Field Of Computer Science Research Articles

Enhancing EEG-based brain-computer interface systems through efficient machine learning classification techniques

Advances in the fields of neuroscience and computer science have greatly enhanced the human brain’s ability to communicate and interact with the surrounding environment. In addition, recent steps in machine learning (ML) have increased the use of electroencephalography (EEG)-based BCIs for artificial intelligence (AI) applications. The prevailing challenge in recording EEG sensor data is that the captured signals are mixed with noise, which makes their effective use difficult. Therefore, strengthening the classification stage becomes extremely important and plays a major role in addressing this problem. In this study, we chose five most widely used classification models that obtained the best results in this field and tested them on two open-source databases. We also focused on improving the hyperparameters of each algorithm to obtain best results. Our results indicate excellent results on the first dataset and acceptable for most models on the second, while RF showed superior performance on both with an accuracy of 100% on the first dataset and 86.47% on the second. This was achieved with the lowest training costs, and better performance compared to previous works we evaluated that used the same databases. These results provide valuable insights and advance the development of brain-computer interface (BCI) technology and design.

Factors Influencing Undergraduate Final Year Project Idea Selection: A Comprehensive Review for IT and CS Students

This study sought to identify and categorize factors influencing undergraduate students' Final Year Project (FYP) idea selection in Information Technology (IT) and Computer Science (CS) fields. The study adopted a systematic approach, analyzing relevant literature post-2010 from various databases. The review scrutinized factors under three primary categories: student, supervisor and environment. Eight articles were selected for analysis based on their relevance to IT and CS. Factors were categorized based on recurring themes and their frequencies were examined and presented in detailed tables. Student-centered factors (58.5%) prominently influenced project idea selection, with Student Interest, Future Ambition and Mastering English Language being noteworthy. Supervisor-centered factors (14.6%), such as Supervisor’s Area of Interest and environment-centered factors (26.8%), including Availability of Literature influenced project selection. The study concludes that institutions should prioritize strategies to enhance student-centered factors, supporting interests, future ambitions and language proficiency.

SIGecom Winter Meeting 2024 Highlights

The fourth annual ACM SIGecom Winter Meeting took place on February 15, 2024. Organized by Sigal Oren and Ran Shorrer, this year's meeting brought together researchers from economics, computer science, and adjacent fields to focus behavioral models. The virtual meeting took the form of a workshop including talks and presentations from leading experts on various directions, as well as a fireside chat on getting into the research space and exciting opportunities that lie ahead. We share some highlights from the 2024 Winter Meeting, and additional insights from follow-up interviews with the speakers.

Combination Concept of LSB and PlayFair Cipher for Optimizing Data Security

Data security is now something that is very necessary, with the aim of ensuring that important data and information does not fall into the hands of unauthorized people. The widespread data exchange process provides opportunities for unauthorized parties to take, copy or steal the exchanged data. This is what triggers the importance of securing data when exchange occurs. In the field of computer science, there are many ways that data can be secured. These methods include the concepts of steganography and cryptography. Steganography is a way to hide data in various media, while cryptography is a way to encode data into a form that has no meaning. This research aims to design a system to secure messages in the form of text data using the Playfair cipher cryptography method and Least Significant Bit (LSB) steganography using image media and the image containing the message is not visible to the eye that the image contains a secret message

Posit and floating-point based Izhikevich neuron: A Comparison of arithmetic

Reduced precision number formats have become increasingly popular in various fields of computational science, as they offer the potential to enhance energy efficiency, reduce silicon area, and improve processing speed. However, this is often at the expense of introducing arithmetic errors that can impact the accuracy of a system. The optimal balance must be struck, judiciously choosing a number format using as few bits as possible, while minimising accuracy loss.In this study, we examine one such format, posit arithmetic as a replacement for floating-point when conducting spiking neuron simulations, specifically using the Izhikevich neuron model. This model is capable of simulating complex neural firing behaviours, 20 of which were originally identified by Izhikevich and are used in this study. We compare the accuracy, spike count, and spike timing of the two arithmetic systems at different bit-depths against a 64-bit floating-point gold-standard. Additionally, we test a rescaled set of Izhikevich equations to mitigate against arithmetic errors by taking advantage of posit arithmetic’s tapered accuracy.Our findings indicate that there is no difference in performance between 32-bit posit, 32-bit floating-point, and our 64-bit reference for all but one of the tested firing types. However, at 16-bit, both arithmetic systems diverge from the 64-bit reference, albeit in different ways. For example, 16-bit posit demonstrates an 18× improvement in accumulated spike timing error over a 1000ms simulation compared to 16-bit floating-point when simulating regular (tonic) spiking. This finding holds particular importance given the prevalence of this particular firing type in specific regions of the brain. Furthermore, when we rescale the neuron equations, this error is eliminated altogether. Although current Posit Arithmetic Units are no smaller than Floating Point Units of the same bit-width, our results demonstrate that 64-bit floating-point can be replaced with 16-bit posit which could enable significant area savings in future systems.

Foreign experience of software testing engineers training: analysis of educational standards and programs of American universities

The article aims to explore the U.S. educational system's approach to training software testing specialists, highlighting specific features and methodologies. The study involves analyzing key standards for IT engineer training in the USA, such as the ACM/IEEE Computer Science Curricula, ABET Accreditation Criteria, ISTE Standards for Educators, and CSTA K-12 Computer Science Standards, their overall context and targeting, as well as the inclusion of both fundamental and IT-specific disciplines into their content. In the study of each standard, particular emphasis was placed on defining the place and teaching methods of theory and practice of software testing in the university curriculum guidelines. As it was revealed in the analysis, despite the extremely similar interpretation of basic concepts and practices in the context of software testing, opinions about the methodology of their teaching and the need to study as a separate discipline, differ in a set of reviewed standards. Additionally, the research examines training programs at leading American universities, including MIT, Stanford, and Carnegie Mellon University, to identify how software testing and quality assurance are reflected in their curricula, covering full-time training programs for students in computer science, software engineering and similar related fields. The analysis examined programs in both general and basic disciplines in the context of information technology, and specific to a certain technological or organizational area, such as mobile device development or IT project management. The content of the subjects, the list of modules and lectures, as well as tasks for extracurricular work, available in open sources, was also analyzed and searched for topics related to quality assurance or software testing. As revealed in the study, in most technical universities testing is not covered within a separate discipline, but is taught as a part of ones related to development practices. However, some training programs, in particular the Master's level, still have disciplines, devoted exclusively to quality control, testing management and automation

Application of artificial intelligence to hepatobiliary cancer clinical outcomes research

The rapid evolution of modern technology has made artificial intelligence (AI) an important emerging tool in healthcare. AI, which is a broad field of computer science, can be used to develop systems or machines equipped with the ability to tackle tasks that traditionally necessitate human intelligence. AI can be used to perform multifaceted tasks that involve the synthesis of large amounts of data with the generation of solutions, algorithms, and decision support tools. Various AI approaches, including machine learning (ML) and natural language processing (NLP), are increasingly being used to analyze vast healthcare datasets. In addition, visual AI has the potential to revolutionize surgery and the intraoperative experience for surgeons through augmented reality enhancing surgical navigation in real-time. Specific applications of AI in hepatobiliary tumors such as hepatocellular carcinoma and biliary tract cancer can improve patient diagnosis, prognostic risk stratification, as well as treatment allocation based on ML-based models. The integration of radiomics data and AI models can also improve clinical decision making. We herein review how AI may be of particular interest in the care of patients with complex cancers, such as hepatobiliary tumors, as these patients often require a multimodal treatment approach.

Role Of Artificial Intelligence in Big Database Management

This exploration article digs into the significant effect of (artificial intelligence) AI, on big Data base management in the field of computer science by utilizing the broad abilities of artificial intelligence AI technologies, this study investigates different strategies to improve the oversight and administration of huge information bases. Through an exhaustive assessment of existing academic works, itemized contextual investigations, and master experiences, the article disentangles the mind-boggling combination of Ai’s integration and its important impacts on database management rehearses. Covering an extensive variety of artificial intelligence aspects, for example, AI, regular language handling, and profound realizing, this academic request plans to explain the complex job of computer-based intelligence in the domain of big database management. As a critical commitment to scholarly talk, this research article fills in as an original work, planning the groundbreaking excursion of Ai transformative into the center of data base management system, introducing another time of development and productivity in the computerized scene.

How much can ChatGPT really help computational biologists in programming?

ChatGPT, a recently developed product by openAI, is successfully leaving its mark as a multi-purpose natural language based chatbot. In this paper, we are more interested in analyzing its potential in the field of computational biology. A major share of work done by computational biologists these days involve coding up bioinformatics algorithms, analyzing data, creating pipelining scripts and even machine learning modeling and feature extraction. This paper focuses on the potential influence (both positive and negative) of ChatGPT in the mentioned aspects with illustrative examples from different perspectives. Compared to other fields of computer science, computational biology has (1) less coding resources, (2) more sensitivity and bias issues (deals with medical data), and (3) more necessity of coding assistance (people from diverse background come to this field). Keeping such issues in mind, we cover use cases such as code writing, reviewing, debugging, converting, refactoring, and pipelining using ChatGPT from the perspective of computational biologists in this paper.

A Human-Centric Approach towards Equity and Inclusion in AI Education

Artificial Intelligence (AI) has become pervasive in modern lives, with AI generative tools driving further transformation. However, a notable issue persists: the underrepresentation of females and individuals from ethnic and racial minorities in the tech industry. Despite generally positive attitudes toward technology among young students, this enthusiasm often does not extend to aspirations for careers in the field. To address this disparity, many schools in the United States are now offering computer science and AI courses at the high school level. Nevertheless, students from underrepresented groups often feel disconnected from these subjects, leading to low enrollment rates. Research underscores that students' career aspirations are solidified between the ages of 10-14 yrs, highlighting the importance of engaging them with computer science and computing skills during this formative period. Leveraging the Bourdieusian concept of social capital, this paper proposes educational interventions tailored for elementary schools. By nurturing students' technical social capital, these interventions aim to foster an inclusive ecosystem from an early age, when aspirations are taking shape. Ultimately, the goal is to enhance the accessibility of computer science education and related skills, empowering young students from underrepresented groups to pursue higher studies and careers in computer science and AI fields.

Are the bibliometric growth patterns of excellent scholars similar? From the analysis of ACM Fellows

The growth of excellent scholars provides paradigmatic career paths leading to research success, as their research capabilities ultimately manifest as fluctuations in bibliometric indexes. Examining the commonalities in the trajectories of these bibliometric indexes displays the universal characteristics of their growth process, and furtherly shows exemplary routes to scientific success. In this study, we examine 287 excellent scholars elected as ACM Fellows in the field of computer science from 2016s to 2020s. Based on their changes in productivity, impact, and comprehensive abilities, we categorize them into three categories, four categories, and six categories, respectively. Most of these scholars experience continuous growth in productivity during the early development stages, maintaining a prolonged period of high productivity in the mid-later maturity stages. Their impact rises smoothly and consistently, while the growth of their comprehensive abilities is relatively gradual, remaining at above-average levels in the mid-later maturity stages. Furthermore, the level of recognition within the scientific research community varies for different categories of scholars, and there are also differences in the growth patterns between scholars from Asia and those from Western regions.

Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection

Outliers are observation values that are very different from most observations. The presence of outliers in data can have a negative impact on research but can contain important information for other research. So, identifying outliers before conducting data analysis is a crucial thing to do. Outlier detection methods/techniques were first pioneered by researchers in statistics. However, due to rapid technological advances which have an impact on the ease of collecting extensive data, the development of outlier detection techniques is now handled mainly by researchers in the field of computer science (data mining) using computing facilities. This research aims to examine the results of simulation studies by comparing methods for identifying several outliers using statistical approaches and data mining algorithm approaches in various predetermined data scenarios. Based on the scenario carried out, the outlier detection method using a statistical approach is generally better than the outlier detection method using a data mining-based approach. Suggestions for further research are to improve the data mining method by focusing more on statistical analysis apart from focusing on data processing computing time so that the expected results of outlier detection are faster and more precise.

AI-based predictive approach via FFB propagation in a driven-cavity of Ostwald de-Waele fluid using CFD-ANN and Levenberg–Marquardt

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into computational science has ushered in a new era of innovation and efficiency in various fields, with particular significance in computational fluid dynamics (CFD). Several methods based on AI and Machine Learning (ML) have been standardized in many fields of computational science, including computational fluid dynamics (CFD). This study aims to couple CFD with artificial neural networks (ANNs) to predict the fluid forces that arise when a flowing fluid interacts with obstacles installed in the flow domain. The momentum equation elucidating the flow has been simulated by adopting the finite element method (FEM) for a range of rheological and kinematic conditions. Hydrodynamic forces, including pressure drop between the back and front of the obstacle, surface drag, and lift variations, are measured on the outer surface of the cylinder via CFD simulations. This data has subsequently been fed into a Feed-Forward Back (FFB) propagation neural network for the prediction of such forces with completely unknown data. For all cases, higher predictivity is achieved for the drag coefficient (CD) and lift coefficient (CL) since the mean square error (MSE) is within ± 2% and the coefficient of determination (R) is approximately 99% for all the cases. The influence of pertinent parameters like the power law index (n) and Reynolds number (Re) on velocity, pressure, and drag and lift coefficients is also presented for limited cases. Moreover, a significant reduction in computing time has been noticed while applying hybrid CFD-ANN approach as compared with CFD simulations only.

Investigating Lexical and Syntactic Differences in Written and Spoken English Corpora

This paper presents an analysis of the differences between written text and the transcription of spoken text using current Natural Language Processing (NLP) methods. The purpose of the study is to investigate the long and rich history of attempts to differentiate spoken and written text in fields such as linguistics, communication, and rhetoric, which date back to the early 20th century. Given the availability of large quantities of machine-readable data and machine learning algorithms that can handle them, it is possible to use a large number of derived features. The research focuses on syntactic and lexical differences in written books and transcriptions of speeches by United States presidents. The analysis investigates morphological, lexical, syntactical, and text-level aspects. In this process, multiple features have been considered including lexical diversity, syllable count, frequency of parts of speech, and features relating to the parse tree, like the average length of noun phrases, and the use of interrogative sentences, among others. This study will enhance our understanding of the difference between written text and the transcription of spoken text in various disciplines including computer science, applied linguistics, communication, and similar fields.

ProQuiz

ProQuiz is an app designed for educational purposes, specifically to help users prepare for multiple-choice exams, especially in the field of computer science. The app aims to make studying enjoyable by incorporating elements of gamification. The primary objective of ProQuiz is to assist users in getting ready for subjective tests used in admissions and recruitment processes, with a focus on the computer science domain. The app is organized into three main modules: computer science, reasoning, and analytical skills. Each of these modules covers various sub-categories to address different subjects. During the quiz, users have access to helpful features such as hints, the ability to skip questions, and a pause/life-lines option. These features can be used only once per user. The app also provides feedback on progress throughout the quiz, and upon completion, it displays the user's results. The overall aim is to provide users with an engaging platform for practicing and enhancing their knowledge in a variety of subjects related to computer science. This research paper examines the development and effectiveness of ProQuiz. Through user testing and analysis, we evaluate its usability and engagement. Additionally, we propose enhancements, including personalized learning paths and social features. This paper contributes insights into innovative educational technology and suggests avenues for future improvement of ProQuiz.

Comparative Analysis of Scientific Approaches in Computer Science: A Quantitative Study

Computer Science is an interdisciplinary field drawing its foundations from a multitude of scientific and engineering domains. The study of Computer Science necessitates the integration of concepts from various fields, blending theoretical frameworks with practical applications. This dual approach, combining abstraction and design, allows for a comprehensive understanding of computational systems. Over the years, the historical evolution of Computer Science has witnessed the emergence of numerous sub-disciplines that increasingly communicate and overlap, driven by the advancement of communication technologies and the growing need for a holistic perspective in understanding complex systems. This interdisciplinary synergy is crucial in addressing contemporary challenges that are inherently multifaceted, requiring inputs from diverse scientific areas. As our world becomes more interconnected and dominated by intricate technological systems, the reductionist approach proves inadequate. Instead, a holistic view, which acknowledges and leverages the interdependencies among various scientific disciplines, becomes imperative. This paper explores the multifaceted nature of Computer Science, highlighting its foundational concepts, historical development, and the integration of theory and practice. It delves into how the convergence of different scientific fields within Computer Science fosters innovation and addresses complex real-world problems. By examining the interdisciplinary interactions and their implications, this study underscores the importance of a comprehensive approach in advancing the field of Computer Science and its applications in solving modern-day challenges.

Mental Health Analysis AI Chatbot

The project work is a practical experience of the knowledge one has. The documentation leads a way to the concept to present the thinking and the upgradation of various techniques into the project. This project entitled “Mental Health Analysis AI Chatbot” is a practical project based on some trends of computer science. Every day the world is searching new techniques in the field of computer science to upgrade human limitations into machines to get more and more accurate and meaningful data. More and more mental health issues such as depression are getting known and recognized by our society today. However, not all of them can receive appropriate treatment. There are many of us still facing the problem of getting the appropriate mental health services every day. We cannot deny the fact that not everyone can get mental healthcare services as they might face some difficulties such as financial problems. Therefore, we may look for new solutions to fix this mental health issue. This demand for solving this issue has led to the proposal of technology as a solution. Chatbot, also known as a conversational agent which can participate in the conversation might be considered one of the solutions too. By mimicking the conversation between human counselor and patient, it can provide counselor service to the patient at some point. However, to further improve the quality of the counselor service, the improvement of the chatbot has to be carried out. By using deep learning, this proposed chatbot can recognize the meaning of the conversation and give a relevant response.

Performance optimization for a scintillating glass electromagnetic calorimeter at the EIC

The successful realization of the EIC scientific program requires the design and construction of high-performance particle detectors. Recent developments in the field of scientific computing and increased availability of high performance computing resources have made it possible to perform optimization of multi-parameter designs, even when the latter require longer computational times (for example simulations of particle interactions with matter). Procedures involving machine-assisted techniques used to inform the design decision have seen a considerable growth in popularity among the EIC detector community. Having already been realized for tracking and RICH PID detectors, it has a potential application in calorimetry designs. A SciGlass barrel calorimeter originally designed for EIC Detector-1 has a semi-projective geometry that allows for non-trivial performance gains, but also poses special challenges in the way of effective exploration of the design space while satisfying the available space and the cell dimension constraints together with the full detector acceptance requirement. This talk will cover specific approaches taken to perform this detector design optimization.

Journalism and Communication Education Enabled by Artificial Intelligence: Reflections on the Construction of Majors and Professional Groups

With the rapid development of artificial intelligence technology, its application in the field of news communication has become an important driving force to promote education reform and professional construction in this field. The purpose of this study is to explore the practice and thinking of journalism and communication education enabled by artificial intelligence, especially in the construction of majors and professional groups. By using literature analysis and case study, this paper systematically reviews the application status of artificial intelligence technology in news gathering, editing, distribution and user interaction, and analyzes how these technologies affect the curriculum setting, teaching methods and students' ability cultivation in news communication education. Further, based on expert interviews and questionnaire surveys, this study identifies the challenges and opportunities in AI-enabled journalism and communication education, and puts forward corresponding strategies for the construction of majors and professional groups. The research results show that the integration of artificial intelligence technology has brought new teaching content and methods to journalism and communication education, promoted the cultivation of students' interdisciplinary ability, and also put forward higher requirements for the education system and educators. In order to cope with these changes, it is suggested that journalism and communication major should strengthen the cross-integration with computer science, data science and other fields in the construction of professional groups, and strengthen practical teaching and project-driven learning to cultivate students' innovative ability and critical thinking ability. This study provides theoretical guidance and practical reference for the professional construction and teaching reform of journalism and communication education in the era of artificial intelligence.

Artificial Intelligence (AI): Evolution, Methodologies, and Applications

Abstract: Artificial intelligence, also known as AI, is a technology that enables computers and machines to emulate human intelligence and problem-solving abilities. Computers can perform a wide range of advanced functions thanks to the use of artificial intelligence (AI) technologies. These functions include the ability of machines to see, comprehend, and translate spoken and written language, analyzing data, making recommendations, and more. Artificial intelligence is considered a field of computer science that encompasses other areas like machine learning and deep learning, data analytics, linguistics, software engineering, and so on. These disciplines often involve the development of AI algorithms that are based on the decision-making processes of the human brain, that has the ability to learn and memorize from existing data and allows more precise classifications or predictions over a period of time. AI is the foundation for innovation in modern computing, discovering the value for both individuals and businesses. For illustration, AI is used to extricate content and information from pictures and documents, turns unstructured content into business-ready structured data, and unlocks valuable insights. AI has the ability to perform tasks that would otherwise require human intelligence or intervention, when combined with other technologies such as sensors, and robotics. It is used in many areas of life, including education, finance, healthcare, and manufacturing. Here are some examples of AI in different areas: Facial detection and recognition, Text editors, Digital assistants, Self-driving cars, and many more.