Intelligent Transportation systems (ITS) present a range of challenges that need to be addressed to ensure successful deployment, seamless integration, robust interconnection, and robust security. These challenges ari...
详细信息
ISBN:
(纸本)9798350319439
Intelligent Transportation systems (ITS) present a range of challenges that need to be addressed to ensure successful deployment, seamless integration, robust interconnection, and robust security. These challenges arise from the complex nature of ITS, the multitude of stakeholders involved, and the need to balance technological advancements with privacy and security concerns. Vehicle trajectory prediction is a critical task for improving traffic management and safety. It can be used to develop advanced traffic control systems, enhance autonomous vehicle navigation, and support real-world applications in intelligent transportation systems. This paper presents a detailed comparison and study on predicting vehicle trajectories using the NGSIM dataset, a publicly available dataset of high-quality vehicle trajectory data. A practical data set had been used to extract the main features of ITS, then we implemented two machine learning (ML) models for vehicle trajectory prediction by applying classification and regression tasks of both machine learning models, Random Forest (RF) and Support Vector Machine (SVM). We evaluated the models using classification accuracy, precision, recall, F1-score, mean squared error (MSE), and R-squared. The results show that both models can predict vehicle velocity and type with reasonable accuracy. However, the random forest outperformed the SVM on the regression task, while the SVM outperformed the random forest on the classification task. We also studied key factors that influence the performance of vehicle trajectory prediction models and proposed suggestions for improving their performance.
This proposal aims to suggest improvements for the article titled 'Low-Cost Data Centers in Developing Countries' with the aim of enhancing its content, relevance, and practicality. Our expertise in data infra...
This paper explores the convergence of Artificial Intelligence (AI) and Vehicular Ad-Hoc Networks (VANETs), with a particular focus on the impact and potential of Explicable Artificial Intelligence (XAI) to improve in...
详细信息
Cloud Computing is fundamentally revolutionizing the use, storage, management and processing of data and IT applications, providing increased flexibility, improved accessibility and substantial cost savings for busine...
详细信息
This special issue includes a selection of the artefacts presented at the 18th international Federated conference on Distributed Computing Techniques (DiScoTec 2023), held at the NOVA University Lisbon (Lisbon, Portug...
详细信息
This special issue includes a selection of the artefacts presented at the 18th international Federated conference on Distributed Computing Techniques (DiScoTec 2023), held at the NOVA University Lisbon (Lisbon, Portugal), in June 18-23, 2023. The federated conference included: COORDINATION 2023, the 25th internationalconference on Coordination Models and Languages);DAIS 2023, the 23rd internationalconference on Distributed applications and Interoperable systems;and FORTE 2023, the 43rd internationalconference on Formal Techniques for Distributed Objects, Components, and systems. All the three conferences welcomed submissions describing technological artefacts, including innovative prototypes supporting the modelling, development, analysis, simulation, or testing of systems in the broad spectrum of distributed computing subjects. The artefact evaluation chairs have selected a subset of high- quality accepted artefacts to be invited for submission to this special issue. Following the revision process, nine artefacts have been accepted to be part of this special issue. The published contributions include different types of artefacts, including programming libraries, frameworks, as well as tools for the analysis, verification, and simulation of distributed systems.
The Internet of Things (IoT) is an innovative technology that is revolutionizing the global economy and acquired significant recognition across various sectors, notably within the healthcare field. IoT cameras play a ...
详细信息
ISBN:
(纸本)9798350319439
The Internet of Things (IoT) is an innovative technology that is revolutionizing the global economy and acquired significant recognition across various sectors, notably within the healthcare field. IoT cameras play a crucial role in facilitating real-time monitoring of the human body through video streams. However, this kind of IoT equipment is vulnerable to various threats. Traditional encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest Shamir Adleman) are commonly used to encrypt textual data. However, they are not suitable for digital image encryption due to computational constraints. Consequently, researchers have proposed cryptographic approaches based on chaotic systems, which offer high speed, security, and low computational costs. In this paper, a new 2D-ZasHen map is proposed, resulting from a combination of two existing 2D maps, along with a new secure chaos-based image encryption approach for IoT healthcare monitoring. The proposed approach involves three significant processes which are key generation, permutation and substitution, inspired by the global existing scheme. Moreover, it considers the confusion and diffusion processes as a single step, executed simultaneously in the same round. This approach effectively meets the requirements of both confusion and diffusion processes, optimizing time efficiency, enhancing security, and ensuring robust encryption and decryption phases. Extensive tests have demonstrated the robustness of the proposed chaos-based approach cryptosystem against differential and entropy attacks, the randomness of the encrypted images, and their sensitivity to slight changes.
Time-To-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the ...
详细信息
This paper outlines my PhD project, which focuses on using artificial intelligence to classify and extract semantic information from breast radiology reports, specifically within the Algerian healthcare context. Curre...
详细信息
Intelligent and sustainable vehicle networking (ISVN) is a new paradigm for transportation that makes use of developments in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication to encourage coll...
详细信息
ISBN:
(纸本)9798350319439
Intelligent and sustainable vehicle networking (ISVN) is a new paradigm for transportation that makes use of developments in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication to encourage collaboration between vehicles and the infrastructure in order to enhance traffic flow, safety, and environmental effect. Traffic accidents are a major public safety concern, resulting in significant casualties and economic losses each year, as well as a significant contributor to air pollution and greenhouse gas emissions. In order to increase traffic accident management's efficacy and efficiency and support a more environmentally friendly transportation system, this article suggests an integrated ISVN-ML traffic accident management system. The proposed system leverages ISVN sensors and cameras to collect data about traffic accidents in real time. This data is then transmitted to an ML-based traffic accident management system, which uses it to predict the number of killed and injured people involved in the accident and to immediately prioritize the dispatch of emergency responders. Additionally, the system provides real-time information to police stations and ambulance services to help them respond to accidents more quickly and efficiently. After the police station has completed its investigation of the accident, the details of the accident are sent back to the ML-based traffic accident management system to improve the accuracy of the system's predictions and make the system more efficient over time. Overall, the proposed system is a promising approach to improving traffic safety, reducing the economic costs associated with traffic accidents, and contributing to a more sustainable transportation system.
Artificial intelligence has transformed the automotive industry, giving rise to safer and more comfortable semi-Autonomous vehicles. This review evaluates the critical role of recommendation systems in the development...
详细信息
暂无评论