Developing a Conceptual Framework for Zakat Collection and Distribution Impact on Social Welfare Through Implications of SDGs

Although Islamic social finance including zakat has witnessed an upsurge in development in quantum, which reached US$2 trillion in 2015 and is projected to exceed US$3 trillion by 2020, due to a lack of transparency, trust, and timely disclosure to public has resulted in inefficient of zakat collection and ineffective disbursement for the wellbeing of recipients. The reason for this could be neglecting adoption of financial technology specifically blockchain and artificial intelligence system in zakat management to enhance proper collection and efficient distributions timely and effectively reporting to public. Therefore, this research proposes an ‘Islamic Social Welfare Financial Technology' (ISW FinTech) as an innovative framework that assesses zakat institutions operational efficiency of zakat collection, transparency, and effective distribution that would lead to wellbeing of zakat recipients. This framework is based on new six clusters according to their needs and priority, which convert them from being zakat recipients into zakat payers.

Structure of the Decision Support System in the Development of Software Products

This study examined the problems of calculating the time to create a software product using the example of the company "RKIT" LLC. The article discussed the analysis of the most effective, from the point of view of the authors, methods for assessing the complexity of projects. As a result of the review, the authors suggested to create a decision support system. This system will consist of two blocks: an automated information computing system based on the PERT method and an artificial intelligence system in the form of an expert system, which is a repository of expert knowledge. The creation of a DSS will reduce the time for experts to make decisions and reduce the likelihood of a decrease in the profitability of the project, which will lead to an increase in the company's profit.

A remote MQTT-based data monitoring system for energy efficiency in industrial environments

The concept of the new industry seeks not only to improve production processes, but also to bring solutions to environmental problems, in addition to reducing resource consumption, while maintaining high yields. This constant search for process optimization has been the main agent in the development of new technologies aimed at improving the performance of industrial production lines. Thus, this article proposes to raise some important concepts of Industry 4.0, and present the development of a remote IoT-based system that, through MQTT and Modbus protocols, will be responsible for monitoring the entire electrical network of an industrial plant, sending its data to the cloud, where it can be monitored and analyzed by the industry management sector or even by an artificial intelligence system, in a simple and effective way, in real time and from anywhere, in order to assist in decision-making focused on energy efficiency.

Development of an Artificial Intelligence System for the Instruction and Control of Cooperating Mobile Robots

<p>This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different users. Feedback coordination is robust to variation in weights and thresholds for failure detection. This permits the correction of suboptimal allocations arising from greedy task allocation, incorrect initial task specifications or unexpected failures. By being robust within the tested limits, weights and thresholds can be intuitively selected. However, other parameters such as ideal achievement data can be difficult to accurately characterise in some instances. A hierarchical hybrid deliberative-reactive navigation system for memory constrained heterogeneous robots to navigate obstructed environments is developed. Deliberative control is developed using a modified version of the A* algorithm and a rectangular occupancy grid map. A novel two-tiered path planner executes on limited memory mobile robots utilising the memory of a computationally powerful robot to enable navigation beyond localised regions of a large environment. Reactive control is developed using a modified dynamic window approach and a polar histogram technique to remove the need for periodic path planning. A range of simulation experiments in different sized environments is conducted to assess the performance of the two-tiered path planning strategy. The path planner is able to achieve superior or comparable execution times to non-memory constrained path planning when small sized local maps are employed in large global environments. Performance of hybrid deliberative-reactive navigation is assessed in a range of simulated environments and is also validated on a real robot. The developed reactive control system outperforms the dynamic window method.</p>