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All Journal JOIV : International Journal on Informatics Visualization
Khaleefah, Shihab Hamad
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System Khaleefah, Shihab Hamad; A. Mostafa, Salama; Gunasekaran, Saraswathy Shamini; Khattak, Umar Farooq; Yaacob, Siti Salwani; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2701

Abstract

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.
Applying Deep Learning Models to Breast Ultrasound Images for Automating Breast Cancer Diagnosis Khaleefah, Shihab Hamad; Lojungin, Eva Cabrini; Mostafa, Salama A.; Baharum, Zirawani; Aldulaimi, Mohammed Hasan; Ghazal, Taher M.; Alo, Salam Omar; Hidayat, Rahmat
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.1912

Abstract

Breast cancer is a result of uncontrolled human cell division. The vast growth of breast cancer patients has been an issue worldwide. Most of the patients are women, but breast cancer also affects men with a much lesser percentage. Breast cancer might lead to death for those who are suffering from it. Numerous types of research have been done to make an early diagnosis of breast cancer. It has been proven that the tumor can be detected by using an ultrasound image. Artificial Intelligence techniques have been used to detect breast cancer fundamentally. This paper studies the effectiveness of deep learning (DL) techniques in automating breast cancer diagnosis. Subsequently, the paper evaluates the diagnosis performance of three DL models utilizing the criteria of accuracy, recall, precision, and f1-score. The Densenet-169, U-Net, and ConvNet DL models are selected based on the examination of the related work. The DL diagnosis process involves identifying two types of breast cancer tumors: benign and malignant. The evaluation outcomes of the DL models show that the most effective model for diagnosing breast cancer among the three is the ConvNet, which achieves an accuracy of 91%, a recall of 83%, a precision of 85%, and an F1-score of 83%.
Co-Authors A. Mostafa, Salama Alde Alanda, Alde Aldulaimi, Mohammed Hasan Alo, Salam Omar Baharum, Zirawani Ghazal, Taher M. Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Lojungin, Eva Cabrini Mostafa, Salama A. Rahmat Hidayat Yaacob, Siti Salwani