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Topic Review
Deep Learning in Causality Mining
Deep learning models for causality mining (CM) can enhance the performance of learning algorithms, improve the processing time, and increase the range of mining applications.
  • 1.0K
  • 08 Nov 2021
Topic Review
Continuous Pain Intensity Monitoring
The continuous pain intensity recognition system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities.
  • 1.0K
  • 20 Oct 2023
Topic Review
The Methods of Fall Detection
Falls by an older person are a significant public health issue because they can result in disabling fractures and cause severe psychological problems that diminish a person’s level of independence. Falls can be fatal, particularly for the elderly. Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems.
  • 1.0K
  • 09 Jun 2023
Topic Review
Artificial Intelligence in Cancer Research
Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies.
  • 1.0K
  • 09 Dec 2022
Topic Review
Machine Vision and Industry 4.0 to Industry 5.0
With the emergence of artificial intelligence (AI) and its integration into various intelligent robotics, the Fourth Industrial Revolution, also known as Industry 4.0, managed to trigger changes. Its need has been emphasized in multiple situations, such as that of the COVID-19 pandemic, entering every area of human life, with Industry 4.0 being more and more involved in production processes. Industry 4.0 is an emerging concept that is multidisciplinary and complex. Leveraging not just one, but a patchwork of technologies that can work individually as well as in combination, Industry 4.0 strives to achieve a more general digital transformation with high expectations both in the production of products and services in real-time. This effort is mainly based on advanced computers with fast processors able to store, manage, process, and analyze a large amount of data, spending less time and resources than ever before.
  • 1.0K
  • 08 Mar 2024
Topic Review
Automated Fact Verification Systems
The rapid growth in Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy.
  • 1.0K
  • 01 Dec 2023
Topic Review
State-of-the-Art on Recommender Systems for E-Learning
Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization.
  • 1.0K
  • 06 Dec 2022
Topic Review
A New Container Throughput Forecasting Paradigm under COVID-19
COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. Combining this with change-point analysis and empirical mode decomposition (EMD), this uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, here tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. 
  • 1.0K
  • 24 Mar 2022
Topic Review
Open-Domain Conversational AI
There are different opinions as to the definition of AI, but according to, it is any computerised system exhibiting behaviour commonly regarded as requiring intelligence. Conversational AI, therefore, is any system with the ability to mimick human–human intelligent conversations by communicating in natural language with users. Conversational AI, sometimes called chatbots, may be designed for different purposes. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. 
  • 1.0K
  • 24 Jun 2022
Topic Review
Intelligent Question Answering System
Intelligent question answering system is an innovative information service system which integrates natural language processing, information retrieval, semantic analysis and artificial intelligence. The system mainly consists of three core parts, which are question analysis, information retrieval and answer extraction. Through these three parts, the system can provide users with accurate, fast and convenient answering services.
  • 1.0K
  • 20 Aug 2024
Topic Review
Diabetic Foot with Exercise Therapy
Diabetic foot (DF) is a long-term diabetes complication that can increase morbidity and mortality in addition to affecting mobility and the overall well-being of patients. In particular, the DF has a complex multifactorial pathogenesis that makes it difficult to prevent and treat. In this sense, it is well known that the prevention and treatment of DF disease requires a multidisciplinary approach. Physical activity has always been considered a potential pillar in the prevention of DFD. More recently, it has been reported, that physical activity can contribute in the wound healing phase. Unfortunately, to date, there is no clear and definitive evidence on the role that protocols of physical activity can play in the treatment of patients at risk or with DFD. In order to pursue this objective, it is important to standardize exercise training protocols for the prevention or treatment of these patients. Moreover, it is now possible to organize innovative methods of conducting, monitoring and analysing physical activity performed by patients, even remotely.
  • 1.0K
  • 29 Mar 2022
Topic Review
Human–Autonomous Taxis Interactions
With the increasing deployment of autonomous taxis in different cities around the world, recent studies have stressed the importance of developing new methods, models and tools for intuitive human–autonomous taxis interactions (HATIs). Street hailing is one example, where passengers would hail an autonomous taxi by simply waving a hand, exactly like they do for manned taxis.
  • 1.0K
  • 30 May 2023
Topic Review
Pedestrian Tracking in Autonomous Vehicles
Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking.
  • 1.0K
  • 29 Feb 2024
Topic Review
Quantum Machine Learning
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed.
  • 1.0K
  • 01 Jun 2023
Topic Review
Forecasting Pollution in Urban Area
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions.  Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.
  • 1.0K
  • 16 Jul 2021
Topic Review
Single-Agent Reinforcement Learning and Multi-Agent Reinforcement Learning
Flexible job shop scheduling (FJSP) is regarded as an effective measure to deal with the challenge of mass personalized and customized manufacturing in the era of Industry 4.0, and is widely extended to many real applications. Single-Agent Reinforcement Learning (SARL) is the algorithm only contains one agent that makes all the decisions for a control system. Multi-Agent Reinforcement Learning (MARL) is the algorithm comprises multiple agents that interact with the environment through their respective policies.
  • 1.0K
  • 08 Jan 2024
Topic Review
Deep Learning Architectures for Multivariate Time-Series Forecasting
Deep learning algorithms, renowned for their ability to extract intricate patterns from complex datasets, have proven particularly adept at handling the multifaceted time-series data characteristic of smart city IoT applications. Deep learning architectures model complex relationships through a series of nonlinear layers—the set of nodes of each intermediate layer capturing the corresponding feature representation of the input.
  • 1.0K
  • 27 Oct 2023
Topic Review
Segmentation of Liver Tumor in Computed Tomography Scan
Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from computed tomography (CT) scans is critical for identifying liver cancer.
  • 1.0K
  • 15 Sep 2022
Topic Review
Neuromorphic Sentiment Analysis Using Spiking Neural Networks
Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. 
  • 1.0K
  • 20 Sep 2023
Topic Review
Classification of Low Illumination Image Enhancement Methods
As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. According to the learning method used, people can classify existing low-illumination enhancement methods into four categories, i.e., supervised learning, unsupervised learning, semi-supervised learning, and zero-shot learning methods.
  • 997
  • 06 May 2023
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