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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Anand, Vatsala Gupta, Sheifali Gupta, Rupesh Alshahrani, Hani Hamdi, Mohammed Al Reshan, Mana Saleh Shaikh, Asadullah Chitkara University Institute of Engineering and Technology Chitkara University Punjab India College of Computer Science and Information Systems Najran University Department of Computer Science Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran University Department of Information Symens Najran61441 Saudi Arabia

ISBN: (纸本)9798350327595

Plant disease diagnosis is the process of identifying and characterizing diseases that affect plants. The primary goal of plant disease diagnosis is to determine the cause of the disease, and to develop appropriate control and management strategies to minimize its impact on crop yields and quality. In this research, four transfer learning models such as AlexNet, VGG16, MobileNetV2, and InceptionV3 has been used for the classification of plant disease into different categories. Out of all the four transfer learning models, the best performing model is InceptionV3 with the value of accuracy as 0.92 and precision as 0.84. © 2023 IEEE.

关键词： Convolutional neural networks Crop diseases Diagnosis Plant disease detection

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Transformers for Vision: A Survey on Innovative Methods for computer Vision

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IEEE Access 2025年 13卷 95496-95523页

作者： Hassija, Vikas Palanisamy, Balamurugan Chatterjee, Arpita Mandal, Arpita Chakraborty, Debanshi Pandey, Amit Chalapathi, Gss Kumar, Dhruv Deemed to be University School of Computer Engineering Odisha Bhubaneswar751024 India Bennett University School of CSET Gautam Buddha Nagar Greater Noida201310 India Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani xPilani Campus Vidya Vihar Rajasthan Pilani333031 India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Vidya Vihar Rajasthan Pilani333031 India

Transformers have emerged as a groundbreaking architecture in the field of computer vision, offering a compelling alternative to traditional convolutional neural networks (CNNs) by enabling the modeling of long-range dependencies and global context through self-attention mechanisms. Originally developed for natural language processing, transformers have now been successfully adapted for a wide range of vision tasks, leading to significant improvements in performance and generalization. This survey provides a comprehensive overview of the fundamental principles of transformer architectures, highlighting the core mechanisms such as self-attention, multi-head attention, and positional encoding that distinguish them from CNNs. We delve into the theoretical adaptations required to apply transformers to visual data, including image tokenization and the integration of positional embeddings. A detailed analysis of key transformer-based vision architectures such as ViT, DeiT, Swin Transformer, PVT, Twins, and CrossViT are presented, alongside their practical applications in image classification, object detection, video understanding, medical imaging, and cross-modal tasks. The paper further compares the performance of vision transformers with CNNs, examining their respective strengths, limitations, and the emergence of hybrid models. Finally, current challenges in deploying ViTs, such as computational cost, data efficiency, and interpretability, and explore recent advancements and future research directions including efficient architectures, self-supervised learning, and multimodal integration are discussed. © 2013 IEEE.

关键词： Convolutional neural networks

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Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection 38

Suitable is the Best: Task-Oriented Knowledge Fusion in Vuln...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Wang, Jingjing Huang, Minhuan Nie, Yuanpin Li, Xiang Du, Qianjin Kong, Wei Deng, Huan Kuang, Xiaohui Institute of Systems Engineering Academy of Military Sciences PLA China Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Zhejiang Sci-Tech University China

Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code comprising multiple functional modules or diverse vulnerability subtypes. In this paper, we present a knowledge fusion-based vulnerability detection method (KF-GVD) that integrates specific vulnerability knowledge into the Graph Neural Network feature learning process. KF-GVD achieves accurate vulnerability detection across different functional modules of the Linux kernel and vulnerability subtypes without compromising general task performance. Extensive experiments demonstrate that KF-GVD outperforms SOTAs on function-level and statement-level vulnerability detection across various target tasks, with an average increase of 40.9% in precision and 26.1% in recall. Notably, KF-GVD discovered 9 undisclosed vulnerabilities when employing on C/C++ open-source projects without ground truth. © 2024 Neural information processing systems foundation. All rights reserved.

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IOT & Machine Learning Models to Develop The Architecture For Assessment Of Security Threats.

IOT & Machine Learning Models to Develop The Architecture Fo...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Alotibi, Mohammed Masaad Rajeyyagari, Sivaram Al-Omari, Omaia Mohammed Alymani, Mofadal Planning and Digital Transformation Ministry of Health Riyadh Saudi Arabia College of Computing and Information Technology Department of Computer Engineering Shaqra Saudi Arabia College of Computing and Information Technology Department of Information Systems Shaqra Saudi Arabia

ISBN: (纸本)9798350317060

The Internet of Things (IoT) and other recent advancements in communication and information technology have profoundly impacted and enhanced people's quality of life. IoT systems are vulnerable to previously unanticipated cyber threats such as denial of service, jamming, phishing, obfuscations eavesdropping, spoofing and invasions because of the widespread availability and rising demand for connected devices. The emerging cyber-physical security problem is difficult to avoid and guard against using conventional methods. Securing IoT systems calls for robust, dynamic, and current security mechanisms. When addressing emerging security concerns in cyber-physical systems (CPS), machine learning (ML) technology is often regarded as the most cutting- edge and promising option. This literature review explains how IoTs are built, looks into the many threats they face, and discusses the most up-to-date efforts being made to ensure their security using machine learning. In addition, it covers some of the research obstacles that may arise in the future while trying to implement security measures in IoT infrastructure. © 2023 IEEE.

关键词： Internet of things

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Towards Aspect Based Components Integration Framework for Cyber-Physical System

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computers, Materials & Continua 2022年第1期70卷 653-668页

作者： Sadia Ali Yaser Hafeez Muhammad Bilal Saqib Saeed Kyung Sup Kwak University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si17035Korea Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982DammamSaudi Arabia Department of Information and Communication Engineering Inha UniversityIncheon22212Korea

Cyber-Physical systems(CPS)comprise interactive computation,networking,and physical *** integrative environment of CPS enables the smart systems to be aware of the surrounding physical *** systems,such as smart health care systems,smart homes,smart transportation,and smart cities,are made up of complex and dynamic *** components integration development approach should be based on the divide and conquer *** way multiple interactive components can reduce the development complexity *** reusability enhances efficiency and consistency in CPS,encapsulation of component functionalities and a well-designed user interface is vital for the better end-user’s Quality of Experience(QoE).Thus,incorrect interaction of interfaces in the cyber-physical system causes system ***,interface failures occur due to false,and ambiguous requirements analysis and ***,to resolve this issue semantic analysis is required for different stakeholders’viewpoint analysis during requirement specification and components *** work proposes a framework to improve the CPS component integration process,starting from requirement specification to prioritization of components for *** semantic analysis and assessing the reusability of specifications,the framework uses text mining and case-based reasoning *** framework has been tested experimentally,and the results show a significant reduction in ambiguity,redundancy,and irrelevancy,as well as increasing accuracy of interface interactions,component selection,and higher user satisfaction.

关键词： Cyber-physical systems component-based development casebased reasoning prioritization requirement management specification text mining

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Career Track Based Capstone Course Design for information systems Curriculum 14

Career Track Based Capstone Course Design for Information Sy...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Gottipati, Swapna Shim, Kyong Jin Datta, Subhajit School of Computing and Information Systems Singapore Management University Singapore Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

The aim of technical education is to design a career-based curriculum that imparts a set of skills to students and enable them to contribute to a nation's economic progress. With their multifarious uses, information systems (IS) lie at the heart of modern economies. IS curricula offer several tracks for students to pursue career paths based on their interests and opportunities. To offer students an opportunity to apply the knowledge they glean from classroom instructions on real-world problems, capstone projects are essential elements in most, if not all, IS programs. As IS programs increasingly offer a wider range of career paths for students, the design and delivery of such capstone projects present a set of unique challenges. In this paper, we examine the set of challenges and share our experience from designing and delivering a career track-based capstone course for the first time at a large university. Feedback from students and faculty supervisors indicates that the course was able to meet its learning objectives to a notable extent and offered important pointers to future improvements in the course design. © 2023 IEEE.

关键词： information systems

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Alzheimer's Disease Classification via Pre-trained Convolutional Neural Network Variants Utilizing Multi-level Thresholding based on Kapur's Entropy 2

Alzheimer's Disease Classification via Pre-trained Convoluti...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Mahendran, Nivedhitha Durai Raj Vincent, P.M. Srm Institute of Science and Technology School of Computing Technologies Department of Networking and Communications Kattankulathur Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372892

Neurological diseases are the disorders of brain and the nerves that run throughout the human body. There is a range of symptoms that arise as a result of chemical, structural, or electrical modifications in the regions of brain. One such neurological disease is the Alzheimer's Disease (AD), is considered as a slow and progressive disease that majorly affects the cognitive abilities of the individual. It is most common forms of dementia without any proper cure. However, early diagnosis and treatment might slow down the symptoms and improve the lifestyle of the individual. The most common techniques to identify the neurological disorders are imaging of the brain regions, namely, CT, MRI, PET, etc. The learning approaches, such as Deep Learning and Machine Learning are identified to be effective in analyzing these images to provide better accuracies in detecting the AD. In this study, four versions of Convolutional Neural Networks (CNN) are used to classify AD and Control cases. Further, the Kapur's thresholding technique is also implemented to segment the images for improving the classification accuracy of AD. From the results shown in the study, the inference shows that the VGG16 of CNN provides considerable improvement than the other deep learning models before and after the implementation of thresholding. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of the Programming Languages Preferred by Novice Programmers for Solving Programming Problem 4

Analysis of the Programming Languages Preferred by Novice Pr...

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4th Asia Service Sciences and Software engineering Conference, ASSE 2023

作者： Amin, Md Faizul Ibne Rahman, Md. Mostafizer Shirafuji, Atsushi Watanobe, Yutaka Department of Computer and Information Systems University of Aizu Aizu-Wakamatsu Fukushima Japan Dhaka University of Engineering and Technology Gazipur Dhaka Bangladesh

ISBN: (纸本)9798400708534

In ICT education, most of the courses particularly those focused on programming, are designed to enhance computational and practical skills. However, the selection of appropriate programming languages holds great significance for novice programmers embarking on their journey of learning programming. This paper presents a comprehensive analysis utilizing real-world data to gain insights into the prevalent trends in programming language selection for problem-solving among novice programmers. The study leverages solution submission logs from an online judge (OJ) system, employed as an educational tool within an introductory programming course. The analysis specifically focuses on topic #1 consists of four problems of this course. Our investigation revolves around programmers' first attempts to solve these problems, taking into account the programming languages they employ. The findings of our detailed statistical analysis shed light on the preferred programming languages used by programmers to tackle these problems. These insights not only aid fledgling novice programmers in making informed decisions when selecting a programming language, but also offer educators, instructors, and academic institutions valuable information for curating programming language choices within introductory programming courses. © 2023 ACM.

关键词： Big data

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Ensemble Text Classification with TF-IDF Vectorization for Hate Speech Detection in Social Media

Ensemble Text Classification with TF-IDF Vectorization for H...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sathishkumar, R. Karthikeyan, T. Praveen, Kumar P Shamsundar, S.M. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Sri Manakula Vinayagar Engineering College Department of Computer Science and Business Systems Puducherry India Sri Manakula Vinayagar Engineering College Department of Information Technology Puducherry India

ISBN: (纸本)9798350315127

The development of artificial intelligence (AI) has changed how hate speech is detected. In hate speech identification using machine learning, a number of methods are used to automatically find text that uses vocabulary that is considered to be derogatory, discriminatory, or motivated by hatred. Supervised learning techniques like neural networks, decision trees, and SVMs need a labelled dataset comprising samples of hate speech and non-hate speech. This project investigates the use of AI and machine learning techniques to automatically detect material that uses offensive, intolerant, or hostile words. A voting classifier and TF-IDF representations are combined to improve classification accuracy. The ensemble of classifiers, powered by AI approaches, shows impressive accuracy in identifying hate speech by training five different classifiers (Random Forest, Bagging, Support Vector Machine, AdaBoost, and Gradient Boosting) on a labelled dataset of tweets. The TF-IDF representation prioritises textual terms, whereas the ensemble method uses classifier diversity to capture distinctive patterns. Results from experiments show the strategy's effectiveness, with precision 0.95, recall 0.96, f1-score 0.95 and accuracy 0.97 for detecting hate speech. By successfully utilising AI's capacity to fight hate speech, this research helps the development of a diverse and secure online environment. The suggested approach works well for automatically identifying hate speech, making the internet a safer and more welcoming place for all users. © 2023 IEEE.

关键词： Stochastic systems

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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