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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Articles

https://doi.org/10.51271/JCEEES-0031

: 52 : 44

Citation : Erdal, E., Ergüzen, A., & Önal, A. (2026). Application and performance evaluation of the dynamic bit-level encoding algorithm (DEA) in text compression: a cross-domain perspective. J Comp Electr Electron Eng Sci, 4(1), 1-8.

Original Article - Computer Engineering

Development of a personalized cardio exercise and diet tracking mobile application: CardioFit IOS

https://doi.org/10.51271/JCEEES-0032

: 31 : 58

Citation : Uyan, Y., Sarısoy, F., Yılmaz, B., & Jira, H. (2026). Development of a personalized cardio exercise and diet tracking mobile application: CardioFit IOS. J Comp Electr Electron Eng Sci, 4(1), 9-16.

https://doi.org/10.51271/JCEEES-0033

: 24 : 72

Citation : Evgin, M. S., & Toklu, S. (2026). Investigation of fine-tuned BERT models for sentiment analysis in COVID-19 tweets using a fuzzy logic-based ensemble approach. J Comp Electr Electron Eng Sci, 4(1), 17-26.

Original Article - Electronic Engineering

Intelligent diagnosis of tomato leaf diseases using YOLOv8

https://doi.org/10.51271/JCEEES-0034

: 59 : 52

Citation : Candan, H., Aydilek, H. & Erten, M. Y. (2026). Intelligent diagnosis of tomato leaf diseases using YOLOv8. J Comp Electr Electron Eng Sci, 4(1), 27-33.

Review - Computer Engineering

Detection of Alzheimer's disease from magnetic resonance images with deep learning

https://doi.org/10.51271/JCEEES-0035

: 69 : 43

Citation : Güngör, A. & Barışçı, N. (2026). Detection of Alzheimer’s disease from magnetic resonance images with deep learning. J Comp Electr Electron Eng Sci, 4(1), 34-45.
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