JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

eISSN: 2980-177X
Creative Commons CC BY-NC-ND 4.0 International License.

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Full Issue

Articles

Original Article - Computer Engineering

Personalized daily-weekly workout arrangement application "cardio fit"

https://doi.org/10.51271/JCEEES-0006

: 388 : 311

Citation : Ataman Z, Heybet ME, Sarısoy F, Yılmaz B, Günay M, Şenel Ö. Personalized daily-weekly workout arrangement application “cardio fit”. J Comp Electr Electron Eng Sci. 2023;1(2):25-28.

Original Article - Computer Engineering

Cryptocurrency analysis using machine learning and deep learning approaches

https://doi.org/10.51271/JCEEES-0007

: 509 : 237

Citation : Muhammed Sakran F. Cryptocurrency analysis using machine learning and deep learning approaches. J Comp Electr Electron Eng Sci. 2023;1(2):29-33.

Original Article - Electrical Engineering

Controlling the operation of the dc motor by using pid with metaheuristic technology

https://doi.org/10.51271/JCEEES-0008

: 537 : 171

Citation : Khalaf KAK, Teke M. Controlling the operation of the dc motor by using pid with metaheuristic technology. J Comp Electr Electron Eng Sci. 2023;1(2):34-40.

Original Article - Computer Engineering

Classification of heart disease dataset with k-NN optimized by pso and gwo algorithms

https://doi.org/10.51271/JCEEES-0009

: 753 : 152

Citation : Lüy M, Metin NA. Classification of heart disease dataset with k-NN optimized by pso and gwo algorithms. J Comp Electr Electron Eng Sci. 2023;1(2):41-45.

Original Article - Computer Engineering

Machine learning methods on quantized vectors

https://doi.org/10.51271/JCEEES-0010

: 485 : 167

Citation : Tanyeri U, Dindar T, Kökver Y, Koçak NF. Machine learning methods on quantized vectors. J Comp Electr Electron Eng Sci. 2023;1(2):46-49.
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