Optimization of Dualband Microstrip mm-Wave Antenna with Improved Directivity for Mobile Application Using Genetic Algorithm

Gestational diabetes mellitus (GDM) is frequently described as glucose intolerance for pregnancy women. GDM patients currently practice the traditional method (record book) for recording blood glucose readings and keeping track of meal intake. This practice is not efficient and impractical for monitoring glucose level for GDM patients when we compared with mobile health monitoring technologies available today. Although, many applications have been developed for diabetes patients, but we do not found any application appropriate for GDM monitoring. In this study, we describe the design and development of mobile application for GDM monitoring using genetic algorithm that aims to predict recommended meal intake. We developed the mobile application for the GDM patients to maintain their blood glucose level through their meals. We tested the components of the mobile application and found that the prediction algorithm has successfully predicted the next meal intake according to the patient blood glucose levels. We hope this study will encourage research on development of selfmonitoring applications to improve blood glucose control for GDM.

Download Full-text

Improved Genetic Algorithm and Mobile Application for an Up-to-date Traveling Salesman Problem

2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) ◽

10.1109/ismsit50672.2020.9255207 ◽

2020 ◽

Author(s):

Muhammet Tayyip Muslu ◽

Yunus Dogan

Keyword(s):

Genetic Algorithm ◽

Traveling Salesman Problem ◽

Mobile Application ◽

Traveling Salesman ◽

Improved Genetic Algorithm

Download Full-text

Evolving Mobile Prototypes towards the Best-Suited Design and Interaction Schema Using the Genetic Algorithm

International Journal of Mobile Human Computer Interaction ◽

10.4018/ijmhci.2014040101 ◽

2014 ◽

Vol 6 (2) ◽

pp. 1-9

Author(s):

Ragaad AlTarawneh ◽

Shah Rukh Humayoun

Keyword(s):

Genetic Algorithm ◽

Mobile Application ◽

Time Pressure ◽

Delivery Time ◽

Application Development ◽

Mobile Environment ◽

Touch Gestures ◽

Mobile Application Development ◽

New Challenges ◽

Mobile Market

The recent advances in the mobile environment, such as multi-touch gestures paradigm, introduce new challenges for the interaction designers in producing the best-suited final prototype. Moreover, the short delivery-time pressure of the current mobile market makes it harder to perform the detailed evaluations for selecting the best prototype amongst the created ones. In this vision paper, we propose an approach for evolving the created prototypes towards the final prototype with the best-suited design and interaction schema. Our approach is based on using the Genetic Algorithm for searching the best solution (prototype with the best-suited design and interaction schema) from the set of created prototypes during the design phase. The proposed approach suits the mobile application development and would enhance the interaction designers' ability of producing the final prototype of the target mobile application in an efficient and effective way.

Download Full-text

WeRoute: Route Optimization Web-Based System and Driver Mobile Application

International Journal of Artificial Intelligence ◽

10.36079/lamintang.ijai-0802.314 ◽

2021 ◽

Vol 8 (2) ◽

pp. 78-87

Author(s):

Ang Pei Ying ◽

Justtina Anantha Jothi ◽

Nursakirah ARM

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Mobile Application ◽

Route Optimization ◽

Optimal Route ◽

Routing Problem ◽

Web Based ◽

Cost Controls ◽

Web Based System

This paper intends to conceptualise an optimisation solution for vehicle routing that can get the best routing result and release the most optimal route to the driver, namely WeRoute. The objectives of the paper are to manage the data efficiently, save time, reduce cost, enhance customer satisfaction, and decrease the emission of carbon. Moreover, this is also known as the vehicle routing problem, which deals with a range of variables, including drivers, stops, roads, and customers. The method, Genetic algorithm, was developed to improve the efficiency of generating feasible routes for a project. A team of drivers and several stops are needed to generate the solution of optimising the vehicle routing. It can be said that the more drivers or stops, the more complicated the problem becomes, such as cost controls and vehicle limitations. Thus, a route optimisation tool slowly becomes the key to ensuring the delivery business as efficiently as possible.

Download Full-text