Research Title: Computer-aided biopharmaceutical model development

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: Any medication that is derived from or partially synthesized from a naturally occurring biological source is referred to as a biopharmaceutical. The research and development of a new medicine is a complex process, and forecasting pharmacokinetics and drug targeting has proven difficult for scientists. During the discovery and development stages, particularly the early ones, it is crucial to understand the biopharmaceutical profile of drugs. A new drug’s introduction into the market is a labor-intensive procedure that is expensive in terms of both time and money. Drug development and discovery are said to take between 10 and 14 years and cost more than $1 billion. By offering a quick and inexpensive method to assess the bioperformance of medications, computational modeling has developed into a vital tool that accelerates the formulation development process. In-silico screening models have been widely used in the research and development of biopharmaceuticals, such as gastrointestinal absorption simulation, in silico computational modeling, monoclonal antibodies, predicting the corneal permeability of some medicinal agents, and the identification of potential compounds to treat diseases. The concepts and advancements of computer-aided biopharmaceuticals are briefly discussed in this chapter.

Keywords: Computer-aided biopharmaceutical model development

Research Title: Mechanisms of Antibacterial Drug Resistance

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: Antibacterial resistance is an escalating worldwide public health challenge with substantial ramifications for global well-being. It has emerged as a critical global health concern. As defined by the World Health Organization (WHO), antimicrobial resistance (AMR) denotes the capacity of microorganisms to endure antimicrobial therapies. Resistance mechanisms involve a change in the target site, efflux pumps, PBP mutation, Porin modification, and enzymatic inhibition. Factors influencing antibacterial resistance encompass inappropriate antibiotic use and environmental and biological factors. The misuse and overuse of antibiotics, coupled with the adaptive capabilities of bacteria, contribute to the emergence of resistant strains. This complex issue demands a multifaceted approach that includes responsible antibiotic use, the development of new therapeutic strategies, and enhanced surveillance measures.

Keywords: Antibacterial Drug Resistance

Research Title: Sentiment Analysis of COV19 Impact on Education and Economy Sectors using Arabic Tweets

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

2023 14th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: The coronavirus (COVID-19) infected millions of people around the world. Due to the disease's rapid spread, all governments have decided to implement lockdowns. The two key pillars of any nation, education and economy were negatively impacted. This study investigates public opinion on COVID-19 effects on these two key sector by collecting Arabic tweets from Twitter from the MENA region. Sentiment analysis was applied using the collected dataset for each sector separately. We created five cutting-edge deep learning models to apply them in the prediction process. Such models named Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), LSTM plus CNN, BERT, and AraBERT. The outcomes showed that in two datasets, the LSTM with CNN and LSTM provided the greatest performance results in the education data set, while BERT and AraBERT has the best performance in the economy dataset. The results presented that these algorithms created information that helped several government decision-makers make important and wise decisions on the standard of living in the Arab World Country.

Keywords: COVID-19 , Deep learning , Economics , Sentiment analysis , Social networking (online) , Biological system modeling , Education

Research Title: Sentiment Analysis of COV19 Impact on Education and Economy Sectors using Arabic Tweets

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

2023 14th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: The coronavirus (COVID-19) infected millions of people around the world. Due to the disease's rapid spread, all governments have decided to implement lockdowns. The two key pillars of any nation, education and economy were negatively impacted. This study investigates public opinion on COVID-19 effects on these two key sector by collecting Arabic tweets from Twitter from the MENA region. Sentiment analysis was applied using the collected dataset for each sector separately. We created five cutting-edge deep learning models to apply them in the prediction process. Such models named Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), LSTM plus CNN, BERT, and AraBERT. The outcomes showed that in two datasets, the LSTM with CNN and LSTM provided the greatest performance results in the education data set, while BERT and AraBERT has the best performance in the economy dataset. The results presented that these algorithms created information that helped several government decision-makers make important and wise decisions on the standard of living in the Arab World Country.

Keywords: COVID-19 , Deep learning , Economics , Sentiment analysis , Social networking (online) , Biological system modeling , Education

Research Title: Predicting Bug Severity Using Machine Learning and Ensemble Learning Techniques

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

2023 14th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: Software quality can be adversely affected by various bugs in the system. Bug identification and fixing are part of every software development life cycle. However, in practice, large and long-lived systems may encounter an enormous number of bugs during their lifetime. Additionally, identifying and fixing a large number of software bugs requires a vast amount of allocated resources that may hinder the software budget. Thus, proper management including bug classification and prioritization is needed. Therefore, this paper employs modern Machine Learning techniques to help developers identify and classify bugs based on various factors including their severity level. Six different datasets are used to evaluate the proposed ML models. Features are extracted from bug descriptions using natural language processing techniques like TF-IDF. Evaluated models include K-Nearest Neighbors, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, and various ensemble learning methods. All candidate models are evaluated based on their accuracy for each of the used datasets. Results show that the Neural Networks model exceeds on all six datasets, achieving an accuracy that ranges from 93% to 95%. It also exceeds in other measures as well including precision, recall, and F1-Score. These results indicate that the proposed method performs satisfactorily when identifying bugs in source code.

Keywords: Support vector machines , Source coding , Computer bugs , Neural networks , Software quality , Feature extraction , Software systems

Research Title: A Deep Learning Approach for Varicocele Detection from Ultrasound Images

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

2023 14th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: Varicocele is a disease characterized by abnormal dilatation of the scrotal venous plexus pampiniformis. It is a common cause of infertility and may cause pain or discomfort in some cases. In this article, we present a new approach for automatic classification of varicocele using Deep Learning convolutional neural networks. The available dataset consists of images converted to different color modes. Each color mode dataset is partitioned, augmented, and trained using a Deep Learning network. Experiments were conducted with all possible combinations of four different pre-trained models and over three color modes to determine the best combination that achieves the highest performance, with and without augmentation. Training and testing were evaluated using various metrics. Analysis of the results demonstrated the effectiveness of the proposed system. Results showed an accuracy of 83.1% with the RGB color mode when using ResNet50, and that the effect of augmentation was small.

Keywords: Deep learning , Measurement , Training , Ultrasonic imaging , Image color analysis , Pain , Gray-scale

Research Title: Pronoun Replacement Approach for Enhancing Arabic Text Summarization

Author: Aya Adnan Oqla Miqdady, Published Year: 2022

2022 13th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: The world witnessed massive and unprecedented growth in the amount of information available online. Identifying and consuming needed content has been difficult and time-consuming. Therefore, automatic text summarization to obtain accurate and relevant collections of the vast amount of available information is essential. Methodologies for automatic summarization of Arabic text remain immature, due to the complexity inherent in the Arabic language in terms of structure and morphology. The summarization process is based on the frequency which the trainer used to fetch important sentences to compute the keywords weightage that aims to construct the summary. This study proposes replacing pronouns with proper nouns before applying the Arabic text summarizer. This enhances the quality of the summarization process and helps reader better understand the summary.

Keywords: Natural Language Processing (NLP) , Text Summarization , Pronoun Replacement

Research Title: A CNN and Image-Based Approach for Malware Analysis

Author: Aya Adnan Oqla Miqdady, Published Year: 2022

2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA),

Faculty: Information Technology

Abstract: Malware attacks have various types, patterns, and volumes and have become more sophisticated and severe. Using machine learning to classify and detect malware is one of the approaches to mitigate malware attacks. However, malware classification suffers from some challenges such as the time required in manipulating a huge number of malware files. This paper proposes a Convolutional Neural Network (CNN) model and a pre-processing approach to solve the aforementioned issue. The contribution of this paper is based on converting the dataset into RGB images followed by scale maximization step. In addition, the paper proposes a preprocessing approach for the input datum of images. The results prove that the proposed preprocessing methods have a strong impact on enhancing the overall accuracy by increasing the accuracy from 92.5% to 98%.

Keywords: Convolution , Neural networks , Data preprocessing , Machine learning , Gray-scale , Market research , Malware

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