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Topic Review
Augmented Reality in Professional Training
Professional training is defined as a set of behaviors and acts with the purpose of increasing the employees’ professional skills to carry out a particular job in a better manner. Such a definition highlights three important features of professional training. First, its purpose is educational, which focuses on employee development (e.g., skill acquisition and knowledge growth) rather than performance improvement. Augmented reality (AR) is defined as a technology-enhanced environment where virtual objects (augmented components) can be overlaid into the real world. Azuma (1997) identified three technical features of AR: a combination of the real and virtual world, real-time interaction, and accurate 3D registration of virtual and real objects.
  • 1.2K
  • 27 Jan 2022
Topic Review
Deep Learning Models to Predict Prosthetic Ankle Torque
Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers.
  • 1.2K
  • 25 Sep 2023
Topic Review
Machine Learning in Agricultural Big Data
Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address problems such as farmers’ decision making, water management, soil management, crop management, and livestock management. Crop management includes yield prediction, disease detection, weed detection, crop quality, and species recognition. On the other hand, livestock management considers animal welfare and livestock production. 
  • 1.2K
  • 08 Apr 2022
Topic Review
Machine Learning-Based Text Classification Comparison
The growth in textual data associated with the increased usage of online services and the simplicity of having access to these data has resulted in a rise in the number of text classification research papers. Text classification has a significant influence on several domains such as news categorization, the detection of spam content, and sentiment analysis. The classification of Turkish text is the research focus since only a few studies have been conducted in this context. Researchers utilize data obtained from customers’ inquiries that come to an institution to evaluate the proposed techniques. Classes are assigned to such inquiries specified in the institution’s internal procedures. The Support Vector Machine, Naïve Bayes, Long Term-Short Memory, Random Forest, and Logistic Regression algorithms were used to classify the data. The performance of the various techniques was then analyzed after and before data preparation, and the results were compared. The Long Term-Short Memory technique demonstrated superior effectiveness in terms of accuracy, achieving an 84% accuracy rate, surpassing the best accuracy record of traditional techniques, which was 78% accuracy for the Support Vector Machine technique. The techniques performed better once the number of categories in the dataset was reduced. Moreover, the findings show that data preparation and coherence between the classes’ number and the number of training sets are significant variables influencing the techniques’ performance.
  • 1.2K
  • 01 Sep 2023
Topic Review
Machine Learning-Based Facial Palsy Detection and Evaluation
Automated solutions for medical diagnosis based on computer vision form an emerging field of science aiming to enhance diagnosis and early disease detection. The detection and quantification of facial asymmetries enable facial palsy evaluation.  Deep learning methods allow the automatic learning of discriminative deep facial features, leading to comparatively higher performance accuracies.
  • 1.2K
  • 22 Jan 2024
Topic Review
Machine Learning-based Driver Drowsiness Detection Using Visual Features
Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. Image-based systems are the most commonly used techniques for detecting driver drowsiness. Facial parameters such as the eyes, mouth, and head can be used to identify many visual behaviors that fatigued people exhibit.
  • 1.2K
  • 06 Jun 2023
Topic Review
AI Agent Model for Extrinsic Emotion Regulation
Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. To address this gap, a computational model for intelligent agents has been proposed for facilitating emotion regulation in individuals. This model is grounded in a multidimensional emotion representation and on J. Gross’s theoretical framework of emotion regulation. In this apporach, an intelligent agent selects the most appropriate regulation strategies to reach or maintain an individual’s emotional equilibrium considering the individual’s personality traits and specific characteristics.
  • 1.2K
  • 11 Mar 2024
Topic Review
The Urban Transit Routing Problem
The Urban Transit Routing Problem (UTRP) is a challenging problem in transportation planning that involves designing and optimizing transit route networks for urban areas. The objective is to find the most efficient routes for public transportation vehicles, considering factors such as travel time, passenger demand, transfer connections, vehicle capacities, operating costs, and environmental impacts. 
  • 1.2K
  • 21 Aug 2023
Topic Review
Deep Learning for Motor Imagery Brain–Computer Interface
The field of brain–computer interface (BCI) enables us to establish a pathway between the human brain and computers, with applications in the medical and nonmedical field. Brain computer interfaces can have a significant impact on the way humans interact with machines. In recent years, the surge in computational power has enabled deep learning algorithms to act as a robust avenue for leveraging BCIs. 
  • 1.2K
  • 17 Oct 2023
Topic Review
Compression of Neural Networks
Researchers propose the value-locality-based compression (VELCRO) algorithm for neural networks. VELCRO is a method to compress general-purpose neural networks that are deployed for a small subset of focused specialized tasks.
  • 1.2K
  • 08 Nov 2021
Topic Review
AI-Powered Diagnosis of Skin Cancer
Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. Artificial Intelligence (AI)-based methods can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. 
  • 1.2K
  • 27 Feb 2023
Topic Review
Deep Learning Approaches for Distance Estimation
Visual impairment (VI) is a significant public health concern that affects people of all ages and is caused by a range of factors, including age-related eye diseases, genetic disorders, injuries, and infections. Therefore, governments of different countries are attempting to design various assistive living facilities for individuals with visual impairments. Machine learning techniques have greatly improved object recognition accuracy in computer vision [8]. This has led to the development of sophisticated models that can recognize objects in complex environments. 
  • 1.2K
  • 16 Oct 2023
Topic Review
Regional-to-Local Point-Voxel Transformer
Semantic segmentation of large-scale indoor 3D point cloud scenes is crucial for scene understanding but faces challenges in effectively modeling long-range dependencies and multi-scale features. Researchers present RegionPVT, a novel Regional-to-Local Point-Voxel Transformer that synergistically integrates voxel-based regional self-attention and window-based point-voxel self-attention for concurrent coarse-grained and fine-grained feature learning. The voxel-based regional branch focuses on capturing regional context and facilitating inter-window communication. The window-based point-voxel branch concentrates on local feature learning while integrating voxel-level information within each window.
  • 1.2K
  • 20 Oct 2023
Topic Review
A Benchmark Dataset for Wearable Low-Light Pedestrian Detection
Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of pedestrians. With this in mind, a low-light pedestrian detection (called HRBUST-LLPED) dataset by capturing pedestrian data on campus using wearable low-light cameras is introduced.
  • 1.2K
  • 19 Dec 2023
Topic Review
Deep Anomaly Detection for In-Vehicle Monitoring
Deep learning approaches to the detection of visual data instances that markedly digress from regular sequences have been mostly focusing on outdoor video-surveillance scenarios, mainly regarding abnormal behaviour and suspicious or abandoned object detection. However, with the increasing importance of public and shared transportation for urban mobility, it becomes imperative to provide autonomous intelligent systems capable of detecting abnormal behaviour that threatens passenger safety. In-vehicle monitoring becomes particularly relevant for Shared Autonomous Vehicles, which do not have a driver responsible for assuring the well-being and safety of passengers; such vehicles must be accompanied by reliable autonomous in-vehicle surveillance systems.
  • 1.2K
  • 17 Oct 2022
Topic Review
Machine Learning Methods
Machine learning (ML) has a well-established reputation for successfully enabling automation through its scalable predictive power.
  • 1.2K
  • 15 Nov 2022
Topic Review
Artificial Intelligence-Assisted Programming Tasks
Artificial intelligence (AI)-assisted programming can enable software engineers to work more efficiently and effectively with the existing software tools such as OpenAI ChatGPT, Github Copilot, DeepMind AlphaCode, Amazon Codewhisperer, Replit Ghostwriter, Microsoft IntelliCode and Codedium, especially in situations where complex algorithms are being used that involve large amounts of code (i.e., Big Code regime). It also strikes a balance between productivity and ensuring safety, security, and reliability within the programming development environment. There are two main categories of AI-assisted programming tasks related to software naturalness: generation and understanding. The former includes code generation, code completion, code translation, code refinement, and code summarization. The latter is concerned with understanding code and includes defect detection and clone detection.
  • 1.2K
  • 03 Jul 2023
Topic Review
Oximetry for Obstructive Sleep Apnea
Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs.
  • 1.2K
  • 11 Oct 2023
Topic Review
Internet-of-Things-Based Smart Monitoring System
With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources.
  • 1.2K
  • 29 May 2023
Topic Review
Algorithms for Spam Detection
Spam emails have become a pervasive issue, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. 
  • 1.2K
  • 16 Oct 2023
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