Article Per Year (5 Year)
p-Index From 2020 - 2025
1.642
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Assegie, Tsehay Admassu
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 9 Documents
Search

 1 
An optimized K-Nearest Neighbor based breast cancer detection Assegie, Tsehay Admassu
Journal of Robotics and Control (JRC) Vol 2, No 3 (2021): May (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this research, a grid search is employed to find the optimal parameter and an optimized K-Nearest Neighbor (KNN) based breast cancer detection model is proposed. The grid search is used to find the best combinations of parameters that could produce better breast cancer detection accuracy. Moreover, this study explored the effect of parameter tuning on the performance of KNN algorithm foe breast cancer detection. The findings of this research reveals that parameter tuning has a significant effect on the performance of the proposed model. The effect of parameter tuning on the performance of KNN algorithm is experimentally tested using Wisconsin breast cancer dataset collected from kaggle data repository. Finally, we have compared the performance of the KNN algorithm with the tuned hyper-parameter and with default hyper-parameter. The result analysis on the performance of the KNN algorithm on breast cancer detection on the test dataset reveals that the accuracy of the proposed optimized model is 94.35% and the performance of the KNN algorithm with the default hyper-parameter is 90.10%.
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.
Prediction of the risk of developing heart disease using logistic regression Salau, Ayodeji Olalekan; Assegie, Tsehay Admassu; Markus, Elisha Didam; Eneh, Joy Nnenna; Ozue, ThankGod Izuchukwu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1809-1815

Abstract

Heart disease (HD) accounts for more deaths every year than other illnesses. World Health Organization (WHO) assessed 17.9 million life losses caused by heart disease in 2016, demonstrating 31% of all international life losses. Three-quarters of these life losses occur in low and middle-income nations. Machine learning (ML), due to advanced precision in pattern recognition and classification, demonstrates to be in effect in complementing decision-making and threat prediction from the huge number of HD data created by the healthcare sector. Thus, this study aims to develop a logistic regression model (LRM) for predicting the risk of getting HD in ten years. The study explores the different methodologies for improving the performance of base LRM for predicting whether a person gets HD after ten years or not. The result demonstrates the capability of LRM in predicting the risks of getting HD after ten years. The LRM achieves 97.35% accuracy with the recursive feature elimination and random under-sampling. This implies that the LRM can play an important role in precautionary methods to avoid the risk of HD.
Security-based low-density parity check encoder for 5G communication Rajangam, Balamurugan; Alagarsamy, Manjunathan; Radhakrishnan, Chirakkal Rathish; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan; Quansah, Andrew; Chowdhury, Nur Mohammad; Chowdhury, Ismatul Jannat
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7019

Abstract

The fifth generation (5G) of mobile telecommunication standards is intended to offer better performance and efficiency. One of the most significant difficulties in delivering safe data transfer through the transmission channel in the emerging 5G technology is channel-coding security. This research primarily focused on offering information transmission that is secure in the place of novel assaults such as side-channel attacks. In this research, we present a low-density parity check (LDPC) encoder that is constructed using the multiplicative masking method to overcome side-channel attacks, one of the most significant security concerns for the upcoming 5G system. As a result, it offers greater security, reduced complexity, and higher performance. Power, area, and delay can all be calculated using LDPC codes. Comparing multiplicative masking implemented LDPC encoders to ordinary channel coding techniques in terms of security seen that multiplicative masking implemented LDPC encoders offer more security. The program Xilinx ISE 14.7 can synthesize the analysis. The advantage of multiplicative masking is that it offers a promising level of security through the principle of randomization, which is the foundation of the procedure. According to the analysis, the secured transmission of the data by the proposed multiplicative masking implemented LDPC encoder is increased.
Evaluation of structural failure resistance of glass fiber reinforced concrete beams Getachew Chikol, Yilachew; Assegie, Tsehay Admassu; Mohmmad, Shaimaa Hadi; Salau, Ayodeji Olalekan; Yanhui, Liu; Braide, Sepiribo Lucky
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6620

Abstract

Glass fiber reinforced concrete (GFRC) is a composite material that is widely used in construction due to its high strength and durability. GFRC is made by adding glass fibers to the concrete mix, which increases the tensile strength of the material. This paper evlautes the shear resistance (SR) of sliced glass fiber (30 mm) GFRC beams. The shear resistance of GFRC beams can be significantly improved by adding glass fibers to the concrete mix. However, further research is needed to fully understand the shear behavior of GFRC and to optimize its design for maximum shear resistance. The result indicates that shear fracture glass fiber is a better alternative for increasing a shear resistance input mechanism.
An opinionated sentiment analysis using a rule-based method Zeleke Mekonen, Mareye; Assegie, Tsehay Admassu; Palit, Shamik; Kalyan Kumar, Angati; Sinha Roy, Chandrima; Priya Kompala, Chandi; Kumar Napa, Komal
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8568

Abstract

The categorization of opinions into positive, negative, or neutral facilitates information gathering, pinpointing individual weaknesses, and streamlining the decision-making process. Precision in opinion classification enables decision-makers to extract valuable insights, make well-informed decisions, and execute suitable actions. Sentiment analysis is language-specific due to the distinct morphological structures unique to each language, distinguishing them from one another. This study implemented a rule-based sentiment analysis approach for Kafi-noonoo opinionated texts, leveraging a rule-based system tailored for smaller datasets that operate based on a predefined set of rules. The rule-based mechanism calculates the overall polarity of a given sentence by applying a set of rules and categorizes it into positive, negative, or neutral sentiments upon identifying sentimental terms from a dedicated file. While the analysis utilized 1,500 words sourced from Facebook and music review samples, the modest sample size yielded satisfactory results. Performance evaluation metrics such as precision, recall, and F-measure were employed, indicating positive word scores of 91%, 86%, and 88.4%, and negative word scores of 80%, 75%, and 77%, respectively.
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.
Soybean leaf disease detection and classification using deep learning approach Adimas, Ayenew Kassie; Mekonen, Mareye Zeleke; Assegie, Tsehay Admassu; Singh, Hemant Kumar; Mazumdar, Indu; Gupta, Shashi Kant; Salau, Ayodeji Olalekan; Tin, Ting Tin
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.v14i4.8585

Abstract

In Ethiopia, where soybeans are mainly involved, manual observation has traditionally been relied upon for detecting soybean leaf diseases. However, the manual process is susceptible to numerous issues such as labor-intensiveness, inconsistency, and subjectivity. While previous studies have explored automated classification for soybean leaf disease detection, they primarily focused on binary classification, overlooking the complexity and diversity of soybean leaf diseases, which hinders effective management strategies. This study introduces deep learning algorithms and computer vision for automated soybean leaf disease identification and classification in soybean leaves. By comparing pre-trained convolutional neural network (CNN) models (VGG16, VGG19, and ResNet50V2), a dataset of 3078 soybean leaf images was curated, representing various diseases. Image preprocessing techniques augmented the dataset to 6,958 images, enhancing the model's accuracy and generalization performance. VGG16 demonstrated outstanding performance with a test accuracy of 99.35%, highlighting its promising performance and generalization potential.
Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model Assegie, Tsehay Admassu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 3 No. 1 (2021): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v3i1.196

Abstract

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm
Co-Authors Adimas, Ayenew Kassie Alagarsamy, Manjunathan Ayalew, Amogne Andualem Braide, Sepiribo Lucky Chowdhury, Ismatul Jannat Chowdhury, Nur Mohammad Eneh, Joy Nnenna Ganesan, Sangeetha Getachew Chikol, Yilachew Gupta, Shashi Kant Kalyan Kumar, Angati Kumar Napa, Komal Kumar, Napa Komal Manivannan, Bommy Markus, Elisha Didam Mazumdar, Indu Mekonen, Mareye Zeleke Mohmmad, Shaimaa Hadi Ozue, ThankGod Izuchukwu Palit, Shamik Prasad, Battula Krishna Priya Kompala, Chandi Purushothaman, Raguraman Quansah, Andrew Radhakrishnan, Chirakkal Rathish Rajangam, Balamurugan Salau, Ayodeji Olalekan Singh, Hemant Kumar Sinha Roy, Chandrima Suresh, Tamilarasi Tegegne, Melaku Lake Tin, Ting Tin Yanhui, Liu Zeleke Mekonen, Mareye