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

作者： Kate, Priya Pacharney, Utkarsha Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Department of Computer Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

5G technology represents a progressive leap in Wi-Fi conversation, providing unheard-of pace, connectivity, and capacity. Its deployment has profound implications for modern engineering, impacting industries such as telecommunications, healthcare, transportation, and clever towns. With speeds up to one hundred instances quicker than 4G and latency as little as 1 millisecond, 5G permits real-time facts trade, fostering improvements like independent vehicles, far-flung surgeries, and the Internet of Things (IoT). Currently, Engineers are challenged to design infrastructure that will assist the dense community of gadgets related to 5G, necessitating advancements in antenna design, power performance, and statistics security. The era also quickens the adoption of part computing, reducing reliance on centralised facts centres and ensuring quicker processing near the date supply. The transformative impact of 5G extends beyond connectivity. It reshapes the layout and capability of present-day engineering structures, emphasising adaptability, resilience, and scalability. As 5G continues to conform, its function in driving innovation and redefining engineering paradigms becomes increasingly glaring. © 2025 IEEE.

关键词： 5G mobile communication systems

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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IoT-Based Intelligent Energy Management for EV Charging Stations

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1853-1861页

作者： Dasi, Swathi Bondalapati, Swarupa Rani Subbaraju, Mummidi P. Nimma, Divya Jangir, Pradeep Reddy, R. Vijaya Kumar Zareena, N. Electronics and Communication Engineering Department Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Aditya University Andhra Pradesh Surampalem India University of Southern Mississippi United States Biosciences Department Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India associated with Applied Science Research Center Applied Science Private University Amman Jordan. Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Computer Science and Engineering Department R.V.R. & J. C. College of Engineering Guntur India

The electronics panels and controllers used in electric vehicle (EV) charging stations need to function securely and optimally in countries such as India, where the external temperature remains high for a significant part of the year. This study is the first to use a solar-battery storage integrated switchable glazing architecture to supply HVAC (heating, ventilation, and air conditioning) in the control rooms of electric vehicle charging stations throughout the day. The EV charging station has been equipped with a rooftop-mounted solar PV source as part of an initiative to promote renewable energy and sustainable forms of mobility. The utilization of a vanadium redox flow battery (VRFB) by the system ensures energy security through the provision of a durable solution for storing energy over an extended period of time. A smart scheduling system utilizing IoT technology was demonstrated to effectively meet the glazing load need of buildings in real-time, taking into account dynamic climatic conditions. This system incorporates solar PV, VRFB storage, and the neighborhood power grid. The efficiency of the suggested method was validated using four separate transitory scenarios: sunny, intermittently cloudy, prolongedly cloudy, and low solar radiation with frequent grid outages. Due to its versatility to be applied in various situations, the suggested method has the potential to greatly enhance capacity. © (2024), (International Association of Engineers). All rights reserved.

关键词： Air conditioning

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Interoperability and Standardization in the Internet of Things 9

Interoperability and Standardization in the Internet of Thin...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Saidala, Ravi Kumar Haseeb, V.V. Padmapriya, G. Jayabalan, S. Priya, V. Kalai Shaikh, Anwar Ahamed Alliance University Department of Computer Science Engineering Karnataka Bangalore560076 India Nam College Department of Computer Science Kerala Tamil Nadu Kallikkandy 670693 India School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Panimalar Engineering College Department of Computer Science And Business System Tamil Nadu Chennai600123 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Puduvoyal601206 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350365092

This research highlights the importance of standards and interoperability in the IoT. An essential component of IoT ecosystems is interoperability, which refers to the ability for various systems and devices to converse and share data. Formats for data, methods of communication, and safeguards are all outlined by standards. Widespread adoption and scalability are made possible through interoperability and standards, which allow devices from many manufacturers to communicate with one another. This research investigates the alliances, regulatory authorities, and commercial associations that cooperate in the creation and advancement of open standards that can be embraced by other entities. The main objective of the research is to showcase the construction of an Internet of Things device capable of communicating with various embedded peripheral protocols utilizing Plug and Play (PnP) technology. Moreover, the research examines the impact of regulatory frameworks on the IoT ecosystem and their role in fostering interoperability. An IoT ecosystem necessitates a standardized and open environment to provide optimal performance in terms of connectivity, data exchange, and innovation. This research contributes to the progress of harmonizing and achieving interoperability among World Wide Web Things. © 2024 IEEE.

关键词： Interoperability

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Biotic Stress and Severity Classification of Pear Leaves Disease with Ensemble Learning

Biotic Stress and Severity Classification of Pear Leaves Dis...

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2024 IEEE International Conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Sugunadevi, C. Maheswari, B. Uma Cmr Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350390759

Leaf disease frequently arises as pears plants mature. With a particular emphasis on categorizing the pear leaf disease, the proposed research work suggests the implementation of the CNN architecture that we use in this study to create this disease identification and classification method comprised of ResNet50, InceptionV3, VGG16, and VGG19. These neural networks have been pre-trained on the ImageNet dataset. The ensemble methodology that we describe produces final predictions for the test samples based on the recall, precision, accuracy, and f1-score scores of four standard evaluation metrics. Even though there is an increase in the amount of accuracy when the models are ensembled, the remarkable combination is InceptionV3 with VGG16 with an accuracy of 77.25%, precision of 79.34%, recall of 77.25% and F1 score of 76.50% and with the severity classification as accuracy 45.29%, precision 46.88%, recall 45.29% and F1 score 45.50% respectively. © 2024 IEEE.

关键词： Plant diseases

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Recession Forecasting: Domain-Specific Feature Selection Methodology 1

Recession Forecasting: Domain-Specific Feature Selection Met...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Katale, Tejaswini Hase, Jayashri Chotrani, Aakash Ozarde, Sakshi Vishwakarma Institute of Technology Department of Computer Engineering Pune India K. K Wagh Institute of Engineering and Research Department of Computer Science and Design Nashik India LDRP Institute of Technology and Research Department of Information Technology Gandhinagar India

ISBN: (纸本)9798331518943

Existing feature selection algorithms often choose features based on their individual importance and consider them as independent entities, which may overlook underlying interrelationships amongst the features. This may result in overfitting because only highly correlated features are selected without considering their collective contribution. Hence it is crucial to take into account both individual feature importance and feature interactions. To mitigate this issue, this research paper proposes a Novel Methodology for Domain Wise feature selection approach which is capable of identifying the interrelationships by focusing on Domain-Wise feature selection. It ensures that correlated and similar features are considered together. It is implemented on a Recession Dataset consisting of 293 observations and 45 features. It groups similar features in 8 domains and further arranges them in descending order of Average Correlation values. For each mini dataset it considers only those data points which are nearest to the mean of that domain and merges all unique observations from these domains and creates a new dataset. Further applying Preprocessing steps - Outlier Elimination, Up sampling and Feature Selection. This paper also discusses how the order of preprocessing can impact the overall result of the evaluation metrics. The efficacy of the algorithm is checked by the accuracy scores of Static Probit Model-98% and Vector Auto Regression-88%. Therefore, by considering both the significance of individual features and the relationships between them the suggested approach successfully addresses the shortcomings of traditional feature selection algorithms. © 2024 IEEE.

关键词： Feature Selection

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Enhancing the Resolution of ChestX-ray Images with SRGAN and Sub-Pixel CNN 7

Enhancing the Resolution of ChestX-ray Images with SRGAN and...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Reddy, K. Lokeshwar Sudharson, S. Natarajan, B. Theetchenya, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science And Engineering Chennai India Computer Science And Engineering Vellore Institute of Technology Chennai India Computer Science And Engineering Sona College of Technology Salem India

ISBN: (纸本)9798350371406

A crucial diagnostic tool for respiratory and heart disorders is chest X-ray imaging. However, the limited resolution of acquired images can hinder accurate interpretation and subsequent treatment decisions. To address this challenge, we employ deep learning-based super-resolution techniques, specifically SRGAN and ESPCN, to enhance the chest X-ray images. In this study, we proposed a modified ESPCN and present our findings on the application of SRGAN and ESPCN for enhancing chest Xray images. The enhanced image resolution enables radiologists to visualize fine details and subtle abnormalities that were previously difficult to discern. This enhancement significantly improves image interpretation and diagnostic accuracy. Moreover, our findings demonstrate the potential of super-resolution techniques for medical education and training. Through extensive experimentation, we saw significant visual improvements, as evidenced by PSNR(peak signal-to-noise ratio) of SRGAN and proposed ESPCN are 40.46 and 40.17, and achieved SSIM values 0.84 and 0.71 respectively. In conclusion, our study showcases the efficacy of SRGAN and ESPCN in enhancing chest X-ray imaging. By improving image quality and diagnostic accuracy, these techniques have the potential to revolutionize medical diagnostics, leading to better patient outcomes and a more effective healthcare system. © 2023 IEEE.

关键词： Image resolution

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