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:
Most Viewed Latest Alphabetical (A-Z) Alphabetical (Z-A)
Filter:
All Topic Review Biography Peer Reviewed Entry Video Entry
Topic Review
Mobile Applications for Disabilities
A“mobile application” or “mobile app” is an application of software; depending on the technologies engaged, it knows how to be a native application, as well as a web or hybrid application. Mobile applications are designed to be employed on intelligent devices or tablets; they can be transferred from a device company’s distribution platform.
  • 504
  • 22 Jun 2021
Topic Review
Public Perceptions around mHealth Applications
This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during the COVID-19 pandemic: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. The specific objectives of this study are: (1) to examine the difference in communication network structure across the networks generated among the six mHealth apps included in our study; (2) to analyze the sentiment surrounding the six mHealth apps conversations; and (3) to evaluate the performance of a sentiment classifier using machine learning approaches.
  • 504
  • 10 Jan 2022
Topic Review
Multi-UAV Path Planning Algorithms
While several developments have taken place in the field of autonomus guidance and navigation techniques for Unmanned Aerial Vehicle (UAV) systems, obtaining an optimal path planning algorithm remains elusive. With multiple UAVs involvement in missions, these difficulties increase significantly leading to the need for collision free navigation routes from the UAVs' initial positions to target points. Consequently, this entry focuses specifically on Multi-UAV Path Planning Algorithms utilizing Bio -Inspired Algorithms. The findings indicated that bio-inspired algorithms possess substantial potential in addressing multipoint path planning issues and delineate new prospects and implications for the enhancement of this active research domain.
  • 504
  • 03 Jul 2023
Topic Review
Deep Neural Networks
The fundamental principles and structures of deep learning (DL) are examined herein. The specific roles and functions of the diverse layers that make up deep networks are discussed, and the importance of evaluation metrics, which serve as crucial tools for gauging the effectiveness of these models, are emphasized. Commonly used architectures in medical image segmentation are also introduced.
  • 504
  • 29 Nov 2023
Topic Review
Medical Case-Based Multiple-Choice Questions
Moving from old-style multiple-choice questions (MCQs) to ones that are more related to real clinical situations is really important. It helps in growing critical thinking and practical use, especially since MCQs are still the primary method for testing knowledge in medicine.
  • 503
  • 15 Jan 2024
Topic Review
Human Action Recognition Methods
In the field of artificial intelligence, human action recognition is an important part of research in this area, making human interaction with the external environment possible. While human communication can be conveyed with words, facial expressions, written text, etc., the relationship between computers and sensors to understand human intentions and behaviour is now a popular area of research. As a result, more and more researchers are devoting their time and experience to the study of human action recognition.
  • 502
  • 28 Jul 2023
Topic Review
Multiple-Instance Learning Methods
Multiple-instance learning has become popular due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. 
  • 501
  • 27 Oct 2023
Topic Review
Artificial Intelligence in the Diagnosis of Dairy Cows
With the rapid growth of computational power and data transfer capabilities, machine learning (ML) and artificial intelligence (AI) are also making inroads into animal husbandry and veterinarian research. In particular, Infrared thermography (IRT) is being increasingly used for health monitoring and the diagnosis of dairy cows, especially in studies related to heat stress, which causes severe losses, helping us analyze its effects on nutrition, milk production, reproduction, etc. There is plenty of evidence for the potential benefits of using IRT for monitoring udder health status in dairy cows and for the early detection of mastitis. Its role in detecting hoof lesions and lameness has also been reported. The growth of the population and the increase of quality standards has set a requirement for the production of more and better quality food. The capabilities and potential benefits of IRT make systems for the automatic collection and processing of thermographic information and decision-making particularly important.
  • 501
  • 06 Nov 2023
Topic Review
Machine Learning-Based Text Classification Comparison
The growth in textual data associated with the increased usage of online services and the simplicity of having access to these data has resulted in a rise in the number of text classification research papers. Text classification has a significant influence on several domains such as news categorization, the detection of spam content, and sentiment analysis. The classification of Turkish text is the research focus since only a few studies have been conducted in this context. Researchers utilize data obtained from customers’ inquiries that come to an institution to evaluate the proposed techniques. Classes are assigned to such inquiries specified in the institution’s internal procedures. The Support Vector Machine, Naïve Bayes, Long Term-Short Memory, Random Forest, and Logistic Regression algorithms were used to classify the data. The performance of the various techniques was then analyzed after and before data preparation, and the results were compared. The Long Term-Short Memory technique demonstrated superior effectiveness in terms of accuracy, achieving an 84% accuracy rate, surpassing the best accuracy record of traditional techniques, which was 78% accuracy for the Support Vector Machine technique. The techniques performed better once the number of categories in the dataset was reduced. Moreover, the findings show that data preparation and coherence between the classes’ number and the number of training sets are significant variables influencing the techniques’ performance.
  • 500
  • 01 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. 
  • 499
  • 09 Jul 2021
Topic Review
AI and Neural Network Algorithms
Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into the daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a num- ber of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active development of artificial intelligence (AI) and neural networks. A neural algorithm application in Micro-Electro-Mechanical System microcantilever biosensors is anticipated through the associated application of the principal com-ponent analysis approach. Researchers investigation has more scientific study value, because there are currently no favorable reports on the market regarding the use of AI with Micro-Electro-Mechanical System microcantilever sensors.
  • 499
  • 13 Sep 2022
Topic Review
Intrusion Detection System in IoT Wi-Fi Networks
The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this research was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for deauthentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. 
  • 499
  • 15 Oct 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.
  • 499
  • 21 Sep 2022
Topic Review
Machine Learning Used to Combat COVID-19
Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19.
  • 498
  • 20 Sep 2022
Topic Review
Neural Load Disaggregation
Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of energy savings. NILM received significant attention, reflected by the number of contributions and systematic reviews published yearly.
  • 498
  • 09 Feb 2023
Topic Review
Arbitrary-Oriented Object Detection in Aerial Images
Objects in aerial images often have arbitrary orientations and variable shapes and sizes. As a result, accurate and robust object detection in aerial images is a challenging problem. 
  • 497
  • 24 May 2023
Topic Review
Network Slicing
5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. 
  • 497
  • 07 Jul 2022
Topic Review
Flatfeet Severity-Level Detection
Flat foot is a postural deformity in which the plantar part of the foot is either completely or partially contacted with the ground. In the clinical practices, X-ray radiographs have been introduced to detect flat feet because they are more affordable to many clinics than using specialized devices. 
  • 496
  • 16 Oct 2023
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. 
  • 496
  • 07 Apr 2024
Topic Review
Cards Against Calamity Learning Game: Civics, Negotiation, Sustainability
Learning games for instruction constitute a progressively important and mutually universal challenge for academics, researchers, and software engineers worldwide. Gaming offers immersive space for interaction and co-creation of successful negotiation and conflict management, civic learning and sustainable development attributes in higher education and workplace context. 
  • 495
  • 16 Nov 2022
  • Page
  • of
  • 58
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
ScholarVision Creations
Feedback