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Abdul Azis Abdillah
The School of Engineering, University of Birmingham, Birmingham, United Kingdom
Chemical Engineering, Chemistry & Bioengineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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A Simple Insight into Convolutional Neural Network Research Using VOSviewer, Python, and Gen-AI Samsul Arifin; Ade Kurniawan; Muhammad Faisal; Merios Gusan Putra; Tiawan; Dani Lukman Hakim; Abdul Azis Abdillah; Wiwik Wiyanti
Recent in Engineering Science and Technology Vol. 4 No. 2 (2026): RiESTech Vol. 4 No. 2 Years 2026
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v4i2.130

Abstract

This study presents a bibliometric analysis of research trends in Convolutional Neural Networks (CNN) published in 2023. Using the Scopus database, metadata for 2,185 journal articles was extracted based on criteria including open access status, English language, and classification under Computer Science. The research employed a systematic methodology involving data extraction, preprocessing, and network visualization. VOSviewer was used to map co-authorship networks, keyword co-occurrence, and citation patterns, while Python supported advanced data processing, topic modeling, and trend analysis. Keyword analysis highlighted the prominence of terms such as "deep learning," "learning systems," "image classification," and "object detection," indicating the diverse and interdisciplinary applications of CNN technology. The co-authorship network revealed China, India, and the United States as key centers of international research collaboration, demonstrating global engagement in advancing CNN studies. Citation analysis showed a skewed distribution where most publications received between zero and two citations, though some articles garnered significantly higher attention, with citations reaching up to 57 within the same year of publication. This suggests that a few studies have rapidly influenced the field despite the overall low citation count typical of recent papers. By integrating quantitative bibliometric techniques with AI-assisted qualitative insights, this study offers a comprehensive overview of the dynamic and rapidly evolving landscape of CNN research in 2023, guiding future academic and practical endeavors.
Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries Idrus Assagaf; Agus Sukandi; Parulian Jannus; Sonki Prasetya; Asep Apriana; Ega Edistria; Abdul Azis Abdillah
Recent in Engineering Science and Technology Vol. 3 No. 4 (2025): RiESTech Vol. 3 No. 4 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i04.127

Abstract

Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for optimizing battery management and ensuring operational reliability. This study compares the performance of two deep learning architectures—a Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (1D-CNN)—in predicting RUL using datasets from CALCE batteries B35, B36, and B37. Data preprocessing involved outlier removal, missing value handling, and feature normalization, with key features extracted including Resistance, Constant Voltage Charging Time (CVCT), and Constant Current Charging Time (CCCT). Correlation analyses confirmed strong relationships between these features and RUL. Both models were trained and validated on preprocessed data, and their predictive accuracies were assessed using Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated that while both architectures effectively captured battery degradation patterns, the MLP consistently outperformed the 1D-CNN, achieving on average 5% lower RMSE and 1.5% higher R2 across all tested batteries. These findings suggest that simpler fully connected networks may suffice for this forecasting task under the given feature set and preprocessing conditions. This work provides valuable insights into neural network model selection for battery health prognostics, guiding the development of efficient and accurate predictive maintenance strategies.
Co-Authors Ade Kurniawan Agus Sukandi Asep Apriana Dani Lukman Hakim Ega Edistria Idrus Assagaf Merios Gusan Putra Muhammad Faisal Parulian Jannus Sonki Prasetya Tiawan Wiwik Wiyanti