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

共 6842 条记录，以下是221-230 订阅

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Enhancing Academic Performance Predictions Using Deep Belief Networks: A Novel Approach for Educational data Analysis 3

Enhancing Academic Performance Predictions Using Deep Belief...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Zahoor, Irwa Mani, M.S. Radha Manga Saravanan, B. Kumar, Ajay Bhattacharya, Chaitali Kar, Rohit Department of Computer Science and Engineering Islamic University of Science and Technology Jammu and Kashmir Awantipora India Department of Information Technology SRKR Engineering College Andhra Pradesh Bhimavaram India Department of CSE Sri Eshwar College of Engineering Tamilnadu Coimbatore India Tecnia Institute of Advanced Studies Rohini New Delhi India Tecnia Institute of Advanced Studies CDL Wazirpur New Delhi India Department of Management St. Claret College Autonomous Karnataka Bengaluru India

ISBN: (纸本)9798331532420

The capacity to predict students' performance is vital in matters related to deep learning and its relationship to educational data and, more generally, to higher education. Precise performance prediction facilitates both the selection of appropriate courses and the creation of efficient long-term study plans. It helps with predicting how well students will do in class, and it lets administration and educators keep tabs on students to support the need for training program integration. Preprocessing, feature selection, and model training are all part of the suggested method. Transforming raw data into a usable format is a crucial step in machine learning. It helps fix problems in real-world datasets and produce better outcomes. Principle Component Analysis (PCA) is frequently employed in feature selection. All through the training, DGNN was always in use. This novel approach outperforms GNN and RNN with an average accuracy of 94.27%. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on Sugarcane Leaf Defect Detection Approaches 3

A Review on Sugarcane Leaf Defect Detection Approaches

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Patel, Avi Sutaria, Kamal Adhvaryu, Rachit Computer Science & Engineering Department PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331532420

Sugarcane is a critical crop, and its productivity is significantly influenced by leaf defects caused by various diseases. Accurate and timely detection of these defects is essential for effective crop management. This review paper explores the state-of-the-art approaches for detecting sugarcane leaf defects, focusing on machine learning (ML), deep learning (DL), and transfer learning techniques. The motivation behind this review is to address the limitations of existing methods and propose future improvements. The objective is to provide a comprehensive overview of the current methodologies, evaluate their performance, and highlight areas for future research. This review aims to contribute to the development of more accurate and robust detection systems that can be effectively implemented in real-world agricultural settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Federated learning with Heterogeneous Edge Devices 3

Optimizing Federated Learning with Heterogeneous Edge Device...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Islam, Mohammad Munzurul Alawad, Mohammed Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9798350372977;9798350372984

In the era of Internet of thing (IoT), data is massively generated by the distributed sensors. Federated learning (FL) has emerged as a privacy-preserving and secure DL framework to incorporate knowledge from data stored in different locations and train a DL model on the combined data without revealing the local data to a centralized server. This approach is limited by several challenges including the computation and communication cost. In this paper, we propose a novel FL framework that reduces the computation and communication cost through model pruning. It also, considers the device and data heterogeneity in IoT devices. This paper considers the problem of how to stochastically measure a large and complex information field (i.e., the criticality of parameters in local DL models based on local datasets) with optimally few observations. We adopt sparsity-promoting transform domain regularization and integrate compressive sensing with Bayesian learning to improve the measurement accuracy of criticality values in large-scale information fields while minimizing the overall computational efforts and time. The proposed approach is evaluated on two model architectures and two datasets. We demonstrate that our FL framework effectively addresses the heterogeneity present in local devices while surpassing the performance of state-of-the-art FL approaches, both with IID (independent and identically distributed) and non-IID (non-independent and identically distributed) data distributions.

关键词： federated learning model compression compressive sensing

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Exploratory Analysis of Heart Attack and Breast Cancer Early Stage Prediction 3

Exploratory Analysis of Heart Attack and Breast Cancer Early...

引用

3rd international conference on Trends in Electrical, Electronics and Computer engineering, TEECCON 2024

作者： Vandana, C.P. Kashyap, M Shivani Yashas, D. Singh, Neha New Horizon College of Engineering Dept of Information Science & Engg. Bengaluru India New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331505714

This paper involves the development of a web based platform with an integrated machine learning algorithm to address problems pertaining to heart disease and breast cancer. It enables users to select either the heart attack or breast cancer datasets and use machine learning models such as Logistic Regression, K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest and XG Boost. The website provides basic information about the datasets such as the total number of records in a dataset, number of classes in a dataset, and also allows users to generate graphs for better visualization of the data. The evaluation of the performance of each of the models is based on various metrics such as accuracy, F1 score, precision, recall and mean squared error. This way it is possible to assess the performance of the models in relation to each of the available data sets which makes it easy for users to pick the most effective model. There are also inputs for the users which would be their health indicators like age, sex, level of cholesterol, heart rate among others. Using these values as well as the picked model, the website estimates the possibility of the user developing heart disease or breast cancer. Apart from predicting health risks, there is an emphasis on the evaluation of health risks by different machine learning models and thus assists users in finding the most accurate prediction model regarding their healthcare problems. Overall, the website provides both predictive healthcare insights and a deeper understanding of how various algorithms perform in real-world scenarios. © 2024 IEEE.

关键词： K-Nearest Neighbor (KNN) Logistic Regression Predictive Analytics Random Forest Support Vector machine (SVM) XGBoost

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App Based Cardiovascular Disease Prediction using machine learning Algorithms 3

App Based Cardiovascular Disease Prediction using Machine Le...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Manjula, B.M. Dhanush, C.O. Gurudeep, G.H. Likhith, M.Y. Nitte Meenakshi Institute Of Technology Department Of Electronics And Communication Engineering Bengaluru India

ISBN: (纸本)9798350318609

Study investigates machine learning (ML) algorithms for early detection of cardiovascular diseases (CVDs), given their significant global impact. Utilizing demographic data, medical history, and lab results, algorithms like Logistic Regression, Support Vector machine, Decision Tree, Random Forest, and K-Nearest Neighbors are examined. An ensemble model, incorporating Random Forest, is developed for improved accuracy, initially achieving 95%. Hyperparameter optimization boosts accuracy to 99%. An Android app interface facilitates easy access to the predictive model, empowering users for CVD risk assessment. These findings underscore ML' s potential in early CVD detection, offering advancements in predictive healthcare. This contributes to addressing CVD burdens and improving public health outcomes. © 2024 IEEE.

关键词： Decision trees

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Progress and Research Trends of Artificial Intelligence Incorporation in Protein Structure Prediction 3

Progress and Research Trends of Artificial Intelligence Inco...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Lau, Maggie Santa Clara Univ Sch Engn Santa Clara CA 95053 USA

ISBN: (纸本)9798350372977;9798350372984

Protein synthesis is closely regulated in nature, and in the field of protein design, scientists have attempted to understand and replicate those processes due to the importance of proteins. Existing protein structure prediction methods include physics-based approaches like molecular dynamics or knowledge-based methods such as energy functions, but these methods are computationally costly and time-consuming. Artificial Intelligence (AI) technology has been applied to protein structure prediction due to its ability to address current challenges more effectively. This paper will review the significant progress made recently in protein structure prediction using both classical machine learning and deep learning methods. Advancements in protein structure prediction using computational methods have been made possible due to the increase of available data, and representative examples of six databases used in protein structure prediction will be provided in this review. These public databases have diverse entries such as protein sequences, protein structures, protein interactions, enzyme nomenclature, and proteomes, which will facilitate more research in the protein design field. Despite the advancements with the incorporation of AI, several challenges remain, and the proposed solutions and research trends will be discussed in this paper.

关键词： Protein Design Protein Structure Prediction Deep learning machine learning

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A machine learning Approach to Predictive Modeling for Breast Cancer Prediction 3

A Machine Learning Approach to Predictive Modeling for Breas...

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Rahman, Md Shohanur Rahman, Md. Atikur Nipa, Tania Ahmed Pranto, Md Asif Rahman Daffodil International University Department of CSE Dhaka Bangladesh

ISBN: (纸本)9798350388282

Breast cancer is a global problem that highlights the need for accurate and efficient diagnostic methods. The aim of this study is to improve patient outcomes by using machine learning approaches to boost early breast cancer diagnosis accuracy. This study examines the accuracy of six distinct machine learning algorithms using a sizable dataset Naive Bayes, Random Forest, Decision Trees, K-nearest neighbors (KNN), Logistic Regression, and Support Vector machines (SVM) can all be utilized. The models provide promising results, with accuracy levels between 94% and 97%. In addition to accuracy evaluations, this study fully assesses these models using a variety of metrics and uses techniques like cross-validation to guarantee resilience and dependability. Additionally, to maximize model performance and interpretability, the study investigates the possibilities of ensemble approaches and feature selection strategies. The findings of the study offer valuable insights into the application of machine learning methods to enhance the detection of breast cancer. These results demonstrate the effectiveness of several algorithms and present prospects for more advancements in this critical field of medicine. © 2024 IEEE.

关键词： Decision trees

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Contribution to Predicting Electricity Load using Random Forest 3

Contribution to Predicting Electricity Load using Random For...

引用

3rd international conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Marzak, Zahira Mouatassim, Salma Benhra, Jamal Benabbou, Rajaa ENSEM Casablanca Morocco

ISBN: (纸本)9798331522056

The growing demand for sustainable energy solutions and the integration of renewable resources have increased the need for accurate electricity demand forecasting, which is an essential element in effective power system management. This study evaluates the application of the Random Forest (RF) algorithm, an ensemble learning technique, for predicting Short-Term Electricity Load (STEL). Using historical data including load, weather conditions, and calendar variables, we optimized the model through feature engineering and hyperparameter tuning, in an effort to capture the dynamic interactions between consumer behavior, climate effects, and seasonal patterns, all of which significantly influence electricity demand. The results demonstrate the RF model's superiority over traditional methods, achieving high accuracy measured by Mean Absolute Percentage Error (MAPE). © 2024 IEEE.

关键词： Electricity Load Forecasting Energy Management Ensemble learning machine learning Predictive Analytics Random Forest

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Analysis and Performance Evaluation of Credit Card Fraud by Multi-model ML 3

Analysis and Performance Evaluation of Credit Card Fraud by ...

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md Aminul Asif Imran, A.T.M. Rahman, Md Habibur Pabel, Md Amran Hossen Mishra, Bhupesh Kumar Basu, Kashinath University of Gloucestershire Data Science School of Engineering and Technology Cheltenham United Kingdom Dhaka Bangladesh University of Chittagong Department of Mathematics Chittagong Bangladesh Wright State University Master of Science in Business Analytics United States United Kingdom Oxford Brookes University Data Science School of Engineering Computing and Mathematics United Kingdom

ISBN: (纸本)9798350388282

Using credit cards is a convenient and efficient payment mechanism. Credit card fraud significantly impacts financial loss, mental health, and the reputation of financial institutions. This study would incorporate an analysis of the preceding statement about eliminating several obstacles associated with the availability of public data, the presence of unbalanced data, the dynamic nature of fraud tendencies, and the prevalence of false alarms. The authors discuss various machine-learning techniques used to detect credit card fraud. Extreme learning, Decision Trees, Random Forests, Support Vector machines, Logistic Regression, and XG Boost are among these techniques on the European card benchmark dataset. A comparative evaluation of the effectiveness of machine learning has also been conducted, and precision is increased by incorporating multiple layers. The study's findings indicate significant improvements in several crucial metrics, including accuracy, f1-score, precision, and AUC curves. XGBoost model achieves optimized values of 99.9\% accuracy. The proposed model outperforms contemporary machine learning approaches in credit card fraud detection. It has been observed that the combination of data balancing techniques significantly reduces the occurrence of false negatives. This study has substantial potential for credit card fraud detection applications in the real world, providing an effective and efficient method for addressing this ongoing critical issue. © 2024 IEEE.

关键词： Decision trees

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Position Estimation for Mobile Robots using machine learning 3

Position Estimation for Mobile Robots using Machine Learning

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Varma, Kallepalli Rahul Sreenivasan, Medha Srivani, N. Bhattacharya, Shourjyo Glace Varghese, T. Kochuvila, Sreeja Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Robot localization is a critical challenge present in autonomous robots to estimate their position within the environment, ensuring accurate navigation and task execution. This research project compares the position estimation capabilities of advanced machine learning methods Gradient Boosting and K-Nearest Neighbors. In this work machine learning (ML) models are built using a dataset from *** that tracked Received Signal Strength Indicator (RSSI) readings and tested them against strong performance measurements. This work findings show GB provides better results than KNN because it reaches an R2 score of 0.9341. These insights are applicable in various fields, including autonomous vehicles, industrial automation, service robotics, and multi-robot coordination, guiding the selection of appropriate algorithms for specific localization challenges. © 2025 IEEE.

关键词： Gradient Boosting K-Nearest Neighbors machine learning Navigation Robot localization

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