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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Prasad, Battula Krishna
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Computer Science & IT Electrical & Electronics Engineering

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Amharic event text classification from social media using hybrid deep learning Ayalew, Amogne Andualem; Tegegne, Melaku Lake; Manivannan, Bommy; Suresh, Tamilarasi; Kumar, Napa Komal; Prasad, Battula Krishna; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2264-2270

Abstract

This study aims to develop a hybrid deep-learning model for detecting and classifying Amharic text. Various natural language applications, such as information extraction, event extraction, conversation, text summarization, and require an automatic event classification. However, existing studies focused on classification, giving little attention to the preprocessing and feature extraction techniques. To address this problem, this work proposed a hybridized deep learning-based Amharic social media text event classification model. The model consists of word-to-vector (Word2vecv) word embedding techniques to capture the semantic and syntactic representation. Convolutional neural network (CNN) is used to extract short-length text features. Additionally, bidirectional long-short memory (Bi-LSTM) is used to extract features from long Amharic sentences and classify those events based on their classes. The dataset used for training and testing consists of 6,740 labeled Amharic text sentences, collected from social media. The result shows an accuracy of 94.8% in detecting and classifying Amharic text events.
Meta-learning for malaria diagnosis: evaluating stacking models for enhanced classification performance Napa, Komal Kumar; Murugan, Sangeetha; Subramanian, Sathya; Saravanan, Durga Devi; Nageswari, Devana; Prasad, Battula Krishna
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10158

Abstract

Accurate malaria detection is crucial for effective disease management, particularly in regions with limited medical resources. Deep learning models have shown promising results in automated diagnosis, yet real-world deployment often faces challenges such as computational cost and model interpretability. This study evaluates multiple deep learning architectures—VGG16, ResNet50, InceptionV3, MobileNetV2, and DenseNet121—on the publicly available National Institutes of Health (NIH) malaria cell image dataset (27,558 images), and enhances their performance using stacking ensemble learning with different meta-learners. Among individual models, DenseNet121 achieved the highest accuracy of 88.00%, while MobileNetV2 had the lowest at 84.80%. Implementing stacking with logistic regression as the meta-learner improved accuracy to 89.40%, while random forest increased it to 90.10%. The best performance was achieved with XGBoost as the meta-learner, attaining an accuracy of 91.20%, precision of 92.10%, recall of 90.80%, and an F1-score of 91.40%—representing a 3.2% accuracy improvement over the best individual model. The classification report further confirms superior performance in distinguishing infected and uninfected cases. These results highlight the potential of stacking with advanced meta-learners to support health workers in rapid, reliable malaria diagnosis, ultimately aiding timely treatment, and improving patient outcomes in clinical and field settings.
Co-Authors Assegie, Tsehay Admassu Ayalew, Amogne Andualem Kumar, Napa Komal Manivannan, Bommy Murugan, Sangeetha Nageswari, Devana Napa, Komal Kumar Salau, Ayodeji Olalekan Saravanan, Durga Devi Subramanian, Sathya Suresh, Tamilarasi Tegegne, Melaku Lake