Your browser does not fully support modern features. Please upgrade for a smoother experience.
Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort by:
Most Viewed Latest Alphabetical (A-Z) Alphabetical (Z-A)
Filter:
All Topic Review Biography Peer Reviewed Entry Video Entry
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.
  • 930
  • 23 Sep 2022
Topic Review
Weakly Supervised Object Detection for Remote Sensing Images
To account for the lack of fine-grained annotations, such as object bounding boxes, several object detection methods have been developed that leverage only coarse-grain annotations (especially image-level labels indicating only the presence or absence of an object). This approach is called inexact Weak Supervision and introduces a new branch of Object Detection called Weakly Supervised Object Detection. Given an image, Remote Sensing Fully Supervised Object Detection (RSFSOD) aims to locate and classify objects based on Bounding Boxes annotations. Differently from RSFSOD, Remote Sensing Weakly Supervised Object Detection aims to precisely locate and classify object instances in Remote Sensing Images using only image-level labels or other types of coarse-grained labels (e.g., points or scribbles) as ground truth. 
  • 928
  • 24 Nov 2022
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.
  • 928
  • 06 May 2024
Topic Review
PCB Defect Based on Improved YOLOv7
The printed circuit board (PCB) holds immense importance in the electronic industry as a crucial component for the development of electronic products. PCBs are becoming increasingly integrated and smaller due to the excellent craftsmanship, precise wiring, and rapid development of integrated circuits.
  • 925
  • 11 May 2023
Topic Review
Gastrointestinal Tract Disorders
Globally, gastrointestinal (GI) tract diseases are on the rise. If left untreated, people may die from these diseases. Early discovery and categorization of these diseases can reduce the severity of the disease and save lives. Automated procedures are necessary, since manual detection and categorization are laborious, time-consuming, and prone to mistakes.
  • 924
  • 21 Aug 2023
Topic Review
Facility Location Problem
The facility location problem (FLP) is a complex optimization problem that has been widely researched and applied in industry.
  • 923
  • 31 Oct 2023
Topic Review
The Robustness of Machine Learning in Network Security
Utilizing machine learning (ML) based methodologies for Network Intrusion Detection Systems (NIDSs) engenders valid concerns, primarily stemming from the inherent vulnerabilities of current ML models to various security threats.
  • 923
  • 08 Nov 2023
Topic Review
Bayes Factor and Prior Elicitation
The Bayes factor is a ratio of the marginal likelihood of two competing models. The marginal likelihood for a model class is a weighted average of the likelihood over all the parameter values represented by the prior distribution. Therefore, carefully choosing priors and conducting a prior sensitivity analysis play an essential role when using Bayes factors as a model selection tool. This section briefly discusses the prior distributions, prior elicitation, and prior sensitivity analysis.
  • 922
  • 24 Feb 2022
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).
  • 922
  • 08 Mar 2024
Topic Review
Public Transport COVID-19-Safe: New Barriers and Policies
The COVID-19 emergency forced cities worldwide to adopt measures to restrict travel and implement new urban public transport solutions. The discontinuity and reduction of services made users recognize public transport systems as contamination vectors, and the decrease in the number of passengers can already be seen in several places. Countermeasures that reduce the contact with other passengers—directly (limit the number of passengers in vehicles) or indirectly (operate with large vehicles)—and increase offers are possible solutions to make users feel safe while riding. 
  • 918
  • 28 Mar 2022
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. 
  • 918
  • 16 Oct 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. 
  • 918
  • 22 Dec 2023
Topic Review
Computer Vision Technology for Physical Exercise Monitoring
Physical activity is movement of the body or part of the body to make the muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time.
  • 917
  • 06 Feb 2023
Topic Review
Incremental Deep Learning for Defect Detection in Manufacturing
Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase.
  • 917
  • 23 Feb 2024
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.
  • 916
  • 30 Sep 2021
Topic Review
Encoding Techniques for Gait Analysis
Gait refers to the movement patterns of an individual’s walk. It encompasses the rhythm, speed, and style of movement which require a strong coordination of the upper and lower limbs. The dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion.
  • 916
  • 19 Jan 2024
Topic Review
GAN-Based Applications in Parkinson’s Disease Diagnosis and Treatment
Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson’s Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues.
  • 914
  • 22 Nov 2023
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.
  • 912
  • 01 Mar 2023
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.
  • 912
  • 09 Jul 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. 
  • 910
  • 21 Apr 2022
  • Page
  • of
  • 59
Featured Entry Collections
>>

Featured Reprints

>>
Encyclopedia of Digital Society, Industry 5.0 and Smart City
Encyclopedia of Engineering
Encyclopedia of Social Sciences
Encyclopedia of Medieval Royal Iconography
Academic Video Service
Feedback