the proceedings contain 99 papers. the topics discussed include: development of a lamped parameter model for flow distribution and windage analysis in electric machines;advancing electric vehicle motor design optimiza...
ISBN:
(纸本)9798331507510
the proceedings contain 99 papers. the topics discussed include: development of a lamped parameter model for flow distribution and windage analysis in electric machines;advancing electric vehicle motor design optimization - overview of a novel approach for fault characterization;a new single-phase integrated battery charger for electric vehicles using open-winding PMSM drive and applying model predictive control;design and optimization of electromechanical actuators for aerospace applications;a comparative analysis of two different types of permanent magnet motors with outer-rotor structure for direct drive applications;electromagnetic forces on the end windings and terminal connections of large electric machines: calculations, considerations, and capabilities for stress analysis;and fast charging stations and battery swap stations for electric vehicles: a decision-making model.
For the past few years, detecting object surface defects using deep learning has become an important tool in industry and a major research area for researchers involved. there is a wide variety of articles on object s...
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this research presents a novel approach to automating the generation of expert-level questions on specific topics, by leveraging advanced large language models. the proposed technique ensures grammatical as well as se...
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ISBN:
(纸本)9798350350661;9798350350654
this research presents a novel approach to automating the generation of expert-level questions on specific topics, by leveraging advanced large language models. the proposed technique ensures grammatical as well as semantic accuracy due by utilizing a large language model and employs a KNN classifier to extract relevant context from large number of extensive text embeddings. this method accommodates entire textbooks of related fields within a single learner, eliminating the need for multiple learners across different fields. Pretrained on extensive SQaAD data, the Pegasus model generates questions from concise contexts, offering potential applications in examination institutes, competitive exams like SSC and UPSC, interview rounds and beyond. the system requires fine-tuning to generate specific topic related questions by feeding data specific to the topic only. Traditional question generation methods, such as Cloze or discourse cue-based approaches, may generate questions but needed to be explicitly programmed and they do not learn semantic meaning of the question hence compromising question quality. the limitations of rule-based grammatical methods suggests the necessity for a system that comprehends sentence logic and semantics. To address this, the research combines the Universal Sentence Encoder, K-Nearest Neighbor (KNN) Classifier, and Pegasus model. the Universal Sentence Encoder converts sentences into vectors, preserving semantic meaning, and allowing machine learningalgorithms to process textual data. the KNN classifier fetches context similar to user-defined keywords, ensuring grammatically and semantically correct paragraphs. Pegasus, using on KNN-derived context, generates grammatically questions, enabling scalable and reliable question generation across diverse topics and fields
Existing representation learning usually neglects two important issues, the intra-class representation diversity and underexploited label utilization, especially the negative feedback during training process. Fortunat...
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the rapid rise of 5G, IoT, and HetNets introduces challenges in optimizing energy efficiency, scalability, and performance. Despite extensive research, a gap remains in integrating diverse optimization techniques acro...
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In the ever-evolving domain of cybersecurity, malware detection and classification are critical for safeguarding digital infrastructure. this study explores the application of various machine learningalgorithms, incl...
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ISBN:
(纸本)9798350367782;9798350367775
In the ever-evolving domain of cybersecurity, malware detection and classification are critical for safeguarding digital infrastructure. this study explores the application of various machine learningalgorithms, including K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and XGBoost, to classify malware using the Microsoft Malware Classification Challenge (BIG 2015) dataset. the dataset consists of diverse malware families, each exhibiting unique characteristics. A comprehensive preprocessing framework was applied, including data cleaning, feature extraction from binary and assembly files, and the creation of new features to improve model accuracy. Our experimental results indicate that Random Forest and XGBoost significantly outperform other models, achieving accuracies as high as 98%. Logistic Regression provided strong interpretability, making it suitable for applications where understanding model decisions is crucial. this study demonstrates the effectiveness of ensemble learning models in malware classification and highlights their potential for integration into real-world cybersecurity solutions, ensuring timely detection and prevention of cyber threats.
the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: Dataset Balancing Techniques and Supervised Learnin...
ISBN:
(纸本)9789819769940
the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: Dataset Balancing Techniques and Supervised learningalgorithms for Predictive Analysis of Rice and Corn Yields;fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence;predictive Analysis for Early Detection of Breast Cancer through Artificial Intelligence algorithms;boosting Security: An Effective Approach to Intrusion Detection in Wireless Sensor Networks with AdaBoost Classifiers;Chat2Fluency: Enhancing Language learningthrough Conversational AI;REED-NET: Residual Enhanced Encoder-Decoder Network for Low-Dose CT Reconstruction;comparative Analysis of Large Language Models;Visualizing Insights to Empower HR Decision-Making: A Data-Driven Approach;handling Uncertainty in Parkinson’s Disease Voice Data Using Intuitionistic Fuzzy Entropy Measure;Exploring Shopping Opportunities and Elevating Customer Experiences through AI-Powered E-Commerce Strategies;ioT-Based Vehicle Class Detection for Smart Traffic Control;Domain Adaptation for NER Using mBERT;SQL Query Recommendation Based on Matrix Factorization;retenSure: Ensemble learning for Managing Employee Attrition;Subject–Verb Agreement Error Handling Using RNN Architectures;ad-Spend Analytics;an Efficient Real-Time Word-Level Recognition of Indian Sign Language;innovating Drug Design for Alzheimer’s Disease via Reinforcement learning for Enhanced Molecular Generation;an Efficient Real-Time Recognition of Static Kannada Sign Language;unveiling Diagnostic Clarity: A Machine learning Approach to Distinguish Borderline Personality Disorder and Bipolar Disorder for Enhanced Mental Health Diagnostics;DLSTM with Adam Waterwheel optimization for Groundwater Level Prediction in India;PhishGuard: Machine learning Model for Real-Time URL Detection;Improved Grasshopper optimization with Squeezenet (IGO-SNet)
the sparrow search algorithm (SSA) has emerged as a promising swarm intelligence optimization technique, particularly in the context of maximum power point tracking (MPPT) for wave energy generation systems. It has de...
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Patrol pathoptimization is of great significance in various real applications. At present, there are many ways to optimize the patrol path from various angles, and there are also many studies on traditional ant algor...
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A photovoltaic (PV) system is highly sensitive to dynamic changes in environmental conditions. Improving the maximum power point tracking (MPPT) algorithm is one of the most cost-effective ways to enhance its performa...
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