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Topic Review
Smart Parking
Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are.
  • 996
  • 21 Dec 2023
Topic Review
Deep Learning Methods in Image Matting
Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical scenarios, such as in image and video editing.
  • 995
  • 14 Jun 2023
Topic Review
Reconstructing Superquadrics from Intensity and Color Images
The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. Researchers extend these reconstruction methods to intensity and color images. 
  • 994
  • 09 Aug 2022
Topic Review
U-Net_dc
Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions, and improving the efficiency of treatment and the useful effect of drugs. However, traditional image segmentation models are not ideal solutions for cancer cell image segmentation due to the fact that cancer cells are highly dense and vary in shape and size. To tackle this problem, researchers propose a novel U-Net-based image segmentation model, named U-Net_dc, which expands twice the original U-Net encoder and decoder and, in addition, uses a skip connection operation between them, for better extraction of the image features.
  • 994
  • 14 Jul 2023
Topic Review
Automatic Diagnosis of Glaucoma from Retinal Images
Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. Researchers obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.
  • 993
  • 25 May 2023
Topic Review
Machine Learning Methods for Stock Market Prediction
Stock market prediction models are developed with different goals. The primary focus of stock market prediction has been on forecasting the price of a share for a specific future period. The price of a share is a numerical value, and its variation over time is often treated as a time series in various studies. 
  • 990
  • 21 Jul 2023
Topic Review
Biometrics Mobile Authentication
Touch screen devices have evolved rapidly in recent years as demand and manufacture have skyrocketed. While smartphone capability continues to grow, progress in security has stagnated. This increasing gap between smartphone ability and security poses a significant problem. Physiological biometrics involve the unique physical characteristics of an individual such as their face, fingerprints, or iris. Behavioral biometrics involve how a person interacts with their device such as their typing, swiping, or tapping patterns. Biometrics authentication applies the user’s unique biological or behavioral features to phone security, which is more difficult to replicate by attackers in comparison to knowledge-based authentication.
  • 989
  • 10 Jul 2023
Topic Review
Brain Immunoinformatics
Breakthrough advances in informatics of the last decade have thoroughly influenced the field of immunology. In particular, the immunoinformatics of the central neural system is referred to as neuroimmunoinformatics (NII). This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using a bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and protein-protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII opts to individualize treatment strategies, personalize disease prognosis and treatment response.   
  • 987
  • 28 Mar 2022
Topic Review
DIBR Distortion Mask Prediction Using Synthetic Images
Deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and the Depth Image-Based Rendering (DIBR) process in a multi-view video system. Due to the lack of Multi-view Video plus Depth (MVD) data, a deep learning-based model using more synthetic Synthesized View Images (SVI) is proposed, in which a random irregular polygon-based SVI synthesis method is proposed to simulate the DIBR distortion based on existing massive RGB/RGBD data. In addition, the DIBR distortion mask prediction network is embedded to further enhance the performance.
  • 985
  • 22 Nov 2022
Topic Review
Reinforcement Learning
Reinforcement Learning (RL) is an approach in Machine Learning that aims to solve dynamic and complex problems, in which autonomous entities, called agents, are trained to take actions that will lead them to an optimal solution
  • 985
  • 25 Jul 2023
Topic Review
Machine-Learning Forensics
A world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time.
  • 983
  • 21 Sep 2023
Topic Review
Vision-Based Pose Estimation of Non-Cooperative Target
In the realm of non-cooperative space security and on-orbit service, a significant challenge is accurately determining the pose of abandoned satellites using imaging sensors. Traditional methods for estimating the position of the target encounter problems with stray light interference in space, leading to inaccurate results.
  • 982
  • 18 Dec 2023
Topic Review
Data Extraction Approach for Empirical Agent-Based Model Development
Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence.
  • 982
  • 29 Sep 2023
Topic Review
Deep Learning Methods in Plant Taxonomy
Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features.
  • 981
  • 26 Jul 2023
Topic Review
Computational Thinking
Computational Thinking (CT) has been widely regarded as an essential ability to solve problems by applying basic knowledge of computer science in technological societies. Initially, CT was defined as using the fundamental concepts of computer science to solve problems, design systems, and understand human behaviors.
  • 981
  • 28 Oct 2021
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.
  • 980
  • 21 Sep 2022
Topic Review
Convolution Neural Network  and Transformer-Based Human Pose Estimation
Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images.
  • 979
  • 03 Aug 2023
Topic Review
Sleep Spindle
Sleep spindles are bursts of neural oscillatory activity that are generated by interplay of the thalamic reticular nucleus (TRN) and other thalamic nuclei during stage 2 NREM sleep in a frequency of ~10 –12 Hz for at least 0.5 seconds. After generation in the TRN, spindles are sustained and relayed to the cortex by a thalamo-thalamic and thalamo-cortical feedback loops regulated by both GABAergic and NMDA-receptor mediated glutamatergic neurotransmission. Sleep spindles have been found in all tested mammalian species and in vitro cells. Research supports that spindles (sometimes referred to as "sigma bands" or "sigma waves") play an essential role in both sensory processing and long term memory consolidation. Until recently, it was believed that each sleep spindle oscillation peaked at the same time throughout the neocortex. It was determined that oscillations sweep across the neocortex in circular patterns around the neocortex, peaking in one area, and then a few milliseconds later in an adjacent area. It has been suggested that this spindle organization allows for neurons to communicate across cortices. The time scale at which the waves travel at is the same speed it takes for neurons to communicate with each other.
  • 978
  • 12 Oct 2022
Topic Review
Machine-Learning-Based Chemoinformatics
In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis.
  • 977
  • 18 Jul 2023
Topic Review
Measure of Presortedness
In computer science, a measure of presortedness of a sequence represents how much work is required to sort the sequence. If the sequence is pre-sorted, sorting the sequence entirely require little work, hence it is expected to have a small measure of presortedness. In particular, the measure of a sorted sequence is 0. Some sorting algorithms are more efficient on pre-sorted list, as they can use this pre-work into account to avoid duplicate work. The measure of presortedness allows to formalize the notion that an algorithm is optimal for a certain measure.
  • 974
  • 07 Nov 2022
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