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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Artificial Intelligence
Sola, Adetoro Mayowa
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Performance analysis of a neuromodel for breast histopathology decision support system Ojo, Adedayo Olukayode; Sola, Adetoro Mayowa; Ojo, Florence Omolara; Onibonoje Oluwafemi, Moses
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v14.i1.pp102-108

Abstract

Breast cancer detection and diagnosis are crucial in reducing mortality rates among women globally. This research article explores an artificial intelligence technique for early breast cancer detection, aiding doctors in making informed decisions for improved patient management. The study employs histopathological analysis of breast tissue microscopically to detect abnormalities, with the aim of categorizing normal tissue, benign lesions, in situ carcinoma, and invasive carcinoma. The proposed technique utilizes an artificial neural network trained using the resilient backpropagation algorithm (RP_ANN). The study further compares the observed performance with those of three other algorithms, including gradient descent algorithm (GDA_ANN), Levenberg-Marquardt algorithm (LM_ANN), and layer sensitivity-based (LSB_ANN) algorithm based on various evaluation metrics. RP_ANN and LSB_ANN demonstrated superior performance, with high validation and training variance accounted for (VAF) and low root mean squared error (RMSE). The results underscore the potential of deep learning-based algorithms for improving breast cancer detection, promising better patient outcomes and enhanced diagnostic accuracy.
Network Anomaly Detection System using Transformer Neural Networks and Clustering Techniques Isijola, Ayomitope; Asefon, Michael; Ogude, Ufuoma; Sola, Adetoro Mayowa; Adebowale, Temiloluwa; Akunekwu, Isabella
International Journal of Artificial Intelligence Vol 12 No 1: June 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01201.837

Abstract

This study proposes a hybrid approach for network anomaly detection by integrating a Transformer-based model with clustering techniques. The methodology begins with the application of K-means clustering as a preprocessing step to group similar network traffic data, thereby reducing data complexity and highlighting significant patterns. The clustered data is then fed into a Transformer model, which utilizes multi-head self-attention mechanisms to capture intricate temporal dependencies and contextual relationships within sequential data. This dual-stage approach enhances the model’s ability to differentiate between normal and anomalous behaviors in network traffic. Trained on a network security dataset, the system effectively identifies both common and rare attack types. According to the results, the suggested ensemble classifier outperformed existing deep learning models with an accuracy of over 99.5%, 98.5%, and 99.9% on the UNSW-NB15 dataset. The synergy between the unsupervised pattern recognition of clustering and the deep learning capabilities of Transformers enables a scalable and adaptable solution for real-world network security applications, making it suitable for proactive cyber threat detection and mitigation.
Co-Authors Adebowale, Temiloluwa Akunekwu, Isabella Asefon, Michael Isijola, Ayomitope Ogude, Ufuoma Ojo, Adedayo Olukayode Ojo, Florence Omolara Onibonoje Oluwafemi, Moses