Vol. 13 No. 3 (2025)

					View 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

Research Articles

  • Hybrid RNN-LSTM Architecture for Sentiment Analysis of Algerian Dialectal Social Media Content

    Djalila Boughareb, Ammar Chahir Menasri
    50-57
    DOI: https://doi.org/10.58190/ijamec.2025.128
  • Performance Comparison of SVM Kernel Functions for Date Fruit Classification

    Hüseyin Bulduk, Kadir Sabancı
    58-64
    DOI: https://doi.org/10.58190/ijamec.2025.129
  • Trends and Research Directions in Electricity Load Forecasting: A Bibliometric Analysis

    Oya KILCI , Abdulkadir OZTURK, Muslume Beyza YILDIZ , Sude Nur ATIK, Elham Tahsin YASIN , Zeki Berk TONGUÇ , Murat Koklu
    65-74
    DOI: https://doi.org/10.58190/ijamec.2025.130
  • Predicting School Dropout Among International Students in Türkiye Using Machine Learning: A Case Study of Yemeni Students

    Anas ALHARDI, Selahattin ALAN
    75-83
    DOI: https://doi.org/10.58190/ijamec.2025.131
  • A Hybrid Model Approach Based on Swin Transformer and EfficientNetV2 for Maize Variety Classification

    Hüseyin Bulduk, Kadir Sabancı
    84-92
    DOI: https://doi.org/10.58190/ijamec.2025.132