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All Journal International Journal of Advances in Applied Sciences Jurnal Jaringan Telekomunikasi
Musthafa, Namya
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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The Number of Nodes Effect to Predict the Electrical Consumption in Seven Distinct Countries Safarudin, Yanuar Mahfudz; Musthafa, Namya; Elkhidir, Abdelrahman; Khan, Shah Zahid
Jurnal Jaringan Telekomunikasi Vol 13 No 4 (2023): Vol. 13 No. 04 (2023) : December 2023
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jartel.v13i4.826

Abstract

This paper presents a machine learning-based approach for forecasting electrical consumption in seven selected countries across different geographical categories. The data, sourced from The International Energy Agency, is analysed and condensed to focus on specific nations: Northern (Norway, Canada), Southern (Chile, Australia), Four-season (France, Japan), and a Tropical country (Colombia). The unique electrical consumption patterns influenced by regional climate characteristics make this study compelling for machine learning applications. From the dataset comprising over 132,000 records from January 2010 to May 2023 across 53 countries, a refined dataset focusing on 791 data points from seven specifically chosen countries to simplify the study. A significant part of the paper details the machine learning design for electrical consumption forecasting. Specifically, Artificial neural network architecture is proposed to predict consumption. The input features encompass the year, month, and country, with the output being the anticipated electrical usage.
Enhanced skin cancer classification via Xception model Memon, Qurban Ali; Musthafa, Namya; Masud, Muhammad; Al Ameri, Ghaya
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp69-76

Abstract

Skin cancer is a prevalent and deadly cancer, and early detection is crucial for improving treatment success. Intelligent technologies are currently being used to classify skin lesions. The fundamental goal of this experimental research is to investigate biomedical skin cancer datasets to develop an effective approach for determining whether a cancer is malignant or benign. Well-known deep learning classification models (convolutional neural network (CNN) (sequential), ResNet50, InceptionV3, and Xception) are employed to train and categorize the dataset images. Two large and balanced datasets are collected and employed in this research. One is used to compare the performance of the employed model algorithms. Next, the selected model(s) are again trained on the second dataset for validation and generalization purposes. It turns out that the performance of the Xception model is superior and can be generalized. The performance results obtained from various simulations are tabulated and graphed. Comparative results are also presented.
Co-Authors Al Ameri, Ghaya Elkhidir, Abdelrahman Khan, Shah Zahid Masud, Muhammad Memon, Qurban Ali Yanuar Mahfudz Safarudin