International Journal of Advanced Science and Computer Applications最新文献

NEW INTEGRAL TRANSFORM AND SOME OF ITS RELATIONS AND APPLICATIONS 新积分变换及其一些关系和应用

International Journal of Advanced Science and Computer Applications Pub Date : 2024-07-18 DOI: 10.47679/ijasca.v5i1.85

Ahmed Mohamed Abdel Abdallah

引用次数: 0

Critical Success Factors of Digital Transformation in the Higher Education Sector 高等教育领域数字化转型的关键成功因素

International Journal of Advanced Science and Computer Applications Pub Date : 2024-07-18 DOI: 10.47679/ijasca.v5i1.84

Asma Aleidi

引用次数: 0

Examining Blockchain Platforms: Finding the Perfect Fit for Various Topics 考察区块链平台：为各种主题寻找完美契合点

International Journal of Advanced Science and Computer Applications Pub Date : 2024-07-18 DOI: 10.47679/ijasca.v5i1.89

Adil El Mane, Younes Chihab

引用次数: 0

Performance Analysis of Quicksort Algorithm: An Experimental Study of Its variants Quicksort 算法的性能分析：对其变体的实验研究

International Journal of Advanced Science and Computer Applications Pub Date : 2024-07-18 DOI: 10.47679/ijasca.v5i1.80

Dr Shorman

引用次数: 0

SOLAR POWER INTEGRATED GREEN CAMPUS FRAMEWORK FOR ELECTRIC VEHICLE CHARGING INFRASTRUCTURE 太阳能综合绿色校园电动汽车充电基础设施框架

International Journal of Advanced Science and Computer Applications Pub Date : 2024-07-18 DOI: 10.47679/ijasca.v5i1.87

V. Sri Priya, S.Brindha

{"title":"SOLAR POWER INTEGRATED GREEN CAMPUS FRAMEWORK FOR ELECTRIC VEHICLE CHARGING INFRASTRUCTURE","authors":"V. Sri Priya, S.Brindha","doi":"10.47679/ijasca.v5i1.87","DOIUrl":"https://doi.org/10.47679/ijasca.v5i1.87","url":null,"abstract":"Global warming presents a serious threat to the environment and human livelihoods, with the residential building and transportation sectors being major contributors to greenhouse gas emissions. Electric vehicles (EVs) have gained prominence as a sustainable alternative to traditional fossil fuel-powered vehicles. The success of EVs hinges on efficient charging infrastructure. This research focuses on transportation pollution and greenhouse gas emissions, emphasizing the role of EVs. The study explores the importance of Electric Vehicle Charging Station (EVCS) location selection and introduces the concept of a Green Campus (GC) approach to enhance sustainability. As the world phases out carbon-producing vehicles like trains and buses, electrified transportation offers a greener alternative. However, to support the growing adoption of electric vehicles, charging infrastructure must expand and become more seamless. Some entities are exploring solar panels to power EVs, reducing their carbon footprint. The study proposes an EVSC-GC service architecture that aims to minimize carbon dioxide emissions, reduce electricity costs, and enhance charging efficiency. It leverages telematics, digital systems, and roadside cameras to optimize fuel consumption. Additionally, electronic wallets facilitate convenient payment for charging costs. This suggested EVSC-GC model improves charging demand, charging time, time distribution, and traveling velocity compared to existing methods, making electric mobility more sustainable and efficient.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UTILIZING MULTIPLE MODALITIES FOR PARKINSON’S DETECTION 利用多种模式检测帕金森病

International Journal of Advanced Science and Computer Applications Pub Date : 2024-07-18 DOI: 10.47679/ijasca.v4i2.82

B Nithya Sree, Lakshmi M R, B Swetha Sree, B Nandini, H Shravani

{"title":"UTILIZING MULTIPLE MODALITIES FOR PARKINSON’S DETECTION","authors":"B Nithya Sree, Lakshmi M R, B Swetha Sree, B Nandini, H Shravani","doi":"10.47679/ijasca.v4i2.82","DOIUrl":"https://doi.org/10.47679/ijasca.v4i2.82","url":null,"abstract":"The research explores how machine learning methods can aid in the early identification of Parkinson's disease. It examines two distinct aspects: hand movements and vocal features. Unique datasets tracking the progressive changes in these symptoms over time are explored. Specialized techniques are employed to extract the most distinguishing hand motions and speech characteristics, which serve as potential biomarkers. In contrast to conventional approaches that depend exclusively on a single feature, this multi-modal approach combines both hand movement and voice biomarkers into a unified computational model. Overall, the research illustrates the promising potential of machine learning tools to enable earlier intervention for medical purposes, while emphasizing that the focus remains on aiding clinicians rather than replacing specialized assessments. The study does not aim at individual diagnosis but rather explores avenues for supporting healthcare professionals. Future research endeavors involve developing multi-modal models that encompass a wide range of aspects associated with this complex and variable condition.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 97","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhance Teaching using Google Classroom as a Digital Tool 将谷歌教室作为数字化工具加强教学

International Journal of Advanced Science and Computer Applications Pub Date : 2024-07-02 DOI: 10.47679/ijasca.v4i1.86

Prasanna Dahal, Lubna Zaghlool

引用次数: 0

Knowledge Graph-based JingFang Drug Efficacy Analysis With a Supportive Randomized Controlled Influenza-like Illness Clinical Trial 基于知识图谱的京房药效分析与辅助性随机对照流感样疾病临床试验

International Journal of Advanced Science and Computer Applications Pub Date : 2024-06-04 DOI: 10.47679/ijasca.v4i2.79

Yuqing Li, Zhitao Jiang, Zhiyan Huang, Wenqiao Gong, Yanling Jiang, Guoliang Cheng

{"title":"Knowledge Graph-based JingFang Drug Efficacy Analysis With a Supportive Randomized Controlled Influenza-like Illness Clinical Trial","authors":"Yuqing Li, Zhitao Jiang, Zhiyan Huang, Wenqiao Gong, Yanling Jiang, Guoliang Cheng","doi":"10.47679/ijasca.v4i2.79","DOIUrl":"https://doi.org/10.47679/ijasca.v4i2.79","url":null,"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"12 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of shear wall residential beam height based on machine learning 基于机器学习的剪力墙住宅梁高预测

International Journal of Advanced Science and Computer Applications Pub Date : 2024-05-21 DOI: 10.47679/ijasca.v5i1.76

Dejiang Wang, Lijun Chen

{"title":"Prediction of shear wall residential beam height based on machine learning","authors":"Dejiang Wang, Lijun Chen","doi":"10.47679/ijasca.v5i1.76","DOIUrl":"https://doi.org/10.47679/ijasca.v5i1.76","url":null,"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"35 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

REAL-TIME MONITORING FOR DETECTING LAKE POLLUTION AND BIOTIC CONSERVATION 检测湖泊污染和生物保护的实时监测

International Journal of Advanced Science and Computer Applications Pub Date : 2024-05-15 DOI: 10.47679/ijasca.v4i2.73

Dr. Shalini S, K Mounika Sree, Prajwal M H, Nitin Reddy N V, P Govardhan Reddy

引用次数: 0