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Topic Review
Video Summarization
During the last few years, several technological advances have led to an increase in the creation and consumption of audiovisual multimedia content. Users are overexposed to videos via several social media or video sharing websites and mobile phone applications. For efficient browsing, searching, and navigation across several multimedia collections and repositories, e.g., for finding videos that are relevant to a particular topic or interest, this ever-increasing content should be efficiently described by informative yet concise content representations. A common solution to this problem is the construction of a brief summary of a video, which could be presented to the user, instead of the full video, so that she/he could then decide whether to watch or ignore the whole video. Such summaries are ideally more expressive than other alternatives, such as brief textual descriptions or keywords. 
  • 906
  • 16 Oct 2023
Topic Review
Arabic Text Clustering
Arabic text clustering is an essential topic in Arabic Natural Language Processing (ANLP). Its significance resides in various applications, such as document indexing, categorization, user review analysis, and others. 
  • 905
  • 20 Nov 2023
Topic Review
Assistive Technology for Dementia People
There has been a significant increase in the number of people diagnosed with dementia (PLWD). With diminishing public health and social care resources, there is substantial need for assistive technology-based devices that support independent living. The term assistive technology (AT) is used to describe electronic devices that can be used to support PLWD’s lifestyles. These devices can improve the living standards of PLWD, encourage independence, and may decrease hospital admission rates. Furthermore, assistive technology can reduce the stress of caring for PLWD. AT devices that support remote assistance of PLWD can play a vital role in mitigating loneliness and stress caused by pandemics, reducing the need for home visits and hospitalization, thus reducing the costs associated with caregiver services. Reducing the risks of virus transmission within care homes are also a major consideration.
  • 903
  • 30 Sep 2021
Topic Review
Equilibrium Optimizer Algorithm
The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems.
  • 901
  • 04 Sep 2023
Topic Review
A Lightweight Object Detection Network with Attention Modules
Object detection methods based on deep learning typically require devices with ample computing capabilities, which limits their deployment in restricted environments such as those with embedded devices.
  • 901
  • 22 Nov 2023
Topic Review
Artificial Pancreas Control Strategies for Type 1 Diabetes
This entry presents a comprehensive survey about the fundamental components of the artificial pancreas (AP) system including insulin administration and delivery, glucose measurement (GM), and control strategies/algorithms used for type 1 diabetes mellitus (T1DM) treatment and control. 
  • 898
  • 17 Dec 2021
Topic Review
Piano Performance in Unimodality and Multimodality
With the rise in piano teaching in recent years, many people have joined the ranks of piano learners. However, the high cost of traditional manual instruction and the exclusive one-on-one teaching model have made learning the piano an extravagant endeavor. Most existing approaches, based on the audio modality, aim to evaluate piano players' skills. These methods overlook the information contained in videos, resulting in a one-sided and simplistic evaluation of the piano player's skills.
  • 897
  • 09 Jul 2023
Topic Review
Transfer Learning Strategies
Discriminatively trained models perform well if labeled data are available in abundance, but they do not perform adequately for tasks with scarce datasets as this limits their learning abilities. To address this issue, Large language models (LLMs) were first pretrained on large unlabeled datasets using the self-supervised approach, where the learning was then transferred discriminatively on specific tasks. As a result, transfer learning helps to leverage the capabilities of pretrained models and is advantageous, especially in data-scare settings. For example, generative pretrained transformer (GPT) used the generative language model objective for pretraining, followed by discriminative finetuning. Compared to pretraining, the transfer learning process is inexpensive and converges faster than training the model from scratch. Additionally, pretraining uses an unlabeled dataset and follows a self-supervised approach, whereas transfer learning follows a supervised technique using a labeled dataset particular to the downstream task. The pretraining dataset comes from a generic domain, whereas, during transfer learning, data come from specific distributions (supervised datasets specific to the desired task).
  • 897
  • 08 Mar 2024
Topic Review
Designing for Hybrid Intelligence
A taxonomy and survey of crowd-machine interaction is proposed. Specifically, this summary aims to provide a glimpse into the unique characteristics of artificial intelligence (AI)-powered crowdsourcing by characterizing its uses, limitations, and prospects when seen from a socio-technical perspective grounded on hybrid machine-crowd interaction. To this end, a scoping review of the existing literature was performed  in order to frame the relevant aspects of this particular form of hybrid intelligence in light of the progress reported in prior research when considering human-algorithmic arrangements at a massive scale. From understanding the role of crowd-AI ethicality to the analysis of the spatio-temporal characteristics of crowd activity and the behavioral traces left by crowd workers as a way of improving performance outcomes and user experience (UX) design.
  • 895
  • 01 Mar 2023
Topic Review
Markov Modeling of Acute Respiratory Distress Syndrome
This project focuses on utilizing mathematical Markov chain modeling as a stochastic process to analyze the stages of Acute Respiratory Distress Syndrome (ARDS). ARDS, characterized by a spectrum of severity ranging from floors to death, presents a complex clinical challenge. By employing Markov chain modeling, we aim to provide a structured framework for understanding the dynamic progression of ARDS. Our approach involves constructing a Markov chain that represents the transition of patients through various stages of ARDS, including floors, mild, moderate, severe, and ultimately death. Each stage is associated with specific clinical characteristics and outcomes, forming the basis of our modeling framework. In addition to describing the natural progression of ARDS, our project involves reviewing current clinical guidelines for managing the condition. We propose to examine the impact of each guideline on patient outcomes and the transition through different ARDS stages. By systematically analyzing the effects of various interventions and treatment strategies, we aim to provide insights into optimizing patient care and improving outcomes in ARDS management. Ultimately, this project serves as a comprehensive exploration of ARDS progression, providing healthcare professionals with a valuable framework for thinking about the condition. By integrating mathematical modeling with clinical guidelines, we seek to enhance our understanding of ARDS and contribute to more effective treatment approaches tailored to individual patient needs.
  • 895
  • 06 May 2024
Topic Review
Detection/Classification of Knee Injuries from MR Images
Magnetic resonance imaging (MRI) is a technique for mapping the interior structure of the body as well as specific aspects of functioning. 
  • 894
  • 16 Dec 2021
Topic Review
Detection of Suspicious Behaviors from Movement Trajectory Data
Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods are mostly focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors.  Due to advances in positioning technology and the increasing number of cameras, smart mobile terminals, and WLAN networks, large amounts of fine-grained personal trajectory data are collected. Such a large number of trajectories provide people with an unprecedented opportunity to automatically discover helpful knowledge, such as identifying suspicious movements and unusual activities. Therefore, crimes can be prevented if people’s suspicious behaviors can be automatically detected by mining the semantic information that is hidden in the trajectory data.
  • 892
  • 23 Sep 2022
Topic Review
Methods for Imaging and Evaluation of Scoliosis
Scoliosis is defined as a three-dimensional spinal deformity consisting of a lateral curvature greater than 10 degrees with rotation of the vertebrae within the curve. It can be identified as congenital, neuromuscular or idiopathic. Idiopathic scoliosis (IS) can be further classified by age of onset: infantile (birth to two years), juvenile (three to nine years), and adolescent (10 years and older). It is the most common pediatric musculoskeletal disorder that causes a three-dimensional (3D) spinal deformity. The deformity is always 3D because it also involves an axial rotation of the vertebrae, not just displacement and rotation in the frontal plane. Adolescent IS is the most common form because the spinal deformity evolves during periods of significant physical growth. IS is diagnosed when other etiological factors cannot be identified, such as congenital neurological or musculoskeletal anomalies, or inflammatory or demyelinating processes leading to primary or secondary motor neuron damage (myotonia, myopathy, etc.).
  • 890
  • 15 Feb 2022
Topic Review
AI-Based Unmanned Aerial Vehicles Networks
To enhance the overall performance of the unmanned aerial vehicles (UAVs) networks and to address some specific problems, new features in the network are being designed as autonomous features. This approach not only provides optimum solutions for the targeted problems but also supports the dynamic properties of a UAV network. 
  • 890
  • 07 Jun 2023
Topic Review
AMC Using Residual Learning and Squeeze–Excitation Blocks
Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise.
  • 890
  • 10 Oct 2023
Topic Review
Application of Artificial Intelligence in a Cephalometric Analysis
The application of artificial intelligence (AI) has become more and more widespread in medicine and dentistry. It may contribute to improved quality of health care as diagnostic methods are getting more accurate and diagnostic errors are rarer in daily medical practice. The accuracy of determining cephalometric landmarks using widely available commercial AI-based software and advanced AI algorithms was presented. Most AI algorithms used for the automated positioning of landmarks on cephalometric radiographs had relatively high accuracy. At the same time, the effectiveness of using AI in cephalometry varies depending on the algorithm or the application type, which has to be accounted for during the interpretation of the results.
  • 889
  • 15 Aug 2023
Topic Review
AI and XAI for Visual Quality Assurance
Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes.
  • 889
  • 02 Feb 2024
Topic Review
Sensor-Based Human Action Recognition
Sensor-based Human Action Recognition (HAR) is a fundamental component in human–robot interaction and pervasive computing. It achieves HAR by acquiring sequence data from embedded sensor devices (accelerometers, magnetometers, gyroscopes, etc.) of multiple sensor modalities worn at different body locations for data processing and analysis.
  • 888
  • 28 Sep 2023
Topic Review
Semantic Change Detection for High Resolution RS Images
Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts research on faster and more accurate change detection methods. 
  • 888
  • 22 Dec 2023
Topic Review
SeAttE—Embedding Model Based on Knowledge Graph Completion
SeAttE is a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion. SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. 
  • 887
  • 21 Apr 2022
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