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Topic Review
Blockchain-Based Traffic Bottleneck Management System
To alleviate traffic congestion, it is necessary to effectively manage traffic bottlenecks. In existing research, travel demand prediction for traffic bottlenecks is based on travel behavior assump￾tions, and prediction accuracy is low in practice. Thus, the effect of traffic bottleneck management strategies cannot be guaranteed. Management strategies are often mandatory, leading to problems such as unfairness and low social acceptance. To address such issues, this paper proposes managing traffic bottlenecks based on shared travel plans. To solve the information security and privacy prob￾lems caused by travel plan sharing and achieve information transparency, travel plans are shared and regulated by blockchain technology. To optimize the operation level of traffic bottlenecks, travel plan regulation models under scenarios where all/some travelers share travel plans are proposed and formulated as linear programming models, and these models are integrated into the blockchain with smart contract technology. Furthermore, travel plan regulation models are tested and verified using traffic flow data from the Su-Tong Yangtze River Highway Bridge, China. The results indicate that the proposed travel plan regulation models are effective for alleviating traffic congestion. The vehicle transfer rate and total delay rate increase as the degree of total demand increases; the vehicle transfer rate increases as the length of the time interval decreases; and the vehicle transfer rate and total delay rate increase as the number of vehicles not sharing their travel plans increases. By using the model and method proposed in this paper, the sustainability of urban economy, society, and environment can be promoted.
  • 43
  • 27 Mar 2024
Topic Review
Artificial Intelligence-Based Support in Cardiology
Artificial Intelligence (AI)-based algorithms, in particular, Deep Neural Networks (DNNs), have recently revolutionized image creation. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. For example, an AI-based algorithm for the segmentation of pigmented skin lesions has been developed, which enables diagnosis in the earlier stages of the disease, without invasive medical procedures. With flexibility and scalability, AI can be also considered an efficient tool for cancer diagnosis, particularly in the early stages of the disease.
  • 49
  • 27 Mar 2024
Topic Review
Data Storage and Retrieval Using IPFS and Blockchain
Blockchain technology has been successfully applied in recent years to promote the immutability, traceability, and authenticity of previously collected and stored data. However, the amount of data stored in the blockchain is usually limited for economic and technological issues. Namely, the blockchain usually stores only a fingerprint of data, such as the hash of data, while full, raw information is stored off-chain. This is generally enough to guarantee immutability and traceability, but misses to support another important property, that is, data availability. This is particularly true when a traditional, centralized database is chosen for off-chain storage. For this reason, many proposals try to properly combine blockchain with decentralized IPFS storage. However, the storage of data on IPFS could pose some privacy problems. This entry proposes a solution that properly combines blockchain, IPFS, and encryption techniques to guarantee immutability, traceability, availability, and data privacy.
  • 99
  • 26 Mar 2024
Topic Review
Data Gathering and Disease Detection in Healthcare WSN
Wireless sensor networks (WSNs) consist of a multitude of distributed devices, equipped with sensors, to monitor physical or environmental conditions. These devices, also known as nodes, collaboratively pass their data through the network to a main location or sink where the data can be observed and analyzed. WSNs have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. 
  • 56
  • 22 Mar 2024
Topic Review
Synthetic Datasets
With the consistent growth in the importance of machine learning and big data analysis, feature selection stands to be one of the most relevant techniques in the field. Extending into many disciplines, the use of feature selection in medical applications, cybersecurity, DNA micro-array data, and many more areas is witnessed. Machine learning models can significantly benefit from the accurate selection of feature subsets to increase the speed of learning and also to generalize the results. Feature selection can considerably simplify a dataset, such that the training models using the dataset can be “faster” and can reduce overfitting. Synthetic datasets were presented as a valuable benchmarking technique for the evaluation of feature selection algorithms.
  • 47
  • 20 Mar 2024
Topic Review
Improved Feature Selection for Social Internet of Things
The Social Internet of Things (SIoT) ecosystem tends to process and analyze extensive data generated by users from both social networks and Internet of Things (IoT) systems and derives knowledge and diagnoses from all connected objects. To overcome many challenges in the SIoT system, such as big data management, analysis, and reporting, robust algorithms should be proposed and validated. 
  • 33
  • 20 Mar 2024
Topic Review
Connecting the Elderly Using VR
An innovative approach for creating a social virtual reality (VR) platform that seamlessly blends art, technology, artificial intelligence (AI), and VR. Developed as part of a European project, the methodology is designed to safeguard and improve neurological, cognitive, and emotional functions, with a particular emphasis on promoting mental health.
  • 54
  • 19 Mar 2024
Topic Review
Detection of Hate Speech in Arabic
Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. 
  • 55
  • 19 Mar 2024
Topic Review
Non-Iterative Cluster Routing
In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes.
  • 322
  • 19 Mar 2024
Topic Review
Multi-View Stereo Method
As a 3D reconstruction method, multi-view stereoscopic (MVS) plays a vital role in 3D computer vision, and has a wide range of applications in the fields of virtual reality, augmented reality, and autonomous driving. With the rapid development of deep learning technology in the field of computer vision, the learning-based multi-view stereo method has produced advanced results.
  • 107
  • 18 Mar 2024
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