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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
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
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
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
Breast Density and Pre-Trained Convolutional Neural Network
Breast density describes the amount of fibrous and glandular tissue in a breast compared with the amount of fatty tissue. The breast density is assigned to one of four classes in the mammogram report based on the ACR BI-RADS standard. Convolutional Neural Network (CNN) are a type of artificial neural network usually used for classification and computer vision tasks. Therefore, CNNs are considered efficient tools for medical imaging classification.
  • 1.1K
  • 21 Jun 2022
Topic Review
Railway Track Fault Detection
Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. 
  • 1.1K
  • 29 Aug 2023
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
Quantization Methods of  Defense against Membership Inference Attacks
Machine learning deployment on edge devices has faced challenges such as computational costs and privacy issues. Membership inference attack (MIA) refers to the attack where the adversary aims to infer whether a data sample belongs to the training set. In other words, user data privacy might be compromised by MIA from a well-trained model. Therefore, it is vital to have defense mechanisms in place to protect training data, especially in privacy-sensitive applications such as healthcare. 
  • 1.1K
  • 19 Sep 2023
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
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
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
Approach for Overlapped Segmentation of Bacterial Cell Images
Scanning electron microscopy (SEM) techniques have been extensively performed to image and study bacterial cells with high-resolution images. Bacterial image segmentation in SEM images is an essential task to distinguish an object of interest and its specific region.
  • 1.1K
  • 15 Dec 2022
Topic Review
Classification of Sleep Stages Using Telemetry Polysomnography
Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment as it captures diverse physiological signals. Recent advancements have shown that simpler machine learning models, when coupled with sophisticated feature extraction techniques, can yield accurate and reliable results comparable to those achieved by complex deep learning methods. These simpler models not only reduce the computational burden but also offer greater interpretability, a feature that is highly valued in clinical settings for both diagnostic and treatment purposes. Therefore, integrating simpler machine learning algorithms with advanced feature extraction can serve as an effective and efficient approach for sleep stage classification in research and clinical applications.
  • 1.1K
  • 25 Aug 2023
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
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
Automation in Interior Space Planning
In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality.
  • 1.1K
  • 08 Aug 2023
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
DeepSORT
DeepSORT is an intelligent tracking technology that can continuously track multiple objects in complex scenarios, such as crowded areas or environments with occlusions. By integrating the appearance features and motion patterns of the targets, it is widely applied in fields like security surveillance, autonomous driving, and sports analysis, significantly enhancing the stability and accuracy of tracking.
  • 1.1K
  • 24 Mar 2025
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