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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. 
  • 414
  • 25 Jan 2024
Topic Review
Imaging Modalities for COVID-19 Diagnosis
The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. The X-ray and computed tomography (CT) can image the lungs of patients with COVID-19. Lung imaging can reveal the niduses’ spatial location and the infection’s extent. 
  • 413
  • 11 Jan 2023
Topic Review
Hybrid Deep Belief Network in Traffic Flow Prediction
Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. The presented hybrid deep belief network (AST2FP-OHDBN) model initially normalizes the traffic data using min–max normalization.
  • 412
  • 21 Nov 2022
Topic Review
Recommendation Systems for e-Shopping
The interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers.
  • 412
  • 21 Dec 2023
Topic Review
Intelligent Source Code Completion Assistants
As artificial intelligence advances, source code completion assistants are becoming more advanced and powerful. Existing traditional assistants are no longer up to all the developers’ challenges. Traditional assistants usually present proposals in alphabetically sorted lists, which does not make a developer’s tasks any easier (i.e., they still have to search and filter an appropriate proposal manually). As a possible solution to the presented issue, intelligent assistants that can classify suggestions according to relevance in particular contexts have emerged. Artificial intelligence methods have proven to be successful in solving such problems. Advanced intelligent assistants not only take into account the context of a particular source code but also, more importantly, examine other available projects in detail to extract possible patterns related to particular source code intentions. This is how intelligent assistants try to provide developers with relevant suggestions. 
  • 412
  • 17 Jan 2024
Topic Review
Severity Identification of Parkinson’s Disease
Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Movement disorders caused by PD may not remain the same in different patients. Thus, it is essential to develop an automated tool to evaluate a patient’s gait.
  • 411
  • 14 Apr 2023
Topic Review
Sign Language Recognition Techniques
Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. Building a sign language recognition system using deep learning technology plays a vital role in interpreting sign language to ordinary individuals and the reverse. This system would ease the process of communication between deaf and normal people.
  • 411
  • 10 Oct 2023
Topic Review
ISTD Based on Background-SuppressionProximal Gradient and GPU Acceleration
Infrared Small-Target Detection (ISTD) is an important component of infrared search and tracking, aiming to exploit the thermal radiation difference between a target and its background to achieve long-range target detection. According to the definition by the Society of Photo-Optical Instrumentation Engineers (SPIE), small targets typically refers to objects in a 256 × 256 image with an area of fewer than 80 pixels, accounting for approximately 0.12% of the total image area.
  • 411
  • 25 Dec 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.
  • 411
  • 23 Feb 2024
Topic Review
Green Artificial Intelligence and Digitalization Facilitate
Green AI (Artificial Intelligence) and digitalization facilitate the “Dual-Carbon” goal of low-carbon, high-quality economic development. Green AI is moving from “cloud” to “edge” devices like TinyML, which supports devices from cameras to wearables, offering low-power IoT computing. 
  • 410
  • 26 Sep 2023
Topic Review
Brain Tumor  Segmentation
Brain tumor segmentation plays a crucial role in the diagnosis, treatment planning, and monitoring of brain tumors. Accurate segmentation of brain tumor regions from multi-sequence magnetic resonance imaging (MRI) data is of paramount importance for precise tumor analysis and subsequent clinical decision making. The ability to delineate tumor boundaries in MRI scans enables radiologists and clinicians to assess tumor size, location, and heterogeneity, facilitating treatment planning and evaluating treatment response. Traditional manual segmentation methods are time-consuming, subjective, and prone to inter-observer variability. Therefore, the automatic segmentation algorithm has received widespread attention as an alternative solution. For instance, the self-organizing map (SOM) is an unsupervised exploratory data analysis tool that leverages principles of vector quantization and similarity measurement to automatically partition images into self-similar regions or clusters. Segmentation methods based on SOM have demonstrated the ability to distinguish high-level and low-level features of tumors, edema, necrosis, cerebrospinal fluid, and healthy tissue.
  • 410
  • 04 Mar 2024
Topic Review
AI-Based Fault Diagnosis of Centrifugal Pump
The fault-related impulses in the centrifugal pump (CP) vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge.
  • 409
  • 10 Nov 2023
Topic Review
Intrusion Detection and Datasets
With the significant increase in cyber-attacks and attempts to gain unauthorised access to systems and information, Network Intrusion-Detection Systems (NIDSs) have become essential detection tools. Anomaly-based systems use machine learning techniques to distinguish between normal and anomalous traffic. They do this by using training datasets that have been previously gathered and labelled, allowing them to learn to detect anomalies in future data. However, such datasets can be accidentally or deliberately contaminated, compromising the performance of NIDS.
  • 409
  • 30 Jan 2024
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.
  • 409
  • 22 Feb 2024
Topic Review
Document-Level Multimodal Sentiment Analysis
An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. Sentiment analysis at the document level aims to identify the opinion on a main topic expressed by a whole document.
  • 408
  • 01 Jun 2023
Topic Review
Deep Learning Methods of Small Object Detection
Remote sensing methodology has been increasingly applied in fields such as forest fire detection, iceberg detection, ship detection, floating object detection, and agriculture monitoring. Synthetic aperture radar (SAR) images can be used to construct two-dimensional images, which can be further reconstructed into multidimensional images if necessary. When the SAR focuses on the target application, such as biomass detection in the forest, ship detection in the ocean, and vehicle count on land, it comprises many small objects. Detecting these small images is less effortless than general object detection in typical images.
  • 408
  • 14 Jul 2023
Topic Review
Optimizing Session-Aware Recommenders
Recommendation mechanisms have emerged as vital tools for the filtering of information in various aspects of life. They are widely used in commercial platforms, including e-commerce sites like Amazon. Session-based or session-aware recommendation is more attractive due to the recommendation accuracy.
  • 407
  • 26 Feb 2024
Topic Review
Soybean Monitoring and Management
The interest in deep learning in agriculture has been continuously growing since the inception of this type of technique in the early 2010s. Soybeans, being one of the most important agricultural commodities, has frequently been the target of efforts in this regard. It can be challenging to keep track of a constantly evolving state of the art.
  • 406
  • 23 Aug 2023
Topic Review
Privacy and Security in Sustainable Smart City Applications
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML).
  • 406
  • 12 Dec 2023
Topic Review
Arabic Mispronunciation Recognition System Using LSTM Network
The widespread use of CALL (computer-assisted language learning) systems attests to their success in helping people improve their language and speech skills. CALL is predominantly concerned with addressing pronunciation errors in non-native speakers’ speech. Accurate mispronunciation detection, voice recognition, and accurate pronunciation evaluation are all activities that may be accomplished with CALL.
  • 405
  • 05 Sep 2023
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