Topic Review
Diffusion-Based Method for Pavement Crack Detection
Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. Some studies have already applied the feature learning capability of generative models to crack detection. 
  • 185
  • 01 Apr 2024
Topic Review
Enhancing Communication Security an In-Vehicle Wireless Sensor Network
The adoption of wireless sensor networks (WSNs) in vehicle systems represents a transformative development in the automotive sector, broadening the scope of functionalities from simple monitoring and control to facilitating sophisticated driver assistance and self-driving features. Secure in-vehicle communication systems are integral to the realization of fully connected and automated urban environments, where vehicles and city systems operate in harmony to optimize city life.
  • 190
  • 01 Apr 2024
Topic Review
The Convergence of AI, 6G, and Wireless Communication
In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems.
  • 292
  • 27 Mar 2024
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.
  • 175
  • 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.
  • 389
  • 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.
  • 388
  • 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. 
  • 163
  • 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.
  • 206
  • 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. 
  • 95
  • 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.
  • 179
  • 19 Mar 2024
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