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Topic Review
Carbonate Reservoirs Permeability Prediction
Permeability is a crucial property that can be used to indicate whether a material can hold fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and expensive task while using traditional techniques. Traditional methods often demand a significant amount of time, resources, and manpower, which are sometimes beyond the limitations of under developing countries.
  • 994
  • 10 Oct 2023
Topic Review
State-of-the-Art on Recommender Systems for E-Learning
Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization.
  • 993
  • 06 Dec 2022
Topic Review
Random Forest, Feedforward Neural Network, GRU and FinGAT
Stock prediction has garnered considerable attention among investors, with a recent focus on the application of machine learning techniques to enhance predictive accuracy. Prior research has established the effectiveness of machine learning in forecasting stock market trends, irrespective of the analytical approach employed, be it technical, fundamental, or sentiment analysis. 
  • 991
  • 18 Dec 2023
Topic Review
Open-Domain Conversational AI
There are different opinions as to the definition of AI, but according to, it is any computerised system exhibiting behaviour commonly regarded as requiring intelligence. Conversational AI, therefore, is any system with the ability to mimick human–human intelligent conversations by communicating in natural language with users. Conversational AI, sometimes called chatbots, may be designed for different purposes. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. 
  • 987
  • 24 Jun 2022
Topic Review
Human–Autonomous Taxis Interactions
With the increasing deployment of autonomous taxis in different cities around the world, recent studies have stressed the importance of developing new methods, models and tools for intuitive human–autonomous taxis interactions (HATIs). Street hailing is one example, where passengers would hail an autonomous taxi by simply waving a hand, exactly like they do for manned taxis.
  • 987
  • 30 May 2023
Topic Review
Pedestrian Tracking in Autonomous Vehicles
Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking.
  • 987
  • 29 Feb 2024
Topic Review
Multiscale-Deep-Learning Applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task.
  • 986
  • 26 Oct 2022
Topic Review
DeepSORT
DeepSORT is an intelligent tracking technology that can continuously track multiple objects in complex scenarios, such as crowded areas or environments with occlusions. By integrating the appearance features and motion patterns of the targets, it is widely applied in fields like security surveillance, autonomous driving, and sports analysis, significantly enhancing the stability and accuracy of tracking.
  • 985
  • 24 Mar 2025
Topic Review
Deep Learning in Causality Mining
Deep learning models for causality mining (CM) can enhance the performance of learning algorithms, improve the processing time, and increase the range of mining applications.
  • 985
  • 08 Nov 2021
Topic Review
Automated Fact Verification Systems
The rapid growth in Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy.
  • 983
  • 01 Dec 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.   
  • 982
  • 28 Mar 2022
Topic Review
Segmentation of Liver Tumor in Computed Tomography Scan
Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from computed tomography (CT) scans is critical for identifying liver cancer.
  • 981
  • 15 Sep 2022
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.
  • 981
  • 10 Jul 2023
Topic Review
Deep Learning Architectures for Multivariate Time-Series Forecasting
Deep learning algorithms, renowned for their ability to extract intricate patterns from complex datasets, have proven particularly adept at handling the multifaceted time-series data characteristic of smart city IoT applications. Deep learning architectures model complex relationships through a series of nonlinear layers—the set of nodes of each intermediate layer capturing the corresponding feature representation of the input.
  • 981
  • 27 Oct 2023
Topic Review
Forecasting Pollution in Urban Area
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions.  Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.
  • 980
  • 16 Jul 2021
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.
  • 971
  • 12 Oct 2022
Topic Review
Quantum Machine Learning
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed.
  • 971
  • 01 Jun 2023
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.
  • 971
  • 21 Dec 2023
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.
  • 969
  • 22 Nov 2022
Topic Review
Classification of Low Illumination Image Enhancement Methods
As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. According to the learning method used, people can classify existing low-illumination enhancement methods into four categories, i.e., supervised learning, unsupervised learning, semi-supervised learning, and zero-shot learning methods.
  • 967
  • 06 May 2023
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