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A comparative study of machine learning classification alorithms on acceleration data

Hızlanma verileri üzerinde makine eğitimi sınıflandırma aloritmalarının karşılaştırmalı bir çalışması

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  1. Tez No: 840875
  2. Yazar: MUHAMMAD AHMED MAJEED
  3. Danışmanlar: Assist. Prof. Dr. NURİ BİNGÖL, DR. ÖĞR. ÜYESİ FAYYAZ AHMAD
  4. Tez Türü: Yüksek Lisans
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2023
  8. Dil: İngilizce
  9. Üniversite: Üsküdar Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Bilgisayar Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Bilgisayar Mühendisliği Bilim Dalı
  13. Sayfa Sayısı: 130

Özet

Human activity recognition using machine learning has gained significant attention in recent years. The application of machine learning techniques to recognize human activities by analyzing acceleration data is focused on in this thesis research. The motivation for this research arises from the increasing popularity of wristband gadgets, mobile applications, smartwatches, etc., which collect gyroscope, gravity, rotation, and acceleration data, as well as the advancements in machine learning. Python and MATLAB® code was used in this research to extract 66 features from raw acceleration data associated with six human activities: walking, standing, sitting, upstairs, downstairs, and jogging. Four classification algorithms were implemented: Decision Tree, Support Vector Machine (SVM), Naïve Bayes, and K Nearest Neighbors (KNN). Classification models capable of accurately predicting various human activities were created using these algorithms. Across the different algorithms, the accuracy levels of the classification models varied. An accuracy of 87.4% was achieved by the Decision Tree algorithm, 90.9% by Naïve Bayes, 95.3% by Support Vector Machine, and 88.0% by KNN. Several metrics, including accuracy, precision, recall, and F1-score, were used to evaluate the performance of each algorithm. The numerical experimental results demonstrate that each algorithm has its strengths and limitations in recognizing human activities. Among these algorithms, the Support Vector Machine algorithm demonstrated the highest accuracy of 95.3%. The effectiveness of machine learning techniques in human activity recognition is showcased in this study, highlighting the superiority of the Support Vector Machine algorithm in accurately identifying human activities using raw data.

Özet (Çeviri)

Human activity recognition using machine learning has gained significant attention in recent years. The application of machine learning techniques to recognize human activities by analyzing acceleration data is focused on in this thesis research. The motivation for this research arises from the increasing popularity of wristband gadgets, mobile applications, smartwatches, etc., which collect gyroscope, gravity, rotation, and acceleration data, as well as the advancements in machine learning. Python and MATLAB® code was used in this research to extract 66 features from raw acceleration data associated with six human activities: walking, standing, sitting, upstairs, downstairs, and jogging. Four classification algorithms were implemented: Decision Tree, Support Vector Machine (SVM), Naïve Bayes, and K Nearest Neighbors (KNN). Classification models capable of accurately predicting various human activities were created using these algorithms. Across the different algorithms, the accuracy levels of the classification models varied. An accuracy of 87.4% was achieved by the Decision Tree algorithm, 90.9% by Naïve Bayes, 95.3% by Support Vector Machine, and 88.0% by KNN. Several metrics, including accuracy, precision, recall, and F1-score, were used to evaluate the performance of each algorithm. The numerical experimental results demonstrate that each algorithm has its strengths and limitations in recognizing human activities. Among these algorithms, the Support Vector Machine algorithm demonstrated the highest accuracy of 95.3%. The effectiveness of machine learning techniques in human activity recognition is showcased in this study, highlighting the superiority of the Support Vector Machine algorithm in accurately identifying human activities using raw data.

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