Applications In Computer Science Research Articles

Computing edge version of metric dimension of certain chemical networks

In the modern digital sphere, graph theory is a significant field of research that has a great deal of significance. It finds widespread application in computer science, robotic directions, and chemistry. Additionally, graph theory is used in robot network localization, computer network problems and the formation of various chemical structures for networks. Moreover, it finds uses in exploring diffusion mechanisms and scheduling aircraft as well. The present research project examines and concentrates on the edge version of metric dimension of the Concealed Non-Kekuléan Benzenoid Hydrocarbon, Polythiophene, Backbone DNA network and Bakelite networks. All the mentioned networks have constant edge metric dimension except Bakelite network, as demonstrated by the results. If we talk about the applications of these networks, Polythiophene are used to treat prion disorders. It is also capable of detecting metal ions. The concept of Bakelite, which finds applications in the jewelry, electrical, cookware, sports, and clock industries, had an impact on the invention of modern polymers. The functions of DNA include information encoding, replication, mutation, and recombination gene expression.

Domination on Bipolar Fuzzy Graph Operations: Principles, Proofs, and Examples

Bipolar fuzzy graphs, capable of capturing situations with both positive and negative memberships, have found diverse applications in various disciplines, including decision-making, computer science, and social network analysis. This study investigates the domain of domination and global domination numbers within bipolar fuzzy graphs, owing to their relevance in these aforementioned practical fields. In this study, we introduce certain operations on bipolar fuzzy graphs, such as intersection, join, and union of two graphs. Furthermore, we analyze the domination number and the global domination number for various operations on bipolar fuzzy graphs, including intersection, join, and union of fuzzy graphs and their complements.

Online learning system for English speech automatic recognition based on hidden Markov model algorithm and conditional random field algorithm

E-learning provides a flexible way of learning, which can provide corresponding learning content and tasks according to students’ interests, abilities and learning progress, so that students can choose learning content and learning progress more independently. In traditional teaching methods, teachers need to spend a lot of time and energy to evaluate students’ learning. The e-learning system can automatically record students’ learning behavior and learning results, and give feedback and evaluation in time during the learning process. Automatic speech recognition systems have been widely applied in various fields, and the demand for more user-friendly and convenient user operating systems is becoming increasingly urgent. This article proposes an English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm to improve recognition accuracy and stability, and applies it to the computer field. Label inference and optimization for speech recognition were carried out using the conditional random field algorithm. By establishing the correlation between features and labels, label inference was performed on the speech signal, which was compared with existing speech recognition systems to evaluate whether the recognition accuracy and stability of the new system had been improved. The results show that the English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm exhibits high performance in terms of recognition accuracy and stability. Compared with traditional speech recognition systems, this system has made significant improvements in the application of computer science.

Sequent systems for consequence relations of cyclic linear logics

Linear Logic is a versatile framework with diverse applications in computer science and mathematics. One intriguing fragment of Linear Logic is Multiplicative-Additive Linear Logic (MALL), which forms the exponential-free component of the larger framework. Modifying MALL, researchers have explored weaker logics such as Noncommutative MALL (Bilinear Logic, BL) and Cyclic MALL (CyMALL) to investigate variations in commutativity. In this paper, we focus on Cyclic Nonassociative Bilinear Logic (CyNBL), a variant that combines noncommutativity and nonassociativity. We introduce a sequent system for CyNBL, which includes an auxiliary system for incorporating nonlogical axioms. Notably, we establish the cut elimination property for CyNBL. Moreover, we establish the strong conservativeness of CyNBL over Full Nonassociative Lambek Calculus (FNL) without additive constants. The paper highlights that all proofs are constructed using syntactic methods, ensuring their constructive nature. We provide insights into constructing cut-free proofs and establishing a logical relationship between CyNBL and FNL.

From Local Atomic Environments to Molecular Information Entropy.

The similarity of local atomic environments is an important concept in many machine learning techniques, which find applications in computational chemistry and material science. Here, we present and discuss a connection between the information entropy and the similarity matrix of a molecule. The resulting entropy can be used as a measure of the complexity of a molecule. Exemplarily, we introduce and evaluate two specific choices for defining the similarity: one is based on a SMILES representation of local substructures, and the other is based on the SOAP kernel. By tuning the sensitivity of the latter, we can achieve good agreement between the respective entropies. Finally, we consider the entropy of two molecules in a mixture. The gain of entropy due to the mixing can be used as a similarity measure of the molecules. We compare this measure to the average and best-match kernel. The results indicate a connection between the different approaches and demonstrate the usefulness and broad applicability of the similarity-based entropy approach.

A Systematic Review of Artificial Intelligence: A Future Guide to Sustainable Agriculture

The population is expected to grow rapidly and reach 10 billion people by the year 2050. As a result, there will be a greater need for food. The conventional techniques employed by the farmers proved insufficient to meet these demands. For this, new automated techniques were unveiled. Along with other cutting-edge computer science applications, farming has long made use of technologies like artificial intelligence. The focus has shifted in recent years to consider the applications of this new technology. A significant percentage of humanity's nutrition has come from agriculture for thousands of years, with the most significant contribution being the broad adoption of efficient farming techniques for a variety of crops. The application of artificial intelligence (AI) in agriculture has sparked a revolution in the field, and AI technology has made the agro-based commercial sector operate more profitably. Artificial intelligence (AI) technologies have the power to transform the future and address problems. This will make it easier for farmers to learn about climate variance and pests that lower crops. The use of AI focuses on identifying damaged crops and enhancing the ability of healthy crops to provide higher yields. This paper gives a thorough analysis of AI models used in agricultural applications. It also examines the use of AI models to specify sustainable goals. This article explores the challenges and opportunities for utilizing AI to develop future generations of sustainable agriculture through this comprehensive review.

Kernel Paradigms: A Comprehensive Analysis, Applications, and Future Directions in Computer Science

Kernel Paradigms: A Comprehensive Analysis, Applications, and Future Directions in Computer Science

Application of the New Mapping Method to Complex Three Coupled Maccari’s System Possessing M-Fractional derivative

In this academic investigation, an innovative mapping approach is applied to complex three coupled Maccari’s system to unveil novel soliton solutions. This is achieved through the utilization of M-Truncated fractional derivative with employing the new mapping method and computer algebraic syatem (CAS) such as Maple. The derived solutions in the form of hyperbolic and trigonometric functions. Our study elucidates a variety of soliton solutions such as periodic, singular, dark, kink, bright, dark-bright solitons solutions. To facilitate comprehension, with certain solutions being visually depicted through 2-dimensional, contour, 3-dimensional, and phase plots depicting bifurcation characteristics utilizing Maple software. Furthermore, the incorporation of M-Truncated derivative enables a more extensive exploration of solution patterns. Our study establishes a connection between computer science and soliton physics, emphasizing the pivotal role of soliton phenomena in advancing simulations and computational modeling. Analytical solutions are subsequently generated through the application of the new mapping method. Following this, a thorough examination of the dynamic nature of the equation is conducted from various perspectives. In essence, understanding the dynamic characteristics of systems is of great importance for predicting outcomes and advancing new technologies. This research significantly contributes to the convergence of theoretical mathematics and applied computer science, emphasizing the crucial role of solitons in scientific disciplines.

Exploring the Efficacy of Machine Learning Tools for Analyzing Student Performance in Computer Science Applications

This research study examines how ML techniques can assess computer science student performance. The study compares current and past performance metrics, identifies salient performance assessment features, and outlines strategies for sustaining or enhancing student achievement in the context of the COVID-19 pandemic, which has shifted the paradigm toward online learning. Context: With the rise of digital learning platforms and the changing educational environment, ML approaches are being used to get useful insights from massive student data sets. The COVID-19 pandemic has highlighted the need for improved virtual student learning monitoring and support methods. Objective: The purpose of this study is to determine if ML methods can accurately assess student computer science proficiency. Compare students' current performance measures to their prior records to identify COVID-19-related changes. To choose relevant characteristics for performance evaluation using ML algorithms and statistics. Methods: The research uses a mixed-methods approach, combining quantitative academic record analysis with qualitative student survey and interview data. Results: show substantial relationships between performance metrics and external influences, including remote learning. ML models identify academic performance factors, enabling targeted interventions and instructional improvements. Conclusion: Integrating ML technologies can improve computer science instruction and student achievement. Data-driven insights help instructors change teaching tactics, identify at-risk pupils, and create a dynamic learning environment that promotes academic success. ML techniques may help educational institutions negotiate distant learning and achieve inclusive, equitable, and successful results.

Locating-dominating number of certain infinite families of convex polytopes with applications

A convex hull of finitely many points in the Euclidean space Rd is known as a convex polytope. Graphically, they are planar graphs i.e. embeddable on R2. Minimum dominating sets possess diverse applications in computer science and engineering. Locating-dominating sets are a natural extension of dominating sets. Studying minimizing locating-dominating sets of convex polytopes reveal interesting distance-dominating related topological properties of these geometrical planar graphs. In this paper, exact value of the locating-dominating number is shown for one infinite family of convex polytopes. Moreover, tight upper bounds on γl−d are shown for two more infinite families. Tightness in the upper bounds is shown by employing an updated integer linear programming (ILP) model for the locating-dominating number γl−d of a fixed graph. Results are explained with help of some examples. The second part of the paper solves an open problem in Khan (2023) [28] which asks to find a domination-related parameter which delivers a correlation coefficient of ρ>0.9967 with the total π-electronic energy of lower benzenoid hydrocarbons. We show that the locating-dominating number γl−d delivers such a strong prediction potential. The paper is concluded with putting forward some open problems in this area.

Chemical Applications on General Zagreb Indices of Composite Graphs

A topological index is a quantity computed from the molecular graph, that finds application in chemistry, material science, computer science, and biological application-driven fields. Recently, the general Zagreb indices were studied, and methods for their calculation for composite graphs were established. In the present paper, we provide examples for chemical applications of these methods, computing the first and second general Zagreb indices of several composite molecular species, in particular of biphenyl, naphthalene, biphenylene, and bicoronylene. In this paper, we compute the Nirmala index and its corresponding exponential of chloroquine, hydroxychloroquine and remdesivir. Also we determine the inverse Nirmal indices of chloroquine, hydroxychloroquine and remdesivir.

ANALYZING THE ROLE OF COMPUTER SCIENCE IN SHAPING MODERN ECONOMIC AND MANAGEMENT PRACTICES. BIBLIOMETRIC ANALYSIS

The application of computer science in management and economics is a rapidly growing field that combines the analytical and technological capabilities of computer science with the strategic and operational needs of management and economics. The main aim of this research paper is to analyze the main academic contributors, sources, and international collaborations from 2014 to 2022 in computer science in the areas of management and economics, as well as to analyze the main subtopics developed over time. Bibliometric techniques were used to carry out the literature review, which allows an objective analysis of the academic literature through quantitative indicators. The results reveal a significant shift towards data-driven decision making in management, with artificial intelligence and machine learning improving predictive analytics, operational efficiency, and economic forecasting and modeling, highlighting the essential role of digital transformation in these disciplines, with significant implications for researchers, practitioners and decision-makers. It concludes that all stakeholders should work to develop a more informed and innovative approach to maximize the exploitation of the potential offered by computational sciences in different contexts. This includes the integration of advanced computational tools to improve decision making and operational efficiency, or the exploitation of computational models for more effective forecasting and policy decision making, as well as the continuous analysis of emerging areas in this field, being aware of the ethical, privacy and security challenges presented by these technologies, in order to ensure a responsible and equitable application.

Binary relations applied to the fuzzy substructures of quantales under rough environment

Abstract Binary relations (BIRs) have many applications in computer science, graph theory, and rough set theory. This study discusses the combination of BIRs, fuzzy substructures of quantale, and rough fuzzy sets. Approximation of fuzzy subsets of quantale with the help of BIRs is introduced. In quantale, compatible and complete relations in terms of aftersets and foresets with the help of BIRs are defined. Furthermore, we use compatible and complete relations to approximate fuzzy substructures of quantale, and these approximations are interpreted by aftersets and foresets. This concept generalizes the concept of rough fuzzy quantale. Finally, using BIRs, quantale homomorphism is used to build a relationship between the approximations of fuzzy substructures of quantale and the approximations of their homomorphic images.

Machine Learning-Based Kernel Selector for SpMV Optimization in Graph Analysis

Sparse Matrix and Vector multiplication (SpMV) is one of the core algorithms in various large-scale scientific computing and real-world applications. With the rapid development of AI and big data, the input vector in SpMV becomes sparse in many application fields. Especially in some graph analysis calculations, the sparsity of the input vector will change with the running of the program, and the non-zero element distribution of the adjacency matrix of some graph data has the power law property, leading to serious load imbalance, which requires additional optimization means. Therefore, the optimal SpMV kernel may be different, and a single SpMV kernel can no longer meet the acceleration requirements. In this paper, we propose a decision tree-based adaptive SpMV framework, named DTSpMV, that can automatically select appropriate SpMV kernels according to different input data in iterations of graph computation. Based on the analysis of computing patterns, bit-array compression algorithms, and serial and parallel algorithms, we encapsulate nine SpMV kernels within the framework. We explore machine learning-based kernel selectors in terms of both accuracy and runtime overhead. Experimental results on NVIDIA Tesla T4 GPU show that our adaptive framework achieves the arithmetic average performance improvement of \(152\% \) compared to the SpMV kernel in cuSPARSE.

The Application of AI and Computer Science in the Context of International Law and Governance “Opportunities and Challenges”

Purpose: The purpose of "Governing Artificial Intelligence: Ethical, Legal, and Technical Opportunities and Challenges" is to examine the impact of artificial intelligence (AI) on various societal aspects and to underscore its potential benefits for human rights, social welfare, and economic growth. The essay emphasizes the necessity of regulating AI systems in a fair, transparent, and responsible manner, especially in high-risk sectors.
 Materials and Methods: Research Design: The essay employs a literature review and analysis approach to explore the ethical, legal, and technical challenges associated with governing AI.
 Method of Data Collection: Data collection primarily involves gathering information from existing literature, reports, and expert opinions. Analysis and Presentation: The data is analyzed qualitatively to provide insights into the complexities of AI governance. The findings are presented systematically to address different dimensions of the topic.
 Findings: The essay presents a comprehensive analysis of the ethical, legal-regulatory, and technological challenges in governing AI. It highlights the need for robust governance frameworks to ensure the responsible development and deployment of AI systems.
 Implication to Theory, Practice, and Policy: The study is informed by various theories on ethics, governance, and technology. Validation of these theories is achieved through a critical examination of existing literature and empirical evidence. Practitioners are recommended to adopt principles of fairness, transparency, and accountability in the development and deployment of AI systems. Additionally, continuous monitoring and evaluation mechanisms should be established to ensure compliance with ethical standards. Policymakers are encouraged to enact regulations that promote the ethical and responsible use of AI technologies. This includes establishing clear guidelines for AI development, deployment, and accountability mechanisms to address potential risks and ensure societal well-being.

Bridging theory and application: An in-depth study of the greatest common divisor and least common multiple in mathematics and real-world settings

The study of the Least Common Multiple (LCM) and the Greatest Common Divisor (GCD) explores the fundamental field of number theory. In addition to being essential to theoretical number theory, these mathematical ideas have extensive applications in computer science, algebra, and real-world settings like network routing and scheduling. Using techniques like the Euclidean algorithm for GCD calculation and prime factorization for LCM, the study starts by examining the fundamental concepts and notations of GCD and LCM. Then the author goes further into the detail about how these ideas are used to polynomial equations, highlighting how they may be used to solve challenging issues and simplify algebraic expressions. The studys practical applications, which demonstrate how GCD and LCM optimize scheduling tasks and boost network routing efficiency, are presented in the conclusion. The results of this study highlight the broad impact and adaptability of LCM and GCD in theoretical and applied mathematics, providing valuable insights for further research in these domains.

Optimized Distance-Based Algorithm for Cloud Space—Tourist Route Recommendation for Heritage Town, Pondicherry, India

Architecture, often regarded as a three-dimensional art form, relies on a multitude of abstractions during the design process. In this context, architectural elements are effectively represented through vector-based data processing techniques, offering a canvas for creative exploration. To further our understanding of spatial configurations and generate multiple design insights, mathematical structures known as graphs come into play. Graph theory, a fundamental component of this process, it is a mathematical field that studies relationships and connections between objects using vertices and edges, finding applications in computer science, network analysis, and more finds application in architectural design. This study delves into the application of graph theory in the analytical exploration of urban networks, with a focus on tourism parameters in the picturesque town of Pondicherry, Tamil Nadu, India. Traditionally, urban network analysis seeks to determine the shortest distance between origin and destination, a metric rooted in accessibility. However, in leisure destinations, a novel approach involving an optimized distance-based algorithm is introduced. This innovative methodology aims to facilitate efficient access to all tourist destinations within the spatial extent while minimizing discrete physical distances. To illustrate this concept, we take the iconic French Quarters in the White Town of Pondicherry as a case study. By applying the optimized distance-based algorithm, we endeavor to uncover the most efficient route for tourists to navigate and leisurely explore the area. The outcome of this analysis reveals a path graph structure, which, when compared to a circular graph structure, demonstrates superior efficiency in guiding tourists through the space. The implications of this tourist route extend beyond the realm of tourism itself. It has the potential to enhance the organization of tourist activities, allowing stakeholders to manage and regulate the flow of visitors. Additionally, it presents an opportunity to promote and develop the region’s often-underestimated tourist destinations, which can, in turn, have a positive impact on the local economy. In summary, the integration of graph theory into architectural and urban design methodologies enriches the approach to tourism and offers a holistic perspective on spatial optimization.

Study for Some Eccentricity-based Topological Indices of Second Type of Dominating David-derived Network.

Dominating David-derived networks are widely studied due to their fractal nature, with applications in topology, chemistry, and computer sciences. The use of molecular structure descriptors is a standard procedure that is used to correlate the biological activity of molecules with their chemical structures, which can be useful in the field of pharmacology. This article's goal is to develop analytically closed computing formulas for eccentricity-based descriptors of the second type of dominating David-derived derived network. Thermodynamic characteristics, physicochemical properties, and chemical and biological activities of chemical graphs are just a few of the many properties that may be determined using these computation formulas. Vertex sets were initially divided according to their degrees, eccentricities, and cardinalities of occurrence. The eccentricity-based indices are then computed using some combinatorics and these partitions. Total eccentricity, average eccentricity, and the Zagreb index are distance-based topological indices utilized in this study for the second type of dominating David-derived network, denoted as D_2 (m). These calculations will assist the readers in estimating the fractal and difficult-to-handle thermodynamic and physicochemical aspects of chemical structure. Apart from configuration and impact resistance, the D_2 (m) design has been used for fundamental reasons in a variety of technical and scientific advancements.

An Introduction to Computational Science and Computer Development

As our economy and society continue to evolve, computer technology is also evolving and innovating. The emergence of computer technology has greatly facilitated human work and learning, and has brought great influence to all walks of life. For a long time, computer technology has deeply penetrated into people's production and life, and become an indispensable and important part. To a certain extent, computers have contributed to the development of the national economy, and at the same time, it has also brought a lot of convenience to the people. This initiative not only significantly improves the quality of life and productivity of the general public, but also an important indicator of the overall quality of a country's population. Through the study, it is found that with the continuous development of science and technology, the trend of combining computer technology and communication technology is becoming more and more obvious and has made certain achievements. Although the development of computer science and technology is very short, but the scope of its application for people's lives and the reform and development of related work is unparalleled by any other technology. The application of computer science and technology basically involves all aspects of domestic life, from the most basic food, clothing, housing and transportation to human communication and all kinds of production work, at the same time, computer science and technology is also the core of other technological reforms and innovations, and other technological advances relying on computer science and technology, cloud computing technology, cloud storage and its intelligent regulation and control of the new technology have achieved a certain degree of fundamental independent innovation. This research will focus on the computer science and technology, which is the core of other technological reforms and innovations. This study will systematically analyze the practical application and development of computer science and technology, and carefully sort out the main reasons for the development of computer science and technology and the future development of the market outlook.

Application of Computer Science Technology in Computer Education

The arrival of the era of big data, making the computer in all walks of life are widely used in the teaching process is also inseparable from the application of computers, some of the traditional teaching methods have also been replaced by computer technology, computer teaching has become the mainstream of the times, and has become a very effective means of teaching as well as teaching tools. The application and popularization of computers has brought great convenience to people's daily life, and has a great effect on the improvement of teaching quality. In the process of computer popularization, it is widely used in real life. And the computer as an education and learning tools, for computer education itself has great significance. In assisting teachers in the management of teaching and practical work at the same time, but also to provide students with richer learning resources and learning efficiency, so that the teaching and learning process is more concise and easy. The arrival of the era of big data, making the computer in all walks of life are widely used in the teaching process is also inseparable from the application of computers, some of the traditional teaching methods have also been replaced by computer technology, computerized teaching has become the mainstream of the times, and has become a very effective means of teaching as well as the teaching tools of the times. The application and popularization of computers has brought great convenience to people's daily life, and has a great effect on the improvement of teaching quality. Especially in the cultivation of talents in colleges and universities, the application of computers can have a great role in promoting the cultivation of innovation ability of college students. This article briefly analyzes the application of computer science and technology in computer education, starting from computer science and technology.