Medical devices and pharmaceutical drugs undergo packaging procedures to ensure their stability and integrity remain intact throughout post-production shipping and storage, prior to their clinical utilization. Throughout delivery and storage, the packaging may interact either directly or indirectly with the drug product or medical device, potentially leading to chemical reactions between the two. The role of packaging is paramount in ensuring success, safeguarding the product, and facilitating its sale. Similar to other items found in supermarkets, prescription pharmaceuticals necessitate packaging that addresses various needs, including security, promptness, safety, product identity, quality assurance, patient well-being, and product excellence. Packaging represents both a scientific and artistic endeavour, involving the consideration of numerous factors, beginning with the fundamental design and technology utilized to package the product securely, while also ensuring its protection, presentation, and compliance with manufacturing standards during transportation, storage, and consumption. To uphold the physiochemical, biological, and chemical stability of drugs, packaging professionals design containers capable of withstanding the pressures encountered during supply and shipping processes. Enhancements in the field of prescription drug development have long emphasized the importance of packaging expertise. This serialization process is crucial for bolstering drug security within the supply chain while maintaining drug quality, thereby minimizing the risk of counterfeit drugs infiltrating the distribution network.
{"title":"Improving the Integrity of Pharmaceutical Serialization with Enterprise Technologies","authors":"Hariprasad Mandava","doi":"10.32628/cseit2410338","DOIUrl":"https://doi.org/10.32628/cseit2410338","url":null,"abstract":"Medical devices and pharmaceutical drugs undergo packaging procedures to ensure their stability and integrity remain intact throughout post-production shipping and storage, prior to their clinical utilization. Throughout delivery and storage, the packaging may interact either directly or indirectly with the drug product or medical device, potentially leading to chemical reactions between the two. The role of packaging is paramount in ensuring success, safeguarding the product, and facilitating its sale. Similar to other items found in supermarkets, prescription pharmaceuticals necessitate packaging that addresses various needs, including security, promptness, safety, product identity, quality assurance, patient well-being, and product excellence. Packaging represents both a scientific and artistic endeavour, involving the consideration of numerous factors, beginning with the fundamental design and technology utilized to package the product securely, while also ensuring its protection, presentation, and compliance with manufacturing standards during transportation, storage, and consumption. To uphold the physiochemical, biological, and chemical stability of drugs, packaging professionals design containers capable of withstanding the pressures encountered during supply and shipping processes. Enhancements in the field of prescription drug development have long emphasized the importance of packaging expertise. This serialization process is crucial for bolstering drug security within the supply chain while maintaining drug quality, thereby minimizing the risk of counterfeit drugs infiltrating the distribution network.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"108 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352379","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 security considerations of the votes are based on blockchain technology using cryptographic hashes to secure end-to-end verification. To this end, a successful vote cast is considered as a transaction within the blockchain of the voting application. Therefore, a vote cast is added as a new block (after successful mining) in the blockchain as well as being recorded in data tables at the backend of the database. The system ensures only one-person, one-vote (democracy) property of voting systems. This is achieved by using the voter’s unique face image, which is matched at the beginning of every voting attempt to prevent double voting. The Face Recognition is the study of physical or behavioral characteristics of human being used for the identification of person. So implement real time authentication system using face biometrics for authorized the person for online voting system. This work claims to score voting method and data management challenges in blockchain and provides an improved manifestation of the electronic voting process. Score-based voting methods, also known as range voting or rated voting, are electoral systems where voters are allowed to express their preferences for candidates or options by assigning numerical scores to them. Unlike traditional voting methods where voters choose a single candidate, score-based systems enable voters to provide a more nuanced and detailed assessment of their preferences. It is important here to note that cryptographic hash for a voter is the unique hash of voter by which voter is known in the blockchain. This property facilitates achieving verifiability of the overall voting process. Furthermore, this id is hidden and no one can view it even a system operator cannot view this hash therefore achieving privacy of individual voters.
投票的安全性考虑基于区块链技术，使用加密哈希值来确保端到端的验证。为此，成功投出的一票被视为投票应用程序区块链中的一笔交易。因此，投票将作为一个新区块（成功挖掘后）添加到区块链中，并记录在数据库后台的数据表中。该系统只确保投票系统的一人一票（民主）属性。这是通过使用选民的唯一人脸图像来实现的，在每次投票开始时都会进行匹配，以防止重复投票。人脸识别是对人的身体或行为特征的研究，用于识别人的身份。因此，使用人脸生物识别技术实现实时身份验证系统，以授权个人使用在线投票系统。这项工作声称对区块链中的投票方法和数据管理挑战进行评分，并提供了电子投票过程的改进表现形式。基于分数的投票方法，也称为范围投票或评级投票，是允许选民通过给候选人或选项分配数字分数来表达其偏好的选举系统。与投票人选择单一候选人的传统投票方法不同，基于分数的系统使投票人能够对自己的偏好做出更细致入微的评估。这里需要注意的是，选民的加密哈希值是区块链中已知选民的唯一哈希值。这一特性有助于实现整个投票过程的可验证性。此外，这个 ID 是隐藏的，任何人都无法查看，甚至系统操作员也无法查看这个哈希值，因此实现了选民个人隐私的保护。
{"title":"An Efficient Blockchain Enabled Score Voting with Face Recognition","authors":"Madhubal A. M","doi":"10.32628/cseit24103130","DOIUrl":"https://doi.org/10.32628/cseit24103130","url":null,"abstract":"The security considerations of the votes are based on blockchain technology using cryptographic hashes to secure end-to-end verification. To this end, a successful vote cast is considered as a transaction within the blockchain of the voting application. Therefore, a vote cast is added as a new block (after successful mining) in the blockchain as well as being recorded in data tables at the backend of the database. The system ensures only one-person, one-vote (democracy) property of voting systems. This is achieved by using the voter’s unique face image, which is matched at the beginning of every voting attempt to prevent double voting. The Face Recognition is the study of physical or behavioral characteristics of human being used for the identification of person. So implement real time authentication system using face biometrics for authorized the person for online voting system. This work claims to score voting method and data management challenges in blockchain and provides an improved manifestation of the electronic voting process. Score-based voting methods, also known as range voting or rated voting, are electoral systems where voters are allowed to express their preferences for candidates or options by assigning numerical scores to them. Unlike traditional voting methods where voters choose a single candidate, score-based systems enable voters to provide a more nuanced and detailed assessment of their preferences. It is important here to note that cryptographic hash for a voter is the unique hash of voter by which voter is known in the blockchain. This property facilitates achieving verifiability of the overall voting process. Furthermore, this id is hidden and no one can view it even a system operator cannot view this hash therefore achieving privacy of individual voters.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"120 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362503","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}
Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.
{"title":"Age and Gender voice Recognition using Deep learning","authors":"Santhiya S, N. Nanda Kumar","doi":"10.32628/cseit2410336","DOIUrl":"https://doi.org/10.32628/cseit2410336","url":null,"abstract":"Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"350 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380866","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 trends in cloud computing development have become fundamental building blocks to many business information system models and innovations, the architects are designing cloud systems to be as effective and beneficial as possible. In this paradigm, open-source cloud platforms are part of the design philosophy that drives innovation in cloud services. The adoption of open-source cloud has become pervasive in the modern enterprise; this has been accelerated by perceived benefits of openness. The power in the community of developers and openness fosters development of hardened, secure and reliable solutions. These features enable a collaborative source code, modification and customization geared towards innovation with positive impact on the design, aligning perfectly with the dynamic nature of open-source cloud solutions. However, the slow adoption rate in the modern enterprises has been attributed to a lack of understanding of open-source cloud adoption. This study explores the determinants of open-source cloud adoption in the context of higher learning institutions. A deductive thematic analysis technique was utilized in the study.
{"title":"Towards Open-Source Cloud Adoption : Exploring the Determinants","authors":"Chirchir P. K, Muhambe T. M, Obare E. O","doi":"10.32628/cseit24103120","DOIUrl":"https://doi.org/10.32628/cseit24103120","url":null,"abstract":"The trends in cloud computing development have become fundamental building blocks to many business information system models and innovations, the architects are designing cloud systems to be as effective and beneficial as possible. In this paradigm, open-source cloud platforms are part of the design philosophy that drives innovation in cloud services. The adoption of open-source cloud has become pervasive in the modern enterprise; this has been accelerated by perceived benefits of openness. The power in the community of developers and openness fosters development of hardened, secure and reliable solutions. These features enable a collaborative source code, modification and customization geared towards innovation with positive impact on the design, aligning perfectly with the dynamic nature of open-source cloud solutions. However, the slow adoption rate in the modern enterprises has been attributed to a lack of understanding of open-source cloud adoption. This study explores the determinants of open-source cloud adoption in the context of higher learning institutions. A deductive thematic analysis technique was utilized in the study.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"7 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382786","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}
Ardon Kotey, Allan Almeida, Nihal Gupta, Dr. Vinaya Sawant
Birds are meaningful to a wide audience including the public. They live in almost every type of environment and in almost every niche (place or role) within those environments. The monitoring of species diversity and migration is important for almost all conservation efforts. The analysis of long-term audio data is vital to support those efforts but relies on complex algorithms that need to adapt to changing environmental conditions. Convolutional neural networks (CNNs) are powerful toolkits of machine learning that have proven efficient in the field of image processing and sound recognition. In this paper, a CNN system classifying bird sounds is presented and tested through different configurations and hyperparameters. The MobileNet pre-trained CNN model is finetuned using a dataset acquired from the Xeno-canto bird song sharing portal, which provides a large collection of labeled and categorized recordings. Spectrograms generated from the downloaded data represent the input of the neural network. The attached experiments compare various configurations including the number of classes (bird species) and the color scheme of the spectrograms. Results suggest that choosing a color map in line with the images the network has been pre-trained with provides a measurable advantage. The presented system is viable only for a low number of classes.
{"title":"Bird Sound Classification : Leveraging Deep Learning for Species Identification","authors":"Ardon Kotey, Allan Almeida, Nihal Gupta, Dr. Vinaya Sawant","doi":"10.32628/cseit24103127","DOIUrl":"https://doi.org/10.32628/cseit24103127","url":null,"abstract":"Birds are meaningful to a wide audience including the public. They live in almost every type of environment and in almost every niche (place or role) within those environments. The monitoring of species diversity and migration is important for almost all conservation efforts. The analysis of long-term audio data is vital to support those efforts but relies on complex algorithms that need to adapt to changing environmental conditions. Convolutional neural networks (CNNs) are powerful toolkits of machine learning that have proven efficient in the field of image processing and sound recognition. In this paper, a CNN system classifying bird sounds is presented and tested through different configurations and hyperparameters. The MobileNet pre-trained CNN model is finetuned using a dataset acquired from the Xeno-canto bird song sharing portal, which provides a large collection of labeled and categorized recordings. Spectrograms generated from the downloaded data represent the input of the neural network. The attached experiments compare various configurations including the number of classes (bird species) and the color scheme of the spectrograms. Results suggest that choosing a color map in line with the images the network has been pre-trained with provides a measurable advantage. The presented system is viable only for a low number of classes.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"73 S105","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382719","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}
Decoding stocks is extensively utilized in the financial sector by numerous organizations. It is volatile in nature, so it’s tough to predict the prices of stock. Numerous methodologies exist for tackling this task, including logistic regression, support vector machines (SVM), autoregressive conditional heteroskedasticity (ARCH) models, recurrent neural network (RNN), convolutional neural networks (CNN), backpropagation, Naïve Bayes, among others. Among these, Long Short-Term Memory (LSTM) stands out as particularly adept at handling time series data. The primary aim is to discern prevailing market trends and achieve accurate stock price forecasts. Leveraging LSTM and RNN , we strive for error free stock price predictions, with promising results.
{"title":"Decoding Stocks Patterns Using LSTM","authors":"Dr. Madhur Jain, Shilpi Jain, Ankit Gupta","doi":"10.32628/cseit2410328","DOIUrl":"https://doi.org/10.32628/cseit2410328","url":null,"abstract":"Decoding stocks is extensively utilized in the financial sector by numerous organizations. It is volatile in nature, so it’s tough to predict the prices of stock. Numerous methodologies exist for tackling this task, including logistic regression, support vector machines (SVM), autoregressive conditional heteroskedasticity (ARCH) models, recurrent neural network (RNN), convolutional neural networks (CNN), backpropagation, Naïve Bayes, among others. Among these, Long Short-Term Memory (LSTM) stands out as particularly adept at handling time series data. The primary aim is to discern prevailing market trends and achieve accurate stock price forecasts. Leveraging LSTM and RNN , we strive for error free stock price predictions, with promising results.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"75 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123375","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 field of medical imaging, Diffusion Imaging (DI) has emerged as a powerful technique for investigating the microstructural properties of biological tissues. However, the complexity of DI analysis software often poses a significant barrier to its widespread adoption, as it typically requires proficiency in Python programming and command-line interactions. This technical barrier can limit the accessibility of DI technology to individuals without extensive technical expertise, hindering its potential impact in various medical and research applications To address this challenge, we propose a novel solution that leverages the capabilities of Query Markup Language (QML) to develop a user-friendly interface for Diffusion Imaging. By combining the power of Python technology, which forms the core of DI analysis, with the intuitive interface design capabilities of QML, our project aims to democratize DI analysis and make it accessible to a broader audience, including medical professionals, researchers, and students. Our research focuses on bridging the gap between the technical complexities of DI analysis and user accessibility. The proposed QML-powered interface will feature modern UI elements with fluid animations, ensuring a seamless and engaging user experience. Crucially, it will abstract away the intricacies of Python programming and command-line interactions, allowing users to concentrate on the analysis and interpretation of DI data without the burden of technical hurdles.
在医学成像领域，扩散成像（DI）已成为研究生物组织微观结构特性的强大技术。然而，由于扩散成像分析软件通常需要熟练掌握 Python 编程和命令行交互，其复杂性往往成为其广泛应用的重大障碍。为了应对这一挑战，我们提出了一种新颖的解决方案，利用查询标记语言（QML）的功能为扩散成像开发用户友好型界面。通过将构成弥散成像分析核心的 Python 技术与 QML 的直观界面设计功能相结合，我们的项目旨在实现弥散成像分析的民主化，让更多的受众（包括医疗专业人员、研究人员和学生）能够使用弥散成像分析。我们的研究重点是缩小 DI 分析技术复杂性与用户可访问性之间的差距。拟议的 QML 界面将采用现代 UI 元素和流体动画，确保无缝和引人入胜的用户体验。最重要的是，它将抽象出 Python 编程和命令行交互的复杂性，使用户能够专注于 DI 数据的分析和解释，而不必为技术障碍所累。
{"title":"QML Powered Interface for Diffusion Imaging","authors":"Rupali Jadhav, Ajay Jadhav, Vinay Ghate, Gitesh Mahadik, Praneeth Shetty","doi":"10.32628/cseit2410329","DOIUrl":"https://doi.org/10.32628/cseit2410329","url":null,"abstract":"In the field of medical imaging, Diffusion Imaging (DI) has emerged as a powerful technique for investigating the microstructural properties of biological tissues. However, the complexity of DI analysis software often poses a significant barrier to its widespread adoption, as it typically requires proficiency in Python programming and command-line interactions. This technical barrier can limit the accessibility of DI technology to individuals without extensive technical expertise, hindering its potential impact in various medical and research applications To address this challenge, we propose a novel solution that leverages the capabilities of Query Markup Language (QML) to develop a user-friendly interface for Diffusion Imaging. By combining the power of Python technology, which forms the core of DI analysis, with the intuitive interface design capabilities of QML, our project aims to democratize DI analysis and make it accessible to a broader audience, including medical professionals, researchers, and students. Our research focuses on bridging the gap between the technical complexities of DI analysis and user accessibility. The proposed QML-powered interface will feature modern UI elements with fluid animations, ensuring a seamless and engaging user experience. Crucially, it will abstract away the intricacies of Python programming and command-line interactions, allowing users to concentrate on the analysis and interpretation of DI data without the burden of technical hurdles.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119689","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}
A crucial component of contemporary banking is now online banking. Due to the present password- based authentication paradigm’s inadequacies in terms of efficiency and robust, as well as their suspectibility to automated attacks, several attempts are successful in gaining access to social network accounts. The easiest solution is to add more identifying features, like one-time PIN numbers that are created by the user’s own device(like a smart phone) or sent to them via SMS to the single factor(Password-based) authentication procedure. With the help of this technology, client’s identities may be instantly and conveniently verified. The goal of this project is to create an online banking system that authenticates customer’s using real-time facial recognition technology. The system will be made to offer a safe and convenient user interface that enables users to perform financial operation like bill payment, money transfers, and balance queries. A facial recognition algorithm, such Grassmann learning, which can record and evaluate customer’s facial traits in real time, will be included into the system. To confirm customer’s identification, the algorithm will match the customer’s facial traits with those in the bank’s database. The technology would give users a safe and convenient interface to conduct real-time banking transactions. Notifications about banking amount transactions are sent to the user in this suggested netbanking application.
{"title":"Utilizing Real – Time Face Recognition Based Bio-Metric System for Online Transaction","authors":"A. Dhivya, K. Aashika, S. Pavitha, G. Varshini","doi":"10.32628/cseit24103108","DOIUrl":"https://doi.org/10.32628/cseit24103108","url":null,"abstract":"A crucial component of contemporary banking is now online banking. Due to the present password- based authentication paradigm’s inadequacies in terms of efficiency and robust, as well as their suspectibility to automated attacks, several attempts are successful in gaining access to social network accounts. The easiest solution is to add more identifying features, like one-time PIN numbers that are created by the user’s own device(like a smart phone) or sent to them via SMS to the single factor(Password-based) authentication procedure. With the help of this technology, client’s identities may be instantly and conveniently verified. The goal of this project is to create an online banking system that authenticates customer’s using real-time facial recognition technology. The system will be made to offer a safe and convenient user interface that enables users to perform financial operation like bill payment, money transfers, and balance queries. A facial recognition algorithm, such Grassmann learning, which can record and evaluate customer’s facial traits in real time, will be included into the system. To confirm customer’s identification, the algorithm will match the customer’s facial traits with those in the bank’s database. The technology would give users a safe and convenient interface to conduct real-time banking transactions. Notifications about banking amount transactions are sent to the user in this suggested netbanking application.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120425","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}
For academics, the process of retrieving information from large datasets, known as data mining, has become a captivating area of research. The concept of utilizing data mining techniques to extract information has been in existence for several decades. The dataset was initially designed to be divided into sections and analyzed using classification and clustering methods to explore its intrinsic characteristics. They make their forecasts based on these features. These predictions have been generated in the field of educational data mining for several purposes, such as forecasting student achievement using individual traits and assisting students in identifying suitable professors and courses. These targets have been derived from the analysis of student attrition and retention. Our study is centered around the aims of student attrition and retention. In addition, we have discovered intriguing indicators that contribute to the prediction of students' success, indicating the most competent instructors, and helping them with their choice of courses.
{"title":"An Effective Optimization in Education System using Decision Support Systems","authors":"Abhijeet Joshi, Dr. A. S. Kapse","doi":"10.32628/cseit24103111","DOIUrl":"https://doi.org/10.32628/cseit24103111","url":null,"abstract":"For academics, the process of retrieving information from large datasets, known as data mining, has become a captivating area of research. The concept of utilizing data mining techniques to extract information has been in existence for several decades. The dataset was initially designed to be divided into sections and analyzed using classification and clustering methods to explore its intrinsic characteristics. They make their forecasts based on these features. These predictions have been generated in the field of educational data mining for several purposes, such as forecasting student achievement using individual traits and assisting students in identifying suitable professors and courses. These targets have been derived from the analysis of student attrition and retention. Our study is centered around the aims of student attrition and retention. In addition, we have discovered intriguing indicators that contribute to the prediction of students' success, indicating the most competent instructors, and helping them with their choice of courses.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"64 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123275","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. R. Gopi, A. Srimathi, S. S. S. Sudaroli, R. Gayathri Devi, R. Jayashree
Global food security is largely dependent on the agriculture sector, and technological developments are becoming necessary to meet the growing need for efficient and sustainable farming methods. This paper presents a revolutionary Agriculture Robot that is intended to improve overall crop productivity and resource usage by streamlining the procedures of water spraying and seed sowing. The Agriculture Robot integrates state-of-the- art technologies, including precision navigation systems, real-time sensors, and automation mechanisms. The robot is equipped with a precise seed dispensing system that ensures optimal seed placement, spacing, and depth, promoting uniform crop germination. Additionally, the robot features an efficient water spraying mechanism, utilizing advanced sensors to assess soil moisture levels and crop health, enabling targeted and judicious irrigation practices. The robot employs advanced algorithms and sensors to precisely sow seeds with optimal spacing and depth, ensuring uniform germination and maximizing crop yield. Real-time soil moisture sensors and crop health monitoring enable the robot to make data-driven decisions for targeted water spraying. This minimizes water wastage while maintaining optimal moisture levels for crop growth. Farmers can remotely monitor and control the Agriculture Robot through a user- friendly interface. This feature enhances flexibility and allows farmers to adapt to changing conditions promptly. By integrating cutting-edge technologies, the Agriculture Robot presented in this paper addresses the challenges of labour- intensive and resource-inefficient traditional farming methods. The implementation of this robot has the potential to revolutionize agriculture by increasing productivity, reducing environmental impact, and contributing to sustainable and precision farming practices.
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