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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是741-750 订阅

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A Hybrid Intrusion Detection System using Explainable AI for Enhanced Accuracy and Transparency 3

A Hybrid Intrusion Detection System using Explainable AI for...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Hemalatha, A. Vimal Kumar, M.N. Graf, F. Twinkle Anlin Sahaya Infant Tinu, M. Pavithra, P. Suresh, R. E.G.S. Pillay Engineering College Department of Information Technology Nagapattinam611002 India Sona College of Technology Department of Mechatronics Engineering Salem636005 India Madanapalle517325 India Rohini College of Engineering and Technology Department of Biomedical Engineering Anjugramam629401 India Tjs Engineering College Department of Computer Science and Engineering Kavaraipettai Peruvoyal Gummidipoondi601201 India Tjs Engineering College Department of Electronics and Communication Engineering Kavaraipettai Peruvoyal Gummidipoondi601201 India

ISBN: (纸本)9798331509675

Intrusion detection system is highly effective and easy to understand in the situation of ubiquitous cyber threats. The trustworthiness and interpretability of traditional intrusion detection systems are balanced due to their dependence on transparent deep learning (DL) and machine learning (ML)methods. To overcome these issues, this research proposes a hybrid IDS which makes use of Explainable AI(XAI). An integration of ML methods like random forest and support vector machine with DL methods like CNN ensure efficient detection accuracy in the system. The decision-making process can be better efficient with the help of XAI methods like SHAP and LIME, which increases transparency. The CICIDS 2017 dataset is used to validate the feature extraction of framework, model training and interpretability components. The result show that proposed method performs better with 98.5% accuracy rate and better precision, recall and F1-socre when compared to existing methods. This method provides useful data for securing networks while reducing the number of false positives and negatives. Through the integration of advanced machine learning/deep learning methods with XAI, the proposed IDS ensures dependability, openness and practicality. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on Federated learning's Contribution to Trustworthy and Responsible Artificial Intelligence 24

Research on Federated Learning's Contribution to Trustworthy...

引用

3rd international Symposium on Robotics, Artificial Intelligence and Information engineering, RAIIE 2024

作者： Huang, Shijia Liang, Yaxin Shen, Fangzhou Gao, Fanru Fu Foundation School of Engineering and Applied Science Columbia University New York10027 United States Viterbi School of Engineering Electrical and Electronic Engineering University of Southern California Los Angeles90007 United States Department of Mathematics and Statistics San Jose State University San Jose95129 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9798400718311

As an emerging machine learning paradigm, federated learning allows multiple partners to work together to complete specific machine learning tasks while ensuring that the original data of participants is not leaked. This method effectively solves the problem of"data islands" in traditional centralized machine learning, and has been widely used in many fields such as smart healthcare, smart cities, and finance. Federated learning (FL), as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism for sharing learning experiences without directly exchanging data. Federated learning, as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism to share learning experiences without the need to directly exchange data. This study explores the contribution of federated learning in building trustworthy AI systems, reducing the risk of data leakage by designing privacy protection mechanisms and enhancing data security measures, and enhancing the generalizability and robustness of models by using multi-party participation in the updating and validation of models. In addition, we have introduced a new way to assess the fairness of the model, aiming to achieve more fair and unbiased decision-making in a variety of use cases. In experimental analysis, our results show that by implementing these strategies, federated learning can significantly improve the transparency and fairness of AI systems, while achieving high model performance and low privacy risk on multiple cross-domain datasets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Earthquake Forecasting using Time Series data 3

Earthquake Forecasting using Time Series Data

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Shah, Dhairya Shah, Jay Parwanda, Vidhi Vyas, Soham Mishra, Shakti Pandit Deendayal Energy University SOT Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9798350354171

Predicting seismic events with high accuracy remains a crucial challenge due to the complex nature of earthquake phenomena and the devastating impact they can have on human life and infrastructure. This study introduces a novel approach by integrating convolutional neural networks (CNN) with long short-term memory (LSTM) units to enhance the prediction of earthquake events. Utilizing a comprehensive dataset that includes 37,478 global earthquake instances, the model processes spatial and temporal features such as geographic location, time, magnitude, depth, and additional derived time series data. Through rigorous preprocessing, including label encoding of geographical locations and normalization of all relevant features, we prepare the dataset for effective machine learning modelling. The proposed CNN+LSTM model aims to capture both spatial dependencies through CNN layers and temporal sequences via LSTM layers, addressing the dynamic and multi-dimensional nature of seismic data. Preliminary results indicate improved accuracy in predicting the magnitude and likelihood of earthquake occurrences, offering promising implications for early warning systems and preparedness strategies. Future work will focus on refining the model through hyperparameter tuning and expanding the dataset to include more granular temporal features. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of machine learning Algorithms for Predicting Undergraduate Academic Performance 3

Comparative Analysis of Machine Learning Algorithms for Pred...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Aisyah, Euis Sitinur Manongga, Danny Iriani, Ade Santoso, Sugeng University of Raharja Faculty of Science and Technology Tangerang Indonesia Satya Wacana Christian University Faculty of Information Technology Salatiga Indonesia

ISBN: (纸本)9798350367492

Accurate prediction of student academic performance is crucial for educational institutions and parents to implement proactive intervention strategies. This study employs machine learning algorithms to predict the graduation outcomes of undergraduate students, focusing on the effectiveness of various algorithms. We used the Random Forest algorithm and compared its performance with Decision Tree, Nave Bayes, Artificial Neural Network, and Support Vector machine, utilizing the Orange data mining tool. Our evaluation metrics included Area Under Curve (AUC), accuracy, F1-score, precision, and recall, applied to a dataset of 387 students from the class of 2017 who graduated on time in 2021 and 2022. The features considered were ID, study program, age, sex, GPA of the first semester and GPA of the second semester. The Random Forest algorithm achieved the highest accuracy at 95%. This research contributes to the early identification of students at risk of academic failure and highlights the most effective machine learning algorithm for predicting academic outcomes in higher education. © 2024 IEEE.

关键词： Adversarial machine learning

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

3rd International Conference on Mechanical and Energy Techno...

引用

3rd international conference on Mechanical and Energy Technologies, ICMET 2023

ISBN: (纸本)9789819749461

The proceedings contain 50 papers. The special focus in this conference is on Mechanical and Energy Technologies. The topics include: A Numerical Investigation on Estimation and Validation of Power Law Constants for TiB2 Reinforced AA2024 Composite Material;Power Flow Control of the Grid-Integrated DG System Using Hybrid Aquila Optimizer‑Tangent Search Algorithm;Design and Analysis of Manual Pallet Stacker by Using FEM Method;Design and Development of an IoT Counter Using ESP8266 for 6 Axis MIG Welding Robot;In-Depth Examination of the Mechanical Properties of AA6061/MoS2/SiC Hybrid Composites;optimization of Microchannel Heat Sink Shapes to Enhancing Electronic Cooling Efficiency;empowering Next-Generation Energy Infrastructure Through IoT for Climate Resilience;safeguarding Cryptocurrency Transactions: Leveraging Blockchain and machine learning for Enhanced Financial Security;utilizing machine learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms;improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods;revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and machine learning Enhances Accuracy and Efficiency;harnessing Medical databases and data Mining in the Big data Era: Advancements and Applications in Healthcare;machine learning-Powered Design and Implementation for Classification of Missing data in IoT Applications;ioT-Based Security Detection for Cloud Web Applications: Leveraging Internet of Things Approaches;Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT;navigating Cross-Lingual Natural Language Processing: Challenges, Strategies, and Applications;fostering Understanding: Bridging the Gap Between Black-Box Models and Human Interpretability with Explainable Artificial Intelligence;semantic Analysis and machine learning Techniques for Enhancing Content-Based Image Retrieval.

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Selected Replay with Distillation for Continual Semantic Segmentation 3

Selected Replay with Distillation for Continual Semantic Seg...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Luo, Yuan Liu, Jun Zeng, Shuqi Hu, Zhuoya Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331506582

Although deep neural networks have demonstrated exceptional performance in various fields, especially in image processing, they still face some significant unresolved challenges. Catastrophic forgetting is one of the typical challenges commonly encountered in deep learning models. This issue becomes apparent when new training data introduces new categories to adapt to environmental changes, while the old training data is no longer available. As a result, the model tends to forget previously learned knowledge. In recent years, a variety of solutions and incremental learning frameworks have been proposed to address this issue, particularly showing effectiveness in classification and detection tasks. In this paper, we extend the problem to semantic segmentation, which involves pixel-level classification. We conducted an in-depth analysis of the root causes of catastrophic forgetting and identified two key issues arising from the unavailability of old data samples during the new training phase. First, previously learned knowledge is often mistakenly labeled as background in the new training phase, causing a 'background shift' phenomenon. Second, the data distribution of old and new classes becomes highly imbalanced. To tackle these issues, we propose a strategy that combines replay and knowledge distillation to effectively alleviate catastrophic forgetting. © 2024 IEEE.

关键词： Deep reinforcement learning

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Advanced Feature Extraction Techniques for machine learning-Based Fault Analysis in Power Grids 3

Advanced Feature Extraction Techniques for Machine Learning-...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Tatraiya, Pratham Priyadarshi, Himanshu Manipal University Jaipur Jaipur India

ISBN: (纸本)9798350354379

As the infrastructure develops and more elements are added in the daily livelihood of humankind, dependable electrical power distribution is critical for keeping society and economies running smoothly. Modern power grids are complex, with wide networks and diverse components, requiring advanced problem detection and management systems for its upkeep. The growing demand of reliable power supply, as well as the necessity for long-term power infrastructure, emphasize the importance of reliable fault detection systems. This work aims to improve the resilience and reliability of electricity grids by using machine learning (ML) techniques to detect and analyze faults in power grids, using extensive feature extraction. The original base dataset, generated on an IEEE 6-bus grid system for consistent benchmarking, of three phase voltages and currents will be transformed into a dataset inclusive of time and other derivations, namely, voltage and current unbalance factor, volt-age and current harmonic distortion and the root-mean-square current and voltages. The results show a massive improvement in accuracy and related metrics between the base dataset, hovering between 34%-45% for various ML techniques to the improved dataset, yielding accuracy in the range of 87% -92% for respective techniques. © 2024 IEEE.

关键词： Power distribution faults

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machine-learning-Based Analysis for Problem Detection in Electric Vehicles 3rd

Machine-Learning-Based Analysis for Problem Detection in Ele...

引用

3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Priya, R. Karpaga Guru, V. Raj, T. Kirthick Department of EEE Saveetha Engineering College Tamil Nadu Chennai602105 India

ISBN: (纸本)9789819737444

Electric vehicles (EVs) are gaining popularity because of their energy efficiency and positive environmental impacts. However, challenges arise from the intricacy of EV systems and the need for reliable, safe, and efficient functioning. This study explores the use of machine learning algorithms to get around these challenges and enhance the upkeep and performance of EVs. This project’s primary objective is to use machine learning techniques to monitor the condition of EV components, identify issues, anticipate maintenance needs, and improve various aspects of EV operation. Through the application of machine learning algorithms to a variety of data obtained from sensors and vehicle systems, abnormalities and possible problems may be identified in advance. This proactive strategy aims to prolong the lifespan of important components, especially motors and batteries, and avoid breakdowns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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3rd international conference on Intelligent Systems Design and engineering Applications, ISDEA 2024

3rd International Conference on Intelligent Systems Design a...

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3rd international conference on Intelligent Systems Design and engineering Applications, ISDEA 2024

ISBN: (纸本)9789819792429

The proceedings contain 10 papers. The special focus in this conference is on Intelligent Systems Design and engineering Applications. The topics include: Japanese Road Lane Line Recognition Based on TwinLiteNet;empowering Investigators: The Role of Smart Technology in Fingerprint Examination;a Comparative Evaluation of Thai Word Segmentation Techniques for Profanity Classification;a machine Thematic Classification Method and Its Application in Automatically Analyzing Social Media Content;including Assembly Relationship Complexity into Assembly Line Balancing Problem;iterated Local Search Metaheuristic for the Vehicle Routing Problem with Drones;research on Estimation of Human Elbow Joint Rotational Acceleration Based on Mechanomyography and Broad learning System;design and Research of Advanced Test Equipment for Simulating the External Light Environment of Aircraft Cockpit.

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Stock Market Prediction Using machine learning - A Survey 3

Stock Market Prediction Using Machine Learning - A Survey

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Amal, P. Rodrigues, Jolan Naik, Rajat R. Kumar, K. M. Chaman Shetgaonkar, Pratiksha Goa University Srieit Dept. of Computer Engineering Goa India

ISBN: (纸本)9798350381931

The stock market scenario currently is very volatile and prone to sudden changes. The market is in such a state due to factors like the pandemic, conflicts between nations, and the overall recession that's been going on. Many indices worldwide are down from their all-time highs, but the market also shows signs of potential growth albeit it is very slow. In such a situation traders and investors must carefully consider their goals and the risks they're willing to take. It is here where stock market prediction techniques could prove to be the determining factor with respect to making suitable investments. This paper's goal is to describe and compare the various algorithms that are being used for the sole purpose of stock market predictions. © 2024 IEEE.

关键词： Investments

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