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Topic Review
Denoising Technique for CT Images
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. 
  • 966
  • 07 Apr 2024
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.
  • 965
  • 18 May 2023
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.
  • 964
  • 14 Jul 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.
  • 964
  • 29 Sep 2023
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.
  • 962
  • 29 Mar 2022
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.
  • 962
  • 09 Jan 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.
  • 960
  • 28 Oct 2021
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.
  • 960
  • 21 Sep 2022
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. 
  • 959
  • 09 Aug 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.
  • 959
  • 20 Dec 2022
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.
  • 958
  • 08 Jan 2024
Topic Review
ChatGPT in Teaching Practice
The emergence of new tools, especially those based on AI, raises concerns that technology may replace the teacher in the classroom. ChatGPT can support and automate the activities of educators, but their role as mentors who provide guidance and more profound assessment of learner abilities and role models cannot be entirely replaced by technology. ChatGPT, a generative artificial intelligence (GAI) representative, can create quizzes and assignments that are automatically checked and graded, generate feedback, and provide personalized learning content depending on the learners’ results.
  • 957
  • 06 Nov 2023
Topic Review
Deep Learning Methods for Retinal Disease Diagnosis
The advancement of digital medical imaging has brought about a significant change in ophthalmology as it has introduced effective technologies that help in the detection of such diseases. By improving early detection through image analysis and identifying minuscule anomalies, Artificial Intelligence (AI) has considerably coped with retinal diseases. Different Machine Learning (ML) and Convolutional Neural Networks (CNNs) are efficient at analyzing images and are particularly incredible at recognizing complex patterns in medical images.
  • 956
  • 21 Oct 2023
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
  • 955
  • 25 Jul 2023
Topic Review
Cardiac Failure Forecasting
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation.
  • 955
  • 15 Aug 2023
Topic Review
Machine-Learning Forensics
A world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time.
  • 955
  • 21 Sep 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. 
  • 954
  • 09 Jul 2021
Topic Review
Data-Driven Methods in Power Grids
Applications of data-driven methods in power grids are motivated by the need to predict and mitigate intermittency in a (future) grid that is expected to lean heavily on renewables.
  • 954
  • 22 Jun 2022
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.
  • 954
  • 03 Aug 2023
Topic Review
Measure of Presortedness
In computer science, a measure of presortedness of a sequence represents how much work is required to sort the sequence. If the sequence is pre-sorted, sorting the sequence entirely require little work, hence it is expected to have a small measure of presortedness. In particular, the measure of a sorted sequence is 0. Some sorting algorithms are more efficient on pre-sorted list, as they can use this pre-work into account to avoid duplicate work. The measure of presortedness allows to formalize the notion that an algorithm is optimal for a certain measure.
  • 953
  • 07 Nov 2022
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