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Topic Review
Quality of OpenStreetMap Data
OpenStreetMap (OSM) is a potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators. Improving the quality of these crowdsourcing data has significant implications for monitoring and achieving SDGs, such as zero hunger, sustainable cities, ensuring tenure security, and preserving biodiversity. The quality of OpenStreetMap (OSM) has been widely concerned as a valuable source for monitoring some sustainable development goals (SDG) indicators. Improving its semantic quality is still challenging. As a kind of solution, road type prediction plays an important role. However, most existing algorithms show low accuracy, owing to data sparseness and inaccurate description. 
  • 670
  • 19 Dec 2023
Topic Review
Automation of Competency-Based E-Learning Systems
In today’s learning environment, e-learning systems are becoming a necessity. A competency-based student portfolio system is also gaining popularity. Due to the variety of e-learning systems and the increasing mobility of students between different learning institutions or e-learning systems, a higher level of automated competency portfolio integration is required.
  • 669
  • 27 Dec 2022
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.
  • 667
  • 10 Jun 2022
Topic Review
A Sub-Second Method for SAR Image Registration
For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks.
  • 666
  • 24 Oct 2023
Topic Review
Intelligent Virtual Agents
The use of intelligent virtual agents (IVA) to support humans in social contexts will depend on their social acceptability. Acceptance will be related to the human’s perception of the IVAs as well as the IVAs’ ability to respond and adapt their conversation appropriately to the human. Adaptation implies computer-generated speech (synthetic speech), such as text-to-speech (TTS).
  • 665
  • 08 Jul 2022
Topic Review
Artificial Intelligence for COVID-19 Containment
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats.
  • 665
  • 30 Mar 2023
Topic Review
Traffic Pattern in Smart Cities
Smart cities have large-scale infrastructures that have been developed to monitor a wide variety of urban occurrences. This is done to improve the quality of urban life. In most instances, they place a very restricted and specific emphasis on (e.g., monitoring the traffic). They are expensive, need the management of specialists, and are not universally well-liked among residents since they focus on topics that are not (often) of public importance. 
  • 665
  • 23 Oct 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. 
  • 665
  • 18 Feb 2024
Topic Review
eDenoizer
eDenoizer effectively orchestrates both the denoizer and the model defended by the denoizer simultaneously. In addition, the priority of the CPU side can be projected onto the GPU which is completely priority-agnostic, so that the delay can be minimized when the denoizer and the defense target model are assigned a high priority.
  • 665
  • 08 Oct 2022
Topic Review
Semantic Segmentation of Medical Images
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. Lesion detection is one of the primary objectives of medical imaging, as the size and location of lesions are often directly associated with a patient’s diagnosis, treatment, and prognosis. Since the development of computer vision algorithms, however, researchers have begun to utilize these algorithms in the field of medical imaging.
  • 664
  • 02 Dec 2022
Topic Review
Predictability of Thalassemia Using AI
Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients.
  • 664
  • 23 Nov 2023
Topic Review
Proliferative Diabetic Retinopathy Diagnosis
Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. 
  • 663
  • 21 Jul 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. 
  • 662
  • 13 May 2022
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.
  • 662
  • 01 Aug 2022
Topic Review
Hybrid Evolutionary Approaches for Feature Selection
The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works.
  • 661
  • 26 Jul 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.
  • 661
  • 13 Oct 2023
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.
  • 661
  • 04 Sep 2023
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. 
  • 658
  • 22 Dec 2023
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.
  • 656
  • 24 Oct 2023
Topic Review
Decentralized Federated Learning and Knowledge Graph Embedding
Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning to address potential threats in order to enhance data security.
  • 656
  • 15 Dec 2023
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