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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks 4

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Rajasekar, P. Tiwari, Praveen Prakash Sharmila, L. Garg, Akriti Srinivas, Rayudu Tallapragada, V. V. Satyanarayana Srm Institute of Science and Technology Department of Data Science and Business System Tamil Nadu Kattankulathur603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Aditya College of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Surampalem533437 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. Recursive Hilbert Transform (RHT) is used to extract time domain features such as mean, Root Mean Square (RMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like Deep Residual Neural Network (DRNN), Convolutional Neural Network (CNN), and Recursive Neural Network (RNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Mean square error

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management 3

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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3rd IEEE International Conference on Electrical Power and Energy systems, ICEPES 2024

作者： Antonius Alijoyo, Franciskus Vidhate, Deepak A. Kaur, Chamandeep Nivedan, V. Rani, V. Kanpur Balakumar, A. School of Business and Information Technology STMIK LIKMI Bandung Indonesia Dr. Vithalrao Vikhe Patil College of Engineering Department of Information Technology Maharashtra Ahmednagar India Jazan University Department of Computer Science Jazan Saudi Arabia Sri Ramakrishna College of Arts & Science Department of Electronics and Communication System Coimbatore India Hindustan Institute of Technology and Science Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350390728

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future. © 2024 IEEE.

关键词： Cloud platforms

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Carter’s case [D] admits the 2nd Canonical Form of the Killing Tensor

arXiv

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arXiv 2023年

作者： Kokkinos, D. Papakostas, T. Department of Information and Communication System Engineering University of Aegean Samos Karlovassi Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Crete Heraklion Greece

The study of the Canonical Forms of the Killing Tensor concerns the simultaneous resolving of the Integrability Conditions of the Killing Tensor along with the Einstein’s Field Equations employing the framework of Newman-Penrose Formalism. We present all the Petrov Types admitting the 2nd and 3rd Canonical Forms of Killing Tensor in Vacuum in the frame of General Theory of Relativity. During the investigation of the Type D solution of 2nd Canonical form of the Killing Tensor the Carter’s Case [D] solution in Vacuum emerged. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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Leveraging Data Analytics to Enhance Smart Home Efficiency and Security

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Karthi Vivekanandan B. Kaleeswari P L. Natchiammai Arun Anthonisamy R. Vijayabharathi Manjunathan Alagarsamy Department of Electrical and Electronics Engineering Builders Engineering College Tirupur India Department of Electronics and Communication Engineering Easwari Engineering College (Autonomous) Chennai India Department of Electronics and Communication Engineering Mookambigai College of Engineering Pudukkottai India Department of Computer Science and Business System Panimalar Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices’ energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security.

关键词： Performance evaluation Energy consumption Data analysis Smart homes Machine learning Market research Energy efficiency Security Object recognition Optimization

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A Novel Ensemble Deep Learning Framework for Breast Cancer Prediction

A Novel Ensemble Deep Learning Framework for Breast Cancer P...

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2023 International Conference on Innovative Computing, Intelligent communication and Smart Electrical systems, ICSES 2023

作者： Shaker Reddy, Pundru Chandra Nithyapriya, S. Sharma, Nipun Maheswari, S. Jayaram, B. Katale, Tejaswini SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Anna University Artificial Intelligence and Data Science Bannari Amman Institute of Technology Tamil Nadu Chennai India Presidency University Bangalore Electronics and Communication Engineering Karnataka India Sathyabama Institute of Science and Technology Department of Mathematics Tamil Nadu Chennai India Computer Science and Business System RMK Engineering College Tamil Nadu Kavaraipettai India Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350319200

Because of its high incidence and high mortality rate, breast cancer requires reliable means of early detection and diagnosis to improve survival rates for its patients. With advantages including increased accuracy and efficiency, deep learning (DL) and machine learning (ML) methods are quickly becoming indispensable in the fight against breast cancer. However, there is a need for additional study because existing approaches have scalability and performance constraints. Utilizing a pre-trained ResNet50V2 and ensemble-based ML approaches, we offer a hybrid approach to reliable breast cancer diagnosis in this study. Learning and extracting latent trends from complex breast-cancer(BC) images is made possible by DL's incorporation, with ML strategies contributing interpretability and generalizability. We performed wide experiments utilizing a openly accessible Invasive-Ductal-Carcinoma(IDC) dataset based on breast histopathology images of varying sample sizes. The resilience and great performance of our hybrid model are supported by the results of our extensive experiments. When compared to state-of-the-art models, our 95% accuracy rate, 94.5 precision, 94.2 recall, and 94.6 F1 score are all improvements. We also found that the best ML model to use with the ResNet50V2 architecture is the Light Boosting Classifier (LGB). This study's findings provide substantial contributions to the field of breast cancer diagnosis thanks to its novel approach, in-depth performance analysis, and trustworthy evaluation. In addition, it may help doctors make better decisions, provide better care, and boost results for people with breast cancer. © 2023 IEEE.

关键词： breast histopathology-image Breast-cancer DL ML transfer-learning

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Adaptive Intrusion Detection in Cyber-Physical systems Using Reinforcement Learning-Based Autoencoders

Adaptive Intrusion Detection in Cyber-Physical Systems Using...

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Integrated Intelligence and communication systems (ICIICS), International Conference on

作者： N. Rajathi Divya. S s. Anitha Elavarasi G. Saritha V. Jeya Ramya Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments.

关键词： Adaptation models Adaptive systems Accuracy Autoencoders Intrusion detection Reinforcement learning Cyber-physical systems Real-time systems Critical infrastructure Anomaly detection

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Investigation of Traffic Radar Coverage Efficiency under Different Placement Strategies

Investigation of Traffic Radar Coverage Efficiency under Dif...

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International Conference on Internet of Things 2024, ICIoT 2024

作者： Yao, Shun Zhou, Jin Chieng, David Kwong, Chiew Foong Lee, Boon Giin Li, Jianqiang Chen, Yusen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Zhejiang Communication Investment Group Co. Ltd. Zhejiang China

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring. © The Institution of engineering & Technology 2024.

关键词： Multiobjective optimization

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An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms

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Human-Centric Intelligent systems 2023年第3期3卷 211-231页

作者： Hossain, Md. Bipul Shama, Anika Adhikary, Apurba Raha, Avi Deb Uddin, K. M. Aslam Hossain, Mohammad Amzad Islam, Imtia Murad, Saydul Akbar Munir, Md. Shirajum Bairagi, Anupam Kumar Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of System Engineering University of South Alabama Mobile USA Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Mandy Dental College and Hospital Dhaka Bangladesh School of Computer Science and Computer Engineering The University of Southern Mississippi Hattiesburg USA Department of Electrical and Computer Engineering Old Dominion University Norfolk USA

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body’s metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the pre-processing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. We found Random Forest (RF) is giving the maximum evaluation score in all sectors in our dataset, and Naive Bayes is performing very poorly. Moreover selecting the feature by using the feature importance method RF provides the best accuracy of 91.42%, precision of 92%, recall of 92% and F1-score of 92%. Further, by analyzing the characteristics and behavior of the dataset, we identified the most important features (TSH, T3, TT4, and FTI) of the dataset. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms to detect and diagnose thyroid disease. Finally, we did some explainability analysis of our best classifier to understand the internal black-box of our machine learning model and datasets. This study could further pave the way for the researcher as well as healthcare professionals to analyze thyroid disease in real time applications.

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural Computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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