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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC)
Suresh, Tamilarasi
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier Assegie, Tsehay Admassu; Suresh, Tamilarasi; Purushothaman, Raguraman; Ganesan, Sangeetha; Kumar, Napa Komal
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18412

Abstract

Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned.  With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.
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.
An enhanced key schedule mechanism to improve the security strength of the data encryption standard algorithm Zeleke Mekonen, Mareye; Kumar Napa, Komal; Andulalem Ayalew, Amogne; Manivannan, Bommy; Suresh, Tamilarasi; Senthil Murugan, Janakiraman; Admassu Assegie, Tsehay
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9712

Abstract

The rapid growth of internet accessibility requires strong data security measures, mainly for safeguarding sensitive information. Since many threats and attacks steal our private data. Data encryption standard (DES) is one of the cryptographic methods that uses a symmetric key encryption method to resist various types of cryptographic attacks. This work proposes an improved key scheduling algorithm (KSA) to enhance DES security. The modified KSA is evaluated using criteria such as frequency test, hamming weight, and bit difference to measure round key randomness and resilience. Moreover, the avalanche effect is evaluated to assess the diffusion and confusion character of the generated ciphertext. The final result indicates that the enhanced KSA attains better frequency distribution (0.89-1.0), increased hamming weight consistency (97.13%), and high bit transition rates compared to the original DES KSA. These enhancements demonstrate increased randomness and complexity, making the algorithm more resistant to brute-force and other cryptographic attacks. Our proposed work shows enhanced security capabilities, albeit with increased computational requirements, and establishes a foundation for future improvement in symmetric key cryptography.
Co-Authors Admassu Assegie, Tsehay Andulalem Ayalew, Amogne Assegie, Tsehay Admassu Ayalew, Amogne Andualem Ganesan, Sangeetha Kumar Napa, Komal Kumar, Napa Komal Manivannan, Bommy Prasad, Battula Krishna Purushothaman, Raguraman Salau, Ayodeji Olalekan Senthil Murugan, Janakiraman Tegegne, Melaku Lake Zeleke Mekonen, Mareye