In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides mor...
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In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.
Virtual Reality (VR) and Augmented Reality (AR) have witnessed a surge in popularity, revolutionizing various industries and enhancing user experiences. As these technologies continue to evolve, ensuring secure user i...
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The Internet of Things (IoT) is a large-scale network of devices capable of sensing, data processing, and communicating with each other through different communication protocols. In today's technology ecosystem, I...
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The Internet of Things (IoT) is a large-scale network of devices capable of sensing, data processing, and communicating with each other through different communication protocols. In today's technology ecosystem, IoT interacts with many application areas such as smart city, smart building, security, traffic, remote monitoring, health, energy, disaster, agriculture, industry. The IoT network in these scenarios comprises tiny devices, gateways, and cloud platforms. An IoT network is able to keep these fundamental components in transmission under many conditions with lightweight communication protocols taking into account the limited hardware features (memory, processor, energy, etc.) of tiny devices. These lightweight communication protocols affect the network traffic, reliability, bandwidth, and energy consumption of the IoT application. Therefore, determining the most proper communication protocol for application developers emerges as an important engineering problem. This paper presents a straightforward overview of the lightweight communication protocols, technological advancements in application layer for the IoT ecosystem. The survey then analyzes various recent lightweight communication protocols and reviews their strengths and limitations. In addition, the paper explains the experimental comparison of Constrained Applications Protocol (CoAP), Message Queuing Telemetry (MQTT), and WebSocket protocols, more convenient for tiny IoT devices. Finally, we discuss future research directions of communication protocols for IoT.
Federated Learning (FL) offers significant advancements in user/data privacy, learning quality, model efficiency, scalability, and network communication latency. However, it faces notable security challenges, particul...
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Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) technique to determine the various sentiments of a customer in a single comment regarding different aspects. The increasing online data con...
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In the context of small software development teams, this research article gives a thorough investigation of the adoption of test-driven development (TDD) approaches. It aims to highlight the benefits that TDD offers, ...
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Pre-trained language models (PLMs) play a crucial role in various applications, including sensitive domains such as the hiring process. However, extensive research has unveiled that these models tend to replicate soci...
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The escalating global trend of traffic accidents with subsequent loss of lives is a matter of grave concern that requires immediate attention. Extensive efforts have been made to mitigate accidents and develop effecti...
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The escalating global trend of traffic accidents with subsequent loss of lives is a matter of grave concern that requires immediate attention. Extensive efforts have been made to mitigate accidents and develop effective prevention strategies. This research paper focuses on a comprehensive analysis of traffic accidents in Seoul, aiming to identify factors and accident types that contribute to increased severity. To achieve this, we introduced a new approach called "TrafficNet: A Hybrid CNN-FNN Model" to evaluate effects of various parameters on the severity of traffic accidents in Seoul. Our main objective was to classify accidents into four distinct levels of severity: minor injuries, slander, fatalities, and injury reports. To assess the effectiveness of our proposed model, we conducted comprehensive experiments using publicly available traffic accident data provided by Seoul Metropolitan Government. These experiments involved six different models, including five machine learning models (decision tree, random forest, k-nearest neighbor, gradient boosting, and support vector machine) and one deep learning model (multilayer perceptron). The proposed model demonstrated exceptional performance, surpassing all other models and previous research findings using the same dataset. On the test dataset, TrafficNet achieved an impressive accuracy of 93.98% with a precision of 94.31%, a recall of 93.98%, and an F1-score of 93.89%. Copyright 2023. The Korean Institute of Information Scientists and Engineers
Locating objects Non-Line-of-Sight is an important challenge in many fields such as defense applications, autonomous vehicles, natural disasters, etc. With the advancement of signal processing techniques, there has be...
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Emotions are intrinsic to human nature, playing a vital role in human cognition. Emotions are closely intertwined with rational decision-making, perception, human interaction, and human intelligence. EEG has emerged a...
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