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
Color Image Steganography Using Gradient Selective Bezier Curves Suhaila Najim Mohammed Iraqi Journal of Science, 2023 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.
Image Segmentation Using PSO-Enhanced K-Means Clustering and Region Growing Algorithms Nassir H. Salman, Suhaila N. Mohammed Iraqi Journal of Science, 2021 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.
Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors Yossra Ali, Suhaila Mohammed, and International Journal of Intelligent Engineering and Systems, 2021 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.
Anomaly Based Intrusion Detection System Using Hierarchical Classification and Clustering Techniques Hala Bahjat, Suhaila N. Mohammed, Wafaa Ahmed, Sumaya Hamad, Shayma Mohammed Proceedings International Conference on Developments in Esystems Engineering Dese, 2020 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.
Automatic voice activity detection using fuzzy-neuro classifier Journal of Engineering Science and Technology, 2020
Speech emotion recognition using MELBP variants of spectrogram image Suhaila Mohammed, Hassan Alia, and International Journal of Intelligent Engineering and Systems, 2020 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.
A novel facial emotion recognition scheme based on graph mining Alia K. Hassan, Suhaila N. Mohammed Defence Technology, 2020 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.
English numbers recognition based on sign language using line-slope features and PSO-DBN optimization method Journal of Engineering Science and Technology, 2020
A Ranked-Aware GA with HoG Features for Infant Cry Classification. SN Mohammed, AJ Jabir International Journal of Intelligent Engineering & Systems 16 (6) , 2023 2023 Citations: 1
Color Image Steganography Using Gradient Selective Bezier Curves SN Mohammed Iraqi Journal of Science, 3625-3641 , 2023 2023 Citations: 4
Diagnosis of COVID-19 Infection via Association Rules of Cough Encoding SN Mohammed ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17 (1 … , 2023 2023 Citations: 1
Image segmentation using pso-enhanced k-means clustering and region growing algorithms NH Salman, SN Mohammed Iraqi Journal of Science, 4988-4998 , 2021 2021 Citations: 12
The Effect of the Number of Key-Frames on the Facial Emotion Recognition Accuracy SN Mohammed, A K Abdul Hassan Engineering and Technology Journal 39 (1), 89-100 , 2021 2021 Citations: 4
Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors. YH Ali, SN Mohammed International Journal of Intelligent Engineering & Systems 14 (1) , 2021 2021 Citations: 1
Anomaly based intrusion detection system using hierarchical classification and clustering techniques H Bahjat, SN Mohammed, W Ahmed, S Hamad, S Mohammed 2020 13th International Conference on Developments in eSystems Engineering … , 2020 2020 Citations: 9
Covid-19 diagnostics from the chest x-ray image using corner-based weber local descriptor SN Mohammed, AK Abdul Hassan, HM Rada Big data analytics and artificial intelligence against covid-19: innovation … , 2020 2020 Citations: 3
Automatic voice activity detection using fuzzy-neuro classifier SN Mohammed, AK Hassan Journal of Engineering Science and Technology 15 (5), 2854-2870 , 2020 2020 Citations: 10
A novel facial emotion recognition scheme based on graph mining AK Hassan, SN Mohammed Defence Technology 16 (5), 1062-1072 , 2020 2020 Citations: 71
Speech Emotion Recognition Using MELBP Variants of Spectrogram Image. SN Mohammed, AK Abdul Hassan International Journal of Intelligent Engineering & Systems 13 (5) , 2020 2020 Citations: 16
English Numbers Recognition Based On Sign Language Using Line-Slope Features and PSO-DBN Optimization Method SN Mohammed, H Rada Journal of Engineering Science and Technology 15 (3), 1855-1867 , 2020 2020 Citations: 3
A survey on emotion recognition for human robot interaction SN Mohammed, AKA Hassan Journal of computing and information technology 28 (2), 125-146 , 2020 2020 Citations: 38
Automatic computer aided diagnostic for COVID-19 based on chest X-ray image and particle swarm intelligence SN Mohammed, F Alkinani, Y Hassan International Journal of Intelligent Engineering and Systems 13 (5), 63-73 , 2020 2020 Citations: 32
Spin-Image Descriptors for Text-Independent Speaker Recognition SN Mohammed, JJ Adnan, AA Zaid Emerging Trends in Intelligent Computing and Informatics 1073, 216-226 , 2020 2020 Citations: 8
Emotion Recognition Based on Texture Analysis of Facial Expressions Using Wavelets Transform SN Mohammed, LE George Australian Journal of Basic and Applied Sciences 11 (5), 1-11 , 2017 2017 Citations: 4
Block-based Image Steganography for Text Hiding Using YUV Color Model and Secret Key Cryptography Methods SN Mohammed, S Ahmed, G Mohammed, DA Abduljabbar Australian Journal of Basic and Applied Sciences 11 (7), 37-41 , 2017 2017 Citations: 4
The effect of classification methods on facial emotion recognition & accuracy SN Mohammed, LE George, HA Dawood Br. J. Appl. Sci. Technol 14 (4), 1-11 , 2016 2016 Citations: 4
Illumination-Invariant Facial Components Extraction Using Adaptive Contrast Enhancement Methods SN Mohammed, L George British Journal of Applied Science & Technology 12 (3), 1-13 , 2016 2016 Citations: 9
Emotion Detection Using Facial Image Based on Geometric Attributes SN Mohammed, LE George MSc Thesis. University of Baghdad , 2016 2016 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
A novel facial emotion recognition scheme based on graph mining AK Hassan, SN Mohammed Defence Technology 16 (5), 1062-1072 , 2020 2020.0 Citations: 71
A survey on emotion recognition for human robot interaction SN Mohammed, AKA Hassan Journal of computing and information technology 28 (2), 125-146 , 2020 2020.0 Citations: 38
Automatic computer aided diagnostic for COVID-19 based on chest X-ray image and particle swarm intelligence SN Mohammed, F Alkinani, Y Hassan International Journal of Intelligent Engineering and Systems 13 (5), 63-73 , 2020 2020.0 Citations: 32
Speech Emotion Recognition Using MELBP Variants of Spectrogram Image. SN Mohammed, AK Abdul Hassan International Journal of Intelligent Engineering & Systems 13 (5) , 2020 2020.0 Citations: 16
Image segmentation using pso-enhanced k-means clustering and region growing algorithms NH Salman, SN Mohammed Iraqi Journal of Science, 4988-4998 , 2021 2021.0 Citations: 12
Automatic voice activity detection using fuzzy-neuro classifier SN Mohammed, AK Hassan Journal of Engineering Science and Technology 15 (5), 2854-2870 , 2020 2020.0 Citations: 10
Anomaly based intrusion detection system using hierarchical classification and clustering techniques H Bahjat, SN Mohammed, W Ahmed, S Hamad, S Mohammed 2020 13th International Conference on Developments in eSystems Engineering … , 2020 2020.0 Citations: 9
Illumination-Invariant Facial Components Extraction Using Adaptive Contrast Enhancement Methods SN Mohammed, L George British Journal of Applied Science & Technology 12 (3), 1-13 , 2016 2016.0 Citations: 9
Subject Independent Facial Emotion Classification Using Geometric Based Features SN Mohammed, LE George Research Journal of Applied Sciences, Engineering and Technology 11 (9 … , 2015 2015.0 Citations: 9
Spin-Image Descriptors for Text-Independent Speaker Recognition SN Mohammed, JJ Adnan, AA Zaid Emerging Trends in Intelligent Computing and Informatics 1073, 216-226 , 2020 2020.0 Citations: 8
Emotion Detection Using Facial Image Based on Geometric Attributes SN Mohammed, LE George MSc Thesis. University of Baghdad , 2016 2016.0 Citations: 6
Color Image Steganography Using Gradient Selective Bezier Curves SN Mohammed Iraqi Journal of Science, 3625-3641 , 2023 2023.0 Citations: 4
The Effect of the Number of Key-Frames on the Facial Emotion Recognition Accuracy SN Mohammed, A K Abdul Hassan Engineering and Technology Journal 39 (1), 89-100 , 2021 2021.0 Citations: 4
Emotion Recognition Based on Texture Analysis of Facial Expressions Using Wavelets Transform SN Mohammed, LE George Australian Journal of Basic and Applied Sciences 11 (5), 1-11 , 2017 2017.0 Citations: 4
Block-based Image Steganography for Text Hiding Using YUV Color Model and Secret Key Cryptography Methods SN Mohammed, S Ahmed, G Mohammed, DA Abduljabbar Australian Journal of Basic and Applied Sciences 11 (7), 37-41 , 2017 2017.0 Citations: 4
The effect of classification methods on facial emotion recognition & accuracy SN Mohammed, LE George, HA Dawood Br. J. Appl. Sci. Technol 14 (4), 1-11 , 2016 2016.0 Citations: 4
Biometrics Systems Challenges in a Post-COVID-19 Pandemic World: A review SN Mohammed, FS Alkinani Al-Mansour Journal 39, 1-27 , 0 Citations: 4
Covid-19 diagnostics from the chest x-ray image using corner-based weber local descriptor SN Mohammed, AK Abdul Hassan, HM Rada Big data analytics and artificial intelligence against covid-19: innovation … , 2020 2020.0 Citations: 3
English Numbers Recognition Based On Sign Language Using Line-Slope Features and PSO-DBN Optimization Method SN Mohammed, H Rada Journal of Engineering Science and Technology 15 (3), 1855-1867 , 2020 2020.0 Citations: 3
A Ranked-Aware GA with HoG Features for Infant Cry Classification. SN Mohammed, AJ Jabir International Journal of Intelligent Engineering & Systems 16 (6) , 2023 2023.0 Citations: 1
