Hawraa Hassan Abbas
Curriculum Vitae
Research Interests
Published researchs
الموقع الالكتروني
Name: Hawraa Hassan Abbas Scientific title: Professor Education July 2014 to January 2017 Cardiff University, UK, PhD PhD title “3D face morphology classification for medical application” Sep 1999 to Sep 2001 – Baghdad University, Iraq, MSc MSc title: “Color Image Processing for Medical Application” Sep 1995 to Sep 1999 –Baghdad University, Iraq, BSc in computer and control Engineering. Employment history 2001-2003 –Associate Lecturer, School of Eng., Baghdad University, Iraq. 2004-2008- Lecturer, School of Eng., Baghdad University, Iraq. 2009-2013- Head of Electronic Dept., School of Eng., Baghdad University, Iraq. 2013- 2023: Teacher in Electric and Electronic Dept., Engineering school, Kerbala University, Iraq. 2017-until Now: Visitor researcher in Engineering school, Cardiff University, UK 2020- 2023: The head of computer centre in Kerbala University 2023 -until now : The Assistant to the President of the University for Administrative and Financial Affairs in Alzahraa University for women Editor in the following journals: 1- American Journal of Information Science and Technology(AJIST) 2- SCIREA Journal of Mathematics 3- SCIREA Journal of Information Science and Systems Science 4- SCIREA Journal of Computer. Professional Body Membership: From 2014 Member of the British Machine Vision Association (BMVA) From 2010 Member of the Institute of Electrical and Electronic Engineers (IEEE)
• Computer vision • Medical image processing • Logic design • Computer Network Protocols • 3D Modelling • Robotic • IOT system design
There are no published researchs
Efficient resource allocation for electric car production can be achieved by anticipating aluminum future pricing. For the electric car sector to maintain a steady supply chain, efficient production, and long-term growth, accurate forecasts are essential for policymakers to plan resource policies. The accuracy of ensemble learning models in predicting Aluminum prices was analyzed using Gene Expression programming and long short-term memory (LSTM) method. The analysis was based on monthly frequency spot settlement price data of Aluminum, copper, silver, and crude oil prices, as well as currency inflation rates of USA, China, and Peru. The study period spanned from January 1, 1994 to December 31, 2022, and also included the gross domestic product (GDP) of USA, China, and other metal minerals. Initially, we assess the level of multicollinearity between each chosen input parameter and Aluminum price volatility using Variance inflation factor-based multicollinearity. The primary innovation of this study lies in the creation of a highly precise model that utilizes the LSTM algorithm to estimate monthly Aluminum prices. This model serves as a revolutionary forecasting method. An extensive evaluation of various ensemble learning models reveals that XGBoost is the optimal model for accurately forecasting monthly Aluminum prices. The LSTM-based recurrent neural network model exhibited the lowest error, with a Mean Relative Error (MRE) ranging from 0.001 to 0.008, RMSE ranging from 54.527 to 136.044, and R2 ranging from 0.95 to 0.983. The findings demonstrated that the LSTM-based super learner models had superior performance compared to the CatBoost, LightBoost, Random Forest, XGBoost, and AdaBoost models, as evidenced by higher values of the determination coefficient (R2), value account for (VAF), and other 10 error analysis evaluators. Therefore, the utilization of an LSTM-based super learner model can serve as a dependable approach for forecasting future monthly Aluminum prices. In addition, this study introduces a logical mathematical model utilizing gene expression programming (GEP) to anticipate future Aluminum prices, which can be utilized by other researchers. Thus, we conclude that both the GEP and ensemble learning models, particularly the LSTM-based Super learner model, are appropriate for precise metal price predictions, which can benefit policymakers and assist in resource policy planning. © 2024 Elsevier Ltd
Phase change materials (PCM) are considered promising tools for storing a high density of thermal energy in heat storage systems. The inherent low thermal conductivity of PCM can be remedied by using several thermal conductivity enhancers, especially fins. The effect of one or several thermal conductivity enhancers on the PCM melting was discussed in previous review articles. However, the impact of fin only on the PCM melting was rarely reported. The present article provides a comprehensive review of studies related to the thermal performance of PCM charging in finned annular heat exchangers as LHTES systems. The studies are classified according to the orientations of the LHTES systems (horizontal or vertical). This review includes a survey of several previous studies associated with the charging behaviour of PCM in horizontal and vertical annular thermal storage units equipped with fins. The effects of various factors on charging, such as geometrical factors of different fins, configuration, arrangement installation angle, pitch, number, thickness, and height, are reviewed. It was inferred that inserting the fin suppresses overheating in the top part of the thermal storage unit and improves the melting rate at the bottom part of the thermal storage. Therefore, the overall heat transfer rate is improved, and melting time is reduced. Also, the increasing the number, length, and thickness of fins indicated a pronounced enhancement in the melting process. At the same time, employing the non-uniform distribution of fins and/or using different fin dimensions (length and thickness) between the lower and upper parts of the storage unit improves the charging process. Decreasing the fin number and fin dimensions at the upper part and increasing them at the lower part result in an improvement of the thermal performance of the charging process. Also, decreasing the fin's pitch and installation angle improves the melting rate. Furthermore, using the topology-optimized structure for fins improves the thermal performance. © 2024 Elsevier Ltd
Among different recent technologies proposed for human face classification and recognition, solutions based on analyzing the 3D geometric facial features emerged as a promising academic and practical direction. Researchers have examined both holistic and local approaches to analyzing the 3D face regions to study the impact of facial features in real-life applications such as medical and security implementations. However, a few works have investigated the relevant impact of the extracted geometric features from the descriptive local regions of the human face on identifying human ethnicity. This work proposes a classifier to categorize individuals into their distinctive ethnic groups and deeply analyzes the facial feature variations to highlight the most descriptive parts and features of the human face in race classification. The proposed ML-based classifier is preceded by extracting the 3D facial features from 3D meshes using the recent SIFT and Geodesic distance calculations. In addition, it implements and discusses the initial important preprocessing steps including, cropping the frontal parts, correcting the head pose, selecting the suitable initial key points, aligning the 3D meshes, and implementing the suitable template-based 3D registration. The proposed NN race classifiers are built and evaluated using Headspace, a well-known multi-ethnic dataset, and achieved high accuracy (90% globally, and 100% for the mouth area) especially while using the SIFT features. ©2024 The authors.
Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R2), and variance accounted for (VAF) to determine the best models for predicting powder factor. The results indicate that the decision tree model (MD4) outperforms alternative approaches, such as artificial neural networks and Gaussian Process Regression (GPR). In addition, the research presents an efficient artificial neural network equation (MD2) for estimating the values of optimum powder factor, demonstrating outstanding blasting fragmentation. In conclusion, this research provides significant information for improving the accuracy of powder factor prediction, which is especially advantageous for small-scale blasting operations. © 2024 The Author(s)
In recent years, yearly climatic changes, continuous temperature increases, and the impact of global environmental change have seriously affected agricultural production. The solar greenhouse (SG) system is designed to maintain suitable temperatures and humidity levels for cultivating plants. For this purpose, an earth-to-air heat exchanger (EAHE) can be coupled with the SG to provide the necessary heating and cooling required to maintain suitable conditions for vegetation. This review presents a comprehensive literature survey on SG-EAHE systems. The thermal characteristics of heating and cooling modes are presented for SG-EAHE systems. Reports indicate that integrating EAHE with the SG can meet the heating and cooling needs of the SG while significantly reducing water consumption. The design parameters of EAHE, such as configuration, pipe diameter, pipe length, and buried depth, can affect the performance of SG-EAHE systems. Additionally, integrating photovoltaic (PV) and photovoltaic/thermal (PVT) systems with SG-EAHE systems was discussed. Moreover, the challenges and prospective aspects of SG-EAHE systems were identified. © 2024 The Authors
Eye movement recognition has garnered substantial attention in recent years across diverse disciplines such as Human-Computer Interaction (HCI), medical diagnostics, and assistive technologies. This technology offers transformative possibilities, especially for individuals with paralysis and disabilities. Yet, the deployment of deep learning models for eye movement classification using non-intrusive head-free cameras like webcams remains fraught with challenges. These challenges include the lack of comparative and benchmarking studies that guide researchers and practitioners in choosing appropriate deep learning models that match such complex tasks. To address these challenges, we conducted a meticulous comparative analysis of selected deep learning architectures, including customized and fine-tuned versions of ResNet-18, EfficientNet-B0, and AlexNet. Our analysis primarily aims to evaluate the performance and generalizability for each model across a complex dataset encompassing various conditions. The n-fold cross-validation is employed to assess the robustness of our findings. Our empirical assessments reveal a nuanced landscape. For instance, ResNet-18 excels in terms of accuracy with 99.5% and acquires a competitive small model size of 43MB, while AlexNet acquires around 222MB. While this advantage comes with slightly higher computational and memory overhead compared to models like EfficientNet-B0. This study offers critical insights into the trade-offs involved in selecting an optimal deep learning model for eye movement recognition under real-world conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.