scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

Machine learning techniques based on security management in smart cities using robots

Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Zhang Mengqi ◽  
Wang Xi ◽  
V.E. Sathishkumar ◽  
V. Sivakumar
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Security Management ◽  
Machine Learning Techniques ◽  
Quality Of Services ◽  
Learning Techniques ◽  
Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

scholarly journals Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128325-128338 ◽  
Author(s):  
Saba Ameer ◽  
Munam Ali Shah ◽  
Abid Khan ◽  
Houbing Song ◽  
Carsten Maple ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Air Quality ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

2019 ◽  
Vol 11 (16) ◽  
pp. 1943 ◽  
Author(s):  
Omid Rahmati ◽  
Saleh Yousefi ◽  
Zahra Kalantari ◽  
Evelyn Uuemaa ◽  
Teimur Teimurian ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mountainous Area ◽  
Data Set ◽  
Boosted Regression Tree ◽  
Hazard Exposure ◽  
Learning Techniques

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

Survey of Machine Learning Techniques in Drug Discovery

2019 ◽  
Vol 20 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Natalie Stephenson ◽  
Emily Shane ◽  
Jessica Chase ◽  
Jason Rowland ◽  
David Ries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
State Of The Art ◽  
New Drugs ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Learning Techniques ◽  
Pharmaceutical Industries ◽  
Current Stage

Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

scholarly journals State‐of‐the‐Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System

2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Rahul Kumar Sevakula ◽  
Wan‐Tai M. Au‐Yeung ◽  
Jagmeet P. Singh ◽  
E. Kevin Heist ◽  
Eric M. Isselbacher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular System ◽  
Patient Outcomes ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Improve Patient
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