Ali Manzak
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ali Manzak has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Ab Al-Hadi Ab Rahman
Universiti Teknologi Malaysia
1 shared publication - Ahmad Zuri Sha’aMeri
School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
1 shared publication Dilek Manzak
Georgia Institute of Technology
1 shared publication- Dilek Manzak
Georgia Institute of Technology, Atlanta, GA, USA
1 shared publication - Gökçen Çetinel
Electrical and Electronics Engineering Department, Engineering Faculty, Sakarya University, 54050 Serdivan, Türkiye
1 shared publication - Ibrahim Yousef Alshareef
VeCAD Research Laboratory, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
1 shared publication
Latest Publications
- 15 April 2026
- Published by Elsevier in Knowledge-Based Systems
- Vol. 343
ReLU-DCT and LASP: Real-valued frequency-domain activations and adaptive pooling for DCT-Based CNNs
- 15 April 2026
- Published by Elsevier in Knowledge-Based Systems
- Vol. 343
ReLU-DCT and LASP: Real-valued frequency-domain activations and adaptive pooling for DCT-Based CNNs
- Open Access
- 6 July 2020
- Published by Elsevier in SSRN Electronic Journal
Analysis of Environmental, Economic, and Demographic Factors Affecting COVID-19 Transmission and Associated Deaths in the U.S.A.
COVID-19 has arrived in the USA and spread quickly, causing deaths. The factors affecting the fast spread of COVID-19 and associated deaths are not clearly understood. In this research, we tried to identify the most significant environmental, economic, and demographic features impacting COVID-19 cases and associated mortality in the USA using statistical methods, including linear regression. We have chosen a wide range of feature parameters such as; average daily high temperature, hours of sunshine, population density, average relative humidity, median household income, persons 65 years and over, and percentage of African American, Hispanic and White population. We examined the correlation between the selected features and COVID-19 cases and associated deaths in 50 US counties. The counties are selected from each US state where the most populated city is located. We applied multiple linear regression and backward elimination method to decide which variables have the most impact on Corona related cases and deaths. Using these features, we tried to estimate COVID-19 cases and related deaths in the USA. We repeated the procedure for the state-wise comparison and chosen the parameters for the 50 US states and examined the correlation for the same attributes.
- 6 July 2020
- Published by Elsevier in SSRN Electronic Journal
Open AccessAnalysis of Environmental, Economic, and Demographic Factors Affecting COVID-19 Transmission and Associated Deaths in the U.S.A.
COVID-19 has arrived in the USA and spread quickly, causing deaths. The factors affecting the fast spread of COVID-19 and associated deaths are not clearly understood. In this research, we tried to identify the most significant environmental, economic, and demographic features impacting COVID-19 cases and associated mortality in the USA using statistical methods, including linear regression. We have chosen a wide range of feature parameters such as; average daily high temperature, hours of sunshine, population density, average relative humidity, median household income, persons 65 years and over, and percentage of African American, Hispanic and White population. We examined the correlation between the selected features and COVID-19 cases and associated deaths in 50 US counties. The counties are selected from each US state where the most populated city is located. We applied multiple linear regression and backward elimination method to decide which variables have the most impact on Corona related cases and deaths. Using these features, we tried to estimate COVID-19 cases and related deaths in the USA. We repeated the procedure for the state-wise comparison and chosen the parameters for the 50 US states and examined the correlation for the same attributes.
- 1 August 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1050-1053
Automated Classification of Alzheimer’s Disease using Deep Neural Network (DNN) by Random Forest Feature Elimination
Determining Alzheimer’s Disease (AD) in its early stages is very important to prepare proper care for the patient. In this study, we aimed to create fast and accurate automated classification system to determine AD with the minimum data collected from the patient. Magnetic Resonance Imaging (MRI) is widely used to diagnose AD. When the cost of the technique and risks of the procedures are considered, there is a need for different solutions. With the availability of neural network chips, it is even possible to build portable devices for Alzheimer’s detection. We propose fast and successful method to detect Alzheimer using Deep Neural Network (DNN). To reduce the complexity of the algorithm, Random Forest method was used to eliminate some of the features. Success of Random Forest to eliminate the features and success of DNN to detect AD are discussed.
- 1 August 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1050-1053
Automated Classification of Alzheimer’s Disease using Deep Neural Network (DNN) by Random Forest Feature Elimination
Determining Alzheimer’s Disease (AD) in its early stages is very important to prepare proper care for the patient. In this study, we aimed to create fast and accurate automated classification system to determine AD with the minimum data collected from the patient. Magnetic Resonance Imaging (MRI) is widely used to diagnose AD. When the cost of the technique and risks of the procedures are considered, there is a need for different solutions. With the availability of neural network chips, it is even possible to build portable devices for Alzheimer’s detection. We propose fast and successful method to detect Alzheimer using Deep Neural Network (DNN). To reduce the complexity of the algorithm, Random Forest method was used to eliminate some of the features. Success of Random Forest to eliminate the features and success of DNN to detect AD are discussed.