English العربية کوردی

ئەز   Hivi Ismat Dino

Lecturer

Specialties

Artificial Inteligence

hivi.dino@uoz.edu.krd
07504122962
Iraq , Zakho
2
Awards
7
Published Journal Articles
1
Thesis
0
Training Course

0
Book Chapter
4
Conference
0
Presentation
0
Workshop

Education

Master of Science

University of Zakho لە University of Zakho

2019

Bachelor of Science

University of Zakho لە University of Zakho

2014

Academic Title

Lecturer

2023-10-15

Assistant Lecturer

2020-10-11

Awards

Excellent Paper Award

2022-03
2022 International Conference on Computer Science and Software Engineering (CSASE)

Received the excellence award for "Gene Expression Microarray Data Classification based on PCA and Cuttlefish Algorithm" in recognition for being the best paper at the 2022 International Conference on Computer Science and Software Engineering (CSASE) which was held on 15-17 March (2022)

 2022

Microsoft Imagine cup 2012 Top 10 winner

2012-05
Imagine Cup Kurdistan Region of Iraq 2012

The Microsoft Imagine Cup Competition is the world’s premier student technology competition. We invite all eligible students to use their imagination and passion to create a technology solution that addresses the Imagine Cup 2012 theme: Imagine a world where technology helps solve the toughest problems In ten years, the Imagine Cup has grown to be a truly global competition focused on finding solutions to real-world problems. Since 2003, over 1.4 million students have participated in the Imagine Cup with 358,000 students representing 183 countries and regions registering for the Imagine Cup 2011 competition. The Imagine Cup 2012 competition is your chance to: * Solve tough problems facing the world today, and maybe even turn your ideas into a business. * Learn new technological skills. *Test yourself against the brightest students around the world. * Make new friends. * Win cash, grants, and prizes – plus, a chance for a free trip to Sydney, Australia, next July to compete at the Imagine Cup 2012 Worldwide Finals! What is Imagine Cup Kurdistan Region of Iraq 2012? Regional Competitions take place around the world to designate representatives whom will then represent their country in the international competition. KRG Department of IT and Microsoft have launched Imagine Cup Kurdistan Region of Iraq 2012 to designate the team that will represent Kurdistan in Imagine Cup worldwide. In order to designate this team, the Imagine Cup Kurdistan Region of Iraq 2012 event has been launched. Universities are invited to compete with one another to represent Kurdistan in the worldwide edition of the Imagine Cup.

 2012

Published Journal Articles

Neuroscience & Biobehavioral Reviews (Issue : 106399) (Volume : 179)
Classification of Neurological and Mental Health Disorders Based on Multimodal Approaches: A Comprehensive Review

Disorders of the nervous system and mental health are among the most prevalent, complex, and... See more

Disorders of the nervous system and mental health are among the most prevalent, complex, and devastating health challenges globally, with a significant impact on quality of life. Recently, advances in deep learning-based multimodal methodologies have transformed the classification and detection of these disorders through the utilization of diverse data types including neuroimaging, bio-signals, and clinical evaluations. These multimodal techniques provide a more holistic understanding of complex conditions, addressing the limitations of traditional unimodal methods, which often fail to capture the multifaceted nature of these disorders. Despite the growing body of research, a comprehensive review focusing on the application of deep learning-based multimodal approaches to both neurological and mental health disorders remains lacking. This review fills this gap by offering an in-depth analysis of recent advancements in machine learning and deep learning-based multimodal classification for ten major disorders: five neurological and five mental health-related. It examined key modalities, explored fusion strategies, and provided insights into the strengths and weaknesses of existing multimodal approaches. Additionally, this review highlighted the challenges associated with multimodal data integration, such as data imbalance, model interpretability, and the need for large-scale, high-quality datasets. Furthermore, the review discussed emerging trends and future directions, emphasizing the potential of advanced fusion and computational techniques to enhance the clinical applicability of these models. By synthesizing the current state of research, this review aims to guide future studies and contribute to the development of more accurate, reliable, and accessible diagnostic tools for neurological and mental health disorders.

 2025-10
Multimedia Tools and Applications (Issue : 81) (Volume : 11563)
Person-independent facial expression recognition based on the fusion of HOG descriptor and cuttlefish algorithm

This paper proposes an efficient approach for person-independent facial expression recognition based on the fusion... See more

This paper proposes an efficient approach for person-independent facial expression recognition based on the fusion of Histogram of Oriented Gradients (HOG) descriptor and Cuttlefish Algorithm (CFA). The proposed approach employs HOG descriptor due to its outstanding performance in pattern recognition, which results in features that are robust against small local pose and illumination variations. However, it produces some irrelevant and noisy features that slow down and degrade the classification performance. To address this problem, a wrapper-based feature selector, called CFA, is used. This is because CFA is a recent bio-inspired feature selection algorithm, which has been shown to effectively select an optimal subset of features while achieving a high accuracy rate. Here, support vector machine classifier is used to evaluate the quality of the features selected by the CFA. Experimental results validated the effectiveness of the proposed approach in attaining a high recognition accuracy rate on three widely adopted datasets: CK+ (97.86%), RaFD (95.15%), and JAFFE (90.95%). Moreover, the results also indicated that the proposed approach yields competitive or even superior results compared to state-of-the-art approaches.

 2022-02
Asian Journal of Research in Computer Science (Issue : 10) (Volume : 4)
A Comprehensive Study of Caching Effects on Fog Computing Performance

The rapid advancement in the Internet of things applications generates a considerable amount of data... See more

The rapid advancement in the Internet of things applications generates a considerable amount of data and requires additional computing power. These are serious challenges that directly impact the performance, latency, and network breakdown of cloud computing. Fog Computing can be depended on as an excellent solution to overcome some related problems in cloud computing. Fog computing supports cloud computing to become nearer to the Internet of Things. The fog's main task is to access the data generated by the IoT devices near the edge. The data storage and data processing are performed locally at the fog nodes instead of achieving that at cloud servers. Fog computing presents high-quality services and fast response time. Therefore, Fog computing can be the best solution for the Internet of things to present a practical and secure service for various clients. Fog computing enables sufficient management for the services and resources by keeping the devices closer to the network edge. In this paper, we review various computing paradigms, features of fog computing, an in-depth reference architecture of fog with its various levels, a detailed analysis of fog with different applications, various fog system algorithms, and also systematically examines the challenges in Fog Computing which act as a middle layer between IoT sensors or devices and data centers of the cloud.

 2021-07
Indonesian Journal of Electrical Engineering and Computer Science (Issue : 2) (Volume : 21)
Classification techniques’ performance evaluation for facial expression recognition

Facial expression recognition as a recently developed method in computer vision is founded upon the... See more

Facial expression recognition as a recently developed method in computer vision is founded upon the idea of analyzing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result determination. The highest-ranked six features are applied on six classifiers including multi-layer perceptron, support vector machine, decision tree, random forest, radial based function, and K-Nearest neighbor to figure out the most accurate one when the minimum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. random forest with a total accuracy ratio of 94.23% is illustrated as the most accurate classifier amongst the rest.

 2021-02
Polytechnic Journal (Issue : 1) (Volume : 10)
A Comparison of Four Classification Algorithms for Facial Expression Recognition

Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s.... See more

Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparative approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbour (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on the minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.

 2020-06
Technology Reports of Kansai University (TRKU) (Issue : 5) (Volume : 62)
Impact of cloud computing and internet of things on the future internet

Internet of Things (IoT) and Cloud computing are extremely distinct technologies which are by now... See more

Internet of Things (IoT) and Cloud computing are extremely distinct technologies which are by now playing an important role in our life. It is expected that adopting and using them would be more and more common, that makes them significant components for the Future Internet (FI). The upcoming internet-associated revolution is almost there with the existence of the IoT. IoT empowers connection and communications among tons of devices between themselves to exchange information, knowledge, and data that promotes the quality of our daily lives. Alternatively, appropriate, upon-request, and adaptable network access is provided by Cloud Computing, as a result, makes it possible to contribute computing resources which helps dynamic data be integrated from a variety of data sources. However, Cloud Computing and IoT in FI cannot be implemented without an abundance of issues and problems. In this research paper, we will be endeavoring to outline and put light on the prime and main concepts of the Cloud Computing and IoT.

 2020-06
Technology Reports of Kansai University (TRKU) (Issue : 5) (Volume : 62)
Impact of Process Execution and Physical Memory-Spaces on OS Performance

The importance of process monitoring applications continues to grow. Generally, many of the developments in... See more

The importance of process monitoring applications continues to grow. Generally, many of the developments in process monitoring are being driven by access to more and more data. Process monitoring is important to understand the variation in a process and to assess its current state. Process monitoring and controlling an organization is of high importance for all process management initiatives. The parallel execution of numerous threads can perform significant increases in performance and overall efficiency in the computer systems. In many computer systems, numerous performance counters are available. For example, performance counters may provide a count of the number of threads executing at a given time. This paper presented an overview of the main research works in the field of the process and thread controlling and monitoring related to physical memory to measure the performance of the operating system. The main finding is that developing strategies needed to make it easy to deal with the monitoring of a large number of data. Furthermore, the used strategies produced improvements of up to 25% and achieved very good results. Besides, the performance of applications with process control executed much faster than without.

 2020-06

Thesis

2019-11-11
Facial Expression Recognition Based on Dual Feature Selection

This thesis provides a technique for Facial expressions from still images of human faces based... See more

This thesis provides a technique for Facial expressions from still images of human faces based on Dual-Feature-Selection (DFS) concept. This work includes substantive problems such as; variety in facial datasets, the instances consisted in each dataset, the number of facial expressions experienced, utilizing an accurate technique for face detection, utilizing multiple feature selection methods for selecting the minimum number of most characterized facial features, utilizing multiple classification algorithms, and diversity of structure models. A newly collected dataset named (KURD) dataset along with two existed datasets CK+ and JAFFE are used as a data source for validating this approach. Viola-Jones algorithm is used for face detection. Correlation Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR) and Relief Feature Selection (ReliefF) are used as a feature selector. K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Decision Tree (DTJ48) and Naïve Bayes (NB) algorithms are used as a classifier of expressions. For discovering a suitable model of each classifier, hundreds of tests are performed dynamically. The results of various models are compared, and it is perceived that these techniques provide high rates of recognition. The accuracy of classifiers was quite high for six classes of KURD and CK+ datasets. Experiments are performed using Matlab programming and Weka Data Mining Tool.

 2019

Conference

2022 International Conference on Computer Science and Software Engineering (CSASE)
 2022-04
Gene Expression Microarray Data Classification based on PCA and Cuttlefish Algorithm

The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection... See more

The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.

2020 International Conference on Advanced Science and Engineering (ICOASE)
 2020-12
COVID-19 Diagnosis Systems Based on Deep Convolutional Neural Networks Techniques: A Review

The rapidly spreading of the viral disease “COVID-19” causes millions of infections and deaths worldwide. It causes a devastating impact on lifestyle, public health, and the global economy. This motivates the researchers to invent and... See more

The rapidly spreading of the viral disease “COVID-19” causes millions of infections and deaths worldwide. It causes a devastating impact on lifestyle, public health, and the global economy. This motivates the researchers to invent and develop innovative and automated methods to detect COVID-19 at its early stages. It is necessary to isolate the positive cases quickly to prevent this epidemic and treat affected patients. Many diagnosis methods are proposed to perform accurate and fast detection for COVID-19, such as Reverse transcription-polymerase Chain Reaction (RT -PCR). The clinical studies indicate that the severity of COVID-19 cases depends on the virus's amount within infected lungs. Chest X-ray (CXR) and Computed Tomography (CT) images are useful imaging methods for diagnosing COVID-19 cases. Deep Convolutional Neural Network (DCNN) is a machine learning technique usually used in computer vision applications. This review focuses on utilizing the DCNN methods for building an automated Computer-Aided Diagnosis (CADs) system to detect and classify the infected cases of the COVID-19 disease accurately and fast. These techniques are used to extracts valuable information by analyzing a massive amount of CXR and CT images that can critically impact on screening of Covid-19. DCNN techniques proved their robustness, potentiality, and advancement by comparing them among the other learning algorithms. It is worth noting that DCNN is an essential tool for supporting physicians' clinical decisions.

2020 International Conference on Advanced Science and Engineering (ICOASE)
 2020-08
Queuing Theory Model of Expected Waiting Time for Fast Diagnosis nCovid-19: A Case Study

This Queuing theory analysis has been used in hospitals and other healthcare settings; its use in this sector is not widespread. Consequently, the queue waiting line is an effective scientific tool in the performance of... See more

This Queuing theory analysis has been used in hospitals and other healthcare settings; its use in this sector is not widespread. Consequently, the queue waiting line is an effective scientific tool in the performance of waiting lines analysis. The main parameters used to measure the performance of the systems are the length of the queue line, utilization of the server, and the delays for arrivals. This study aims to avoid others to expose to epidemics such as (nCovid-19), because of becoming a big global problem today in the world. Also, to know the length for the expected and actual waiting times to diagnosis the arrivals to Duhok city. Collection and execution are done within one week with one activity healthcare team's (HCT) in the main entry Duhok city, port. Data was collected utilize individual conceptions and records from Saturday through to Thursday, which is the most critical time setting. The main interest in this study was calculating the average waiting time spent after the process to improve arrivals satisfaction using the queuing theory model. The results of this analysis indicate for each citizen checked by the healthcare team may be waiting 32.44 minutes in a queue. Also, was estimate to provide the results of system capabilities with characterization related to the expected waiting time. Finally, Medical staff at the city outlets can estimate; how many arrivals will be waiting in the line and the number of clients that will walk away each day.

2019 International Conference on Advanced Science and Engineering (ICOASE)
 2019-04
Facial Expression Classification Based on SVM, KNN and MLP Classifiers

Facial Expression Recognition (FER) has been an the active topic of papers that were researched during the 1990s till now, according to its importance, FER has achieved an extremely role in the image processing area.... See more

Facial Expression Recognition (FER) has been an the active topic of papers that were researched during the 1990s till now, according to its importance, FER has achieved an extremely role in the image processing area. FER typically performed in three stages include face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which can recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of the facial expressions. For validating the method, the Extended Cohn-Kanade (CK+) the dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce the dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.