International Journal of Applied Methods in Electronics and Computers

About the Journal

The International Journal of Applied Methods in Electronics and Computers (IJAMEC) is a peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of electronics and computer science. Established in 2013, the journal provides a platform for interdisciplinary studies and innovative applications addressing real-world challenges in these dynamic fields.

The journal welcomes research papers in all significant areas of electronics and computers that have practical applications in scientific problem-solving. It allows authors to retain unrestricted copyright and publishing rights. Open Access under the CC-BY license. IJAMEC does not impose any fees for article submissions or processing. It is free of charge.

ISSN: 3023-4409

DOI Prefix: 10.58190/ijamec

Frequency: Quarterly

Announcements

International Journal of Applied Methods in Electronics and Computers is now indexed in the ICI Journals Master List for 2024

12-09-2025

We are thrilled to announce that the International Journal of Applied Methods in Electronics and Computers (ISSN: 3023-4409) has successfully passed the evaluation process and is now indexed in the ICI Journals Master List for 2024. Furthermore, the journal has been awarded an Index Copernicus Value (ICV) of 100.00 for 2024.

Current Issue

Vol. 13 No. 3 (2025)

This issue of the International Journal of Applied Methods in Electronics and Computers (Vol. 13, No. 3, 2025) presents diverse and impactful studies that leverage machine learning, deep learning, and bibliometric methods across applications in natural language processing, agriculture, energy forecasting, and education analytics. Featured articles include a hybrid RNN-LSTM model tailored for sentiment analysis in Algerian dialectal social media content, a comparative study of SVM kernel functions for classifying date fruit varieties, and a bibliometric overview highlighting key trends in electricity load forecasting. The issue also explores the use of machine learning in predicting school dropout rates among international students in Türkiye, and introduces a hybrid deep learning approach combining Swin Transformer and EfficientNetV2 for maize variety classification.

Published: 30-09-2025