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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711-720 订阅

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SVM-Plus: An Improved machine learning Classifier to Predict Loss-of-Coolant Accidents in Pressurized Water Reactor 3

SVM-Plus: An Improved Machine Learning Classifier to Predict...

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

作者： Khan, Mushfiqur Rashid Shohanuzzaman, Md. Hosen, Iqbal Hassan, Md. Shamim Institute of Nuclear Power Engineering BUET Dhaka Bangladesh Islamic University Department of ICT Kushtia Bangladesh University of Asia Pacific Department of EEE Dhaka Bangladesh

ISBN: (纸本)9798350388282

In pressurized water reactors (PWRs), it is essential to make appropriate predictions about loss-of-coolant accident (LOCA) to continue operating safely and effectively. LOCA prediction in a PWR is very complex due to the dynamics and complicated interactions present. Prediction models currently in use are not as accurate, reliable, or timely as needed for efficient decision-making and risk reduction. However, the need for improved model interpretability, the exploration of diverse machine learning (ML) models, the incorporation of real-time data for dynamic prediction, and a deeper understanding of model uncertainty in LOCA scenarios are crucial. To address these problems, this research investigates an improved ML model known as SVM-Plus, which predicts the LOCA in PWRs with more efficiency. Firstly, the dataset is collected from a marine PWR simulator containing 3668 samples. Moreover, we generated 1500 samples using generative adversarial networks (GANs). Some data preprocessing steps (cleaning, integration, clustering, etc.), are utilized, and our proposed hybrid model (SVM-Plus) is applied with 30% testing data. Lastly, the outcome of this hybrid model is evaluated using a confusion matrix. We obtained 99.613% accuracy, 0.986 recall, 0.00386 mean absolute error (MAE), and 0.00387 normalized mean absolute percentage error (NMAPE) for predicting LOCA in PWR. SVM-Plus is a new classifier for predicting LOCA in PWR. The adaptability of the SVM-Plus model is strengthened by utilizing a diverse dataset, uncovering intricate patterns and relationships beyond traditional SVM capabilities. However, real-world validation necessitates evaluating the model with actual observations from a PWR to ascertain its performance. © 2024 IEEE.

关键词： Support vector machines

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Predicting Customer Lifetime Value in E-commerce: A data-Driven Approach to Enhance Customer Retention Strategies 3

Predicting Customer Lifetime Value in E-commerce: A Data-Dri...

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

作者： Dey, Sonali Radha, D. Kirthika Devi, V.S. Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees,hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting. © 2025 IEEE.

关键词： Sales

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TriBoost and Beyond: Advanced machine learning Approaches for Diabetes Risk Prediction 3

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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

作者： Katlariwala, Soham Biren Gandhi, Vaibhav C. Patel, Nirav Parmar, Devendra Desai, Ayushi Indian Institute of Technology Madras B.S. Data Science and Applications Chennai India University Department of Computer Engineering Mbit Anand India Parul University Parul Institute of Technology Vadodara India

ISBN: (纸本)9798331509675

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as;Random Forest, AdaBoost, Support Vector machine, CatBoost, Decision Tree, LS-SVM, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, LightGBM, XGBoost, and neural networks. Most emphasis is put on TriBoost that is a novel ensemble model which integrates XGBoost, CatBoost, and LightGBM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed TriBoost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use. © 2025 IEEE.

关键词： Adversarial machine learning

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3rd international conference on Innovation in engineering, ICIE 2024

3rd International Conference on Innovation in Engineering, I...

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3rd international conference on Innovation in engineering, ICIE 2024

ISBN: (纸本)9783031615818

The proceedings contain 108 papers. The special focus in this conference is on Innovation in engineering. The topics include: Transmission Power Based Congestion Control Using Q-learning Algorithm in Vehicular Ad Hoc Networks (VANET);collaborative Robot Laboratory Setup for Repeatable Force and Speed Experiments;towards Global Sustainability: Exploratory Analysis Through Unsupervised machine learning Techniques;structural Kinematic Analysis of a Wheelchair Mechanism Designed for Disabled Persons;application of Graph Theory in Designing the Communication System of a Robotic Production Cell;machine learning Algorithms in Scheduling Problems: An Overview and Future Paths;on the Relation Between the Smith Predictor and Algebraic Control Approach for Time Delay Systems: A Case Study;image as a Way of Processing Multidimensional Production data for Product Quality Prediction Using Deep learning;OPC-UA in Digital Twins—A Performance Comparative Analysis;design Improvements for an Inspection Rover;decoding the Effect of Descriptive Statistics on Informed Decision-Making;Simulation Control of Three-Tank-System Using 2DOF Controller;development of a Serious Game to Support Alzheimer’s Patients: First Insights;e-voting Platform Based on Blockchain;the Transformation of Maintenance with the Arise of Industry 4.0;real-World Comparison of Predictive Controllers Based on Internal and External Description of the Controlled System;the Use of Virtual Reality in Lower-Limb Robotic Rehabilitation;the Contribution of Photovoltaic Systems to Sustainable Agriculture—An Analysis of Agrivoltaic Systems;integration of Augmented Reality and IoT Elements in the Maintenance 4.0;A Fuzzy Logic-Based Risk Assessment Tool for IT Startups;wireless Localization System Applied to a Kitting Pick-to-light System;Posture Correction Device Based on IMU Sensors and CNN.

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machine learning-Driven Rate Adaptation Strategies for Cross-Layer Routing in MANETs 3

Machine Learning-Driven Rate Adaptation Strategies for Cross...

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

作者： Sathish, K. Chitra, D. Rajesh Sharma, R. Sungheetha, Akey Rajavimalanaathan, L. Ellappan, V. Saveetha University TamilNadu Chennai India Department of ECE Mahendra Engineering College Tamil Nadu Namakkal India Department of CSE Alliance University Bangalore India

ISBN: (纸本)9798331527549

A machine learning-Driven Rate Adaptation Strategy for Cross-Layer Routing in Mobile Ad-Hoc Networks (MANET) represents the main contribution of the research which solves QoS challenges in real-time applications. The protocol uses machine learning algorithms to detect dynamic changes in network conditions which include link quality assessment and queue status monitoring and congestion monitoring. This network design unites data from physical along with MAC and network layer components to reach optimal resources utilization. Real-time traffic patterns together with node mobility data get processed by machine learning models which lead to adaptive congestion control and rate adaptation decisions. Through its approach the protocol identifies several node-disjoint paths which boost network resistance to link failures. Computer simulation results prove that the protocol delivers superior packet delivery performance and lower end-to-end delay and enhanced adaptability under changing traffic conditions and mobile node environments. The new methodology surpasses conventional methods by offering strong backing for applications that require delayed tolerance and vast bandwidth needs while handling modern MANETs so well through a complete performance solution. © 2025 IEEE.

关键词： Mobile ad hoc networks

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Advancements in Battery Technology for Electric Vehicles: Integrating machine learning for Enhanced Performance 3

Advancements in Battery Technology for Electric Vehicles: In...

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3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Saravanan, V. Reddy, G. Bharath Parida, Ankita Chandana, K. Chandrasekar, P. Madhan, A. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India Cvr College of Engineering Department of Mechanical Engineering Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Eee Tamil Nadu Chennai India Velammal College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798350364569

The development of batteries will have a major impact on how electric vehicles (EVs) evolve. The State of Charge (SoC) and State of Health (SoH) evaluations made by current Battery Management Systems (BMS) are frequently erroneous due to the use of antiquated algorithms. It results in inefficient use of energy and lower battery lifespans. To improve BMS performance, the research suggests a unique method that integrates machine learning (ML) techniques. ML models are trained to predict battery behavior more accurately and adaptively by utilizing extensive information from battery sensors, past usage logs, and environmental variables. Certain machine learning algorithms, such as LSTM for estimating SoC, SVM for evaluating SoH, and RL for optimizing charging, exhibit better performance metrics in comparison to conventional techniques. The results show significant improvements over the existing systems in terms of energy efficiency (+12 units), battery lifespan (+25 years), and charging time (-8 hours). The ML-integrated BMS offers dependable and sustainable electric transportation solutions in addition to improving EV performance. © 2024 IEEE.

关键词： State of charge

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Deep Reinforcement learning-Based UAV Path Planning Algorithm 3

Deep Reinforcement Learning-Based UAV Path Planning Algorith...

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3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Wang, Kunfu Hui, Ma Hou, Jiajun Song, Xiaming CSSC Systems Engineering Research Institute Beijing100094 China CSSC HAIFENG AVIATION TECHNOLOGY CO. LTD. Beijing100070 China Department of the Second System Integration of CEC Great Wall Sheng Fei Fan Information System Co. Ltd Beijing102200 China

ISBN: (纸本)9798350365443

In the unmanned aerial vehicle (UAV)-enabled wireless charging sensor networks systems, the UAV is able to replace the base station for data collection. Each deployed sensor and the data it collects have their own unique values. Therefore, the UAV should collect as much data as possible from different sensors. We formulated the UAV trajectory planning problem with the goal of maximizing the amount of data collected. Due to the time-varying character of the WSNs and the immediacy of UAV path planning, we apply deep reinforcement learning (DRL) technique to tackle this challenge. In order to collect as much data as possible, we used a Double Deep Q-Network (DDQN) with a data collection action selection strategy. The simulation results show that compared to the DQN algorithm, the DDQN algorithm guided drone access instructions can collect more data. © 2024 IEEE.

关键词： Deep reinforcement learning

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Research on learning resource recommendation based on collaborative filtering algorithm 3

Research on learning resource recommendation based on collab...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Li, Yuncheng Shandong Institute of Commerce and Technology Ji'nan China

ISBN: (纸本)9781510680449

In the internet learning environment, the diversity of online learning platforms and the complexity of learning content make it difficult for learners to efficiently select suitable learning resources. Collaborative filtering algorithm, as a widely used recommendation technology, can effectively solve this problem. This article adopts a project-based collaborative filtering algorithm, using the Slope one algorithm to reduce data sparsity. By calculating the similarity between resources, a resource similarity matrix is constructed, and then weighted average is used for prediction scoring to recommend personalized learning resources for users. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Collaborative filtering

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3rd international conference on Innovation in engineering, ICIE 2024

3rd International Conference on Innovation in Engineering, I...

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3rd international conference on Innovation in engineering, ICIE 2024

ISBN: (纸本)9783031615740

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Research on learning Terminal System for English Teaching Practice in Artificial Intelligence 3

Research on Learning Terminal System for English Teaching Pr...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Cao, Yu Xi'an Eurasia University Global Language Center Xi'an710065 China

ISBN: (纸本)9798350380989

This paper is mainly for the English test terminal research and development. It has realized the functions of question bank, examination, examination statistics and so on. The main feature of the system is that the whole process of using cryptography and digital signature technology, the paper generation process and the content of the examination questions are more fully processed, and also provides a safe and flexible authorized access control mechanism, a complete function of dynamic query and statistical analysis, intelligent paper design. It converts speech recognition and English into Chinese intonation, and designs and develops English translation system at the same time. The deviation between the basic frequency curve of the established tone system and the original sound is only 0.02, and the basic frequency curve is very close. This method can obtain good speech production results, and the generated sounds are diversified and accurate. The CPU usage of this system is 27.8%. Compared with the mobile terminal system and the WEB system, the CPU usage decreased by 19.0%. © 2024 IEEE.

关键词： Artificial intelligence English teaching English test learning terminal system spoken language

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