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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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Language Bridge - Bridging Voices Through NLP and Spoken Input Transcription 10

Language Bridge - Bridging Voices Through NLP and Spoken Inp...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anu Lisba Raj, M. Nixan, Nimble Sankar Krishna, K. Sunny, Sweety Sara Sreetha, E.S. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This paper concentrates on enhancing customer care and booking services with the introduction of a system named Language Bridge. Utilizing advanced technologies, in-cluding speech recognition and natural language processing, the Language Bridge system aims to elevate customer satisfaction by effectively addressing language barriers. Through precise speech recognition capabilities, the system seamlessly converts spoken input into text, offering a user-centric experience that accommodates various dialects. The overarching goal is to redefine global standards for customer service, providing users with a seamless and inclusive platform that transcends linguistic limitations. Through the integration of cutting-edge technologies, Language Bridge endeavors to set a new benchmark in customer care and booking services, fostering improved communication and accessibility for diverse user bases. © 2024 IEEE.

关键词： Customer satisfaction

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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Enhancing Artwork Resilience Through Variable Embedding Technique 2

Enhancing Artwork Resilience Through Variable Embedding Tech...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Deepalakshmi, R. Senthilmurugan, K.R. Preetha, S. Aishwarya, S.Sakthi Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350328202

In the rapidly evolving digital world, safeguarding creative works against unapproved modifications has emerged as a critical issue. Because of the shortcomings of traditional procedures, stronger solutions are being studied. The study uses a novel application of variable embedding technology to address the important problem of maintaining copyright integrity for digital artwork. This paper provides a systematic analysis of the method's effectiveness and a detailed understanding of how appropriate it is to certain image characteristics. And, the paper has illustrated the method's resistance to possible attacks by in-depth experimental investigations, offering a viable path for protecting copyrighted digital art. This study intends to add to the discussion about enhancing authenticity and data protection in the field of artistic expression as the world of digital media expands further. © 2024 IEEE.

关键词： Copyrights

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Enhancing Classroom Accessibility for Deaf Students through Sign Language Recognition 10

Enhancing Classroom Accessibility for Deaf Students through ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Monisha, K.B. Sushmitha, G. Shanthalakshmi Revathy, J. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This paper addresses the communication barriers faced by the deaf and voiceless community by introducing a system that translates sign language into spoken or written language. The primary aim is to foster better communication between these communities and the hearing population, ultimately supporting academic success for individuals with hearing impairments. By utilizing the deep learning model VGG19, the system is able to recognize sign language with an astounding 99.97% accuracy rate, which is important for guaranteeing fair access to education. Because VGG19 can efficiently extract complicated features from images, a property that is essential for precisely interpreting the intricate hand gestures and motions that are a part of sign language communication, it is used in sign language recognition. It highlights the importance of real-time captions and interpretations as transformative tools in educational environments. Furthermore, the paper explores the implementation of this technology, outlining its benefits for educators and deaf students alike. Through examination of challenges, and future directions, it underscores the growing significance of sign language recognition in education, emphasizing its role in promoting inclusivity and enhancing learning experiences. © 2024 IEEE.

关键词： Students

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Bridge the Generalization Gap: A Comprehensive Framework for Enhance Reliability and Robustness in Deep Learning Models

Bridge the Generalization Gap: A Comprehensive Framework for...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vasanth, S. Madhu Karthick, A. Vijay Akash, S. Jovita, B. Kanishka, P. Sruthi, R. Rmk College of Engineering and Technology Department of Computer Science and Engineering Thiruvallur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Deep reinforcement learning (DRL) has been successful in a wide range of domains but its broad acceptance is limited by the generalization gap, which measures the performance difference between an environment a model was trained with versus new, unseen environments. This gap implies a challenge that needs to be addressed for DRL models used in real-world adaptation. This paper considers a strategy whose methodology outlines combining domain randomization, multitask learning, and regularization techniques. The study trains the models on multiple tasks and diverse environments, thus generalizing the ability of models. Our regularization strategy keeps the model from over-specialization to the training data and ensures improved performance. Benchmark environments are evaluated based on our approach, reducing the generalization gap with improvement in average returns in unseen environments and increasing the resilience of DRL models. In this regard, our study opens up the avenue for a significant contribution to the generalization capacity of more robust DRL models for real-world applicability. © 2024 IEEE.

关键词： Deep reinforcement learning

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A deep learning-based early alzheimer’s disease detection using magnetic resonance images

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Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, Lalitha Poovaraghan, R.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s Disease (AD) is a degenerative, chronic condition of the brain for which there is now no effective treatment. However, there are medications that can slow its development. In order to stop and control the development of AD, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct AD stages. This work employs a deep learning methodology, especially CNN. The proposed approach makes use of well-known models that have already been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve greater accuracy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling approach, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset was employed for a comprehensive assessment of the method proposed, utilizing well-known performance metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class AD stage classifications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, par

关键词： Magnetic resonance

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Comparative Analysis of Human Behavioural for Analysing Actions in Sport Videos using Artificial Intelligence and computer Vision 2

Comparative Analysis of Human Behavioural for Analysing Acti...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gupta, Nidhi G. Bardekar, A.A. Sipna College of Engineering & Technology Department of Computer Science & Engineering Amravati India

ISBN: (纸本)9798331528713

Human behavior research is becoming more and more important in human-centered software. Today, many public spaces utilize video monitoring because of improvements in technology and decreased costs. However, the majority of surveillance systems on the market today are limited to identifying odd behavior and gathering data in the form of stored films. Therefore, to reduce workloads and increase productivity, improving methods for recognizing human behavior is critical. Recent advancements in computer vision and sports video analysis have produced notable gains that make several vital functions possible. To offer more sophisticated data - including intricate, thorough analyses in sports like badminton, cricket, basketball, and soccer - research has mostly concentrated on computer vision algorithms utilized to do various jobs. Identifying specific players and teams, as well as their actions and behaviors, throughout practices, competitions, and other events is known as human behavioral recognition, or HBR. In an unidentified video sequence, HBR seeks to determine the sort of action, its length, and the individual doing it. Performance monitoring is the main objective of HBR in sports, and it might involve a variety of tasks such as recognizing the player, tracking their every move, comparing the activities they've just completed, or automatically analyzing the outcomes of those comparisons. This paper provides an extensive overview of human behavioral comprehension for computer vision and artificial intelligence-based action analysis in sports videos. This paper also explores published works in a range of sports-related application-specific tasks and the opinions of current scholars on them. © 2024 IEEE.

关键词： Deep learning

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