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Advancements in Electrical, electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： R. Ayyappa C. H. Hussaian Basha S. Senthilkumar K. Balasubramanian S Arunkumar S. Venkatesh Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering SR University Telangana India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the field of Parkinson’s’ disease diagnosis and progression monitoring, functional and structural connection patterns in the brain, which can be observed by modalities such as functional magnetic resonance imaging (MRI) and electroencephalography (EEG) are becoming more recognized as promising bio markers. An innovative use of Graph Neural Network (GNNs) is proposed in this study for analyzing patterns of brain connectivity. By utilizing the graph structure of brain networks, the researchers identify disturbances that are unique to Parkinson’s disease conditions. In the proposed framework, brain regions are represented as nodes, and the edges of the graph correspond to functional or structural connections derived from either fMRI or EEG data, depending on the modality used for analysis. The investigation of the brain regions and the connection that are most pertinent to the diagnosis can be accomplished through the utilization of attention weights in feature importance analysis. In recognizing brain network changes in Parkinson’s disease, the findings indicates that the GNN perform much better than traditional machine learning methods. Additionally, these research offers significant advancements into mechanism of neuronal degeneration leads attention to certain regions of the brain and connected throughout the brain that are impacted by Parkinson’s disease. This work contributes to a more comprehensive knowledge of neural network disruptions in neurodegenerative disease and enhances the potential of GNN as diagnostic tool.

关键词： Accuracy Functional magnetic resonance imaging Motors Graph neural networks Electroencephalography Telecommunication computing Planning Pattern recognition Biological neural networks Diseases

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A Novel Bridgeless Interleaved-Based Dual-Output Boost-Type PFC Rectifier 17

A Novel Bridgeless Interleaved-Based Dual-Output Boost-Type ...

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17th IEEE International Conference on Compatibility, Power electronics and Power engineering, CPE-POWERENG 2023

作者： Monteiro, Vitor Martins, Joao Afonso, Joao L. University of Minho Campus de Azurem ALGORITMI Research Centre / LASI Department of Industrial Electronics Portugal NOVA University of Lisbon Faculty of Sciences and Technology Electrical and Computer Engineering Department Portugal

ISBN: (纸本)9798350300048

Power factor correction (PFC) rectifiers is an old topic of research, which is assuming extra applicability to alleviate power quality complications in modern power grids. In this background, this paper proposes a novel PFC rectifier, which has as core differentiating characteristics the bridgeless structure, the interleaved current control, the operation with three voltage levels, and the dual output voltage. The proposed PFC rectifier works as a boost voltage topology, but offering an asymmetrical split dc-link, which can be advantageous in many applications as an AC-DC front-end converter. For all power range, the proposed PFC rectifier ensures a sinusoidal AC current in phase with the voltage, and controlled dc-link asymmetrical voltages. The paper outlines a broad clarification of the proposed topology, the operation principle, the employment of the control theory, and a set of computational results, attained with an AC voltage of 230 V-50 Hz and a split DC-link voltage stabilized at 400 V. The proof of the topology and control of the proposed PFC rectifier is performed in steady-state, and also for sudden variations of the operation power. © 2023 IEEE.

关键词： Topology

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Inflated 3D ConvNet for detection of sign language 4

Inflated 3D ConvNet for detection of sign language

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4th International Conference on Signal Processing and Machine Learning, CONF-SPML 2024

作者： Chandana, D. Tushara, M. Sri, A. Ramya Sakhamuri, Sridevi Abualigah, Laith Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram522502 India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman Jordan

ISBN: (纸本)9781510674721

Every human has their own kind of disabilities, we all try to live and overcome them in our life. We educate ourselves to overcome them, we invent technology to achieve our goals. Sign Language is a communication path for deaf-mute people through hand gestures and actions. Sign Language helps people who can’t speak sign language to interact with the people who can speak sign language, this deep learning paper aims to help build a communication bridge for this reason. We used Amazon Rekognition service which uses Deep CNN algorithm for the detection of static images of the signs. As most of the signs are for words they are in the form of videos. We used the I3D algorithm for the classification of videos of the signs of words. The PyTorch framework provides support for CuDNN (NVIDIA CUDA Deep Neural Network) which provides fast GPU implementations for the deep neural networks. The Experimental results has shown that the models used has displayed good results in detecting the words. © 2024 SPIE.

关键词： Deep neural networks

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AppleDiseaseNet-B7: A Robust Solution for Apple Leaf Disease Classification and Model Evaluation

AppleDiseaseNet-B7: A Robust Solution for Apple Leaf Disease...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan G. Balamuralikrishnan N Soundiraraj Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Apple leaf diseases, like cedar apple rust, black rot, and apple scab, pose severe obstacles to apple farming and impact crop productivity and quality. The sophisticated classification model AppleDiseaseNet-B7, known for its efficiency and outstanding performance, was created using the EfficientNetB7 architecture and is presented in this paper. The model improves its feature extraction capabilities by utilizing pretrained weights from ImageNet, which makes it a good fit for differentiating between various apple leaf diseases. AppleDiseaseNet-B7 incorporates numerous unique layers intended to increase classification precision. A Global Average Pooling layer streamlines feature maps, complicated patterns are captured by a dense layer with ReLU activation, overfitting is reduced by a dropout layer, and class prediction is accomplished by a final dense layer with softmax activation. The model's resilience is further increased using thorough data preparation, and augmentation approaches from the PlantVillage and Kaggle Apple Leaf Disease datasets. A comprehensive comparison is made between AppleDiseaseNet-B7's performance and other well-known models, including DenseNet121, ResNet50, and AlexNet. The analysis shows that AppleDiseaseNet-B7 regularly performs better than comparable models in terms of accuracy, precision, recall, and F1 score, among other criteria. This comparison demonstrates how well AppleDiseaseNet-B7 performs in classifying apple leaf diseases, providing a reliable and effective method for managing and detecting agricultural diseases. The study emphasizes how crucial sophisticated model selection is to enhancing crop health monitoring and disease control.

关键词： Analytical models Accuracy Transfer learning Crops Streaming media Data models Robustness Diseases Residual neural networks Resilience

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Cellular automata basedmulti-bit stuck-at fault diagnosis for resistive memory

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Frontiers of Information technology & Electronic engineering 2022年第7期23卷 1110-1126页

作者： Sutapa SARKAR Biplab Kumar SIKDAR Mousumi SAHA Department of Electronics and Communication Engineering Seacom Engineering CollegeHowrahWest Bengal 711302India Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyHowrahWest Bengal 711103India Department of Computer Science and Engineering National Institute of TechnologyDurgapurWest Bengal 713209India

This paper presents a group-based dynamic stuck-at fault diagnosis scheme intended for resistive randomaccess memory(ReRAM).Traditional static random-access memory,dynamic random-access memory,NAND,and NOR flash memory are limited by their scalability,power,package density,and so ***-generation memory types like ReRAMs are considered to have various advantages such as high package density,non-volatility,scalability,and low power consumption,but cell reliability has been a *** memory operation is caused by permanent stuck-at faults due to extensive use of write-or memory-intensive *** increased number of stuck-at faults also prematurely limit chip ***,a cellular automaton(CA)based dynamic stuck-at fault-tolerant design is proposed here to combat unreliable cell functioning and variable cell lifetime issues.A scalable,block-level fault diagnosis and recovery scheme is introduced to ensure readable data despite multi-bit stuck-at *** scheme is a novel approach because its goal is to remove all the restrictions on the number and nature of stuck-at faults in general fault *** proposed scheme is based on Wolfram’s null boundary and periodic boundary CA *** special classes of CAs are introduced for 100%fault tolerance:single-lengthcycle single-attractor cellular automata(SACAs),single-length-cycle two-attractor cellular automata(TACAs),and single-length-cycle multiple-attractor cellular automata(MACAs).The target micro-architectural unit is designed with optimal space overhead.

关键词： Resistive memory Cell reliability Stuck-at fault diagnosis Single-length-cycle single-attractor cellular automata Single-length-cycle two-attractor cellular automata Single-length-cycle multiple-attractor cellular automata

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Use Of Machine Learning On SAR Data To Classify Submerged And Flooded Regions

Use Of Machine Learning On SAR Data To Classify Submerged An...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ajay Kumar Maurya Prashansha Vasistha Bambam Kumar Dharmendra Singh Department of Electronics and Communication Engineering National Institute of Technology Patna Patna INDIA Computer Science & Engineering Banasthali Vidyapith Rajasthan INDIA Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Roorkee INDIA

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

In the past few years very much, attention has been given to flood monitoring. Along with flood detection, it is also essential to classify the fully merged, submerged, and without merged regions, which are helpful in various decision-making and environmental studies, but it is onerous to classify due to the mixed scattering response from these classes. However, there is some difference in the scattering response, which is hard to classify using only ordinary techniques. Machine learning (ML) is apt to deal with such mixed-class problems and classify them with acceptable accuracy. Therefore, in this paper, ML (Xgboost and Radom Forest) models are applied to the extracted optimum features from the Sentinel-1 synthetic aperture radar (SAR) data to classify the submerged and flooded regions. By combining ML algorithms with time series SAR data, flood monitoring systems can provide accurate and timely information about flooded and submerged areas, enhancing flood management strategies and reducing the impact on vulnerable communities.

关键词： Machine learning algorithms Accuracy Time series analysis Scattering Machine learning Feature extraction Classification algorithms Floods Synthetic aperture radar Monitoring

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Improving Microgrid Reliability and Efficiency Through Energy Storage Systems with a Pyramidal Dilation Attention Convolutional Neural Network for Renewable Energy Integration 4

Improving Microgrid Reliability and Efficiency Through Energ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Vijayakumar, Bondu Purushothaman, K.E. Khabiya, Priyanka Garg, Akriti Rajesha, N. Kaliappan, S. Madanapalle Institute of Technology & Science Department of Electrical and Electronics Engineering Andhra Pradesh Annamayya517325 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai600062 India Mandsaur University Department of Computer Science Engineering Madhya Pradesh Mandsaur458001 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India Lovely Professional University Division of Research and Development Delhi Punjab Jalandhar144411 India

ISBN: (纸本)9798331540364

Microgrid (MG) design and deployment are heavily reliant on reliability. Integrating renewable energy sources (RESs) into power grids poses considerable challenges for maintaining grid reliability due to their intermittent nature. This paper proposes a novel approach to enhance grid stability by incorporating Energy Storage Systems (ESS) coupled with Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). The goal of the proposed method is to improve efficiency and lower electricity costs. The proposed PDACNN optimizes the control of Energy Storage Systems by providing more trustworthy forecasts of energy demand as well as supply. By, then the proposed method is implemented in MATLAB and evaluated their performance with various existing methods. The PDACNN method surpasses existing techniques in both efficiency and cost-effectiveness. It achieves a high efficiency of 95.7% and reduces total electricity costs to $650/kWh, outperforming the Artificial Neural Network (ANN), and Recurrent NN (RNN) and Convolutional NN (CNN) methods. © 2024 IEEE.

关键词： Energy Storage Systems Grid Stability Microgrids Pyramidal Dilation Attention Convolutional Neural Network Reliability Renewable Energy Sources

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Machine Learning Model Comparison for Flood Detection Application from Satellite Images

Machine Learning Model Comparison for Flood Detection Applic...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Amruta Sonavale Midhun Chakkaravarthy Surampudi Srinivasa Rao Hishamuddin Bin M. Salleh Jagannath Jadhav Department of Computer Science and Multimedia Lincoln University College Malaysia Department of Electronics and Communication Engineering Malla Reddy College of Engineering and Technology Telangana India Department of Electronics and Communication Engineering KLE college of engineering and Technology Chikodi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Backgrounds: Flooding, a rapidly increasing and increasingly frequent natural disaster, requires effective management, even if unavoidable, and the development of an effective classifier for image detection is crucial for effective response. Methods: The study uses machine learning methods like closest K-nearest neighbors, logistic regression, support vector classification, and decision tree as classifiers. The accuracy of these algorithms varies. The dataset, consisting of ground and space-based sources, is used to determine their accuracy rates. The most accurate classifier is used for forecasting flooding in advance, allowing for faster evacuation and care for those in need. Result: In this case, we could anticipate the likelihood of flooding by analyzing historical rainfall data. The support vector machine produced an efficient accuracy of 93.92%. Conclusion: The accuracy of each classifier is shown, and detection is performed mostly with great precision. The outcomes of any image tests conducted are shown as well.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Data models Real-time systems Classification algorithms Satellite images Floods Meteorology

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Fresh and Rotten Fruit Detection Using Deep CNN and MobileNetV2

Fresh and Rotten Fruit Detection Using Deep CNN and MobileNe...

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International Conference on Advances in IoT, Security with AI, ICAISA 2023

作者： Singh, Abhilasha Gupta, Ritu Kumar, Arun Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Uttar Pradesh Ghaziabad India Department of Information Technology Bhagwan Parshuram Institute of Technology IP University New Delhi India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Delhi-NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819950843

Accurately recognizing rotten fruits has been important, particularly in the agricultural sector. Generally, manual efforts are utilized to classify fresh and rotting fruits which can be tedious sometimes. Unlike humans, machines do not get tired from performing the same task repeatedly. This study suggested a technique for detecting flaws in fruit images, which might reduce human effort, slash manufacturing costs and save time. The rotten fruits may contaminate the good fruits if the flaws are not found within time. Therefore, to prevent the spread of rottenness, a model has been put forward. Based on the fruit images provided as input, the recommended system can identify rotten and fresh fruits. Images of oranges, bananas, and apples have been considered in this paper. Using a convolutional neural network along with max pooling and MobileNetV2 architecture, the features from the input images are collected, based on which images are subsequently classified. On a Kaggle dataset, the suggested model’s performance is evaluated, and by using MobileNetV2, it gets the greatest accuracy in training data (99.56%) and validation set (99.69%). The max pooling had a validation accuracy of 95.01% and a training accuracy of 94.97%. According to the results, the suggested CNN model can differentiate between fresh and rotten apples in an efficient manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Fruits

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The role of WES and BESS in the provision of operating electricity reserves-a review

The role of WES and BESS in the provision of operating elect...

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Power engineering Conference (SAUPEC), Southern African Universities

作者： Siphelele Joseph Mndiya Mukovhe Ratshitanga Senthil Krishnamurthy Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa

Active power reserves are required for an electrical system to work effectively. These reserves are modified regularly to meet system generation and demand to maintain a constant electrical frequency. Regulation reserves, spinning reserves, and non-spinning reserves are the three main forms of operating reserves. Reserves can be used in both normal and extraordinary conditions to maintain supply and demand balance. There have been various attempts to do research into how renewable generation might contribute to the provision of reserves now that large-scale wind energy has been integrated into the grid, but these efforts have been hampered by the stochastic and intermittent character of renewable generation. When proper control techniques are put in place, wind turbines can participate in the provision of operating reserves. This paper discusses the role of wind energy systems and battery energy storage systems in the provision of operating reserves. This research also looks at South Africa’s operating reserve margin and storage capacity.

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