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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Kumar Napa, Komal
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques Kumar Napa, Komal; Kalyan Kumar, Angati; Murugan, Sangeetha; Mahammad, Kamaluru; Admassu Assegie, Tsehay
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp730-736

Abstract

Chronic heart disease (CHD) is a common complication among patients suffering in the cardiological intensive care unit, often resulting in poor prognosis and high mortality. Early prediction of CHD can reduce mortality by preventing the severity of the disease. This study evaluated the efficacy of on recursive feature elimination for predicting CHD using supervised learning techniques for predicting CHD. The study employed 1190 Cleveland Hungarian CHD dataset. Different supervised learning techniques (support vector machine, decision tree, k-nearest neighbor, Naive Bayes, stochastic gradient descent, adaptive boosting, and multilayer perceptron) were used to study the efficacy of the recursive feature elimination. Chest pain type, sex, blood sugar level, angina, depression, and slope were associated with CHD occurrence. The accuracy of the K-nearest neighbor and decision tree model was 89.91% for the feature-selected dataset indicating good predictive ability. Ultimately, the support vector machine and logistic regression with the selected features exhibited good discriminatory ability for early prediction of CHD. Thus, the recursive feature elimination is a good approach to develop a a model with higher accuracy to predict CHD.
Scalability and performance of decision tree for cardiovascular disease prediction Admassu Assegie, Tsehay; Kumar Napa, Komal; Thulasi, Thiyagu; Kalyan Kumar, Angati; Thiruvarasu Vasantha Priya, Maran Jeyanthiran; Dhamodaran, Vigneswari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2540-2545

Abstract

As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the performance of a decision tree for predicting cardiovascular disease. The performance evaluation was carried out by employing a confusion matrix, cross-validation score, model complexity, and training score for varying sizes of training samples. The experiment depicted that, the decision tree model was 88.8% accurate in predicting the presence or absence of cardiovascular disease. Therefore, the implementation of the decision tree is beneficial for the prediction and early detection of heart disease events in patients.
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.
Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network Alayu Melkamu, Yenework; Purushothaman, Raguraman; Sujatha, Madugula; Kumar Napa, Komal; Zeleke Mekonen, Mareye; Admassu Assegie, Tsehay; Olalekan Salau, Ayodeji
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.v14i1.8863

Abstract

Mobile ad hoc networks (MANETs) operate without fixed infrastructure, with mobile nodes acting as both hosts and routers. These networks face challenges due to node mobility and limited resources, causing frequent changes in topology and instability. Clustering is essential to manage this issue. Significant research has been devoted to optimal clustering algorithms to improve cluster-based routing protocols (CBRP), such as the weighted clustering algorithm (WCA), optimal stable clustering algorithm (OSCA), lowest ID (LID) clustering algorithm, and highest connectivity clustering (HCC) algorithm. However, these protocols suffer from high re-clustering frequency and do not adequately account for energy efficiency, leading to network instability and reduced longevity. This work aims to improve the CBRP to create a more stable and long-lasting network. During cluster head (CH) selection, nodes with high residual energy or degree centrality are chosen as CH and backup cluster head (BCH). This approach eliminates the need for re-clustering, as the BCH can seamlessly replace a failing CH, ensuring continuous cluster maintenance. The proposed modified cluster-based routing protocol (MCBRP) evaluated network simulator 2 (ns2) demonstrates that MCBRP is more energy-efficient, selecting optimal CH and balancing the load to enhance network stability and longevity.
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 Alayu Melkamu, Yenework Andulalem Ayalew, Amogne Assegie, Tsehay Admassu Dhamodaran, Vigneswari Kalyan Kumar, Angati Mahammad, Kamaluru Manivannan, Bommy Murugan, Sangeetha Olalekan Salau, Ayodeji Palit, Shamik Priya Kompala, Chandi Purushothaman, Raguraman Senthil Murugan, Janakiraman Sinha Roy, Chandrima Sujatha, Madugula Suresh, Tamilarasi Thiruvarasu Vasantha Priya, Maran Jeyanthiran Thulasi, Thiyagu Zeleke Mekonen, Mareye