Field Of Computer Science Research Articles

A streamlined and adaptive approach for accrediting a French education system program by ABET-EAC

IntroductionIn recent times, there has been a notable increase in the demand for accreditations, driven by their essential role in recognizing diplomas, elevating the quality of higher education, facilitating student mobility, enabling the pursuit of multiple degrees, and influencing university rankings. This surge in demand reflects the evolving landscape of higher education in a world that is becoming progressively interconnected and competitive.MethodsThis paper presents a comprehensive methodology for accrediting study programs in French higher education institutions worldwide that are seeking the Accreditation Board for Engineering and Technology (ABET) accreditation. ABET accreditation is a globally recognized organism of quality for education programs in engineering, computer science, and related fields. The methodology proposed in this paper focuses on aligning ABET’s requirements and standards with the specific context of the French Education System (FES). It provides clear guidelines and practical steps for higher education institutions to effectively navigate the accreditation process. The methodology covers the verification of existing study programs, identification of areas for improvement, implementation of corrective actions, and continuous progress monitoring. It emphasizes the importance of continuous improvement and aligning programs with the Program Educational Objectives (POEs). Concrete examples and best practices are shared to illustrate successful implementation of the methodology developed based on the example of the engineering college programs at our university.Results and discussionThe results obtained in this case study demonstrate the applicability and effectiveness of this approach within a specific educational context. The approach presented in this contribution can serve as a model for other countries seeking to adapt ABET accreditation requirements to their own higher education systems, further promoting diversity and openness. This reinforces the credibility of the methodology and paves the way for its use in other institutions and programs that operate within similar systems.

The Application of Artificial Intelligence (AI) in Library and Information Centre

Artificial intelligence has taken over many industries and is seen as a continuation of human intelligence. Artificial intelligence applications in libraries have revolutionized the information industry. Additionally, it has given modern libraries' development fresh life. It is believed that integrating artificial intelligence into library operations will open up new internet resources for libraries. Virtual reality, which engages users with libraries and improves information literacy abilities, is one of the valid innovations that librarians are constantly utilizing to engage and expand services for their patrons. It wouldn't be incorrect to argue that the development of the computer accelerated the process of digitization, much as the discovery of the wheel ushered in the mechanical age of human existence. Humans are the only animals with the innate ability to think for themselves. With the power of independent thought, humans have created a great deal of innovative technologies. One example of them is the development of the computer. The most significant development in computer technology that humans have made with the use of their intelligence is artificial intelligence. The goal of the computer science field of artificial intelligence is to build computers with human-like intelligence. Almost everywhere that computers are used, artificial intelligence is now being deployed. The need for this is growing daily, namely in the areas of science, health, automobiles, engineering, climates, business, pharmaceuticals, and academic libraries. AI must be used in libraries for both technical and library services purposes. The application of AI will expedite and improve the quality of work done in libraries, allowing them to offer a greater number of services with fewer staff members. KEYWORDS: Artificial Intelligence, Big Data, Internet of Things, Smart Library.

Deployment of AI Tools and Technologies on Academic Integrity and Research

Academic integrity is a set of ethical ideals and values that guide the behavior of individuals in academic and educational settings. It encompasses honesty, trustworthiness, fairness, and a commitment to upholding the highest standards of ethical conduct in the quest for knowledge, learning, and research. Academic integrity is essential in maintaining the trustworthiness, reputation, and effectiveness of educational institutions and scholarly communities. Whereas, AI, or Artificial Intelligence, is a broad field of computer science that focuses on creating frameworks, software, or machines that can perform tasks that would typically require human intelligence. These tasks include problem-solving, learning from experience, understanding natural language, recognizing patterns, and making choices. AI systems aim to mimic or replicate human cognitive functions, and they can range from simple rule-based systems to highly complex, autodidactic neural networks. AI can significantly impact academic integrity and research in both positive and potentially challenging ways.

A Survey on Fake News Detection in Social Media Using Graph Neural Networks

Nowadays, social media has become the key source of information for anyone seeking about current events across the world. This information may be fake or real news. On social media platforms, fake news negatively impacts politics, the economy, and health, and affects the stability of society. The research on fake news detection has received widespread attention in the field of computer science. There are many effective methods of fake news detection technology including natural language processing (NLP) and machine learning techniques, primarily focusing on content analysis and user behavior. While these methods have shown promise, they often fall short in capturing the complex relational and propagation patterns inherent in social networks. Fake news exhibits distinct features such as misleading headlines, and fabricated content, making its detection challenging. To address these issues, Graph Neural Networks (GNNs) have been introduced as a superior solution. GNNs are particularly effective in processing graph-structured data, allowing them to model the intricate connections and dissemination patterns of news in social networks more accurately. This study provides an overview A variety of false information and their characteristics and discusses various techniques and features used in fake news detection. As well as advanced GNN-based techniques and datasets used to implement practical fake news detection systems from multiple perspectives and future research directions. In addition, tables and summary figures help researchers understand the full picture of fake news detection. Finally, the object of this review is to help other researchers improve fake news detection models using GNNs.

A review on applications and uses of Artificial Intelligence

Artificial intelligence (AI) is a field of computer science which aims to mimic cognitive knowledge of humans to machines. The growth of AI in the past decade is tremendous and soon in the future it will be widely used in many fields. This growth might involve replacing humans or helping humans to finish their work quicker. Even though the use of AI is beneficial in all fields, it also has lots of limitations associated with it when used in any field. The potential barriers which hinder the growth of AI include the development of suitable algorithms, data security and privacy issues. Hence, it is crucial to analyse the advantages and limitations before implementing AI in any field to determine the role of AI in that particular field. AI’s growth is most notable in the four fields namely medicine, banking, education and agriculture. This paper analyses the uses and the applications of AI along with the advantages, drawbacks and limitations of using AI in each field.

Artificial intelligence in pharmacy an overview

Intelligent technologies will someday replace or enhance human talent in many areas. Artificial intelligence is the intelligence exhibited by robots or software. It falls under the field of computer science. Computer scientists are becoming more and more interested in studying artificial intelligence because it has significantly enhanced human lives in a variety of ways. Over the past 20 years, artificial intelligence has led to a major boost in the performance of industrial and service systems. Expert systems are a rapidly developing technology that are the result of research into artificial intelligence. Artificial intelligence applications known as expert systems are widely used today to solve complex problems in a range of fields, including science, engineering, business, medicine, and weather forecasting. Artificial intelligence technology have led to improvements in quality and efficiency in certain sectors. This page gives a summary of the technology and some possible uses for it. This paper will also look at the current applications of artificial intelligence technologies in the fields of computer games, accounting databases, hospital inpatient care, medical image classification, network intrusion, Power System Stabilization (PSS) design, and network security against hackers.

Turkish Language Prospective Teachers’ Perceptions of Metaphors Regarding Artificial Intelligence

The concept of Artificial Intelligence (AI) initially emerged as a term in the field of computer science. In the subsequent years, this concept transcended its origins and became relevant across various domains of human life. Nowadays, it’s possible to encounter AI in nearly every aspect of human life. In this context, it’s considered noteworthy to examine individuals’ metaphorical perceptions of AI. Accordingly, the purpose of the research is determined as the investigation of Turkish prospective teachers’ metaphorical perceptions of AI. Phenomenology, a qualitative research method, is employed in the study. The study group consists of 115 voluntary Turkish prospective teachers, 94 of whom are female and 21 are male, studying at the Faculty of Education at Artvin Çoruh University. Among the prospective teachers, 36 are first-year students, 37 are second-year students, 26 are third-year students, and 16 are fourth-year students. Data for the research are collected through semi-structured interview forms prepared by the researchers. Content analysis method is used for data analysis, and the data are presented by tabulating them along with frequency values. The analysis reveals that 115 Turkish language prospective teachers produced a total of 110 metaphors. Among these metaphors, 21 belong to male prospective teachers, while 89 belong to female prospective teachers. Metaphors such as human (f19), robot (f10), future (f4), ocean (f3), storm (f2), spring (f2), assistant (f4) are found to be generated by both female and male prospective teachers. In addition to positive metaphors, Turkish language prospective teachers also generated negative metaphors like threat (f2), massacre (f1), monster (f1), etc., concerning AI. Consequently, although Turkish language prospective teachers developed some negative metaphors, it’s observed that their perceptions of AI are predominantly positive.

Analysis of Two Hybrid Schemes to Solve the Benjamin-Bona-Mahony (BBM) Equation

In this research, we compare and contrast two numerical methods, the Laplace Adomian Decomposition Method (LADM) and the Elzaki Projected Differential Transform Method (EPDTM), for solving the Benjamin-Bona-Mahony (BBM) equation, and this helps in evaluating their performance, assessing robustness, gaining insights into solution behavior, validating results, generalizing their applicability, and advancing the field. This rigorous comparisons validates the efficacy of the methods as we compare the exact results and the results of LADM and EPDTM, plotting the convergent of the two methods to identify the method that is highly convergent and accurate. Results identify the Elzaki Projected Differential Transform Method (EPDTM) as having a simple and accurate numerical method for solving the BBM equation. EPDTM enhances accessibility, computational efficiency, accuracy, robustness, and validation of the solutions, supporting the understanding and analysis of wave phenomena described by the BBM equation. This study contributes to the advancement of the field of numerical analysis and computational science as practical applications of BBM equation in various fields, including coastal engineering, wave modeling, and wave stability analysis cannot be over emphasize.

Neural Networks within Generative AI: A Review from a Marketing Research Perspective

Focusing on the role of neural networks within Generative Artificial Intelligence, this paper reviews their operating principles, recent developments, and implications for research in the marketing discipline. Special emphasis is placed on generative networks that produce synthetic data with a high degree of similarity to original data – such as realistic images, textual, or audio content – thereby expanding the potential for applied marketing practices. Besides significant opportunities opened up by Generative Artificial Intelligence, the review also elaborates on challenges, including technical obstacles like achieving an adequate Nash equilibrium between competing network components, and application-related considerations such as finding reliable metrics to evaluate their business performance. Broader implications are also analysed from recent publications, including ethical dilemmas related to data authenticity. As a result, this study provides a distinct perspective within the intersecting fields of marketing and computer science in the discourse surrounding Generative Artificial Intelligence, underscoring the research innovations arising from neural network models.

AG-SpTRSV: An Automatic Framework to Optimize Sparse Triangular Solve on GPUs

Sparse Triangular Solve (SpTRSV) has long been an essential kernel in the field of scientific computing. Due to its low computational intensity and internal data dependencies, SpTRSV is hard to implement and optimize on GPUs. Based on our experimental observations, existing implementations on GPUs fail to achieve the optimal performance due to their sub-optimal parallelism setups and code implementations, and lack of consideration of the irregular data distribution. Moreover, their algorithm design lacks the adaptability to different input matrices, which may involve substantial manual efforts of algorithm redesigning and parameter tuning for performance consistency. In this work, we propose AG-SpTRSV, an automatic framework to optimize SpTRSV on GPUs, which provides high performance on various matrices while eliminating the costs of manual design. AG-SpTRSV abstracts the procedures of optimizing an SpTRSV kernel as a scheme and constructs a comprehensive optimization space based on it. By defining a unified code template and preparing code variants, AG-SpTRSV enables fine-grained dynamic parallelism and adaptive code optimizations to handle various tasks. Through computation graph transformation and multi-hierarchy heuristic scheduling, AG-SpTRSV generates schemes for task partitioning and mapping, which effectively address the issues of irregular data distribution and internal data dependencies. AG-SpTRSV searches for the best scheme to optimize the target kernel for the specific matrix. A learned lightweight performance model is also introduced to reduce search costs and provide an efficient end-to-end solution. Experimental results with SuiteSparse Matrix Collection on NVIDIA Tesla A100 and RTX 3080 Ti show that AG-SpTRSV outperforms state-of-the-art implementations with geometric average speedups of 2.12x ∼ 3.99x. With the performance model enabled, AG-SpTRSV can provide an efficient end-to-end solution, with preprocessing times ranging from 3.4 to 245 times of the execution time.

Self-care among breast cancer survivors: A concept analysis

Background: Breast cancer survivors have an excellent prognosis and a high survival rate, thus each survivor requires self-care abilities throughout their complex therapy. The concept of self-care is not a new concept in the nursing field, but it is necessary to analyze it as there are still inconsistencies in its use and unclear definitions, especially in the context of breast cancer survivors. Purpose: The purpose of this study was to describe the concept of self-care among breast cancer survivors by employing the Walker and Avant’s analysis method. Methods: The Walker and Avant’s concept analysis method comprises of eight systematic steps. The literature search was conducted through electronic databases, such as ScienceDirect, MEDLINE, EBSCO, ProQuest and SAGE, with the search constraints ranging from 2003 to 2023. Results: The literature search identified 115 articles in the fields of medicine, nursing, sociology, psychology, economics, anthropology, pharmacy, computer science and midwifery. Following the analysis, 30 articles were selected for this study. The concept of self-care was defined as: 1) survivor activity; 2) survivor awareness; 3) self-management; 4) prevention of morbidity and recurrence; 5) improved health. Meanwhile, the concept’s antecedents included self-efficacy, self-care agency and social support, and the consequences included symptom control, improved coping, improved well-being, and improved quality of life. Conclusions: Understanding the attributes of self-care among breast cancer survivors along with their antecedents and consequences could facilitate its development in nursing practices. The self-care concept is utilized for self-care assessment and implementation of appropriate interventions in the context of breast cancer survivors who have completed the therapy.

TRANSAKSI CRYPTOCURRENCY DITINJAU DALAM PERSPEKTIF HUKUM POSITIF DAN HUKUM ISLAM

The Globalization system has had a huge impact on modern society, especially in the fields of science, technology, information and communication, all of which are developing rapidly. The most famous currency is Bitcoin or cryptocurrency. Because there are now no clear boundaries and no recognition of the existence of cryptocurrencies in Indonesia, these cryptocurrencies are facing a number of polemics regarding their use throughout the world, including in that country. The debate over whether Bitcoin is halal or haram in Islam continues among scholars who study the Koran, hadith, ijma and other Islamic books. One important area of expertise in the world of information security is cryptography. Today, professionals in the fields of computer science and mathematics discovered a new application of encryption that could improve the quality of life of local communities: the buying and selling of digital currencies known as cryptocurrencies. Digital currencies that are not subject to government regulation are known as cryptocurrencies; this does not include official cash. This cryptocurrency is the forerunner to the creation of digital currency which is now known as Bitcoin currency, which can be used like other types of money. And the result of the discussion is that whether scholars allow cryptocurrency or not. This research uses a qualitative method where this method focuses on in-depth observation. Therefore, the use of qualitative methods in this research can produce a study of a phenomenon that is more comprehensive with literature research. Based on the background information provided previously, the author identifies problems that require further investigation. Among other things, what is the meaning of cryptocurrency in the current understanding and context and what is Islamic law regarding the use of cryptocurrency as a transaction tool.

Formation of Competences in the Field of Information Security for Future Bachelors of Applied Informatics

The purpose of the study is to develop a model for developing competencies in the field of information security for bachelors in the field of Applied Information Science. The relevance of this problem is determined by modern requirements for enterprise employees and, accordingly, the process of their training. Demand for qualified employees who have skills not only in their field of activity, but also in areas relevant to the information society, such as information culture and the ability to provide an information enterprise.Materials and research methods. To achieve this goal, a competency-based approach was used to activate the process of developing the readiness and ability of subjects to make independent decisions, express independent judgments and at the same time participate in a common cause, create relationships with other participants in joint activities. The following methods were used in the work: analysis of pedagogical, scientific, technical and methodological literature on the research problem, systematization and generalization and visual presentation of the results.Results. As part of the study, a tabular model for the formation of competencies in the field of information security was developed based on the competency-based approach. The article reflects a model for developing competence in the field of information security using the example of additional professional competencies - 2 (the ability to take part in project management, organizing IT infrastructure and information security management) in the process of studying the discipline “Information Security”. The component blocks of the developed model are described: diagnostic, motivational, theoretical, practical and control-analytical. In accordance with the stages of this process of developing competencies and the tasks to be solved at each stage, various innovative forms and methods are presented. The results of the paper show that the process of developing competencies in the field of information security among bachelors in the “Applied Information Science” area is complex and multifaceted. Even though bachelors of applied computer science will not perform the same job functions as information security specialists, their training should be sufficient to enable them to design and build safe and reliable information systems. The pedagogical search is not completed, but it has acquired the necessary boundaries, unique centers of crystallization around which the academic discipline is designed.Conclusion. We can conclude that training specialists in the field of information security is an urgent task of modern society, the solution of which is dependent on scientific justification and identification of a set of necessary competencies that a modern specialist should have. The components of competencies in the field of information security that we have identified, the generalized tabular model of these competencies that we have proposed, as well as the stages, methods and forms of developing these competencies among bachelors of Applied Information Science require additional study. The conducted research allows us to outline ways to solve the multifaceted and complex problem of forming students’ competencies in the field of information security, taking into account modern requirements for the training of specialists not only in the field of computer science and information technology.

Pushing Boundaries: AI and Computer Science in the Era of Technological Revolution

The advent of Artificial Intelligence (AI) has heralded a transformative era in Computer Science, revolutionizing various facets of technology. This paper explores the profound impact of AI on the field of Computer Science, delving into its advancements, innovations, and the potential future trajectory of technology. The primary objective of this paper is to elucidate the significant role of AI in shaping the landscape of Computer Science. Through comprehensive research and analysis, it aims to provide insights into the evolution of AI, its applications, and the implications for future technological developments. This study employs a library research approach, gathering information from academic journals, conference proceedings, books, and reputable online sources. By synthesizing existing literature and scholarly works, it seeks to construct a comprehensive overview of the advancements and innovations in AI within the realm of Computer Science. The findings reveal a dynamic and rapidly evolving field driven by AI technologies. From machine learning algorithms to neural networks and deep learning models, AI has revolutionized data analysis, pattern recognition, and decision-making processes. Moreover, AI-driven applications such as natural language processing, computer vision, and robotics have reshaped various industries, including healthcare, finance, transportation, and manufacturing. However, alongside these advancements come ethical considerations, privacy concerns, and challenges related to algorithmic biases and societal implications. Looking ahead, the future of technology promises further integration of AI into various domains, leading to unprecedented opportunities and challenges in the realm of Computer Science

Analysing and predicting learning behaviours in computer science: A case study of the blended teaching mode in Digital Signal Processing course

Blended learning is the latest and inevitable trend in the development of education. Although blended learning research is on the rise, fewer studies examine the learning behaviour of college students in blended learning environments. This study aimed to investigate the learning behaviours of students in the field of computer science and examine these behaviours using data mining algorithms, taking the teaching practice of the Digital Signal Processing course as a case study. A total of 18 behavioural indicators were extracted and divided into three categories: basic learning behaviours, self-regulated learning behaviours, and extended learning behaviours. Data analysis of the behavioural indicators yielded the following conclusions: (1) Students did not have the habit of watching course playback and were less receptive to multiple online learning platforms; (2) Students’ midterm performance and duration of livestream watching directly affected their basic learning behaviours, with all indicators of self-regulated and extended learning behaviours showing significant correlations; (3) The clustering of learning behaviours yielded four different learner patterns, which calls for personalised teaching strategies; (4) The random forest algorithm had an accuracy of 95.4% in predicting performance of the four types of learners.

Reassessment of the Shirley and model functions within X-ray photoelectron spectroscopy analysis utilizing comprehensive search methodologies of Bayesian inference

The analysis of X-ray photoelectron spectra often faces challenges due to the lack of standardization in modeling approaches, background subtraction methods, and computational algorithms within the field of computer science. The interpretation of XPS data significantly relies on the unique expertise and judgment of individual researchers. Therefore, the objective of this study is to highlight the difficulties associated with analytical methods that depend heavily on the discretion of individual scientists, to elucidate the prevailing models and background subtraction techniques, and to suggest improvements to these methodologies. This endeavor aims to enhance the reliability and reproducibility of XPS analysis, thereby contributing to the advancement of research in this area. By utilizing the information criterion as part of a thorough search methodology in Bayesian inference, we show that our sophisticated analytical techniques significantly outperform others in the analysis of actual X-ray photoelectron spectroscopy (XPS) spectra. This improvement is evidenced through enhanced accuracy and reliability in spectral interpretation, underscoring the efficacy of our methods in practical applications of XPS.

Making Computer Science Accessible through Universal Design for Learning in Inclusive Education

The field of technology and computer science (CS) is developing dynamically. Just as anyone can learn computers at any age, students with special educational needs (SEN) also aspire to acquire IT (information technology) knowledge on an equal footing with all other students. However, one of the obstacles facing students with SEN is the lack of educational materials and programs for CS in secondary schools. The authors have designed teaching materials and assignments that promote inclusion. This study aims to evaluate the impact of teaching resources developed based on universal design for learning (UDL) to make the school’s CS course accessible to all students. The experiment involved 16 students and five teachers. For 8 weeks, students studied computer science using training materials based on UDL. Assessment of knowledge outcome indicators, particularly programming skills, was conducted before and after the experiment. After studying computer science through specific tasks, the interviewees demonstrated a higher level of assimilation of the subject, as indicated by the subsequent test results (mean = 12.13, standard deviation = 1.20), compared to the pre-experimental test (mean = 8.94, standard deviation = 1.12). The study demonstrated that using special UDLbased tasks to teach CS makes it more accessible and has a positive impact on students with special educational needs.

Analysing Flipped Classroom Themes Trends in Computer Science Education (2007–2023) Using CiteSpace

Flipped classroom (FC) overcomes many drawbacks of traditional teaching and has a wide range of applications in the field of (computer science) CS education. Systematically analysing the research themes and keywords of the flipped classroom in CS teaching is of great significance in guiding the improvement of the teaching quality of the flipped classroom. In this study, we used CiteSpace to statistically analyse the data of 229 articles published in the core library of Web of Science from 2007 to July 18, 2023, and visualize author co-citation clustering, and keyword co-occurrence. The results of the study showed that (1) the statistical analysis indicated that the application of flipped classroom in CS education has entered a trough period; (2) based on the cluster of author co-citation network, three research themes of CS flipped implementation, online learning, and skill development were identified in this study. (3) Based on the co-occurrence of keywords, this study identified three research hotspots: integration strategies, flipping effectiveness, and challenges faced.

Travel Salesman Problem Using Dynamic Programming

In the fields of operations research and computer science, the traveling salesman problem (also known as TSP) is a classic algorithmic issue. One method for solving TSP is dynamic programming, which identifies the optimal path at the lowest possible cost. It starts by determining the shortest path between two points, and then it expands to identify routes to other sites. In order to solve the Travel salesman Problem, Dynamic Programming was used in this study as a a solution technique. In addition to a real-world application from AM-EXPRESS NIGERIA LIMITED, illustrative examples of travel salesman difficulties were taken into consideration and handled utilizing the dynamic programming technique. The finding's outcome showed that the least amount of money needed to provide logistical services is ₦6500, and the shortest possible paths or routes were found.

The man-lighthouse and the man-anchor

The article is dedicated to the memory of Yury Nikolaevich Stolyarov, an outstanding Soviet and Russian scientist and pedagogue, facilitator of science and higher education, the largest specialist in the field of library science, documentology, book science and computer science, Doctor of Pedagogical Sciences, Professor, Honored Worker of the Higher School of the Russian Federation, President of the Library Science Department of the International Academy of Informatization, full member of the Russian Academy of Humanities, Honorary Professor of Moscow State Institute of Culture and Chelyabinsk State Institute of Culture. The memoirs and reflections on Yury N. Stolyarov as a mentor and a friend are presented, and the authors' personal impressions of this in many ways amazing personality are given.