Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort:
Hottest Latest Alphabetical (A-Z) Alphabetical (Z-A)
Type:
All Topic Review Biography
Topic Review
Sensor Network Environments
New technologies have driven the rise of what is being termed as the fourth industrial revolution. The introduction of this new revolution is amalgamating the cyber and physical worlds, bringing with it many benefits, such as the advent of industry 4.0, the internet of things, cloud technologies and smart homes and cities. These new and exciting areas are poised to have significant advantages for society; they can increase the efficiency of many systems and increase the quality of life of people. However, these emerging technologies can potentially have downsides, if used incorrectly or maliciously by bad entities. The rise of the widespread use of sensor networks to allow the mentioned systems to function has brought with it many security vulnerabilities that conventional “hard security” measures cannot mitigate. It is for this reason that a new “soft security” approach is being taken in conjunction with the conventional security means. Trust models offer an efficient way of mitigating the threats posed by malicious entities in networks that conventional security methods may not be able to combat. 
  • 380
  • 19 Aug 2022
Topic Review
Hyperledger
Internet of things (IoT) systems based on blockchain have significant shortcomings in terms of scalability, flexibility, robustness, and privacy. To address these issues, Hyperledger is considered as an ideal technology and attracted a lot of attention. In addition to having the general characteristics of blockchain, Hyperledger achieves new empowerment in four aspects: security, interoperability, consensus, and performance.
  • 453
  • 19 Aug 2022
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. 
  • 1.5K
  • 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.
  • 535
  • 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.
  • 435
  • 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.
  • 838
  • 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
  • 3.0K
  • 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.
  • 595
  • 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. 
  • 760
  • 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.
  • 654
  • 12 Aug 2022
  • Page
  • of
  • 371
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
ScholarVision Creations
Feedback