In this manuscript, we introduce a new integral transform calledA. M. Abdallah transform which is a generalization of the Jafari and polynomialintegral transform for solving differential, partial and integral equations. Theproposed integral transform is applied to show high accuracy, efficiency andsimplicity.
在本手稿中，我们介绍了一种新的积分变换，称为 A. M. 阿卜杜拉变换。M. Abdallah 变换，它是贾法里和多项式积分变换的一般化，用于求解微分方程、偏微分方程和积分方程。所提出的积分变换在应用中显示出高精度、高效率和简便性。
{"title":"NEW INTEGRAL TRANSFORM AND SOME OF ITS RELATIONS AND APPLICATIONS","authors":"Ahmed Mohamed Abdel Abdallah","doi":"10.47679/ijasca.v5i1.85","DOIUrl":"https://doi.org/10.47679/ijasca.v5i1.85","url":null,"abstract":"In this manuscript, we introduce a new integral transform calledA. M. Abdallah transform which is a generalization of the Jafari and polynomialintegral transform for solving differential, partial and integral equations. Theproposed integral transform is applied to show high accuracy, efficiency andsimplicity.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825416","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}
Digital transformation (DT) has a significant impact on higher education institutions (HEIs), which is directly related to the development and performance improvement. There is, however, lack of understanding of the critical success factors of digital transformation in HEIs. Based on a review of the related literature, the study identified the critical success factors of digital transformation including digital literacy as central to DT process. This identification led to the development of the initial conceptual framework. Such findings can help to develop appropriate strategies and policies for better implementation of digital transformation programs for improving HEIs (managers, academics, and staff) in their DT.
{"title":"Critical Success Factors of Digital Transformation in the Higher Education Sector","authors":"Asma Aleidi","doi":"10.47679/ijasca.v5i1.84","DOIUrl":"https://doi.org/10.47679/ijasca.v5i1.84","url":null,"abstract":"Digital transformation (DT) has a significant impact on higher education institutions (HEIs), which is directly related to the development and performance improvement. There is, however, lack of understanding of the critical success factors of digital transformation in HEIs. Based on a review of the related literature, the study identified the critical success factors of digital transformation including digital literacy as central to DT process. This identification led to the development of the initial conceptual framework. Such findings can help to develop appropriate strategies and policies for better implementation of digital transformation programs for improving HEIs (managers, academics, and staff) in their DT.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826497","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}
This research compares some of the most well-liked Blockchain platforms. Depending on the orientation and the target domain, it greatly varies from one to the next. To offer readers a thorough grasp of the advantages and disadvantages of each platform, essential elements, including scalability, security, interoperability, and administration, are explored. The researchers who want to investigate the best user interface for developing and generating their Blockchain architecture quickly, analysing the nodes, saving transactions, and spreading data to all network members will benefit from comparisons between Blockchain platforms before and after. This paper will assist researchers in learning more about Blockchain platforms, their configuration/installation difficulty, and other details like the programming languages used in the structure, the description, and the outcome of a medium to expert IT researcher and the challenges that surpass him during the installation phase. Researchers will value this concept since it will save them money and time.
本研究比较了一些最受欢迎的区块链平台。根据定位和目标领域的不同，各平台之间存在很大差异。为了让读者全面了解每个平台的优缺点，本研究探讨了可扩展性、安全性、互操作性和管理等基本要素。研究人员若想研究快速开发和生成区块链架构、分析节点、保存交易以及向所有网络成员传播数据的最佳用户界面，就必须对区块链平台进行前后比较，从而从中获益。本文将帮助研究人员更多地了解区块链平台、其配置/安装难度、其他细节，如结构中使用的编程语言、描述、中等水平到专业水平的 IT 研究人员的成果以及在安装阶段遇到的挑战。研究人员会重视这一概念，因为这将为他们节省金钱和时间。
{"title":"Examining Blockchain Platforms: Finding the Perfect Fit for Various Topics","authors":"Adil El Mane, Younes Chihab","doi":"10.47679/ijasca.v5i1.89","DOIUrl":"https://doi.org/10.47679/ijasca.v5i1.89","url":null,"abstract":"This research compares some of the most well-liked Blockchain platforms. Depending on the orientation and the target domain, it greatly varies from one to the next. To offer readers a thorough grasp of the advantages and disadvantages of each platform, essential elements, including scalability, security, interoperability, and administration, are explored. The researchers who want to investigate the best user interface for developing and generating their Blockchain architecture quickly, analysing the nodes, saving transactions, and spreading data to all network members will benefit from comparisons between Blockchain platforms before and after. This paper will assist researchers in learning more about Blockchain platforms, their configuration/installation difficulty, and other details like the programming languages used in the structure, the description, and the outcome of a medium to expert IT researcher and the challenges that surpass him during the installation phase. Researchers will value this concept since it will save them money and time.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 91","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825079","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}
The Quicksort algorithm is often the best practice choice for sorting due to its remarkable efficiency on average cases, small constant factors hidden in the θ(n log n) notation, and its in-place sorting nature. This paper provides a comprehensive study and empirical results of the Quicksort algorithm and its variants. The study encompasses all Quicksort variants from 1961 to the present. Additionally, the paper compares the performance of different versions of Quicksort in terms of running time on integer arrays that are sorted, reversed, and randomly generated. Our work will be invaluable to anyone interested in studying and understanding the Quicksort algorithm and its various versions.
{"title":"Performance Analysis of Quicksort Algorithm: An Experimental Study of Its variants","authors":"Dr Shorman","doi":"10.47679/ijasca.v5i1.80","DOIUrl":"https://doi.org/10.47679/ijasca.v5i1.80","url":null,"abstract":"The Quicksort algorithm is often the best practice choice for sorting due to its remarkable efficiency on average cases, small constant factors hidden in the θ(n log n) notation, and its in-place sorting nature. This paper provides a comprehensive study and empirical results of the Quicksort algorithm and its variants. The study encompasses all Quicksort variants from 1961 to the present. Additionally, the paper compares the performance of different versions of Quicksort in terms of running time on integer arrays that are sorted, reversed, and randomly generated. Our work will be invaluable to anyone interested in studying and understanding the Quicksort algorithm and its various versions.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 91","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824505","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}
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.
{"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}
B Nithya Sree, Lakshmi M R, B Swetha Sree, B Nandini, H Shravani
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.
{"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}
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
Google Classroom 作为在线学习管理系统 (LMS) 越来越受欢迎，而且 Google for Education 还提供了一系列免费工具，值得了解一下。[1] Google Classroom 是一个非常适合使用的平台，因为它与 Google Suite for Education 中的其他应用程序（如 Gmail、Google Calendar、Google Docs、Google Slides 和 Google Meet）配合得非常好。将所有这些便捷的工具集中在一处，有助于在进行在线教学时尽可能简化操作。 谷歌教室可用于授课的大部分环节，包括设置任务、添加文件和批改学生作业[2]。在本作品中，我们将介绍如何创建谷歌教室、邀请学生加入课堂、添加作业和材料、批改作业和留下反馈意见。这项工作的目的是让教师能够创建一个在线课堂区域，在其中管理学生需要的所有文件。教师可以在课堂上布置作业，学生完成作业后交上来评分。
{"title":"Enhance Teaching using Google Classroom as a Digital Tool","authors":"Prasanna Dahal, Lubna Zaghlool","doi":"10.47679/ijasca.v4i1.86","DOIUrl":"https://doi.org/10.47679/ijasca.v4i1.86","url":null,"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","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"1 1‐6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686763","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}
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.
{"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}
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.
{"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}
Dr. Shalini S, K Mounika Sree, Prajwal M H, Nitin Reddy N V, P Govardhan Reddy
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.
{"title":"REAL-TIME MONITORING FOR DETECTING LAKE POLLUTION AND BIOTIC CONSERVATION","authors":"Dr. Shalini S, K Mounika Sree, Prajwal M H, Nitin Reddy N V, P Govardhan Reddy","doi":"10.47679/ijasca.v4i2.73","DOIUrl":"https://doi.org/10.47679/ijasca.v4i2.73","url":null,"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974867","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}
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