An electrical protection system in an industry Works by detecting and acting against abnormal conditions in the electrical system with the objective of guaranteeing the safety of people, protecting equipment and ensuring the continuity of industrial process. Taking into account the importance of guaranteeing adequate electrical protection system in an industrial activity in this research a proposal for the protection system for an industrial electrical network is presented. As a previous step proposal, the methodology desingned for the coordination of protections for an industrial electrical network was proposed. The proposal was designed taking into account four operating scenarios required to calibrate the industry’s protection devices. The short circuit analysis maximum in each of the system bars for each of the scenarios allowed determining the maximum phase failure currents it is 44.44% for emergency 1 and 66.66% for emergency 2, while the maximum ground fault current was founding emergency scenario2. At the news to the four scenarios of the industrial network in the actuation times of the devices of protection, there is not considerable variation; this is justified by the current time graph because when there is a serious short circuit current, the action time should be shorter. On the contrary, when there is a small current time will be greater

By Alex Paúl Cruz Gonzales , Xavier Santiago Salazar Defaz, Xavier Alfonso Proaño Maldonado, Franklin Hernán Vásquez Teneda

Cloud computing has gained popularity with advancements in virtualization technology and the deployment of 5G. However, scheduling workload in a heterogeneous multi-cloud environment is a complicated process. Users of cloud services want to ensure that their data is secure and private, especially sensitive or proprietary information. Several research works have been proposed to solve the challenges associated with cloud computing. The proposed Adaptive Priority based scheduling (PBS) focuses on reducing data access completion time and computation expense for task scheduling in cloud computing. PBS assigns tasks depending on its size and selects the minimum cost path for data access. It contains a task register, scheduler, and task execution components for efficient task execution. The proposed system also executes a double signature mechanism for data privacy and security in data storage. This study correlates the perfo}rmance of three algorithms, PBS, (Task Requirement Degree) TRD and (recommended a Risk adaptive Access Control) RADAC, in terms of task execution time and makespan time. The experimental results demonstrate that PBS outperforms TRD and RADAC in both metrics, as the number of tasks increases. PBS has a minimum task execution time and a lower makespan time than the othertwo algorithms

By K.Prathap Kumar, K. Rohini

Introduction: Organizational climate is a key factor in employee performance and satisfaction. In this study, the validity and reliability of an organizational climate scale in agroindustrial companies in Peru was examined. Objective: To analyze the psychometric properties of an organizational climate scale adapted to Peruvian Spanish. Methods: A methodological study was carried out. Demographic data were collected, as well as responses to an organizational climate questionnaire. Results: The data were analyzed using confirmatory factorial analysis (CFA). The reliability of the instrument was high (α = 0.92). However, the factor loadings of several items were not adequate, so a unidimensional model was tested, then a model with adequate factor loadings, and finally an optimal model. In this last 9-item model, the fit was optimal, and the factor loading was adequate for all items. Conclusion: Overall, the organizational climate scale demonstrated good reliability and validity in this context of agroindustrial companies in Peru. However, some items needed to be revised to improve the scale's accuracy. These findings provide a valuable tool for measuring the organizational climate in these types of companies and pave the way for future research in this field.

By Flor Damiano-Aulla, Jeydi Raqui-Rojas, Víctor D. Álvarez-Manrique, Liset Z. Sairitupa-Sanchez, Wilter C. Morales-García

The use of computer networks has become necessary in most human activities. However, these networks are exposed to potential threats affecting the confidentiality, integrity, and availability of data. Nowadays, the security of computer networks is based on tools and software such as antivirus software. Among the techniques used for machine protection, firewalls, data encryption, etc., were mentioned. These techniques constitute the first phase of computer network security. However, they remain limited and do not allow for full network protection. In this paper, a Network Intrusion Detection System (NIDS) was proposed for binary classification. This model was based on ensemble learning techniques, where the base models were carefully selected in a first layer. Several machine learning algorithms were individually studied to choose the best ones based on multiple metrics, including calculation speed. The SMOTE technique was used to balance the data, and cross-validation was employed to mitigate overfitting issues. Regarding the approaches used in this research, a stacking and voting model was employed, trained, and tested on a UNSW-NB15 dataset. The stacking classifier achieved a higher accuracy of 96%, while the voting approach attained 95.6%.

By Anouar Bachar, Omar EL Bannay

Cell standby, particularly picocell sleep mode (SM), is a prominent strategy for reducing energy consumption in 5G networks. The emergence of multi-state sleep states necessitates new optimization approaches. This paper proposes a novel energy optimization strategy for 5G heterogeneous networks (HetNets) that leverages macrocell-picocell coordination and machine learning. The proposed strategy focuses on managing the four available picocell sleep states. The picocell manages the first three states using the Q-learning algorithm, an efficient reinforcement learning technique. The associated macrocell based on picocell energy efficiency controls the final, deeper sleep state. This hierarchical approach leverages localized and network-wide control strengths for optimal energy savings. By capitalizing on macrocell-picocell coordination and machine learning, this work presents a promising solution for achieving significant energy reduction in 5G HetNets while maintaining network performance.

By Macoumba Fall, Mohammed Fattah, Mohammed Mahfoudi, Younes Balboul, Said Mazer, Moulhime El Bekkali, Ahmed D. Kora

Introduction: the events that occurred with the pandemic caused a drastic change in all activities with direct contact due to the high risk of contagion, with educational centers being affected by the closure measures and the imposition of virtual classes to continue with student preparation, leading many students to see the need to have a computer to take their classes, eventually showing boredom due to the lack of desire to be in front of a computer, This to a certain extent weakens their interest in learning and affects their learning because mobile devices have become more important due to the various applications that provide students with information. For this reason, we propose mobile learning that allows students to have more information, as well as interaction with different students so that they have the opportunity to learn on a constant basis.

Objective: the objective is to create an automatic mobile learning system for the constant preparation of the student community.

Method: a methodology based on a client-server model to take advantage of the various educational resources accompanied by the good support it provides the subjects for students with the interaction of a mobile application.

Results: through the operation of the system, it was visualized that the tests carried out with the students were presented with an efficiency of 96,70 %,

Conclusions: this system presents a high efficiency that allows to reinforce the subjects that need more prominence in the student’s learning and progress of level through the teacher’s evaluations.

By Lucía Asencios-Trujillo, Djamila Gallegos-Espinoza, Lida Asencios-Trujillo, Livia Piñas-Rivera, Carlos LaRosa-Longobardi, Rosa Perez-Siguas

Confronting the undeniably complicated versatile correspondence organization, knowledge is the advancement heading of organization versatile improvement innovation later on. Portable correspondence information is a significant part representing things to come data society. AI calculation is embraced in the versatile improvement plot, which can facilitate different enhancement goals as per the progressions of climate and state and understand the ideal boundary arrangement. Canny portable terminal hardware is turning out to be increasingly well known. The combination and advancement of social, portable and area administrations make the conventional informal organization easily change to versatile correspondence organization. AI is a part of man-made consciousness. Its examination objective is to construct a framework which can advance a few guidelines from information and apply them to the resulting information handling. In light of chart hypothesis, this paper tackles the issue of correspondence network information really, and concentrates on the calculation of huge information examination in view of AI.

By Vijaya Saradhi Thommandru, T. Suma, A. Mary Odilya Teena, A. Muthukrishnan, P Thamaraikannan, S. Manikandan

In Jepara, Central Java, Indonesia, significant correlations between high rainfall and wind speed impact multiple sectors including health, agriculture, and infrastructure. This study aims to predict the effects of extreme weather by employing nonparametric regression based on Fourier series estimators. Data from December 2023 to March 2024, sourced from NASA, were analyzed using sinus, cosinus, and combined Fourier functions to model the dynamic and seasonal fluctuations of weather variables. This approach allows for a flexible modeling of these previously undefined functional relationships. The analysis revealed that the combined function model was superior, achieving an optimal Generalized Cross-Validation (GCV) score of 0.236498 with a Fourier coefficient K=3, indicating a well-fitted model. Moreover, this model exhibited a low Mean Absolute Percentage Error (MAPE) of 1.887, demonstrating high predictive accuracy. These findings not only affirm the efficacy of Fourier series in nonparametric regression for weather forecasting but also underscore its potential in informing public policy and bolstering disaster preparedness in Jepara and similar regions vulnerable to extreme weather conditions

By Ihsan Fathoni Amri, Nur Chamidah, Toha Saifudin, Ariska Fitriyana Ningrum, Ariska Fitriyana Ningrum, Saeful Amri

Introduction: The Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge.
Methods: This article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system.
Result and Discussion: In consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability.
Conclusion: In terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience.

By Veera V Rama Rao M, Kiran Sree Pokkuluri, N.Raghava Rao, Sureshkumar S, Balakrishnan S, Shankar A

AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced response time, enhanced logistics route optimization, improved demand planning as well as real-time inventory control. Thus, the idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics. SCO-AI implementation has seen significant improvements in inventory turnover rate, rates of on-time delivery as well as overall supply chain costs. In this period of high business turbulence, such kind of research builds up the robustness of a given supply chain wh

By Anber Abraheem Shlash Mohammad, Iyad A.A Khanfar, Badrea Al Oraini, Asokan Vasudevan, Suleiman Ibrahim Mohammad, Zhou Fei

Introduction: Information has become a crucial asset for companies in decision making and performance evaluation. Information technologies, such as Business Intelligence, allow data to be converted into relevant information. The implementation of a Datamart, a specialized database, stands out as a solution to analyze specific data from a business area.
Objective: The main objective is to determine how the implementation of a Datamart affects data analysis in the sales area of the company.
Method: A bibliographic review of various sources was carried out using the PICO keywords. In addition, filters were applied to limit the search to relevant articles published in the last 5 years in Spanish or English. Then, 31 relevant documents that highlighted the implementation of Datamarts in the sales area were evaluated.
Results: Predominant Datamart development methods were identified, such as the Kimball and Hefesto methodologies. Likewise, effectiveness was measured through indicators such as processing time, report generation, user satisfaction and availability of information.
Conclusions: In conclusion, a well-implemented Datamart can be a key tool to improve data management and analysis in the sales area of a company.

By Luz Castillo-Cordero, Milagros Contreras-Chihuán, Brian Meneses-Claudio

One of the main challenges faced by teachers, researchers, and students today is efficiently filtering reliable and useful information available on the internet, as well as in scientific academic databases. To address this phenomenon, the bibliometrics tool is used, which involves understanding the number of publications, analyzing them, and determining their trend based on the application of filters and relationships of scientific concepts in specialized topics. There are other technological tools that allow finding bibliographic information on the internet, such as artificial intelligence (AI) specifically through the ChatGPT chatbot (Generative Pre-trained transformer). The objective of this article is to identify the differences between the results of a bibliometric analysis from Scopus and ChatGPT; the research type is documentary; the search strategy for the bibliometric analysis was "Dynamic Capabilities." Findings show that there are differences between the data obtained from the two bibliometric analyses, including authors, subject areas, affiliations, and keywords; it should be noted that the use of ChatGPT is a basic and simple tool that complements the bibliometric analysis provided by an academic database like Scopus; it is suggested to compare the results analytically and manually at all times, which is of interest to academia and the development of theoretical frameworks in research work.

By Ana Karen Romero, Deyanira Bernal, Reyna Christian Sánchez

Introduction: technological advances have led to the creation of artificial intelligence, implementing it in tasks until recently developed directly by man, as in the case of parking lot surveillance.
Objective: to learn about the application of a vehicle license plate recognition system with artificial intelligence for the detection of alerted vehicles at the National University of Ucayali during the period 2022-2023.
Methods: qualitative approach study, inductive method and descriptive research level; the population consisted of university personnel over 19 years of age, regardless of gender and whose employment status was by appointment or contract, among whom a non-probabilistic sampling was applied, established in thirteen people, to whom an interview composed of twelve items was applied and who filled out an informed consent form, guaranteeing confidentiality, to have reliable data and scientific integrity of the same.
Results: there are favorable and unfavorable opinions; the former are contributed by people who understand the process and agree with its implementation, while the latter respond to doubts generated by the lack of information and institutional communication.
Conclusions: it is necessary to improve the communication system to avoid misinterpretations, doubts, and confusions in the use of private data, giving the users of the campus the certainty that the advances, in cooperation with the competent authorities, result in an adequate progress for the organization and control of their assets.

By Jackie Frank Chang Saldaña, Lincoln Fritz Cachay Reyes, Julio Cesar Pastor Segura, Liz Sobeida Salirrosas Navarro

Natural Language Processing (NLP) is an interdisciplinary field that investigates the fascinating world of human language with the goal of creating computational models and algorithms that can comprehend, produce, and analyze natural language in a way that is similar to humans. LLMs still encounter issues with loud and unpolished input material despite their outstanding performance in natural language processing tasks. TextRefine offers a thorough pretreatment pipeline that refines and cleans the text data before using it in LLMs to overcome this problem . The pipeline includes a number of actions, such as removing social tags, normalizing whitespace, changing all lowercase letters to uppercase, removing stopwords, fixing Unicode issues, contraction unpacking, removing punctuation and accents, and text cleanup. These procedures work together to strengthen the integrity and quality of the input data, which will ultimately improve the efficiency and precision of LLMs. Extensive testing and comparisons with standard techniques show TextRefine's effectiveness with 99% of the accuracy.

By Ekta Dalal, Parvinder Singh

Introduction: modern possibilities of using digital technologies in vocational education are actively used to improve the training of specialists and adapt them to the requirements of the labour market. The purpose of the article is to analyse the integration of information technology into innovative teaching methods and to study the improvement of the quality of vocational education in the digital age. Methodology: the type of research is quantitative. The authors used such scientific methods: comparison and content analysis. The materials were processed from 02.09.2023 to 21.12.2023. A survey of teachers of vocational education institutions (140 people) was also conducted, based on which the main opinions on the state and prospects of digitalisation in this area are presented.
Results: it was showed how often and effectively digital technologies are used and what innovative tools teachers use. It is also demonstrated that the difficulties in reforming the material base of education are recognised as extremely significant in Ukrainian reality. The importance of continuous professional development is emphasised, as digital technologies are developing rapidly.
Conclusions: it was summarised the results of the study, emphasising that the digitalisation of vocational education aims to ensure the proper development of education in line with the current challenges of the labour market

By Hanna Kravchenko, Zoya Ryabova, Halyna Kossova-Silina, Stepan Zamojskyj, Daria Holovko

The primary focus of this article is dedicated to a thorough investigation of the Fixed Load Transportation Problem (FCTP) and the proposition of an exceedingly efficient resolution method, with a specific emphasis on the achievement of optimal transportation plans within practical time constraints. The FCTP, recognized for its intricate nature, falls into the NP-complete category, notorious for its exponential growth in solution time as the problem's size escalates. Within the realm of combinatorial optimization, metaheuristic techniques like the Dragonfly algorithm and genetic algorithms have garnered substantial acclaim due to their remarkable capacity to deliver high-quality solutions to the challenging FCTP. These techniques demonstrate substantial potential in accelerating the resolution of this formidable problem. The central goal revolves around the exploration of groundbreaking solutions for the Fixed Load Transportation Problem, all while concurrently minimizing the time investment required to attain these optimal solutions. This undertaking necessitates the adept utilization of the Dragonfly algorithm, an algorithm inspired by natural processes, known for its adaptability and robustness in solving complex problems. The FCTP, functioning as an optimization problem, grapples with the multifaceted task of formulating distribution plans for products originating from multiple sources and destined for various endpoints. The overarching aspiration is to minimize overall transportation costs, a challenge that mandates meticulous considerations, including product availability at source locations and demand projections at destination points. The proposed methodology introduces an innovative approach tailored explicitly for addressing the Fixed Charge Transport Problem (FCTP) by harnessing the inherent capabilities of the Dragonfly algorithm. This adaptation of the algorithm's underlying processes is precisely engineered to handle large-scale FCTP instances, with the ultimate objective of unveiling solutions that have hitherto remained elusive. The numerical results stemming from our rigorous experiments unequivocally underscore the remarkable prowess of the Dragonfly algorithm in discovering novel and exceptionally efficient solutions. This demonstration unequivocally reaffirms its effectiveness in overcoming the inherent challenges posed by substantial FCTP instances. In summary, the research represents a significant leap forward in the domain of FCTP solution methodologies by seamlessly integrating the formidable capabilities of the Dragonfly algorithm into the problem-solving process. The insights and solutions presented in this article hold immense promise for significantly enhancing the efficiency and effectiveness of FCTP resolution, ultimately benefiting a broad spectrum of industries and logistics systems, and promising advancements in the optimization of transportation processes.

By Ismail Ezzerrifi Amrani, Ahmed Lahjouji El Idrissi, Abdelkhalek BAHRI, Ahmad El ALLAOUI

Introduction: currently, the integration of innovative technologies plays a crucial role in students' academic formation. In this context, ChatGPT emerges as a cutting-edge tool with the potential to transform the educational experience.
Objective: to assess the attitude of university students towards the use of ChatGPT as a learning resource.
Methods: a quantitative study, non-experimental design and observational and descriptive type. The sample was determined through simple random sampling and consisted of 269 university students of both genders who were administered the Attitudes towards the Use of ChatGPT Scale, an instrument with adequate metric properties.
Results: the attitude towards the use of ChatGPT as a learning resource was predominantly rated at a medium level, as were the affective, cognitive, and behavioral dimensions. This suggests that students enjoy using ChatGPT as a tool in their learning process and consider it facilitates and improves their educational experience. However, they expressed concern about the possibility of this tool generating inaccurate results.
Conclusions: the attitude of university students towards the use of ChatGPT as a learning resource was rated at a medium level. Likewise, it was determined that as students progressed in their academic training, they developed a more favorable attitude towards the use of ChatGPT.

By Edwin Gustavo Estrada-Araoz, Yolanda Paredes-Valverde, Rosel Quispe-Herrera, Néstor Antonio Gallegos-Ramos, Freddy Abel Rivera-Mamani, Alfonso Romaní-Claros

Abstract
Ensuring accurate forecasts of Global Horizontal Irradiance (GHI) stands as a pivotal aspect in optimizing the efficient utilization of solar energy resources. Machine learning techniques offer promising prospects for predicting global horizontal irradiance. However, within the realm of machine learning,the importance of feature selection cannot be overestimated, as it is crucial in determining performance and reliability of predictive models. To address this, a comprehensive machine learning algorithm has been developed, leveraging advanced feature importance techniques to forecast GHI data with precision. The proposed models draw upon historical data encompassing solar irradiance characteristics and environmental variables within the Ouarzazate region, Morocco, spanning from 1st January 2018, to 31 December 2018, with readings taken at 60-minute intervals. The findings underscore the profound impact of feature selection on enhancing the predictive capabilities of machine learning models for GHI forecasting. By identifying and prioritizing the most informative features, the models exhibit significantly enhanced accuracy metrics, thereby bolstering the reliability, efficiency, and practical applicability of GHI forecasts. This advancement not only holds promise for optimizing solar energy utilization but also contributes to the broader discourse on leveraging machine learning for renewable energy forecasting and sustainability initiatives.

By Benchikh Salma, Jarou Tarik, Lamrani Roa, Nasri Elmehdi

Total employment among college grads is now under significant pressure, and structural conflicts are quite visible. People are starting to take notice of the serious job crisis that college students face. According to the research study, college students' employment cognition is a key factor in this problem. The importance of enhancing students' employment cognition cannot be overstated. According to the article, knowing the important aspects that could affect one's job cognition is the first step in improving students' employment cognition. SEM analysis shows these characteristics were positively and substantially associated with employment cognition. This article aims to use big data technologies to conduct extensive studies and analyses on AI employment cognition and occupational contradictions. The first step is implementing a scientific approach to building a multi-level linked big data management platform for Employment Cognition. The platform will be used during the development of Employment career advancement. The subsequent objective is to build an employment team by including information resources. Finally, the results show a huge variation in self-ability cognition and a remarkable difference in how college students think about their job capacity due to new work. Their physiological features and societal expectations are related to this. However, the most critical factor in enhancing the quality of college graduates' jobs is for the relevant department to improve the nurturing of their prior abilities in this area.

By Hong Xiang, Anrong Wang, Wenxi Tan, Xiaoju Dai , Le Zhang

    The option to create inventory is not always the optimal choice due to the associated expenses and space requirements. Nevertheless, there are instances where a shortage of materials on customer lines can result in substantial financial penalties. This constant contradiction places supply chain managers in a perplexing predicament, especially when considering the amplification of inventory through the bullwhip effect as it moves across different stages. Moreover, the uncertain backdrop created by unforeseen events intensifies this already critical situation, compelling managers to seek novel decision-making approaches. These approaches should enable the simulation of risks and the selection of suitable scenarios, particularly within the intricate domain of stochastic and dynamically evolving supply chains. In this study, we introduce a new decision-making model rooted in the fuzzy logic concept introduced by Loutfi Zadeh in 1965. This model is applied to criteria assessed by experts, representing the most pertinent parameters for guiding inventory optimization. The chosen criteria encompass Lead Time, Equipment Production Reliability, and Warehousing Costs. This model exhibits the potential to unearth intricate patterns and associations among variables that conventional statistical methods struggle to reveal. Notably, the integration of fuzzy logic for inventory prediction yields promising outcomes, extendable to the realm of artificial intelligence, where comprehensive inference rules facilitate effective decision-making.

By Hatim Lakhouil, Aziz Soulhi

Data de-duplication in cloud storage is crucial for optimizing resource utilization and reducing transmission overhead. By eliminating redundant copies of data, it enhances storage efficiency, lowers costs, and minimizes network bandwidth requirements, thereby improving overall performance and scalability of cloud-based systems. The research investigates the critical intersection of data de-duplication (DD) and privacy concerns within cloud storage services. Distributed Data (DD), a widely employed technique in these services and aims to enhance capacity utilization and reduce transmission bandwidth. However, it poses challenges to information privacy, typically addressed through encoding mechanisms. One significant approach to mitigating this conflict is hierarchical approved de-duplication, which empowers cloud users to conduct privilege-based duplicate checks before data upload. This hierarchical structure allows cloud servers to profile users based on their privileges, enabling more nuanced control over data management. In this research, we introduce the SHA method for de-duplication within cloud servers, supplemented by a secure pre-processing assessment. The proposed method accommodates dynamic privilege modifications, providing flexibility and adaptability to evolving user needs and access levels. Extensive theoretical analysis and simulated investigations validate the efficacy and security of the proposed system. By leveraging the SHA algorithm and incorporating robust pre-processing techniques, our approach not only enhances efficiency in data de-duplication but also addresses crucial privacy concerns inherent in cloud storage environments. This research contributes to advancing the understanding and implementation of efficient and secure data management practices within cloud infrastructures, with implications for a wide range of applications and industries.

By Rajendran Bhojan, Manikandan Rajagopal, Ramesh R

The rise of artificial intelligence (AI) and natural language processing (NLP) has revolutionized many aspects of daily life, particularly in the field of development of medical research articles. the use of AI in scientific writing has both advantages and disadvantages. As AI tools gain in popularity and their application becomes more ubiquitous, it's essential to consider how they may affect the future of medical literature. This work aims to describe a number of IT-based tools that contribute to scientific research and writing as ChatGPT, Gemini, Elicit, SCISPACE... Each tool has its own advantages and applications, not to mention shortcomings that can affect the quality of medical research. To conclude artificial intelligence tools have emerged as catalysts for innovation in healthcare research, providing motivation and driving progress even amidst challenges. Therefore, it's crucial to confront the obstacles related to AI and to tackle ethical and regulatory issues to enhance research quality and scientific output.

By Hind Berrami, Manar Jallal, Zineb Serhier, Mohammed Bennani Othmani

Introduction; Artificial intelligence (AI) and Machine Learning have become buzzwords lately due to technological changes and data quality testing, especially in shape and finish analysis. Lots of research has been conducted for linear regression algorithms to predict the price in different sectors for share stock, rental properties, prices of used cars etc. This study provides suitable data split ratio for optimum cost estimation based on linear regression model. In present days there is an increasing demand for having own car for every middle class family therefore this have given opportunity to motor vehicle business to offer wide range of used vehicle for re-sale especially companies like Maruti Suzuki, Tata motors & Mahendra motors in Indian motor vehicle industries. Therefore, it is important to know the current value of your car before spending your hard-earned money on any item.
Objective; The objective of this paper is finding appropriate value of cars in Metropolitans or even in state capitals. Features like model, mileage, AC, seating capacities, fuel type automatic will be taken into account when doing this. This estimate is designed to help customers find the right options to suit their needs.
Methods; We have used a linear regression model to estimate the value of the respective car.
Results; For doing this price prediction in this paper using liner regression we have tried to find the optimum accuracy of model by varying data split ratio for training and test data set and concluded with the result that 80/20 ratio is the best ratio with optimum model accuracy for business domain analysis with labelled data set.
Conclusion; The findings underscore the importance of careful consideration when selecting a data split ratio for price prediction models in the used vehicle market. The insights gleaned from this study can inform future research and contribute to the development of more accurate and reliable regression models in similar domains.

By Md Alimul Haque, Md Shams Raza, Sultan Ahmad, Md Alamgir Hossain, Hikmat A. M. Abdeljaber, A. E. M. Eljialy, Sultan Alanazi, Jabeen Nazeer

This paper examines the relationship between economic growth and institutional quality in the context of the Moroccan economy. Using annual data from 1970 to 2020 and an Autoregressive Distributed Lag (ARDL) cointegration approach, we analyze the long-run and short-run nexus between these two variables. The statistical tests performed, including the ADF and Phillips Perron tests, indicate integration at different orders, and the bounds cointegration test proposed by Pesaran was also conducted. The study finds that institutional quality has a positive short-term impact on economic growth. Furthermore, in the long term, the study reveals that institutional quality continues to positively influence economic growth in Morocco (P-value=0.01<5%). These results contribute valuable insights to the existing empirical literature and can guide policymakers and stakeholders in implementing institutional reforms to promote economic development.

By El Houssaine fathi, Ahlam qafas, Youness Jouilil

This research examined the link between digital competencies and technostress among university instructors in remote settings in Peru, with the goal of identifying if improving digital skills could help mitigate technostress. A non-experimental, quantitative methodology was employed, gathering data via standardized surveys such as the DigCompEdu Check-In and RED TIC. The participant group comprised 120 teachers, whose responses were analyzed using logistic regression in SPSS v27. Descriptive findings indicated that 55.6% of the teachers demonstrated a high level of professional commitment, and 58.9% showed proficient digital pedagogical skills. Inferential analysis showed a significant correlation between digital competencies and technostress, with a Nagelkerke index of 0.622, suggesting that about 62.2% of the variation in technostress could be explained by differences in digital competencies. The study concludes that enhancing digital competencies among teachers could substantially reduce their technostress, emphasizing the need to effectively integrate these skills into teaching practices to improve the educational experience in virtual settings.

By Karina Raquel Bartra-Rivero, Lida Vásquez-Pajuelo, Geraldine Amelia Avila-Sánchez, Elba María Andrade-Díaz, Gliria Susana Méndez-Ilizarbe, Jhonny Richard Rodriguez-Barboza , Yvonne Jacqueline Alarcón-Villalobos

The aim of this study is to derive the important factors that influence levels of happiness in Korea, and to identify which factors are particularly important among these influencing factors. To achieve this goal, topic modelling analysis, machine learning analysis and neural network analysis methods were utilized. The Netminer 4.5 program was used for topic modelling analysis and machine learning analysis, and SPSS MODELER 18 was used to perform neural network analysis. Two types of analysis data were used in this study. The first consisted of 1,000 papers relating to happiness published in academic journals managed by the Springer publishing company, which were used to derive happiness-influencing factors. The second consisted of a survey conducted in 2020 by the Community Well-being Center of the Graduate School of Public Administration at Seoul National University in Korea. A total of 16,655 people responded to this survey. The analysis results of the study are as follows. Important variables that affect the level of happiness of Korean residents are: family life, social status, income, health, and perceptions of inequality. Analysis using neural network analysis of the most important factors influencing happiness showed that satisfaction with family life had the most important influence. This suggests that policies that can improve the quality of family life, such as family-friendly work environments, childcare support, and domestic violence prevention and response programmes, will become important in the future.

By Ji-Hyun Jang, Nemoto Masatsuku

Introduction: The exponential growth of data generation has led to an escalating concern for data privacy on a global scale. This work introduces a pioneering approach to address the often overlooked data privacy leakages associated with quasi-identifiers, leveraging artificial intelligence, machine learning and data correlation analysis as foundational tools. Traditional data privacy measures predominantly focus on anonymizing sensitive attributes and exact identifiers, leaving quasi-identifiers in their raw form, potentially exposing privacy vulnerabilities.
Objective: The primary objective of the presented work, is to anonymise the quasi-identifiers to enhance the overall data privacy preservation with minimal data utility degradation.
Methods: In this study, the authors propose the integration of ℓ-diversity data privacy algorithms with the OPTICS clustering technique and data correlation analysis to anonymize the quasi-identifiers.
Results: To assess its efficacy, the proposed approach is rigorously compared against benchmark algorithms. The datasets used are - Adult dataset and Heart Disease Dataset from the UCI machine learning repository. The comparative metrics are - Relative Distance, Information Loss, KL Divergence and Execution Time.
Conclusion: The comparative performance evaluation of the proposed methodology demonstrates its superiority over established benchmark techniques, positioning it as a promising solution for the requisite data privacy-preserving model. Moreover, this analysis underscores the imperative of integrating artificial intelligence (AI) methodologies into data privacy paradigms, emphasizing the necessity of such approaches in contemporary research and application domains.

By Sreemoyee Biswas, Vrashti Nagar, Nilay Khare, Priyank Jain, Pragati Agrawal

Introduction: The article examines the issue of digital modernization in Latin America, where, despite over a decade of efforts, progress has been slow. It focuses on the importance of e-government for modern public administration, highlighting the limited digitization of activities.

Objective: To evaluate the theoretical-conceptual development of the relationship between digital modernization and public administration.

Methodology: The bibliometric technique was used, drawing from Scopus documents and employing a specific search protocol, resulting in 1,602 records with metadata.

Results: There is shown growth in research since 2003, with studies primarily concentrated in the United States, the United Kingdom, and the Netherlands. Original articles in social sciences are highlighted, emphasizing the role of digital modernization in transparency and democratization of public administration.

Conclusion: While there have been advancements in research since 2003, Latin American countries face significant challenges compared to other regions. The need for greater collaboration and research in this area in Latin America is emphasized to leverage the benefits of digital modernization. It is suggested to establish specific policies and strategies to drive governmental digitization and enhance the efficiency of public services, closing the existing gap.

By Merly Enith Mego Torres , Lindon Vela Meléndez, Juan Diego Dávila Cisneros, Roibert Pepito Mendoza Reyna

Small and Medium Enterprises (SMEs) are essential to the global economy, promoting employment, innovation and sustainable development. Effective sales information management is critical to your success, involving the collection, storage, analysis and application of data about customers, products, distribution channels, prices and market trends. Proper management of this data allows SMEs to understand customer demands, identify market opportunities and optimize their sales strategies.
However, SMEs face significant challenges in this area, such as technological limitations, budget constraints and data complexity, which can lead to manual processes, lack of visibility in the supply chain and loss of competitiveness. The research carried out in the La Maná canton, in Cotopaxi, Ecuador, supports the implementation of a sales information management system due to its intense commercial activity and the presence of SMEs in sectors such as agriculture and commerce.
The study adopted a mixed methodology, which combined literature review and field research, using inductive and deductive approaches. Managers, administrators and workers from two companies were surveyed. The results indicate that this methodology is effective in achieving the research objectives, underscoring the importance of integrating various methodologies to obtain a complete understanding of the topic

By Gissela Yajaira Hinojosa Barreto, Nathaly Beatriz Chávez García, Jaime Mesías Cajas

The study focused on the design of an advanced algorithm for the optimal sizing of protection systems against atmospheric discharges in architectural structures, applying the rolling sphere method. This technique facilitated the incorporation of user-specified parameters through an advanced graphical interface. The methodology began with the exhaustive accumulation of data relevant to the project. Risk indices were estimated through sophisticated risk analysis software applications. If adjustments were required, the process continued; If not, the building was considered to be adequately secured. The ground resistivity was evaluated according to IEEE Std. 81, and the rolling sphere method was implemented according to IEC 662305-3. The grounding systems were configured in accordance with IEEE Std. 142 and IEEE Std. 80. To analyze the interaction of electrical discharges with the protected building, the electrical equivalents of elements such as meshes, fused copper rods were computed. , and conductors positioned horizontally and vertically. Using these data, a model was built in ATPDraw, interconnected with Python for the generation of graphical representations of the current waves in the different protection subsystems. To conclude and corroborate the effectiveness of the process, the risk indices were reevaluated. The validation of the algorithm was achieved by minimizing the margin of error to insignificant levels by incorporating standardized data proposed by organizations such as IEC and IEEE, thus confirming the precision of the designed algorithm

By Carlos Ivan Quinatoa Caiza, Alex Ivan Paguay Llamuca , Xavier Alfonso Proaño Maldonado

According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the disease's fatality rate. Early cancer detection raises the chance of survival by up to 8%. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy

By G. Meenalochini, D. Amutha Guka, Ramkumar Sivasakthivel, Manikandan Rajagopal

Background: Performance evaluation is essential to ensure quality healthcare services, especially in the field of nursing. Objective: The objective of this study was to analyze the factorial structure, reliability, and invariance by sex and age of the work performance scale in Peruvian nurses. Methods: Confirmatory factor analysis (CFA) was conducted to evaluate the internal structure of the scale, and psychometric properties including reliability and convergent validity were determined. Additionally, factorial invariance was evaluated according to participants' sex and age. Results: The CFA supported the structure of three factors (Task Performance, Counterproductive Behaviors, Contextual Performance) and showed adequate and stable psychometric properties for a 12-item version (: χ2 = 231.09, df = 78; CFI = 0.97, TLI = 0.96, RMSEA = 0.06 (90% CI: 0.05-0.06), and SRMR = 0.03). Strict factorial invariance was demonstrated for both sex and age, and adequate internal consistency was found for each dimension, as well as convergent validity. Conclusions: The work performance scale, in its 12-item version (IWPQ-P), is a valid and reliable measure for evaluating work performance in Peruvian nurses. Its factorial invariance by sex and age makes it a useful tool for future research and practical applications in nursing performance evaluation.

By Irma Chalco-Ccapa, Gaby Torres-Mamani, Mardel Morales-García, Alcides A Flores-Saenz, Liset Z. Sairitupa-Sanchez, Maribel Paredes-Saavedra, Wilter C. Morales-García

Social networks have become pivotal in education, offering opportunities for inclusive learning experiences. This study seeks to understand the role of social networks in educational inclusion by analyzing students' usage, motivations, and perceived benefits. It focuses on identifying usage patterns, main activities, and perceptions regarding the impact of social networks on communication, interpersonal relationships, and access to educational information. A quantitative approach was employed, gathering data through a questionnaire from 355 university students of the specialty of secondary education in Lima during the 2023-2 semester. Statistics on social media usage, predominant activities, and perceived benefits associated with their use were analyzed. Findings revealed high social media usage, with WhatsApp (96.9%) being the most used platform, followed by Facebook (63.4%) and Instagram (40.6%). Main activities were entertainment (67%), family communication (60.8%), and education (57.2%). Students also valued improved interpersonal relationships (31.5%) and access to information (69.9%). Social networks play a crucial role in educational inclusion, providing opportunities for communication, collaboration, and information access. The need to balance their use and address challenges like digital dependency, prioritizing student well-being in the digital age, is emphasized.

By Milagros Maria Erazo-Moreno, Gloria María Villa-Córdova, Geraldine Amelia Avila-Sánchez, Fabiola Kruscaya Quispe-Anccasi, Segundo Sigifredo Pérez-Saavedra, Jhonny Richard Rodriguez-Barboza

The monograph focuses on examining public policies in Ecuador to mitigate violence against children and adolescents, this in the context that the rates of violence in the country have increased over the years and the ways in which they are produces are diverse, as well as the aggressors are no longer only found in the family environment but also in the school environment and in other areas where the minor has participation. In this sense, a review of the regulations in force in the country is carried out to assess their coverage and effectiveness based on international instruments on which they are based. The result of this review has made it possible to identify that despite the diversity of legal instruments, children's rights continue to be violated, which infers the need for actions to reinforce the guarantees of their compliance.

By Alexandra Marisol Barcia Maridueña, Iván Andrés Muñoz Mata, Marcia Lisbeth Verdugo Arcos, Thalía Lilibeth Figueroa Suárez

Introduction: Social Capital in organizations is an intangible asset that represents the favourable relationships that exist between work teams, within an organization and externally, to different interest groups.
Objective: This study examined the link between internal relational social capital (RSC) and external RSC with innovation in small industrial firms in Tabasco, Mexico. There was also an inquiry into how much internal RSC and external RSC explain innovation.
Methods: The design was nonexperimental, cross-sectional, descriptive, correlational, and explanatory. Linear regression analysis was used.
Results: Significant positive relationships was identified between internal RSC and external RSC and innovation. The internal RSC and external RSC contributed significantly to the explaining of innovation. Areas of opportunity were identified for these firms in process design and formal research activities for new raw materials, production procedures and patent generation. Conclusion: To promote innovation, managers of small industrial companies must continue to establish strategies and practices to strengthen RSC.

By Edith Georgina Surdez Pérez, María del Carmen Sandoval Caraveo, Maribel Flores Galicia

With the rapid rise of information technology, the amount of unstructured data from the data lake is rapidly growing and has become a great challenge in analyzing, organizing and automatically classifying in order to derive the meaningful information for a data-driven business. The scientific document has unlabeled text, so it's difficult to properly link it to a topic model. However, crafting a topic perception for a heterogeneous dataset within the domain of big data lakes presents a complex issue. The manual classification of text documents requires significant financial and human resources. Yet, employing topic modeling techniques could streamline this process, enhancing our understanding of word meanings and potentially reducing the resource burden. This paper presents a comparative study on metadata-based classification of scientific documents dataset, applying the two well-known machine learning-based topic modelling approaches, Latent Dirichlet Analysis (LDA) and Latent Semantic Allocation (LSA). To assess the effectiveness of our proposals, we conducted a thorough examination primarily centred on crucial assessment metrics, including coherence scores, perplexity, and log-likelihood. This evaluation was carried out on a scientific publications corpus, according to information from the title, abstract, keywords, authors, affiliation, and other metadata aspects. Results of these experiments highlight the superior performance of LDA over LSA, evidenced by a remarkable coherence value of (0.884) in contrast to LSA's (0.768).

By Mohamed CHERRADI

In today's digital age, information technologies have revolutionized how companies manage their business operations and strategies. The application of these technologies in business management has demonstrated significant impacts in various sectors. The main objective was to analyze the scientific production related to the main applications of digital technologies to business management. The research paradigm was mixed through developing a bibliometric study and a thematic analysis of relevant sources. The SCOPUS database was used during the period 2000 – 2024. A total of 85 investigations were obtained. The behavior of investigations behaved heterogeneously while starting in 2019; it experienced notable growth with a maximum peak in 2023 of 24 investigations. The thematic analysis corroborated the importance of digital transformation for business management and the critical role played by the designed introduction of digital technologies. The findings allow us to affirm that it is a heterogeneous field, influenced by various disciplines and in the process of consolidation, due to the range of potentialities it offers.

By Carlos Alberto Gómez-Cano, Verenice Sánchez-Castillo, Rolando Eslava-Zapata

The maritime industry has increasingly integrated advanced technologies such as AI, Blockchain, Big Data, and IoT, transforming traditional port operations into smart facilities aimed at enhancing global trade competitiveness. A particular focus has been on improving tracking and tracing services, with Blockchain technology emerging as pivotal for ensuring data integrity, transparency, and traceability across supply chains. This article proposes a blockchain-based tracking and tracing system model tailored for monitoring containers in Moroccan ports. Utilizing the Unified Modeling Language (UML), the model seeks to optimize resource allocation and boost stakeholder satisfaction through detailed diagrams and functional data requirements depiction. Despite challenges such as IoT terminal platform connectivity and operator resitance, successful implementation was achieved, establishing a foundational framework for a comprehensive container monitoring system. This model provides valuable insights for supply chain professionals and scholars interested in item tracking, aiming to integrate Blockchain with technologies like RFID, GPS, RTLS, QR Codes, BLE, and IoT sensors to enhance port operation efficiency and container management effectiveness. By leveraging these integrated technologies, ports can further improve operational efficiency and ensure accurate traceability of containers throughout the supply chain, contributing to overall trade facilitation and economic growth..

By Safia Nasih, Sara Arezki, Taoufiq Gadi

Implementing artificial intelligence in agriculture can improve efficiency, reduce pollution, and promote more effective agricultural production. Efficient irrigation management avoids wasting water and ensures that plants receive the right amount of water at the right time. The purpose of this research is to present an intelligent irrigation system based on neural networks and fuzzy logic, to avoid the presence of pests due to excess relative humidity in rose crops in Cotopaxi. A mixed methodology was used. The SCRUM methodology, Android Studio as an integrated development environment, a relational database management system and the Mobile-D method were used as software elements. For the prototype construction, the main hardware element that was used was the Arduino Board. The system for irrigating automated water using fuzzy logic took less time than manual irrigation. Training actions were proposed for employers and employees in the use and maintenance of the automated irrigation system, to maintain continuous improvement in the process

By Manuel William Villa Quisphe, José Augusto Cadena Moreano, Juan Carlos Chancusig Chisag

Introduction: During the last decade, it has become evident that the impact of a referee's decision in professional sports turns out to be a turning point in the outcome of a competition, often generating discomfort among fans and competitors. It is for this reason that technological assistants were implemented in sports to help in referee decision making.
Objective: Review and analyze those technological solutions based on the use of artificial intelligence techniques capable of serving as technological assistants in support of referee decision-making in highly competitive professional sports.
Method: The PICO methodology was used for the selection process of scientific publications of the PRISMA declaration. Finding 21 scientific publications extracted from the SCOPUS database that comply with the proposed guidelines, which were reviewed and analyzed to obtain information with added value.
Results: It was found that the proposed technological assistants reached a level of precision greater than 90% in certain sports. Likewise, those limitations were found that reduce the operational quality of these solutions. As found those algorithms, models, methods and approaches of artificial intelligence most used and recommended for future research studies.
Conclusions: In conclusion, the implementation of technological assistants based on artificial intelligence in referee decision making in professional sports has proven to be an effective tool, achieving significant levels of precision.

By Rafael Thomas-Acaro, Brian Meneses-Claudio

Currently the use of pesticides in agriculture has expanded in the search for greater productivity. These products can harm people's health in various ways. These effects can be captured through the use of genotoxicity biomarkers. The objective of this research is to systematize studies on biomarkers of genotoxicity of people exposed to pesticides in South America. The PRISMA method was applied to determine the studies to be analyzed. 15 documents met the inclusion criteria. Among the adverse health effects perceived in studies are neurological, respiratory, dermatological and endocrine disorders, as well as an increased risk of cancer. The main biomarkers identified are the comet assay, the cytokinesis blockade micronucleus assay, and the buccal cytoma micronucleus assay. Polymerase chain reaction, chromosomal aberrations, flow cytometry, and fluorescence in situ hybridization were also taken into account. Limitations were determined by biomarker. The usefulness of using multiple biomarkers is highlighted for a more complete and precise evaluation of pesticide exposure and genotoxic damage in agricultural workers in South America. The establishment of protective measures for workers against the use of pesticides and opting for the use of pesticides of biological origin will contribute to the preservation of people's health.

By Edisson Vladimir Maldonado Mariño, Dario Orlando Siza Saquinga, Diego Eduardo Guato Canchinia, Alexander Javier Ramos Velastegui

Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each technique has tradeoffs between precision and processing speed. Two-stage detectors like Faster R-CNN are highly accurate but slow at 7 FPS. Single-shot detectors like SSD are faster at 22 FPS but less precise. YOLO is extremely fast at 45 FPS but has lower accuracy. This paper reviews prominent deep learning vehicle detectors. It proposes a new integrated method combining YOLOv3 detection, optical flow tracking and trajectory analysis to enhance both accuracy and speed. Results on highway and urban datasets show improved precision, recall and F1 scores compared to YOLOv3 alone. Optical flow helps filter noise and recover missed detections. Trajectory analysis enables consistent object IDs across frames. Compared to other CNN models, the proposed technique achieves a better balance of real-time performance and accuracy. Occlusion handling and small object detection remain open challenges. In summary, deep learning has enabled major progress but enhancements in model architecture, training data and occlusion handling are needed to realize the full potential for traffic management applications. The integrated method proposed offers improved performance over baseline detectors. We have achieved 99 % accuracy in our project

By Patakamudi Swathi, Dara Sai Tejaswi, Mohammad Amanulla Khan, Miriyala Saishree, Venu Babu Rachapudi, Dinesh Kumar Anguraj

E-governance combines the use of electronic means in interaction between government and citizens, government and business, and within government operations to enhance democratic, governmental, and business aspects of governance. Thus, e-governance is built on a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational dimension such as e-governance. The objective was to design and validate an instrument to measure e-governance based on three factors: a) e-administration, b) e-services, and c) e-democracy; Methods: Based on the level of importance given to each factor (sample of 2042 Latin American citizens), as well as the relationships between them, an analysis of the importance of each factor is carried out; Results: After the confirmatory analysis, the definitive instrument with which e-governance can be measured by other researchers and future research is obtained, considering the three selection factors, namely: e-administration, e-services and e-democracy; Conclusions: This research contributes to political science through the design and validation of an instrument consisting of 39 items that can be used to measure e-governance according to the dimensions proposed by the United Nations Educational, Scientific and Cultural Organization.

By Ángel Emiro Páez Moreno, Carolina Parra Fonseca

The intricate nature of remote sensing image dehazing poses a formidable challenge due to its multifaceted characteristics. Considered as a preliminary step for advanced remote sensing image tasks, haze removal becomes crucial. A novel approach is introduced with the objective of dehazing an image employing an encoder-decoder architecture embedded in a generative adversarial network (GAN). This innovative model systematically captures low-frequency information in the initial phase and subsequently assimilates high-frequency details from the remote sensing image. Incorporating a skip connection within the network serves the purpose of preventing information loss. To enhance the learning capability and assimilate more valuable insights, an additional component, the multi-scale attention module, is introduced. Drawing inspiration from multi-scale networks, an enhanced module is meticulously designed and incorporated at the network's conclusion. This augmentation methodology aims to further enhance the dehazing capabilities by assimilating context information across various scales. The material for fine-tuning the dehazing algorithm has been obtained from the RICE-I dataset that serves as the testing ground for a comprehensive comparison between our proposed method and other two alternative approaches. The experimental results distinctly showcase the superior efficacy of our method, both in qualitative and quantitative terms. Our proposed methodology performed better with respect to contemporary dehazing techniques in terms of PSNR and SSIM although it requires longer simulation times. So it could be concluded that we contributed a more comprehensive RS picture dehazing methodology to the existing dehazing methodology literature.

By Mallesh Sudhamalla, Dr. D. Haripriya

Qualitative data analysis in academic research is a challenge. In this context, the use of tools such as ATLAS.ti has emerged as a potential solution to improve the understanding and management of data in the analysis of in-depth interviews. The main objective of the research was to analyze the perspectives of Tourism and Hospitality Management students on the use of ATLAS.ti in the analysis of interviews in qualitative research. The methodology employs a qualitative approach and a descriptive-interpretative design. Data were collected through in-depth interviews and focus groups directed to 40 students of the X cycle who conducted this approach in their research to opt for the bachelor’s degree in Tourism and Hospitality Administration during the years 2022 and 2023. The findings reveal that the use of ATLAS.ti in qualitative data analysis is highly beneficial, facilitating the coding, organization, and identification of emerging patterns in in-depth interviews. The relevance of its effective use in qualitative analysis is highlighted, improving data management, and understanding of participants' perspectives. It is concluded that it is a valuable and effective tool in this context, although the need for researchers to acquire a deep understanding of the tool and receive adequate training is emphasized. It is suggested that they focus on continuous training in its use and constant practice of its advanced functionalities, especially in areas such as coding and code creation, to achieve a deeper interpretation of qualitative data.

By Miriam Viviana Ñañez-Silva, Julio Cesar Quispe-Calderón, Patricia Matilde Huallpa-Quispe, Bertha Nancy Larico-Quispe

Communication technology have advanced quickly since the COVID-19 epidemic started, providing consumers with additional benefits and conveniences. Concerns over the privacy and confidentiality of this data have grown in importance as initiatives that promote the use of audio and video to enhance interpersonal interactions become more common. In the context of the Internet of Things (IoT), audio communications security is essential in the biomedical domain. Sensitive medical data may be compromised in these connections, which include exchanges between patients and doctors and broadcasts of vital signs. To protect patient privacy and reduce cybersecurity threats, strong security measures such as data encryption must be put in place. Our study attempts to address these issues in this environment. Comparative examination of the Chacha20, Salsa20, and Camellia encryption algorithms enabled us to ascertain that Chacha20 performs exceptionally well when it comes to audio file decryption and encryption speed. The results of our trials attest to this encryption method's astounding effectiveness and efficacy. We have also used the noise reduction technique, which is frequently used in audio security to enhance the quality of recordings and make it easier to identify significant information in audio signals. Then, Fourier transform technique, which is also used to analyze audio files and can be used to identify changes, extract hidden information, and authenticate audio files. By doing this, the audio files security and integrity are strengthened.

By Mohammed Amraoui , Imane Lasri , Fouzia Omary, Mohamed Khalifa Boutahir, Yousef Farhaoui

Health Information Technologies (HIT) has a significant chance of enhancing the standard of medical treatment, but their acceptance faces major obstacles including low adoption rates and professional hesitancy. Limited research on HIT adoption, especially in poor nations, adds to this problem and clearly challenges health care managers and researchers. It emphasizes the need of knowing the elements influencing acceptance, choice, and usage of healthcare technology to improve user adoption willingness. Using past studies from several nations, this paper investigates the elements driving HIT adoption within the prism of the Theory of Planned Behavior (TPB). Using a Systematic Literature Review (SLR) under direction from the PRISMA framework guaranteed an open and exhaustive study. With eight publications compared to six from wealthy countries, the results expose a notable trend: emerging countries help more to promote HIT adoption research. Furthermore, the combination of TPB with other theories like the Technology Acceptance Model (TAM) provides a whole framework for grasp the elements influencing HIT uptake. Core TPB components include subjective norms, attitude, and perceived behavioral control are well known in industrialized nations and supported by TAM's perceived utility and simplicity of use, along with demographic elements, therefore stressing a user-centric approach. Research on emerging nations, particularly China, shows, on the other hand, a wide spectrum of variables on HIT adoption including personal, technical, social, and institutional ones. The results greatly improve our knowledge of HIT adoption seen from the TPB perspective and provide insightful analysis for legislators developing sensible plans for HIT implementation.

By Anber Abraheem Shlash Mohammad, Iyad A.A Khanfa, Badrea Al Oraini, Asokan Vasudevan, Suleiman Ibrahim Mohammad, Ala'a M. Al-Momani

Introduction: Currently, information technologies have one characteristic in common: their volatility. This is why it is important that companies have methodologies that allow adequate management of the continuity of the services offered through them.
Objective: In this sense, the purpose of this systematic literature review is to identify the most appropriate methodologies that can be implemented in companies to deal with these unforeseen interruptions.
Method: With a study based on a PICO question, the search for relevant literature in a scientific database was proposed using a search equation based on keywords.
Results: The studies offer qualitative results that mainly allow reducing response times before incidents of unforeseen interruptions, among the most notable is that the proposed systems help increase the success rate of recovery procedures by 80%, allow identifying and apply integration technologies that allow improving business continuity systems, among others. However, there is a knowledge gap for which the implementation of these methods is suggested for future proposals in order to achieve quantitative results that can be presented through metrics.
Conclusions: In conclusion, the present systematic literature review carried out the analysis and a comparison of the methodologies proposed by the authors and analyzes the results achieved in each of them, suggesting that 69% of the articles mention an origin of the associated interruptions to logical failures, 75% of the studies indicate that business continuity plans mostly have a preventive focus and 44% suggest continuous testing of plans to ensure their effectiveness.

By Renzo Huapaya-Ruiz, Brian Meneses-Claudio

Weaving saturation can have harmful consequences, such as problems with loom performance, accelerated wear of mechanical parts and loss of raw materials. To avoid these problems, when designing and creating new fabrics, the densities and yarn qualities must be carefully matched with the weaves to ensure successful testing. To facilitate this task, this study focuses on the development of a practical fuzzy logic model for predicting the saturation of new fabrics. An experimental part was carried out to validate this fuzzy model. The fabric samples used in this study came from three different types of weaves, namely plain, twill and satin. These samples also included five weft counts (Nm) and eight different densities. The results obtained using the fuzzy logic model developed were compared with experimental values. The prediction results were satisfactory and precise, demonstrating the effectiveness of the fuzzy logic model developed. The mean absolute error of the calculated fuzzy model was 1.97%. It was therefore confirmed that this fuzzy model was both fast and reliable for predicting the saturation of new fabric

By Mhammed EL BAKKALI, Redouane MESSNAOUI, Mustapha ELKHAOUDI, Omar CHERKAOUI, Aziz SOULHI

Introduction: the concern about the limited progress in public institutions in Peru in the field of digitization of processes, despite the existence of legislation in force with coordinated actions from the State, to advance the digital development of the country.
Objective: analyze the current situation of the system of document processing through digital signatures and administrative management in public universities.
Methods: bibliographic research developed through a systematic review of repositories of Peruvian universities dated since 2019 and with the support of Google Scholar.
Results: the findings showed that the existing advances continue to be scarce despite having demonstrated the benefits they bring to these entities in the use of human resources, materials, and time costs, as well as in the streamlining of their administrative processes, in line with the global trend of zero paper.
Conclusions: un effort should be made to convey the benefits achieved with the application of this system, to overcome the doubts expressed by the respondents and to achieve an adequate implementation of the system

By Lincoln Fritz Cachay Reyes, Jackie Frank Chang Saldaña, Julio Cesar Pastor Segura, Liz Sobeida Salirrosas Navarro, Janet Yvone Castagne Vasquez

Introduction: the development of research competencies among university students is a crucial aspect of contemporary academic education. These competencies have not only become indispensable for professional advancement but are also essential for societal progress. However, their development is not always uniform, and their acquisition is associated with various variables.

Objective: to determine the variables associated with research competencies in university students from Southern Peru.

Methods: a quantitative, non-experimental, cross-sectional descriptive study was conducted. The sample consisted of 302 university students selected through probabilistic sampling. Data collection was done using the Research Competencies Questionnaire, which had adequate metric properties.

Results: research competencies of 72.8% of students were moderately developed, 17.5% were not developed, while 9.6% were fully developed. Furthermore, upon evaluating dimensions, it was found that organizational, communicational, and collaborative skills were also moderately developed. Additionally, it was determined that research competencies were significantly associated with membership in research groups and the number of weekly hours students dedicated to research activities (p<0.05).

Conclusions: membership in a research group and greater dedication of hours were associated with a higher level of development of research competencies. Moreover, overall, it was determined that the majority of students had a moderate level of development of these competencies.

By Edwin Gustavo Estrada-Araoz, Marilú Farfán-Latorre, Willian Gerardo Lavilla-Condori, Dominga Asunción Calcina-Álvarez, Luis Iván Yancachajlla-Quispe

The purpose of this article is to study translation as a human speech act in the context of artificial intelligence. Using the method of analysing the related literature, the article focuses on the impact of technological changes on traditional approaches and explores the links between these concepts and their emergence in linguistics and automatic language processing methods. The results show that the main methods include stochastic, rule-based, and methods based on finite automata or expressions. Studies have shown that stochastic methods are used for text labelling and resolving ambiguities in the definition of word categories, while contextual rules are used as auxiliary methods. It is also necessary to consider the various factors affecting automatic language processing and combine statistical and linguistic methods to achieve better translation results. Conclusions - In order to improve the performance and efficiency of translation systems, it is important to use a comprehensive approach that combines various techniques and machine learning methods. The research confirms the importance of automated language processing in the fields of AI and linguistics, where statistical methods play a significant role in achieving better results.
Keywords: technological changes, linguistics, innovations, language technologies, automatic translation

By Nataliia Yuhan, Yuliia Herasymenko, Oleksandra Deichakivska, Anzhelika Solodka, Yevhen Kozlov

In the post-pandemic scenario, a study was conducted at I.E. 50499 Justo Barrionuevo Álvarez in Cusco, Peru, to investigate the relationship between the use of information technologies and digital competencies among teachers. With a sample of 54 teachers, a structured questionnaire was administered to assess their competencies. The results revealed a direct positive correlation between the use of technologies and digital competencies, with a Spearman's Rho coefficient of 0.877, indicating a significant relationship. Correlations between the use of information technologies and the dimensions of digital competencies ranged from moderate to high. Significant correlations were observed in areas such as problem-solving (Rho=0.457), information and digital literacy (Rho=0.633), and security (Rho=0.743), among others. These findings suggest that, despite limited experience and limited knowledge of digital technologies among teachers in the institution, there is a notable relationship between the use of these technologies and their digital competencies. This study underscores the need for further training in information technologies for teachers in non-modernized urban contexts and for those who are older adults with limited prior experience in the digital domain. Enhancing digital competencies is crucial for adapting to the educational challenges in this new era of education

By Lida Vásquez-Pajuelo, Jhonny Richard Rodriguez-Barboza, Karina Raquel Bartra-Rivero, Edgar Antonio Quintanilla-Alarcón, Wilfredo Vega-Jaime, Eduardo Francisco Chavarri-Joo

Introduction: Early diagnosis of Cardiovascular Disease (CVD) is vital in reducing mortality rates. Artificial intelligence and machine learning algorithms have increased the CVD prediction capability of clinical decision support systems. However, the shallow feature learning in machine learning and incompetent feature selection methods still pose a greater challenge. Consequently, deep learning algorithms are needed to improvise the CVD prediction frameworks. Methods: This paper proposes an advanced CDSS for CVD detection using a hybrid DL method. Initially, the Improved Hierarchical Density-based Spatial Clustering of Applications with Noise (IHDBSCAN), Adaptive Class Median-based Missing Value Imputation (ACMMVI) and Clustering Using Representatives-Adaptive Synthetic Sampling (CURE-ADASYN) approaches are introduced in the pre-processing stage for enhancing the input quality by solving the problems of outliers, missing values and class imbalance, respectively. Then, the features are extracted, and optimal feature subsets are selected using the hybrid model of Information gain with Improved Owl Optimization algorithm (IG-IOOA), where OOA is improved by enhancing the search functions of the local search process. These selected features are fed to the proposed Chaotic Rat Swarm Optimization-based Convolutional Neural Networks (CRSO-CNN) classifier model for detecting heart disease. Results: Four UCI datasets are used to validate the proposed framework, and the results showed that the OOA-DLSO-ELM-based approach provides better heart disease prediction with high accuracy of 97.57%, 97.32%, 96.254% and 97.37% for the four datasets. Conclusions: Therefore, this proposed CRSO-CNN model improves the heart disease classification with reduced time complexity for all four UCI datasets.

By Sasirega. D, Krishnapriya.V

Introduction: Thesis juries are a group of academics or experts whose purpose is to ensure the integrity and rigor in the processes of evaluation and academic defense of theses, as well as to provide critical and constructive feedback aimed at improving their quality.
Objective: To evaluate the scientific production in the Scopus, Web of Science, and Scielo databases of the thesis juries of the Faculty of Education of a public university in Peru.
Methods: Bibliometric, retrospective, and descriptive research that included 69 teachers who served as thesis juries during the period 2020-2023. The scientific production of the thesis committees was identified through the search of their publications registered in the Scopus, Web of Science, and Scielo databases.
Results: 56.5% of the teachers who served as thesis juries had no scientific production registered in the Scopus, Web of Science, or Scielo databases, while 43.5% did have some scientific production in these databases. Additionally, it was found that the scientific production of the teachers was mainly based on original articles, published in Spanish, and self-financed.
Conclusions: The scientific production in the Scopus, Web of Science, and Scielo databases of the thesis juries of the Faculty of Education of a public university in Peru was low. Therefore, it is imperative to implement policies aimed at strengthening their research and writing skills.

By Edwin Gustavo Estrada-Araoz, Guido Raúl Larico-Uchamaco, José Octavio Ruiz-Tejada, Jair Emerson Ferreyros-Yucra, Alex Camilo Velasquez-Bernal, Cesar Elias Roque-Guizada, María Isabel Huamaní-Pérez, Yasser Malaga-Yllpa

Efficiently managing loans granted can have an immediate effect on the profitability and viability of a financial institution. Considering this, this study determined the impact of past-due loans on the profitability of the Peruvian Municipal Savings and Loan Banks during the period 2022. A quantitative, non-experimental, cross-sectional, correlational-causal methodology was used, which employed documentary analysis and the design of a data sheet. The population and sample consisted of 11 Municipal Savings and Loan Associations, which have been approved and are inspected by the Superintendence of Banking, Insurance and Private Pension Fund Administrators. A positive and moderate correlation of Rho = 460 and a significance level greater than 0.05 (0.154 > 0.05) was found, that is, overdue loans have a positive, but not significant, impact on the profitability of these financial institutions. The behaviors of these variables allow us to conclude that having more past-due loans will not always result in lower profitability, since there may be other factors that help mitigate this negative impact.

By Cesar Alvino Poemape Alfaro, Miguel Fernando Ramos Romero, Flor de María Lioo Jordan, Viviana Inés Vellón Flores, Jesús Jacobo Coronado Espinoza, Abraham César Neri Ayala

The contribution of technology in the development of our daily activities has taken a giant step in the dependence of the citizen-technology-society with the integration of the Internet without glimpsing a border. It is therefore necessary to safeguard personal information if you have an active digital life. The identification of the factors and causes that lead to identity theft is a requirement for the technical and operational literacy of citizens, who are easy victims. This article aims to analyze some aspects of causes and factors of identity theft of citizens of the municipality of the center of the State of Tabasco. A quantitative instrument was designed, applied via Internet to a population of 3,158. The results show that citizens are unaware of several aspects of security in the environment of digital services, which, depending on gender, age and level of education, are captive in some scenario of digital insecurity.

By Gilberto Murillo González, German Martínez Prats, Verónica Vázquez Vidal

In the university context, it is a concern to improve English learning in students, given that much scientific information is found in this language, hence the interest in examining the related factors. The objective was to determine the relationship between academic self-efficacy (AA) and linguistic anxiety about learning English (ALAI). The research was quantitative, basic and descriptive correlational design, with a non-probabilistic sample of 246 students from a state university. The validity of the instruments was evaluated by means of expert judgment and factor analysis. Cronbach's alpha reliability of the AA was 0.938 and of the ALAI 0.908. The results showed a significant inverse correlation between the variables (r = -0.403, p = 0.001). It is concluded that the higher the AA, the lower the ALAI, which merits improving students' self-efficacy.

By Rafael Emiliano Sulca Quispe, Víctor Enrique Lizama Mendoza, Luisa Margarita Díaz Ricalde de Arenas, Carlos Heraclides Pajuelo Camones, Juan Pablo Trujillo Soncco

The article explores the increasing influence of artificial intelligence (AI) in education, addressing contemporary challenges and highlighting its significance in refining teaching methods and enhancing learning efficiency. It is a structured literature review that systematically analyzes existing literature on AI in education, drawing insights from prominent researchers to understand current and future trends. Four key questions guide the analysis: the relationship between education and AI, their interaction, AI's contribution to educational evolution, and research challenges. The study employs a systematic review of literature, focusing on works by eminent scholars such as Lee, Memarian, and Yuan, selected from the Scopus database spanning from 1986 to 2024. It follows a structured approach to gather and analyze data from selected studies. The article progresses by presenting an introduction to the topic, outlining the methodology, and summarizing and analyzing key findings from selected literature. It explores the intrinsic relationship between education and AI, their interaction, and AI's role in evolving the educational process. Major findings underscore the importance of a cautious and ethical approach to integrating AI in education. Despite its potential benefits, challenges and shortcomings in current research are acknowledged, urging for further exploration and consideration of ethical implications.

By Zouheir Boussouf, Hanae Amrani, Mouna Zerhouni Khal, Fouad Daidai

Introduction: in the educational context, the integration of artificial intelligence is transforming the way teachers teach and students learn. However, there are challenges that teachers must face when incorporating artificial intelligence into their pedagogical practice.
Objective: to evaluate the perspectives of Peruvian basic education teachers on the implementation of artificial intelligence in the educational context.
Methods: a quantitative, non-experimental, cross-sectional descriptive study was conducted. The sample consisted of 125 basic education teachers selected through probabilistic sampling. These participants were administered a scale designed to evaluate their perspectives on artificial intelligence, which demonstrated adequate metric properties.
Results: it was found that teachers had a partial knowledge of what artificial intelligence is and its scope. Among the advantages of artificial intelligence, it stands out that it was an effective teaching resource and a necessary tool to provide personalized education. However, among the disadvantages highlighted are concerns that it could foster academic dishonesty, doubts about its reliability, and a lack of confidence in its ability to guarantee the confidentiality of information.
Conclusions: the perspective of basic education teachers on the implementation of artificial intelligence in the educational context is heterogeneous. Although they recognize the disadvantages and have a partial knowledge of what artificial intelligence is and its scope, they show willingness to explore and take advantage of its possibilities in the educational field.

By Edwin Gustavo Estrada-Araoz, Jhemy Quispe-Aquise, Yasser Malaga-Yllpa, Guido Raúl Larico-Uchamaco, Giovanna Rocio Pizarro-Osorio, Marleni Mendoza-Zuñiga, Alex Camilo Velasquez-Bernal, Cesar Elias Roque-Guizada, María Isabel Huamaní-Pérez

This study examined how artificial intelligence (AI) has transformed the personalization of the virtual educational process in higher education programs. A systematic review of literature published between 2012 and 2023 was carried out, evaluating empirical studies, reports and review articles available in academic databases such as IEEE Xplore, SpringerLink and Google Scholar. Methods discussed include intelligent tutoring systems, learning analytics, and recommendation systems. The results showed that AI significantly improved the personalization of learning. Intelligent tutoring systems provide real-time adaptive feedback, adjusting content and pacing based on students' individual needs, improving their understanding and retention. Learning analytics helps identify student behavior patterns and predict academic issues, thereby facilitating timely interventions that help improve performance. Additionally, recommender systems personalize study materials based on student preferences and progress, thereby optimizing the educational experience. However, significant challenges have been identified, such as the need to protect data privacy and mitigate algorithmic biases that can affect the fairness and efficiency of these systems. In conclusion, the integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance. However, there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefits

By Elizabeth Magdalena Recalde Drouet, David Mauricio Tello Salazar, Tatiana Lizbeth Charro Domínguez, Pablo Jordán Catota Pinthsa

The area of medical imaging specialization, specifically in the context of brain tumor segmentation, has long been challenged by the inherent complexity and variability of brain structures. Traditional segmentation methods often struggle to accurately differentiate between the diverse types of tissues within the brain, such as white matter, grey matter, and cerebrospinal fluid, leading to suboptimal results in tumor identification and delineation. These limitations necessitate the development of more advanced and precise segmentation techniques to enhance diagnostic accuracy and treatment planning. In response to these challenges, the proposed study introduces a novel segmentation approach that combines the Grey Wolf Optimization approach and the Cuckoo Search approach within a Fuzzy C-Means (FCM) framework. The integration of GWO and CS is designed to leverage their respective strengths in optimizing the segmentation of brain tissues. This hybrid approach was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing segmentation methods. The study observed a 4,9 % improvement in accuracy, 3,5 % increase in precision, 4,5 % higher recall, 3,2 % less delay, and 2,5 % better specificity in tumor segmentation. The implications of these advancements are profound. By achieving higher precision and accuracy in brain tumor segmentation, the proposed method can substantially aid in early diagnosis and accurate staging of brain tumors, eventually leading to more effective treatment planning and improved patient outcomes. Furthermore, the integration of GWO and CS within the FCM process sets a new benchmark in medical imaging, paving the way for future investigation in the field of study

By Ayesha Agrawal, Vinod Maan

Introduction: since 2008, Morocco’s Tayssir program has been a key public initiative aimed at combating school dropout rates, by offering conditional cash transfers to households with school-aged children, particularly targeting rural communities with high poverty rates. This initiative seeks to ensure equitable access to education, regardless of socioeconomic status, and boosted school attendance rates.

Objective: to assess the impact of the Tayssir program on reducing school dropout rates in rural Morocco and to examine the effectiveness of targeting strategies and incentives provided to families.

Methods: the study utilized cross-sectional data from the Household Survey Panel Data. Propensity score matching (PSM) techniques were employed to estimate the program’s impact on school dropout rates, comparing beneficiaries with a control group not participating in the program. Various statistical analyses were conducted to explore the characteristics of participants and to validate the logistic model used.

Results: the propensity score matching analysis revealed a statistically significant reduction in school dropout rates among beneficiaries of the Tayssir program. The average treatment effect on the treated (ATET) demonstrated a decrease in dropout rates by approximately 43 % using one-to-one matching, 42,7 % with k-nearest neighbor, and 38,6 % via kernel matching methods. Furthermore, no significant gender differences were observed in the program’s impact.

Conclusions: the Tayssir program has significantly contributed to reducing school dropout rates in rural Morocco, ensuring better access to education for children from disadvantaged backgrounds. The program’s effectiveness underscores the importance of targeted interventions and conditional cash transfers in promoting educational attainment. Future recommendations include expanding the beneficiary base, refining targeting mechanisms, and establishing a unified social registry to improve program governance.

By Mohamed Bouincha, Youness Jouilil, Mustapha Berrouyne

Background: Job satisfaction is a key focus in organizational behavior studies, particularly relevant in the healthcare sector and nursing. It influences patient care quality and staff retention and is shaped by the work environment, working conditions, managerial support, and interactions among colleagues. However, there is limited research specifically addressing the job satisfaction of nurses in Peru, a critical area in health administration. Objective: This study aimed to evaluate the metric properties of the S20/23 job satisfaction scale among Peruvian nurses. Methods: An instrumental research design was employed using a non-probabilistic sample of 325 nurses from two hospitals in Lima, Peru. The Chilean version of the S20/23 scale was used, comprising four dimensions of job satisfaction (relationship with supervision, physical work space, professional fulfillment, and training and decision-making opportunities). Data analysis included descriptive statistics, confirmatory factor analysis (CFA), and reliability tests using Cronbach's Alpha and McDonald's Omega. Results: The CFA revealed a satisfactory fit for the four-dimensional structure with 18 items (χ2 = 387.290, df = 124, p < .001, CFI = 0.92, TLI = 0.90, RMSEA = 0.08, SRMR = 0.05). The scale also demonstrated high reliability for each dimension: relationship with supervision (α = 0.90, ꞷ = 0.87), physical work space (α, ꞷ = 0.92), professional fulfillment (α, ꞷ = 0.88), and training and decision-making opportunities (α = 0.88, ꞷ = 0.84), with acceptable factor loadings (>0.70). Conclusions: The adapted 18-item S20/23 scale is a valid and reliable tool for assessing job satisfaction among Peruvian nurses. The study highlights the importance of specific job satisfaction dimensions, such as relationships with supervisors and professional development opportunities, in the Peruvian nursing context.

By Allison Ramirez-Cruz, Caleb Sucapuca, Mardel Morales-García, Víctor D. Álvarez-Manrique, Alcides A Flores-Saenz, Wilter C. Morales-García

Introduction: The scientific production of universities plays a crucial role in the generation and dissemination of knowledge, as well as in strengthening the position of academic institutions on both national and international levels.
Objective: To evaluate the scientific production in the Scopus database of a public university in southern Peru.
Methods: A bibliometric and retrospective investigation was conducted. Documents indexed in the Scopus database were analyzed by evaluating the quantity of documents, authors, journals where the documents were published, types of documents, language of publication, funding, areas of knowledge to which the documents belong, and co-authorship networks.
Results: A total of 763 indexed documents were identified in the Scopus database, showing a trend towards increased production in recent years. The majority of indexed documents were characterized by being original articles, published in foreign journals and in English language, and self-financed. Additionally, it was observed that more documents were published in the areas of Social Sciences and Agricultural and Biological Sciences.
Conclusions: In recent years, significant growth has been observed in the scientific production in the Scopus database of a public university in southern Peru. Therefore, it is imperative to promote an institutional research culture, focused on the development of research skills, with the purpose of increasing both the quantity and quality of publications.

By Duverly Joao Incacutipa-Limachi, Edwin Gustavo Estrada-Araoz, Yony Abelardo Quispe-Mamani, Euclides Ticona-Chayña, Adderly Mamani-Flores

Effective presentation slides are crucial for conveying information efficiently, yet existing tools lack content analysis capabilities. This paper introduces a content-based PowerPoint presentation generator, aiming to address this gap. By leveraging automated techniques, slides are generated from text documents, ensuring original concepts are effectively communicated. Unstructured data poses challenges for organizations, impacting productivity and profitability. While traditional methods fall short, AI-based approaches offer promise. This systematic literature review (SLR) explores AI methods for extracting Data from unstructured details. Findings reveal limitations in existing methods, particularly in handling complex document layouts. Moreover, publicly available datasets are task-specific and of low quality, highlighting the need for comprehensive datasets reflecting real-world scenarios[1]. The SLR underscores the potential of Artificial-based approaches for information extraction but emphasizes the challenges in processing diverse document layouts. Proposed is a framework for constructing high-quality datasets and advocating for closer collaboration between businesses and researchers to address unstructured data challenges effectively.

By Rajkumar N, Balusamy Nachiappan, C. Kalpana, Mohanraj A, B Prabhu Shankar, C Viji

Introduction: Digital transformation in the Peruvian educational sector has experienced a significant boost after facing the COVID-19 pandemic. During the period between 2020 and 2023, various innovative methods have been implemented to ensure the continuity of the academic year.
Objective: Explain how the digital transformation was carried out in the Peruvian educational sector after facing the COVID-19 pandemic to the present (2020 – 2023).
Method: Examples from many institutions, statistical studies and scientific and technological references were taken into account to achieve the objective. Throughout this work we are analyzing the different and innovative methods used by teachers to provide continuity to the academic year and how digital challenges were overcome.
Results: 78 documents from Scopus and Scielo were reviewed, leaving 62 after filtering. These cover 8 categories on the impact of the pandemic on education, the transition to online teaching, job skills, challenges and advantages of virtual education, innovation in higher education, educational evaluation in virtual environments, educational internationalization and challenges for teachers during the COVID-19 pandemic.
Conclusions: In conclusion, the digital transformation in the Peruvian educational sector after the COVID-19 pandemic has been fundamental to guarantee the continuity of the teaching-learning process.

By Anali Alvarado-Acosta, Jesús Fernández-Saavedra, Brian Meneses-Claudio

Introduction: The knowledge of artificial intelligence (AI) by university professors provides them with the ability to effectively integrate these innovative technological tools, resulting in a significant improvement in the quality of the teaching and learning process.
Objective: To assess the level of knowledge about AI in a sample of Peruvian university professors.
Methods: Quantitative study, non-experimental design and descriptive cross-sectional type. The sample consisted of 55 university professors of both sexes who were administered a questionnaire to assess their level of knowledge about AI, which had adequate metric properties.
Results: The level of knowledge about AI was low for 41.8% of professors, regular for 40%, and high for 18.2%. This indicates that there is a significant gap in the knowledge of university professors about AI and its application in education, which could limit their ability to fully leverage AI tools and applications in the educational environment and could affect the quality and effectiveness of teaching. Likewise, it was determined that age and self-perception of digital competencies of professors were significantly associated with their level of knowledge about AI (p<0.05).
Conclusions: Peruvian university professors are characterized by presenting a low level of knowledge about AI. Therefore, it is recommended to implement training and professional development programs focused on artificial intelligence, in order to update and improve their skills in this field.

By Edwin Gustavo Estrada-Araoz, Yesenia Veronica Manrique-Jaramillo, Víctor Hugo Díaz-Pereira, Jenny Marleny Rucoba-Frisancho, Yolanda Paredes-Valverde, Rosel Quispe-Herrera, Darwin Rosell Quispe-Paredes

By using digital technologies to streamline procedures and increase the productivity of public services, e-government modernizes administrative management and makes government more accessible and responsive to citizens' requests for assistance. The purpose of this study was to determine the relationship between e-government and administrative management in the Provincial Municipality of Huaura, Peru. Using a sample of 129 administrative workers and a population of 194 administrative workers, a quantitative, non-experimental, cross-sectional and correlational methodology was developed. Participants completed a survey-questionnaire. The results showed a substantial relationship between administrative management and e-government in the Provincial Municipality of Huaura, with a sig. of less than 5 % and an Rho value of 0.596. This allowed us to deduce that the planning, organization, management and control of the entity's public resources will improve to the extent that a more solid electronic infrastructure is implemented, political will and institutional architecture, governance through transformations and organizational redesign, and whether or not its citizens have the necessary tools or knowledge to access online information and services.

By Víctor Joselito Linares-Cabrera, María Amelia Díaz-Nicho de Linares, Abrahán Cesar Neri-Ayala, Cesar Armando Díaz-Valladares, Pablo Cesar Cadenas-Calderón, Gladys Magdalena Aguinaga-Mendoza

In recent days, traffic prediction has been essential for modern transportation networks. Smart cities rely on traffic management and prediction systems. This study utilizes state-of-the-art deep learning and machine learning techniques to adjust to changing traffic conditions. Modern DL models, such as LSTM and GRU, are examined here to see whether they may enhance prediction accuracy and provide valuable insights. Repairing problems and errors connected to weather requires hybrid models that integrate deep learning with machine learning. These models need top-notch training data to be precise, flexible, and able to generalize. Researchers are continuously exploring new approaches, such as hybrid models, deep learning, and machine learning, to discover traffic flow data patterns that span several places and time periods. Our current traffic flow estimates need improvement. Some expected benefits are fewer pollutants, higher-quality air, and more straightforward urban transportation. With machine learning and deep learning, this study aims to improve traffic management in urban areas. Long Short-Term Memory (LSTM) models may reliably forecast traffic patterns.

By Syed Aleem Uddin Gilani, Murad Al-Rajab, Mahmoud Bakka

This study aims to analyze the use of project-based learning with augmented reality (AR) on the communication skills of autistic children through systematic literature review and theoretical bibliometric analysis of research on autistic children's communication skills sourced from Scopus from 2013 to 2022. The research method is a systematic literature review with analysis theory and bibliometric analysis with VOSviewer and RStudio applications. This research was conducted in several stages, namely determining i) research questions; ii) inclusion and exclusion criteria; iii) quality assessment; iv) data collection; and v) bibliometric analysis. The results of this study note that research on the communication skills of autistic children is still a research trend that is of great interest to researchers with an increase in research occurring from 2015 to 2022. Countries from the Americas and Asia have contributed the most to research on the communication skills of autistic children. The subject areas of psychology and social science are the subject areas with the most contributions, namely 22% each of the 165 articles found. There is a relationship between the term project (P), communication skills (CS), and autism spectrum disorder (ASD). This relationship is shown by the link strength P→CS is 2 and the link strength CS→ASD is 4. The characteristic requirements for autistic children for project-based learning with AR are middle class autistic children. The characteristics possessed by project-based learning can help in training the level of communication skills of autistic children and it would be better if assisted with the use of AR.

By Dwi Fitria Al Husaeni, M. Munir, R. Rasim, Laksmi Dewi, Azizah Nurul Khoirunnisa

Introduction: The main areas of application of artificial intelligence for algorithmic analysis and optimization of information flows in tasks of multiparametric diagnostics by means of computer engineering are considered. The issues of globalization of all areas of humanitarian, scientific, technical and engineering activities of human society are considered. It is noted that the common denominator of all directions is information flows. The main tools for their management and algorithmic analysis are multi-parametric methods of artificial intelligence.
Methods: One of its most relevant areas has been highlighted - the use of evolutionary algorithms in combination with modern diagnostic systems based on computer engineering. The possibility of using intelligent analysis of data from biophysical laser systems in assessing the state of “living matter” - the biological media of the human body - is considered.
Results: Through algorithmic optimization, a set of new cancer detection markers was determined: the statistical parameters of optical anisotropy maps wavelet coefficients linear distributions - the differences between these markers lie in the range from 4 to 20 times; the asymmetry of the wavelet coefficients autocorrelation function - the differences between these markers lie within two orders of magnitude; for normal state, the wavelet coefficients distributions are multifractal; for prostate cancer, the distributions of the wavelet amplitude coefficients are multifractal.
Conclusions: A comparative study of the algorithmic optimization of differences of cancer through the use of multiparametric statistical, correlational, fractal and wavelet analysis of polarization tomograms of optical anisotropy of blood layers of donors and prostate cancer sicks is presented

By Volodymyr Yakhno, Vadym Kolumbet, Petar Halachev, Vladyslav Khambir, Ruslan Ivanenko

This overview traced the evolution of data management, transitioning from traditional ETL processes to addressing contemporary challenges in Big Data, with a particular emphasis on data ingestion and schema matching. It explored the classification of data ingestion into batch, real-time, and hybrid processing, underscoring the challenges associated with data quality and heterogeneity. Central to the discussion was the role of schema mapping in data alignment, proving indispensable for linking diverse data sources. Recent advancements, notably the adoption of machine learning techniques, were significantly reshaping the landscape. The paper also addressed current challenges, including the integration of new technologies and the necessity for effective schema matching solutions, highlighting the continuously evolving nature of schema matching in the context of Big Data.

By Oumaima El Haddadi, Max Chevalier, Bernard Dousset, Ahmad El Allaoui, Anass El Haddadi, Olivier Teste

Integrating artificial intelligence (AI) into sales processes at a business level, specifically, in the segmentation of potential customers, is currently a very important issue for the promotion of your products and services. The present study focused on the analysis of the effectiveness of the machine learning approach used in mass consumption companies for the segmentation of potential customers. To achieve this objective, a systematic review of the literature will be carried out with a qualitative approach and supported by the PRISMA methodology. The results achieved in the review carried out showed that machine learning algorithms present better results compared to other approaches; Furthermore, regarding customer segmentation, this can be done through grouping, which is one of the most recognized machine learning techniques. It is concluded that it is necessary to expand the methods provided by this approach, using them to extract knowledge from unstructured, monitoring, and network data to achieve descriptive, causal, and prescriptive analyses; In addition, to outline the journey that customers take when purchasing and deploy decision support capabilities. All these benefits, at a business level, are provided by machine learning, reason enough for the proposed marketing strategies to be based on the information it offers.

By Eduardo Rafael Jauregui Romero, Javier Alca Gomez, Manuel Eduardo Vilca Tantapoma, Orlando Tito Llanos Gonzales

The finishing process in the textile sector is recognized as one of the most complex. This complexity arises from the diversity of structures, the multiple steps involved, the use of complex machinery, the variety of materials, chemicals and dyes, and the need to combine creativity and precision. Therefore, it is crucial to have tools that can improve efficiency, flexibility, and decision-making in this complex area. This literature review aims to provide relevant information on the use of digital engineering in the field of textile finishing. In this review, we used a systematic literature review methodology to examine how digital engineering is applied in the dyeing and finishing process. The data for this study was collected from reputed databases such as Science Direct, IEEE Xplore, Textile Research Journal and Google Scholar. We used the Prisma framework to select relevant articles, which led to the exclusive inclusion of journal articles in our literature review. A comprehensive framework has been developed to understand the impacts of using digital engineering. The approach presented in this framework provides a comprehensive and highly effective approach to addressing the complex challenges associated with ambiguity, modifications and subtleties frequently observed in the ennobling process. The results of various studies explored different aspects, such as properties of textile materials, chemicals and dyes, performance of finishing machines, organizational performance of finishing companies, as well as health concerns and safety at work. Although these studies have provided valuable solutions, they unfortunately remain insufficient to meet the requirements of the finishing process, which remains a complex area characterized by uncertainties, variations, and subtleties inherent to the practice. This particularity of each dyed and finished product promotes an environment conducive to experimentation and continued research.

By Mostafa Elkhaoudi, Mhammed El Bakkali, Redouane Messnaoui, Omar Cherkaoui, Aziz Soulhi

This study analyzes the potential of Blockchain technology to improve security and privacy in the management and verification of digital identities, aspects that currently face challenges. Through a literature review, it was found that Blockchain offers a decentralized approach that provides greater control to users over their data through cryptographic mechanisms. The cases examined demonstrate benefits such as efficiency and automation in identity processes. However, further research is required to address pending challenges and achieve widespread application considering the particularities of each context. The objective is to analyze how this technology can positively transform the way digital identity is managed in an inclusive and privacy-respecting manner.

By Edith Mariela Quispe Sanabria, Julio Cesar Pizarro Avellaneda, Edward Eddie Bustinza Zuasnabar, Ana Mónica Huaraca García, Lizet Doriela Mantari Mincam, Hilario Romero Giron, Yesser Soriano Quispe