Suhaila N. Mohammed
@uobaghdad.edu.iq
Department of Computer Science
University of Baghdad
Suhaila Najim Mohammed received the BSc degree (2010) in computer science from Baghdad University, Iraq. She received the MSc degree (2016) in computer science from the University of Baghdad, Iraq. In 2011, she started the work as a lecturer in computer science department at college of science, University of Baghdad, Iraq. Currently, she is working toward the PhD degree at the University of Technology, Iraq.
EDUCATION
Doctor of Philosophy in Computer Science
RESEARCH INTERESTS
Multimedia, Computer Vision, Pattern Recognition, Soft Computing, Data Mining, Artificial Intelligence, Machine Learning, Image Processing, Signal Processing, Deep Learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Suhaila Najim Mohammed
University of Baghdad College of Science
Internet technology has revolutionized the landscape of communication technologies in the modern era. However, because the internet is open to the public, communication security cannot be guaranteed. As a result, data concealment approaches have been developed to ensure confidential information sharing. Various methods have emerged to achieve the goal of secure data communication via multimedia documents. This study proposes a method, which is both adaptable and imperceptible, for concealing a secret text in a color image. From an adaptivity perspective, image corners are detected using the Harris corner detection algorithm and utilized as anchor points for picking the optimal hiding regions of interest using Bezier curve interpolation. On the other hand, because human vision is less sensitive to aberrations in edge regions, imperceptibility is guaranteed by utilizing curves that cross through these regions. Experiments indicate that utilizing gradient selective Bezier curves for secret text concealment can keep the imperceptibility even when the payload capacity is increased.
Nassir H. Salman and Suhaila N. Mohammed
University of Baghdad College of Science
Image segmentation is a basic image processing technique that is primarily used for finding segments that form the entire image. These segments can be then utilized in discriminative feature extraction, image retrieval, and pattern recognition. Clustering and region growing techniques are the commonly used image segmentation methods. K-Means is a heavily used clustering technique due to its simplicity and low computational cost. However, K-Means results depend on the initial centres’ values which are selected randomly, which leads to inconsistency in the image segmentation results. In addition, the quality of the isolated regions depends on the homogeneity of the resulted segments. In this paper, an improved K-Means clustering algorithm is proposed for image segmentation. The presented method uses Particle Swarm Intelligence (PSO) for determining the initial centres based on Li’s method. These initial centroids are then fed to the K-Means algorithm to assign each pixel into the appropriate cluster. The segmented image is then given to a region growing algorithm for regions isolation and edge map generation. The experimental results show that the proposed method gives high quality segments in a short processing time.
Yossra Ali, , Suhaila Mohammed, and
International Journal of Intelligent Engineering and Systems The Intelligent Networks and Systems Society
Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based features and color based features. The extracted features are finally fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed and different combinations of feature types are attempted. The achieved results showed that when using combined vectors of local descriptors, the system gives the desired accuracy which is 100%. The achieved result demonstrates the effectiveness of using local descriptors in solving malaria infection detection problem.
Hala Bahjat, Suhaila N. Mohammed, Wafaa Ahmed, Sumaya Hamad, and Shayma Mohammed
IEEE
With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vectors to determine the sub-class of each attack type are selected. Features are evaluated to measure its discrimination ability among classes. K_Means clustering algorithm is then used to cluster each class into two clusters. SFFS and ANN are used in hierarchical basis to select the relevant features and classify the query behavior to proper intrusion type. Experimental evaluation on NSL-KDD, a filtered version of the original KDD99 has shown that the proposed IDS can achieve good performance in terms of intrusions detection and recognition.
Suhaila Mohammed, , Hassan Alia, and
The Intelligent Networks and Systems Society
Speech emotion recognition finds many applications in the daily life like conversational agents, human robot interaction, call centres etc. However; the task of emotion recognition from speech signal is not trivial due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the signal in an accurate manner. Image processing techniques are exploited in this paper to solve speech emotion recognition problem. After converting the signal into 2D spectrogram image representation, four forms of Extended Local Binary Pattern (ELBP) are generated to serve as a source for feature extraction stage. The histograms of multiple blocks from ELBP variants are computed and fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the achieved results showed that when using combined vectors of MELBP, the system gives the best accuracy which is 72.14%. The achieved result outperforms state-of-the-art results on the same database.
Suhaila Mohammed, , Fatin Alkinani, Yasmin Hassan, , and
The Intelligent Networks and Systems Society
COVID-19 is a vital zoonotic illness caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) COVID-19 is a very wide-spread among humans thus the early detection and curing of the disease offers a high opportunity of survival for patients Computed Tomography (CT) plays an important role in the diagnosis of COVID-19 As chest radiography can give an indicator of coronavirus Though, an automated Computer Aided Diagnostic (CAD) system for COVID-19 based on chest X-Ray image analysis is presented in this article It is designed for COVID-19 recognition from other MERS, SARS, and ARDS viral pneumonia The optimal threshold value for the segmentation of a chest image is deduced by exploiting Li s' method and particle swarm intelligence Laws' masks are then applied to the segmented chest image for secondary characteristics highlighting After that, nine different vectors of attributes are extracted from the Grey Level Co-occurrence Matrix (GLCM) representation of each Law's mask result Support vector machine ensemble models are then built based on the extracted feature vectors Finally, a weighted voting method is utilized to combine the decisions of ensemble classifiers Experimental findings show an accuracy of 98 04 % It indicates that the suggested CAD scheme can be a promising supplementary COVID-19 diagnostic tool for clinical doctors © 2020, Intelligent Network and Systems Society
Alia K. Hassan and Suhaila N. Mohammed
Elsevier BV
Abstract Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. To reduce the number of generated sub-graphs, overlap ratio metric is utilized for this purpose. After encoding the final selected sub-graphs, binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification. Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the final system accuracy was 90.00%. The results show significant accuracy improvements (about 2%) by the proposed system in comparison to current published works in SAVEE database.
S. N. Mohammed, A. K. Abdul Hassan, and H. M. Rada
Springer International Publishing
With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined.
Suhaila N. Mohammed, Adnan J. Jabir, and Zaid Ali Abbas
Springer International Publishing
Building a system to identify individuals through their speech recording can find its application in diverse areas, such as telephone shopping, voice mail and security control. However, building such systems is a tricky task because of the vast range of differences in the human voice. Thus, selecting strong features becomes very crucial for the recognition system. Therefore, a speaker recognition system based on new spin-image descriptors (SISR) is proposed in this paper. In the proposed system, circular windows (spins) are extracted from the frequency domain of the spectrogram image of the sound, and then a run length matrix is built for each spin, to work as a base for feature extraction tasks. Five different descriptors are generated from the run length matrix within each spin and the final feature vector is then used to populate a deep belief network for classification purpose. The proposed SISR system is evaluated using the English language Speech Database for Speaker Recognition (ELSDSR) database. The experimental results were achieved with 96.46 accuracy; showing that the proposed SISR system outperforms those reported in the related current research work in terms of recognition accuracy.