Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Control Problems of Power Systems

2021 ◽  
pp. 83-117
Author(s):  
Samira Sadeghi ◽  
Ali Hesami Naghshbandy ◽  
Parham Moradi ◽  
Navid Rezaei
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Power Systems ◽  
Control Problems

NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi139-vi139
Author(s):  
Jan Lost ◽  
Tej Verma ◽  
Niklas Tillmanns ◽  
W R Brim ◽  
Harry Subramanian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Prediction Accuracy ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Resection ◽  
Support Vector ◽  
Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals Existential Methods on Diabetes Detection using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3034-3039
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Literature Review ◽  
Learning Techniques ◽  
The World ◽  
Diabetes Prediction

Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM). In this paper, the literature review is done on diabetes prediction using Machine Learning and Deep Learning techniques. Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy. DL algorithms like CNN and LMST used in diabetic retinopathy images.

scholarly journals A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 518 ◽  
Author(s):  
Hafsa Khalid ◽  
Muzammil Hussain ◽  
Mohammed A. Al Ghamdi ◽  
Tayyaba Khalid ◽  
Khadija Khalid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Ct Scan ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
Learning Techniques

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

Clustering in Power Systems Using Innovative Machine Learning/Deep Learning Methods

2021 ◽  
pp. 151-176
Author(s):  
Mohammad Hossein Rezaeian Koochi ◽  
Mohammad Hasan Hemmatpour ◽  
Payman Dehghanian
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Power Systems ◽  
Learning Methods

Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Load Forecasting in Power System

2021 ◽  
pp. 119-135
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Morteza Nazari-Heris ◽  
Behnam Mohammadi-Ivatloo ◽  
Somayeh Asadi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Power System ◽  
Load Forecasting

NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi145-vi145
Author(s):  
Niklas Tillmanns ◽  
Avery Lum ◽  
W R Brim ◽  
Harry Subramanian ◽  
Ming Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Controlled Vocabulary ◽  
Machine Learning Algorithms ◽  
Average Score ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Bias Analysis ◽  
Number Of Patients

Abstract PURPOSE Nowadays Machine learning (ML) algorithms are often used for segmentation of gliomas, but which algorithms provide the most accurate method for implementation into clinical practice has not fully been identified. We performed a systematic review of the literature to characterize the methods used for glioma segmentation and their accuracy. METHODS In accordance to PRISMA, a literature review was performed on four databases, Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection first in October 2020 and in February 2021. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Publications were screened in Covidence and the bias analysis was done in agreement with TRIPOD. RESULTS Sixty-six articles were used for data extraction. BRATS and TCIA datasets were used in 36.6% of all studies, with average number of patients being 141 (range: 1 to 622). ML methods represented 45.3% of studies, with deep learning used in 54.7%; Dice score for the tumor core ranged from 0.72 to 0.95. The most common algorithm used in the machine learning papers was support vector machines (SVM) and for deep learning papers, it was Convolutional Neural Networks (CNN). Preliminary TRIPOD analysis yielded an average score from 12 (range: 7-16) with the majority of papers demonstrating deficiencies in description of the ML algorithm, funding role, data acquisition and measures of model performance. CONCLUSION In the last years, many articles were published on segmentation of gliomas using machine learning, thus establishing this method for tumor segmentation with high accuracy. However, the major limitations for clinically applicable use of ML in glioma segmentation include more than one-third of publications use the same datasets, thus limiting generalizability, increase the likelihood of overfitting, show and lack of ML network description and standardization in accuracy reporting.

scholarly journals Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

2021 ◽  
pp. 32-42
Author(s):  
Navod Neranjan .. ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Smart Grid ◽  
Power Systems ◽  
Energy Distribution ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Directions ◽  
Architectural Elements ◽  
Efficient Power

In the 21st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG. In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well.

scholarly journals Conceptual view approach of Machine Learning Based Recommendation System

2021 ◽  
Vol 23 (07) ◽  
pp. 1165-1173
Author(s):  
G NageswaraRao ◽  
◽  
P Om Sreeja ◽  
T Anusha ◽  
K Kethan Surya Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Human Brain ◽  
Recommendation System ◽  
Motion Pictures ◽  
Learning System ◽  
Learning Approaches ◽  
Learning Models ◽  
Neural Learning

The paper explains the use of machine learning approaches and especially throws light on the issue of user-based recommender frameworks. The new sort of framework which has been received by this exploration is a blend of profound learning baed and client recommender type arrangement of AI. Therefore, the model of a hybrid system of deep learning system has been incorporated into this research which used the convolutional neural learning models. This system of learning has been explained as the method which is used to study various users’ preferences in order to see their clicks. The information utilizes considering the inclinations or proposals of the clients is utilized in such a manner to direct these machines. In the client proposals frameworks, the innovation of computerized reasoning is utilized with the goal that the machines could learn things like a human brain. In the section of the literature review, the researcher has emphasized the various models which are used in machine learning. The systems which play a role in the users’ recommender systems involve examining the preferences of these users who use these systems. The system which has been utilized for this exploration is examining different characters who watch various motion pictures which have a place with two classifications of activity and parody. Thus, the information which has been gathered examined and anticipated the inclinations of these clients by considering the aa around gave information. Hence, there are various datasets that are used in this paper to predict the users’ preferences.

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