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Special Issue on Multidisciplinary Sciences and Advanced Technology (SI-MSAT 2026)
Guest Editors: Prof. Paul Andrew
Deadline: 31 December 2026

Special Issue on Artificial Intelligence for Energy Transition and Decarbonization (SI-AIETD26)
Guest Editors: Dr. Elkhatib Kamal, Dr. Reza Ghorbani, Dr. Ahmed Ragab, Prof. Mohamed Kouki
Deadline: 31 December 2026

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Keyword: machine learning models
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Open AccessArticle
11 Pages, 1,676 KB Download PDF
Comparative Analysis of Supervised Machine Learning Models for PCOS Prediction Using Clinical Data

by Ranyah Taha, Huda Zain El Abdin and Tala Musleh
Journal of Engineering Research and Sciences, Volume 4, Issue 6, Page # 16-26, 2025; DOI: 10.55708/js0406003
Abstract: Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women of reproductive age, commonly resulting in irregular menstrual cycles, elevated androgen levels, and the presence of polycystic ovaries. It is a major cause of infertility and is often linked with metabolic complications such as insulin resistance and obesity. Symptoms vary and may include acne,… Read More

(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))

Open AccessArticle
10 Pages, 1,295 KB Download PDF
Soil Properties Prediction for Agriculture using Machine Learning Techniques

by Vijay Kumar, Jai Singh Malhotra, Saurav Sharma and Parth Bhardwaj
Journal of Engineering Research and Sciences, Volume 1, Issue 3, Page # 09-18, 2022; DOI: 10.55708/js0103002
Abstract: Information about soil properties help the farmers to do effective and efficient farming, and yield mo . An attempt has been made in this paper to predict the soil properties using machine learning approaches. The main properties of soil prediction are Calcium, Phosphorus, pH, Soil Organic Carbon, and Sand. These properties greatly affect the production… Read More

(This article belongs to the Section Environmental Engineering (EVE))

Open AccessArticle
15 Pages Download PDF
Metamodel based Optimization for Analog Integrated Circuits

by Vasile Grosu and Nicolae Patache
Journal of Engineering Research and Sciences, Volume 5, Issue 7, Page # 1-15, 2026; DOI: js0507001
Abstract: In recent times, machine learning applications have become an important component of the analog integrated circuit development cycle. Several phases such as circuit sizing, optimization or pre-silicon verification have benefited from machine learning automation. For developing such machine learning models, a certain amount of training and testing samples needs to be acquired through circuit simulations.… Read More

(This article belongs to the Special Issue on SP8 (Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26)) and the Section Electronic Engineering (EEE))

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