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Topic Review
Automatic Detection of Cardiovascular Disorders
The main cause of death worldwide is cardiovascular disease (CVD), which claims more than 17 million lives each year. CVD disease creates other pathological issues with the heart, heart valves, or blood vessels.
  • 1.0K
  • 03 Jan 2023
Topic Review
Obfuscated Memory Malware Detection
Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. 
  • 1.0K
  • 24 Jul 2023
Topic Review
Deep Learning Methods for Solving the NLI Problem
Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment relation of a pair of sentences, called the premise and hypothesis. If the premise entails the hypothesis, the pair is labeled as an “entailment”. If the hypothesis contradicts the premise, the pair is labeled a “contradiction”, and if there is not enough information to infer a relationship, the pair is labeled as “neutral”.
  • 1.0K
  • 26 Apr 2024
Topic Review
A Robust Vehicle Detection Model for LiDAR Sensor
Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. 
  • 1.0K
  • 15 Jun 2023
Topic Review
Artificial Intelligence in Cancer Research
Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies.
  • 1.0K
  • 09 Dec 2022
Topic Review
Continuous Pain Intensity Monitoring
The continuous pain intensity recognition system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities.
  • 1.0K
  • 20 Oct 2023
Topic Review
Brain Tumor Segmentation
Segmentation of brain tumor images from magnetic resonance imaging (MRI) is a challenging topic in medical image analysis. The brain tumor can take many shapes, and MRI images vary considerably in intensity, making lesion detection difficult for radiologists. Image segmentation is the action of grouping pixels according to predefined criteria, in order to build regions or classes of pixels. There are several methods of image segmentation: methods based on contours, regions, classification, or hybrid. Segmentation and its automation remain today one of the major challenges in MRI, mainly in relation to brain tumor images, in order to help the practitioner in his daily practice, in the presence of a huge volume of images. 
  • 1.0K
  • 26 Sep 2022
Topic Review
Self-Healing in Cyber–Physical Systems Using Machine Learning
The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality.
  • 1.0K
  • 12 Oct 2023
Topic Review
The Methods of Fall Detection
Falls by an older person are a significant public health issue because they can result in disabling fractures and cause severe psychological problems that diminish a person’s level of independence. Falls can be fatal, particularly for the elderly. Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems.
  • 1.0K
  • 09 Jun 2023
Topic Review
Predictive Maintenance of Ball Bearing Systems
In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses.
  • 1.0K
  • 01 Feb 2024
Topic Review
Classification of Sleep Stages Using Telemetry Polysomnography
Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment as it captures diverse physiological signals. Recent advancements have shown that simpler machine learning models, when coupled with sophisticated feature extraction techniques, can yield accurate and reliable results comparable to those achieved by complex deep learning methods. These simpler models not only reduce the computational burden but also offer greater interpretability, a feature that is highly valued in clinical settings for both diagnostic and treatment purposes. Therefore, integrating simpler machine learning algorithms with advanced feature extraction can serve as an effective and efficient approach for sleep stage classification in research and clinical applications.
  • 1.0K
  • 25 Aug 2023
Topic Review
Deep Reinforcement Learning for Vision-Based Navigation of UAVs
Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this research describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This research applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The research compare three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving The novelty of this research lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. The findings could have practical implications for the development of safer and more efficient drones in the future.
  • 1.0K
  • 13 Dec 2023
Topic Review
A New Container Throughput Forecasting Paradigm under COVID-19
COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. Combining this with change-point analysis and empirical mode decomposition (EMD), this uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, here tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. 
  • 1.0K
  • 24 Mar 2022
Topic Review
Machine Vision and Industry 4.0 to Industry 5.0
With the emergence of artificial intelligence (AI) and its integration into various intelligent robotics, the Fourth Industrial Revolution, also known as Industry 4.0, managed to trigger changes. Its need has been emphasized in multiple situations, such as that of the COVID-19 pandemic, entering every area of human life, with Industry 4.0 being more and more involved in production processes. Industry 4.0 is an emerging concept that is multidisciplinary and complex. Leveraging not just one, but a patchwork of technologies that can work individually as well as in combination, Industry 4.0 strives to achieve a more general digital transformation with high expectations both in the production of products and services in real-time. This effort is mainly based on advanced computers with fast processors able to store, manage, process, and analyze a large amount of data, spending less time and resources than ever before.
  • 1.0K
  • 08 Mar 2024
Topic Review
Railway Track Fault Detection
Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. 
  • 998
  • 29 Aug 2023
Topic Review
A Unified Framework for RGB-Infrared Transfer
Infrared(IR) images (both 0.7-3 µm and 8-15 µm) offer radiation intensity texture information that visible images lack, making them particularly helpful in daytime, nighttime, and complex scenes. Many researchers are studying how to translate RGB images into infrared images for deep learning-based visual tasks such as object tracking, crowd counting, panoramic segmentation, and image fusion in urban scenarios. The utilization of the RGB-IR dataset in the aforementioned tasks holds the potential to provide comprehensive multi-band fusion data for urban scenes, thereby facilitating precise modeling across different scenarios. In addressing the challenge of accurately generating high-radiance textures for the targets in the infrared spectrum, the proposed approach aims to ensure alignment between the generated infrared images and the radiation feature of ground-truth IR images.
  • 998
  • 18 Dec 2023
Topic Review
Arbitrary-Oriented Object Detection in Aerial Images
Objects in aerial images often have arbitrary orientations and variable shapes and sizes. As a result, accurate and robust object detection in aerial images is a challenging problem. 
  • 995
  • 24 May 2023
Topic Review
Deep Learning-Based Diagnosis of Alzheimer’s Disease
Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. 
  • 994
  • 01 Jun 2022
Topic Review
Forensic Methods for Image Inpainting
The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. Efforts have been devoted to developing forensic methods for image inpainting. They can be roughly divided into the following two categories: conventional inpainting forensics methods and deep learning-based inpainting forensics methods.
  • 994
  • 26 Jun 2023
Topic Review
Automation in Interior Space Planning
In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality.
  • 994
  • 08 Aug 2023
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