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Topic Review
Computer Vision in Self-Steering Tractors
Agricultural machinery, such as tractors, is meant to operate for many hours in large areas and perform repetitive tasks. The automatic navigation of agricultural vehicles can ensure the high intensity of automation of cultivation tasks, the enhanced precision of navigation between crop structures, an increase in operation safety and a decrease in human labor and operation costs.
  • 1.5K
  • 24 Feb 2022
Topic Review
Dementia
Dementia is a syndrome that is characterised by the decline of different cognitive abilities. A high rate of deaths and high cost for detection, treatments, and patients care count amongst its consequences. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support, appropriate medication, and maintenance, as far as possible, of engagement in intellectual, social, and physical activities. The early detection of Alzheimer Disease (AD) is considered to be of high importance for improving the quality of life of patients and their families. In particular, Virtual Reality (VR) is an expanding tool that can be used in order to assess cognitive abilities while navigating through a Virtual Environment (VE).
  • 1.5K
  • 05 Feb 2021
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.
  • 1.5K
  • 03 Jul 2023
Topic Review
Interval Scheduling
Interval scheduling is a class of problems in computer science, particularly in the area of algorithm design. The problems consider a set of tasks. Each task is represented by an interval describing the time in which it needs to be executed. For instance, task A might run from 2:00 to 5:00, task B might run from 4:00 to 10:00 and task C might run from 9:00 to 11:00. A subset of intervals is compatible if no two intervals overlap. For example, the subset {A,C} is compatible, as is the subset {B}; but neither {A,B} nor {B,C} are compatible subsets, because the corresponding intervals within each subset overlap. The interval scheduling maximization problem (ISMP) is to find a largest compatible set - a set of non-overlapping intervals of maximum size. The goal here is to execute as many tasks as possible. In an upgraded version of the problem, the intervals are partitioned into groups. A subset of intervals is compatible if no two intervals overlap, and moreover, no two intervals belong to the same group (i.e. the subset contains at most a single representative interval of each group). The group interval scheduling decision problem (GISDP) is to decide whether there exists a compatible set in which all groups are represented. The goal here is to execute a single representative task from each group. GISDPk is a restricted version of GISDP in which the number of intervals in each group is at most k. The group interval scheduling maximization problem (GISMP) is to find a largest compatible set - a set of non-overlapping representatives of maximum size. The goal here is to execute a representative task from as many groups as possible. GISMPk is a restricted version of GISMP in which the number of intervals in each group is at most k. This problem is often called JISPk, where J stands for Job. GISMP is the most general problem; the other two problems can be seen as special cases of it:
  • 1.5K
  • 07 Nov 2022
Topic Review
Resource Allocation Schemes for 5G Network
Resource allocation is an important aspect of any cellular network environment. It plays a significant part in maintaining friendly access for end-users, business partners, and customers of cellular-based applications. Resource allocation has great benefits for the cellular network environment.
  • 1.5K
  • 21 Oct 2021
Topic Review
Concept of the Robo-Advisor with Digital Twin
The term “digital twin” (DT) refers to a digital representation of an individual that has the capability of integrating any digital data with virtually real-time data and generating advanced analytics for feedback, recommendation, and alternative solutions for users. 
  • 1.4K
  • 11 May 2022
Topic Review
Traditional Computer-Vision Methods Implemented in Sports
Automatic analysis of video in sports is a possible solution to the demands of fans and professionals for various kinds of information. Analyzing videos in sports has provided a wide range of applications, which include player positions, extraction of the ball’s trajectory, content extraction, and indexing, summarization, detection of highlights, on-demand 3D reconstruction, animations, generation of virtual view, editorial content creation, virtual content insertion, visualization and enhancement of content, gameplay analysis and evaluations, identifying player’s actions, referee decisions and other fundamental elements required for the analysis of a game. Recent developments in video analysis of sports have a focus on the features of computer vision techniques, which are used to perform certain operations for which these are assigned, such as detailed complex analysis such as detection and classification of each player based on their team in every frame or by recognizing the jersey number to classify players based on their team will help to classify various events where the player is involved. In higher-level analysis, such as tracking the player or ball, many more such evaluations are to be considered for the evaluation of a player’s skills, detecting the team’s strategies, events and the formation of tactical positions such as midfield analysis in various sports such as soccer, basketball, and also various sports vision applications such as smart assistants, virtual umpires, assistance coaches. A higher-level semantic interpretation is an effective substitute, especially in situations when reduced human intervention and real-time analysis are desired for the exploitation of the delivered system outputs.
  • 1.4K
  • 19 May 2022
Topic Review
Re-Identification in Urban Scenarios
Multi-object Re-Identification (ReID), based on a wide range of surveillance cameras, is nowadays a vital aspect in modern cities, to better understand city movement patterns among the different infrastructures, with the primary intention of rapidly mitigate abnormal situations, such as tracking car thieves, wanted persons, or even lost children. Given an image or video of an object-of-interest (query), object identification aims to identify the object from images or video feed taken from different cameras. 
  • 1.4K
  • 24 Feb 2022
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
Applying Machine Learning in Retail Demand Prediction
In the realm of retail supply chain management, accurate forecasting is paramount for informed decision making, as it directly impacts business operations and profitability. 
  • 1.4K
  • 19 Oct 2023
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.
  • 1.4K
  • 19 Apr 2022
Topic Review
Technological Sustainability and Artificial Intelligence Algor-ethics
Since 2018, a new terminology has been developed, called Algor-ethics, indicating the necessity for a dedicated study concerning the evaluation of an ethics applied to technology, to Algorithms and to Artificial Intelligence (AI). At the same time, since 1987, when the concept of sustainability was introduced, the discussion on this issue has become increasingly lively and has now spread to every area of life. An application of the concept of sustainability to technological processes and in particular to the elaboration of AI systems, for the construction of an ethics that could confer meaning, motivations and impetus in using new technologies and Artificial Intelligence is proposed here. Researchers first try to build an ethical framework, here called Dynamical Techno-Algor-Ethical Composition, to define the interaction between the most important ethical ingredients involving the human person in relation to technology, taking a person-centered approach. Out of this it will emerge a possible structure and definition of Technological Sustainability. The second step consist of evaluating the process for the elaboration of an AI algorithm as a concrete application of the previously analyzed framework, to set ethical contents composing what researchers might call a good and sustainable algorithm.
  • 1.4K
  • 06 Apr 2022
Topic Review
Lightweight IoT Intrusion Detection Systems
Cyber security has become increasingly challenging due to the proliferation of the Internet of Things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet and is expected to reach 73.1 ZB (zettabytes) by 2025. IoT devices have limited computational capabilities and thus researchers have shifted their focus onto designing lightweight intrusion-detection system (IDS) that can deliver the needed security requirements while operating on those thin devices.
  • 1.4K
  • 29 Aug 2023
Topic Review
LiFi Network
Light Fidelity (LiFi), a new technology that uses light to transmit data as a high-speed wireless connection system from a wide spectrum of domains.
  • 1.4K
  • 01 Dec 2021
Topic Review
Applications of Deep Reinforcement Learning in Other Industries
Reinforcement Learning (RL) is an approach to simulate the human’s natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. 
  • 1.4K
  • 29 Nov 2021
Topic Review
Machine Learning Algorithms in Manufacturing
The quality-control process in manufacturing must ensure the product is free of defects and performs according to the customer’s expectations. Maintaining the quality of a firm’s products at the highest level is very important for keeping an edge over the competition. To maintain and enhance the quality of their products, manufacturers invest a lot of resources in quality control and quality assurance. During the assembly line, parts will arrive at a constant interval for assembly. The quality criteria must first be met before the parts are sent to the assembly line where the parts and subparts are assembled to get the final product. Once the product has been assembled, it is again inspected and tested before it is delivered to the customer. Because manufacturers are mostly focused on visual quality inspection, there can be bottlenecks before and after assembly. The manufacturer may suffer a loss if the assembly line is slowed down by this bottleneck. To improve quality, state-of-the-art sensors are being used to replace visual inspections and machine learning is used to help determine which part will fail.
  • 1.4K
  • 19 Oct 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.
  • 1.4K
  • 28 Sep 2021
Topic Review Peer Reviewed
Tokenization in the Theory of Knowledge
Tokenization is a procedure for recovering the elements of interest in a sequence of data. This term is commonly used to describe an initial step in the processing of programming languages, and also for the preparation of input data in the case of artificial neural networks; however, it is a generalizable concept that applies to reducing a complex form to its basic elements, whether in the context of computer science or in natural processes. In this entry, the general concept of a token and its attributes are defined, along with its role in different contexts, such as deep learning methods. Included here are suggestions for further theoretical and empirical analysis of tokenization, particularly regarding its use in deep learning, as it is a rate-limiting step and a possible bottleneck when the results do not meet expectations.
  • 1.4K
  • 11 Apr 2023
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. 
  • 1.4K
  • 08 Aug 2022
Topic Review
Unmanned Aerial Vehicles and Federated Learning
Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy.
  • 1.4K
  • 22 Jul 2022
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