Amit V. Zambare1, Dhananjay A Kulkarni, Mahesh B Patole
The present study aimed to develop a nutraceutical ready-to-serve (RTS) beverage using pear fruit to enhance its nutritional value and extend its shelf life. Preliminary investigations were conducted based on the standards specified for RTS fruit drinks to create a suitable recipe. The RTS beverage was prepared with four levels of pear juice (35%, 45%, 55%, and 65%), sugar (10%, 15%, and 20%), and 0.2% citric acid. Sensory evaluation was carried out with the help of 30 trained panelists at seven-day intervals to determine the optimal juice concentration for the RTS. The sensory evaluation indicated significant differences between treatments concerning color, taste, consistency, and overall acceptability. The RTS beverage with 45% pear juice content and 15% sugar with 0.2% citric acid was selected as the best combination. A storage study was conducted to assess the effects on total soluble solids (TSS), acidity, and pH of the RTS over 35 days, with investigations at seven-day intervals. The study observed a slight increase in TSS from 17.5% to 18.9% and pH from 2.54 to 3.31, along with a slight decrease in acidity from 0.32% to 0.21%, when stored at room temperature (approximately 38°C to 41°C). Keywords: Pear fruit, RTS, TSS, Acidity, pH, storage study
{"title":"Development of Pear Fruit RTS Beverage","authors":"Amit V. Zambare1, Dhananjay A Kulkarni, Mahesh B Patole","doi":"10.55041/ijsrem36833","DOIUrl":"https://doi.org/10.55041/ijsrem36833","url":null,"abstract":"The present study aimed to develop a nutraceutical ready-to-serve (RTS) beverage using pear fruit to enhance its nutritional value and extend its shelf life. Preliminary investigations were conducted based on the standards specified for RTS fruit drinks to create a suitable recipe. The RTS beverage was prepared with four levels of pear juice (35%, 45%, 55%, and 65%), sugar (10%, 15%, and 20%), and 0.2% citric acid. Sensory evaluation was carried out with the help of 30 trained panelists at seven-day intervals to determine the optimal juice concentration for the RTS. The sensory evaluation indicated significant differences between treatments concerning color, taste, consistency, and overall acceptability. The RTS beverage with 45% pear juice content and 15% sugar with 0.2% citric acid was selected as the best combination. A storage study was conducted to assess the effects on total soluble solids (TSS), acidity, and pH of the RTS over 35 days, with investigations at seven-day intervals. The study observed a slight increase in TSS from 17.5% to 18.9% and pH from 2.54 to 3.31, along with a slight decrease in acidity from 0.32% to 0.21%, when stored at room temperature (approximately 38°C to 41°C). Keywords: Pear fruit, RTS, TSS, Acidity, pH, storage study","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"10 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797096","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}
Wireless sensor networks (WSNs) are particularly desirable for real-time applications because of their small size, low cost, and simplicity of installation. Nevertheless, WSNs may need to be modified or redesigned due to a variety of internal or external circumstances, which is difficult for conventional, explicitly planned WSN systems to manage. Machine learning (ML) approaches can be used to solve this problem. ML makes it possible for networks to learn from their experiences and adapt without requiring reprogramming or human intervention.A prior investigation [1] examined machine learning methods for WSNs between 2002 and 2013. We review ML-based algorithms for WSNs from 2014 to March 2018 in this revised study, stressing their advantages, drawbacks, and effects on network lifetime. We also discuss machine learning techniques for energy harvesting, congestion control, mobile sink scheduling, and synchronization. The survey discusses why certain ML approaches are selected for particular WSN difficulties and offers a statistical analysis of the data obtained. We also talk about some outstanding issues in the sector. Keywords: Wireless sensor networks, Machine learning, Energy efficiency, Network lifetime, Data aggregation
{"title":"AN OVERVIEW OF MACHINE LEARNING ALGORITHMS FOR WIRELESS SENSOR NETWORKS","authors":"Pritam Nanda, Sasmita Tripathy","doi":"10.55041/ijsrem36829","DOIUrl":"https://doi.org/10.55041/ijsrem36829","url":null,"abstract":"Wireless sensor networks (WSNs) are particularly desirable for real-time applications because of their small size, low cost, and simplicity of installation. Nevertheless, WSNs may need to be modified or redesigned due to a variety of internal or external circumstances, which is difficult for conventional, explicitly planned WSN systems to manage. Machine learning (ML) approaches can be used to solve this problem. ML makes it possible for networks to learn from their experiences and adapt without requiring reprogramming or human intervention.A prior investigation [1] examined machine learning methods for WSNs between 2002 and 2013. We review ML-based algorithms for WSNs from 2014 to March 2018 in this revised study, stressing their advantages, drawbacks, and effects on network lifetime. We also discuss machine learning techniques for energy harvesting, congestion control, mobile sink scheduling, and synchronization. The survey discusses why certain ML approaches are selected for particular WSN difficulties and offers a statistical analysis of the data obtained. We also talk about some outstanding issues in the sector. Keywords: Wireless sensor networks, Machine learning, Energy efficiency, Network lifetime, Data aggregation","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797765","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 become a significant phenomenon in the business environment in recent times, with a particular impact on the industrial sector. This study explores the complex dynamics of the industrial sector's digital transition with an emphasis on the effects on global commerce. The research sheds light on the potential and problems that Digital transformation in manufacturing presents by using a through methodology that includes literature evaluation. In this paper the digital transformation in the manufacturing sector and how it will affect Indian manufacturing companies' competitiveness and market positioning in the future. Additionally, it looks at how trade laws, trade practices, and digitalization processes interact within the Indian manufacturing sector to provide insight into how these variables are amongst themselves. Using a nuanced investigation, the study seeks to accomplish two principal goals: first, assessing the effects of digital transformation on the manufacturing sector in India; and second, examining how trade practices and policies both influence and are influenced by the digital revolution, consequently influencing international trade. Keywords: Digital Transforming, Manufacturing Sector, Global Commerce, Digitalisation, Trade Practices etc.
{"title":"Impact of Digital Transformation on Indian Manufacturing Industry","authors":"Jyoti Yadav","doi":"10.55041/ijsrem36835","DOIUrl":"https://doi.org/10.55041/ijsrem36835","url":null,"abstract":"Digital Transformation (DT) has become a significant phenomenon in the business environment in recent times, with a particular impact on the industrial sector. This study explores the complex dynamics of the industrial sector's digital transition with an emphasis on the effects on global commerce. The research sheds light on the potential and problems that Digital transformation in manufacturing presents by using a through methodology that includes literature evaluation. In this paper the digital transformation in the manufacturing sector and how it will affect Indian manufacturing companies' competitiveness and market positioning in the future. Additionally, it looks at how trade laws, trade practices, and digitalization processes interact within the Indian manufacturing sector to provide insight into how these variables are amongst themselves. Using a nuanced investigation, the study seeks to accomplish two principal goals: first, assessing the effects of digital transformation on the manufacturing sector in India; and second, examining how trade practices and policies both influence and are influenced by the digital revolution, consequently influencing international trade. Keywords: Digital Transforming, Manufacturing Sector, Global Commerce, Digitalisation, Trade Practices etc.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"76 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798355","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}
In the construction industry, maintaining structural integrity is pivotal for safety, efficiency, and economic viability. Traditional inspection methods, often sporadic and reliant on visual assessments, can overlook critical issues, especially in challenging environments where access is restricted or hazardous. The integration of IoT (Internet of Things) technology has revolutionized structural health monitoring by enabling continuous, remote data collection and analysis through sophisticated sensor networks. These networks, comprising wireless sensors strategically placed across buildings or infrastructure, monitor a range of parameters including temperature, humidity, light levels, vibration, and structural strain. This real-time data is transmitted wirelessly to central hubs or gateways, typically utilizing cost-effective solutions like Raspberry Pi devices programmed with Python for efficient data management. The collected data is then processed and stored in cloud servers, leveraging the scalability and accessibility of cloud computing to facilitate advanced signal processing and analysis. MATLAB is utilized for its robust capabilities in numerical computing and visualization, presenting the data in graphical formats that highlight trends, anomalies, and potential deterioration patterns. Crucially, this system incorporates an alert mechanism, notifying stakeholders via email of critical sensor readings or emerging issues, enabling swift responses to prevent accidents or structural failures. The adoption of IoT-enabled structural health monitoring offers multifaceted benefits to the construction industry and broader economic landscape. By continuously monitoring infrastructure health, this approach allows for early detection of defects or wear, facilitating proactive maintenance interventions that can significantly extend the service life of buildings and infrastructure. This proactive maintenance not only enhances safety and reliability but also reduces long-term costs associated with reactive repairs and unplanned downtime. Moreover, by minimizing the need for frequent physical inspections, IoT technology contributes to environmental sustainability by reducing carbon emissions associated with transportation and improving operational efficiency through data-driven decision-making. These efficiencies translate into tangible economic gains, as stakeholders can optimize resource allocation, prioritize maintenance efforts, and mitigate the financial impacts of unexpected structural failures or degradation. From a safety perspective, IoT-enabled monitoring systems enhance risk management by providing real-time insights into structural conditions. By identifying potential hazards or weaknesses early on, stakeholders can implement targeted interventions to mitigate risks and ensure compliance with stringent safety regulations. This proactive approach not only protects human lives but also safeguards investments in infrastructure by preemptively ad
在建筑行业，保持结构的完整性对于安全、效率和经济可行性至关重要。传统的检测方法往往是零星的，依赖于目测评估，可能会忽略关键问题，尤其是在限制进入或危险的挑战性环境中。物联网（IoT）技术的集成通过先进的传感器网络实现了连续的远程数据收集和分析，从而彻底改变了结构健康监测。这些网络由战略性放置在建筑物或基础设施中的无线传感器组成，可监测一系列参数，包括温度、湿度、光照度、振动和结构应变。这些实时数据以无线方式传输到中心集线器或网关，通常采用的是具有成本效益的解决方案，如使用 Python 编程的 Raspberry Pi 设备，以实现高效的数据管理。然后，收集到的数据将被处理并存储在云服务器中，利用云计算的可扩展性和可访问性来促进高级信号处理和分析。MATLAB 具有强大的数值计算和可视化功能，能以图形格式显示数据，突出显示趋势、异常和潜在的恶化模式。最重要的是，该系统集成了警报机制，可通过电子邮件将关键传感器读数或新出现的问题通知利益相关者，以便迅速采取应对措施，防止事故或结构故障的发生。采用由物联网支持的结构健康监测可为建筑行业和更广泛的经济领域带来多方面的益处。通过持续监测基础设施的健康状况，这种方法可以及早发现缺陷或磨损，促进主动维护干预，从而大大延长建筑物和基础设施的使用寿命。这种主动维护不仅能提高安全性和可靠性，还能降低与被动维修和计划外停工相关的长期成本。此外，物联网技术最大限度地减少了频繁的实际检查，减少了与运输相关的碳排放，并通过数据驱动的决策提高了运营效率，从而促进了环境的可持续发展。这些效率可以转化为实实在在的经济收益，因为利益相关者可以优化资源分配，确定维护工作的优先次序，并减轻意外结构故障或退化造成的经济影响。从安全角度来看，物联网监控系统可实时了解结构状况，从而加强风险管理。通过早期识别潜在危险或薄弱环节，利益相关者可以实施有针对性的干预措施，以降低风险并确保遵守严格的安全法规。这种积极主动的方法不仅能保护人的生命，还能在问题升级为代价高昂的紧急情况之前先发制人地加以解决，从而保障对基础设施的投资。此外，通过利用基于云的数据存储和分析，这些系统还能让利益相关者以前所未有的方式获得全面、可操作的见解。
{"title":"Structural Health Monitoring Using IOT","authors":"Karanam Rajasekhar, Mr. Zeeshan Khan","doi":"10.55041/ijsrem36802","DOIUrl":"https://doi.org/10.55041/ijsrem36802","url":null,"abstract":"In the construction industry, maintaining structural integrity is pivotal for safety, efficiency, and economic viability. Traditional inspection methods, often sporadic and reliant on visual assessments, can overlook critical issues, especially in challenging environments where access is restricted or hazardous. The integration of IoT (Internet of Things) technology has revolutionized structural health monitoring by enabling continuous, remote data collection and analysis through sophisticated sensor networks. These networks, comprising wireless sensors strategically placed across buildings or infrastructure, monitor a range of parameters including temperature, humidity, light levels, vibration, and structural strain. This real-time data is transmitted wirelessly to central hubs or gateways, typically utilizing cost-effective solutions like Raspberry Pi devices programmed with Python for efficient data management. The collected data is then processed and stored in cloud servers, leveraging the scalability and accessibility of cloud computing to facilitate advanced signal processing and analysis. MATLAB is utilized for its robust capabilities in numerical computing and visualization, presenting the data in graphical formats that highlight trends, anomalies, and potential deterioration patterns. Crucially, this system incorporates an alert mechanism, notifying stakeholders via email of critical sensor readings or emerging issues, enabling swift responses to prevent accidents or structural failures. The adoption of IoT-enabled structural health monitoring offers multifaceted benefits to the construction industry and broader economic landscape. By continuously monitoring infrastructure health, this approach allows for early detection of defects or wear, facilitating proactive maintenance interventions that can significantly extend the service life of buildings and infrastructure. This proactive maintenance not only enhances safety and reliability but also reduces long-term costs associated with reactive repairs and unplanned downtime. Moreover, by minimizing the need for frequent physical inspections, IoT technology contributes to environmental sustainability by reducing carbon emissions associated with transportation and improving operational efficiency through data-driven decision-making. These efficiencies translate into tangible economic gains, as stakeholders can optimize resource allocation, prioritize maintenance efforts, and mitigate the financial impacts of unexpected structural failures or degradation. From a safety perspective, IoT-enabled monitoring systems enhance risk management by providing real-time insights into structural conditions. By identifying potential hazards or weaknesses early on, stakeholders can implement targeted interventions to mitigate risks and ensure compliance with stringent safety regulations. This proactive approach not only protects human lives but also safeguards investments in infrastructure by preemptively ad","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"43 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799020","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}
Tijisha Mol J, Anna George, Khadheeja S Shahul, Subhala R
DiGeorge Syndrome (DGS), or 22q11.2 deletion syndrome (22q11DS), is a genetic disorder characterized by a microdeletion on chromosome 22 at band q11.2, presenting with a range of clinical features including immunodeficiency, hypoparathyroidism, and congenital heart disease. Partial DiGeorge Syndrome involves a subset of these features, complicating diagnosis and management. This case study describes a 45-year-old female with partial DiGeorge Syndrome who presented with a seizure disorder, hypothyroidism, and recurrent infections. Following a fall, she sustained multiple facial bone fractures and developed respiratory symptoms. Her management included intensive care for seizure activity, correction of electrolyte imbalances, and treatment for respiratory complications and infections. The patient required multidisciplinary care involving neurologists, endocrinologists, and pulmonologists. The patient's complex presentation highlighted the need for comprehensive, coordinated medical care to address the multifaceted impacts of partial DiGeorge Syndrome. Key management strategies included continuous monitoring, endocrine support, and targeted antibiotic therapy. This case underscores the importance of a multidisciplinary approach in managing partial DiGeorge Syndrome, emphasizing the need for integrated care to improve patient outcomes and quality of life. KEYWORDS: DiGeorge Syndrome, Seizure Disorder, Hypothyroidism, Recurrent Infection
{"title":"A Complex Case of Partial Digeorge Syndrome with Multiple System Involvement: Seizure Disorder, Hypothyroidism and Recurrent Infection","authors":"Tijisha Mol J, Anna George, Khadheeja S Shahul, Subhala R","doi":"10.55041/ijsrem36819","DOIUrl":"https://doi.org/10.55041/ijsrem36819","url":null,"abstract":"DiGeorge Syndrome (DGS), or 22q11.2 deletion syndrome (22q11DS), is a genetic disorder characterized by a microdeletion on chromosome 22 at band q11.2, presenting with a range of clinical features including immunodeficiency, hypoparathyroidism, and congenital heart disease. Partial DiGeorge Syndrome involves a subset of these features, complicating diagnosis and management. This case study describes a 45-year-old female with partial DiGeorge Syndrome who presented with a seizure disorder, hypothyroidism, and recurrent infections. Following a fall, she sustained multiple facial bone fractures and developed respiratory symptoms. Her management included intensive care for seizure activity, correction of electrolyte imbalances, and treatment for respiratory complications and infections. The patient required multidisciplinary care involving neurologists, endocrinologists, and pulmonologists. The patient's complex presentation highlighted the need for comprehensive, coordinated medical care to address the multifaceted impacts of partial DiGeorge Syndrome. Key management strategies included continuous monitoring, endocrine support, and targeted antibiotic therapy. This case underscores the importance of a multidisciplinary approach in managing partial DiGeorge Syndrome, emphasizing the need for integrated care to improve patient outcomes and quality of life. KEYWORDS: DiGeorge Syndrome, Seizure Disorder, Hypothyroidism, Recurrent Infection","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"23 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799507","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}
Concrete is highly versatile, capable of withstanding harsh environments and achieving inspirational forms. Modern advancements focus on enhancing its performance through innovative chemical admixtures and supplementary cementitious materials (SCMs). SCMs, often industrial byproducts like fly ash, silica fume, ground granulated blast furnace slag, and steel slag, replace a portion of Portland cement, reducing costs and environmental impact while improving concrete properties. Silica fume, a particularly successful SCM, significantly enhances concrete's strength and durability, especially in high-strength applications. Steel slag, a byproduct of steel manufacturing, shows potential as an aggregate in concrete, despite its tendency to expand due to free lime and magnesium oxides. Proper treatment and the use of pozzolanic materials like silica fume can mitigate this expansion. This study investigates the mechanical properties of concrete mixes using ACC brand slag cement, fly ash cement, and their blend (1:1), modified with 10% and 20% silica fume. Natural sand (zone II, IS 383-1982) serves as the fine aggregate, and steel slag (20 mm down) as the coarse aggregate, mixed in a 1:1.5:3 ratio. Tests on 7-day, 28- day, and 56-day compressive strengths, flexural strength, porosity, and capillary absorption were conducted. Key findings include an increased water requirement with higher silica fume content, higher early strength gain with fly ash cement, and better later strength with slag cement. Silica fume reduces capillary absorption and porosity, particularly with fly ash cement. Keywords: Concrete, Steel Slag, Silica Fume, Supplementary Cementitious Materials, Durability, Strength.
{"title":"Synergistic Effects of Silica Fume And Steel Slag And Steel Slag In Advanced Concrete Composites","authors":"Rishabh Hirwani, Dr. R.R.L Birali","doi":"10.55041/ijsrem36843","DOIUrl":"https://doi.org/10.55041/ijsrem36843","url":null,"abstract":"Concrete is highly versatile, capable of withstanding harsh environments and achieving inspirational forms. Modern advancements focus on enhancing its performance through innovative chemical admixtures and supplementary cementitious materials (SCMs). SCMs, often industrial byproducts like fly ash, silica fume, ground granulated blast furnace slag, and steel slag, replace a portion of Portland cement, reducing costs and environmental impact while improving concrete properties. Silica fume, a particularly successful SCM, significantly enhances concrete's strength and durability, especially in high-strength applications. Steel slag, a byproduct of steel manufacturing, shows potential as an aggregate in concrete, despite its tendency to expand due to free lime and magnesium oxides. Proper treatment and the use of pozzolanic materials like silica fume can mitigate this expansion. This study investigates the mechanical properties of concrete mixes using ACC brand slag cement, fly ash cement, and their blend (1:1), modified with 10% and 20% silica fume. Natural sand (zone II, IS 383-1982) serves as the fine aggregate, and steel slag (20 mm down) as the coarse aggregate, mixed in a 1:1.5:3 ratio. Tests on 7-day, 28- day, and 56-day compressive strengths, flexural strength, porosity, and capillary absorption were conducted. Key findings include an increased water requirement with higher silica fume content, higher early strength gain with fly ash cement, and better later strength with slag cement. Silica fume reduces capillary absorption and porosity, particularly with fly ash cement. Keywords: Concrete, Steel Slag, Silica Fume, Supplementary Cementitious Materials, Durability, Strength.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"32 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800651","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}
Fuzzy association rules with its linguistic annotations and human interpretable form, has provided a convenient extension of association concepts to quantified attributes. The applicability is extended by combining extraction of both positive and negative association rules. Interestingness measures are used to filter out the useful and correct set of actionable association rules from the larger set of rules mined by association rule mining algorithms. Many measures such as Support, Confidence, Conviction and Certainty Factor, with their own area of applicability and statistical significance are popular. The wide range of measures is usually based on frequency counts or probability of occurrence of certain attribute patterns. Binary attributes uses a 2×2 contingency table as the basis for defining different measures. This paper presents concept of fuzzy support matrix using fuzzy partitions, as a natural extension of contingency table for the different interestingness measures. Those can be defined in a uniform and consistent manner. It uses the existing interestingness measures defined in new form using fuzzy support and illustrate these concepts using known data sets. This paper represent active research directions aimed at advancing the capabilities, applicability, and efficiency of fuzzy association rule mining in handling modern data challenges across various domains. Keywords: Interestingness measures; Association Rules mining; Fuzzy sets.
{"title":"Introducing Concept of Fuzzy Support Matrix for Interestingness Measures","authors":"Swati Ramdasi","doi":"10.55041/ijsrem36778","DOIUrl":"https://doi.org/10.55041/ijsrem36778","url":null,"abstract":"Fuzzy association rules with its linguistic annotations and human interpretable form, has provided a convenient extension of association concepts to quantified attributes. The applicability is extended by combining extraction of both positive and negative association rules. Interestingness measures are used to filter out the useful and correct set of actionable association rules from the larger set of rules mined by association rule mining algorithms. Many measures such as Support, Confidence, Conviction and Certainty Factor, with their own area of applicability and statistical significance are popular. The wide range of measures is usually based on frequency counts or probability of occurrence of certain attribute patterns. Binary attributes uses a 2×2 contingency table as the basis for defining different measures. This paper presents concept of fuzzy support matrix using fuzzy partitions, as a natural extension of contingency table for the different interestingness measures. Those can be defined in a uniform and consistent manner. It uses the existing interestingness measures defined in new form using fuzzy support and illustrate these concepts using known data sets. This paper represent active research directions aimed at advancing the capabilities, applicability, and efficiency of fuzzy association rule mining in handling modern data challenges across various domains. Keywords: Interestingness measures; Association Rules mining; Fuzzy sets.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"14 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801252","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}
Prof. Shivaji Goroba Shinde, Mr. Shubham Suresh Patil
In past days, capture images with very high quality and good size is so easy because of rapid improvement in quality of capturing device with less costly but superior technology. Videos are a collect of sequential images with a constant time interval. So video can provide also more information about our object when scenarios about to changing with respect to time. Therefore, manually handling videosit can be quite impossible. That time all that need an automatic devise to process these videos. In this thesis one such attempt has been made to track objects in videos. Many algorithms and technology have been developed to automate monitoring the object in a video file. Object detection and tracking is a one of the challenging task in computer vision. Mainly there are three basic steps in video analysis: Detection of objects of Interest from moving objects, Tracking of that interested objects in consecutive frames, and Analysis of object tracks to understand their behavior Some common choice to choose suitable feature to categories, visual objects are intensity, shape, color and feature points. In this thesis, we studied about mean shift tracking based on the color pdf, optical flow tracking based on the intensity and motion; SIFT tracking based on scale invariant local feature points. Keywords: real-time, object detection, tracking, surveillance
在过去，由于捕捉设备的质量迅速提高，成本较低但技术卓越，因此捕捉高质量、大尺寸的图像变得非常容易。视频是时间间隔不变的连续图像的集合。因此，当场景随时间发生变化时，视频也能提供更多关于物体的信息。因此，手动处理视频是不可能的。这时就需要一种自动装置来处理这些视频。在本论文中，我们就进行了这样的尝试，以跟踪视频中的物体。目前已开发出许多算法和技术来自动监控视频文件中的物体。物体检测和跟踪是计算机视觉领域的一项具有挑战性的任务。视频分析主要有三个基本步骤：从移动物体中检测感兴趣的物体，在连续帧中跟踪感兴趣的物体，以及分析物体轨迹以了解其行为。在本论文中，我们研究了基于颜色 pdf 的均值移动跟踪、基于强度和运动的光流跟踪、基于尺度不变局部特征点的 SIFT 跟踪。关键词： 实时、物体检测、跟踪、监控
{"title":"A Review of Real Time Image Processing for Object Detection","authors":"Prof. Shivaji Goroba Shinde, Mr. Shubham Suresh Patil","doi":"10.55041/ijsrem36808","DOIUrl":"https://doi.org/10.55041/ijsrem36808","url":null,"abstract":"In past days, capture images with very high quality and good size is so easy because of rapid improvement in quality of capturing device with less costly but superior technology. Videos are a collect of sequential images with a constant time interval. So video can provide also more information about our object when scenarios about to changing with respect to time. Therefore, manually handling videosit can be quite impossible. That time all that need an automatic devise to process these videos. In this thesis one such attempt has been made to track objects in videos. Many algorithms and technology have been developed to automate monitoring the object in a video file. Object detection and tracking is a one of the challenging task in computer vision. Mainly there are three basic steps in video analysis: Detection of objects of Interest from moving objects, Tracking of that interested objects in consecutive frames, and Analysis of object tracks to understand their behavior Some common choice to choose suitable feature to categories, visual objects are intensity, shape, color and feature points. In this thesis, we studied about mean shift tracking based on the color pdf, optical flow tracking based on the intensity and motion; SIFT tracking based on scale invariant local feature points. Keywords: real-time, object detection, tracking, surveillance","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"12 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802088","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 project aims to develop the implementation of a quiz web application using the Django framework, aimed at providing an intuitive and efficient platform for conducting quizzes online. The project focuses on creating a user-friendly interface for both quiz creators and participants, incorporating features such as user authentication, quiz creation, real- time quiz taking, and performance analytics. Utilizing Django's powerful tools and libraries, the application offers seamless integration of components like views, templates, models, and URLs, ensuring scalability, flexibility, and security. Key functionalities include the ability to create quizzes with customizable settings, such as time limits and question types, dynamic rendering of quiz content using templates, and interaction with a backend database for storing and retrieving quiz data. Additionally, the application provides comprehensive performance analytics for quiz creators and participants, enabling insights into quiz performance, participant engagement, and areas for improvement. Through its robust architecture and user-centric design, the quiz web application aims to revolutionize the way quizzes are conducted and experienced in an online environment, catering to educators, trainers, and organizations seeking an efficient and engaging quiz platform. Key Words: django,quiz web application,user-friendly interface
{"title":"CREATING A WEB APPLICATION AND ELEVATE LEARNING QUIZ","authors":"M. Ramya, S. Prakash, D. Tamilselvam","doi":"10.55041/ijsrem36815","DOIUrl":"https://doi.org/10.55041/ijsrem36815","url":null,"abstract":"This project aims to develop the implementation of a quiz web application using the Django framework, aimed at providing an intuitive and efficient platform for conducting quizzes online. The project focuses on creating a user-friendly interface for both quiz creators and participants, incorporating features such as user authentication, quiz creation, real- time quiz taking, and performance analytics. Utilizing Django's powerful tools and libraries, the application offers seamless integration of components like views, templates, models, and URLs, ensuring scalability, flexibility, and security. Key functionalities include the ability to create quizzes with customizable settings, such as time limits and question types, dynamic rendering of quiz content using templates, and interaction with a backend database for storing and retrieving quiz data. Additionally, the application provides comprehensive performance analytics for quiz creators and participants, enabling insights into quiz performance, participant engagement, and areas for improvement. Through its robust architecture and user-centric design, the quiz web application aims to revolutionize the way quizzes are conducted and experienced in an online environment, catering to educators, trainers, and organizations seeking an efficient and engaging quiz platform. Key Words: django,quiz web application,user-friendly interface","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"21 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799117","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}
High-performance concrete (HPC) is defined as concrete that meets special combinations of performance and uniformity requirements that cannot always be achieved routinely using conventional constituents and normal mixing, placing, and curing practices. Ever since the term HPC was introduced into the industry, it has been widely used in large-scale concrete construction that demands high strength, high flowability, and high durability. High-strength concrete is always a type of HPC, but HPC is not always high-strength concrete. Specifying high-strength concrete does not ensure durability. Achieving a product that simultaneously fulfills all desired properties is challenging. Pozzolanic materials such as Ground Granulated Blast Furnace Slag (GGBS), silica fume, rice husk ash, fly ash, and high reactive metakaolin can be used in concrete as partial replacements for cement. These pozzolans are essential for producing HPC. In this study, X-ray diffraction (XRD) tests were conducted on these materials to analyze their constituents. Maintaining a minimal water-cement ratio is crucial, necessitating the use of superplasticizers, which play a significant role in HPC production. The study involved testing materials like rice husk ash, GGBS, and silica fume to achieve the desired properties. XRD tests were conducted on these pozzolanic materials to analyze their content. Synthetic fiber (Recron fiber) was added in varying percentages (0.0%, 0.1%, 0.2%, 0.3% by total weight of concrete), and concrete was cast. Additionally, different percentages of silica fume were used to replace cement while keeping the fiber content constant, and concrete was cast. Two types of cement were used: Portland slag cement and ordinary Portland cement. Mortar, cubes, cylinders, and prisms were prepared, followed by compressive, splitting tensile, and flexural tests. Porosity and permeability tests were also conducted. To achieve the performance characteristics not attainable with conventional concrete, numerous trial mixes were required to select the optimal material combinations. Keywords: High-Performance Concrete, Pozzolanic Additives, Fiber-Reinforced Concrete, Ground Granulated Blast Furnace Slag, Silica Fume, Rice Husk Ash, X-Ray Diffraction, Superplasticizer, Synthetic Fiber, Mechanical Properties, Durability.
{"title":"Evaluating The Impact of Pozzoloniz Additives On The Mechanical Properties of Fiber-Reinforced Concrete","authors":"Om prakash, Dr. R.R.L Birali","doi":"10.55041/ijsrem36844","DOIUrl":"https://doi.org/10.55041/ijsrem36844","url":null,"abstract":"High-performance concrete (HPC) is defined as concrete that meets special combinations of performance and uniformity requirements that cannot always be achieved routinely using conventional constituents and normal mixing, placing, and curing practices. Ever since the term HPC was introduced into the industry, it has been widely used in large-scale concrete construction that demands high strength, high flowability, and high durability. High-strength concrete is always a type of HPC, but HPC is not always high-strength concrete. Specifying high-strength concrete does not ensure durability. Achieving a product that simultaneously fulfills all desired properties is challenging. Pozzolanic materials such as Ground Granulated Blast Furnace Slag (GGBS), silica fume, rice husk ash, fly ash, and high reactive metakaolin can be used in concrete as partial replacements for cement. These pozzolans are essential for producing HPC. In this study, X-ray diffraction (XRD) tests were conducted on these materials to analyze their constituents. Maintaining a minimal water-cement ratio is crucial, necessitating the use of superplasticizers, which play a significant role in HPC production. The study involved testing materials like rice husk ash, GGBS, and silica fume to achieve the desired properties. XRD tests were conducted on these pozzolanic materials to analyze their content. Synthetic fiber (Recron fiber) was added in varying percentages (0.0%, 0.1%, 0.2%, 0.3% by total weight of concrete), and concrete was cast. Additionally, different percentages of silica fume were used to replace cement while keeping the fiber content constant, and concrete was cast. Two types of cement were used: Portland slag cement and ordinary Portland cement. Mortar, cubes, cylinders, and prisms were prepared, followed by compressive, splitting tensile, and flexural tests. Porosity and permeability tests were also conducted. To achieve the performance characteristics not attainable with conventional concrete, numerous trial mixes were required to select the optimal material combinations. Keywords: High-Performance Concrete, Pozzolanic Additives, Fiber-Reinforced Concrete, Ground Granulated Blast Furnace Slag, Silica Fume, Rice Husk Ash, X-Ray Diffraction, Superplasticizer, Synthetic Fiber, Mechanical Properties, Durability.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"26 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800701","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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