Preferred Language
Articles
/
IherN48BVTCNdQwCvGOw
Dynamic Background Subtraction in Video Surveillance Using Color-Histogram and Fuzzy C-Means Algorithm with Cosine Similarity
...Show More Authors

The background subtraction is a leading technique adopted for detecting the moving objects in video surveillance systems. Various background subtraction models have been applied to tackle different challenges in many surveillance environments. In this paper, we propose a model of pixel-based color-histogram and Fuzzy C-means (FCM) to obtain the background model using cosine similarity (CS) to measure the closeness between the current pixel and the background model and eventually determine the background and foreground pixel according to a tuned threshold. The performance of this model is benchmarked on CDnet2014 dynamic scenes dataset using statistical metrics. The results show a better performance against the state-of the art background subtraction models.

Scopus Clarivate Crossref
View Publication
Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
MR Brain Image Segmentation Using Spatial Fuzzy C- Means Clustering Algorithm
...Show More Authors

conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

View Publication Preview PDF
Crossref
Publication Date
Tue Sep 08 2020
Journal Name
Baghdad Science Journal
Turbid of Water By Using Fuzzy C- Means and Hard K- Means
...Show More Authors

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

View Publication Preview PDF
Scopus (1)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Tue Sep 08 2020
Journal Name
Baghdad Science Journal
Turbid of Water By Using Fuzzy C- Means and Hard K- Means
...Show More Authors

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

View Publication
Scopus (1)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Tue Jul 30 2024
Journal Name
Iraqi Journal Of Science
Frame-Based Change Detection Using Histogram and Threshold to Separate Moving Objects from Dynamic Background
...Show More Authors

      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

View Publication Preview PDF
Scopus Crossref
Publication Date
Wed Mar 01 2017
Journal Name
2017 Annual Conference On New Trends In Information & Communications Technology Applications (ntict)
An efficient color quantization using color histogram
...Show More Authors

View Publication
Scopus (6)
Crossref (5)
Scopus Crossref
Publication Date
Mon Feb 21 2022
Journal Name
Iraqi Journal For Computer Science And Mathematics
Fuzzy C means Based Evaluation Algorithms For Cancer Gene Expression Data Clustering
...Show More Authors

The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic

... Show More
View Publication
Crossref (1)
Crossref
Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
Image Steganography using Dynamic Threshold based on Discrete Cosine Transform
...Show More Authors

The art of preventing the detection of hidden information messages is the way that steganography work. Several algorithms have been proposed for steganographic techniques. A major portion of these algorithms is specified for image steganography because the image has a high level of redundancy. This paper proposed an image steganography technique using a dynamic threshold produced by the discrete cosine coefficient. After dividing the green and blue channel of the cover image into 1*3-pixel blocks, check if any bits of green channel block less or equal to threshold then start to store the secret bits in blue channel block, and to increase the security not all bits in the chosen block used to store the secret bits. Firstly, store in the cente

... Show More
View Publication Preview PDF
Scopus (6)
Crossref (4)
Scopus Crossref
Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching
...Show More Authors

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare

... Show More
View Publication Preview PDF
Scopus (2)
Crossref (1)
Scopus Crossref
Publication Date
Fri Sep 30 2022
Journal Name
Journal Of The Iraqi University
Exclusion optimal portfolio from outlier by using fuzzy c-means clustering - analytical research at the Iraqi Stock Exchange
...Show More Authors

This research aims to solve the problem of selection using clustering algorithm, in this research optimal portfolio is formation using the single index model, and the real data are consisting from the stocks Iraqi Stock Exchange in the period 1/1/2007 to 31/12/2019. because the data series have missing values ,we used the two-stage missing value compensation method, the knowledge gap was inability the portfolio models to reduce The estimation error , inaccuracy of the cut-off rate and the Treynor ratio combine stocks into the portfolio that caused to decline in their performance, all these problems required employing clustering technic to data mining and regrouping it within clusters with similar characteristics to outperform the portfolio

... Show More
View Publication Preview PDF
Publication Date
Wed Sep 01 2021
Journal Name
Baghdad Science Journal
Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis
...Show More Authors

This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obta

... Show More
View Publication Preview PDF
Scopus (2)
Crossref (1)
Scopus Clarivate Crossref