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Kim, Daehyon
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Computer Science & IT

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Preprocessing Effects in Road Traffic Scene Analysis Using Machine Learning Models Kim, Daehyon
Informatics and Software Engineering Vol. 1 No. 2 (2023): December 2023
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v1i2.148

Abstract

Image-based real-time object detection is a key issue in integrated automatic traffic monitoring and control systems and is one of the main research topics in Intelligent Transportation Systems (ITS). Despite the growing interest in the application of image processing techniques for traffic scene analysis, vehicle detection algorithms are still unreliable in complex real-world backgrounds. Machine learning models are increasingly being applied to automatic video-based object detection and traffic scene analysis due to their excellent performance in real-time pattern recognition. However, the performance of machine learning models is highly dependent on the properties of the input vectors. Studies have shown that the preprocessing of raw image data obtained from digital video can improve the predictive performance of machine learning models. The purpose of this study is to investigate the predictive performance of machine learning models using preprocessing methods such as image size reduction and image filtering. The experiment was performed with the Backpropagation model, which is one of the most popular machine learning models, and the predictive performance was compared to see how it could be affected by different preprocessing techniques.   Deteksi objek real-time berbasis gambar adalah isu kunci dalam sistem pemantauan dan kontrol lalu lintas otomatis terintegrasi dan merupakan salah satu topik penelitian utama dalam Sistem Transportasi Cerdas. Meskipun minat yang meningkat dalam penerapan teknik pemrosesan gambar untuk analisis scene lalu lintas, algoritma deteksi kendaraan masih belum dapat diandalkan dalam latar belakang dunia nyata yang kompleks. Model pembelajaran mesin semakin diterapkan pada deteksi objek berbasis video otomatis dan analisis scene lalu lintas karena kinerja mereka yang sangat baik dalam pengenalan pola real-time. Namun, kinerja model pembelajaran mesin sangat bergantung pada sifat vektor masukan. Studi telah menunjukkan bahwa pra-pemrosesan data gambar mentah yang diperoleh dari video digital dapat meningkatkan kinerja prediktif dari model pembelajaran mesin. Tujuan dari penelitian ini adalah untuk menyelidiki kinerja prediktif dari model pembelajaran mesin menggunakan metode pra-pemrosesan seperti reduksi ukuran gambar dan penyaringan gambar. Percobaan dilakukan dengan model Backpropagation, yang merupakan salah satu model pembelajaran mesin paling populer, dan kinerja prediktifnya dibandingkan untuk melihat bagaimana hal itu dapat dipengaruhi oleh berbagai teknik pra-pemrosesan.
Assessing the Predictive Performance of Machine Learning Algorithms: DBNs, Fuzzy ARTMAP, and SVMs Kim, Daehyon
Informatics and Software Engineering Vol. 2 No. 2 (2024): December 2024
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v2i2.255

Abstract

The field of machine learning is rapidly advancing, and selecting the most suitable algorithm for predictive tasks remains a critical challenge. This study evaluates the predictive performance of three prominent machine learning algorithms: Deep Belief Networks (DBNs), Fuzzy ARTMAP, and Support Vector Machines (SVMs). Experiments on pattern recognition using image data from construction sites showed that DBNs achieved the highest predictive accuracy. In this study, experiments were conducted on a pattern recognition problem using image data from construction sites. The experimental results demonstrated that DBNs exhibited the highest predictive accuracy with the data used in this study. Algorithms such as DBNs, Fuzzy ARTMAP, and SVMs are representative models of machine learning methods, and their predictive power can vary depending on the type of data and the problem context. Therefore, future research should incorporate extended analyses with more diverse datasets and problem domains. Nonetheless, the findings of this study provide valuable guidelines for selecting appropriate algorithms for practical problem-solving and offer practical insights for practitioners aiming to optimize predictive accuracy across various machine learning applications.   Bidang pembelajaran mesin berkembang pesat, dan memilih algoritma yang paling sesuai untuk tugas-tugas prediktif masih merupakan tantangan penting. Studi ini memberikan evaluasi komprehensif terhadap kinerja prediktif dari tiga algoritma pembelajaran mesin terkemuka: Deep Belief Networks (DBNs), Fuzzy Adaptive Resonance Theory Mapping (FuzzyARTMAP), dan Support Vector Machines (SVMs). Dalam penelitian ini, percobaan dilakukan pada masalah pengenalan pola menggunakan data gambar dari lokasi konstruksi. Hasil eksperimen menunjukkan bahwa DBN menunjukkan akurasi prediksi tertinggi dibandingkan data yang digunakan dalam penelitian ini. Algoritma seperti DBN, FuzzyARTMAP, dan SVM merupakan model representatif dari metode pembelajaran mesin, dan kekuatan prediksinya dapat bervariasi bergantung pada jenis data dan konteks masalah. Oleh karena itu, penelitian di masa depan harus menggabungkan analisis yang diperluas dengan kumpulan data dan domain masalah yang lebih beragam. Meskipun demikian, temuan penelitian ini memberikan pedoman berharga dalam memilih algoritma yang tepat untuk pemecahan masalah praktis dan menawarkan wawasan praktis bagi para praktisi yang ingin mengoptimalkan akurasi prediksi di berbagai aplikasi pembelajaran mesin.
Unified Predictive Modeling: Enhancing Accuracy with DBNs, Fuzzy ARTMAP, and SVMs Kim, Daehyon
Informatics and Software Engineering Vol. 3 No. 1 (2025): June 2025
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v3i1.425

Abstract

Amid the global surge in artificial intelligence, the field of machine learning is advancing rapidly, and selecting the most suitable algorithm for prediction tasks remains a crucial challenge. This paper introduces a novel ensemble model that combines three machine learning algorithms—Deep Belief Networks (DBNs), Fuzzy ARTMAP, and Support Vector Machines (SVMs)—to enhance predictive performance. Each machine learning model possesses unique strengths, and by integrating these models, it is possible to overcome individual model limitations and achieve more accurate and reliable predictions. DBNs excel at learning hierarchical representations and capturing complex patterns, Fuzzy ARTMAP is proficient in handling imprecise and ambiguous data, and SVMs are renowned for their robustness in high-dimensional spaces. Thus, the integrated framework leverages the complementary strengths of each model while mitigating their weaknesses. In this study, the proposed ensemble model's predictive power was validated through experiments on image data collected from actual construction sites for construction automation research. The prediction performance of the proposed ensemble model was evaluated and compared with that of individual models such as DBNs, Fuzzy ARTMAP, and SVMs, demonstrating its superiority. The experimental results showed that the proposed model outperformed each individual algorithm in terms of prediction accuracy, clearly illustrating the effectiveness of the ensemble approach.
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