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Topic Review
GAN-Based Tabular Data Generator for Constructing Synopsis
In data-driven systems, data exploration is imperative for making real-time decisions. However, big data are stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data. The use of Generative Adversarial Networks (GANs) for generating tabular data has emerged as a pivotal method in AQP for constructing accurate synopses. Moreover, the advancement of tabular GAN architectures addresses the specific challenges encountered in synopsis construction. These advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems. 
  • 599
  • 25 Jan 2024
Topic Review
Edge-Guided Multimodal Transformers Change Detection
Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion.
  • 599
  • 05 Mar 2024
Topic Review
Facial Expression Recognition System Using Convolutional Neural Network
Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. An individual’s facial expressions (FEs) or countenance convey their psychological reactions and intentions in response to a social or personal event. These expressions convey non-verbal stealth messages. With technological advancements, human behavior can be understood through facial expression recognition (FER). To express emotions, humans use facial expressions as their primary nonverbal communication method.
  • 598
  • 17 Nov 2023
Topic Review
DiabeticSense
Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. Researchers was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions.
  • 598
  • 29 Dec 2023
Topic Review
Robot Landing Method on Overhead P[ower Transmission Lines
Hybrid inspection robots have been attracting increasing interest in recent years, and are suitable for inspecting long-distance overhead power transmission lines (OPTLs), combining the advantages of flying robots (e.g., UAVs) and climbing robots (e.g., multiple-arm robots). Due to the complex work conditions (e.g., power line slopes, complex backgrounds, wind interference), landing on OPTL is one of the most difficult challenges faced by hybrid inspection robots.
  • 595
  • 01 Sep 2023
Topic Review
Real-Time Intelligent Detection System for Illegal Wearing
Ensuring personal safety and preventing accidents are critical aspects of power construction safety supervision. However, current monitoring methods are inefficient and unreliable as most of them rely on manual monitoring and transmission, which results in slow detection and delayed warnings regarding violations.
  • 593
  • 27 Jul 2023
Topic Review
Multi-Scene Mask Detection
Deep learning for mask detection has important demand in medical and industrial production, which reflects the application of neural networks and image sensors in daily life. During an epidemic of respiratory viruses, mask detection can effectively supervise the wearing of masks, thereby reducing the risk of virus transmission.
  • 593
  • 04 Dec 2023
Topic Review
Cross-Modal Dynamic Attention Neural Architecture
Detecting anomalies in data streams from smart communication environments is a challenging problem that can benefit from novel learning techniques. The Attention Mechanism is a very promising architecture for addressing this problem. It allows the model to focus on specific parts of the input data when processing it, improving its ability to understand the meaning of specific parts in context and make more accurate predictions.
  • 592
  • 15 Sep 2023
Topic Review
Incremental Scene Classification Using Dual Knowledge Distillation
Conventional deep neural networks face challenges in handling the increasing amount of information in real-world scenarios where it is impractical to gather all the training data at once. Incremental learning, also known as continual learning, provides a solution for lightweight and sustainable learning with neural networks.
  • 591
  • 08 Feb 2024
Topic Review
Algorithms for Histopathology Image Detection and Segmentation
Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images.
  • 590
  • 27 Nov 2023
Topic Review
Matrix Factorization Recommendation Algorithm Based on Attention Interaction
Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users.
  • 589
  • 26 Feb 2024
Topic Review
Web Search Results Exploration for Blind Users
In the contemporary digital landscape, web search functions as a pivotal conduit for information dissemination. Nevertheless, blind users (BUs) encounter substantial barriers in leveraging online services, attributable to intrinsic deficiencies in the information structure presented by online platforms. A critical analysis reveals that a considerable segment of BUs perceive online service access as either challenging or unfeasible, with only a fraction of search endeavors culminating successfully. 
  • 588
  • 21 Nov 2023
Topic Review
Insulator Defect Detection
Insulators, as important components of high-voltage transmission lines, serve the functions of electrical separation and support for conductors. Due to their long-term outdoor exposure to sunlight, rain, climate changes, and chemical corrosion, insulators often suffer from self-exploding defects, causing the disconnection of insulator strings and interfering with their performance, thus affecting the safety and stability of power systems. Insulator detection methods are generally divided into two types. The first is manual inspection, where workers directly observe insulators to identify defective parts. However, this method is time-consuming and not safe. The second is intelligent inspection, which can effectively locate defective parts by carrying edge detection equipment on drones for regular inspection of insulators. This is also the current mainstream inspection method.
  • 588
  • 26 Jan 2024
Topic Review
Alzheimer’s Disease, Machine Learning and Feature Selection Methods
Alzheimer’s disease (AD) is a prevalent form of dementia that accounts for up to 80% of all dementia cases. The use of machine learning and feature selection methods in predicting AD based on gene expression data is a rapidly evolving area of research. 
  • 586
  • 02 Jun 2023
Topic Review
Gait Recognition
Gait recognition aims to identify a person based on his unique walking pattern. Compared with silhouettes and skeletons, skinned multi-person linear (SMPL) models can simultaneously provide human pose and shape information and are robust to viewpoint and clothing variances. 
  • 586
  • 27 Nov 2023
Topic Review
Data Gathering and Disease Detection in Healthcare WSN
Wireless sensor networks (WSNs) consist of a multitude of distributed devices, equipped with sensors, to monitor physical or environmental conditions. These devices, also known as nodes, collaboratively pass their data through the network to a main location or sink where the data can be observed and analyzed. WSNs have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. 
  • 586
  • 22 Mar 2024
Topic Review
Sequential Tracking Models, Physics-Based Models and Hybrid Models
Spatio-temporal, geo-referenced datasets are rapidly expanding and will continue to do so in the near future due to technological advancements as well as social and commercial factors. The introduction of the automatic identification system (AIS), which allows neighboring ships to communicate frequently with their location and navigation status via a radio signal, has enabled researchers to get their hands on datasets rich in spatio-temporal information. AIS data are collected from satellites and ground stations located all over the world. AIS data facilitates the mapping and characterization of maritime human and vessel activities, thus allowing for the real-time geo-tracking and identification of vessels equipped with AIS. Hence, in addition to its initial application in collision avoidance, AIS is now also a massive data source of unparalleled quality for diverse tracking tasks. The AIS dataset contains the location and motion features of the vessels. Each data point or row in the AIS data file is represented by a time-sequenced node that contains the vessel’s coordinates, speed, and traveling direction. Each node also has an associated time stamp indicating the data collection time. AIS dataset is suitable for  the track association problem solving approach for its spatio-temporal characteristics.
  • 584
  • 07 Aug 2023
Topic Review Peer Reviewed
AI-Driven Non-Destructive Testing Insights
Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This entry provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect detection accuracy and reduce inspection times. Challenges related to data quality, ethical considerations, and regulatory standards were discussed as well. By summarizing established knowledge and highlighting advancements, this entry serves as a valuable reference for engineers and researchers, contributing to the development of safer and more efficient infrastructure management practices. 
  • 584
  • 22 Nov 2024
Topic Review
Improving Visual Defect Detection
Reliable functionality in anomaly detection in thermal image datasets is crucial for defect detection of industrial products. Nevertheless, achieving reliable functionality is challenging, especially when datasets are image sequences captured during equipment runtime with a smooth transition from healthy to defective images. This causes contamination of healthy training data with defective samples. Anomaly detection methods based on autoencoders are susceptible to a slight violation of a clean training dataset and lead to challenging threshold determination for sample classification. 
  • 582
  • 06 Sep 2023
Topic Review
Models for Enhancing Autonomous Driving Accuracy
Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. 
  • 582
  • 12 Oct 2023
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