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Journals / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 14

Data Pre-processing Approach with ML Algorithms for Accuracy and Authenticity Detection of Big Data Sourced from the Social Internet of Things and a Case Study

Sosyal Nesnelerin İnternetinden Kaynaklanan Büyük Verilerin Doğruluğu ve Gerçekliğinin Tespiti için ML Algoritmalarıyla Veri Ön İşleme Yaklaşımı ve Bir Vaka Çalışması

Deniz Kızılaslan(İSTANBUL AYDIN ÜNİVERSİTESİ)0009-0009-7128-4075
Şükrü Mustafa Kaya(İSTANBUL AYDIN ÜNİVERSİTESİ)0000-0003-2710-0063
Pages
63–73
Publication Date
2026-03-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Additionally, the results emphasize the need for larger, more diverse datasets, and suggest incorporating deep learning and automated bias mitigation methods in future research.

📌 3 Key Takeaways

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Objective & Hypothesis:The Social Internet of Things (SIoT), which integrates sensor data with social media interactions, produces massive volumes of unstructured data that require accurate and reliable preprocessing for meaningful analysis.
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Key Findings & Data:This study investigates the effectiveness of various machine learning (ML) classification algorithms in detecting the accuracy and authenticity of SIoT-derived data.A dataset comprising 17,500 user records collected from mobile devices and social media platforms was analyzed using five ML classifiers: Logistic Regression, Naive Bayes, K-Nearest Neighbor (K-NN), Random Forest, and Support Vector Machines (SVM).
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Conclusion & Impact:Additionally, the results emphasize the need for larger, more diverse datasets, and suggest incorporating deep learning and automated bias mitigation methods in future research.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Nesnelerin İnterneti · Sosyal Nesnelerin İnterneti · Büyük Veri

Abstract

The Social Internet of Things (SIoT), which integrates sensor data with social media interactions, produces massive volumes of unstructured data that require accurate and reliable preprocessing for meaningful analysis. This study investigates the effectiveness of various machine learning (ML) classification algorithms in detecting the accuracy and authenticity of SIoT-derived data.A dataset comprising 17,500 user records collected from mobile devices and social media platforms was analyzed using five ML classifiers: Logistic Regression, Naive Bayes, K-Nearest Neighbor (K-NN), Random Forest, and Support Vector Machines (SVM). Through extensive hyperparameter tuning and 5-fold cross-validation, the Random Forest and SVM models exhibited the highest performance, achieving accuracy scores of 0.58 and 0.57, respectively. SVM also obtained the best AUC value of 0.64, highlighting its strength in distinguishing authentic from manipulated data. Additionally, the results emphasize the need for larger, more diverse datasets, and suggest incorporating deep learning and automated bias mitigation methods in future research.

Özet

IoT, çevremizdeki sayısız fiziksel olayı algılayan, bunları veriye dönüştüren ve bu verileri farklı ortamlara veya dijital sistemlere aktaran sensörler dünyasıdır. Nesnelerin İnterneti tabanlı teknolojilerin kullanım alanları sürekli artmakta ve IoT altyapısını destekleyecek teknolojiler geliştirilmektedir. Ancak algılama katmanında üretilen büyük miktardaki büyük verinin etkin bir şekilde yönetilebilmesi için ön işleme tabi tutulması ve büyük veri standartlarıyla uyumlu hale getirilmesi gerekmektedir. Büyük verinin etkin bir şekilde yönetilebilmesi için veri seti standartlarının iyileştirilmesi gerekmekte ve daha kaliteli bir veri seti için farklı veri işleme yöntemleri geliştirilmektedir. Bu çalışmada, mobil cihazlar kullanılarak IoT algılama katmanında üretilen verilerin doğruluğu ve güvenilirliğini belirlemek amacıyla literatür taraması yapılmıştır. Ayrıca çalışmada, mobil cihazlar ve sosyal platformlar üzerinde üretilen verilerden oluşan veri seti ML sınıflandırma algoritmaları ile incelenmiştir. Veri seti üzerinde Lojistik Regresyon (LR), Naive Bayes (NB), Rastgele Orman (RF), K-en yakın komşu (kNN), Destek Vektör Makineleri (SVM) sınıflandırma algoritmaları ile doğruluk ve güvenilirlik açısından karşılaştırmalar ve değerlendirmeler yapılmıştır. ML sınıflandırma algoritmaları üzerinde yapılan çalışmalar sonucunda, Rastgele Orman ve Destek Vektör Makineleri algoritmalarının yakın oranlarda doğruluk sağladığı görülmektedir. Bu çalışma, IoT algılama katmanında mobil cihazlar kullanılarak elde edilen verilerin doğruluğunu ve güvenilirliğini artırmak için etkili algoritmaların belirlenmesine katkıda bulunmaktadır.

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Cite This Article

Kızılaslan, D., Kaya, Ş. M. (2026). Data Pre-processing Approach with ML Algorithms for Accuracy and Authenticity Detection of Big Data Sourced from the Social Internet of Things and a Case Study. Balkan Journal of Electrical and Computer Engineering, Cilt: 14, 63–73. https://doi.org/10.17694/bajece.1712376

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