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Topic Review
A Taxonomic Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant prior information.
  • 873
  • 20 Jun 2023
Topic Review
Dementia Severity Scale Based on MRI
In the clinical treatment of Alzheimer’s disease, one of the most important tasks is evaluating its severity for diagnosis and therapy. However, traditional testing methods are deficient, such as their susceptibility to subjective factors, incomplete evaluation, low accuracy, or insufficient granularity, resulting in unreliable evaluation scores. 
  • 873
  • 11 Aug 2023
Topic Review
CV and AI in Seaport Parking Space Allocation
Urban expansion has ushered in a landscape of opportunities and challenges across housing, transportation, education, health, and the economy. In response to these evolving dynamics, the application of artificial intelligence (AI) and computer vision (CV) technologies has emerged as a transformative solution. While smart traffic monitoring and advanced parking distribution systems have eased urban pressures, optimizing mobility remains pivotal in the context of burgeoning smart cities.
  • 873
  • 20 Sep 2023
Topic Review
Advancements in Glaucoma Diagnosis
The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population.
  • 873
  • 14 Mar 2024
Topic Review
Object Detection of Remote Sensing Image
Remote sensing image object detection tasks play a pivotal role in the realm of airborne and satellite remote sensing imagery, representing invaluable applications. Remote sensing technology has witnessed remarkable progress, enabling the capture of copious details that inherently reflect the contours, hues, textures, and other distinctive attributes of terrestrial targets. It has emerged as an indispensable avenue for acquiring comprehensive knowledge about the Earth’s surface. The primary objective of remote sensing image object detection is to precisely identify and locate objects of interest within the vast expanse of remote sensing images. This task finds extensive implementation across significant domains, including military reconnaissance, urban planning, environmental monitoring, soil science, and maritime vessel surveillance. With the incessant advancement of observational techniques, the availability of high-quality remote sensing image datasets, encompassing richer and more intricate information, has unlocked immense developmental potential for the ongoing pursuit of remote sensing image object detection.
  • 872
  • 23 Oct 2023
Topic Review
Input Enhancement (Computer Science)
In computer science, input enhancement is the principle that processing a given input to a problem and altering it in a specific way will increase runtime efficiency or space efficiency, or both. The altered input is usually stored and accessed to simplify the problem. By exploiting the structure and properties of the inputs, input enhancement creates various speed-ups in the efficiency of the algorithm.
  • 871
  • 02 Dec 2022
Topic Review
Inpainting Methods
Image inpainting is sometimes called an inverse problem, and usually these types of problems are ill-posed. The problem of inpainting consists in finding the best approximation to fill in the region inside the source image and comparing it with the ground truth. All the algorithms that tackle this problem begin with the assumption that there must be some correlation between the pixels present inside the image, either from a statistical or from a geometrical perspective.
  • 870
  • 20 Feb 2024
Topic Review
Development in Vision-Based On-Road Behaviors Understanding
On-road behavior analysis is a crucial and challenging problem in the autonomous driving vision-based area. Several endeavors have been proposed to deal with different related tasks and it has gained wide attention recently. Much of the excitement about on-road behavior understanding has been the labor of advancement witnessed in the fields of computer vision, machine, and deep learning. Remarkable achievements have been made in the Road Behavior Understanding area over the last years.
  • 867
  • 18 Apr 2022
Topic Review
Data Mining Techniques for Students’ Performance Predictive Analysis
The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required.
  • 865
  • 22 Dec 2023
Topic Review
Wrist-Based Electrodermal Activity Monitoring for Stress Detection
With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible.
  • 864
  • 22 Dec 2023
Topic Review
Ontology-Based Regression Testing
Regression testing is a relevant research field focused on ensuring software works correctly after being modified or when new functionalities are added. The widespread use of information technology techniques has enabled the rapid development of applications.
  • 862
  • 04 Nov 2021
Topic Review
Representation Learning for Electronic Health Records
An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections.
  • 862
  • 04 Aug 2023
Topic Review
Pedestrian Identification and Classification in Autonomous Vehicles
Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. This paper demonstrates that the use of image augmentation on training data can yield varying results. 
  • 861
  • 10 Jan 2022
Topic Review
Generative AI
Generative AI models harness the capabilities of neural networks to discern patterns and structures within existing datasets and create original content. These AI models draw inspiration from human neuronal processes, learning from data inputs to create new output that matches learned patterns.
  • 860
  • 22 Feb 2024
Topic Review
Intrusion Detection System
The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, an IDS using a recurrent neural network based on gated recurrent units (GRUs) was developed and long short-term memory (LSTM) through a computing unit to form Cu-LSTMGRU was improved. 
  • 859
  • 30 Sep 2022
Topic Review
A Machine Learning-Based Sustainable University Field Training Framework
The proposed sustainable University Field Training (SUNFIT) is an educational data mining framework based on the pedagogical strategies of preparing, conducting, and assessing computing students’ skills in courses involving practical industry engagement.
  • 859
  • 29 May 2023
Topic Review
Artificial Intelligence and Radiomics Techniques
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. 
  • 858
  • 30 Dec 2022
Topic Review
Existing Approaches for Single-Image Super-Resolution
Deep learning has been introduced to single-image super-resolution (SISR). These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment.
  • 858
  • 04 Jul 2023
Topic Review
Strawberry Ripeness Classification
Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment.
  • 858
  • 07 Feb 2024
Topic Review
Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
Inductive relationship prediction for knowledge graphs, as an important research topic, aims to predict missing relationships between unknown entities and many practical applications. Most of the existing approaches to this problem use closed subgraphs to extract features of target nodes for prediction; however, there is a tendency to ignore neighboring relationships outside the closed subgraphs, which leads to inaccurate predictions. In addition, they ignore the rich commonsense information that can help filter out less compelling results.
  • 858
  • 07 Mar 2024
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