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Topic Review
SeAttE—Embedding Model Based on Knowledge Graph Completion
SeAttE is a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion. SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. 
  • 887
  • 21 Apr 2022
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. 
  • 887
  • 14 Nov 2023
Topic Review
Weakly Supervised Object Detection for Remote Sensing Images
To account for the lack of fine-grained annotations, such as object bounding boxes, several object detection methods have been developed that leverage only coarse-grain annotations (especially image-level labels indicating only the presence or absence of an object). This approach is called inexact Weak Supervision and introduces a new branch of Object Detection called Weakly Supervised Object Detection. Given an image, Remote Sensing Fully Supervised Object Detection (RSFSOD) aims to locate and classify objects based on Bounding Boxes annotations. Differently from RSFSOD, Remote Sensing Weakly Supervised Object Detection aims to precisely locate and classify object instances in Remote Sensing Images using only image-level labels or other types of coarse-grained labels (e.g., points or scribbles) as ground truth. 
  • 886
  • 24 Nov 2022
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.
  • 885
  • 12 May 2023
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.
  • 883
  • 11 Nov 2022
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. 
  • 882
  • 21 Oct 2022
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. 
  • 879
  • 27 Oct 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.
  • 879
  • 07 Jan 2025
Topic Review
Public Perceptions around mHealth Applications
This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during the COVID-19 pandemic: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. The specific objectives of this study are: (1) to examine the difference in communication network structure across the networks generated among the six mHealth apps included in our study; (2) to analyze the sentiment surrounding the six mHealth apps conversations; and (3) to evaluate the performance of a sentiment classifier using machine learning approaches.
  • 878
  • 10 Jan 2022
Topic Review
Non-Iterative Cluster Routing
In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes.
  • 877
  • 19 Mar 2024
Topic Review
Encoding Techniques for Gait Analysis
Gait refers to the movement patterns of an individual’s walk. It encompasses the rhythm, speed, and style of movement which require a strong coordination of the upper and lower limbs. The dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion.
  • 874
  • 19 Jan 2024
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. 
  • 872
  • 16 Aug 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. 
  • 870
  • 29 Jun 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.
  • 869
  • 13 Feb 2023
Topic Review
IDS Using Feature Extraction with ML in IoT
With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy.
  • 869
  • 22 Nov 2023
Topic Review
Quality of Pinot Noir Wine
Wine quality is an important concept for each of these disciplines, as well as for both wine producers and consumers. Any technique that could help producers to understand the nature of wine quality and how consumers perceive it, will help them to design even more effective marketing strategies.
  • 866
  • 01 Nov 2022
Topic Review
Incremental Deep Learning for Defect Detection in Manufacturing
Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase.
  • 866
  • 23 Feb 2024
Topic Review
Advancements in Glaucoma Diagnosis
The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population.
  • 865
  • 14 Mar 2024
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. 
  • 863
  • 19 Jan 2024
Topic Review
Product Promotion in Internet Market
The influence of Internet marketing has grown so much that producers must now reconfigure their businesses from offline operation to online presence simply to meet user expectations. Thus, the development of an intelligent information system for product promotion online is quite relevant. It may lead to automatized selection of competing products and advertising content, a subsequent increase in the effectiveness of advertisements, and a decrease in costs for Internet ad placements. 
  • 861
  • 08 Sep 2023
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