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Topic Review
Noise-Tolerant Data Reconstruction for Wireless Sensor Network
Maintaining data dependability within wireless sensor network (WSN) systems has significant importance. Nevertheless, the deployment of systems in unattended and hostile areas poses a major challenge in dealing with noise. Consequently, several investigations have been conducted to address the issue of noise-affected data recovery.
  • 490
  • 21 Sep 2023
Topic Review
Vehicular Routing and Intelligent Transportation Systems
Urban areas all over the world, from New York's skyscraper-filled skyline to Casablanca's busy streets, have been coping with an exponential surge in vehicle traffic in recent years. This phenomena highlights the larger socioeconomic dynamics influencing current period as well as the world's rising obsession with autos. The effects of this traffic increase are being felt most acutely in emerging powerhouses and developed countries with their advanced industries and economies that are rapidly industrializing and urbanizing. A series of difficulties have arisen as a result of the growth in vehicle traffic. Cities are now frequently congested with traffic, turning once-smooth thoroughfares into figurative parking lots during rush hours. In addition to trying commuters' patience, congestion like this has real-world economic repercussions.  The need for transportation increases logically as cities grow in population with younger citizens. What is particularly alarming, though, is the glaring inconsistency in many urban areas: while the number of automobiles increases, there is a glaring delay in improving road infrastructure and bolstering safety measures. The promise of effortless urban mobility is in danger of becoming an uncontrollable nightmare due to this imbalance.
  • 486
  • 10 Nov 2023
Topic Review
Brain Pathology Classification of MR Images
A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. 
  • 484
  • 08 Sep 2023
Topic Review
Trustworthy Artificial Intelligence
Artificial Intelligence is an indispensable element of the modern world, constantly evolving and contributing to the emergence of new technologies. Artificial Intelligence techniques must inspire users’ trust because they significantly impact virtually every industry and person. For this reason, systems using Artificial Intelligence are subject to many requirements to verify their trustworthiness in various aspects.
  • 482
  • 30 Jan 2024
Topic Review
Decomposition for Multivariant Traffic Time Series
Data-driven modeling methods have been widely used in many applications or studies of traffic systems with complexity and chaos. The empirical mode decomposition (EMD) family provides a lightweight analytical method for non-stationary and non-linear data.  A large amount of traffic data in practice are usually multidimensional, so the EMD family cannot be used directly for those data.
  • 480
  • 07 Jun 2023
Topic Review
Green Energy Transportation Systems on Urban Air Quality
Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO22, SO22, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO22 and SO22 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.
  • 478
  • 21 Sep 2023
Topic Review
Deep Learning-Based IVIF Approaches
Infrared and visible image fusion (IVIF) aims to render fused images that maintain the merits of both modalities. 
  • 478
  • 17 Oct 2023
Topic Review
Microseismic Monitoring Signal Waveform Recognition and Classification
Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. Microseismic monitoring entails the continuous surveillance of minuscule seismic events during mining activities. These imperceptible events provide valuable information about evolving geological conditions. They serve as early warning signals, offering crucial insights into potential hazards and enabling timely preventive measures. This not only safeguards the well-being of miners but also enhances the overall efficiency and sustainability of mining practices.
  • 472
  • 11 Dec 2023
Topic Review
Non-Contact Video Fire Detection
Fire accidents pose a major threat to the safety of human life and property. Accurate fire detection plays a vital role in responding to fire outbreaks in a timely manner and ensuring the smooth conduct of subsequent firefighting efforts.
  • 470
  • 20 Nov 2023
Topic Review
Auction-based Learning for Knowledge Graph Question Answering
Knowledge graphs are graph-based data models which can represent real-time data that is constantly growing with the addition of new information. The question-answering systems over knowledge graphs (KGQA) retrieve answers to a natural language question from the knowledge graph. Most existing KGQA systems use static knowledge bases for offline training. After deployment, they fail to learn from unseen new entities added to the graph. There is a need for dynamic algorithms which can adapt to the evolving graphs and give interpretable results. The algorithms can adapt to changing environments in real-time, making them suitable for offline and online training. An auction algorithm computes paths connecting an origin node to one or more destination nodes in a directed graph and uses node prices to guide the search for the path. When new nodes and edges are dynamically added or removed in an evolving knowledge graph, the algorithm can adapt by reusing the prices of existing nodes and assigning arbitrary prices to the new nodes. For subsequent related searches, the “learned” prices provide the means to “transfer knowledge” and act as a “guide”: to steer it toward the lower-priced nodes.
  • 468
  • 15 Sep 2023
Topic Review
Explainable Deep Learning in Brain Tumors
Brain tumors (BT) present a considerable global health concern because of their high mortality rates across diverse age groups. A delay in diagnosing BT can lead to death. Therefore, a timely and accurate diagnosis through magnetic resonance imaging (MRI) is crucial. A radiologist makes the final decision to identify the tumor through MRI. However, manual assessments are flawed, time-consuming, and rely on experienced radiologists or neurologists to identify and diagnose a BT. Computer-aided classification models often lack performance and explainability for clinical translation, particularly in neuroscience research, resulting in physicians perceiving the model results as inadequate due to the black box model. Explainable deep learning (XDL) can advance neuroscientific research and healthcare tasks.
  • 463
  • 07 Dec 2023
Topic Review
Deep Learning Models in Prostate Cancer Diagnosis
Prostate imaging refers to various techniques and procedures used to visualize the prostate gland for diagnostic and treatment purposes. Deep learning (DL) architectures have shown promising effectiveness and relative efficiency in prostate cancer (PCa) diagnosis due to their ability to analyze complex patterns and extract features from medical imaging data.
  • 460
  • 27 Sep 2023
Topic Review
Insider Cyber Security Threat
The COVID-19 pandemic made all organizations and enterprises work on cloud platforms from home, which greatly facilitates cyberattacks. Employees who work remotely and use cloud-based platforms are chosen as targets for cyberattacks. For that reason, cyber security is a more concerning issue and is incorporated into almost every smart gadget and has become a prerequisite in every software product and service. There are various mitigations for external cyber security attacks, but hardly any for insider security threats, as they are difficult to detect and mitigate. Thus, insider cyber security threat detection has become a serious concern.
  • 459
  • 26 Jan 2024
Topic Review
Techniques for Credit Card Fraud Detection
Fraudulent activities are on the rise within the financial sector, with an escalating trend observed in credit card fraud. The incidence of credit card fraud is expanding swiftly in tandem with the increasing daily usage of credit cards. The Federal Trade Commission (FTC) report underscores the severity of the issue, noting that 2021 marked the most challenging year in history for identity theft. It is crucial to note that many cases of identity theft go unreported, suggesting that the actual number may surpass the reported figures. The FTC report emphasises the need for innovative approaches to safeguard the financial well-being of both consumers and businesses. In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. 
  • 453
  • 17 Jan 2024
Topic Review
Future Healthcare with Ambient Intelligence and IoMT
Imagine technology that is perceptive, adaptable, and perfectly in tune with human needs. This is the promise of Ambient Intelligence (AMI), a groundbreaking advancement in IT with transformative potential across various domains, especially healthcare. By merging the power of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT), AMI creates a dynamic and responsive medical environment. The survey dives deep into the integration of AMI techniques in IoMT, providing essential insights for both researchers and practitioners eager to innovate in the healthcare sector. 
  • 451
  • 19 Jun 2024
Topic Review
Multi-Access Edge Computing
Multi-access edge computing (MEC), based on hierarchical cloud computing, offers abundant resources to support the next-generation Internet of Things network.
  • 449
  • 22 Sep 2023
Topic Review
Customer Segmentation in the Retail Market
Peoples’ awareness of online purchases has significantly risen. This has given rise to online retail platforms and the need for a better understanding of customer purchasing behaviour. Retail companies are pressed with the need to deal with a high volume of customer purchases, which requires sophisticated approaches to perform more accurate and efficient customer segmentation. Customer segmentation is a marketing analytical tool that aids customer-centric service and thus enhances profitability. 
  • 449
  • 20 Oct 2023
Topic Review
Visual Tracking Related to Age or Gender Information
Visual tracking of multiple targets, also referred to as multiple object tracking (MOT), since the target can be any moving object or entity, is a well-investigated computer vision task. Actually, the goal is to detect one or more targets in a time-variate scene and then obtain their trajectories in terms of following their tracklets, for a given video sequence. This is completed by associating newly detected instances with current ones. Typically, the association part assumes a prediction task whose aim is to favor the most possible correspondence among detections of consecutive frames for a given target. When the targets of interest are real people, resulting detections from this procedure are usually post-processed so as to extract useful information related, for instance, with their age or gender. 
  • 449
  • 11 Dec 2023
Topic Review
Social Recommender Systems
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data.
  • 449
  • 11 Dec 2023
Topic Review
Occluded Face Recognition
Occlusion in facial photos poses a significant challenge for machine detection and recognition. Consequently, occluded face recognition for camera-captured images has emerged as a prominent and widely discussed topic in computer vision. The standard face recognition methods have achieved remarkable performance in unoccluded face recognition but performed poorly when directly applied to occluded face datasets. The main reason lies in the absence of identity cues caused by occlusions. Therefore, a direct idea of recovering the occluded areas through an inpainting model has been proposed. 
  • 449
  • 26 Jan 2024
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