Journal of Advanced Research in Computing and Applications

Meta Analysis of Quantitative Trait Loci (QTL) for Growth and Yield Related Traits Under Drought Stress in Wheat (Triticum aestivum L.)

Siti Athirah Ismail — 2024-12-31

Wheat is a critical crop in temperate climate zones, with demand rising in both urbanized and industrialized countries. However, drought stress significantly impacts global wheat yield, highlighting the need for breeding programs focused on drought tolerant wheat. This study conducted a QTL meta analysis to identify growth characteristics and yield related traits associated with drought tolerance in wheat. Stable QTLs were targeted across different varieties and experimental environments to ensure the success of these breeding programs. The study compiled drought tolerant QTLs from 27 previous studies, identifying a total of 951 QTLs, which were used to create a consensus map. Of these, 538 QTLs were related to yield characteristics, while 413 were linked to growth characteristics associated with drought tolerance. BioMercator V4.2.3 software was employed to generate consensus maps and perform the meta analysis. The consensus map, derived from the combined data, consisted of 457 QTLs spanning a total genetic map length of 727,819 cM. The meta analysis identified 140 MQTLs, with MQTL 5B:7 and 3B:4 emerging as the most significant, harboring 24 and 20 QTLs, respectively. These chromosomes contain major QTLs associated with drought tolerance. The findings provide valuable insights that can be utilized in breeding programs to enhance drought tolerance, yield, and growth in wheat.

The Designing of Geoinformation Backend-Frontend to Improve Landslide Mitigation Application

Iksal Yanuarsyah — 2024-12-31

The necessity of designing geoinformation backend-frontend is to improve landslide mitigation application built for landslide disaster management is discussed in this article. The design of geoinformation backend-frontend is critical to ensure that the landslide mitigation application will run appropriately. This research seeks to address the geoinformation backend-frontend with three subject which are input to the landslide mitigation map objective, determination of selected evacuation sites and landslide hazard mitigation services presented in the form of application. The development of landslide mitigation was done online using the waterfall model. Web-based applications are developed in programming languages as the backend. Web applications can run on the Internet or an intranet (LAN network) and can manage data centrally. The system architecture design was made based on the client-server model, where the client sends and requests JSON data from the web service located on the server with the support of using Leaflet JS API as the base map layer. The geoinformation will communicate with the database using JSON String to make changes and retrieve data in the database. The JSON String is obtained from the Web Services Application Programming Interface (API), which is available for data processing in the system database. The front end is presented as a website. This section is created using HTTP, CSS, and JavaScript so that the URL can work and be displayed on the website properly. Meanwhile Frontend developers have the task of developing all the visual components of the website and keeping the interface attractive. With a series of advantages and models built, some things could be improved in the design process, such as the choice of relevant data, analysis methods and service features that can be presented as a more interactive mobile application.

Overview of Visual Literacy and Facility Support in Higher Education - Case of Indonesia and Malaysia

Nadia Sigi Prameswari — 2024-12-31

Visual literacy is a skill that is owned by someone as a form of visual communication. Visual literacy skills are crucial to learn, especially for university students to receive and translate objects more quickly in order to improve and increase their learning achievements. The main objective of this study was identified and discuss the relationship between the understanding of visual literacy of students and university teachers in Indonesia and Malaysia to the learning media used and the use of university facilities. This study employed qualitative research, using purposive sampling method. Six respondents who were students and lecturers from the State University of Semarang (UNNES), Indonesia and Universiti Teknologi Mara (UiTM), Kelantan, Malaysia particapated in this study. The results of the sudy indicated that Indonesian and Malaysian respondents had visual literacy knowledge, although there were differences in visual interest and duration. The results further showed that in visual terms, Indonesian respondents tended to prioritize quantity, while Malaysian respondents prioritized quality. In addition, the use of learning media for Indonesian respondents tended to be less varied than that of Malaysian respondents. The results also found that the campuses in both countries provided supporting facilities. Indonesian respondents however have not used it well, while Malaysian respondents indicated that they use campus facilities to learn visual literacy. The aim of this study is to contribute to the process of improving visual literacy learning in Indonesia and Malaysia and further adding to the body of knowledge.

A Comparative Performance Analysis of Several Machine Learning Classifiers on The Credit Card Data

Nor Aishah Ahad — 2024-12-31

As customers tendencies learn rapidly about online transactions due to the outbreak of the Covid-19 pandemic, fraudulent transactions that mimic legitimate transactions will continue to increase unabated. Over the years, fraudulent transactions have been committed but the supervised and unsupervised machine learning classifiers and fraud detection techniques could not stop the fraud from occurring at the onset. However, fraudulent cases are detected after post-occurrence thereby recognizing these techniques as non-smart detection models because fraud could only be detected using past data sets. Various fraud detection or classifier techniques using machine learning techniques such as decision trees, random forests, k-means, and neural networks have been studied. Also, the possibility of overfitting due to the branch growth associated with the decision trees is a classifier performance problem. The application of the probability axiomatic metric (PAM) to determine the accuracy of the models is a classification evaluation problem because of the relatively moderate error rate. To overcome the above shortfalls, we proposed a threshold Mahalanobis distance (TMD) classifier and adopted BETH as a performance evaluation metric. The result revealed that TMD is comparable to neural network (NN), k-means, isolation forest, XGBoost, and random forest based on the credit card data set. The analysis showed that TMD and NN have higher efficiency compared to other classifiers. This study demonstrated that the TMD classifier is unique and could be applied to extract legitimate and illegitimate transactions from customer transaction data sets. The study concludes that TMD is robust and comparable to NN and the classification errors associated with BETH are minimal compared to PAM for both TMD and NN.

Using Gray Wolf Optimization for Joint Request Offloading and Resource Scheduling in 5G Network which Use Mobile Edge Computing

Safaa Alali — 2024-12-31

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds. Therefore, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to maximize the system welfare of requests. Then we proposed gray wolf optimization algorithm referred as (GWO) to solve this problem. The simulation results showed that (GWO) outperforms existing methods in terms of system welfare and can maintain good performance in a dynamic network.