Research Title: Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

Journal of Imaging,

Faculty: Information Technology

Abstract: Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

Keywords: Artificial intelligence (AI); convolutional neural network (CNN); deep learning (DL); malaria parasites

Research Title: Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

Journal of Imaging,

Faculty: Information Technology

Abstract: Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

Keywords: Artificial intelligence (AI); convolutional neural network (CNN); deep learning (DL); malaria parasites

Research Title: DRDM: Deep Learning Model for Diabetic Retinopathy Detection

Author: Aya Adnan Oqla Miqdady, Published Year: 2024

International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS),

Faculty: Information Technology

Abstract: The application of Artificial Intelligence is being applied in the medical industry at a quick pace, and it is currently serving as the main source of support for clinical practice solutions. Clinical practice accuracy could be improved and costs could be decreased with the use of deep learning techniques. To diagnose Diabetic Retinopathy, an effective and dependable method for automatic screening must be identified. However, deep-learning models may face difficulties due to a lack of data in several medical fields. The Diabetic Retinopathy Detection Model (DRDM), a deep learning model, is proposed in this research to identify retinal images as either infected or uninfected. The data transformation approach is used to address the lack of Diabetic Retinopathy data, which helps prevent overfitting by doubling the data. The paper shows that building a highly complex model like EfficientNetB3 or VGG16 is not necessary to achieve high performance, where, the experiment's test results approved that the DRDM model outperforms such pre-trained models. Furthermore, it took much less time for the DRDM model to produce these results.

Keywords: Deep learning , Industries , Diabetic retinopathy , Developing countries , Retina , Data models , Sustainable development

Research Title: An Adaptive Query Approach for Extracting Medical Images for Disease Detection Applications

Author: Aya Adnan Oqla Miqdady, Published Year: 2024

Arabian Journal for Science and Engineering,

Faculty: Information Technology

Abstract: Different applications heavily benefit from automatic deep learning including image classification, segmentation, and analysis. It significantly adds value to clinical systems through computer-aided detection, curing planning, diagnosis, and therapy through the acquisition of the most informative images. However, this deep learning approach faces one of the main hurdles in image processing: the necessity of a large, labeled dataset. Actually, such requirements in medical image analysis applications are considered excessively costly to acquire. Active learning methods can mitigate such issues by reducing the number of annotated images while raising the model’s performance. This paper introduces an active learning framework based on a novel sampling technique, where it queries the unannotated samples that behave differently from current training set samples. The adaptive sampling method is optimized by stochastic gradient descent approximation. This optimization leads to the construction of an adaptable and robust system that meets the needs of medical control systems. Moreover, such novelties contribute to a respectful enhancement of the model’s deep network performance when training over a few numbers of annotated images to reach underlying accuracy. The proposed structure outperforms other AL methods, as proved by the experimental results using stochastic gradient descent optimization technique over Skin Cancer, Pediatric Pneumonia, and COVID-19 datasets, which achieved an accuracy of 72.5%, 90%, and 90.5 using only 42.8%, 8%, and 5% human-labeled training data, respectively.

Keywords: Deep Learning, Active Learning, Sampling Technique, Stochastic Gradient Descent, Medical Image

Research Title: ArSa-Tweets: A Novel Arabic Sarcasm Detection System Based on Deep Learning Model

Author: Aya Adnan Oqla Miqdady, Published Year: 2024

Heliyon,

Faculty: Information Technology

Abstract: Sarcasm in Sentiment Analysis (SA) is important due to the sense of sarcasm in sentences that differs from their literal meaning. Analysis of Arabic sarcasm still has many challenges like implicit indirect idioms to express the opinion, and lack of Arabic sarcasm corpus. In this paper, we proposed a new detecting model for sarcasm in Arabic tweets called the ArSa-Tweet model. It is based on implementing and developing Deep Learning (DL) models to classify tweets as sarcastic or not. The development of our proposed model consists of adding main improvements by applying robust preprocessing steps before feeding the data to the adapted DL models. The adapted DL models are LSTM, Multi-headed CNN-LSTM-GRU, BERT, AraBert-V01, and AraBert-V02. In addition, we proposed ArSa-data as a golden corpus that consists of Arabic tweets. A comparative process shows that our proposed ArSa-Tweet method has the most impact accuracy rate based on deploying the AraBert-V02 model, which obtains the best performance results in all accuracy metrics when compared with other methods.

Keywords: Deep learning (DL), Sarcasm, Sentiment analysis (SA), Machine learning, Natural language processing (NLP), Tweets

Research Title: FACTORS INFLUENCING SERVICE RECOVERY PERFORMANCE

Author: Owais Barkat Hamad Al-graibah, Published Year: 2016

INTERNATIONAL CONFERENCE ON POSTGRADUATE RESEARCH, Penang, Malaysia

Faculty: Business

Abstract: Service recovery performance has emerged as an important topic for academicians and practitioners over the last two decades. Many studies have attempted to uncover the factors that influence the performance of service recovery. The purpose of this study is to develop comprehensive conceptual model of the factors that influence service recovery performance. An intensive literature review from the available studies are reviewed for the development of the research model. Three main construct are incorporated in this study i.e. frontline employees (rewards, empowerment, teamwork, training, and commitment), organizational strategies (compensations, verbal action, leadership, and justice), customers (personality, and purchasing experience). The model and its related hypotheses are presented and the limitation is discussed.

Keywords: Service recovery performance, frontline employees, strategies, customers

Research Title: Predictors of E-banking Service Adoption in Malaysia Using an Extended Technology Acceptance Model

Author: Owais Barkat Hamad Al-graibah, Published Year: 2020

International Journal of Contemporary Management and Information Technology , 1

Faculty: Business

Abstract: Electronic banking (E-banking) is a service that can ease the financial transaction. However, users have several concerns when dealing with online banking. aims to develop an extended a model to predict and explain customers’ behavioural intentions with regard to adopting online banking. The proposed model incorporates four variables to provide a more comprehensive investigation about online banking. Data was collected from graduate students in Malaysia. The results show that the proposed model has moderate explanatory power. In addition, the results ease of use and customer attitude are significantly related to the adoption of E-Banking. In contrast perceived usefulness and risk have no significant association with the adoption of E banking. Decision makers have to ensure that the E-banking is easy to use and have to provide clear instruction for using the services.

Keywords: E-banking TAM Usefulness Ease of use

Research Title: ONLINE CONSUMER RETENTION IN SAUDI ARABIA DURING COVID 19: THE MODERATING ROLE OF ONLINE TRUST

Author: Owais Barkat Hamad Al-graibah, Published Year: 2020

JOURNAL OF CRITICAL REVIEWS , VOL 7

Faculty: Business

Abstract: Online customer retention has become essential for the success of businesses during COVID 19. A shift in customers toward the online has increased the important of this variable. Nevertheless, few studies examined the predictors of online customer retention. Based on social exchange theory service quality model, and technology acceptance model, this study proposes that attitude, customer satisfaction, ease of use and responsiveness will have a direct effect on online customer retention among customers of retailers in Saudi Arabia. The study also proposes that online trust will moderate the relationship. Data was collected from Saudi online customers. A total 224 respondents were obtained. The data was analysed using smart partial least square. The findings showed that responsiveness, customer satisfaction, ease of use and attitude are important for online customer retention. In addition, the findings indicated that the online trust can moderate the effect of the variables with online customer retention. Retailers are advised to enhance the delivery time and reward customers for late delivery to increase their retention.

Keywords: Online trust; Customer Satisfaction; Online customer retention; Attitude; COVID 19.

Research Title: Customer retention in five-star hotels in Jordan: The mediating role of hotel perceived value

Author: Owais Barkat Hamad Al-graibah, Published Year: 2020

Management Science Letters , 10

Faculty: Business

Abstract: Customer retention (CR) has become increasingly important due to high competition among hotels and countries. However, most of previous studies focus on this variable in the context of restaurants. This study aims to examine the factors that affect the CR among customers of five-star hotels in Jordan. Based on the literature, the study proposes that physical environment (PE), customer satis faction (CS), service quality (SQ), and perceived consumption value (PCV) will affect the CR. In addition, the study proposes that hotel perceived value (HPV) will mediate the effects between the variable. The population of this study is the hotels in Amman, the capital of Jordan. Using a random sampling technique, a total of 301 responses are collected from seven brands. The findings indi cated that PE, SQ, PCV, and CS were important predictors of CR. The findings also show that HPV mediated partially the effect of PE and SQ on CR while a full mediator was found between CS and CR. Decision makers are advised to improve the PE and the HPV.

Keywords: Customer Retention Customer Satisfaction Physical Environment Service Quality

Research Title: Brand Equity and Loyalty in the Airline Industry: The Role of Perceived Value and Online Word of Mouth

Author: Owais Barkat Hamad Al-graibah, Published Year: 2020

International Journal of Innovation, Creativity and Change, Volume 14, Issue 9,

Faculty: Business

Abstract: Brand loyalty is an important term in marketing studies. Its relationship with brand equity is fragmented and inconclusive. The purpose of this study is to examine the effect of brand awareness, brand experience, and brand quality on brand equity. The study also aims to understand the relationship between brand equity and brand loyalty and the role of online word of mouth and perceived value between the variables. The population of this study is the customers of the airline industry in Jordan. The data of this study was collected using purposive sampling. A total of 213 respondents participated in this study. The findings were derived using Smart PLS. Brand quality, brand experience and brand awareness affected significantly the brand equity. Brand equity has a significant effect on brand loyalty. The findings also showed that online word of mouth partially mediated the effect of brand equity on brand loyalty. Perceived value did not moderate this relationship between brand equity and brand loyalty. Decision makers are advised to enhance the brand quality by offering additional services in the online and offline environment.

Keywords: Brand Experience, Brand Loyalty, Brand Equity, Online Word of Mouth, Perceived Value, Airline Industry