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International Journal of Advanced Science and Computer Applications
Published by UK Institute
ISSN : 28097599     EISSN : 28097467     DOI : https://doi.org/10.47679/ijasca
Core Subject : Science, Agriculture, Social, Engineering,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering
International Journal of Advanced Science and Computer Applications (IJASCA) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented the whole spectrum of Advanced Science and Computer Applications. Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two international reviewers. Accepted papers will be freely accessed in this website
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 46 Documents
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Real-Time Monitoring For Detecting Lake Pollution And Biotic Conservation Shalini S; sree, K Mounika; Prajwal M H; Nitin Reddy N V; P Govardhan Reddy
International Journal of Advanced Science and Computer Applications Vol. 4 No. 2 (2025): September 2025
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v4i2.73

Abstract

This research unveils a comprehensive system designed to tackle plastic pollution in lakes autonomously, eliminating the necessity for human intervention. By harnessing sensor data and camera imagery processed through the YOLO algorithm, the system identifies plastic debris. It then calculates the debris density and compares it against a preset threshold. Once the threshold is exceeded, an automated email alert containing the density data is sent to relevant authorities. Additionally, water quality sensors are integrated to continuously monitor environmental conditions. Regular updates are provided to enable proactive measures in pollution prevention. This endeavor showcases the utilization of advanced technology to address environmental challenges and safeguard aquatic ecosystems' health. By employing automated detection and monitoring mechanisms, the system offers a sustainable approach to combat plastic pollution in lakes, fostering environmental conservation endeavors.
Prediction of shear wall residential beam height based on machine learning Wang, Dejiang; Chen, Lijun
International Journal of Advanced Science and Computer Applications Vol. 4 No. 2 (2025): September 2025
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v5i1.76

Abstract

The beam height is an important design parameter that influences structural properties such as load-bearing capacity and stability of beams. In the early stages of structural design, the existing methods for determining beam height mainly include empirical formulae. However, empirical methods are highly subjective, lack accuracy, and are poorly adapted to complex conditions. This paper establishes a beam height prediction model for shear wall residential structures. Using structural design data from projects built by a real estate company across various regions in China, a large dataset of beam heights was collected. The Permutation Feature Importance (PFI) method and six unique machine learning (ML) models were used to rank the importance of input variables. The Gradient Boosting (GB) model, consistent with the feature ranking obtained from PFI, was selected. The model evaluation method was then used to select the number of input features for the GB model, and grid search and K-fold cross-validation were employed to optimize the GB prediction model. This model was compared with a prediction model obtained from a Back Propagation Neural Network (BPNN). Finally, the SHAP method was used to interpret the "black box" machine learning model. The results show that the GB model has higher accuracy compared to the BPNN model, and the input features of the proposed GB model contribute to the beam height in accordance with mechanical laws, demonstrating the model's rationality. The research findings can provide a reference for initial beam height design.
Knowledge Graph-based JingFang Drug Efficacy Analysis With a Supportive Randomized Controlled Influenza-like Illness Clinical Trial Li, Yuqing; Jiang, Zhitao; Huang, Zhiyan; Gong, Wenqiao; Jiang, Yanling; Cheng, Guoliang
International Journal of Advanced Science and Computer Applications Vol. 4 No. 2 (2025): September 2025
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v4i2.79

Abstract

This paper presents a novel methodology for drug efficacy analysis using a knowledge graph, validated by a randomized controlled clinical trial. To provide a comprehensive understanding of drug treatment effects, a learning-based workflow is developed to mine drug-disease entities and relations from literature. These relations build a knowledge graph used for clustering-based drug efficacy analysis. Our tool reports the learned relatedness between drugs and diseases, indicating efficacy levels. JingFang is identified as effective for flu and colds. To validate this, a clinical trial was conducted on Influenza-like illness. Between August 25 and October 12, 2020, 106 patients were randomly assigned in a 1:1 ratio to either the combined group (53) or the control group (53). Patients in the combined group received Xinkangtai Ke and JingFang, while the control group received Xinkangtai Ke only for 7 days. The combined group's cure rate was 92.5% (49) compared to 81.1% (43) in the control group (p=0.0852). The very effective rate was 98.1% (52) in the combined group versus 92.5% (49) in the control group (p=0.3692). For middle-aged and elderly participants, the combined group's recovery rate was significantly higher than the control group's (100% vs 78.4%, p=0.0059, 95% CI: 21.6 (8.3, 38.2)). No adverse effects were observed in either group. The results indicate that JingFang is effective for patients with Influenza-like illnesses, especially those over 34 years old. This study highlights the potential of knowledge graph-based analysis in drug efficacy research.
Enhance Teaching using Google Classroom as a Digital Tool Dahal, Prasanna; Zaghlool, Lubna
International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v4i1.86

Abstract

Google Classroom is gaining popularity as an online Learning Management System (LMS) and with the suite of free tools that comes with Google for Education, it is worthwhile knowing about.[1] Google Classroom is a fantastic platform to use because it works really well alongside the other apps in Google Suite for Education such as Gmail, Google Calendar, Google Docs, Google Slides, and Google Meet. The ease of having all these handy tools in one place helps to keep things as simple as possible when teaching online. Google Classroom can be used for most parts of delivering a lesson, from setting tasks, adding files, and marking student assignments [2] In this work we explain how to Create Google classroom, invite students to the class, add assignments and materials, Grade the assignments and leave feedback. The aim of the work is to enable teachers to create an online classroom area in which they can manage all the documents that their students need. Teachers can make assignments from within the class, which their students complete and turn in to be graded
AN IOT-BASED FRAMEWORK FOR EFFICIENT SOLAR POWER GENERATION AND INTEGRATION IN AUTOMOTIVE: INTEGRATION IN AUTOMOTIVE Sri Priya, V.; Dr. S.Brindha
International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v4i1.103

Abstract

Objective: We suggest an Internet of Things (IoT)-based system that uses edge intelligence to anticipate power production effectively and monitors electricity substations and smart solar installations. It ensures dependable and effective power distribution inside industrial Internet of things settings, improving sustainability, safety, and energy management in smart buildings. It also improves decision-making and reduces volatility. Method: Create and execute an IoT-enabled power monitoring system for smart solar panels and substations that incorporates edge intelligence for instantaneous prediction and decision-making. Deploy an IoT-enabled solar charging station for smart homes and Industry 4.0 applications, and use the cloud for sensor data analysis and control. Findings: In order to effectively manage load for commercial, electric, residential, and industrial vehicles, the suggested framework improves the efficiency and dependability of power production and distribution in industrial IoT contexts. The system increases overall efficiency via the mitigation of power fluctuations and eventualities. Furthermore, IoT integration enhances smart building energy management safety and sustainability of energy resources as well as reduced the overall cost by 95% when comparing to the traditional devices. Novelty: For smart solar systems and substations, a novel framework combines edge intelligence with IoT. It includes a sophisticated IoT-based control system that improves power distribution network decision-making. In addition to taking an integrated strategy to energy management and enabling real-time monitoring and prediction of power production in industrial IoT contexts, it emphasizes sustainability, safety, recycling, and reuse in smart buildings.
Integrating OCR and NLP Techniques for Accurate Text Extraction and Plagiarism Detection in Image-Based Content Kumar, Palvadi Srinivas; Prasad, Krishna
International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v4i1.105

Abstract

In the digital age, images often contain valuable text-based information, including numbers, symbols, and other data. Efficient extraction and verification of this content is critical, particularly in academic and content-driven domains where originality is paramount. This paper presents a novel approach to detecting plagiarism in text embedded within images. The proposed method leverages Optical Character Recognition (OCR) to extract text from images and applies Natural Language Processing (NLP) techniques to evaluate the originality of the extracted content. By comparing the text against a comprehensive database of existing sources, the system is capable of identifying potential plagiarism while distinguishing between original and copied content. This approach ensures that not only text in conventional documents but also in images is scrutinized for authenticity, enhancing the reliability of plagiarism detection in diverse content formats. The proposed solution offers an efficient and automated pipeline for image-based text extraction and plagiarism detection, applicable in educational, legal, and content creation environments.

Page 5 of 5 | Total Record : 46

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