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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7123 条记录，以下是121-130 订阅

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Proceedings of the 18th INDIAcom;2024 11th international conference on Computing for Sustainable Global Development, INDIACom 2024

Proceedings of the 18th INDIAcom;2024 11th International Con...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

ISBN: (纸本)9789380544519

the proceedings contain 310 papers. the topics discussed include: blockchain technology and artificial intelligence based integrated framework for sustainable supply chain management system;contribution of microbial mechanism and artificial intelligence in wine production;a study of various clustering algorithms for image segmentation;deep learning based energy-efficient task scheduling in cloud computing;enhancing e-commerce fashion sales through personalized recommendation systems;an investigation of the intelligent and secure child rescue system from borewell;design of a dual band microstrip patch antenna;ml-based blood pressure estimation using converted ppg signal from video;auditing of outsourced data in cloud computing: an overview;and interpretable machine learning models for credit risk assessment.

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Target Localization from mm-wave Point Clouds using Deep learning based Classifiers 11

Target Localization from mm-wave Point Clouds using Deep Lea...

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11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Amjad, Bisma Ahmed, Qasim Z. Khan, Faheem A. Zaharis, Zaharias D. Lazaridis, Pavlos Univ Huddersfield Sch Comp & Engn Huddersfield W Yorkshire England Aristotle Univ Thessaloniki Sch Elect & Comp Engn Thessaloniki Greece

ISBN: (纸本)9798350377873;9798350377866

the implementation of indoor localization is being facilitated through commercially available low-cost mm-wave sensors. these sensors generate point cloud outputs containing noisy estimates of detected targets due to hardware noise and multipath reflections. In contrast to previously studied regression approaches, this study introduces a classification-based approach to predict the angle-of-arrival (AoA) and range of a human target from point clouds obtained from an mm-wave sensor. Our proposed methodology achieves a 7% and 26% improvement in AoA and range prediction, respectively, compared to the baseline models. All experiments have been conducted and validated using real data recorded by the mm-wave sensor.

关键词： mm-Wave angle of arrival deep learning localization indoor positioning system.

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Enhanced Personality Profiling: Unveiling MBTI Traits through Machine learning Analysis 11

Enhanced Personality Profiling: Unveiling MBTI Traits Throug...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kandpal, Harshit Gupta, Deepa Bathla, Ruchika Amity Institute of Information and Technology Amity University Noida Delhi India

ISBN: (纸本)9798350350357

this machine learning paper analyzes the MBTI personality dataset, comprising 16 personality types. It employs a structured approach, including Exploratory data Analysis (EDA), data grouping, visualization, preprocessing, feature engineering, and model training with both 60-40 and improved 70-30 splits. the study compares the performance of various algorithms and concludes with a robust verification process. the insights gained contribute to understanding the interplay between personality traits and associated features, with potential applications in personality assessment and human resources. © 2024 IEEE.

关键词： data visualization

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Collaborative Intrusion Detection System for Intermittent 10 Vs Using Federated learning and Deep Swarm Particle Optimization 11

Collaborative Intrusion Detection System for Intermittent 10...

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11th IEEE international conference on data Science and Advanced Analytics, DSAA 2024

作者： Ullah, Farhan Srivastava, Gautam Mostarda, Leonardo Cacciagrano, Diletta University of Camerino Division of Computer Science Camerino Italy Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada University of Perugia Department of Mathematics and Computer Science Perugia Italy School of Software Northwestern Polytechnical University Xian China

ISBN: (纸本)9798350364941

intelligent vehicles have significantly influenced the advancement of intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. this work presents a Federated learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. this approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. this cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance. © 2024 IEEE.

关键词： Federated learning

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Prevention of Security Attacks at Wireless Network Layers using Machine learning Techniques 18

Prevention of Security Attacks at Wireless Network Layers us...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ramisetty, Sowjanya Ghantasala, G. S. Pradeep Gupta, Gaurav Sonia Kg Reddy College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

Wireless sensor networks have great potential for use in flood control, weather forecasting systems, the military, and the healthcare industry. A WSN's nodes are connected to one another and share information. When sending data between nodes, data security is essential. In the world of networking, security is a top priority. data travels along a path when it is transferred from one location to another, increasing the likelihood that a rogue node would enter the network. Detecting malicious nodes within a network presents a formidable challenge, particularly due to external assaults on data packets during their transmission between nodes. Hackers take advantage of weaknesses to manipulate and alter data as it travels from the source node to the destination node. this paper looked at machine learning algorithm-based preventive measures and layer-level security challenges. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning Security Security attacks Wireless Sensor Networks

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Coverage-aware and Reinforcement learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment 11

Coverage-aware and Reinforcement Learning Using Multi-agent ...

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11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and intelligent Connectivity

作者： Redondo, Jeffrey Yuan, Zhenhui Aslam, Nauman Zhang, Juan Univ Northumbria Newcastle NSW Australia Univ Warwick Coventry W Midlands England

ISBN: (纸本)9798350377873;9798350377866

One effective way to optimize the offloading process is by minimizing the transmission time. this is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data which requires constant updates. this implies that latency and throughput requirements must be guaranteed by the wireless system. To achieve this, adjustable contention windows (CW) allocation strategies in the standard IEEE802.11p have been explored by numerous researchers. Nevertheless, their implementations demand alterations to the existing standard which is not always desirable. To address this issue, we proposed a Q-learning algorithm that operates at the application layer. Moreover, it could be deployed in any wireless network thereby mitigating the compatibility issues. the solution has demonstrated a better network performance with relatively fewer optimization requirements as compared to the Deep Q Network (DQN) and Actor-Critic algorithms. the same is observed while evaluating the model in a multi-agent setup showing higher performance compared to the single-agent setup.

关键词： Quality of service Q-learning Vehicular Network machine learning VANET HD Map

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Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement learning 11

Autonomous Driving using Residual Sensor Fusion and Deep Rei...

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11th RSI international conference on Robotics and Mechatronics, ICRoM 2023

作者： Aghdasian, Amin Jalal Ardakani, Amirhossein Heydarian Aqabakee, Kianoush Abdollahi, Farzaneh Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350308105

this paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars. Our system employs a two-branch fusion method for vehicle image and tracking sensor data, leveraging the strengths of residual structures and identity mapping to enhance agent training. through comprehensive comparisons, we demonstrate the efficacy of information fusion and establish the superiority of our selected algorithm over alternative approaches. Our work advances the field of autonomous driving and demonstrates the potential of reinforcement learning in enabling intelligent vehicle decision-making. © 2023 IEEE.

关键词： Reinforcement learning

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dataset Amount and Augmentation Process Effect for CNN Rice Seed Classification 11

Dataset Amount and Augmentation Process Effect for CNN Rice ...

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11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Wibowo, thomas Tri Hidayat, Sidiq Svamsul Suharjono, Amin Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350389289

Rice seed classification is one of the critical procedures in quality assessment in the agricultural sector, and it can be made easier using machine learning. the classification process using machine learning starts with creating a dataset, preprocessing the data, and then creating and training the model. In the process of preprocessing data, there are times when augmentation is needed to process it before it is trained on the machine learning model that has been created. In this study, the author compares the results of rice seed classification for superior and non-superior classes between previously augmented data and data without augmentation. Another variation was the number of datasets used to see differences in classification results. this research shows that models using augmented data produce higher accuracy and precision than those without augmentation. In this research, the percentage increase in accuracy, precision, recall, and f1 score before and after the data was augmented was 9% to 15%. this research contributes to developing a rice seed identification system, which can increase efficiency and accuracy in the seed selection process. © 2024 IEEE.

关键词： data accuracy

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A Comprehensive Literature Review of Skin Lesions Classification Techniques 11

A Comprehensive Literature Review of Skin Lesions Classifica...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Tiwari, Shubham thapar, Puneet Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350350357

An intelligent system that relies on image input to provide unbiased diagnoses of skin related diseases has become an indispensable screening tool. Healthcare employees have a hefty task due to the effort-intensive and slow process of seeing and manually diagnosing skin lesion conditions based on pictures, which is the typical technique. In order to reduce and alleviate this labor, a considerable deal of research has been done on machine learning and deep learning techniques. While early research focused more on normal machine learning approaches, more recent work has relied on deep learning techniques, mainly based on convolutional neural networks, which have shown amazing results. However, due to the complications with the skin lesions, some challenges remain open. Using deep learning approaches, this review paper offers a thorough study of the classification phases of skin lesions. Involving the classification methods utilized for skin lesion identification. this work examines and assesses the effectiveness of cutting-edge techniques that have been tried on a range of datasets including skin lesions. the research makes use of performance metrics for 7 distinct Convolutional Neural Network-based classification models. © 2024 IEEE.

关键词： intelligent systems

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Dissecting data Practices in Android Apps: A Comparative Study of data Collection and Sharing Behaviors 25th

Dissecting Data Practices in Android Apps: A Comparative Stu...

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25th international conference on intelligent data engineering and automated learning

作者： Nguyen, Triet M. Pham, Nghiem T. Doan, Hieu M. Tran, Khoa D. Tran, Bao Q. Huynh, Khiem G. Tran, Nam B. Vo, Khanh H. FPT Univ Can Tho Vietnam

ISBN: (纸本)9783031777301;9783031777318

the exponential growth of mobile apps within the Android ecosystem has underscored the critical need for robust user privacy and data protection measures. Central to these concerns are the privacy policies that serve as the primary channel of communication between organizations and users, detailing data collection, utilization, and sharing practices. However, the efficacy of these policies is often undermined by their inaccessibility and the legalese that obfuscates their intent, presenting a barrier to informed user consent. this study addresses these challenges by harnessing the capabilities of Generative AI (GenAI) to perform a detailed analysis of data practices in Android apps. Our methodology extends beyond the traditional scope of AI-assisted analysis by not only identifying third-party entities but also by elucidating their data handling purposes. We introduce a classification system that distinguishes between 'Regular' and 'Irregular' app behaviors, offering a benchmark for app evaluation and compliance assessment. Our comparative analysis across various apps reveals patterns and anomalies in data management, providing actionable insights for developers, regulators, and users.

关键词： Android Apps data Collection data Sharing Privacy Policy data Usage Behavior Mobile Privacy

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