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Topic Review
Machine Learning-Based Network Anomaly Detection
Artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. 
  • 1.4K
  • 19 Aug 2022
Topic Review
Urban Digitization Governance in Birth Registration Field
Digitization governance is one of the most significant current discussions in the urban governance field. Especially, with the spreading of Blockchain Technology (BCT), researchers have shown an increased interest in the application of the technology in solving birth registration challenges as a digital infrastructure in developing countries.
  • 500
  • 17 Aug 2022
Topic Review
Hill Climb Assembler Encoding
Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks. HCAE is a light variant of HCMAE and it originates from both AE and AEEO. All the algorithms are based on three key components, i.e., a network definition matrix (NDM), which represents the neural networks, assembler encoding program (AEP), which operates on NDM, and evolutionary algorithm, whose task is to produce optimal AEPs, NDMs, and, consequently, the networks.
  • 412
  • 15 Aug 2022
Topic Review
Multimodal Segmentation Techniques in Autonomous Driving
Semantic Segmentation has become one of the key steps toward scene understanding, especially in autonomous driving scenarios. In the standard formulation, Semantic Segmentation uses only data from color cameras, which suffer significantly in dim lighting or adverse weather conditions. A solution to this problem is the use of multiple heterogeneous sensors (e.g., depth and thermal cameras or LiDARs) as the input to machine learning approaches tackling this task, allowing to cover for the shortcomings of color cameras and to extract a more resilient representation of the scene.
  • 809
  • 15 Aug 2022
Topic Review
Artificial Intelligence-Based Decision Support Systems in Project Sustainability
Decision support systems (DSS) is a computer-based aid, which is designed to assist project managers in decision making when the tasks at hand are of a complex nature. Artificial intelligence (AI) is “the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”. The use of artificial intelligence (AI)-based decision support systems (DSS) has been gaining in popularity. AI technologies are becoming powerful tools throughout the world for improving project management; however, the advancement of construction management is still in its infancy and is adapting to the use of AI at a much slower pace than other sectors
  • 2.9K
  • 15 Aug 2022
Topic Review
Voting-Based Leader-Election Scheme in Lead-Follow UAV Swarm
The recent advances in unmanned aerial vehicles (UAVs) enormously improve their utility and expand their application scope. The UAV and swarm implementation further prevail in Smart City practices with the aid of edge computing and urban Internet of Things. The lead–follow formation in UAV swarm is an important organization means and has been adopted in diverse exercises, for its efficiency and ease of control. The reliability of centralization makes the entire swarm system in risk of collapse and instability, if a fatal fault incident happens in the leader. Researchers propose a voting-based leader election scheme inspired by the Raft method in distributed computation consensus to build a mechanism helping the distributed swarm recover from possible failures.
  • 569
  • 15 Aug 2022
Topic Review
Application Scenarios of Using Knowledge Graph
In dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. 
  • 723
  • 12 Aug 2022
Topic Review
Monocular Depth Estimation with Deep Learning
Significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures.  This is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem.
  • 614
  • 12 Aug 2022
Topic Review
Vision-Based Gait Recognition
Identifying people’s identity by using behavioral biometrics has attracted many researchers’ attention in the biometrics industry. Gait is a behavioral trait, whereby an individual is identified based on their walking style. Over the years, gait recognition has been performed by using handcrafted approaches. Due to several covariates’ effects, the competence of the approach has been compromised. Deep learning is an emerging algorithm in the biometrics field, which has the capability to tackle the covariates and produce highly accurate results. 
  • 472
  • 12 Aug 2022
Topic Review
AI-Based Wormhole Attack Detection Techniques
The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. 
  • 1.1K
  • 12 Aug 2022
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