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All Journal Journal of Computing Theories and Applications Journal of Fuzzy Systems and Control (JFSC) Journal of Future Artificial Intelligence and Technologies
Aghaunor, Tabitha Chukwudi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Journal : Journal of Computing Theories and Applications

 1 
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua; Pribadi, Octara; Kartikadarma , Etika; Setyoko, Bimo Haryo; Widiono, Suyud; Robet, Robet; Aghaunor, Tabitha Chukwudi; Ugbotu, Eferhire Valentine
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
Investigating a SMOTE-Tomek Boosted Stacked Learning Scheme for Phishing Website Detection: A Pilot Study Ugbotu, Eferhire Valentine; Emordi, Frances Uchechukwu; Ugboh, Emeke; Anazia, Kizito Eluemunor; Odiakaose, Christopher Chukwufunaya; Onoma, Paul Avwerosuoghene; Idama, Rebecca Okeoghene; Ojugo, Arnold Adimabua; Geteloma, Victor Ochuko; Oweimieotu, Amanda Enaodona; Aghaunor, Tabitha Chukwudi; Binitie, Amaka Patience; Odoh, Anne; Onochie, Chris Chukwudi; Ezzeh, Peace Oguguo; Eboka, Andrew Okonji; Agboi, Joy; Ejeh, Patrick Ogholuwarami
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14472

Abstract

The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at exploiting interconnected cum shared resources. These exploits seek to compromise an unsuspecting user device cum unit. Increased susceptibility and success rate of these attacks have been traced to user's personality traits and behaviours, which renders them repeatedly vulnerable to such exploits especially those rippled across spoofed websites as malicious contents. Our study posits a stacked, transfer learning approach that seeks to classify malicious contents as explored by adversaries over a spoofed, phishing websites. Our stacked approach explores 3-base classifiers namely Cultural Genetic Algorithm, Random Forest, and Korhonen Modular Neural Network – whose output is utilized as input for XGBoost meta-learner. A major challenge with learning scheme(s) is the flexibility with the selection of appropriate features for estimation, and the imbalanced nature of the explored dataset for which the target class often lags behind. Our study resolved dataset imbalance challenge using the SMOTE-Tomek mode; while, the selected predictors was resolved using the relief rank feature selection. Results shows that our hybrid yields F1 0.995, Accuracy 0.997, Recall 0.998, Precision 1.000, AUC-ROC 0.997, and Specificity 1.000 – to accurately classify all 2,764 cases of its held-out test dataset. Results affirm that it outperformed bench-mark ensembles. Result shows the proposed model explored UCI Phishing Website dataset, and effectively classified phishing (cues and lures) contents on websites.
Co-Authors Abdullahi, Mustapha Barau Afotanwo, Anderson Agboi, Joy Aghware, Fidelis Obukohwo Ako, Rita Erhovwo Anazia, Kizito Eluemunor Binitie, Amaka Patience De Rosal Ignatius Moses Setiadi Eboka, Andrew Okonji Ejeh, Patrick Ogholuwarami Emordi, Frances Uche Emordi, Frances Uchechukwu Ezzeh, Peace Oguguo Geteloma, Victor Ochuko Ibor, Ayei Egu Idama, Rebecca Okeoghene Jumbo, Evans Fubara Kartikadarma , Etika Max-Egba, Asuobite ThankGod Niemogha, Star Umiyemeromesu Nwozor, Blessing Uche Octara Pribadi Odiakaoase, Christopher Chukwufunaya Odiakaose , Christopher Chukwufunaya Odiakaose, Christopher Chukwufunaya Odoh, Anne Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Onochie, Chris Chukwudi Onochie, Christopher Chukwudi Onoma, Paul Avweresuo Onoma, Paul Avweresuoghene Onoma, Paul Avwerosuoghene Orobor, Anderson Ise Oweimieotu, Amanda Enaodona Robet Robet Setyoko, Bimo Haryo Suyud Widiono Ugboh, Emeke Ugbotu, Eferhire Valentine