Journal La Multiapp https://newinera.com/index.php/JournalLaMultiapp <p>International <strong>Journal La Multiapp</strong> ISSN 2721-1290 (Online) and ISSN 2716-3865 (Print) includes all the areas of research activities in all fields Engineering, Technology, Computer Sciences, A<span class="tlid-translation translation" lang="en"><span class="" title="">rchitect</span></span>, Applied Biology, Applied Chemistry, Applied Physics, Material Engineering, Civil Engineering, Military and Defense Studies, Photography, Cryptography, Electrical Engineering, Electronics, Environment Engineering, Computer Engineering, Software Engineering, Electromechanical Engineering, Transport Engineering, Mining Engineering, Telecommunication Engineering, Aerospace Engineering, Food Science, Geography, Oil &amp; Petroleum Engineering, Biotechnology, Agricultural Engineering, Food Engineering, Material Science, Earth Science, Geophysics, Meteorology, Geology, Health and Sports Sciences, Industrial Engineering, Information and Technology, Social Shaping of Technology, Journalism, Art Study, Artificial Intelligence, and other Applied Sciences.</p> en-US u.taghiyev@newinera.com (Urfan Taghiyev) m.hasibnp@gmail.com (Mujib Hasib) Fri, 02 Feb 2024 10:07:56 +0700 OJS 3.1.2.4 http://blogs.law.harvard.edu/tech/rss 60 The Applications based on Video Motion Magnification Techniques https://newinera.com/index.php/JournalLaMultiapp/article/view/814 <p><em>This research study's major goal is to present an important overview of recent work on applications based on Video Motion magnification&nbsp; (VMM) approaches during the course of the last 10 years. Over the past few years, video motion magnification (VMM) technologies have attracted a lot of attention and research, particularly as applications based on video motion have become more and more necessary. With an increase in the number of recommended procedures, surveying and evaluation become necessary. In this study, we will highlight how the survey was focused on several articles that used motion video augmentation techniques in their applications. We contrast these applications as well.</em></p> khalida Amed, Abdul-Wahab Sami lbrahim, Asmaa Sadiq Copyright (c) 2024 Journal La Multiapp http://creativecommons.org/licenses/by-sa/4.0/ https://newinera.com/index.php/JournalLaMultiapp/article/view/814 Fri, 02 Feb 2024 10:07:40 +0700 Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques https://newinera.com/index.php/JournalLaMultiapp/article/view/843 <p><em>Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.</em></p> Ashwaq Katham Mtasher, Esraa Hassan Jawad Al-wakel Copyright (c) 2024 Journal La Multiapp http://creativecommons.org/licenses/by-sa/4.0/ https://newinera.com/index.php/JournalLaMultiapp/article/view/843 Fri, 02 Feb 2024 11:13:01 +0700 Prediction of Mental Health of Elementary School (SD) Students using the Decision Tree Algorithm with K-Fold CV testing in Bone Bolango Regency, Gorontalo Province. https://newinera.com/index.php/JournalLaMultiapp/article/view/853 <p>Mental health is a fundamental component of the WHO definition of health, which means not only being free from disease but also being physically, mentally and socially healthy. Currently, mental health has become a major issue in modern society because if it is good it will enable us to realize our own potential, overcome the normal stresses of life, work productively, and be able to contribute to the society in which we live. In Indonesia, problems related to mental health are related to the lack of mental health detection tools. Meanwhile abroad, much research has been developed regarding mental health detection based on innovative technology using Machine Learning. This research aims to predict mental health using the Social Emotional Health Survey-Secondary (SEHS-S) as a prediction evaluation criterion using Machine Learning with the Decision Tree algorithm method with K-Fold CV testing. The sample in this research was elementary school students in Bone Bolango Regency, Gorontalo Province.</p> <p>&nbsp;</p> Salahuddin Liputo, Frangky Tupamahu Copyright (c) 2024 Journal La Multiapp http://creativecommons.org/licenses/by-sa/4.0/ https://newinera.com/index.php/JournalLaMultiapp/article/view/853 Fri, 02 Feb 2024 14:20:52 +0700 Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration https://newinera.com/index.php/JournalLaMultiapp/article/view/952 <p style="margin-top: 0cm; text-align: justify; text-justify: kashida; text-kashida: 10%;"><em>The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.</em></p> Mohammed F. Al Marjani, Rana K. Mohammed, Ziad O. Ahmed, Yasmin Makki Mohialden Copyright (c) 2024 Journal La Multiapp http://creativecommons.org/licenses/by-sa/4.0/ https://newinera.com/index.php/JournalLaMultiapp/article/view/952 Fri, 02 Feb 2024 14:37:56 +0700 Design of Forecasting for Perishable Product with Artficial Neural Network https://newinera.com/index.php/JournalLaMultiapp/article/view/1000 <p><em>Raw materials are an important part of the manufacturing industry, especially for raw materials that do not last long or have a lifespan. To be able to produce good products, the raw materials used must be of good quality. This happened to company XYZ which operates in the cereal and snack food industry. Inventory control is quite a big challenge for companies. In this year the company experienced losses due to a shortage of finished snacks products, due to finished goods being obsolete due to a lack of accuracy in forecasting snack demand. The research raised forecasting using the Artificial Neural Network method. ANN is known to be able to produce good accuracy values in predicting sales.</em></p> Chintya Salwa Sabhira Copyright (c) 2024 Journal La Multiapp http://creativecommons.org/licenses/by-sa/4.0/ https://newinera.com/index.php/JournalLaMultiapp/article/view/1000 Fri, 02 Feb 2024 15:16:30 +0700