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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是4881-4890 订阅

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AI Based Gym Management System with Body Performance Index measurement and Tips 2

AI Based Gym Management System with Body Performance Index m...

引用

2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Srivastava, Aman Diya Ajith, K. Annamalai, Suresh SRM Institute of Science and Technology School of Computing College of E&T Department of Networking and Communications Tamil Nadu Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

The GMS-BPI Gym System is like a super cool computer program for gyms! It helps gym owners and people who go to the gym. For the owners, it makes things easier like signing up new members, planning classes, fixing gym stuff, and keeping track of money. There's even a fancy screen that shows how the gym is doing. But the awesome part is how it helps people who work out. GMS-BPI uses special gadgets to collect info about how fit they are. So, people can see how healthy they're getting, make goals, and get workout plans just for them. And it figures out a special number called the Body Performance Index to show how fit someone is overall. Recent Techniques - Wearable Fitness Trackers Integration, AI-Powered Personalized workout Plans, VR, AR Techniques for Workout Enhancements. Challenges Faced by the GMS-BPI System - data Privacy and Security Concerns, Integration Complexity with diverse devices, User Engagement & Retention, Accuracy and Reliability of BPI. © 2024 IEEE.

关键词： machine learning

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Multi-modal Multi-scale State Space Model for Medical Visual Question Answering 33rd

Multi-modal Multi-scale State Space Model for Medical Visual...

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33rd international conference on Artificial Neural Networks and machine learning (ICANN)

作者： Chen, Qishen Bian, Minjie He, Wenxuan Xu, Huahu Shanghai Univ Shanghai Peoples R China Shanghai Data Grp Shanghai Peoples R China

ISBN: (纸本)9783031723520;9783031723537

Medical Visual Question Answering (Med-VQA) is pivotal for interpreting medical queries via corresponding images. While multi-modal fusion stages in Med-VQA benefit from attention mechanisms and Transformer-based methods, the latter's computational demands limit scalability. Emerging as a robust alternative, State Space Models (SSMs), particularly the Mamba model, have shown promise in sequence modeling and deep learning network building. However, their limitation to single-modality data processing curtails their direct application in complex vision-language tasks inherent in Med-VQA. Additionally, we identify and address the underutilization of multi-scale visual information in existing Med-VQA frameworks, incorporating it into our fusion process for enhanced context comprehension. Our approach also features an innovative asymmetric fusion structure tailored to bridge the gap between open-ended and close-ended questions, optimizing question answering accuracy. Comparative analyses on benchmark datasets VQA-RAD and SLAKE underscore our method's efficiency, outperforming state-of-the-art Med-VQA models in accuracy while operating with significantly fewer parameters than Transformer-based counterparts. This study not only proposed a powerful Med-VQA model but also broadens the scope of SSMs in tackling complex multi-modal challenges.

关键词： State Space Model Multi-Modal Fusion Medical Visual Question Answering Medical Image

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Abuse Reporting System 6

Abuse Reporting System

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6th IEEE international conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sivadasan, Sreeram Mohamed Jisar, C. Rasmina Murshida Farsana, V. Shijaz, K. Mea Engineering College Department of Computer Science and Engineering India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381689

This project embarks on the development of an abuse reporting system designed to maintain user anonymity and employ advanced machine learning algorithms for genuineness assessment and fraud detection. In the contemporary digital landscape, safeguarding user privacy and ensuring the reliability of abuse reports are paramount. Our project addresses this challenge by integrating state-of-the-art machine learning techniques, enhancing the accuracy and efficiency of abuse reporting process, and mitigating false or malicious reporting. The technical underpinnings of the system involve the creation of a distributed, scalable, and fault-tolerant architecture capable of handling a wide range of abuse reports from diverse sources. We implement advanced natural language processing (NLP) models for textbased report analysis, while image and multimedia reports undergo deep learning-based content analysis. Anonymity preser-vation is achieved through advanced encryption techniques and decentralized data storage. Furthermore, we integrate adaptive machine learning algorithms that continuously evolve to detect novel abuse patterns and emerging fraud strategies, ensuring the system's adaptability to evolving threats. The project includes a comprehensive evaluation of the abuse reporting app's performance, precision, and scalability in handling a large volume of reports while preserving user anonymity. This project represents a significant advancement in the field of abuse reporting systems, offering a technologically robust and privacy-centric solution for contemporary society's increasing demand for secure and effective reporting mechanisms. © 2024 IEEE.

关键词： Anonymity

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Leveraging Ensemble Deep learning Model for Fruit Image Recognition with Explainable AI 5

Leveraging Ensemble Deep Learning Model for Fruit Image Reco...

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5th international conference on Big data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Wang, Yaoqing Yu, Chang Lin, Xiyue Mgbejime, Goodness Temofe Nneji, Grace U. Monday, Happy N. University of Electronic Science and Technology School of Electronic Science and Engineering China Chengdu University of Technology Chengdu China Intelligent Computing Lab Hace Softtech Lagos102241 Nigeria

ISBN: (纸本)9798331506612

Precise classification of fruit images is crucial for a range of agricultural and retail purposes. Although there have been improvements, current methods have limits when it comes to dealing with complex feature extraction and ensuring model resilience. This paper introduces a new framework for categorizing fruits using a combination of advanced deep learning models, namely AlexNet, ResNet50, and EfficientNet. The collection of 43,912 photos across 44 fruit classifications underwent thorough pre-processing and data augmentation procedures. Grad-CAM, SHAP, and LIME were employed to achieve model interpretability, offering valuable insights into the decision-making process of the ensemble model. The experimental results indicate that the ensemble model surpasses the performance of individual models, obtaining 96.9% accuracy, 96.2% precision, 94.5% recall, and 95.4% F1-score. The proposed ensemble model provides a highly efficient solution for automated fruit classification, with substantial implications for agricultural technology and retail automation. © 2024 IEEE.

关键词： Convolutional neural networks

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QoS Optimization via Computation Offloading in Metaverse Environment

QoS Optimization via Computation Offloading in Metaverse Env...

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IEEE international conference on Web Services (IEEE ICWS)

作者： Ge, Zhiyuan Zhang, Pengcheng Jin, Huiying Dong, Hai Ji, Shunhui Li, Jiajia Wang, Qi Hohai Univ Key Lab Water Big Data Technol Minist Water Reso Nanjing Peoples R China Hohai Univ Coll Comp Sci & Software Engn Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China RMIT Univ Sch Comp Technol Melbourne Australia

ISBN: (纸本)9798350368567;9798350368550

The emergence of the metaverse signifies a paradigm shift in Internet technology, offering a comprehensive virtual social platform spanning various domains such as social interaction, gaming, healthcare, and tourism. This new era of the metaverse is facilitated by advancements in next-generation digital technologies including edge computing, artificial intelligence, virtual reality, augmented reality, and blockchain. In the metaverse, the quantity and variety of services requested by users may surpass those in other environments, and existing work cannot be applied to metaverse QoS (Quality of Service) optimization. To address this problem, this paper proposes Meta-PPO, an optimization method for enhancing the QoS of metaverse services using reinforcement learning. Firstly, metaverse services are categorized into virtual scene services and meta-services, providing a comprehensive framework for analysis. Secondly, Meta-PPO, based on the proximal policy optimization algorithm, is introduced to optimize the QoS of metaverse services. This method effectively balances the objectives of minimizing average delay and maximizing resource utilization of mobile devices by making informed offloading decisions for the identified service categories. Simulation results demonstrate the superiority of the proposed method over existing techniques, showcasing its suitability and effectiveness for enhancing the QoS of metaverse service.

关键词： Metaverse Computation offloading Edge computing Reinforcement learning Quality of Service

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Improving Wind Speed Forecast Accuracy with Transformer Architecture: A Deep learning Approach 2

Improving Wind Speed Forecast Accuracy with Transformer Arch...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Ahuja, Muskaan Saini, Sanju Deenbandhu Chhotu Ram University of Science and Technology Sonipat India

ISBN: (纸本)9798350390179

Wind energy is rapidly gaining recognition as a leading renewable energy source worldwide. Accurate forecasting of wind speed is essential for enhancing wind turbine performance, facilitating grid integration, and minimizing the uncertainties in wind energy generation. This paper presents a comparative analysis of different wind speed prediction models, specifically the Kalman Filter, Wavelet Kalman Filter, and Transformer-based models, evaluated on real-world wind speed data. To improve prediction accuracy, wavelet decomposition is used to isolate high-frequency fluctuations from the low-frequency components, creating a more refined model for wind speed forecasting. The models were trained and tested on data collected across all seasons (summer, autumn, winter, and spring) from various geographical locations, ensuring that the results are both robust and generalizable. The findings demonstrate that the proposed Transformer-based model outperforms traditional approaches such as the Kalman Filter and Wavelet Kalman Filter in both accuracy and computational efficiency. This new model achieves high prediction accuracy across all four seasons for both sites, indicating its broad applicability. The key innovation of this work lies in the development of a deep learning model based on the Transformer architecture, which effectively captures temporal dependencies and complex patterns in sequential wind data-insights that traditional models may overlook. © 2024 IEEE.

关键词： Wind forecasting

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Towards Context Aware and Age-Based Book Recommendation System using machine learning for Young Readers 4

Towards Context Aware and Age-Based Book Recommendation Syst...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Singh, Aniket Gandhala, Santhosh Phanitalpak Alahmar, Murhib Gaikwad, Pratik Wable, Upamanyu Yadav, Aman Agrawal, Renuka Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350343670

In an era characterized by an overwhelming abundance of digital content, the significance of personalized book recommendations for young readers cannot be overstated. This research paper introduces a pioneering Content-Based Age-Wise Book Recommendation System, which tackles the challenge of tailoring book suggestions to young audiences. Our system harnesses the power of K-Nearest Neighbors (KNN), Support Vector machine (SVM), Decision Tree, and Naive Bayes techniques to provide intelligent book recommendations based on content while taking into account the user's age category. The paper embarks on a comprehensive exploration of the system's development journey, commencing with data acquisition and preprocessing to ensure the dataset's quality and consistency. Through the utilization of the TF-IDF method, the textual content of books is translated into numerical features, enabling precise content-based analysis. A pivotal aspect of this research involves age group segmentation, a critical component that paves the way for personalized recommendations catering to distinct age categories, including children, teenagers, and young adults. Our research delves into the intricate workings of KNN, which measures book content similarity and is applied within each age group. Additionally, Support Vector machine (SVM), a robust classification algorithm, classifies books into specific categories, allowing for personalized recommendations based on age and genre preferences. Decision Tree is introduced, offering transparency in decision-making, ensuring that users understand the rationale behind book recommendations. Naive Bayes, a probabilistic model, significantly enhances the system's efficiency while considering age and genre. Furthermore, the paper highlights key findings, identifies areas for potential enhancement, and suggests promising avenues for future research. In sum, this research makes a substantial contribution to the ever-evolving realm of recommendation systems by

关键词： Age Group Segmentation Content-Based Filtering K Nearest Neighbor Recommendation System Support Vector machine

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A Comprehensive Approach to Groundwater Quality Prediction and Management in the State of Telangana, India, Using Stacking Ensemble Approach 4

A Comprehensive Approach to Groundwater Quality Prediction a...

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4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Senapati, Chinmayee Patra, Kumar Janardan Swain, Satyaprakash Jayasingh, Suvendra Kumar Senapati, Swagatika Ray, Niranjan Kumar Siksha 'o' Anusandhan Deemed to Be University Institute of Technical Education and Research Department of Civil Engineering Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Bhubaneswar India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

Groundwater, a vital resource for various purposes, faces increasing threats to its quality due to factors like overuse, pollution and climate change. In the semi-arid climate of the state of Telangana, India, exacerbated by intensive agricultural practices, groundwater quality is a significant concern. This study presents a machine learning-based approach for predicting and managing groundwater quality in Telangana, utilizing a stacking ensemble method. Our proposed approach combines decision trees (DT) with meta-models such as logistic regression, K-nearest neighbors (K-NN), Bayesian ridge regression (BRR) and random forest (RF). Trained on a comprehensive dataset from the Telangana Open data portal (2018-2021), the stacking ensemble demonstrates superior performance, achieving an accuracy of 96.07%, surpassing the best individual model (DT) at 92.68%. It not only enhances predictive capabilities but also provides insights into the spatial distribution of groundwater quality, identifying areas vulnerable to contamination. These findings offer valuable information for policymakers, environmental scientists and water resource managers, guiding informed decision-making for sustainable water resource management in Telangana. This research contributes to the ongoing discourse on environmental conservation and the imperative for effective groundwater management. © 2024 IEEE.

关键词： Decision trees

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Bounded Rational User Behavior Analysis-based Cooperative Pricing Scheme for CSs with Single-Output Multi-Port Charging Station

Bounded Rational User Behavior Analysis-based Cooperative Pr...

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2024 international conference on Communications Control and computing Technologies for Smart Grids

作者： Yang, Chen Liu, Lie Leng, Xi Tang, Xiaoying Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Key Lab Crowd Intelligence Empowered 1ow Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9798350318562;9798350318555

The rapid penetration of electric vehicles (EVs) provides notable opportunities for the EV charging industry. However, the frequent occurrence of EV overstay results in the underutilization of charging piles, leading to the low overall efficiency of charging stations (CSs). To address this issue, we present a Cooperative Pricing Scheme for Multi-Type CSs (CPSMT) integrating single-output multi-port (SOMP) CSs. Besides, the bounded rational user behavior facing new SOMP CSs is meticulously analyzed and modeled to tackle obstacles such as users' low acceptance of these emerging CSs. Numerical experiments are conducted to illustrate the effect of bounded rationality on user selection. To derive optimal pricing strategies for CPSMT, we propose a reinforcement learning-based method, creatively utilizing customized statistical data to track the status of each CS. The simulation results show that this advanced scheme remarkably enhances the profitability of CSs, greatly outperforming the system only with traditional CSs.

关键词： EV CS bounded rational user behavior cooperative pricing scheme reinforcement learning

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Evaluating Deep learning Architectures for Lung Disease Classification: A Study of VDSNet, State-of-the-Art Models 2

Evaluating Deep Learning Architectures for Lung Disease Clas...

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2nd international conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Dhiman, Tanu Kumar, Puneet Chandigarh University Department of Computer Science Punjab Mohali India

ISBN: (纸本)9798331519056

Lung-disease detection and classification are crucial for early diagnosis and effective treatment. This paper presents a comprehensive comparison of various deep learning methodologies employed for lung disease classification using chest X-ray images. We investigate three distinct approaches: a collection of well-established CNN models including EfficientNetB1, NasNetMobile, and MobileNetV2;and VDSNet, a specialized model designed for medical image analysis. The research focuses on evaluating these models based on their performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. We also address common challenges in medical image classification such as class imbalance and data augmentation. Our comparative analysis highlights the strengths and limitations of each methodology, providing valuable insights for future research and practical applications in lung disease detection. The results demonstrate that while each approach has its advantages, combining techniques may offer the best performance for accurate and reliable lung disease classification. © 2024 IEEE.

关键词： Lung cancer

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