Baral, Aditi Verma, Neha Adhikari, Image Chitrakar, Sailesh Dahlhaug, Ole Gunnar
Published in
IOP Conference Series: Materials Science and Engineering
This paper presents a comprehensive study of application of different machine learning techniques for the prediction of turbine faults in an early stage. As the need to ensure an optimal turbine operation through early stage fault detection grows, precise predictive models using machine learning techniques are also increasing rapidly. This study pr...
Raza, Hassan akhtar, zafar
This study uses advanced machine learning models to predict stock prices in the Pakistani stock market using 27 technical indicators. It evaluates the predictive capabilities of four techniques, SVM, LSTM, and Random Forest for binary classification of stock price movements. ANN and SVM show the highest accuracy at 85%, followed by Random Forest at...
grigorev, evgenii
This master thesis is dedicated to the analysis of data from users of an application designed to treat nicotine addiction. We have compiled several machine-learning models for solving classification and regression problems. First, we constructed a series of classification models to predict the likelihood of successful completion of therapy in indiv...
Thi Thanh Huong, Nguyen The Hien, Nguyen Thi Hang, Phan Thi Hoai, Cao Dinh Bao, Ho
Published in
IOP Conference Series: Earth and Environmental Science
The objective of this study was to classify the forest status of Ta Dung National Park, Vietnam using integrated satellite imagery and a machine learning algorithm to support biodiversity conservation and forest management. The complexity of land use poses a challenge to producing accurate land cover/land use maps using satellite imagery, particula...
Liu, Yanhui Ma, Shiwei Dong, Lihao Xiao, Ruihua Huang, Junbao Zhou, Pinggen
Published in
Frontiers in Earth Science
Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-indu...
Mathivanan, Ramesh Kanna
Maize metabolism is highly complex and influenced by genetic variation, yet the specific genes contributing to this variation and their links to non-metabolic traits remain less understood. To address this knowledge gap, we identified genes involved in maize metabolic variation and linked them to non-metabolic traits. We utilized a quadruplicate da...
Lafitte, Thomas Robin, Marc Launeau, Patrick Debaine, Françoise
On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach...
Ramana, E.V. Penekalapati, Sai Varun Kumar Namala, Kiran
Published in
E3S Web of Conferences
Non-Destructive Testing (NDT) is important to detect sub-surface defects in the weldments to ensure the quality of weld joints. The weld radiographs are digitized using a high-resolution digital camera. Data augmentation techniques are applied to expand the radiographic image dataset. Multi-class defect classification is done using the Gray-level c...
Flegel, Thomas Neumann, Anja Holst, Anna-Lena Kretzschmann, Olivia Loderstedt, Shenja Tästensen, Carina Gutmann, Sarah Dietzel, Josephine Becker, Lisa Franziska Kalliwoda, Theresa
...
Published in
Frontiers in Veterinary Science
Introduction Clinical reasoning in veterinary medicine is often based on clinicians’ personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual under...
Bhavsar, Maitri Patel, Manish
Published in
ITM Web of Conferences
Early detection of cardiovascular disease symptoms is one of the hardest things for professionals to do. Cardiovascular disease comes in many forms, including stroke, congenital heart disease (CHD), peripheral artery disease (PAD), and coronary artery disease (CAD). Comparing several feature selection methods to accurately predict cardiovascular di...
