Published: September 29, 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.
Labstic Laboratory, Department of Computer Science, University of 8 Mai 1945, Guelma, Algeria; Department of Computer Science, University of 8 Mai 1945, Guelma, Algeria
Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiye
Graduate School of Natural and Applied Sciences, Selcuk University, Konya, TURKIYE; Department of Computer Engineering, Selcuk University, Konya, TURKIYE
Department of Computer Engineering, Selcuk University, Konya, Türkiye
Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiye