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Topic Review
Classification of Low Illumination Image Enhancement Methods
As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. According to the learning method used, people can classify existing low-illumination enhancement methods into four categories, i.e., supervised learning, unsupervised learning, semi-supervised learning, and zero-shot learning methods.
  • 976
  • 06 May 2023
Topic Review
Single-Agent Reinforcement Learning and Multi-Agent Reinforcement Learning
Flexible job shop scheduling (FJSP) is regarded as an effective measure to deal with the challenge of mass personalized and customized manufacturing in the era of Industry 4.0, and is widely extended to many real applications. Single-Agent Reinforcement Learning (SARL) is the algorithm only contains one agent that makes all the decisions for a control system. Multi-Agent Reinforcement Learning (MARL) is the algorithm comprises multiple agents that interact with the environment through their respective policies.
  • 975
  • 08 Jan 2024
Topic Review
Machine Learning Methods for Stock Market Prediction
Stock market prediction models are developed with different goals. The primary focus of stock market prediction has been on forecasting the price of a share for a specific future period. The price of a share is a numerical value, and its variation over time is often treated as a time series in various studies. 
  • 975
  • 21 Jul 2023
Topic Review
Sleep Spindle
Sleep spindles are bursts of neural oscillatory activity that are generated by interplay of the thalamic reticular nucleus (TRN) and other thalamic nuclei during stage 2 NREM sleep in a frequency of ~10 –12 Hz for at least 0.5 seconds. After generation in the TRN, spindles are sustained and relayed to the cortex by a thalamo-thalamic and thalamo-cortical feedback loops regulated by both GABAergic and NMDA-receptor mediated glutamatergic neurotransmission. Sleep spindles have been found in all tested mammalian species and in vitro cells. Research supports that spindles (sometimes referred to as "sigma bands" or "sigma waves") play an essential role in both sensory processing and long term memory consolidation. Until recently, it was believed that each sleep spindle oscillation peaked at the same time throughout the neocortex. It was determined that oscillations sweep across the neocortex in circular patterns around the neocortex, peaking in one area, and then a few milliseconds later in an adjacent area. It has been suggested that this spindle organization allows for neurons to communicate across cortices. The time scale at which the waves travel at is the same speed it takes for neurons to communicate with each other.
  • 974
  • 12 Oct 2022
Topic Review
U-Net_dc
Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions, and improving the efficiency of treatment and the useful effect of drugs. However, traditional image segmentation models are not ideal solutions for cancer cell image segmentation due to the fact that cancer cells are highly dense and vary in shape and size. To tackle this problem, researchers propose a novel U-Net-based image segmentation model, named U-Net_dc, which expands twice the original U-Net encoder and decoder and, in addition, uses a skip connection operation between them, for better extraction of the image features.
  • 974
  • 14 Jul 2023
Topic Review
Convolution Neural Network  and Transformer-Based Human Pose Estimation
Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images.
  • 974
  • 03 Aug 2023
Topic Review
Neuromorphic Sentiment Analysis Using Spiking Neural Networks
Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. 
  • 974
  • 20 Sep 2023
Topic Review
Data Extraction Approach for Empirical Agent-Based Model Development
Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence.
  • 973
  • 29 Sep 2023
Topic Review
Computational Thinking
Computational Thinking (CT) has been widely regarded as an essential ability to solve problems by applying basic knowledge of computer science in technological societies. Initially, CT was defined as using the fundamental concepts of computer science to solve problems, design systems, and understand human behaviors.
  • 972
  • 28 Oct 2021
Topic Review
The Synergistic Relationship between AI and the Economy
Artificial intelligence (AI) is transforming various aspects of the economy, including manufacturing, healthcare, finance, and transportation. AI-powered systems are augmenting human decision-making, reducing operational costs, enhancing productivity, and creating new business models. However, the integration of AI into the economy also poses several challenges, such as job displacement, economic inequality, and ethical concerns. This research explores the complex relationship between AI and the economy, highlighting the opportunities and challenges that arise from their synergy.
  • 970
  • 18 May 2023
Topic Review
Reconstructing Superquadrics from Intensity and Color Images
The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. Researchers extend these reconstruction methods to intensity and color images. 
  • 969
  • 09 Aug 2022
Topic Review
Deep Learning Methods in Image Matting
Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical scenarios, such as in image and video editing.
  • 969
  • 14 Jun 2023
Topic Review
Gastrointestinal Tract Polyp Anomaly Segmentation
Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and in the mucous membrane, which has protrusions of micro-abnormal tissue that increase the risk of incurable diseases such as cancer. A deep learning method, Graft-U-Net, is proposed to segment polyps using colonoscopy frames. Graft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages.
  • 967
  • 21 Sep 2022
Topic Review
Reinforcement Learning
Reinforcement Learning (RL) is an approach in Machine Learning that aims to solve dynamic and complex problems, in which autonomous entities, called agents, are trained to take actions that will lead them to an optimal solution
  • 966
  • 25 Jul 2023
Topic Review
Conflict Prediction in Sub-Saharan Africa
This entry offers policymakers and researchers pragmatic and sustainable approaches to identify and mitigate conflict threats by looking beyond p-values and plausible instruments. We argue that predicting conflict successfully depends on the choice of algorithms, which, if chosen accurately, can reduce economic and social instabilities caused by post-conflict reconstruction. After collating data with variables linked to conflict, we used a grid level dataset of 5928 observations spanning 48 countries across sub-Saharan Africa to predict civil conflict. The goals of the study were to assess the performance of supervised classification machine learning (ML) algorithms in comparison with logistic model, assess the implication of selecting a specific performance metric on policy initiatives, and evaluate the value of interpretability of the selected model. After comparing class imbalance resampling methods, the synthetic minority over-sampling technique (SMOTE) was employed to improve out-of-sample prediction for the trained model. The results indicate that if our selected performance metric is recall, gradient tree boosting is the best algorithm; however, if precision or F1 score is the selected metric, then the multilayer perceptron algorithm produces the best model. 
  • 965
  • 09 Jul 2021
Topic Review
Diabetic Foot with Exercise Therapy
Diabetic foot (DF) is a long-term diabetes complication that can increase morbidity and mortality in addition to affecting mobility and the overall well-being of patients. In particular, the DF has a complex multifactorial pathogenesis that makes it difficult to prevent and treat. In this sense, it is well known that the prevention and treatment of DF disease requires a multidisciplinary approach. Physical activity has always been considered a potential pillar in the prevention of DFD. More recently, it has been reported, that physical activity can contribute in the wound healing phase. Unfortunately, to date, there is no clear and definitive evidence on the role that protocols of physical activity can play in the treatment of patients at risk or with DFD. In order to pursue this objective, it is important to standardize exercise training protocols for the prevention or treatment of these patients. Moreover, it is now possible to organize innovative methods of conducting, monitoring and analysing physical activity performed by patients, even remotely.
  • 965
  • 29 Mar 2022
Topic Review
Hesitant Fuzzy Graph Neural Network-Based Prototypical Network
Few-shot text classification aims to recognize new classes with only a few labeled text instances. Previous studies mainly utilized text semantic features to model the instance-level relation among partial samples. However, the single relation information makes it difficult for many models to address complicated natural language tasks. A novel hesitant fuzzy graph neural network (HFGNN) model that explores the multi-attribute relations between samples is proposed. HFGNN is combined with the Prototypical Network (HFGNN-Proto) to achieve few-shot text classification.
  • 965
  • 20 Dec 2022
Topic Review
Deep Learning Methods in Plant Taxonomy
Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features.
  • 965
  • 26 Jul 2023
Topic Review
Electroencephalogram-Based Emotion Classification
Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the rapidly developing science of machine learning.
  • 964
  • 09 Jan 2023
Topic Review
Path Planning for Agricultural Ground Robots
Ground robots have been developed for a variety of agricultural applications, with autonomous and safe navigation being one of the most difficult hurdles in this development. When a mobile platform moves autonomously, it must perform a variety of tasks, including localization, route planning, motion control, and mapping, which is a critical stage in autonomous operations. 
  • 964
  • 26 Sep 2023
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