Research Title: Recent targeted discovery of phytomedicines to manage endocrine disorder develops due to adapting sedentary lifestyle

Author: Mohammad Bayan, Published Year: 2023

Faculty: Pharmacy

Abstract: Leading a sedentary lifestyle is becoming a significant public health issue nowadays. Sedentary lifestyle appears to be increasingly outspread in many nations despite being linked to a range of chronic health conditions. Most importantly, leading a sedentary lifestyle may lead to endocrinological disorders. The endocrine system is a network of glands and organs located throughout the body. The main function of the endocrine system is to regulate the range of bodily functions through the release of hormones. When the function of the endocrine system is disturbed, then it may lead to hormonal imbalance. For the management of major endocrinological disorder, many phytochemical constituents are used. This work aimed to focus on phytomedicinal herbs to target the endocrine glands. Moreover, a variety of phytoconstituents were found to be effective in the management of major endocrine disorders such as diabetes, hypertension, thyroid, hormonal imbalance. In this chapter, we provide an introduction to sedentary lifestyle, followed by a detailed study of endocrine system and hormones secreted by endocrine glands and major disorders of endocrine glands. We then focus on phytochemical constituents in the form of phytomedicinal herbs used to treat the endocrinological disorders with targeted drug delivery to endocrine glands that can be easily targeted on endocrine glands for the treatment and management of hormonal imbalance.

Keywords: Targeted discovery; Sedentary lifestyle; Endocrine disorder

Research Title: Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Author: Mohammad Taye, Published Year: 2023

Computers, 12

Faculty: Information Technology

Abstract: Abstract In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial neural networks (ANN), has become a big deal in computing because it can learn from data. The ability to learn enormous volumes of data is one of the benefits of deep learning. In the past few years, the field of deep learning has grown quickly, and it has been used successfully in a wide range of traditional fields. In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches. In order to provide a more ideal starting point from which to create a comprehensive understanding of deep learning, also, this article aims to provide a more detailed overview of the most significant facets of deep learning, including the most current developments in the field. Moreover, this paper discusses the significance of deep learning and the various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be utilised. We conclude by identifying possible characteristics for future generations of deep learning modelling and providing research suggestions. On the same hand, this article intends to provide a comprehensive overview of deep learning modelling that can serve as a resource for academics and industry people alike. Lastly, we provide additional issues and recommended solutions to assist researchers in comprehending the existing research gaps. Various approaches, deep learning architectures, strategies, and applications are discussed in this work.

Keywords: machine learning (ML); deep learning (DL); recurrent neural network (RNN); convolutional neural networks (CNN) artificial intelligence (AI)

Research Title: A Novel Traffic characteristics aware and Context Prediction Protocol for Intelligent Connected Vehicles

Author: Maram Bani Younes, Published Year: 2023

IEEE Transactions on Vehicular Technology , Early Access

Faculty: Information Technology

Abstract: Digital maps have been installed and attached to vehicles recently. They help with the GPS receivers to determine the relative locations of vehicles to other existing traffic and objects over the road network such as entrance/exit points, obstacles, road intersections, etc. This helps drivers or autonomous vehicles to decide the most appropriate reaction, in terms of speed, take-over, or stop operations ahead of time. Several daily traveling vehicles do not have digital maps. Besides, digital maps are vulnerable to being destroyed or inaccurate. They require regular updates due to the continuous construction and re-design of the road networks. These constructions aimed to enhance the road design and the traffic efficiency there. Moreover, accidents, broken vehicles, traffic congestion, or other ad-hoc obstacles appear unpredictably over the road network. In this paper, we aim to introduce a prediction protocol that gathers and analyzes the traffic characteristics of vehicles over the investigated road scenario using wireless transceivers in vehicles. Then, it predicts the physical and traffic context based on the analyzed traffic data. This protocol can replace the absent or broken digital maps in vehicles. It also can be used to verify the correctness of the digital map in vehicles. From the experimental results, we can infer that the proposed protocol has succeeded in predicting the road context over highways and downtown scenarios. More accurate and better predictions are acquired by increasing the percentage of wireless transceiver-equipped vehicles.

Keywords: Roads , Road transportation , Protocols , Accidents , Vehicles , Ad hoc networks , Wireless communication

Research Title: SmartLight: A smart efficient traffic light scheduling algorithm for green road intersections

Author: Maram Bani Younes, Published Year: 2023

Ad Hoc Networks, 140

Faculty: Information Technology

Abstract: Traveling vehicles participate in emphasizing the global warming problem due to the gases produced by them. The exponential increase in the number of daily traveling vehicles has exaggerated the world pollution problem threatening the life on the planet. This encourages several environmental organizations to look for designing green vehicles. Moreover, several countries have forced green driving rules and technologies. Road intersections are considered high fuel consumption areas over the road network. This is due to the required power to stop moving vehicles and restart stopped vehicles at these intersections. This work introduces an efficient traffic light scheduling algorithm (SmartLight) that controls the competing traffic flows at the road intersections. It is designed to reduce the total consumed fuel of vehicles and thus it reduces their produced gases. The topology of the road intersection, the context of the competing traffic flows, and the real-time traffic characteristics of each flow are mainly considered to schedule the phases of each located traffic light. The phases of the traffic light cycle are primly set to allow emergency vehicles to pass through the intersection without stopping. Then, the traffic flow that contains heavy vehicles or has the highest weight among the competing traffic flows is assigned the highest priority to pass through the signalized intersection. Finally, the average waiting delay time of each flow on the signalized intersection should not exceed a pre-determined threshold to guarantee fair sharing of the signalized intersection. The scheduling time of each phase is set based on the lengths of platoons of vehicles that are scheduled during that phase from different un-conflicted traffic flows. An experimental study has evaluated the performance of the proposed algorithm (SmartLight) compared to previous traffic light scheduling algorithms in terms of total fuel consumption, gas emission, the average queuing delay time of vehicles, and the throughput of the road intersection.

Keywords: SmartLight: A smart efficient traffic light scheduling algorithm for green road intersections

Research Title: Information Security and Data Management for IoT Smart Healthcare

Author: Maram Bani Younes, Published Year: 2023

Faculty: Information Technology

Abstract: International legislation and health authorities urge and promote healthcare providers to adopt meaningful use of becoming network integrated. As a result, healthcare services are intelligently provided using the Internet of things (IoT)-based principle. However, transiting healthcare providers and organizations to electronic-based systems are vulnerable to information security attacks and cybercrimes [1]. Information security techniques protect information and systems from illegal and unauthorized admission, usage, disclosure, interference, or conversion. This is accomplished by processing the three main elements: confidentiality, integrity, and availability of information. Confident information is available or disclosed only to legal processes and only by authorized people from a healthcare perspective. Therefore, only authorized users can modify and control the integrity and protection of electronic data.

Keywords: Information Security and Data Management for IoT Smart Healthcare

Research Title: Safe and Efficient Advising Traffic System Around Critical Road Scenarios

Author: Maram Bani Younes, Published Year: 2023

International Journal of Intelligent Transportation Systems Research, 21

Faculty: Information Technology

Abstract: Vehicles travel daily over the road networks toward their targeted destinations. The context of the road varies in terms of the geometric design and existing traffic. Accidents repeatedly occur among traveling vehicles. Some areas of road segments over the road network witness a higher rate of traffic accidents compared to other road scenarios. This is usually affected by the geometric design and pavement quality of the road, including its winding and slope. These roads that witness a higher rate of accident occurrence are referenced as critical road scenarios. In this work, an advising traffic system is proposed to recommend the best speed and basic driving behavior around these scenarios. This system considers the geometric design of the road scenario, the weather conditions, and the real-time traffic characteristics (e.g., traffic density and traffic speed) to obtain optimal recommendations for the traveling vehicles there. From the experimental results, we can infer that the proposed system enhances the safety conditions and reduces the accidental rate over the critical road. The proposed system also enhances traffic efficiency in terms of reducing fuel consumption and gas emission over the investigated critical road scenarios.

Keywords: Critical road Curvature Slope Weather conditions Traffic characteristics Traffic recommendation

Research Title: An Object Classification Approach for Autonomous Vehicles Using Machine Learning Techniques

Author: Maram Bani Younes, Published Year: 2023

World Electric Vehicle Journal, 14

Faculty: Information Technology

Abstract: An intelligent, accurate, and powerful object detection system is required for automated driving systems to keep these vehicles aware of their surrounding objects. Thus, vehicles adapt their speed and operations to avoid crashing with the existing objects and follow the driving rules around the existence of emergency vehicles and installed traffic signs. The objects considered in this work are summarized by regular vehicles, big trucks, emergency vehicles, pedestrians, bicycles, traffic lights, and traffic signs on the roadside. Autonomous vehicles are equipped with high-quality sensors and cameras, LiDAR, radars, and GPS tracking systems that help to detect existing objects, identify them, and determine their exact locations. However, these tools are costly and require regular maintenance. This work aims to develop an intelligent object classification mechanism for autonomous vehicles. The proposed mechanism uses machine learning technology to predict the existence of investigated objects over the road network early. We use different datasets to evaluate the performance of the proposed mechanism. Accuracy, Precision, F1-Score, G-Mean, and Recall are the measures considered in the experiments. Moreover, the proposed object classification mechanism is compared to other selected previous techniques in this field. The results show that grouping the dataset based on their mobility nature before applying the classification task improved the results for most of the algorithms, especially for vehicle detection.

Keywords: autonomous vehicle; object detection; object classification; Udacity dataset; BDD100K dataset; machine learning; road network

Research Title: Review of Security Challenges in Mobile Cloud Computing Applications

Author: Maram Bani Younes, Published Year: 2023

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

Faculty: Information Technology

Abstract: Mobiles and smartphones recently store huge amounts of valuable information such as personal information, financial transactions, social applications, and call records. These devices allow the transmission of data, by voice, text, or video, at anytime and everywhere. They enable easy access to the required information. The huge development of mobile devices has introduced the ability to use new and advanced applications. In several scenarios, the advanced applications require a connection to the cloud services. This affects the general environment, infrastructure, and security challenges of mobile applications. In this paper, we mainly aim to investigate the security challenges and issues of Mobile Cloud Computing (MCC) applications. First, we discuss the most popular applications of MCC. Then, we present an adversary and threat model that determines the main security challenges and issues in these applications. We summarize some security techniques, that have been used to tackle these challenges and determine their main requirements. Finally, clear recommendations regarding the directions and required research in this field are given in the paper.

Keywords: Wireless communication , Threat modeling , Cloud computing , Market research , Mobile handsets , Malware , Mobile applications

Research Title: Machine learning based prediction with parameters tuning of multi-label real road vehicles characteristics

Author: Maram Bani Younes, Published Year: 2022

ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, canada

Faculty: Information Technology

Abstract: The real-time traffic characteristics on the road network highly affect the safety conditions and the driving behaviors there. Early detection of crowded areas or hazardous conditions on the road network should affect the drivers' decisions and behavior to guarantee smooth and comfortable trips. Machine learning mechanisms have been mainly used for general prediction after extensive training processes. Over the road networks, trained machines could be really helpful to obtain instant predictions that assist drivers and autonomous vehicles there. However, the quality and efficiency of these machines are affected by several criteria including the quality of the used dataset and the tuning of the parameters of the regression algorithm. In this work, we investigate the performance of the most popular regression algorithms in terms of temporal prediction of the traffic characteristics in a real road scenario. Moreover, we optimize the regression algorithm by tuning the parameters using the grid search technique. From the experimental results, we can clearly notice the enhancements in predicting the traffic characteristics for different periods of time. We have observed that the number of neighbors, the distance, and the metric parameters' values are best tuned with the values of 4, 'Manhattan', and 'Distance', respectively, for the K-Nearest Neighbor (KNN) regression algorithm.

Keywords: traffic prediction, machine learning

Research Title: Towards green driving: A review of efficient driving techniques

Author: Maram Bani Younes, Published Year: 2022

World Electric Vehicle Journal, 13

Faculty: Information Technology

Abstract: The exponential increase in the number of daily traveling vehicles has exacerbated global warming and environmental pollution issues. These problems directly threaten the continuity and quality of life on the planet. Several techniques and technologies have been used and developed to reduce fuel consumption and gas emissions of traveling vehicles over the road network. Here, we investigate some solutions that assist drivers to follow efficient driving tips during their trips. Advanced technologies of communications or vehicle manufacturing have enhanced traffic efficiency over road networks. In addition, several advisory systems have been proposed to recommend to drivers the most efficient speed, route, or other decisions to follow towards their targeted destinations. These recommendations are selected according to the real-time traffic distribution and the context of the road network. In this paper, different high fuel consumption scenarios are investigated over the road networks. Next, the details of efficient driving techniques that were proposed to tackle each case accordingly are reviewed and categorized for downtown and highway driving. Finally, a set of remarks and existing gaps are reported to researchers in this field.

Keywords: green driving; road context; driving assistance; traffic situation