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Topic Review
Electrocardiogram-Based Emotion Recognition
Heart rate variability (HRV) serves as a significant physiological measure that mirrors the regulatory capacity of the cardiac autonomic nervous system. It not only indicates the extent of the autonomic nervous system’s influence on heart function but also unveils the connection between emotions and psychological disorders. 
  • 910
  • 14 Nov 2023
Topic Review Peer Reviewed
Nuclear Magnetic Resonance and Artificial Intelligence
This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications of AI techniques, especially machine learning (ML) and deep learning (DL) in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated. The review also shows where progress is limited.
  • 910
  • 07 Jan 2025
Topic Review
From Simultaneous Localization and Mapping to Situational Awareness
Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM).
  • 908
  • 25 May 2023
Topic Review
AMC Using Residual Learning and Squeeze–Excitation Blocks
Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise.
  • 907
  • 10 Oct 2023
Topic Review
Deep Learning in Optical Coherence Tomography Angiography
Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies.
  • 906
  • 13 Feb 2023
Topic Review
Detection/Classification of Knee Injuries from MR Images
Magnetic resonance imaging (MRI) is a technique for mapping the interior structure of the body as well as specific aspects of functioning. 
  • 904
  • 16 Dec 2021
Topic Review
Application of Artificial Intelligence in a Cephalometric Analysis
The application of artificial intelligence (AI) has become more and more widespread in medicine and dentistry. It may contribute to improved quality of health care as diagnostic methods are getting more accurate and diagnostic errors are rarer in daily medical practice. The accuracy of determining cephalometric landmarks using widely available commercial AI-based software and advanced AI algorithms was presented. Most AI algorithms used for the automated positioning of landmarks on cephalometric radiographs had relatively high accuracy. At the same time, the effectiveness of using AI in cephalometry varies depending on the algorithm or the application type, which has to be accounted for during the interpretation of the results.
  • 903
  • 15 Aug 2023
Topic Review
Customized Deep Sleep Recommender System Using Deep Learning
Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. 
  • 903
  • 16 Aug 2023
Topic Review
Automatic Visual Pollution Detection
Visual pollution, characterized by disorderly and displeasing urban environments, is inherently subjective and challenging to quantify precisely. In recent years, substantial research efforts have been initiated to identify and categorize various forms of visual pollution by applying artificial intelligence and computer vision techniques. The automated recognition of visual disturbances using advanced deep learning methods can aid governmental bodies and relevant authorities in taking proactive measures. 
  • 901
  • 19 Jan 2024
Topic Review
Methods for Imaging and Evaluation of Scoliosis
Scoliosis is defined as a three-dimensional spinal deformity consisting of a lateral curvature greater than 10 degrees with rotation of the vertebrae within the curve. It can be identified as congenital, neuromuscular or idiopathic. Idiopathic scoliosis (IS) can be further classified by age of onset: infantile (birth to two years), juvenile (three to nine years), and adolescent (10 years and older). It is the most common pediatric musculoskeletal disorder that causes a three-dimensional (3D) spinal deformity. The deformity is always 3D because it also involves an axial rotation of the vertebrae, not just displacement and rotation in the frontal plane. Adolescent IS is the most common form because the spinal deformity evolves during periods of significant physical growth. IS is diagnosed when other etiological factors cannot be identified, such as congenital neurological or musculoskeletal anomalies, or inflammatory or demyelinating processes leading to primary or secondary motor neuron damage (myotonia, myopathy, etc.).
  • 900
  • 15 Feb 2022
Topic Review
AI-Based Unmanned Aerial Vehicles Networks
To enhance the overall performance of the unmanned aerial vehicles (UAVs) networks and to address some specific problems, new features in the network are being designed as autonomous features. This approach not only provides optimum solutions for the targeted problems but also supports the dynamic properties of a UAV network. 
  • 899
  • 07 Jun 2023
Topic Review
Multiple-Instance Learning Methods
Multiple-instance learning has become popular due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. 
  • 897
  • 27 Oct 2023
Topic Review
IoT and Sustainable Smart Cities
The Internet of Things (IoT) is an emerging technology and provides connectivity with the physical world using the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. However, it is observed that transmission system in constraint oriented network is still a burning research issue in smart cities. Also, there is an existence of a lot of malicious machines that can damage sustainable services of smart cities and compromised the connected devices. Thus, proposing an efficient solution using a 5G system is a demanding task for a smart environment that efficiently utilizes the communication resources and securing the data over insecure routes.
  • 896
  • 27 Aug 2021
Topic Review
Human Mobility Prediction with Calibration for Noisy Trajectories
Human mobility prediction is a key task in smart cities to help improve urban management effectiveness. However, it remains challenging due to widespread intractable noises in large-scale mobility data. Based on previous research and the statistical analysis of real large-scale data, the researchers observe that there is heterogeneity in the quality of users’ trajectories, that is, the regularity and periodicity of one user's trajectories can be quite different from another. Inspired by this, the researchers propose a trajectory quality calibration framework for quantifying the quality of each trajectory and promoting high-quality training instances to calibrate the final prediction process. The main module of this approach is a calibration network that evaluates the quality of each user's trajectories by learning their similarity between them. It is designed to be model-independent and can be trained in an unsupervised manner. Finally, the mobility prediction model is trained with the instance-weighting strategy, which integrates quantified quality scores into the parameter updating process of the model. Experiments conducted on two citywide mobility datasets demonstrate the effectiveness of the approach when dealing with massive noisy trajectories in the real world.
  • 896
  • 11 Nov 2022
Topic Review
Sensor-Based Human Action Recognition
Sensor-based Human Action Recognition (HAR) is a fundamental component in human–robot interaction and pervasive computing. It achieves HAR by acquiring sequence data from embedded sensor devices (accelerometers, magnetometers, gyroscopes, etc.) of multiple sensor modalities worn at different body locations for data processing and analysis.
  • 896
  • 28 Sep 2023
Topic Review
Hydroponic Monitoring and Controlling System Using ANFIS
Most people are now aware of the importance of a healthy lifestyle, including the importance of consuming vegetables. As a result, the demand for vegetables has increased, and so their production needs to be increased. Currently, most plantations use soil as a growing medium, which is time-consuming and requires a significant amount of space. To modernize cultivation, hydroponic techniques should be adopted. A smart hydroponic system was developed using the adaptive neuro-fuzzy inference system (ANFIS) method, which allows for automatic adjustments based on the collected dataset and remote control through internet of things (IoT) technology.
  • 895
  • 11 Jan 2024
Topic Review
Point Cloud Object Classifications
A point cloud is a set of individual data points in a three-dimensional (3D) space. Proper collection of these data points may create an identifiable 3D structure, map, or model.
  • 894
  • 12 May 2023
Topic Review
Ensemble Learning
Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. 
  • 891
  • 29 Jun 2023
Topic Review
Knowledge-Based Robot Manipulation and Knowledge-Graph Embedding
Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics.
  • 890
  • 17 Nov 2023
Topic Review
COVID-19 Vaccines Related User’s Response Categorization
Respiratory viruses known as coronaviruses infect people and cause death. The multiple crown-like spikes on the virus’s surface give them the name “corona”. The pandemic has resulted in a global health crisis and it is expected that every year people will have to fight against different COVID-19 variants. In this critical situation, the existence of COVID-19 vaccinations provides hope for mankind. Despite severe vaccination campaigns and recommendations from health experts and the government, people have perceptions regarding vaccination risks and share their views and experiences on social media platforms. Social attitudes to these types of vaccinations are influenced by their positive and negative effects. The analysis of such opinions can help to determine social trends and formulate policies to increase vaccination acceptance. 
  • 888
  • 21 Oct 2022
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