Tanweer Alam

and 3 more

Child Tracking System is a mobile application where the parent can monitor their children location in crowded environments. In addition to children, there is also the elderly people, and the disabled people, so the guidance or the person responsible of them can use this application to track their location. The parent or guidance side will have the application in which they can track, and on the other side, the child or the old person or the disabled person will have device that includes the GPS chip. The main goal of this research is to design an application with system that will help parents to keep track of their children, eventually reducing the cases in which the children or the other mentioned categories of people could be lost. The current used solution to this problem is that the children first have a wearable hand wrist in which they print their parent phone number, so when the child is lost there is a center in which the child is being taken and dealt with care till they contact the parent to come and pick the child up. The problem with the current way that it takes time, and there is a risk that child get totally lost or kidnapped before even reaching to any help, so the new way is better to even prevent them to go far away or to be lost for hours, thus the recovery here will be fast unlike the regular used way nowadays. That goal will be achieved throw systematically objectives starting from studying the existed systems, to planning and analysing, going to designing and implementing, and lastly, testing our own system.

Tanweer Alam

and 3 more

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation