{"title":"LSTM Deep Learning Techniques for Wind Power Generation Forecasting","authors":"Ahmed Babiker Abdalla Ibrahim, Kenan Altun","doi":"10.55195/jscai.1471257","DOIUrl":"https://doi.org/10.55195/jscai.1471257","url":null,"abstract":"Wind power generation forecasting is crucial for the optimal integration of renewable energy sources into power systems. Traditional forecasting methods often struggle to accurately predict wind energy production due to the complex and nonlinear relationships between wind speed, weather parameters, and power output. In recent years, deep learning techniques have emerged as promising alternatives for wind power forecasting. This conference paper provides a comprehensive review of deep learning techniques, with a specific focus on Long Short-Term Memory (LSTM) networks, for short-term wind power generation forecasting. Leveraging insights from recent research and empirical evaluations, this paper explores the effectiveness of LSTM networks in capturing temporal dependencies in wind data and improving prediction accuracy. The review highlights the potential of LSTM-based models to enhance the integration of wind energy into power systems and provides guidance for future research in this area.","PeriodicalId":511844,"journal":{"name":"Journal of Soft Computing and Artificial Intelligence","volume":"10 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammed Alperen Horoz, Seda Arslan Tuncer, Çağla Danacı
{"title":"Hybrid Artificial Intelligence Approach to COVID-19 Diagnosis from CT Images: Deep Networks and Classification Analysis","authors":"Muhammed Alperen Horoz, Seda Arslan Tuncer, Çağla Danacı","doi":"10.55195/jscai.1467768","DOIUrl":"https://doi.org/10.55195/jscai.1467768","url":null,"abstract":"Using lung images obtained by computed tomography (CT), this study aims to detect coronavirus (Covid-19) disease with deep learning (DL) techniques. The study included 751 lung CT images from 118 Covid-19 patients and 628 lung CT images from 100 healthy individuals. In total, 70% of the 1379 images were used for training and 30% for testing. In the study, two different methods were proposed on the same dataset. In the first method, the images were trained on AlexNet, VGG-16, VGG-19, GoogleNet and a proposed network. The performance metrics obtained from the five networks were compared and it was observed that the proposed network achieved the highest accuracy value with 95.61%. In the second method, the images were trained on VGG-16, VGG-19, DenseNet-121, ResNet-50 and MobileNet networks. Among the image features obtained from each of these networks, the best 1000 features were selected by Principal Component Analysis (PCA). The best 1000 features were classified with Random Forest (RF) and Support Vector Machines (SVM). According to the classification results, the best 1000 features selected from the features extracted by the VGG-16 and MobileNet networks were obtained with the highest accuracy rate of 93.94% using SVM. It is thought that this study can be a helpful tool in the diagnosis of Covid-19 disease while reducing time and labor costs with the use of artificial intelligence (AI).","PeriodicalId":511844,"journal":{"name":"Journal of Soft Computing and Artificial Intelligence","volume":"63 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hybrid CNN-LSTM Model for Air Quality Prediction: A Case Study for Gurugram","authors":"A. Utku","doi":"10.55195/jscai.1469468","DOIUrl":"https://doi.org/10.55195/jscai.1469468","url":null,"abstract":"One of the most important environmental problems brought about by rapid population growth and industrialization is air pollution. Today, air pollution is generally caused by heating, industry and motor vehicles. In addition, factors such as unplanned urbanization, topographic structure of cities, atmospheric conditions and meteorological parameters, building and population density also cause pollution to increase. Pollutants with concentrations above limit values have negative effects on humans and the environment. In order to prevent people from being negatively affected by these pollutants, it is necessary to know the pollution level and take action as soon as possible. In this study, a hybrid ConvLSTM model was developed in order to quickly and effectively predict air pollution, which has such negative effects on humans and the environment. ConvLSTM was compared with LR, RF, SVM, MLP, CNN and LSTM using approximately 4 years of air quality index data from the city of Gurugram in India. Experimental results showed that ConvLSTM was significantly more successful than the base models, with 30.645 MAE and 0.891 R2.","PeriodicalId":511844,"journal":{"name":"Journal of Soft Computing and Artificial Intelligence","volume":"24 63","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141019807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}