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Topic Review
Multi-UAV Path Planning Algorithms
While several developments have taken place in the field of autonomus guidance and navigation techniques for Unmanned Aerial Vehicle (UAV) systems, obtaining an optimal path planning algorithm remains elusive. With multiple UAVs involvement in missions, these difficulties increase significantly leading to the need for collision free navigation routes from the UAVs' initial positions to target points. Consequently, this entry focuses specifically on Multi-UAV Path Planning Algorithms utilizing Bio -Inspired Algorithms. The findings indicated that bio-inspired algorithms possess substantial potential in addressing multipoint path planning issues and delineate new prospects and implications for the enhancement of this active research domain.
  • 1.4K
  • 03 Jul 2023
Topic Review
Surface Defect Detection and Identification Methods on Leather
Genuine leather manufacturing is a multibillion-dollar industry that processes animal hides from varying types of animals such as sheep, alligator, goat, ostrich, crocodile, and cow. Due to the industry’s immense scale, there may be numerous unavoidable causes of damages, leading to surface defects that occur during both the manufacturing process and the bovine’s own lifespan. Owing to the heterogenous and manifold nature of leather surface characteristics, great difficulties can arise during the visual inspection of raw materials by human inspectors. To mitigate the industry’s challenges in the quality control process, there is a growing interest in leveraging artificial intelligence (AI) and computer vision techniques for automated and accurate leather surface defect detection.
  • 1.4K
  • 21 Aug 2023
Topic Review
An Optimal House Price Prediction Algorithm: XGBoost
An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. 
  • 1.4K
  • 18 Jan 2024
Topic Review
Learning for Unmanned Ground Vehicles
The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. We present a review on the recent contributions in the roboticsliterature adopting learning-based methods to solve the problem of environment perception andinterpretation with the final aim of the autonomous context-aware navigation of ground vehicles inunstructured environments.
  • 1.4K
  • 29 Apr 2021
Topic Review
Edge Artificial Intelligence
Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. 
  • 1.3K
  • 09 Feb 2023
Topic Review
Artificial Intelligence in Adaptive and Intelligent Educational System
There has been much discussion among academics on how pupils may be taught online while yet maintaining a high degree of learning efficiency, in part because of the worldwide COVID-19 pandemic in the previous two years. Students may have trouble focusing due to a lack of teacher–student interaction, yet online learning has some advantages that are unavailable in traditional classrooms. The architecture of online courses for students is integrated into a system called the Adaptive and Intelligent Education System (AIES). In AIESs, reinforcement learning is often used in conjunction with the development of teaching strategies, and this reinforcement-learning-based system is known as RLATES.
  • 1.3K
  • 21 Sep 2022
Topic Review
AI Enabling Technologies in Physical Layer Security
With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods.
  • 1.3K
  • 23 May 2022
Topic Review
Machine Learning and Fuzzy Logic in Electronics
Machine learning is a part of artificial intelligence science and works in close collaboration with data science. The main aim is the collected big data to be processed and studied in such a way to give meaningful knowledge when problems have to be solved or decisions have to be made. Fuzzy logic is another scientific field that is used for modeling, description and evaluation of objects and systems with different levels of complexity, which are characterized with uncertainty, fuzziness and vagueness of their parameters and properties. The application of machine learning and fuzzy logic in electronics is studied to outline the current research topics, scientific achievements and directions for future exploration.
  • 1.3K
  • 07 Dec 2021
Topic Review
Developing IoT Artifacts in a MAS Platform
The Internet of Things (IoT) is a computational paradigm where a massive number (perhaps billions) of ordinary objects are endowed with interconnection capabilities, making them able to communicate and cooperate with other (surrounding) devices, generally via the Internet.. The Internet of Things (IoT) is a growing computational paradigm where all kinds of everyday objects are interconnected, forming a vast cyberphysical environment at the edge between the virtual and the real world. Since the emergence of the IoT, Multi-Agent Systems (MAS) technology has been successfully applied in this area, proving itself to be an appropriate paradigm for developing distributed, intelligent systems containing sets of IoT devices. However, this technology still lacks effective mechanisms to integrate the enormous diversity of existing IoT devices systematically.
  • 1.3K
  • 15 Mar 2022
Topic Review
Convolutional Neural Networks for Image Classification
Convolutional neural networks learn directly from data and are widely used for image recognition and classification. Convolutional Neural Networks (CNNs) have been considered one of the best machine learning algorithms to analyze grid-like structured data, such as images. 
  • 1.3K
  • 07 Jul 2023
Topic Review
Internet of Things Applications in China’s Hospitality Industry
During the current post-epidemic period, hygiene requirements and health needs in the hospitality industry keep increasing, and consumers become more concerned about the cleanliness of hotels and have stronger demands for contactless services in hotels. The growth and popularity of IoT technology in China make it more accessible to a wider range of service industries and provides the basis for the application of IoT in the hospitality industry. The application of IoT devices in hotels mainly includes intelligent robots, intelligent guest control, systems, etc., which helps to realise contactless services in hotels. 
  • 1.3K
  • 12 Jul 2022
Topic Review
Low Rate DDoS Detection Techniques in Software-Defined Networks
Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. 
  • 1.3K
  • 08 Aug 2022
Topic Review
Bi-Directional Text
Bi-directional text is text containing text in both text directionalities, both right-to-left (RTL or dextrosinistral) and left-to-right (LTR or sinistrodextral). It generally involves text containing different types of alphabets, but may also refer to boustrophedon, which is changing text directionality in each row. Some writing systems of the world, including the Arabic and Hebrew scripts or derived systems such as the Persian, Urdu, and Yiddish scripts, are written in a form known as right-to-left (RTL), in which writing begins at the right-hand side of a page and concludes at the left-hand side. This is different from the left-to-right (LTR) direction used by the dominant Latin script. When LTR text is mixed with RTL in the same paragraph, each type of text is written in its own direction, which is known as bi-directional text. This can get rather complex when multiple levels of quotation are used. Many computer programs fail to display bi-directional text correctly. For example, the Hebrew name Sarah (שרה) is spelled: sin (ש) (which appears rightmost), then resh (ר), and finally heh (ה) (which should appear leftmost). Note: Some web browsers may display the Hebrew text in this article in the opposite direction.
  • 1.3K
  • 11 Nov 2022
Topic Review
Financial Technology Influence on the Banking Industry
The synthesis of technology and finance is known as financial technology (Fintech), which brings together two of the biggest industries in harmony. Fintech disruption is a deviation from the norm, resulting in a significant shift in banking services and, as a result, risk. 
  • 1.3K
  • 17 Nov 2022
Topic Review
Radiomics/Deep Learning for Nasopharyngeal Carcinoma
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods.
  • 1.3K
  • 28 Sep 2021
Topic Review
Novel Pooling Methods for Convolutional Neural Networks
Neural network computational methods have evolved over the past half-century. In 1943, McCulloch and Pitts designed the first model, recognized as the linear threshold gate. Hebbian developed the Hebbian learning rule approach for training the neural network. However, would the Hebbian rule remain productive when all the input patterns became orthogonal? The existence of orthogonality in input vectors is a crucial component for this rule to execute effectively. To meet this requirement, a much more productive learning rule, known as the Delta rule, was established. Whereas the delta rule poses issues with the learning principles outlined above, backpropagation has developed as a more complicated learning approach. Backpropagation could learn an infinite layered structure and estimate any commutative function. A feed-forward neural network is most often trained using backpropagation (FFNN).
  • 1.3K
  • 08 Sep 2022
Topic Review
Rheumatoid Arthritis Diagnosis
Rheumatoid arthritis (RA) is a systemic autoimmune disease that preferably affects small joints. As the well-timed diagnosis of the disease is essential for the treatment of the patient, several works have been conducted in the field of deep learning to develop fast and accurate automatic methods for RA diagnosis.
  • 1.3K
  • 29 Dec 2021
Topic Review
Robotic Platform for Horticulture
The modern level of development of infocommunication and computer technologies, microprocessor technology and equipment, communication and positioning makes possible the development and practical application of automated and robotic technologies and technical means to improve the efficiency of agricultural production. Currently, intensive horticulture is becoming increasingly widespread due to rapid fruiting and high yield rates. At the same time, the process of harvesting apples in intensive horticulture is the most time-consuming, and harvesting is carried out mainly by a team of pickers. In the production process of cultivating fruit crops, this is an important final stage which requires the development of automated devices and robotic platforms with a control system capable of offline harvesting.
  • 1.3K
  • 02 Dec 2022
Topic Review
Embedded Machine Learning
Embedded machine learning (EML) can be applied in the areas of accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. 
  • 1.3K
  • 01 Nov 2021
Topic Review
Decentralized Multi-Robot Collision Avoidance
When deploying a multi-robot system, it is ensured that the hardware parts do not collide with each other or the surroundings, especially in symmetric environments. Two types of methods are used for collision avoidance: centralized and decentralized. The decentralized approach has mainly been used in recent times, as it is computationally less expensive.
  • 1.3K
  • 19 Apr 2022
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