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The study will utilize machine learning algorithms such as Random Forest, Gradient Boosting, and Support Vector Regression (SVR) on data collected from the districts of Nalgonda, Yadadri Bhuvanagiri, and Suryapet over the past two years. The project seeks to m…

Accurate and transparent blood glucose forecasting is crucial for effective diabetes management. This paper presents an interpretable deep learning (DL) framework based on multi-task learning (MTL) for forecasting glucose levels at multiple prediction horizons…

E-commerce is evolving rapidly, creating a more competitive market environment. With this development, gaining a competitive advantage has become even more crucial. Companies implement various strategic moves to maintain their position in this competitive mark…

Traffic flow forecasting has an important place in designing a successful intelligent transportation system. The success of the forecasting is related to the accuracy and timely acquisition of the traffic flow data. The inadequacy in the number of data has led…

In this study, diabetes prediction was conducted using XGBoost, Support Vector Machines (SVM), and a Multilayer Artificial Neural Network (ANN). Attributes that cannot be biologically ‘0’ (e.g. glucose, BMI) were first marked as NaN. After filling the missing …

Forecasting earthquakes is challenging due to the difficulty in detecting underground phenomena. However, statistically, 30% of large earthquake foreshocks occur before the main shock, which indicates that, theoretically, it is possible to forecast them. Howev…

In this study, the effect of using a mixture of butanol and diesel fuel in a small diesel engine on engine performance and exhaust emissions is modeled. A comparison has been made with artificial neural networks, which is an up-to-date method, and multiple l…

Complexity in the behavior of asphalt binders are further escalated with geopolymer (fly ash and the alkali liquid) modification thus, making it difficult to predict the performance of the binder accurately. This study employs artificial neural network model…

Teknolojinin ilerlemesi ile birlikte birçok veri dijital ortamlarda kayıt altına alınarak büyük veri yığınları ortaya çıkmıştır. Veri madenciliği sayesinde bu büyük veri yığınlarının içinden anlamlı ve yararlı bilgilerin ortaya çıkarılması için çalışmalar yapı…

The estimation of the sediment yield for Coruh watersheds should be determined in order to calculate the lifespan of the dams that are being already build. In order to determine sediment yield, information regarding topographic situation, vegetation, climate, …

Prediction in the medical field is a challenging problem and as a result many researchers have used different artificial intelligent methods including conventional Self Organizing Map (SOM) to achieve this task. SOM is a specialized clustering technique that h…

Eğitim sektörünün giderek büyüyen yapısında, yükseköğretim kurumları, öğrenci performansının akademik olarak iyileştirilmesi ve öğrencilerin okulu bırakmalarını önlemek için veri madenciliği araçlarını ve tekniklerini kullanmaktadır. Veriler sosyo-ekonomik, de…

The successful estimation of future electricity consumption has a crucial importance in the energy planning. Because, in order to meet rising energy demand, policy makers should formulate electricity supply policies and make critical decisions and develop ne…

Generally, it is limited to obtain all kinds of data for determining of variables which distributed points in any area. Estimates are made from this data for unsampled locations. Thus, the spatial properties of an examined variable are defined all over the are…

Differentiated thyroid cancer (DTC) is the most frequent form of thyroid cancer. Although this type of cancer shows a favourable prognosis, the risk of recurrence remains a critical concern. Early and accurate prediction of the risk of recurrence is essential …

Neural Network has been used in a number of scientific fields including medical diagnosis. It is clear that classification is one of the main challenges in any medical diagnosis. However, the drawbacks of conventional neural network classifier are summarized a…

The integration of increasing renewable energy-based energy production into the existing electricity grid has become of great importance. The biggest difficulty to integration into the power grid is variability and discontinuity of wind energy. To deal with th…

The aim of this study is to determine instantaneous irradiance and annual percentile sunlit time on the available rooftop floor area of Technical and Social Science Vocational School at Usak University by simulation using open source software. The building of …

In the United States, banking desert status is classified annually from that same year’s branch data, producing a contemporaneous snapshot of current access rather than a forecast of which communities are likely to change status next. This study tests whether …

This study presents a Hybrid Digital-Physical Risk Management Framework designed to enhance urban climate resilience through the integration of Industry 4.0 technologies. By combining real-time IoT sensor networks, AI-based predictive analytics, and blockchain…