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 m...
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.
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 ...
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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.
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, preprocess...
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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 stronge...
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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. Whe...
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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...
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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.
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 s...
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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 w...
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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 an...
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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 ...
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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.
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