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Topic Review
Machine Learning for Temperature Estimation
The modern and very effective methods of estimating the temperature of electric motors include machine learning and deep learning. Their unquestionable advantage is that on the basis of the collected measurement data, a function mapping the relationship between the values of the input features and the output is determined. This means that predictive modeling does not require knowledge of the material properties of a given device or having expertise knowledge about its construction.
  • 1.8K
  • 19 Jul 2021
Topic Review
Industrial Control Systems Technologies
Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. However, ensuring the security of these systems is of utmost importance due to the potentially severe consequences of cyber attacks. 
  • 1.8K
  • 21 Nov 2023
Topic Review
Gabor Filters
The use of Gabor filters in image processing has been well-established, and these filters are recognized for their exceptional feature extraction capabilities. These filters are usually applied through convolution.
  • 1.8K
  • 19 Dec 2023
Topic Review
Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging
Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses.
  • 1.8K
  • 18 Mar 2022
Topic Review
Firewall for Securing Smart Healthcare Environment
Firewalls today represent the first line of defense against major attacks, affecting both traditional and modern networks, and enforcing the protection of inside networks from external (and untrusted) networks. The application of an effective set of security practices and policies may indeed keep those systems safe and save entire businesses. Firewalls have a very important function of protecting, filtering, and controlling all traffic sent and received from the computer, Local Area Network (LAN), or Wide Local Area Network (WLAN) internal networks from unauthorized intrusions or external attacks.
  • 1.8K
  • 08 Oct 2021
Topic Review
Impact of Artificial Intelligence on the Job Market
This research explores the impact of artificial intelligence (AI) on the job market, including both its potential benefits and drawbacks. The research discusses how AI can automate repetitive tasks, improve accuracy, and assist workers in performing their jobs more effectively. However, the article also highlights concerns about job displacement, biases and discrimination, and the deskilling of workers. The research examines the impact of AI on different industries and types of jobs and discusses the need for workers to develop complementary skills and for employers to invest in AI technologies that work collaboratively with human workers. The research concludes by highlighting the importance of investing in education and training programs, ensuring ethical and transparent development and deployment of AI, and implementing appropriate policies to support workers who are displaced by AI.
  • 1.8K
  • 22 May 2023
Topic Review
Physical Layer Authentication in Wireless Networks
The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity.
  • 1.8K
  • 15 Mar 2023
Topic Review
Autonomous Vehicle
An Autonomous Vehicle (AV), or a driverless car, or a self-driving vehicle is a car, bus, truck, or any other vehicle that is able to drive from point A to point B and perform all necessary driving operations and functions without any human intervention. An Autonomous Vehicle is normally equipped with different types of sensors to perceive the surrounding environment, including Normal Vision Cameras, Infrared Cameras, RADAR, LiDAR, and Ultrasonic Sensors.  An autonomous vehicle should be able to detect and recognise all type of road users including surrounding vehicles, pedestrians, cyclists, traffic signs, road markings, and can segment the free spaces, intersections, buildings, and trees to perform a safe driving task.  Currently, no realistic prediction expects we see fully autonomous vehicles earlier than 2030. 
  • 1.8K
  • 17 Feb 2021
Topic Review
Big Data Analytics Applications and Opportunities
Big data applications and analytics are vital in proposing ultimate strategic decisions. The existing literature emphasizes that big data applications and analytics can empower those who apply Big Data Analytics during the COVID-19 pandemic.
  • 1.8K
  • 27 Dec 2022
Topic Review
AI Revolution in Digital Finance in Saudi Arabia
In recent years, Artificial Intelligence (AI) has become widespread, driven by abundant daily data production and increased computing power. It finds applications across various sectors, including transportation, education, healthcare, banking, and finance. The financial industry, in particular, is rapidly adopting AI to achieve significant cost savings. AI has the potential to revolutionize financial services by offering tailored, faster, and more cost-effective solutions. Saudi Arabia is emerging as a growing market in this field, emphasizing technology-driven institutions. Despite gaining prominence and government support, AI has yet to play a crucial role in improving the efficiency of financial transactions.
  • 1.8K
  • 27 Nov 2023
Topic Review
Visual Question Answering
Visual question answering (VQA) is a task that generates or predicts an answer to a question in human language about visual images. VQA is an active field combining two AI branches: Natural language processing (NLP) and computer vision. VQA usually has four components: vision featurization, text featurization, fusion model, and classifier. Vision featurization is a part of the multi-model responsible for extracting the vision features. Text featurization is another part of the VQA multi-model responsible for extracting text features. The combination of both features and their processes is the fusion component. The last component is the classifier that classifies the queries about the images and generates the answer.
  • 1.8K
  • 22 Sep 2023
Topic Review
Smart Parking Systems
The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.
  • 1.7K
  • 27 Oct 2020
Topic Review
Approaches for Flow-Shop Scheduling Problems
Flow-shop scheduling problems are classic examples of multi-resource and multi-operation scheduling problems where the objective is to minimize the makespan. Because of the high complexity and intractability of the problem, apart from some exceptional cases, there are no explicit algorithms for finding the optimal permutation in multi-machine environments. 
  • 1.7K
  • 13 Sep 2023
Topic Review
Electrocardiogram Signal Denoising
The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. 
  • 1.7K
  • 29 Nov 2023
Topic Review
Multimodal Segmentation Techniques in Autonomous Driving
Semantic Segmentation has become one of the key steps toward scene understanding, especially in autonomous driving scenarios. In the standard formulation, Semantic Segmentation uses only data from color cameras, which suffer significantly in dim lighting or adverse weather conditions. A solution to this problem is the use of multiple heterogeneous sensors (e.g., depth and thermal cameras or LiDARs) as the input to machine learning approaches tackling this task, allowing to cover for the shortcomings of color cameras and to extract a more resilient representation of the scene.
  • 1.7K
  • 15 Aug 2022
Topic Review
Fuel Consumption and CO2 of Light-Duty Vehicles
Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.
  • 1.7K
  • 20 Jan 2022
Topic Review
Radar Depth and Velocity Estimation
Radar can measure range and Doppler velocity, but both of them cannot be directly used for downstream tasks. The range measurements are sparse and therefore difficult to associate with their visual correspondences. The Doppler velocity is measured in the radial axis and, therefore, cannot be directly used for tracking.
  • 1.7K
  • 08 Jun 2022
Topic Review
Computer Vision and Artificial Intelligence for Fish Recognition
Computer vision has been applied to fish recognition. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the state of the art and to determine the best course of action for new studies.
  • 1.7K
  • 24 Nov 2022
Topic Review
Deep Learning Approaches for Wildfires Using Satellite Data
Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were developed to detect and map wildland fires, estimate their severity, and predict their spread. In this paper, we provide a comprehensive review of recent deep learning techniques for detecting, mapping, and predicting wildland fires using satellite remote sensing data. We begin by introducing remote sensing satellite systems and their use in wildfire monitoring. Next, we review the deep learning methods employed for these tasks, including fire detection and mapping, severity estimation, and spread prediction. We further present the popular datasets used in these studies. Finally, we address the challenges faced by these models to accurately predict wildfire behaviors, and suggest future directions for developing reliable and robust wildland fire models.
  • 1.7K
  • 24 May 2023
Topic Review
Unsupervised Domain Adaptation for Image Classification
Unsupervised domain adaptation (UDA) is a transfer learning technique utilized in deep learning. UDA aims to reduce the distribution gap between labeled source and unlabeled target domains by adapting a model through fine-tuning. To reduce the domain divergence between the source and target domain, there are mainly two main types of UDA methods that have gained significant attention: discrepancy-based UDA methods and adversarial-based UDA methods.
  • 1.7K
  • 31 May 2023
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