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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Bleeding Simulation with Physical Visco-Elasticity and its Application to Virtual Surgery

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SSRN 2023年

作者： Sun, Pengyu Liu, Peter Xiaoping School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Department of Systems and Computer Engineering Carleton University OttawaON Canada

Background and objectives: Being composed of blood cells and plasma, the blood flow has different rheological properties from common fluids. However, current bleeding simulations mostly focus on the morphology of blood, but ignore the special rheological properties of blood flow. In this paper, we consider the effect of the aggregation behavior of red blood cells on the blood flow process, in order to improve the visual fidelity of bleeding ***: In particular, the elastic force caused by viscoelastic fluid elasticity is incorporated into the standard N-S momentum equation, and an improved momentum equation which characterizes the rheological characteristics of viscoelastic blood is constructed. The obtained new momentum equation is solved by using the smooth particle hydrodynamic (SPH) *** and Conclusions: The effect of introducing elastic force on the motion of particles is analyzed from the microscopic particle perspective by designing a two-dimensional particle system. Simulation results show that the presented method improves the simulation realism of the diffusion process of viscoelastic blood, and in the meantime, eliminates the discontinuous boundary due to blood gushing out of the wound. Additionally, the presented method is able to simulate the viscoelastic rheological properties of bleeding without significant distortion even when the number of bleeding particles is small. © 2023, The Authors. All rights reserved.

关键词： Navier Stokes equations

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A Hybrid Model to Predict the Breast Cancer using Stacking and Bagging Model

A Hybrid Model to Predict the Breast Cancer using Stacking a...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： S. Yuvalatha S. Nithyapriya S. Prabhavathy R. Priyadharshini S. Savitha S. Kayathri Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering Christ the King Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering Coimbatore India Department of Computer and Science and Engineering PSR Engineering College Sivakasi India

Breast cancer is a malignant tumor that develops in the cells of the breast tissue. Breast cancer is one of the major causes of death for women globally. In the examination of medical data, breast cancer prediction is a difficult task. To make decisions and accurately distinguish between benign and malignant tumors, physicians and pathologists need certain automated technologies. In this paper, hybrid ensemble technique (Bagging and Stacking) is used to predict the breast tumors as benign and malignant tumors. In the proposed work, the subset of data is created from the initial Wisconsin (Diagnostic) Data Set by bootstrapping technique. Each bootstrap dataset is used to train the weak learner. The weak learners are K-Nearest Neighbors (KNN) Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM). The Logistic Regression (LR) is used as the Meta Learner. The Meta Learner uses the predictions of weak learners as its training data. The proposed hybrid ensemble model obtains an accuracy 98.7%, Precision 98.83%, Recall 98.54%, F1 Score 98.68% and 0.012% error

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IoT Attack Detection and Prevention Through Machine Learning System 2

IoT Attack Detection and Prevention Through Machine Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781957171395

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical fibers

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R Suguna S Deepa T Kanimozhi M Chandru G Arun Harshini M Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture is the backbone of India’s economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity.

关键词： Deep learning Productivity Plant diseases Microorganisms Accuracy Prediction algorithms Real-time systems Classification algorithms System analysis and design Residual neural networks

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Experimental investigation of the impact ethanol-biodiesel-diesel blended fuels on combustion, emission, and performance of compression ignition diesel engine

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Materials Today: Proceedings 2022年 66卷 2830-2835页

作者： Wai, P. Karin, P. Phairote, W. Chollacoop, N. Kosaka, H. Po-ngen, W. Department of Mechanical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok 10520 Thailand National Metal and Materials Technology Center National Science and Technology Development Agency Pathum Thani 12120 Thailand Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo 179-0085 Japan Department of Teacher Training in Mechanical Engineering Faculty of Technical education King Mongkut's University of Technology North Bangkok Bangsue Bangkok 10800 Thailand

This research was directed to reduce the global diesel engine emissions and dependency on finite fossil fuel reserves. The ethanol was blended by weight ratio with commercial "B20" fuel (20% palm oil's biodiesel and 80% diesel) as B20E5 (95% B20 with 5% ethanol), B20E10 (90% B20 with 10% ethanol) and B20E20 (80% B20 with 20% ethanol). The results of the engine's performance, combustion, emission, and agglomerate particles size using blended fuels were compared with the results of based commercial B20 fuel. All fuel samples were tested on a four-cylinder direct injection diesel engine at a constant load of 140Nm with engine speeds of 1000RPM, 1500RPM and 2000RPM. When the engine speed increased, the brake-specific fuel consumption decreased, and the brake thermal efficiency increased. The B20E20 shows the highest brake-specific fuel consumption because of the low energy content of the fuel blend and the highest thermal efficiency because of a better combustion process. The ethanol-blended fuels show higher peaks of in-cylinder pressure and heat release rate than the base B20 fuel, with B20E20 as the highest. Ethanol blended fuels have significant advantages in particulate matters reduction, especially in idle engine speed. The blended fuels decreased soot and CO2 emissions and increased NOx emission. The agglomerate particles size distribution was analysed with 100 samples for each fuel by using Scanning Electron Microscopy (SEM) and Image J tools. The average agglomerate particle size of B20, B20E5, B20E10 and B20E20 are 0.253 µm, 0.245 µm, 0.225 µm and 0.187 µm, respectively. As conclusion, adding ethanol to diesel fuel provide strong advantages on soot reduction and higher engine efficiency due to the enrich of fuel oxygen. © 2022

关键词： Ethanol

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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Advances in Data engineering and Intelligent Computing systems (ADICS), International Conference on

作者： R. Mohandas D.S.John Deva Prasanna Baiju B V S. Nathiya T. Dhiliphan Rajkumar C. Sivashankar Department of Computational Intelligence SRM Institiute of Science and Technology Chennai India Department of Datascience and Business systems SRM Institiute of Science and Technology Chennai India School of Computer Science and Engineering(SCOPE) VIT University Vellore TamilNadu India Department of Electronics and Communication Engineering Prince shri Venkateshwara Padmavathi Engineering College Chennai Dept. Of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennnai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks; therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities.

关键词： Learning systems Industries Pathology Machine learning algorithms Intrusion detection Machine learning Internet of Things

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Status Quo of Hate Content Research using Bibliometric Analysis

Status Quo of Hate Content Research using Bibliometric Analy...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Naganna Chetty Sreejith Alathur Vinayak Pai Department of Information Science & Engineering NMAM Institute of Technology Nitte University Mangaluru India Department of Information Systems Indian Institute of Management Kozhikode India Department of Computer Science & Engineering Manipal Institute of Technology MAHE Manipal India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Expression of hate content through social media is prevalent and a global issue. The objective of this paper is to conduct analysis on hate content research and suggest policies to combat online hate content. The Scopus database containing published articles is queried with a set of keywords and filtering techniques to obtain relevant documents on hate content research. Different bibliometric analysis approaches such as bibliometric coupling, co-occurrence of keywords and citation analysis of documents are applied on selected dataset. Bibliometric analysis resulted with major sources of hate content research, most associated keywords with hate content and top cited articles. From the year 2015 onwards, there is a rapid increase in hate content research. Currently, different social media and its content are the most associated keywords with hate content.

关键词： Couplings Text analysis Social networking (online) Databases Filtering Bibliometrics Citation analysis Blogs

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