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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Cyber attack prediction on IoT and IIOT devices using ensemble based predictive analytic model 4

Cyber attack prediction on IoT and IIOT devices using ensemb...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Kumar, Priyan Malarvizhi Shahwar, Tayyaba Gokulnath, C. Selvaraj, Jeeva University of North Texas Department of Data Science Denton United States SRM Institute of Science and Technology Department of Computing Technologies Chennai India Jain University Global Campus Department of ISE Bangalore India

ISBN: (纸本)9798331518882

The accelerated connectivity across almost all entities of the globe has eventually escalated the number of Internet of Things (IoT) and Industrial IoT (IIoT) devices. These objects need seamless internet access which makes them vulnerable to various types of cyber-attacks at various layers. Many innovative and efficient solutions are developed for preventing these attacks and protecting the devices and data from malicious access. However, new forms of attacks continue to surge every year, which necessitates the development of more adaptive solutions by harnessing the attack profile data. The proposed research work constructs a heterogeneous ensemble model consisting of different types of base machine learning algorithms. These models explore the network profile data of the IoT and IIoT devices to extract inherent and latent features from it and predicts the network activity. Later, the predictions of the individual base learners are aggregated using a meta learner model to give the final prediction. The performance of the proposed ensemble model is validated using standard prediction metrics to assess its performance. As future research extension many base learners could be integrated to form a more efficient and powerful ensemble model with novel aggregation techniques. © 2025 IEEE.

关键词： Industrial internet of things (IIoT)

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Gesture Showdown: Rock Paper Scissors with AI Vision

Gesture Showdown: Rock Paper Scissors with AI Vision

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hemal Babu, H. Yeshwanth, C.D. Shravan, N. Bose, K. Regin Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Institute of Technology Department of AI&DS and Cloud Computing Chennai India

ISBN: (纸本)9798331512965

This paper presents a gesture-based RockPaper-Scissors game that leverages computer vision and hand gesture recognition to enable intuitive human-computer interaction. Utilizing MediaPipe's pre-trained hand tracking module, the system detects and identifies 21 hand landmarks in real-time to classify gestures as rock, paper or scissors based on angular relationships between key points. OpenCV is used for webcam integration and video frame preprocessing, ensuring seamless interaction. The game compares the recognized gesture with a computer-generated move, dynamically displaying the results and resetting after each round for continuous play. The system identifies gestures through precise angle calculations, differentiating between closed fists, open palms and extended fingers. Sample outputs demonstrate real-time gameplay scenarios, highlighting gesture recognition accuracy and user engagement. Future enhancements include refining recognition accuracy, adding multiplayer functionality, introducing customizable gestures, and deploying the game on web and mobile platforms for broader accessibility. This project showcases the potential of integrating AI and computer vision in creating engaging and user-friendly applications. © 2025 IEEE.

关键词： Gesture recognition

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Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

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SN Computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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ERBM: A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials and Continua 2025年第3期83卷 5155-5179页

作者： Mehmmod, Arshad Batool, Komal Sajid, Ahthsham Alam, Muhammad Mansoor Su'ud, Mazliham MohD Khan, Inam Ullah Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Faculty of Computing Riphah International University Islamabad46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia

Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic, where three membership functions are created to classify all the network traffic features as low, medium, or high to remain situationally aware of the environment. Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty. Also, for further classification, machine learning classifiers such as Decision Tree (DT), Random Forest (RF), and Neural Networks (NN) learn complex ways of attacks and make the detection process more precise. A thorough performance evaluation using different metrics, including accuracy, precision, recall, F1 Score, detection rate, and false-positive rate, verifies the supremacy of ERBM over classical IDS. Under extensive experiments, the ERBM enables a remarkable detection rate of 99% with considerably fewer false positives than the conventional models. Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions, the ERBM system provides a unique, scalable, data-driven approach to IoT intrusion detection. This research presents a major enhancement initiative in the context of rule-based IDS, introducing improvements in accuracy to evolving IoT threats. Copyright © 2025 The Authors.

关键词： Membership functions

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Gradient Descent Robustly Learns the Intrinsic Dimension of data in Training Convolutional Neural Networks

arXiv

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arXiv 2025年

作者： Zhang, Chenyang Gao, Peifeng Zou, Difan Cao, Yuan Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong Department of Computer Science School of Computing and Data Science & Institute of Data Science The University of Hong Kong Hong Kong

Modern neural networks are usually highly over-parameterized. Behind the wide usage of over-parameterized networks is the belief that, if the data are simple, then the trained network will be automatically equivalent to a simple predictor. Following this intuition, many existing works have studied different notions of "ranks" of neural networks and their relation to the rank of data. In this work, we study the rank of convolutional neural networks (CNNs) trained by gradient descent, with a specific focus on the robustness of the rank to image background noises. Specifically, we point out that, when adding background noises to images, the rank of the CNN trained with gradient descent is affected far less compared with the rank of the data. We support our claim with a theoretical case study, where we consider a particular data model to characterize low-rank clean images with added background noises. We prove that CNNs trained by gradient descent can learn the intrinsic dimension of clean images, despite the presence of relatively large background noises. We also conduct experiments on synthetic and real datasets to further validate our claim. © 2025, CC BY-SA.

关键词： Convolutional neural networks

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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A Comprehensive Study of OOP-Related Bugs in C++ Compilers

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IEEE Transactions on Software Engineering 2025年 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

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