Data and Metadata

Digital Challenges: The Need to Improve the Use of Information Technologies in Teaching

Fri, 09 Feb 2024 00:00:00 +0000

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

Vehicle license plate recognition system with artificial intelligence for the detection of alerted vehicles at the National University of Ucayali

Sun, 14 Apr 2024 00:00:00 +0000

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.

Technological disinformation: factors and causes of cybernaut identity theft in the digital world

Gilberto Murillo González, German Martínez Prats, Verónica Vázquez Vidal — Fri, 29 Dec 2023 00:00:00 +0000

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.

Big Data De-duplication using modified SHA algorithm in cloud servers for optimal capacity utilization and reduced transmission bandwidth

Rajendran Bhojan, Manikandan Rajagopal, Ramesh R — Sat, 30 Mar 2024 00:00:00 +0000

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.

The effectiveness of education assistance programs using AI innovation. Case for tackling school dropout in Morocco

Mohamed Bouincha, Youness Jouilil, Mustapha Berrouyne — Sat, 13 Apr 2024 00:00:00 +0000

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.

Assessment of the level of knowledge on artificial intelligence in a sample of university professors: A descriptive study

Wed, 24 Apr 2024 00:00:00 +0000

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.

Transformation and digital challenges in Peru during the COVID-19 pandemic, in the educational sector between 2020 and 2023: Systematic Review

Anali Alvarado-Acosta, Jesús Fernández-Saavedra, Brian Meneses-Claudio — Sat, 10 Feb 2024 00:00:00 +0000

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.

Resource allocation on periotity based schuduling and improve the security using DSSHA-256

K.Prathap Kumar, R. Rohini — Thu, 08 Feb 2024 00:00:00 +0000

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

Intelligent Optimization Framework for Future Communication Networks using Machine Learning

Thu, 18 Apr 2024 00:00:00 +0000

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.

Automatic Mobile Learning System for the Constant Preparation of the Student Community

Wed, 31 Jan 2024 00:00:00 +0000

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.

Datamart for the analysis of information in the sales process of the company WC HVAC Engineering

Luz Castillo-Cordero, Milagros Contreras-Chihuán, Brian Meneses-Claudio — Thu, 11 Jan 2024 00:00:00 +0000

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.

Validation of a Job Satisfaction Scale among Health Workers

Tue, 27 Feb 2024 00:00:00 +0000

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.

A dragonfly algorithm for solving the Fixed Charge Transportation Problem FCTP

Thu, 08 Feb 2024 00:00:00 +0000

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.

Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach

Sat, 13 Apr 2024 00:00:00 +0000

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

Fuzzy Decision-Making Model for the inventory leveling under uncertainty condition

Hatim Lakhouil, Aziz Soulhi — Thu, 11 Jan 2024 00:00:00 +0000

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.

Validation of an Organizational Climate Scale in health workers

Wed, 13 Mar 2024 00:00:00 +0000

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.

Economic Growth Unleashed: The Power of Institutional Quality

El Houssaine fathi, Ahlam qafas, Youness Jouilil — Wed, 24 Apr 2024 00:00:00 +0000

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.

Document processing system with digital signatures and administrative management in public universities. A review of the literature

Sun, 14 Apr 2024 00:00:00 +0000

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

A Study of Factors Influencing Happiness in Korea: Topic Modelling and Neural Network Analysis

Ji-Hyun Jang, Nemoto Masatsuku — Thu, 14 Mar 2024 00:00:00 +0000

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.

A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification

G. Meenalochini, D. Amutha Guka, Ramkumar Sivasakthivel, Manikandan Rajagopal — Wed, 07 Feb 2024 00:00:00 +0000

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

Data lake management using topic modeling techniques

Mohamed CHERRADI — Mon, 15 Apr 2024 00:00:00 +0000

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).

Social Capital in Small Industrial Firms and Its Link with Innovation

Sat, 30 Mar 2024 00:00:00 +0000

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.

Technological assistance in highly competitive sports for referee decision making: A systematic literature review.

Rafael Thomas-Acaro, Brian Meneses-Claudio — Wed, 10 Jan 2024 00:00:00 +0000

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.

Investigating the attitude of university students towards the use of ChatGPT as a learning resource

Wed, 24 Apr 2024 00:00:00 +0000

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.

How Digital Competence Reduces Technostress

Thu, 25 Apr 2024 00:00:00 +0000

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.

Applicable methodologies for business continuity management in IT services: A systematic literature review

Renzo Huapaya-Ruiz, Brian Meneses-Claudio — Mon, 08 Jan 2024 00:00:00 +0000

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.

Validation and invariance of an Individual Work Performance Questionnaire (IWPQ-P) in Peruvian Nurses

Wed, 13 Mar 2024 00:00:00 +0000

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.

Overview on Data Ingestion and Schema Matching

Thu, 08 Feb 2024 00:00:00 +0000

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.