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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481-490 订阅

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： data Science

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Multi-fidelity deep neural network surrogate model for aerodynamic shape prediction based on multi-task learning 3

Multi-fidelity deep neural network surrogate model for aerod...

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3rd international conference on Energy and Power engineering, Control engineering (EPECE)

作者： Wu, Pin Liu, Zhitao Zhou, Zhu Song, Chao Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang 621000 Sichuan Peoples R China

ISBN: (纸本)9798350366105;9798350366099

With the increasing demands of refined aerodynamic shape design for modern aircraft, it is very essential for aerodynamic shape optimization to obtain more accurate aerodynamic data. The widely used high-fidelity simulation is usually accurate but extremely time-consuming. Therefore, we propose an innovative multi-fidelity model based on multi-task learning for aerodynamic shape prediction. This model consists of Bezier-Auxiliary Classifier GAN (Bezier-ACGAN), Multi-gate Mixture-of-Experts and Multi-Fidelity (MMoE-MF). Firstly, Bezier-ACGAN is used to construct the subsonic and transonic datasets and is used as an intelligent parameterization method. Secondly, The MMOE-MF model is coupled with the parameters of Bezier-ACGAN to predict different-fidelity of aerodynamic data. The results show that the predicted results of the optimal airfoils agree with the results of high-fidelity simulation well. This method is a promising approach that can convert from low-fidelity data to high-fidelity data in a few seconds.

关键词： Bezier-Auxiliary Classifier GAN Multi-gate Mixtureof-Experts multi-task learning multi-fidelity surrogate model

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Design and Investigate the Deep learning Models for White Blood Cell Classification 3

Design and Investigate the Deep Learning Models for White Bl...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Gaikwad, Prajwal. S. Deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant D. Kalinga University Raipur India AISSMS's Institute of Information Technology Dept. of Computer Engineering Pune India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D.Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350381931

For the purpose of illness prediction, the objective of this study is to develop automated models for the categorization of white blood cells (WBCs). In the human immune system, white blood cells (WBCs) function to combat infections and shield the body from external substances. Eosinophils, neutrophils, monocytes, basophils, and lymphocytes are the components that make up these cells;each of these cell types accounts for a different proportion and is responsible for a distinct set of functions overall. The clinical laboratory technique for counting the specific types of white blood cells (WBCs) has long been an essential component of a testing procedure known as a complete blood count (CBC), which is used to aid in the monitoring of individuals' health. However, such manual processes are time-consuming and error-prone for disease detection. The recently proposed machine and deep learning-based solutions for the blood cell classification delivered better results, however, suffered from challenges as well. These challenges motivate us to propose a novel AI-based approach for automatic WBCs classification in this research work. © 2024 IEEE.

关键词： Cells

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Comparative Analysis of machine learning Algorithms for Early Detection of Chronic Kidney Disease: Performance Evaluation and Insights 3

Comparative Analysis of Machine Learning Algorithms for Earl...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Joan Niveda, J. Yogesh Rajkumar, R. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Bharath Institute of Higher Education and Research Department of Information Technology Chennai India

ISBN: (纸本)9798350391565

Chronic Kidney Disease (CKD) is a major global public health issue, highlighting the importance of early detection to slow down its advancement. This study conducts a comparative analysis of machine learning algorithms aimed at early CKD detection, focusing on performance evaluation and insights into algorithmic efficacy. Leveraging diverse datasets encompassing clinical, demographic, and laboratory parameters, we evaluate the performance of various algorithms including Support Vector machines (SVM), Random Forest (RF), Gradient Boosting machines (GBM), and Convolutional Neural Networks (CNN). Results indicate substantial variations in predictive accuracy, sensitivity, and specificity across different algorithms. SVM demonstrates robust performance with high accuracy and sensitivity, while RF and GBM exhibit competitive results albeit with nuances in predictive characteristics. Additionally, CNN, leveraging deep learning techniques, showcases promising potential, especially in handling complex data structures. Insightful analysis of feature importance highlights significant clinical parameters contributing to predictive models, offering valuable insights into disease mechanisms and risk factors. This study provides a comprehensive overview of machine learning methodologies for early CKD detection, facilitating informed decision-making in clinical practice and paving the way for personalized healthcare interventions aimed at combating the CKD epidemic. © 2024 IEEE.

关键词： Convolutional neural networks

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Uber Rides Prediction using machine learning 3

Uber Rides Prediction using Machine Learning

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

作者： Khedekar, Lokesh S. Chhajed, Ajay S Bhaganagre, Ravishankar C. Kokate, Naina S. Patil, Swaraj Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331509675

Accurately predicting ride demand is critical to improving Uber performance and increasing customer satisfaction. This paper investigates machine learning methods to predict Uber ride requests by analyzing historical data including time, location, and other factors such as weather. After preprocessing and feature engineering, various models (linear regression, decision trees, random forests, and GBR) are evaluated for their prediction performance. GBR emerged as the best model, achieving over 99% accuracy due to its ability to handle complex data and capture nonlinear relationships. This study highlights the importance of physical and environmental features in improving prediction accuracy, allowing Uber to allocate drivers more efficiently and reduce passenger waiting times. These findings provide insight into demand forecasting, improved management, and service quality of ride-hailing platforms. © 2025 IEEE.

关键词： Prediction models

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Comparative Analysis of machine learning Models for Solar Power Prediction Using Time Series Weather data 3

Comparative Analysis of Machine Learning Models for Solar Po...

引用

3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Kane, Sumedh Nilesh Rathore, Akshit Abhijeet Anees, Sanya Kane, Arati Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India Instrumentation and Control Engineering Bharati Vidyapeeth College of Engineering Delhi India

ISBN: (纸本)9798350387193

Smart Grids preside over traditional power grids due to their efficiency and effectiveness. Smart grid implementation has benefited all components of the power system. These grids usually generate energy from renewable energy resources. However, the stability, reliability, and seamless functioning of the grid are adversely affected by the highly weather-dependent characteristics of renewable energy sources. As a result, accurate energy generation forecasting is an essential requirement for the grid's steady and reliable operation. The forecasting techniques for the quantity of electricity produced by solar power plants are discussed in this study. First, the impact of weather on solar power generation is investigated. Second, a machine learning method for solar panel output power prediction based on weather conditions is proposed. Finally, an ensemble approach utilizing different algorithms for machine learning is utilized to boost the accuracy of the proposed model. For the objective of creating prediction models, we utilized a variety of techniques, including temporal convolutional neural networks, fine trees, support vector machines with different kernel functions, artificial neural networks, and long-short-term memory RNNs. Each model's prediction accuracy has been assessed by comparing and contrasting these different methods. A comparative investigation indicated that the Temporal Convolutional Network-based model trumps the others in terms of efficiency. The proposed ensemble model exceeded the conventional individual models after performance comparison with the standard approaches. © 2024 IEEE.

关键词： Prediction models

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Recommendation Method Based on Multi-task learning and Graph Collaborative Filtering 3

Recommendation Method Based on Multi-task Learning and Graph...

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

作者： Wang, Zhi-Min Zhu, Wen-Qiang School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9798331506582

The rapid growth of Internet technology is continuously increasing information volume, leading to the problem of information overload. When faced with a massive amount of information, the focus is on how to quickly and accurately find the desired information, which has led to the emergence of recommendation systems. However, traditional collaborative filtering recommendations are often affected by issues such as cold start and data sparsity, while existing graph-based collaborative filtering methods also suffer from poor representation learning quality and data sparsity. So we propose a recommendation method based on Multi-Task learning and Graph Collaborative Filtering (MTLGCF). This method uses graph collaborative filtering recommendation as the main task, incorporates two contrastive learning tasks as auxiliary tasks, and introduces a data augmentation strategy, enabling the model to perform multi-task learning. By evaluating on three public datasets such as ml-latest-small and comparisons with nine recent recommendation methods, the proposed MTLGCF model shows significant improvements in Recall and NDCG metrics. © 2024 IEEE.

关键词： Contrastive learning

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

3rd IEEE International Conference on Distributed Computing a...

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

ISBN: (纸本)9798350318609

The proceedings contain 299 papers. The topics discussed include: time series modelling approach for predictive analytics;research on the recognition of accounting information distortion by random forest algorithm;reconstruction and update of 3D model of mechanical products based on 3D point cloud data;prediction of soil organic carbon content using machine learning based fuzzy C-means clustering;fault detection and classification in power transmission lines using discrete wavelet transform-based swish recurrent neural network;immersive dramatic space 3D layout using panoramic image reconstruction algorithm;multi-objective optimization of noise in a high-speed railway by a hybrid algorithm;new energy vehicle customer mining model based on machine learning algorithm;and robotization of agriculture using image processing techniques.

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Based on Generative Adversarial Networks Seed Generation Method for Fuzzing 3

Based on Generative Adversarial Networks Seed Generation Met...

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3rd international conference on Big data, Information and Computer Network, BDICN 2024

作者： Cheng, Zekai Guo, Yongkang Li, Xiaoke Hu, Jiahao School of Computer Science and Technology Anhui University of Technolgy Ma'anshan Anhui China

ISBN: (纸本)9798350363821

Fuzzing, as one of the effective methods for vulnerability detection, has shown significant impact in discovering potential software vulnerabilities. However, the quality of seed inputs remains a critical factor influencing the effectiveness of fuzzing. and although in recent years, machine-learning based seed generation methods have achieved some results, there are still some problems leading to the failure of test cases generated by fuzzer variants to trigger more security vulnerabilities. This paper employs Base64 encoding and decoding technology to flexibly expand the types of generated seeds. Using the RelGAN model for seed generation and subsequently modifying the generated files with the hot-spot stitching algorithm, we enhance the quality and diversity of the seeds. This significantly improves the performance of fuzzing on target programs with various input formats. The experimental results show that the method in this paper finds a higher number of unique crashes compared to AFL++ using the original seeds. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of the bandgap reference circuit based on deep learning 3

Design of the bandgap reference circuit based on deep learni...

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

作者： Hu, Li Wang, Jue Sun, Xianfeng Yan, Ru Li, Yongyou School of Information and Electrical Engineering Hangzhou City University Hangzhou China

ISBN: (纸本)9781510680449

In this work, a method combining deep learning and genetic algorithm was applied to assist the design of bandgap reference circuit. Using the neural network to fit the mapping relationship between resistance values and temperature coefficient of bandgap reference circuit has significant improvement over the traditional machine learning algorithms. By using neural network as the fitness function for the genetic algorithm, the optimal design parameters of the circuit have been determined and verified. The temperature coefficient of the bandgap voltage reference is 9.7ppm after optimization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Genetic algorithms

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