Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : KNN


Comparative Analysis of GMM, KNN, and ViBe Background Subtraction Algorithms Applied in Dynamic Background Scenes of Video Surveillance System

Maryam A. Yasir; Yossra H. Ali

Engineering and Technology Journal, 2022, Volume 40, Issue 4, Pages 617-626
DOI: 10.30684/etj.v40i4.2154

Background subtraction is the most prominent technique applied in the domain of detecting moving objects. However, there is a wide range of different background subtraction models. Choosing the best model that addresses a number of challenges is still a vital research area.
Therefore, in this article we present a comparative analysis of three promising algorithms used in this domain, GMM, KNN and ViBe. CDnet 2014 is the benchmark dataset used in this analysis with several quantitative evaluation metrics like precession, recall, f-measures, false positive rate, false negative rate and PWC. In addition, qualitative evaluations are illustrated in snapshots to depict the visual scenes evaluation. ViBe algorithm outperform other algorithms for overall evaluations.

Textual Dataset Classification Using Supervised Machine Learning Techniques

Hanan Q. Jaleel; Jane J. Stephan; Sinan A. Naji

Engineering and Technology Journal, 2022, Volume 40, Issue 4, Pages 527-538
DOI: 10.30684/etj.v40i4.1970

Text classification has been a significant domain of study and research because of the increased volume of text datasets and documents available in digital format. Text classification is one of the major approaches used to arrange digital information via automatically allocating text dataset records or documents into predetermined classes depending on their contents. This paper proposes a technique that implements supervised machine learning algorithms such as KNN, Decision tree, Random Forest, Bernoulli Naive Bayes, and Multinomial Naive Bayes classifiers to classify a dataset into distinct classes. The proposed technique combines the above-mentioned machine learning classifiers with the TF-IDF feature extraction method as a vector space model to achieve more precise classification results. The proposed technique yields high accuracy, precision, recall, and f1-measure metric values for all the implemented classifiers. After comparing the obtained results of different classifiers, it is found that the Random Forest classifier is the best algorithm used to classify the textual dataset records with the highest accuracy value of 0.9995930.

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

Ahmed M. Abed; Sabah A. Gitaffa; Abbas H. Issa

Engineering and Technology Journal, 2021, Volume 39, Issue 5A, Pages 859-869
DOI: 10.30684/etj.v39i5A.2002

Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.

Visual Depression Diagnosis From Face Based on Various Classification Algorithms

Sana A. Nasser; Ivan A. Hashim; Wisam H. Ali

Engineering and Technology Journal, 2020, Volume 38, Issue 11, Pages 1717-1729
DOI: 10.30684/etj.v38i11A.1714

Most psychologists believe that facial behavior through depression differs from facial behavior in the absence of depression, so facial behavior can be utilized as a dependable indicator for spotting depression. Visual depression diagnosis system (VDD) establishes dependents on expressions of the face that are expense-effective and movable. At this work, the VDD system is designed according to the Facial Action Coding System (FACS) to extract features of the face. The key concept of the Facial Action Coding System (FACS) to explain the whole face behavior utilizing Action Units (AUs), every AU is linked to the motion of unique or maybe further face muscles. Six AUs have utilized as depression features; those action units are AUs 4, 5, 6, 7, 10, and 12. The datasets that employed to evaluate the performance of the proposed system are gathered for 125 participants (30 males, 95 females); many of them are among 17-60 years of age. At the final step of the current system, four kinds of classification techniques were applied separately; those classifiers algorithms are KNN, SVM, PCA, and LDA. The outcomes of the simulation indicate that the best outcomes are achieved utilizing the KNN and LDA classifiers, where the success rate is 85%. New classification methods in the VDD system are the key contributions of this research, gather real databases that can utilize to compute the performance of every other VDD system based on face emotions, and choose appropriate features of the face.