Dynamic Weights Equations for Converting Grayscale Image to RGB Image

Alrubaie, Shymaa Akram Hantoush; Hameed, Ahmed Hasan

doi:10.29196/jubpas.v26i8.1677

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…The researchers reached a conclusion found in Eq. ( 1) [40]. Following much testing and investigation, the authors conclude Photographs taken after this procedure are shown in Fig.…”

Section: B) Convert Rgb Images To Grayscale Imagesmentioning

confidence: 87%

Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

2023

IJIES

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Law enforcement and civilian protection are increasingly dependent on surveillance technologies. To prevent social, economic, and environmental harm, video surveillance situations like those in train stations, schools, and hospitals must automatically detect aggressive and suspicious behavior. Intelligent video surveillance systems now enable human interaction monitoring. Despite its security benefits, crowds and the camera's vision make it difficult to distinguish regular from abnormal activity. Therefore, there is a great deal of investigation into the many methods of identifying violent behavior. The detection approach presented in this research can be broken down into three distinct subfields. The classification methods employed led to the formation of these classes. Classifications include Gradient Descent (SGD), Adaptive boosting (ADA), Naive Base (NB), and Logistic Regression (LR) for detecting violent acts with machine learning. Methods for feature extraction and violence detection are derived using Principal Component Analysis (PCA). Dataset learning-based models for violence detection can also be evaluated using the AIRTLab dataset, a model developed to test the robustness of algorithms against false positives. In order to determine if the proposed model's resistance to false positives, accuracy (ACC) metrics were computed on the proposed model and used to set a benchmark for the AIRTLab dataset. According to the research literature, the proposed models are accurate of 98.31% with the logistic regression classifier having superior generalization skills.

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“…The researchers reached a conclusion found in Eq. ( 1) [40]. Following much testing and investigation, the authors conclude Photographs taken after this procedure are shown in Fig.…”

Section: B) Convert Rgb Images To Grayscale Imagesmentioning

confidence: 87%

Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

2023

IJIES

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“…While 0.299, 0.587, and 0.114 represented the constant-coefficient weights. The summation of all constant weights is equal to one value [9].…”

Section: ) Rgb To Gray Imagementioning

confidence: 99%

Design and Implementation of a Touchless Hand Sanitizer Dispenser Mobile Robot

2022

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Two years ago, coronavirus COVID-19 spread all over the world. This virus causes the death of many people. The main problem is the way to maintain healthy hand wash in the circumstance of physical touch to the bottle that causes infection. The purpose of this paper is to design and implement a movable touchless hand sanitizer dispenser mobile robot that identifies the human body by processing the images taken from the camera to estimate the distance between the mobile robot and the human. A microcontroller board which is used to control four DC motors. The all design shows better activity than the usual and manual fixed touchless hand sanitizer design because the movable one can move with an acceptable error to the patient or a healthy person anywhere, he found. People can be protected from any virus or germs by using this proposed design. The main contribution of this work that our design system is useful in Anti-Covid-19 application and the total method has been collected the use of image processing, mobile robot, sensor hardware design, and the overall algorithm that control the application work.

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“…Nilai Wr, Wg, dan juga Wb merupakan koefisien dengan setiap warnanya berdasarkan urutan yaitu 0.299, 0.587, dan 0.114 sehingga didapatkan persamaan (2) [13].…”

Section: E Pre-processingunclassified

Klasifikasi Tumbuhan Angiospermae Menggunakan Algoritma K-Nearest Neighbor Berdasarkan Pada Bentuk Daun

Christopher

Mulyana

2022

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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2) ABSTRAKSekitar 250.000 spesies tanaman yang hidup merupakan tanaman kelompok Angiospermae. Keberagaman tanaman Angiospermae di Indonesia menimbulkan kesulitan bagi seseorang dalam melakukan proses identifikasi tanaman masuk ke kelas monokotil atau dikotil. Hal tersebut yang melatarbelakangi dilakukannya penelitian ini dengan mengklasifikasian tanaman Angiospermae dengan menggunakan algoritma K-Nearest Neighbor. Penelitian terkait pengklasifikasian tanaman manga menggunakan algoritma Backpropagation dengan pengekstraksian fitur menggunakan GLCM menghasilkan akurasi sebesar 49%. Selanjutnya, pengklasifikasian tanaman menggunakan algoritma K-Nearest Neighbor dengan pengekstraksian fitur morfologi daun menghasilkan akurasi sebesar 92%. Terdapat juga pengklasifikasian tanaman jeruk menggunakan algoritma K-Nearest Neighbor dengan pengekstraksian fitur tekstur daun menghasilkan akurasi sebesar 81,48%. Pada penelitian ini, digunakan algoritma pengklasifikasian K-Nearest Neighbor dengan pengekstraksian fitur menggunakan matriks populasi dengan tujuan untuk meneliti tingkat akurasi pengklasifikasian menggunakan pengekstraksian fitur matriks populasi. Data yang digunakan adalah citra digital daun yang akan diekstrak fitur bentuknya menggunakan fitur matriks populasi dengan melakukan image pre-processing dan pendeteksian terlebih dahulu. Data tersebut kemudian dibagi menjadi data training sebesar 70%, dan data test sebesar 30%. Hasil dari penelitian ini adalah nilai akurasi pengujian data test yang didapatkan sebesar 81% dengan jumlah tetangga k=1. Setelah dilakukan pengujian, akan dilakukan evaluasi model dengan menggunakan confusion matrix, yang terdiri dari accuracy, precision, recall, dan f-1 score.

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Dynamic Weights Equations for Converting Grayscale Image to RGB Image

Cited by 12 publications

References 11 publications

Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

Design and Implementation of a Touchless Hand Sanitizer Dispenser Mobile Robot

Klasifikasi Tumbuhan Angiospermae Menggunakan Algoritma K-Nearest Neighbor Berdasarkan Pada Bentuk Daun

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