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Topic Review
Machine-Learning Based Methods for PV
This entry presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. It gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.
  • 1.1K
  • 28 Sep 2021
Topic Review
PET/CT Radiomics in Lung Cancer
Quantitative extraction of imaging features from medical scans (‘radiomics’) has become a major research topic in recent years. Numerous studies have emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment in patients with lung cancer. Furthermore, radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive.
  • 1.1K
  • 17 Feb 2021
Topic Review
Role of Blockchain Technology in COVID-19 Crisis
To obtain adequate performance in resolving issues that are associated with the COVID-19 pandemic, blockchain can be combined with other available technologies to establish a robust healthcare architecture.
  • 1.1K
  • 29 Jan 2022
Topic Review
Vulnerabilities and Potential Threats of Cloud computing
Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology.
  • 1.1K
  • 28 Mar 2022
Topic Review
Deep Learning Techniques for Prediction of Alzheimer’s Disease
Deep learning (DL) has become a prominent issue in the machine learning (ML)  domain in the past few years. ML can be utilized to tackle issues in different sectors. Neuroscience is included in this list. It is well known that detecting malignancies and functioning regions in cognitive systems has been a huge challenge for scientists over the years. The standard approach of detecting the variation in blood oxygen levels can be applied for this purpose. However, completing all the processes can take too long on certain occasions. One benefit of DL approaches over typical ML methods is that the reliability of DL techniques grows with the phases of learning. The efficiency of DL methods tends to rise greatly as more information is provided to them, and they outperform conventional techniques. This is similar to the human brain, which learns more as new information becomes available on a daily basis.
  • 1.1K
  • 13 Oct 2022
Topic Review
Emission Quantification via Passive Infrared Optical Gas Imaging
Passive infrared optical gas imaging (IOGI) is sensitive to toxic or greenhouse gases of interest, offers non-invasive remote sensing, and provides the capability for spatially resolved measurements. It has been broadly applied to emission detection, localization, and visualization.
  • 1.1K
  • 08 Jul 2022
Topic Review
Strategy for Catastrophic Forgetting Reduction in Incremental Learning
Catastrophic forgetting or catastrophic interference is a serious problem in continuous learning in machine learning. It happens not only in traditional machine learning algorithms such as SVM (Support Vector Machine), NB (Naive Bayes), DT (Decision Tree), and CRF (Conditional Random Field) but also in DNNs.
  • 1.1K
  • 05 Jun 2023
Topic Review
Reinforcement Learning in Ad Hoc Vehicular Networks
Ad hoc vehicular networks have been identified as a suitable technology for intelligent communication amongst smart city stakeholders as the intelligent transportation system has progressed. In a highly mobile area, the growing usage of wireless technologies creates a challenging context. To increase communication reliability in this environment, it is necessary to use intelligent tools to solve the routing problem to create a more stable communication system. Reinforcement Learning (RL) is an excellent tool to solve this problem. 
  • 1.1K
  • 01 Jul 2022
Topic Review
Denoising Technique for CT Images
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. 
  • 1.1K
  • 07 Apr 2024
Topic Review
A Unified Framework for RGB-Infrared Transfer
Infrared(IR) images (both 0.7-3 µm and 8-15 µm) offer radiation intensity texture information that visible images lack, making them particularly helpful in daytime, nighttime, and complex scenes. Many researchers are studying how to translate RGB images into infrared images for deep learning-based visual tasks such as object tracking, crowd counting, panoramic segmentation, and image fusion in urban scenarios. The utilization of the RGB-IR dataset in the aforementioned tasks holds the potential to provide comprehensive multi-band fusion data for urban scenes, thereby facilitating precise modeling across different scenarios. In addressing the challenge of accurately generating high-radiance textures for the targets in the infrared spectrum, the proposed approach aims to ensure alignment between the generated infrared images and the radiation feature of ground-truth IR images.
  • 1.1K
  • 18 Dec 2023
Topic Review
Smart Distribution Network Situation Awareness
Due to the rapid development of emerging information and communication technologies (ICT) and advanced metering infrastructure (AMI), distribution networks are in an evolvement from passive to active distribution networks (ADN), also called smart distribution networks (SDN). Operation and maintenance (O&M) cost is an economic factor that the SDN management must consider. Among multiple O&M technologies, situation awareness (SA) emerges and is gradually integrated into the SDN. Facing a high proportion of RES, adequate monitoring, analysis, and prediction of the SDN operating status are urgent. Therefore, comprehensive SA, which contains detection, comprehension, and projection, becomes a significant guarantee for the optimal operation of SDN.
  • 1.1K
  • 24 Feb 2022
Topic Review
NER&RE Techniques on Clinical Texts
Out of the various text mining tasks and techniques, our goal in this paper is to review the current state-of-the-art in Clinical Named Entity Recognition (NER) and Relationship Extraction (RE)-based techniques. Clinical NER is a natural language processing (NLP) method used for extracting important medical concepts and events i.e., clinical NEs from the data. Relationship Extraction (RE) is used for detecting and classifying the annotated semantic relationships between the recognized entities.
  • 1.1K
  • 30 Sep 2021
Topic Review
Troubleshooting Chatbots Applied to ATM Technical Maintenance Support
The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. 
  • 1.1K
  • 21 Jun 2023
Topic Review
Deep Learning-Based Diagnosis of Alzheimer’s Disease
Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. 
  • 1.1K
  • 01 Jun 2022
Topic Review
Multiscale-Deep-Learning Applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task.
  • 1.1K
  • 26 Oct 2022
Topic Review
Coronavirus
Coronaviruses are indeed a huge family of viruses that are found both in humans and animals. Seven different types have been identified, including the ones that caused COVID-19 and the SARS and MERS illnesses.
  • 1.1K
  • 09 Nov 2022
Topic Review
Neural Architecture Search: A Computer Vision Perspective
Deep learning (DL) has been widely studied using various methods across the globe, especially with respect to training methods and network structures, proving highly effective in a wide range of tasks and applications, including image, speech, and text recognition. One important aspect of this advancement is involved in the effort of designing and upgrading neural architectures, which has been consistently attempted thus far. However, designing such architectures requires the combined knowledge and know-how of experts from each relevant discipline and a series of trial-and-error steps. In this light, automated neural architecture search (NAS) methods are increasingly at the center of attention.
  • 1.1K
  • 06 May 2023
Topic Review
3D Object Detection with Differential Point Clouds
3D object detection based on point clouds has many applications in natural scenes, especially in autonomous driving. Point cloud data provide reliable geometric and depth information. 
  • 1.1K
  • 24 Dec 2022
Topic Review
Vision-Based Gait Recognition
Identifying people’s identity by using behavioral biometrics has attracted many researchers’ attention in the biometrics industry. Gait is a behavioral trait, whereby an individual is identified based on their walking style. Over the years, gait recognition has been performed by using handcrafted approaches. Due to several covariates’ effects, the competence of the approach has been compromised. Deep learning is an emerging algorithm in the biometrics field, which has the capability to tackle the covariates and produce highly accurate results. 
  • 1.1K
  • 12 Aug 2022
Topic Review
Random Forest, Feedforward Neural Network, GRU and FinGAT
Stock prediction has garnered considerable attention among investors, with a recent focus on the application of machine learning techniques to enhance predictive accuracy. Prior research has established the effectiveness of machine learning in forecasting stock market trends, irrespective of the analytical approach employed, be it technical, fundamental, or sentiment analysis. 
  • 1.0K
  • 18 Dec 2023
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