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Browsing by Subject "machine learning"

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CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals

Aydemir, Emrah; Dogan, Sengul; Baygin, Mehmet; Ooi, Chui Ping; Barua, Prabal Datta; Tuncer, Turker; Acharya, U. Rajendra (MDPI, 2022)

Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public ...
IoT data analytics architecture for smart healthcare using RFID and WSN

Ogur, Nur Banu; Al-Hubaishi, Mohammed; Ceken, Celal (WILEY, 2022)

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most ...
Nanofiller-Based Novel Hybrid Composite Membranes for High-Capacity Lithium-Sulfur Batteries

Katubi, Khadijah Mohammedsaleh; Saqib, Muhammad; Mubashir, Tayyaba; Tahir, Mudassir Hussain; Halawa, Mohamed Ibrahim; Akbar, Alveena; Basha, Beriham; Sulaman, Muhammad; Alrowaili, Z. A.; Al-Buriahi, M. S. (WILEY, 2023)

Machine learning (ML) analysis has gained huge importance among researchers for predicting multiple parameters and designing efficient donor and acceptor materials without experimentation. Data are collected from literature ...
Passenger demand forecasting for railway systems

Nar, Melek; Arslankaya, Seher (DE GRUYTER POLAND SP Z O O, 2022)

The rapid increase of the population and the number of motor vehicles brought about the transportation problem today. It has brought the efforts of the operators to determine the headway of the vehicles during the day in ...
Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

Katubi, Khadijah Mohammedsaleh; Saqib, Muhammad; Mubashir, Tayyaba; Tahir, Mudassir Hussain; Halawa, Mohamed Ibrahim; Akbar, Alveena; Basha, Beriham; Sulaman, Muhammad; Alrowaili, Z. A.; Al-Buriahi, M. S. (WILEY, 2023)

Machine learning (ML) analysis has gained huge importance among researchers for predicting multiple parameters and designing efficient donor and acceptor materials without experimentation. Data are collected from literature ...
The use of machine learning, density functional theory, and molecular dynamics simulations for the designing and screening of efficient small molecule donors for organic solar cells

Katubi, Khadijah Mohammedsaleh; Pembere, Anthony M. S.; Mehboob, Muhammad Yasir; Al-Buriahi, Mohammed S. (2022)

Indeed, a proper understanding of materials is necessary to get the full benefit from them. For this purpose, multiscale computational modeling is the ultimate need. For machine learning analysis, data is collected from ...