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Topic Review
Cube Surface Light Field Representation
The core idea of the cube surface light field representation is to parameterize the light rays on the two intersections with the cube surface and use the color value at the first intersection of the light ray and the object's surface to be the color of this light ray, constructing a pure ray-based 4D light field representation of the scenes.
  • 656
  • 01 Aug 2022
Topic Review
Chatbots in the Healthcare Industry
Chatbots have become increasingly popular in the healthcare industry. In the area of preventive care, chatbots can provide personalized and timely solutions that aid individuals in maintaining their well-being and forestalling the development of chronic conditions.
  • 654
  • 24 Oct 2023
Topic Review
Heterogeneous Federated Learning via Relational Adaptive Distillation
As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is gaining popularity as a distributed machine learning framework that does not compromise the data privacy of each participant. However, the data held by enterprises and factories in the IoT often have different distribution properties (Non-IID), leading to poor results in their federated learning.
  • 653
  • 13 Oct 2023
Topic Review
Enhancing Ensemble Learning Using CNN for Spoof Fingerprints
Convolutional Neural Networks (CNNs) have demonstrated remarkable success with great accuracy in classification problems. Using an ensemble of neural networks offers a simple yet effective measure to improve performance and robustness beyond that of a single network.
  • 652
  • 18 Jan 2024
Topic Review
Concept Prerequisite Learning with PTM and GNN
Prerequisite chains are crucial to acquiring new knowledge efficiently. Many studies have been devoted to automatically identifying the prerequisite relationships between concepts from educational data. Though effective to some extent, these methods have neglected two key factors: most works have failed to utilize domain-related knowledge to enhance pre-trained language models, thus making the textual representation of concepts less effective; they also ignore the fusion of semantic information and structural information formed by existing prerequisites.
  • 650
  • 04 Sep 2023
Topic Review
Floating Photovoltaic in Underwater Electric Vehicles
Electric vehicles are becoming increasingly necessary in today’s generation systems, with the process of E-vehicles being used not only in automobile manufacturing companies, but also in the design of underwater vehicular technology. Floating photovoltaic in the presence of PV panels is the process of knowing underwater systems with solar panels. 
  • 649
  • 13 May 2022
Topic Review
Cross-Domain Sentiment Analysis in IoT
Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. 
  • 649
  • 01 Sep 2023
Topic Review
Fault Detection Approaches for Lithium-Ion Batteries
Battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. Researchers used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. 
  • 647
  • 18 Feb 2024
Topic Review
Security Threats for Medical Wearables
In the past few years, “smart” objects and products have given rise to significant progress in industry production and its security. Advances in digitization that have occurred in the industry, combined with internet technologies and future-oriented technologies in the field of so-called “smart” objects (machines and products), have led to a new and fundamental paradigm shift in industrial production and in their security.
  • 646
  • 14 Mar 2022
Topic Review
Detecting Misalignment State of Angle Cocks
As one of the key components in the braking system, the angle cock is the switch of the train ventilation duct, which realizes the braking through the air transmission between carriages, so that the train can achieve the purpose of regulating speed or stopping.
  • 646
  • 05 Sep 2023
Topic Review
Hybrid LSTM Model and Air Pollution Prediction
Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, the approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures.
  • 646
  • 22 Sep 2023
Topic Review
Computer-Aided Diagnosis Approach for Breast Cancer
Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world’s leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. A computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist’s mission A new transfer learning approach is introduced for breast cancer classification using a set of pre-trained Convolutional Neural Network (CNN) models with the help of data augmentation techniques. Multiple experiments are performed to analyze the performance of these pre-trained CNN models through carrying out magnification dependent and magnification independent binary and eight-class classifications. Xception model has shown a promising performance through achieving the highest classification accuracy for all experiments.
  • 645
  • 17 Oct 2022
Topic Review
Privacy and Security in Sustainable Smart City Applications
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML).
  • 645
  • 12 Dec 2023
Topic Review
AI-Based Prediction of Dementia
Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently effects over 55 million individuals. Dementia prevention is a global public health priority, and recent ones have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia, through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based large multinational datasets will be validated and integrated in a 18-month trial integrating digital biomarkers, to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data, and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres.
  • 645
  • 10 Jun 2022
Topic Review
Serious Games for Learning Artificial Intelligence Algorithms
Artificial Intelligence (AI) is the technology of the future, as its applications are constantly expanding in every aspect of human life. The spread of the internet has given a great impetus to technologies that apply AI algorithms and make their presence more and more intense in everyday life. However, despite the ubiquitous presence of AI, few people can comprehend its true meaning and reason for its existence, especially the way it is applied. Serious games, that is games with a "serious purpose" other than entertaninment, can play an important role in comprehending and applying AI algorithms.
  • 644
  • 05 Jun 2023
Topic Review
Deep Learning Building Blocks
Industry 4.0 characterizes the transformation from traditional automation to engineered cyber-physical systems with human-like intelligence. Indeed, this gives Artificial Intelligence (AI) the privilege of playing a central role in Industry 4.0. Moreover, the leading branch of AI turned out to be deep learning (DL). DL is an essential subfield of machine learning (ML) characterized by its layered structure of artificial neural networks (ANNs). 
  • 644
  • 23 Jan 2024
Topic Review
Multispectral Facial Recognition in the Wild
Face recognition systems in uncontrolled environments have shown impressive performance improvements. However, most are limited to the use of a single spectral band in the visible spectrum. The use of multi-spectral images makes it possible to collect information that is not obtainable in the visible spectrum when certain occlusions exist (e.g., fog or plastic materials) and in low- or no-light environments. The state of the art regarding face recognition systems in an uncontrolled environment has led to the conclusion that image synthesis methods, mainly with GANs, have been used to combat intrapersonal variations, such as the difference in pose and facial expression. On the other hand, in the area of multispectral face recognition, with a plurality of solutions presented by the use of multispectral images, fusion methods are those that make the most use of images captured in different spectral bands in order to make a decision. The main problem encountered is the limited number of images (and people) in multispectral databases in an uncontrolled environment, which makes it challenging to train convolutional neural networks, which are the most used method for feature extraction.
  • 643
  • 25 Aug 2022
Topic Review
Video Super-Resolution
Super-resolution (SR) refers to yielding high-resolution (HR) images from corresponding low-resolution (LR) images. As a branch of this field, video super-resolution (VSR) mainly utilizes the spatial information of the frame and the temporal information between neighboring frames to reconstruct the HR frame. 
  • 643
  • 27 Oct 2023
Topic Review
Multilingual Evidence for Fake News Detection
The rapid spread of deceptive information on the internet can have severe and irreparable consequences. As a result, it is important to develop technology that can detect fake news. Although significant progress has been made in this area, current methods are limited because they focus only on one language and do not incorporate multilingual information. Multiverse—a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches.
  • 643
  • 02 Feb 2024
Topic Review
Privacy Protection in Mobile Edge Computing
Data sharing and analyzing among different devices in mobile edge computing is valuable for social innovation and development. The limitation to the achievement of this goal is the data privacy risk. Therefore, existing studies mainly focus on enhancing the data privacy-protection capability. On the one hand, direct data leakage is avoided through federated learning by converting raw data into model parameters for transmission. On the other hand, the security of federated learning is further strengthened by privacy-protection techniques to defend against inference attack. However, privacy-protection techniques may reduce the training accuracy of the data while improving the security. Particularly, trading off data security and accuracy is a major challenge in dynamic mobile edge computing scenarios. 
  • 642
  • 22 Dec 2023
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