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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Fathima Mubarakkaa, M. Ummu Sabura, H. Pradiksha, M.S. Shalini, S. Kalpana Devi, A. Department of Computer Science and Engineering India Nellai College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350394580

The proposed college Bus Tracking System presents a holistic solution to rectify the current inadequacies in college bus transportation by harnessing the capabilities of smartphone technology. The primary goal is to establish a precise real-time tracking mechanism for buses, empowering both students and staff to effortlessly monitor bus locations and receive timely notifications. This initiative effectively alleviates prevalent safety concerns, untimely delays, and inconveniences associated with the existing manual procedures. It is particularly noteworthy that this system holds exceptional significance during adverse weather conditions, as it mitigates the discomfort faced by students and staff due to uninformed waiting at designated stops. To ensure the successful realization of this proposed solution, leveraging the mobile GPS system is pivotal. It is imperative to establish a resilient technological framework, prioritize data privacy imperatives, foster extensive user adoption, implement a meticulous maintenance regimen, and seamlessly integrate the system into existing infrastructures. By addressing these considerations, the proposed college Bus Tracking System has the potential to revolutionize college transportation, offering a safer, more efficient, and user-friendly experience for all stakeholders involved. © 2023 IEEE.

关键词： GPS Mobile Devices Open Street Map (OSM) Time consumption

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Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

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Intelligent Automation & Soft Computing 2023年第4期36卷 353-368页

作者： R.Anandha Murugan B.Sathyabama Department of Computer Science and Engineering K.L.N.College of Engineering630612TamilnaduIndia Department of Electronics and Communication Engineering Thiagarajar College of EngineeringMadurai625015TamilnaduIndia

Improved picture quality is critical to the effectiveness of object recog-nition and *** consistency of those photos is impacted by night-video systems because the contrast between high-proﬁle items and different atmospheric conditions,such as mist,fog,dust *** pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient *** recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance ***,the iden-tiﬁcation of objects in the night world also poses further problems because of the distorted backdrop and dim *** Correlation aware LSTM based YOLO(You Look Only Once)classiﬁer method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this *** order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerﬁlter for removing the noise in the *** process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set ***,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalﬁndings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mec

关键词： Object monitoring night vision image SSAN dataset adaptive internal linear embedding uplift linear discriminant analysis recurrent-phase level set segmentation correlation aware LSTM based yolo classiﬁer algorithm

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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computer Modeling in engineering & sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Graph Representation Learning for Recommender Systems

Graph Representation Learning for Recommender Systems

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Manikrao, Kulkarni Manjusha Hiremath, Savitha Manjula, S.H. Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India University of Visvesvaraya College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331529833

Graph data represents information efficiently and can be used to learn subsequent tasks easily. In the domain of biological science, recommender systems, social network analysis graph representation learning has become ubiquitous with tremendous success. A graph structured data can make a system to learn, produce new information and can be analyzed for set of tasks. Deep learning-based recommender systems are using deep graph embeddings for improved accuracy by exploring user item relations. This relation exploration can be utilized for other recommendation task such as link prediction. In this study we are providing overview of graph representation learning with complete analysis of data with graph representation. This study provides importance of graph representation in recommender system to learn node representations. The results and discussion section denotes how simple graph neural network technique successfully applied on graph data for node representation learning and challenges. © 2025 IEEE.

关键词： Contrastive Learning

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Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

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computers, Materials & Continua 2024年第11期81卷 3003-3031页

作者： Hira Akhtar Butt Khoula Said Al Harthy Mumtaz Ali Shah Mudassar Hussain Rashid Amin Mujeeb Ur Rehman Department of Computer Science University of Management and TechnologySialkot51040Pakistan Department of Computer Science and Creative Technologies Global College of Engineering and TechnologyMuscat2546Sultanate of Oman Department of Computer Science University of WahWah Cantt47040Pakistan Department of Computer Science and IT University of ChakwalChakwal48800Pakistan

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)*** Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent *** this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN *** model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant *** adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack *** proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble *** proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in *** provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving *** comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing *** results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

关键词： Table 1(continued)OSI layer Possible DDoS attack Data link MAC Address Flooding Physical Cable disconnection JaMMING physical impersonation

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Smart automated highway lighting system using IoT: a survey

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Energy Informatics 2024年第1期7卷 1-28页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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Deep Learning Model for Human Detection-Enhanced YOLO Comparative Analysis 4

Deep Learning Model for Human Detection-Enhanced YOLO Compar...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, P. Aswathy, R.H. Soms, Nisha Niranjani, V. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri eshwar college of engineering Department of Computer science and engineering Coimbatore India

ISBN: (纸本)9798331540364

Video surveillance systems employing closed-circuit television (CCTV) are crucial for public safety. Accurately detecting humans within video frames is a core component of this technology. This knowledge is crucial for numerous uses, including detecting possible dangers, observing crowd dynamics, and examining patterns of human behavior. This research introduces a new method called YOLOv8, designed to enhance human detection capabilities within CCTV footage. The proposed algorithm, YOLOv8, is built upon the popular object recognition framework, YOLO, but with significant enhancements to improve its accuracy and efficiency in detecting humans. YOLOv8 utilizes deep convolutional neural networks (CNNs) and advanced object recognition techniques to achieve high accuracy in human detection, even in challenging environments such as low light conditions, occlusions, and varying scales of human poses. To assess the effectiveness of YOLOv8, various tests were performed using standard datasets and real-world CCTV surveillance footage. The results demonstrate that YOLOv8 outperforms existing human detection algorithms regarding accuracy, robustness, and real-time processing capability. YOLOv8 achieved an average precision of over 95% in human detection, making it highly suitable for everyday CCTV surveillance. The proposed YOLOv8 algorithm has the potential to significantly improve human detection in security camera footage and enhance public safety. This research contributes to advancing human detection technology and can benefit various domains, including law enforcement, public safety, and video analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting Misinformation in COVID-19 Content: A Machine Learning and Deep Learning Approach with Word Embeddings

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SN computer science 2025年第1期6卷 1-18页

作者： Chabukswar, Arati Shenoy, P. Deepa Venugopal, K.R. Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru 560001 India

Inaccurate information that is purposefully spread for a certain goal known as fake news related to COVID-19 tweets posing threats to public health, harm to the society. But creating a reliable method to identify news of this kind is difficult, particularly when the news is released combining factual and fictitious material. The identification of fake news with COVID-19 is challenging task in Natural Language Processing (NLP). This study uses Machine Learning (ML) models such as Support Vector Machine (SVM), Random Forest (RF), Gaussian Naıve Bayes (NB), Logistic Regression (LR) and Deep Learning (DL) models with several architectures such as Long-short Term memory (LSTM), Bidirectional LSTM with one-hot encoding representation and pre-trained word embeddings such as Word2Vec, GloVe and FastText, trained using 14,571 records on DataCOVID19 dataset obtained by combining datasets from three different sources available in Kaggle and GitHub repository. The tests on a real-world COVID-19 news dataset show that Bi-LSTM model improves performance when built over various embedding techniques such as Word2Vec, GloVe, FastText and One-hot encoding representation achieving highest accuracy score. The findings emphasise the potential of deep learning methods for identifying false news and have important ramifications for curbing the dissemination of false information about COVID-19 tweets known as Fake News Detection (FND). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Deep learning Fake news Machine learning Natural language processing One-hot encoding Social media Word embeddings (Word2Vec, GloVe, FastText)

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Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection 18

Align before Attend: Aligning Visual and Textual Features fo...

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Student Research Workshop, SRW 2024

作者： Hossain, Eftekhar Sharif, Omar Hoque, Mohammed Moshiul Preum, Sarah M. Department of Computer Science and Engineering Department of Computer Science Dartmouth College United States Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Bangladesh

ISBN: (纸本)9798891760905

Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach’s effectiveness with F1-scores of 69.7% and 70.3% for the MUTE and MultiOFF datasets. The scores show approximately 2.5% and 3.2% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://***/eftekhar-hossain/Bengali-Hateful-Memes. © 2024 Association for Computational Linguistics.

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A Survey on Enhancing Image Captioning with Advanced Strategies and Techniques

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computer Modeling in engineering & sciences 2025年第3期142卷 2247-2280页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Sajid Shah Mohammed ELAffendi School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Image captioning has seen significant research efforts over the last *** goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically *** real-world applications rely on image captioning,such as helping people with visual impairments to see their *** formulate a coherent and relevant textual description,computer vision techniques are utilized to comprehend the visual content within an image,followed by natural language processing *** approaches and models have been developed to deal with this multifaceted *** models prove to be stateof-the-art solutions in this *** work offers an exclusive perspective emphasizing the most critical strategies and techniques for enhancing image caption *** than reviewing all previous image captioning work,we analyze various techniques that significantly improve image caption generation and achieve significant performance improvements,including encompassing image captioning with visual attention methods,exploring semantic information types in captions,and employing multi-caption generation ***,advancements such as neural architecture search,few-shot learning,multi-phase learning,and cross-modal embedding within image caption networks are examined for their transformative *** comprehensive quantitative analysis conducted in this study identifies cutting-edgemethodologies and sheds light on their profound impact,driving forward the forefront of image captioning technology.

关键词： Image captioning semantic attention multi-caption natural language processing visual attention methods

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