Browsing by Subject "Machine learning"
Now showing items 1-9 of 9
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Applications of machine learning in classification of biological data
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Publisher and Date:(Kadir Has University, 2018)Machine learning enables computers learn from the data. it has a wide range of application areas. Computational biology and bioinformatics are some areas in which machine learning applications provide accurate solutions to problems. Different types of machine learning tasks are summarized as supervised semi-supervised unsupervised and reinforcement learning. in this thesis we focus on supervised machine learning tasks on biological datasets. We applied multiple machine learning approaches to ...
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A comparative study of surrogate based learning methods in solving power flow problem
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Publisher and Date:(IEEE, 2020)Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase ...
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How to engage consumers through effective social media use-guidelines for consumer goods companies from an emerging market
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Publisher and Date:(Universidad de Talca, 2021-07)This study aims to establish actionable guidelines and provide strategic insights as a means of increasing the social media effectiveness of consumer brands. Post-related factors in addition to the contextual and temporal factors influencing consumer engagement (i.e., reposting, commenting on or liking posts), as an indicator of social media effectiveness, are considered in detail in the research model. Moreover, the model considers differences between industries as well as social media platforms. ...
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Machine Learning Approaches for Predicting Protein Complex Similarity
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Publisher and Date:(Mary Ann Liebert Inc Publ, 2017)Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals electrostatic and desolvation forces) and the similarity of a conformation to its native structure the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this ...
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Machine learning model for maternal quality in sheep
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Publisher and Date:(Organising Committee of the 9th European Conference on Precision Livestock Farming (ECPLF), Teagasc, Animal and Grassland Research and Innovation Centre, 2019)This paper aims to identify determinant traits of ewes by measuring their impact on lamb survival. For that, we devised a machine learning model that correlates ewe traits to lamb survival, and figured out as to which ewe traits explain the correlation and hence help us to identify the better mother. In this study, we kept pregnant ewes under 24 h observation by two researchers starting approximately three days before expected parturition dates. We conducted the study using native and crossbreed ...
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Predicting electricity consumption using machine learning models With R and python
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Publisher and Date:(Kadir Has University, 2016)Electricity load forecasting has become an important field of interest in the last years. Antic- ipating the energy usage is vital to manage resources and avoid risk. Using machine learning techniques it is possible to predict the electricity consumption in the future with high accuracy. This study proposes a machine learning model for electricity usage prediction based on size and time. For that aim multiple predictive models are built and evaluated using two powerful open source tools for machine ...
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Random CapsNet forest model for imbalanced malware type classification task
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Publisher and Date:(Elsevier, 2021)Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracies for predicting malware types. Machine learning-based models need to do heavy feature engineering work, which affects the performance of the models greatly. On the other hand, deep learning-based models require less effort in feature engineering when compared to that of the ...
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Ranking Protein-Protein Binding Using Evolutionary Information and Machine Learning
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Publisher and Date:(Association for Computing Machinery, 2017)Discriminating native-like complexes from false-positives with high accuracy is one of the biggest challenges in protein-protein docking. The relationship between various favorable intermolecular interactions (e.g. Van derWaals electrostatic desolvation forces etc.) and the similarity of a conformation to its native structure is commonly agreed though the precise nature of this relationship is not known very well. Existing protein-protein docking methods typically formulate this relationship as a ...
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Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
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Publisher and Date:(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020)Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated ...
