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.

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.

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.

The use of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, has grown rapidly over the past two decades and continues to expand at a rapid pace. This has resulted in the production of many research papers addressing the use of UAVs in a variety of applications, such as forest firefighting. The main purpose of this paper is to provide a comprehensive overview of UAV-based forest-fire-extinguishing activity (FFEA) operations. To achieve this goal, a systematic literature review was conducted to answer a specific set of questions, which were carefully formulated to address the results of research conducted between 2008 and 2021. This study aims to (i) expand our understanding of the development of UAVs and their current contributions to the FFEA; (ii) identify particularly novel or unique applications and characteristics of UAV-based fire-extinguishing systems; (iii) provide guidance for exploring and revising further ideas in this field by identifying under-researched topics and other areas in which more contributions are needed; and (iv) explore the feasibility of using UAV swarms to enable autonomous firefighting in the forest without human intervention. Of the 1353 articles systematically searched across five databases (Google Scholar, ACM Digital Library, Science Direct, Scopus, and IEEE Explore), 51 highly relevant articles were found to meet the inclusion criteria; therefore, they were analyzed and discussed. The results identified several gaps in this field of study among them the complexity of coordination in multi-robotic systems, the lack of evaluation and implementation of fire extinguishing systems, the inability of handling multiple spot fires, and poor management of time and resources. Finally, based on the conducted review, this paper provides significant research directions that require further investigations by researchers in this field including, the deployment of UAV-based Swarm Robotics, further study on the characteristics of the fire extinguishing systems; design more effective area coverage; and the propose of a self-firefighting model that enables individuals to decide on the course of events efficiently and locally for better utilization and management of time and resources.

In the modern society, Internet provides massive amounts of heterogeneous information, hence Information overload has become an ubiquitous issue. In this paper, we conduct a large scale quantitative study for articles dealing with (1) information overloading; (2) faceted search; and (3) filtering the data in three major databases, namely, Web of Science, ScienceDirect, and IEEE Explore. These three databases have presented 172 articles, which can be classified into four categories. The first category contains review and survey papers related to information overload. The second category includes papers that concentrate on developing theoretical frameworks to reduce information overloading. The third category contains papers dealing with improving structure or architectural of software for filtering the huge data. The fourth category includes papers that provide criteria to evaluate filtering techniques. Finally, our contribution provides further understanding of information overload, and gives an important basis for future research. Moreover, we illustrate that the dynamic faceted filters are more efficient to reduce the information overload.

Local fractional calculus (fractal calculus) plays a crucial role in applications, especially in computer sciences and engineering. One of these applications appears in the theory of chaos. Therefore, this paper studies the dynamics of a fractal complex logistic map and then employs this map to generate chaotic sequences for a new symmetric image encryption algorithm. Firstly, we derive the fractional complex logistic map and investigate its dynamics by determining its equilibria, geometric properties, and chaotic behavior. Secondly, the fractional chaotic sequences of the proposed map are employed to scramble and alter image pixels to increase resistance to decryption attacks. The output findings indicate that the proposed algorithm based on fractional complex logistic maps could effectively encrypt various kinds of images. Furthermore, it has better security performance than several existing algorithms.