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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering 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 Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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MedCare4Home: A Mobile Medical System for Families

MedCare4Home: A Mobile Medical System for Families

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2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

作者： Wang, Mengting Le, Yen Yu, Chen-Hsiang School of Computing and Data Science Wentworth Institute of Technology BostonMA United States Multidisciplinary Graduate Engineering College of Engineering Northeastern University BostonMA United States

ISBN: (纸本)9798350302301

Managing healthcare data is essential for everyone, but it can be challenging and time-consuming. Although several Web and mobile applications existed for personal medical record management, there were few home-based, family-oriented solutions. In this research, we proposed to design an easy-to-use solution for a family. We designed and implemented an innovative Home Medical Care System, MedCare4Home, by deploying a MERN stack Web application to a minicomputer, Raspberry Pi 4, and enabling a local network to connect different devices at home. The users can access the Web application using either a desktop, tablet, mobile phone, or simply a 7-inch LCD touchscreen. MedCare4Home not only allows users to keep track of appointments and organize healthcare documents, but it can also help set up medication reminders and provide a self-report interface for symptom collection for family members easily and securely in one place. The final developed system demonstrated that we achieved the original goal and beyond. We expect to extend our current hardware design and software development to support physiological data collection with external sensors in the future. © 2023 IEEE.

关键词： Internet of things

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © 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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Deep learning approach for Parkinson’s screening with geometric features from spiral and wave drawings

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Multimedia Tools and Applications 2025年 1-29页

作者： Malik, Meenakshi Onyema, Edeh Michael Uddin, Mueen Yadav, Poonam Sharma, Aanchal Jose, Jazlyn Shankar, Achyut Alasim, Fahad Abidi, Mustufa Haider BML Munjal University Gurugram India Department of Mathematics and Computer Science Coal City University Enugu Nigeria Adjunct Faculty Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India College of Computing & Information Technology University of Doha for Science and Technology Doha Qatar Starex University Gurugram India School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh201310 India Industrial Engineering Department College of Engineering King Saud University P. O. Box-800 Riyadh11421 Saudi Arabia Advanced Manufacturing Institute King Saud University Riyadh11421 Saudi Arabia

Parkinson’s disease (PD) is the second most common neurological ailment and affects a substantial percentage of the elderly population. A deterioration in handwriting is a common indication of Parkinson’s disease. Researchers have developed an algorithm-based tool to assist in distinguishing between Parkinson’s disease patients and healthy persons because the disease can be difficult to identify. Unfortunately, previous studies have not tackled this problem or adopted a preventive approach. Examining handwriting is one way to detect PD. After analyzing multiple publications, we examined the 2020 and 2022 articles from Convolutional Neural Network utilizing deep learning techniques. Spiral and wave photos of those in good health and those with Parkinson’s illness were employed in our collection. This investigation used the Random Forest, VGG-16, and ResNet-50 approaches. We applied ResNet-50 for the drawings and got 97% accuracy for spiral pictures over 10 epochs and 98% accuracy for wave images over eight epochs by using feature extraction and preprocessing techniques. This exceptional performance demonstrates ResNet-50’s potential as a potent tool for medical diagnostics, utilizing its deep learning skills to generate precise and reliable results compared to existing studies in which ResNet-50 has 83% accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT 2

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Usharani, R. Sivagami, V.M. Saravanan, K. Pushparani, S. Rekha, K. Sashi Department of Computational Intelligence School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Care College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations. © 2024 IEEE.

关键词： Predictive maintenance

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Mechanistic investigation of highly bendable magnesium alloy sheet fabricated by short-process manufacturing

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Journal of Magnesium and Alloys 2024年第10期12卷 4289-4310页

作者： Shiwei Xu Peijie Xiao Xiaofan Wu John Holmes Frédéric Mompiou Zhenyu Xiao Chen Jin Tianjiao Li Kuaishe Wang Zhuoran Zeng College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan UniversityChangsha 410082China Suzhou Research Institute of Hunan University Suzhou 215131China College of Engineering Computing and CyberneticAustralian National UniversityACT 2601Australia CEMES-CNRS Universitéde Toulouse29 rue J.MarvigToulouse 31055France Qinghai Salt Lake Teli Magnesium Co. Ltd.Xining 810000China International Joint Laboratory for Light Alloys(Ministry of Education) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China School of Metallurgical Engineering Xi’an University of Architecture&TechnologyXi’an 710055China College of Materials Science and Engineering Hunan UniversityChangsha 410082China

In this study,a commercial magnesium alloy AZ31(Mg-3Al-1Zn-0.3Mn)sheet through a short manufacturing process was found to be ductile and highly formable in *** possessing a strong basal texture,the short-processed sheet without any annealing can be bent at a small radius,only 0.2 times its thickness in the 90°bending ***,it could withstand direct deformation by repeated *** in-situ microstructural characterization reveals that extension twin bands with strain localization appear in the bending *** subsequent flattening,these twin bands underwent detwinning,reducing local strain concentrations and enabling further bending *** outstanding bend formability originated from the significant-type dislocation loops slipping on the prismatic crystal planes within dynamic-recrystallized *** grains underwent a uniform refinement to several microns in the short manufacturing process and exhibited low residual *** active prismatic dislocation slip within refined grains was due to its much lower relative activation stress to basal slip(CRSSprism/CRSSbasal of only∼1.6)owing to the effective grain boundary ***,the prismatic dislocation activity was further enhanced when bypassing Al-Mn nano-particles during motion,leaving debris and loops that facilitated easy multiplication.

关键词： Magnesium Deformation Texture In-situ characterization Dislocation slip.

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Multi-Verse Optimization for Node Localization in Wireless Sensor Networks 3

Multi-Verse Optimization for Node Localization in Wireless S...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Pitchaimanickam, B. Muthuvel, P. Rajasekar, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Tamilnadu Kattankulathur603203 India Saveetha Engineering College Saveetha Nagar Department of Ece Chennai Thandalam602117 India School of Computing Kalasalingam Academy of Research and Education Department of Cse Tamilnadu Krishnankoil India

ISBN: (纸本)9798350369083

Wireless Sensor Networks (WSN) are focused on increasing attention to develop their potential applications in healthcare, agriculture, object tracking, and energy management. Sensor nodes are placed in random locations. The information was gathered from the unknown positions of the sensor nodes, and this information may be useless for the applications. The accurate position of the node is required for more sensitive applications of WSN. This work proposes multi-verse optimization as a method for locating unknown nodes. Particle swarm optimization and firefly algorithms are contrasted with the suggested method. The experimental findings demonstrate that, compared to alternative algorithms, the suggested technique yields superior outcomes. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Synergistically Learning Class-specific Tokens for Multi-class Whole Slide Image Classification

Synergistically Learning Class-specific Tokens for Multi-cla...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sun, Pengzhong Wang, Wei Li, Xiangyu Dong, Suyu Li, Shuo Wang, Kuanquan Luo, Gongning Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology Faculty of Computing Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Case Western Reserve University Case School of Engineering Cleveland United States

ISBN: (纸本)9798350337488

The application of transformer architecture in analyzing whole slide images (WSIs) has become increasingly popular due to its remarkable ability to learn complex associations. Nevertheless, a significant drawback emerges in the multiclass analysis of WSIs. The majority of the transformer-based methods available currently rely primarily on a single, class-agnostic token. This approach might not ideally capture the subtleties of class-discriminative information. To address this challenge, we present an innovative approach tailored for multi-class WSI analysis that harnesses the power of class-specific tokens. Central to our method is a novel attention mechanism designed to foster a synergistic learning relationship between patch and class tokens, enhancing the granularity of information captured and ensuring a more comprehensive representation of the WSI. Complementing this, we introduce a dynamic class-centric training strategy designed to optimize token representation learning, ensuring each token is informatively aligned with its corresponding class. Through extensive experimentation on three challenging multi-class WSI analysis datasets, our method consistently demonstrates superior performance, underscoring its potential as a robust solution for multi-class WSI analysis tasks. © 2023 IEEE.

关键词： Multi-class whole slide image analysis Multiple instance learning Transformer

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Evaluation of Biometric Classification and Authentication Using Machine Learning Techniques

Evaluation of Biometric Classification and Authentication Us...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Umasankari, N. Muthukumar, B. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of Cse Sathyabama Institute of Science and Technology Chennai India Department of Information Technology Dmi College of Engineering Chennai India

ISBN: (纸本)9798350334357

This research article proposed the performance measurement of biometric image with computational methodology. This research adopts the following procedures: pre-processing, Feature extraction and Classification. Designing and building the algorithm and simulation programs have been done in a MATLAB environment. My SQL has been utilized for the maintenance of overall dataset. By employing the classification technique comparative analysis was carried out by inspecting three data mining techniques which were: Random Tree, The multilayer Perceptron neural network (MPNN), and the C4.5 decision tree (DT) algorithms. As per the final conclusion, the Random Forest Classifier algorithm exhibits greater performance in contrast to the other techniques. It has been found that the 93.5 % accuracy exhibited by Random Forest Classifier is much greater and enhanced. © 2023 IEEE.

关键词： Classification (of information)

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An Intelligent RL-Based Scheduler to Control Flooding in a Renewable Energy Powered Automatic Water Dam Control System

An Intelligent RL-Based Scheduler to Control Flooding in a R...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Ray, Prishita Geraldine Bessie Amali, D. College of Computing and Information Science Cornell University IthacaNY United States School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325539

The use of non-renewable energy to operate large-scale systems is proving detrimental to the environment through pollution as well as leading to its depletion. As an alternative, the focus of industries, both private and public, is shifting towards the profitable use of renewable energy sources where it is available in abundance for such purposes. Water dams have been built above rivers to tap this natural resource to supply water to the nearby population. However, in the rainy season, excessive flooding wreaks havoc in the surrounding areas, leading to crop failures, damage to households and dam infrastructure. To counter this problem, this paper proposes an intelligent scheduler for an efficient renewable-energy powered automatic water dam control system which is modelled as a non-convex optimization problem. The scheduler uses the Soft Actor Critic with Emphasized Recent Experience and Prioritized Experience Replay Scheduler (SEPS), as the automatic Reinforcement Learning (RL) based scheduling algorithm to manage flooding in the control system through irrigation, hydroelectricity generation and reservoir storage such that it can sufficiently satisfy the needs of domestic households while also ensuring its efficient working. Simulated sensor readings record the current state of the environment and the proposed scheduler can make decisions based on these environmental variables with less human oversight and great accuracy. Experimental results clearly indicate that the SEPS scheduler outperforms its RL counterpart Proximal Policy Optimization Scheduler (PPOS) and the Natural Evolution Strategies Scheduler (NESS) in terms of average rewards obtained for managing floods efficiently and using renewable energy to run the control system. © 2023 IEEE.

关键词： Reservoirs (water)

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