Mohammed Najah Mahdi

Bio: Mohammed Najah Mahdi is an academic researcher from Universiti Tenaga Nasional. The author has contributed to research in topics: Exploratory search & Faceted search. The author has an hindex of 3, co-authored 25 publications receiving 50 citations. Previous affiliations of Mohammed Najah Mahdi include College of Information Technology.

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Abstract: Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

Improving Efficiency and Effectiveness of Robotic Process Automation in Human Resource Management

Abstract: Automation technology is changing and transforming innovation into the industrial landscape and Human Resources (HR) should ensure to adapt and practice its deployment to realise its benefits in time and for cost savings. The implementation of Robotic Process Automation (RPA) in HR can help to offer better service to ensure compliance of the processes with standards and regulations. RPA is a software technology that manages software robots to emulate human actions when interacting with digital platforms. RPA is a solution that could perform repetitions to take over activities carried out by humans. However, a robot is not thought to be able to replace the HR but is, instead, useful to support driven processes. The purpose of the study is to prove the efficiency and effectiveness of RPA in the Human Resource Management System (HRMS) compared to the manual process performed by a human. Different types of components and characteristics were identified to adopt RPA in HRMS based on the data measurement in the implementation process. This study designs and develops an HRMS model using RPA tools to achieve the target process. The model was developed based on a case study of an existing model of RPA in HRMS from an IT consultancy industry. In the HR process, the project uses an application focusing on the parameters of gathering, storing and accessing employees’ information from other modules. Lastly, the gaps in the HRMS to improve productivity are evaluated and explained.

An Artificial Intelligence Approach to Monitor Student Performance and Devise Preventive Measures.

Abstract: A major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students’ academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. The final output of the research turns into a set of supportive measures to carefully monitor students’ performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students.

Software Project Management Using Machine Learning Technique—A Review

Abstract: Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

Paradigm Extension of Faceted Search Techniques A Review

Abstract: Modern populations rely heavily on the worldwide web in searching information because it is the largest human repository of knowledge. However, finding relevant information on the web is often challenging. In the current work, we review analyses and optimize the performance of exploratory and faceted search techniques. Search behavior that is characterized by a large amount of uncertainty about the goals of the search is common in exploratory search. On the other hand, faceted search technique refines search results by a faceted taxonomy in an iterative manner. In addition, facets provide an efficient way to analyze and navigate the search result space. However, we believe that facet selection has been guided by the properties of suboptimal facet and facet term. As a consequence, users may need technical support while searching information. Thus, this paper suggests a natural way of extending the current paradigm employed by traditional search systems, the exploratory search. Our main objective is to provide a framework or a platform which is extensible with plugins and able to provide instances tunable to a particular document collection of choice. In addition, this paper presents a research model based on the prototype that will be developed.

Object-Oriented Analysis and Design.

Abstract: 3. Classes should hide their data, we call this encapsulation, only providing a small number of controlled methods or functions in the interface for accessing that data. These factors taken together enable separation of interface from implementation. The actual designs of the algorithms employed inside the classes can be changed as better, more efficient algorithms are found. After a design is complete, changes in a class implementation must not affect its interface. The effect on current and future users is then only in terms of efficiency: their applications need not be recoded to take advantage of the new class implementation. The advantages of separation of interface from implementation include rapid prototyping and graceful refinement. See Fig. 3.

Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs

Abstract: Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.

Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation

Abstract: To achieve low-carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero-carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO2 emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO2 emission.

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Abstract: Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.