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10th International Conference on Smart computing and Communication, ICSCC 2024

作者： Ranganathan, Chitra Sabapathy Basha, Amjath Ali Jakul Vijayalakshmi, A. Mohankumar, N. Sujatha, S. Sasikala, C. Mphasis Corporation Tamil Nadu Chennai India University of Technology and Applied Sciences Computer Engineering Electrical and Electronic Section Department of Engineering Ibra Oman Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharashtra Nagpur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350363104

Farm production and food security are affected by green leaf diseases. Modern disease detection technologies may discover plant health issues early. This work uses Raspberry Pi, a cheap and flexible single-board computer, along the advanced Random Forest machine learning technique to identify a green leaf disease. To identify crop diseases, the study uses automated, non-invasive technologies. From labeled images of good and sickly leaves, the algorithm extracts color histograms and texture, and form. It feeds a Random Forest classifier these properties for classifying data with high dimensions. Several green leaf diseased plant species were examined. This method detected plant health concerns with high precision. Movement and low cost make the Raspberry Pi ideal for remote and resource-limited agriculture, increasing farming accuracy. Helping farmers and users identify and develop green leaf diseases improves efficient farming. This methodology has the potential to improve crop management, optimize pesticide usage, and increase agricultural productivity. These results might contribute to the development of advanced automatic disease detection techniques to address green leaf diseases in modern agriculture. © 2024 IEEE.

关键词： computer Vision Green Leaf Plant Disease Detection Precision Agriculture raspberry pi

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Safeguarding the Homeless Using ViTransformer in Violence Detection

Safeguarding the Homeless Using ViTransformer in Violence De...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： R Bhuvaneswari Sai Lasya M B Natarajan Tamilselvi M K Geetha M Velammal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai Tamilnadu India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Department of Computer Science and Engineering Karpagam college of Engineering India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically Vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art technology, the research aims to not only identify individuals but also discern their actions and activities within the monitored environment. The methodology involves the initial detection of persons using Vi Transformer, an innovative deep learning model renowned for its accuracy in object recognition tasks. Once a person is successfully identified, this research further scrutinizes their activities, with a particular focus on detecting instances of conflict or fighting. This step holds immense potential or the safety and well-being of homeless individuals, as it enables the swift notification of nearby authorities or support services in cases of altercation. Crucially, this research delves into the performance metrics of both person detection and activity detection experiments, providing valuable insights into the efficacy of the Vi Transformer model in real-world applications. By assessing the system's accuracy, speed, and reliability, this study contributes to the development of proactive strategies for assisting homeless populations, emphasizing the importance of technology in addressing social challenges.

关键词： Surveillance Sociology Smart homes Transformers Safety Security Personnel

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification

Weighted Average Ensembled Probability Pipeline of Pre-train...

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International Conference on computing and Networking Technology (ICCNT)

作者： Rahul Gunti B Rohan Reddy Gunti Swathi G Sharath Kumar Reddy Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems.

关键词： Human computer interaction Solid modeling Sign language Visualization Accuracy Pipelines Virtual reality Streaming media Streams Residual neural networks

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DPFedSAM-Meas: Comparison Of Differential Privacy Federated Learning In Medical Image Classification

DPFedSAM-Meas: Comparison Of Differential Privacy Federated ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Shunrong Wang, Zhi He, Zixuan Li, Yonggang Li, Xiaofan Chi, Haotian Du, Xiaojiang China University of Mining and Technology School of Computer Science Xuzhou221116 China Jiangsu Jingchuang Electric CO. LTD China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Machine learning is widely used in medical image classification tasks. However, medical images often exhibit uneven distribution and high sensitivity to noise. A feasible solution involves using federated learning (FL) with differential privacy (DP), a distributed training method that protects patient privacy. In this work, many studies have proposed improved solutions for localized differential-private federated learning (DP-FL) frameworks. However, these studies are isolated, which would be detrimental to privacy practitioners in designing and using the algorithms. To provide a comprehensive analysis of these algorithms, we propose DPFedSAM-Meas, as a framework for comprehensive utility analysis of DP-FL. In this framework, we employed the state-of-the-art federated learning framework FedSAM. Moreover, we categorize DP algorithms into Laplace DP and Gaussian DP by the underlying DP mechanisms, and into Gradient DP and Parameter DP by the DP position in FL train. DPFedSAM-Meas allows a comparative analysis of these four DP techniques, measuring their model utility, privacy leakage, and overhead when FL uses different network structures. Finally, we evaluate DPFedSAM-Meas on datasets of Pneumonia, Blood, and Path, aiming to investigate the performance of different DP techniques on mainstream deep learning algorithms, including Convolutional Neural Networks (CNN) and Vision Transformers (ViT). © 2024 IEEE.

关键词： Federated learning

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer science 2024年第5期5卷 1-22页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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SVM-Enhanced Dynamic Audiology Screening with IoT-Driven Monitoring for Personalized Hearing Health Management 2

SVM-Enhanced Dynamic Audiology Screening with IoT-Driven Mon...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Ronald, Bindu S. Raman, Ramakrishnan Vekariya, Daxa Baskar, V. Vijaya Rahul, J. Srinivasan, C. Symbiosis Law School Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

The increasing prevalence of hearing impairment across diverse age demographics, particularly in elderly populations, highlights the need for early diagnosis and ongoing therapy. Traditional audiology screening techniques often lack the necessary dynamic flexibility to meet personalized hearing health requirements over time. This system introduces an innovative method for dynamic audiology screening via IoT-based monitoring, using Support Vector Machine (SVM) algorithms for individualized hearing health management. The incorporation of IoT devices allows ongoing, real-time auditory data acquisition from the user's surroundings, recording essential metrics such as sound frequencies, noise levels, and auditory reactions. This data is analyzed using SVM to categorize and identify probable hearing impairments, providing great precision in differentiating between various degrees of hearing loss. The SVM model's strong classification skills render it especially effective in differentiating between varying levels of hearing loss, facilitating early identification and tailored therapy strategies. This tailored method of audiology screening boosts diagnostic precision and facilitates proactive management, perhaps improving long-term results for individuals with hearing impairments. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Multi-RIS Deployment for High Data-Rate Communications

Multi-RIS Deployment for High Data-Rate Communications

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Khan, Muhammad Fawad Mei, Haoran Rahim, Shahnila Peng, Limei Ho, Pin-Han School of Computer Science and Engineering Kyungpook National University Deagu Korea Republic of University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

This paper proposes using multiple reconfigurable intelligent surfaces (RISs) in SISO communications, considering that RISs can flexibly extend the wireless communication range. In a given area with multiple RISs, we propose an algorithm to select the optimal locations from a set of candidate points to deploy the RISs, aiming to maximize the achievable data rate. We investigate the impact of single-hop and multi-hops communication on the achievable data rate for the proposed algorithm and the results show that the deployment height and number of communication hops significantly affect the achievable data rate. © 2022 IEEE.

关键词： Wireless communication Spread spectrum communication Reconfigurable intelligent surfaces SISO communication

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Energy-Efficient Routing in Underwater Wireless Sensor Networks Through Cluster-Dragonfly Optimization 3

Energy-Efficient Routing in Underwater Wireless Sensor Netwo...

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3rd IEEE International Conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Anitha, D. Husain, Saif O. Velpula, Srikanth Rajani, N. Dineshkumar, R. School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Tamilnadu Kattankulathur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Dept.of Ece Bengaluru India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

In this paper, develop a power control scheme for underwater wireless sensor networks that takes into account the impact of communication ranges on network performance, including energy utilization and delay overhead. The primary objective of our scheme is to assign a differentiated optimal communication radius for each sensor node, enhancing overall network efficiency. The proposed method utilizes energy-aware Cluster-Dragonfly Optimization (CDFO), but it might face challenges in handling large-scale optimization, and the scalability of the algorithm may be limited when dealing with high-dimensional or complex optimization. Although the proposed CDFO method exhibits high performance compared to existing methods, such as Self-Adaptive Glow Worm Swarm Optimization (SA-GWO), Improved Coyote Optimization Algorithm (ICOA), Hybrid Cat Cheetah Optimization algorithm (HCCOA), and Chaotic Search-and-Rescue Optimization (CSRO), it may still face challenges in terms of handling large-scale optimization. Nevertheless, the proposed CDFO yields impressive results, achieving a Packet Delivery Ratio (PDR) of 99%, Packet Loss Ratio (PLR) of 4%, Energy Consumption of 9%, and Alive Node count of 330. These outcomes outperform existing methods in the context of Underwater WSN. © 2024 IEEE.

关键词： Optimization

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Advancing In-Situ Bioprinting Through Ant Colony Optimisation: Resolving Dead Zone Challenges in Path Planning 2nd

Advancing In-Situ Bioprinting Through Ant Colony Optimisatio...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Liu, Keyu Huang, Long Feng, Zhiming Ren, Xianlin Chen, Yi School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou China Department of Chemical Engineering University of Manchester Manchester United Kingdom School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819639489

Organ transplantation faces significant challenges due to the limited availability of donor organs, necessitating the exploration of innovative alternatives. Bioprinting, a dynamic form of 3D printing, offers a promising solution by creating complex biological structures layer by layer. This study focuses on addressing the dynamic optimization challenges in in-situ bioprinting through the integration of Ant Colony Optimization (ACO) within the Computational Intelligence Aided Design (CIAD) framework. The dynamic nature of bioprinting environments, characterized by shifting obstacles and changing targets, requires robust algorithms that can adapt to these variations in real-time. ACO, inspired by the foraging behavior of ant colonies, provides effective global optimization with low complexity, making it well-suited for these dynamic conditions. The methodology includes environmental modeling, adaptive path selection, and dynamic dead zone escape through improved state transition rules. Simulations conducted using MATLAB demonstrate that ACO ensures complete area coverage, minimizes track repetition, and significantly reduces the number of turns, thus enhancing the efficiency and success rate of bioprinting. These findings highlight the potential of ACO in advancing dynamic optimization techniques for bioprinting, contributing to the broader fields of evolutionary dynamic optimization and regenerative medicine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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