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Quantum Machine Learning for Rotating Machinery Prognostics and Health Management 5

Quantum Machine Learning for Rotating Machinery Prognostics ...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Tejaswi, Gaddam Prabhakar, Kunal Kunar, S.Deva VFSTR University Department of Computer Science and Engineering AP India

ISBN: (纸本)9798331530013

The study conducted by the prognostics and health management (PHM) entity inquires into the prediction of machine behavior to aid in decision-making through failure modes classification via diagnostics and estimation. This is primarily performed as a classification of the state of rotating machines using diversified The methodologies of Machine Learning (ML) and Deep Learning (DL) have garnered significant attention in contemporary research. In recent times, Quantum Computing (QC) has surfaced as a novel domain of inquiry. With applications in various fields such as optimization, artificial intelligence (AI), gaming, cybersecurity, pharmaceuticals, and the energy sector. Despite hardware limitations, QC is considered an approach to accelerate models and enhance computational power. This paper presents a QML approach for detecting defects in rolling element bearings of rotating machinery using vibration signals. The method employs a hybrid model: a classical neural network to represent a class of parameterized quantum circuits (PQCs), specifically the multi-layer perceptron (MLP). Under different configurations of the Variational Quantum Eigensolver (VQE) system, various types of spin gates and unique two-qubit gates such as CNOT, CZ, and ISWAP are implemented in each PQC configuration. The performance of these PQC configurations is evaluated using two databases of varying complexities: JNU with a failure mode of 10, and CWRRU with a failure mode of 12. Performance results show that all QML models outperform the classical MLP for the two databases considered. This indicates the promising potential of QML models in the field of PHM, despite the current fragile quantum environments. © 2024 IEEE.

关键词： Quantum electronics

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Sentimental Analysis of Amazon Reviews using Map Reducer Technique in Hadoop 18

Sentimental Analysis of Amazon Reviews using Map Reducer Tec...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Byali, Manasi S. Kanakaraddi, Suvarna Badagi, Tulsa Chikaraddi, Ashoka Kle Technological University Department of Computer Science Engineering Hubli India Kle Technological University School of Computer Science Engineering Hubli India

ISBN: (纸本)9789380544519

Sentiment analysis (SA) is an active and dynamic aspect of text mining that focuses on the automated analysis of subjectivity, opinions, and sentiments in textual material. The study explores new SA domains such as resource development, emotion recognition, and transfer learning. The study combines rule-based classification, machine learning, and supervised learning in a novel hybrid technique. The usefulness of this hybrid method is demonstrated through experiments on multiple datasets, including MySpace comments, product reviews, and movie reviews, resulting in improved classification efficacy as judged by both macro and micro-averaged criteria. The study also supports an additional semi-automatic approach in which classifiers cooperate to enhance overall performance. In this article, we have applied SVM, Decision tree, and Linear SVM to the Amazon review dataset and got the output in the form of performance metrics such as CPU-time and maximum memory usage. and we found that Depending on the linearity of the data, a linear SVM or a Decision Tree is more appropriate;select a linear SVM for clearly defined separations and a Decision Tree for intricate, non-linear interactions, customizing the model to the features of the dataset and the goals of the research. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Hadoop Linear Regression MapReduce RDBMS (Relational database Management System) SVM (Support Vector Machine)

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A Smart Home Design with Intelligent Patient Monitoring Features by Using Internet of Things (IoT) Enabled Communication Technology

A Smart Home Design with Intelligent Patient Monitoring Feat...

引用

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

作者： Prince, P. Rajabhushanam, C. Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

The integration of Internet of Things (IoT) technology into smart home systems has revolutionized patient monitoring by providing real-time health insights and automated environmental adjustments. This study focuses on designing and evaluating a smart home system with intelligent patient monitoring features, aimed at enhancing patient safety, comfort, and healthcare delivery. The system employs a network of IoT-enabled devices, including wearable health sensors, environmental monitors, and smart cameras, to track vital signs such as ECG, SPO2, and body temperature, along with environmental parameters like air quality, humidity, and temperature. The system achieved a high accuracy rate, with vital sign sensors demonstrating over 98% reliability across various metrics. A communication framework using Wi-Fi and MQTT protocols ensures seamless data transfer and real-time responsiveness. Edge computing enables low-latency data processing, while cloud analytics provide predictive insights for early health issue detection. Key findings demonstrate the system's efficiency in emergency response, with an average detection-to-alert time of 1.4 seconds, and its ability to adapt the environment based on health conditions with over 95% precision. Automated features such as temperature control, fall detection, and personalized lighting enhance patient care and well-being. This work underscores the transformative potential of smart home technologies in healthcare, paving the way for future advancements in personalized health monitoring systems. © 2024 IEEE.

关键词： Electronic health record

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Hybrid Recommendation Systems using Adaptive Clustering to address Cold start problems 4

Hybrid Recommendation Systems using Adaptive Clustering to a...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Rahman, Md. Mijanur Sifat, Fahim Faisal Islam, Rakibul Molla, Safaeat Khan, Md Rezaul Karim Southeast University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350395914

This study aims to address the 'cold start' problem and solve recommendation systems' current challenges., particularly for new users. The primary goal is to enhance the accuracy and customization of book suggestions in digital libraries and e-commerce sites. Our study uses a hybrid recommendation system., combining content-based and collaborative filtering techniques. Fuzzy user-based clustering enhances collaborative filtering., creating multidimensional user profiles based on user ratings., age., and geographical data. The system architecture illustrated in Figure 1 includes data collection., cleaning., and the creation of a feature-rich dataset. This proposed model handles the complexity of the recommendation system and ensures a systematic approach. By combining collaborative and content-based filtering., this system can solve the cold start problem. The fuzzy c-means clustering plays an essential role in grouping similar users., thus improving new users' accuracy. This system uses a re-recommendation process after user data removal that marks the adaptability of the system. The similarity score uses cosine similarity and evaluates and demonstrates the system's potential to offer customized and accurate book recommendations. This proposed system establishes a new benchmark in the field of recommendation systems. This approach contributes to a more user-centric., intuitive., and responsive approach to book recommendations. By solving cold start problems., our model enhances the user experience in digital reading choice. This study may explore the diverse recommendation scenarios beyond books in future research. © 2024 IEEE.

关键词： Collaborative filtering

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Optimizing Waste Reduction Through Real-Time Defect Detection in Garment Sewing Using Variants YOLOv7

Optimizing Waste Reduction Through Real-Time Defect Detectio...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Uddin, Md. Minhaz Akter Sarmin, Sanzeda Foysal, Sadi Mahmud Rahman, Sadia Risti, Nushara Tazrin Rahman, Md. Khalilur Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

In the era of computer vision to overcome challenges, the introduction of the YOLO model revolutionized real-time computer vision approaches. In the garment industry, the inception of products plays a significant role while increasing the processing time with a good accuracy rate is the big challenge here. A real-time garments defect detection approach using YOLOv7, YOLOv7x, and YOLOv7-w6 on a primary dataset is proposed with a good FPS rate and better accuracy. Maximum traditional garments inception approaches focused on end product defects while this model suggests detecting defects in the sewing phase so that the cost of the rejected end product can be optimized by detecting them before a product goes through all the phases. For this our research focuses on three subclasses of Seam, Stitch, and Hole related to sewing phase defects. To increase the detection rate, the hyperparameter tuning technique is applied to the YOLOv7 model. Three models are proposed based on pre-trained weights of YOLOv7, YOLOv7x, and YOLOv7-w6 to compare the accuracy and FPS rate in terms of implementation in real-world projects. © 2024 IEEE.

关键词： Garment industry

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Analyzing Aggressive Driving Patterns with GPS data: A Machine Learning Approach 6

Analyzing Aggressive Driving Patterns with GPS Data: A Machi...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Islam, Md. Ariful Rahman, Md Mijanur Rahman, Md. Orasur Shakib, Md. Nazmus Green University of Bangladesh Department Of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331531973

Aggressive driving behavior is a critical issue with significant implications for road safety and traffic management. Researchers have extensively studied methods to predict such behaviors, utilizing various data sources and machine learning techniques. This research focuses on predicting aggressive driving behavior using GPS data from the Green University Bus Route dataset. We developed a novel preprocessing algorithm, along with label encoding and standardization techniques, to prepare the dataset for analysis. Seven machine-learning algorithms were used to guess how often aggressive driving would happen. These were Decision Tree classifiers (DTC), AdaBoost classifiers (ABC), Gradient Boosting classifiers (GBC), Extra Tree classifiers (ETC), Support Vector classifiers (SVC), Gaussian Naive Bayes (GNB), and Random Forest classifiers (RFC). Our results indicate that the GBC, ETC, SVC, and RFC outperform other models, with each one achieving the highest accuracy of 99.445%. This study demonstrates the potential of GPS data to effectively predict aggressive driving behavior, offering valuable insights for improving road safety measures. © 2024 IEEE.

关键词： Aggressive driving behavior Driver behavior prediction GPS data analysis Traffic Safety

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Determination of Brain Tumors with Neural Networks and CT Scan Imagery 2

Determination of Brain Tumors with Neural Networks and CT Sc...

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Ragam, Prashanth Gorantla, Bhavitha Sonakshi Eshtamsetti, Yasaswini Maji, Sayanatika Pavithra, Danaveni School of Computer Science and Engineering VIT-AP University Andhrapradesh India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350366570

The process of brain tumor detection is paramount in the field of medical diagnostics. Early iden- tification of a brain tumor significantly enhances the chances of a patient restoring from it. In recent years, Artificial Intel- ligence has emerged as a promising tool that has efficiently assisted brain tumor detection. This research paper intro-duces an Artificial Intelligence system specifically designed to detect brain tumors based on medical imaging data, like Computed Tomography scans. It is harnessing the power of advanced machine learning algorithms, including deep learning and accuracy of brain tumor detection, facilitating timely medical action and proper treatment planning for patients. We have taken models Support Vector Machine (SVM), Radial Basis Function Neural Network (RBFNN), and Back Propagation Neural Network (BPNN) to train on the Computed Tomography images of the brain collected from the Kaggle and achieved an accuracy of 88.88% using RBFNN and BPNN models. © 2024 IEEE.

关键词： Radial basis function networks

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TruChain: A Blockchain-based Access Control to Improve the Security of Smart Water Grid Systems 17

TruChain: A Blockchain-based Access Control to Improve the S...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mondal, Susmita Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several parameters associated with water quality and distribution, leveraging the possibility of performing remote operations. For robust architecture, remote operations rely on application-specific sensors, communication networks, edge control, and cloud computation. Needless to say, the centralized control of edge and cloud infrastructure is prone to attacks and failures, such as unauthorized access or single-point-of-failure. We propose TruChain, a Blockchain-based access control mechanism to eliminate the probability of access takeover and aggregate quorum-based validation on sensory data for transparent provenance reliability. We also provide access revocation to strengthen security by restricting access to sensitive data stored in a privately distributed cloud. We believe our solution has empirical decentralization and a verifiable authorization method by maintaining a tamper-evident, auditable trail of interactive events, building trust without third-party intervention. © 2025 IEEE.

关键词： Cloud security

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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Comparative Study on Machine Learning Algorithms for Breast Cancer Diagnosis 6

Comparative Study on Machine Learning Algorithms for Breast ...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Shah, Hemali Agrawal, Smita Oza, Parita Tanwar, Sudeep Department of Computer Science and Engineering Nirma University Ahmedabad382481 India

Breast Cancer (BC) is the second most found cancer in women worldwide. Different symptoms to identify breast tumors are pain in the breast, lumps, and a change in skin color and texture. In this study, we used mammograms for Breast cancer classification. The radiologist uses computer-aided diagnosis tools for a second opinion diagnosis. Nowadays, Machine learning algorithms are used for the advancement of technology. The proposed research mainly focuses on Machine learning classification algorithms for classifying data as benign and malignant. The datasets used are MIAS and Wisconsin for Breast cancer classification. The algorithms used are Logistic Regression, Naive Bays classifier, Support Vector Machine, Decision tree, and Random Forest. Augmentation was applied to increase the size of the MIAS dataset. The performance of different classifiers is compared using measures like accuracy, precision, recall, and F-measure. Results show that for the MIAS dataset, Random forest gives the highest accuracy of 0.73 compared to all other algorithms. For the Wisconsin dataset, Logistic regression out-performs with 96.49 per cent accuracy in classifying the data. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Random forests

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