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Topic Review
Deep Reinforcement Learning and Games
Deep learning (DL) algorithms were established in 2006 and have been extensively utilized by many researchers and industries in subsequent years. Ever since the impressive breakthrough on the ImageNet classification challenge in 2012, the successes of supervised deep learning have continued to pile up. Many researchers have started utilizing this new and capable family of algorithms to solve a wide range of new tasks, including ways to learn intelligent behaviors in reward–driven complex dynamic problems successfully. The agent––environment interaction expressed through observation, action, and reward channels is the necessary and capable condition of characterizing a problem as an object of reinforcement learning (RL). Learning environments can be characterized as Markov decision problems, as they satisfy the Markov property, allowing RL algorithms to be applied. From this family of environments, games could not be absent. In a game–based environment, inputs (the game world), actions (game controls), and the evaluation criteria (game score) are usually known and simulated. With the rise of DL and extended computational capability, classic RL algorithms from the 1990s could now solve exponentially more complex tasks such as games over time, traversing through huge decision spaces.
  • 732
  • 22 Feb 2023
Topic Review
Securing ATM Payment Transactions
Credit/debit cards are a ubiquitous form of payment at present. They offer a number of advantages over cash, including convenience, security, and fraud protection. In contrast, the inherent vulnerabilities of credit/debit cards and transaction methods have led many payment institutions to focus on strengthening the security of these electronic payment methods. Also, the increasing number of electronic payment transactions around the world have led to a corresponding increase in the amount of money lost due to fraud and cybercrime. This loss of money has a significant impact on businesses and consumers, and it necessitates the development of rigid and robust security designs for securing underlying electronic transaction methods.
  • 732
  • 14 Nov 2023
Topic Review
Multitask-based Shared Feature Learning
Speech emotion recognition (SER), a rapidly evolving task that aims to recognize the emotion of speakers, has become a key research area in affective computing. Various languages in multilingual natural scenarios extremely challenge the generalization ability of SER, causing the model performance to decrease quickly, and driving researchers to ask how to improve the performance of multilingual SER. To solve this problem, an explainable Multitask-based Shared Feature Learning (MSFL) model is proposed for multilingual SER. The introduction of multi-task learning (MTL) can provide related task information of language recognition for MSFL, improve its generalization in multilingual situations, and further lay the foundation for learning MSFs.
  • 731
  • 11 Jan 2023
Topic Review
Artificial Intelligence Techniques in Concrete
Due to the speed of artificial intelligence (AI) techniques in solving engineering problems, there has been a tendency to use these techniques in various fields of civil engineering, including designing construction materials (concrete mixtures for example) or estimating their properties.  As it is hard to predict the compressive strength of concrete due to the different nonlinearities inherent in the mixture designs, various concrete companies are continuously looking to use new methods and technologies to predict the compressive strength. Such methods include numerical modelling and artificial intelligence due to their advantages. 
  • 731
  • 07 Oct 2023
Topic Review
Development of AI in Surgery after SARS-CoV-2 Pandemic
SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. 
  • 730
  • 04 Nov 2021
Topic Review
Color Image Denoising Methods for Impulse Noise
One of the most critical tasks in computer vision applications is image denoising, which involves recovering an image from a degraded noisy version. Impulse noise in digital images is a random variation in the intensity of pixels caused by short-duration pulses of high energy. This type of noise can significantly degrade the quality of images and poses various challenges in real-world applications. 
  • 730
  • 09 Jan 2024
Topic Review
Graph Clustering Algorithms
Graph clustering has received considerable attention, and its applications are numerous, ranging from the detection of social communities to the clustering of computer networks. It is classified as an NP-class problem, and several algorithms have been proposed with specific objectives. There also exist various quality metrics for evaluating them. Having clusters with the required density can be beneficial because it permits the effective deployment of resources.
  • 730
  • 26 Jan 2024
Topic Review
Self-Supervised Representation Learning for Geographical Data
Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a useful tool in the field of geographical information science (GIS). 
  • 726
  • 16 Jun 2023
Topic Review
Fire and Smoke Detection
Wildfires are major natural disasters that can cause extensive damage to ecosystems and threaten human lives. It is an uncontrollable and destructive fire that rapidly spreads through vegetation, grasslands, or other flammable areas. Wildfires are typically triggered by a combination of factors, including the presence of abundant dry vegetation and favorable weather conditions like high temperatures, low humidity, and strong winds. The sources of ignition for wildfires are diverse and can range from natural causes like lightning strikes to human activities such as campfires, careless disposal of cigarettes, or even intentional acts of arson. Besides the destructive nature of wildfires, the smoke from wildfires can have severe human health risks and environmental consequences as it can contribute to air quality degradation, disrupt the balance of ecosystems, and even impact the behavior and survival of wildlife. Therefore, early fire and smoke detection are crucial.
  • 726
  • 12 Oct 2023
Topic Review
Intelligent Source Code Completion Assistants
As artificial intelligence advances, source code completion assistants are becoming more advanced and powerful. Existing traditional assistants are no longer up to all the developers’ challenges. Traditional assistants usually present proposals in alphabetically sorted lists, which does not make a developer’s tasks any easier (i.e., they still have to search and filter an appropriate proposal manually). As a possible solution to the presented issue, intelligent assistants that can classify suggestions according to relevance in particular contexts have emerged. Artificial intelligence methods have proven to be successful in solving such problems. Advanced intelligent assistants not only take into account the context of a particular source code but also, more importantly, examine other available projects in detail to extract possible patterns related to particular source code intentions. This is how intelligent assistants try to provide developers with relevant suggestions. 
  • 726
  • 17 Jan 2024
Topic Review
Weakly Supervised and Unsupervised Methods in Plant Segmentation
Plant segmentation is a challenging computer vision task due to plant images complexity. We need to distinguish plant parts rather than the whole plant. The major complication of multi-part segmentation is the absence of well-annotated datasets. It is very time-consuming and expensive to annotate datasets manually on the object parts level.
  • 724
  • 04 Aug 2023
Topic Review
High-Fidelity Synthetic Face Generation for Rosacea Skin Condition
Similarly to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult, due to privacy concerns. As a result, conditions like rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods of computer-aided diagnosis. Generative adversarial networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images.
  • 724
  • 14 Feb 2024
Topic Review
Low-Dimensional Layered Light-Sensitive Memristive Structures for Machine Vision
Layered two-dimensional (2D) and quasi-zero-dimensional (0D) materials effectively absorb radiation in the wide ultraviolet, visible, infrared, and terahertz ranges. Photomemristive structures made of such low-dimensional materials are of great interest for creating optoelectronic platforms for energy-efficient storage and processing of data and optical signals in real time.
  • 722
  • 15 Mar 2022
Topic Review
Zero-Shot Semantic Segmentation with No Supervision Leakage
Zero-shot semantic segmentation (ZS3), the process of classifying unseen classes without explicit training samples, poses a significant challenge. Despite notable progress made by pre-trained vision-language models, they have a problem of “supervision leakage” in the unseen classes due to their large-scale pre-trained data.
  • 722
  • 29 Aug 2023
Topic Review
Convolutional Neural Network-Based Layer-Adaptive Ground Control Points Extraction
Ground Control Points (GCPs) are of great significance for applications involving the registration and fusion of heterologous remote sensing images (RSIs). However, utilizing low-level information rather than deep features, traditional methods based on intensity and local image features turn out to be unsuitable for heterologous RSIs because of the large nonlinear radiation difference (NRD), inconsistent resolutions, and geometric distortions. Additionally, the limitations of current heterologous datasets and existing deep-learning-based methods make it difficult to obtain enough precision GCPs from different kinds of heterologous RSIs, especially for thermal infrared (TIR) images that present low spatial resolution and poor contrast.
  • 721
  • 02 Jun 2023
Biography
Obinna Johnphill
Obinna Johnphill is a remarkable individual, a married man, and a devoted father of two. His journey in computer science has been one of dedication and continuous pursuit of knowledge. He laid the foundation for his academic career by earning a Bachelor of Science (Hons) in Software Development in 2014 from the University of Wolverhampton. Building on his passion for computer science, he further
  • 721
  • 26 Jul 2023
Topic Review
Matrix Factorization for Enhancing Quality of Recommendations
Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets.
  • 721
  • 14 Nov 2023
Topic Review
Transformer-Based Visual Object Tracking
With the rise of general models, transformers have been adopted in visual object tracking algorithms as feature fusion networks. In these trackers, self-attention is used for global feature enhancement. Cross-attention is applied to fuse the features of the template and the search regions to capture the global information of the object. However, studies have found that the feature information fused by cross-attention does not pay enough attention to the object region. In order to enhance cross-attention for the object region, an enhanced cross-attention (ECA) module is proposed for global feature enhancement.
  • 719
  • 08 Dec 2023
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.
  • 718
  • 20 Mar 2024
Topic Review
Anomaly Detection Algorithms for LAN Failure Prediction
Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. The utilization of machine learning algorithms, specifically decision trees and support vector machines (SVMs), for predicting LAN failures represents a groundbreaking approach in network management.
  • 717
  • 13 Oct 2023
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