Collection

Engineering: Theory and Applications for Machine Learning Guided Evolutionary Optimization in Multi Modal Data Science

Submission status
Closed
Engineering and science growth has caused a lot of improvisation in the task of getting and finding useful information amongst the available raw or numerous data. Processing of raw data into useful information is the fundamental idea of analysis in the field of Information Science. Information Science technologies explore more about analyzing the various forms of data in numerical, text, visual or audio form. Getting the best suitable value among the available data based on the chosen objective is the basic idea of Evolutionary optimization (EO) science. Recently, the EO algorithms are being used to boost the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three aspects of ML: Pre-Processing (e.g., feature selection and resampling), Learning (e.g., parameter selection and membership functions), and Post-Processing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This phenomenon was helpful in various fields of engineering like Medical Science, Network architectures, Database management, Feature engineering, etc., The aim of this issue is to bring out the innovative research in the fields of Information and Optimization Sciences with the scope to improve and solve the futuristic issues in several engineering domains.

Editors

  • B.D. Parameshachari B.D. Parameshachari , 

    B.D. Parameshachari

    Dr. B.D. Parameshachari is currently a Professor and Head of the Department of Telecommunication Engineering at GSSSIETW, Mysuru, India. He has published over 100+ articles in SCI, SCOPUS and other indexed journals and also in conferences. He is also the Founding Chair of IEEE Mysore Subsection and IEEE Information Theory Society - Bangalore Chapter. He serves as Associate Editor, Editorial Board Member, Guest Editor for several reputed indexed journals and as Conference Chair for IEEE Flagship Conferences. His research interest includes Image Processing, Cryptography, Pattern Recognition, Data Science, Sensors and Networks.

  • Liyanage Chandratilak De Silva Liyanage Chandratilak De Silva  &

    Liyanage Chandratilak De Silva

    Dr. Liyanage Chandratilak De Silva is a Professor and Deputy Dean of the Faculty of Integrated Technologies at the University of Brunei Darussalam, Brunei. He has published more than 160 papers in areas like Signal Processing, IoT, Sensor Integration, Power System Analysis and holds one Japanese national patent, which was successfully sold to Sony Corporation Japan, and 1 US and 1 Brunei patent. His works have been cited as one of the pioneering works in bimodal (audio and video signal based) emotion recognition by many researchers. He is a senior member of IEEE USA and the interim chair of IEEE Brunei Darussalam Subsection.

  • Jaroslav Frnda Jaroslav Frnda

    Jaroslav Frnda

    Dr. Jaroslav Frnda, University of Zilina, Slovakia. He received the M.Sc. and Ph.D. degrees from the Department of Telecommunications, VSB—Technical University of Ostrava, in 2013 and 2018, resp. He has been working as Assistant Professor with the Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications at University of Žilina, Slovakia, since 2019. He has authored and coauthored 27 SCI-E and nine ESCI articles in WoS. His research interests include quality of multimedia services in IP networks, data analysis, and machine learning algorithms.

Articles (11 in this collection)