Tran Vu Van Hoa, Thien Chi Nguyen, Tung Thanh Truong, Tuan Anh Nguyen, Hoang Bao Lam, Son Thai Dang
This article explores the efficacy of integrating Landsat and MODIS satellite imagery for comprehensive flood impact analysis. By employing advanced remote sensing technologies and sophisticated data processing techniques, this study offers a methodological framework that enhances the precision and depth of environmental analysis. The core methodology involves the systematic processing of satellite data, including radiometric and geometric corrections, combined with the use of analytical indices such as the Normalized Difference Water Index (NDWI) and the Enhanced Vegetation Index (EVI). These indices play a crucial role in accurately delineating water bodies and assessing the extent of flooding. The approach not only improves the reliability of flood mapping but also contributes to the broader understanding of environmental changes and aids in effective disaster management. Through this study, we demonstrate how strategic data integration can provide valuable insights for policymakers, enhancing responses to environmental crises.
本文探讨了整合 Landsat 和 MODIS 卫星图像进行洪水影响综合分析的功效。通过采用先进的遥感技术和复杂的数据处理技术，本研究提供了一个方法框架，提高了环境分析的精度和深度。核心方法包括对卫星数据进行系统处理，包括辐射和几何校正，并结合使用归一化差异水指数（NDWI）和增强植被指数（EVI）等分析指数。这些指数在准确划分水体和评估洪水范围方面发挥着至关重要的作用。这种方法不仅提高了洪水测绘的可靠性，还有助于更广泛地了解环境变化，并帮助进行有效的灾害管理。通过这项研究，我们展示了战略性数据整合如何为决策者提供有价值的见解，从而加强对环境危机的应对。
{"title":"Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery","authors":"Tran Vu Van Hoa, Thien Chi Nguyen, Tung Thanh Truong, Tuan Anh Nguyen, Hoang Bao Lam, Son Thai Dang","doi":"10.32628/ijsrset2411257","DOIUrl":"https://doi.org/10.32628/ijsrset2411257","url":null,"abstract":"This article explores the efficacy of integrating Landsat and MODIS satellite imagery for comprehensive flood impact analysis. By employing advanced remote sensing technologies and sophisticated data processing techniques, this study offers a methodological framework that enhances the precision and depth of environmental analysis. The core methodology involves the systematic processing of satellite data, including radiometric and geometric corrections, combined with the use of analytical indices such as the Normalized Difference Water Index (NDWI) and the Enhanced Vegetation Index (EVI). These indices play a crucial role in accurately delineating water bodies and assessing the extent of flooding. The approach not only improves the reliability of flood mapping but also contributes to the broader understanding of environmental changes and aids in effective disaster management. Through this study, we demonstrate how strategic data integration can provide valuable insights for policymakers, enhancing responses to environmental crises.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"15 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140674136","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}
Traditional road inspections are manual processes, prone to human error and inefficiencies. This paper presents a novel approach for automated roadway inspection using a Convolutional Neural Network (CNN) model. Our system leverages computer vision techniques to detect potholes and speed breakers on road surfaces from images. We developed a CNN model trained on a comprehensive dataset of road images containing various pothole and speed breaker types, lighting conditions, and road backgrounds. The model achieved an accuracy of 93% in detecting these road defects, demonstrating the effectiveness of deep learning for automated roadway inspections. This system has the potential to significantly improve the efficiency and objectivity of road inspections, leading to faster repairs and improved road safety
{"title":"Roadway Inspection System","authors":"Aditya Patil, Aniket Kshirsagar, Suraj Lokhande, Suraj Jorwar, Prof. Anuja Garande","doi":"10.32628/ijsrset2411259","DOIUrl":"https://doi.org/10.32628/ijsrset2411259","url":null,"abstract":"Traditional road inspections are manual processes, prone to human error and inefficiencies. This paper presents a novel approach for automated roadway inspection using a Convolutional Neural Network (CNN) model. Our system leverages computer vision techniques to detect potholes and speed breakers on road surfaces from images. We developed a CNN model trained on a comprehensive dataset of road images containing various pothole and speed breaker types, lighting conditions, and road backgrounds. The model achieved an accuracy of 93% in detecting these road defects, demonstrating the effectiveness of deep learning for automated roadway inspections. This system has the potential to significantly improve the efficiency and objectivity of road inspections, leading to faster repairs and improved road safety","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140676395","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}
Shaly Wanda Hamzah, Muhammad Nur Aidi, I Made Sumertajaya, Fitrah Ernawati
Women of reproductive age (WRA) are vulnerable to anaemia, iron deficiency (ID), or iron deficiency anaemia (IDA). To identify the factors influencing anaemia, ID, and IDA to WRA in Indonesia, logistic regression analysis was employed. This study aims to determine the prevalence of anaemia, ID, and AID among WRA, as well as to identify influencing factors and evaluate the classification results produced by Logistic Regression methods. The data used were obtained from the Research and Development Agency, Ministry of Health of Indonesia. Haemoglobin data, demographic, and socioeconomic data were derived from the Basic Health Research 2013, and ferritin (Fe) and CRP data used stored serum samples collected in 2013 and analyzed in 2016. The results of this study found that the prevalence of anaemia among WRA in Indonesia is 11%, ID 14%, and AID 9%. Significant factors influencing health conditions include BMI, marital status, family size, malaria, and ARI. Individuals with overweight or obesity have a lower chance of experiencing anaemia, ID, and IDA compared to those who are thin, while individuals who are divorced have a higher risk than those who are unmarried. Additionally, individuals affected by malaria or ARI also have a higher risk of experiencing anaemia. Consumption of animal protein and education also emerges as significant factors affecting ID conditions. Although the model using Multinomial Logistic Regression shows higher accuracy than the binary model, both still have weaknesses in identifying cases of anaemia, ID, and IDA with low sensitivity. Model evaluation indicates that despite proficiency in recognizing normal cases, they still struggle to detect cases of anaemia, ID, and IDA.
育龄妇女（WRA）容易患贫血、缺铁（ID）或缺铁性贫血（IDA）。为了确定影响印度尼西亚育龄妇女贫血、缺铁和缺铁性贫血的因素，我们采用了逻辑回归分析法。本研究旨在确定 WRA 中贫血、缺铁性贫血和缺铁性贫血的患病率，并确定影响因素和评估逻辑回归方法产生的分类结果。所用数据来自印度尼西亚卫生部研究与发展局。血红蛋白数据、人口统计学和社会经济学数据来自《2013年基本健康研究》，铁蛋白（Fe）和CRP数据使用了2013年收集并在2016年分析的储存血清样本。研究结果发现，印尼妇女儿童贫血症患病率为11%，ID为14%，AID为9%。影响健康状况的重要因素包括体重指数、婚姻状况、家庭规模、疟疾和急性呼吸道感染。与瘦弱的人相比，超重或肥胖的人患贫血、ID 和 IDA 的几率较低，而离婚的人比未婚的人风险更高。此外，受疟疾或急性呼吸道感染影响的人患贫血症的风险也较高。动物蛋白摄入量和教育程度也是影响 ID 状况的重要因素。尽管使用多项式逻辑回归的模型比二元模型显示出更高的准确性，但两者在识别贫血、ID 和 IDA 病例方面仍存在弱点，灵敏度较低。模型评估表明，尽管能熟练识别正常病例，但仍难以检测出贫血、ID 和 IDA 病例。
{"title":"Risk Factors for Anaemia, Iron Deficiency, and Iron Deficiency Anaemia in Women of Reproductive Age Using Logistic Regression","authors":"Shaly Wanda Hamzah, Muhammad Nur Aidi, I Made Sumertajaya, Fitrah Ernawati","doi":"10.32628/ijsrset2411260","DOIUrl":"https://doi.org/10.32628/ijsrset2411260","url":null,"abstract":"Women of reproductive age (WRA) are vulnerable to anaemia, iron deficiency (ID), or iron deficiency anaemia (IDA). To identify the factors influencing anaemia, ID, and IDA to WRA in Indonesia, logistic regression analysis was employed. This study aims to determine the prevalence of anaemia, ID, and AID among WRA, as well as to identify influencing factors and evaluate the classification results produced by Logistic Regression methods. The data used were obtained from the Research and Development Agency, Ministry of Health of Indonesia. Haemoglobin data, demographic, and socioeconomic data were derived from the Basic Health Research 2013, and ferritin (Fe) and CRP data used stored serum samples collected in 2013 and analyzed in 2016. The results of this study found that the prevalence of anaemia among WRA in Indonesia is 11%, ID 14%, and AID 9%. Significant factors influencing health conditions include BMI, marital status, family size, malaria, and ARI. Individuals with overweight or obesity have a lower chance of experiencing anaemia, ID, and IDA compared to those who are thin, while individuals who are divorced have a higher risk than those who are unmarried. Additionally, individuals affected by malaria or ARI also have a higher risk of experiencing anaemia. Consumption of animal protein and education also emerges as significant factors affecting ID conditions. Although the model using Multinomial Logistic Regression shows higher accuracy than the binary model, both still have weaknesses in identifying cases of anaemia, ID, and IDA with low sensitivity. Model evaluation indicates that despite proficiency in recognizing normal cases, they still struggle to detect cases of anaemia, ID, and IDA.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"25 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673234","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 study examines the relationship between fiber orientation, functional activity, and growth dynamics in the flexor muscle of a male white Leghorn chick. It tests three hypotheses: similar histochemical fiber typing in muscle mass, distribution patterns influenced by species' functional activities, and fiber growth dynamics related to somatic growth rate. The study confirmed the hypothesis that all three basic fiber types (red, pink, and white) grow exclusively through hypertrophy. True hyperplasia was not evident in any age group, possibly in the late embryonic stage. Some cases of pink and white fibers showed splitting into smaller ones. All three basic fiber types grew by hypertrophy, regardless of location or functional activity. Muscle fiber growth in this muscle mass was directly related to the chick's somatic growth rate.
{"title":"Growth Dynamics of Flexor Muscle Fibers in Developing Male White Leghorn Chicks","authors":"Mayalata Dimpal, Rahul Kundu","doi":"10.32628/ijsrset2411256","DOIUrl":"https://doi.org/10.32628/ijsrset2411256","url":null,"abstract":"The study examines the relationship between fiber orientation, functional activity, and growth dynamics in the flexor muscle of a male white Leghorn chick. It tests three hypotheses: similar histochemical fiber typing in muscle mass, distribution patterns influenced by species' functional activities, and fiber growth dynamics related to somatic growth rate. The study confirmed the hypothesis that all three basic fiber types (red, pink, and white) grow exclusively through hypertrophy. True hyperplasia was not evident in any age group, possibly in the late embryonic stage. Some cases of pink and white fibers showed splitting into smaller ones. All three basic fiber types grew by hypertrophy, regardless of location or functional activity. Muscle fiber growth in this muscle mass was directly related to the chick's somatic growth rate.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"121 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677914","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}
M. Manideepsai, U. Vineeth Goud, CH. Vinay Goud, P. Vignesh Yadav, D. Saidulu
The rise of edge smart IoT devices has led to the development of edge storage systems (ESS) for efficient access to massive edge data. ESS can reduce the load on cloud centers and improve user experience. However, ESS still faces challenges in improving fault tolerance and efficiency. Thus, there is a need for a secure and efficient fault-tolerant storage scheme. Existing schemes have drawbacks like high edge storage overhead, difficulty in protecting edge data privacy, and low data writing performance. To address these issues, we propose a Hierarchical Cloud-Edge Collaborative Fault-Tolerant Storage (HCEFT) model. This model aims to enhance system robustness, reduce edge storage overhead, and ensure edge data privacy. We also introduce an optimization method for data writing in HCEFT, called ECWSS (Erasure Code data Writing method based on Steiner tree and SDN). This method improves the trade-off between data writing time and traffic consumption. Our scheme improves data robustness, availability, and security. Additionally, the writing optimization method reduces data write time by 13%-67% and network traffic consumption by 20%-62%, enhancing network load balance performance.
{"title":"An Optimized Data Storage in A Secure Cloud-Edge Collaboration A Fault Tolerance Approach","authors":"M. Manideepsai, U. Vineeth Goud, CH. Vinay Goud, P. Vignesh Yadav, D. Saidulu","doi":"10.32628/ijsrset2411255","DOIUrl":"https://doi.org/10.32628/ijsrset2411255","url":null,"abstract":"The rise of edge smart IoT devices has led to the development of edge storage systems (ESS) for efficient access to massive edge data. ESS can reduce the load on cloud centers and improve user experience. However, ESS still faces challenges in improving fault tolerance and efficiency. Thus, there is a need for a secure and efficient fault-tolerant storage scheme. Existing schemes have drawbacks like high edge storage overhead, difficulty in protecting edge data privacy, and low data writing performance. To address these issues, we propose a Hierarchical Cloud-Edge Collaborative Fault-Tolerant Storage (HCEFT) model. This model aims to enhance system robustness, reduce edge storage overhead, and ensure edge data privacy. We also introduce an optimization method for data writing in HCEFT, called ECWSS (Erasure Code data Writing method based on Steiner tree and SDN). This method improves the trade-off between data writing time and traffic consumption. Our scheme improves data robustness, availability, and security. Additionally, the writing optimization method reduces data write time by 13%-67% and network traffic consumption by 20%-62%, enhancing network load balance performance.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"108 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678800","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}
Copper(II) soap complexes have been proven their activity against bacteria very effectively. Herein, the influence of biophysical and biomechanical parameters on the activity of Copper(II) soya thiourea complex was evaluated. To this aim, liquid as well as solid growth media were developed by Kirby-Bauer disc diffusion method. The antibacterial activity of Copper(II) soya thiourea complex against the Gram-positive bacterium Staphylococcus aureus was assessed in various concentration of Copper(II) Soya complexes. Copper (II) Soya complexes also resist bacterial growth at higher concentration. This review provides a board overview of Staphylococcus aureus with an emphasis on the Copper(II) soya thiourea complex
事实证明，铜（II）皂络合物具有非常有效的抗菌活性。本文评估了生物物理和生物力学参数对大豆硫脲铜（II）络合物活性的影响。为此，研究人员采用柯比鲍尔（Kirby-Bauer）盘扩散法研制了液体和固体生长培养基。评估了不同浓度的大豆硫脲铜（II）络合物对革兰氏阳性菌金黄色葡萄球菌的抗菌活性。大豆硫脲铜 (II) 复合物在较高浓度下也能抑制细菌生长。本综述对金黄色葡萄球菌进行了全面概述，重点介绍了大豆硫脲铜（II）络合物。
{"title":"Characterization, Comparative Assessment and Antibacterial Potential of Copper(II) Soya Complexes against Staphylococcus Aureus","authors":"Dr. Vandana Sukhadia","doi":"10.32628/ijsrset2411253","DOIUrl":"https://doi.org/10.32628/ijsrset2411253","url":null,"abstract":"Copper(II) soap complexes have been proven their activity against bacteria very effectively. Herein, the influence of biophysical and biomechanical parameters on the activity of Copper(II) soya thiourea complex was evaluated. To this aim, liquid as well as solid growth media were developed by Kirby-Bauer disc diffusion method. The antibacterial activity of Copper(II) soya thiourea complex against the Gram-positive bacterium Staphylococcus aureus was assessed in various concentration of Copper(II) Soya complexes. Copper (II) Soya complexes also resist bacterial growth at higher concentration. This review provides a board overview of Staphylococcus aureus with an emphasis on the Copper(II) soya thiourea complex","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"122 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680365","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}
Video surveillance has become a cornerstone of security for public spaces and private property. However, the effectiveness of this approach is hampered by the limitations of manual monitoring. Human analysts face challenges such as fatigue, distraction, and the sheer volume of video data, leading to missed incidents and inefficient use of resources. This research project proposes a revolutionary solution: intelligent anomaly detection through artificial intelligence (AI). This system transcends the constraints of human observation by automatically identifying deviations from established patterns within video footage. The core concept lies in leveraging the power of AI to analyze various aspects of video data. This includes movement analysis, object recognition, and scene dynamics. Through this comprehensive approach, the system can detect anomalous events that might escape human notice – activities such as loitering, intrusions, or suspicious behavior. This project delves into the design and development of this intelligent anomaly detection system. It explores the vast potential of machine learning techniques, specifically focusing on unsupervised learning and deep learning algorithms. These algorithms play a crucial role in modeling normal behavior within video data. The system then utilizes these models to identify deviations that fall outside the established patterns. By flagging these anomalies, the system empowers security personnel to prioritize their attention on critical events. This significantly enhances overall security efficiency by allowing human analysts to focus on investigating the most relevant situations. This research project seeks to contribute significantly to the advancement of video surveillance technology. By harnessing the power of AI and machine learning, this intelligent anomaly detection system offers a promising approach to enhancing security in public spaces and private property.
{"title":"Unveiling Anomaly : Empowering Video Surveillance through Intelligent Anomaly Detection","authors":"Dikshendra Sarpate, Isha Tadas, Radhesh Khaire, Mokshad Antapurkar, Amisha Sonone","doi":"10.32628/ijsrset2411248","DOIUrl":"https://doi.org/10.32628/ijsrset2411248","url":null,"abstract":"Video surveillance has become a cornerstone of security for public spaces and private property. However, the effectiveness of this approach is hampered by the limitations of manual monitoring. Human analysts face challenges such as fatigue, distraction, and the sheer volume of video data, leading to missed incidents and inefficient use of resources. This research project proposes a revolutionary solution: intelligent anomaly detection through artificial intelligence (AI). This system transcends the constraints of human observation by automatically identifying deviations from established patterns within video footage. The core concept lies in leveraging the power of AI to analyze various aspects of video data. This includes movement analysis, object recognition, and scene dynamics. Through this comprehensive approach, the system can detect anomalous events that might escape human notice – activities such as loitering, intrusions, or suspicious behavior. This project delves into the design and development of this intelligent anomaly detection system. It explores the vast potential of machine learning techniques, specifically focusing on unsupervised learning and deep learning algorithms. These algorithms play a crucial role in modeling normal behavior within video data. The system then utilizes these models to identify deviations that fall outside the established patterns. By flagging these anomalies, the system empowers security personnel to prioritize their attention on critical events. This significantly enhances overall security efficiency by allowing human analysts to focus on investigating the most relevant situations. This research project seeks to contribute significantly to the advancement of video surveillance technology. By harnessing the power of AI and machine learning, this intelligent anomaly detection system offers a promising approach to enhancing security in public spaces and private property.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682876","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}
R. Arivarasu, N. Ramabasi Reddy, K. Madhavi, A. Niranjan
A four-band microstrip patch antenna is designed to work for satellite applications. Out of four bands, one of the bands has a wide band width up to 8 GHz. These microstrip patch antennas can work in the allocated range of 10–40 GHz. The antenna designed can have low return losses and positive gain, which indicates that it can work for practical applications. The designed antenna added stubs on all three corner sides of the microstrip patch antenna for impedance matching. The design used for VSAT applications is the ANSYS HFSS R21. The HFSS (high-frequency structure simulator) software used for analysis of beamwidth, return losses, voltage standing wave ratio (VSWR), gain, gain polar plot, and 3D gain plot has been evaluated and is going to be verified. The gain of the antenna is very high, up to 8.25 dB when compared to the previous design, which was 3.25 dB higher.
{"title":"Microstrip Patch Antenna Development at K Band for Satellite Communication","authors":"R. Arivarasu, N. Ramabasi Reddy, K. Madhavi, A. Niranjan","doi":"10.32628/ijsrset2411225","DOIUrl":"https://doi.org/10.32628/ijsrset2411225","url":null,"abstract":"A four-band microstrip patch antenna is designed to work for satellite applications. Out of four bands, one of the bands has a wide band width up to 8 GHz. These microstrip patch antennas can work in the allocated range of 10–40 GHz. The antenna designed can have low return losses and positive gain, which indicates that it can work for practical applications. The designed antenna added stubs on all three corner sides of the microstrip patch antenna for impedance matching. The design used for VSAT applications is the ANSYS HFSS R21. The HFSS (high-frequency structure simulator) software used for analysis of beamwidth, return losses, voltage standing wave ratio (VSWR), gain, gain polar plot, and 3D gain plot has been evaluated and is going to be verified. The gain of the antenna is very high, up to 8.25 dB when compared to the previous design, which was 3.25 dB higher.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140686754","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 tobacco epidemic continues to grow due to the increasingly irregular and inadequate access to health care in the population and particularly affects LMICs. The occurrence and mobility of different elements in oral smokeless tobacco products STPs were determined because the effects on human health must take into account their ability. We used data from the 2009-2010 national adult tobacco survey a national landline and cell phone survey of adults aged 18 years and older to estimate current use of any tobacco, Cigarettes, cigars, cigarillos, or small cigars or chewing tobacco, snuff or dip water pipes. We stratified estimates by gender, age, education, income, sexual orientation, and US state. Perceptions of tobacco are a relatively unexplored issue in disadvantaged populations in India and France.
{"title":"A Review of Tobacco User and Non-user","authors":"Namrata. N. Nangare, Smitesh. S. Nalage","doi":"10.32628/ijsrset2411229","DOIUrl":"https://doi.org/10.32628/ijsrset2411229","url":null,"abstract":"The tobacco epidemic continues to grow due to the increasingly irregular and inadequate access to health care in the population and particularly affects LMICs. The occurrence and mobility of different elements in oral smokeless tobacco products STPs were determined because the effects on human health must take into account their ability. We used data from the 2009-2010 national adult tobacco survey a national landline and cell phone survey of adults aged 18 years and older to estimate current use of any tobacco, Cigarettes, cigars, cigarillos, or small cigars or chewing tobacco, snuff or dip water pipes. We stratified estimates by gender, age, education, income, sexual orientation, and US state. Perceptions of tobacco are a relatively unexplored issue in disadvantaged populations in India and France.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140708481","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}
Most of the Machine Learning (ML) models used these days establish a complex relationship between the in- dependent variables (X) and dependent variable (y). Without understanding the relationship, we risk introducing undesirable features into the predictions. Biased collection of the data, used to build the model, might bolster these undesirable features. The model might soon become unfit for its intended tasks. This project tries to get deeper insights into such black box machine learning models by looking into various ExplainableAI (XAI) tools and provide it as a service to users. These tools when used in conjunction can make complex models easy to understand and operate for the end-user. Specifically, the tools used would help the user of the machine learning model interact with it and monitor how it behaves on changing certain aspects of the data. To facilitate the better understanding of the achieved outcome, this project uses a weather data-set which is used to classify the air quality.
{"title":"Interpretable AI Services for Enhanced Air Quality Forecasting","authors":"Ketan Shahapure, Samit Shivadekar, Bhrigu Bhargava","doi":"10.32628/ijsrset2411239","DOIUrl":"https://doi.org/10.32628/ijsrset2411239","url":null,"abstract":"Most of the Machine Learning (ML) models used these days establish a complex relationship between the in- dependent variables (X) and dependent variable (y). Without understanding the relationship, we risk introducing undesirable features into the predictions. Biased collection of the data, used to build the model, might bolster these undesirable features. The model might soon become unfit for its intended tasks. This project tries to get deeper insights into such black box machine learning models by looking into various ExplainableAI (XAI) tools and provide it as a service to users. These tools when used in conjunction can make complex models easy to understand and operate for the end-user. Specifically, the tools used would help the user of the machine learning model interact with it and monitor how it behaves on changing certain aspects of the data. To facilitate the better understanding of the achieved outcome, this project uses a weather data-set which is used to classify the air quality.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"67 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707736","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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