In this day and age web-based entertainment is a major region for information examination and exploration work. For Feeling Examination, I select Tweeter handle. I use Tweepy for getting to tweeter information. I perform opinion examination on Indian Political information. I got 117545 tweets of 2019 Indian Political race. I use SVM (Backing Vector Machine) Classifier for feeling Examination. Feeling assessment oversees recognizing and portraying evaluations or sentiments conveyed in source message. Electronic diversion is creating an enormous proportion of feeling rich data as tweets, sees, blog sections, etc. Feeling examination of this client made data is especially useful in knowing the appraisal of the gathering. Twitter feeling assessment is problematic stood out from general assessment examination on account of the presence of work related conversation words and erroneous spellings. The most outrageous limitation of characters that are allowed in Twitter is 140. Data base philosophy and AI approach are the two frameworks used for separating suppositions from the text. In this paper, we endeavor to analyze the twitter posts about electronic things like mobiles, workstations, etc using AI approach.
{"title":"The Emotion Analysis of Indian Political Tweets using Machine Learning","authors":"Parth Sharma, Mansi Vegad","doi":"10.32628/cseit2410255","DOIUrl":"https://doi.org/10.32628/cseit2410255","url":null,"abstract":"In this day and age web-based entertainment is a major region for information examination and exploration work. For Feeling Examination, I select Tweeter handle. I use Tweepy for getting to tweeter information. I perform opinion examination on Indian Political information. I got 117545 tweets of 2019 Indian Political race. I use SVM (Backing Vector Machine) Classifier for feeling Examination. Feeling assessment oversees recognizing and portraying evaluations or sentiments conveyed in source message. Electronic diversion is creating an enormous proportion of feeling rich data as tweets, sees, blog sections, etc. Feeling examination of this client made data is especially useful in knowing the appraisal of the gathering. Twitter feeling assessment is problematic stood out from general assessment examination on account of the presence of work related conversation words and erroneous spellings. The most outrageous limitation of characters that are allowed in Twitter is 140. Data base philosophy and AI approach are the two frameworks used for separating suppositions from the text. In this paper, we endeavor to analyze the twitter posts about electronic things like mobiles, workstations, etc using AI approach.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"83 S3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140709461","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}
These days, object-oriented programming is regarded as an essential programming concept.The moment Simula brought it into life. The use of object-oriented programming (OOP) has expanded in the software real world due to the future growth of the software business and the advancement of software engineering. The following review examines different oop concepts that are essential to object-orientation, in great detail. Many widely used object-oriented programming languages implement various parts of inheritance and polymorphism. We come to the conclusion that much more work needs to be done to find a middle ground so that these can accomplish OOPs features.
{"title":"Survey on Concept of Object-Oriented Programming","authors":"Mansi Dhirajsinh Parmar, Sarthavi Parmar","doi":"10.32628/cseit243647","DOIUrl":"https://doi.org/10.32628/cseit243647","url":null,"abstract":"These days, object-oriented programming is regarded as an essential programming concept.The moment Simula brought it into life. The use of object-oriented programming (OOP) has expanded in the software real world due to the future growth of the software business and the advancement of software engineering.\u0000The following review examines different oop concepts that are essential to object-orientation, in great detail. Many widely used object-oriented programming languages implement various parts of inheritance and polymorphism. We come to the conclusion that much more work needs to be done to find a middle ground so that these can accomplish OOPs features.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"43 9-10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140735146","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}
Abhishek M Nair, Sivaiswarya CK, Sidharth S, Visakh KK, Jibin Joy
Developing an application can be a task if any kind of conflict arises during deploying the code or while running them and it can be due to the storage or the code being not supported by the other party’s system. Thus to provide a solution for this matter, we are introducing the project concept of Dockerized application deployment through a web interface. This proposed project combines the efficiency of Docker containers with a web interface to create a platform for running and managing applications easily. When a programmer or a developer or anyone in the field of programming has conflict in uploading, running or deploying their application code from another programmer’s system to their own due to the inefficiency or lack of facilities in their system, they can use this web interface as a solution. Especially during the time of any rush, they can opt for this web interface as it does not require the installation of a local Docker software and any extra dependency management, as installation of Dockers are a bit time lagging. One of the main factors of this project is that this web interface can be run in any kind of computer system without any extra facilities being added to it. Whether the system is less efficient or high efficient regardless of the type of the system, this web interface is easy to access for the users. Users can upload their application code, build Docker images, and run them directly from the web interface. With the advantage of Docker’s utility methodologies for shipping, testing and deploying code, you can reduce the delay between writing codes and running applications .It has additional features like users can define environment variables for their applications, configure network settings for container communication ,mount persistent volumes to store application data with help of virtual cloud, implement user roles and permissions for secure access control .The front end of the web page is created using NEXT Programming Language meanwhile the backend is applied using NEXT, Docker and Python Flask API. About NEXT Programming Language that in this language, when the front-end is applied the backend function gets directly deployed making us use less effort in creating the webpage. It's a newly created advanced programming language. Overall, this Dockerized application deployment web-interface offers a user-friendly and efficient solution for developers, system administrators, and DevOps teams, streamlining the application development and deployment process.
如果在部署代码或运行代码的过程中出现任何形式的冲突，可能是由于对方的系统不支持存储或代码，那么开发应用程序就是一项艰巨的任务。因此，为了解决这个问题，我们提出了通过网络界面部署 Docker 化应用程序的项目概念。该项目将 Docker 容器的高效性与网络接口相结合，创建了一个可轻松运行和管理应用程序的平台。当程序员、开发人员或编程领域的任何人在从其他程序员的系统上传、运行或部署应用程序代码到自己的系统时，由于其系统效率低下或缺乏设施而产生冲突时，他们可以使用这个网络接口作为解决方案。特别是在时间紧迫的情况下，他们可以选择这个网络界面，因为它不需要安装本地 Docker 软件和任何额外的依赖关系管理，因为 Docker 的安装有点滞后。这个项目的一个主要因素是，这个网页界面可以在任何类型的计算机系统中运行，无需添加任何额外的设施。无论系统的效率是低还是高，也无论系统的类型是什么，用户都可以轻松访问这个网络界面。用户可以上传自己的应用程序代码，构建 Docker 映像，并直接从网络界面运行它们。它还具有其他功能，如用户可以为自己的应用程序定义环境变量，为容器通信配置网络设置，借助虚拟云挂载持久卷以存储应用程序数据，实施用户角色和权限以进行安全访问控制。关于 NEXT 编程语言，当应用前端时，后端功能会被直接部署，使我们在创建网页时更省力。这是一种新创的高级编程语言。总之，这个 Docker 化应用程序部署 Web 界面为开发人员、系统管理员和 DevOps 团队提供了一个用户友好的高效解决方案，简化了应用程序的开发和部署流程。
{"title":"Dockerized Application with Web Interface","authors":"Abhishek M Nair, Sivaiswarya CK, Sidharth S, Visakh KK, Jibin Joy","doi":"10.32628/cseit243646","DOIUrl":"https://doi.org/10.32628/cseit243646","url":null,"abstract":"Developing an application can be a task if any kind of conflict arises during deploying the code or while running them and it can be due to the storage or the code being not supported by the other party’s system. Thus to provide a solution for this matter, we are introducing the project concept of Dockerized application deployment through a web interface. This proposed project combines the efficiency of Docker containers with a web interface to create a platform for running and managing applications easily. When a programmer or a developer or anyone in the field of programming has conflict in uploading, running or deploying their application code from another programmer’s system to their own due to the inefficiency or lack of facilities in their system, they can use this web interface as a solution. Especially during the time of any rush, they can opt for this web interface as it does not require the installation of a local Docker software and any extra dependency management, as installation of Dockers are a bit time lagging. One of the main factors of this project is that this web interface can be run in any kind of computer system without any extra facilities being added to it. Whether the system is less efficient or high efficient regardless of the type of the system, this web interface is easy to access for the users. Users can upload their application code, build Docker images, and run them directly from the web interface. With the advantage of Docker’s utility methodologies for shipping, testing and deploying code, you can reduce the delay between writing codes and running applications .It has additional features like users can define environment variables for their applications, configure network settings for container communication ,mount persistent volumes to store application data with help of virtual cloud, implement user roles and permissions for secure access control .The front end of the web page is created using NEXT Programming Language meanwhile the backend is applied using NEXT, Docker and Python Flask API. About NEXT Programming Language that in this language, when the front-end is applied the backend function gets directly deployed making us use less effort in creating the webpage. It's a newly created advanced programming language. Overall, this Dockerized application deployment web-interface offers a user-friendly and efficient solution for developers, system administrators, and DevOps teams, streamlining the application development and deployment process.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"20 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734573","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}
Jumoke Eluwa, Patrick Omorovan, Dipo Adewumi, Oluwafunmilayo Ogbeide
Cyber threat intelligence (CTI) is a rapidly growing field that plays an essential role in ensuring the security of online systems. CTI refers to the intelligence that is gathered, analyzed, and disseminated to help organizations understand and respond to cyber threats. This information can be used to identify vulnerabilities, detect potential attacks, and develop strategies to mitigate risks. The field of CTI is constantly evolving, as cyber threats become more sophisticated and complex. Legacy security measures like firewalls and anti-virus software are no longer enough to protect organizations from the many threats they face. CTI provides a proactive approach to cybersecurity, by enabling organizations to anticipate and prepare for threats before they occur. CTI relies on the collection and analysis of data from multiple sources, such as open-source intelligence (OSINT), dark web forums, social media, and other threat intelligence streams. The data is analyzed using a wide range of tools and techniques, including machine learning and artificial intelligence, to identify patterns and trends that may indicate a potential threat. One of the key benefits of CTI is its ability to help organizations understand the tactics, techniques, and procedures of attackers. By analyzing the behaviors, strategies, tactics, and actions of threat actors, organizations can develop a more comprehensive understanding of the threats they face and can better prepare for potential attacks.
{"title":"The Evolving Threat Landscape: How Cyber Threat Intelligence Empowers Proactive Defenses against WannaCry Ransomware","authors":"Jumoke Eluwa, Patrick Omorovan, Dipo Adewumi, Oluwafunmilayo Ogbeide","doi":"10.32628/cseit243648","DOIUrl":"https://doi.org/10.32628/cseit243648","url":null,"abstract":"Cyber threat intelligence (CTI) is a rapidly growing field that plays an essential role in ensuring the security of online systems. CTI refers to the intelligence that is gathered, analyzed, and disseminated to help organizations understand and respond to cyber threats. This information can be used to identify vulnerabilities, detect potential attacks, and develop strategies to mitigate risks. The field of CTI is constantly evolving, as cyber threats become more sophisticated and complex. Legacy security measures like firewalls and anti-virus software are no longer enough to protect organizations from the many threats they face. CTI provides a proactive approach to cybersecurity, by enabling organizations to anticipate and prepare for threats before they occur. CTI relies on the collection and analysis of data from multiple sources, such as open-source intelligence (OSINT), dark web forums, social media, and other threat intelligence streams. The data is analyzed using a wide range of tools and techniques, including machine learning and artificial intelligence, to identify patterns and trends that may indicate a potential threat. One of the key benefits of CTI is its ability to help organizations understand the tactics, techniques, and procedures of attackers. By analyzing the behaviors, strategies, tactics, and actions of threat actors, organizations can develop a more comprehensive understanding of the threats they face and can better prepare for potential attacks.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"79 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747393","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 this research into liver tumor categorization within MRI images, diverse machine learning methodologies were scrutinized for their efficacy. The study delved into the integration of shape and texture features, aiming to bolster classification accuracy. Among the algorithms explored, the Extra Trees model emerged as the most promising contender, exhibiting superior performance compared to its counterparts. Leveraging the distinctive capabilities of the Extra Trees model, the study underscored its effectiveness in accurately categorizing liver tumors. This highlights its potential to enhance diagnostic precision in clinical contexts. Through rigorous experimentation and analysis, the research elucidated the significance of incorporating shape and texture features into machine learning frameworks for improved tumor classification. The findings not only contribute to advancing the field of medical imaging but also underscore the importance of leveraging innovative methodologies to address healthcare challenges. Overall, the study sheds light on the promising prospects of employing advanced machine learning techniques in medical imaging for more accurate and efficient diagnosis of liver tumors.
在这项对核磁共振成像图像中的肝脏肿瘤进行分类的研究中，对各种机器学习方法的有效性进行了仔细检查。研究深入探讨了形状和纹理特征的整合，旨在提高分类的准确性。在所探索的算法中，Extra Trees 模型是最有前途的竞争者，与同类算法相比表现出更优越的性能。利用 Extra Trees 模型的独特功能，研究强调了它在准确分类肝脏肿瘤方面的有效性。这凸显了它在提高临床诊断精确度方面的潜力。通过严格的实验和分析，该研究阐明了将形状和纹理特征纳入机器学习框架对改进肿瘤分类的重要意义。研究结果不仅有助于推动医学成像领域的发展，还强调了利用创新方法应对医疗保健挑战的重要性。总之，这项研究揭示了在医学成像中采用先进的机器学习技术以更准确、更高效地诊断肝脏肿瘤的广阔前景。
{"title":"Advanced Machine Learning Techniques for Liver Tumor Classification in MRI Imaging","authors":"Jalpaben Kandoriya, Dr.Sheshang Degadwala","doi":"10.32628/cseit2410233","DOIUrl":"https://doi.org/10.32628/cseit2410233","url":null,"abstract":"In this research into liver tumor categorization within MRI images, diverse machine learning methodologies were scrutinized for their efficacy. The study delved into the integration of shape and texture features, aiming to bolster classification accuracy. Among the algorithms explored, the Extra Trees model emerged as the most promising contender, exhibiting superior performance compared to its counterparts. Leveraging the distinctive capabilities of the Extra Trees model, the study underscored its effectiveness in accurately categorizing liver tumors. This highlights its potential to enhance diagnostic precision in clinical contexts. Through rigorous experimentation and analysis, the research elucidated the significance of incorporating shape and texture features into machine learning frameworks for improved tumor classification. The findings not only contribute to advancing the field of medical imaging but also underscore the importance of leveraging innovative methodologies to address healthcare challenges. Overall, the study sheds light on the promising prospects of employing advanced machine learning techniques in medical imaging for more accurate and efficient diagnosis of liver tumors.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"531 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749758","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}
Matrix multiplication is a fundamental operation in computational applications across various domains. This paper introduces a novel reconfigurable co-processor that enhances the efficiency of matrix multiplication by concurrently executing addition and multiplication operations upon matrix elements of different sizes. The proposed design aims to reduce computation time and improve efficiency for matrix multiplication equations. Experimental evaluations were conducted on matrices of different sizes to demonstrate the effectiveness of the processor. The results reveal substantial improvements in both time and efficiency when compared to traditional approaches. The reconfigurable transformation processor harnesses parallel processing capabilities, enabling the simultaneous execution of addition and multiplication operations by partitioning input matrices into smaller submatrices and performing parallel computations, thus the processor achieves faster results. Additionally, the design incorporates configurable arithmetic units that dynamically adapt to matrix characteristics, further optimizing performance. The experimental evaluations provide evidence of reduction in computation time and improvement in efficiency. present significant benefits over traditional sequential methods. This makes this co-processor ideally fit for domains that require intensive linear algebra computations such as computer vision, machine learning, and signal processing.
{"title":"A High-Speed Floating Point Matrix Multiplier Implemented in Reconfigurable Architecture","authors":"Atri Sanyal, Ashika Jain, Anwesha Dey, Prakash Kumar Gupta","doi":"10.32628/cseit2390661","DOIUrl":"https://doi.org/10.32628/cseit2390661","url":null,"abstract":"Matrix multiplication is a fundamental operation in computational applications across various domains. This paper introduces a novel reconfigurable co-processor that enhances the efficiency of matrix multiplication by concurrently executing addition and multiplication operations upon matrix elements of different sizes. The proposed design aims to reduce computation time and improve efficiency for matrix multiplication equations. Experimental evaluations were conducted on matrices of different sizes to demonstrate the effectiveness of the processor. The results reveal substantial improvements in both time and efficiency when compared to traditional approaches. The reconfigurable transformation processor harnesses parallel processing capabilities, enabling the simultaneous execution of addition and multiplication operations by partitioning input matrices into smaller submatrices and performing parallel computations, thus the processor achieves faster results. Additionally, the design incorporates configurable arithmetic units that dynamically adapt to matrix characteristics, further optimizing performance. The experimental evaluations provide evidence of reduction in computation time and improvement in efficiency. present significant benefits over traditional sequential methods. This makes this co-processor ideally fit for domains that require intensive linear algebra computations such as computer vision, machine learning, and signal processing.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225905","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 every nation, farmers play the most essential role and that is to feed the population. In the urban areas there is lack of open green space for farming and even if the land is available it is infertile for plants to grow on them. Problems faced in urban areas farms are due to the toxic elements let in the soil. The sources of toxic metals and effluents in urban soils are mainly from emissions from industries, automobiles, industrial as well as domestic sewage. In urban areas, people are busy in their work which leads them to buy pesticide and chemically treated food which in injurious to health and they are unable to grow organic vegetable at home due to deficit of space, time and un-fertile soil. Hydroponics is the method of cultivating plants without soil. Water with oxygen and required minerals acts as the cultivation medthod. Smart Hydroponic Farming using the NFT Method helps the farmer to stay connected to their farm anytime and anywhere. This hydroponic system requires special attention to several parameters such as the water temperature, water level, acidity (pH), and the concentration of the nutrient (EC/PPM). We first monitor and collect information from NFT Hydroponic farmer and then systematically evaluate and analyze them. Unfortunately, it is still controlled by using the conventional way (human), for example in controlling the concentrations of nutrient has to be done at least once a day, so much time is wasted. In addressing these issues, we need a system that can be applied and used easily. We built a hydroponic monitoring and automation system that can monitored using sensors connected to the Arduino Uno microcontrollerm, Wi-Fi module ESP8266 and Raspberry Pi 2 Model B microcomputers as the webserver with the concept Internet of Things, in which each block hydroponic farming can communicate with the webserver (broker). Web used as the interface of the system that allows user to monitor and control the NFT hydroponic farming. The NFT hydroponic web interface management systems using a responsive web framework, such as Bootstrap for the front-end, JQuery and JavaScript libraries. The result shows that this system helps farmers to increase the effectivity and efficiency on monitoring and controlling NFT Hydroponic Farm.
对于每个国家来说，农民都扮演着最重要的角色，那就是养活人口。在城市地区，缺少开阔的绿地用于耕种，即使有土地，也很贫瘠，不适合植物生长。城市地区农场面临的问题是土壤中的有毒元素造成的。城市土壤中的有毒金属和污水主要来自工业、汽车、工业和生活污水的排放。在城市地区，人们忙于工作，导致他们购买杀虫剂和经过化学处理的食物，这对健康有害，而且由于缺乏空间、时间和贫瘠的土壤，他们无法在家里种植有机蔬菜。水耕法是一种无土栽培植物的方法。含有氧气和所需矿物质的水是栽培媒介。使用 NFT 方法的智能水耕法可以帮助农民随时随地与他们的农场保持联系。这种水培系统需要特别注意几个参数，如水温、水位、酸度（pH 值）和营养液浓度（EC/PPM）。我们首先监测并收集来自 NFT 水培农户的信息，然后对其进行系统评估和分析。遗憾的是，我们仍在使用传统方法（人工）进行控制，例如，在控制营养液浓度时，每天至少要做一次，因此浪费了大量时间。为了解决这些问题，我们需要一个易于应用和使用的系统。我们利用连接到 Arduino Uno 微控制器、Wi-Fi 模块 ESP8266 和 Raspberry Pi 2 Model B 微电脑的传感器建立了一个水培监控和自动化系统，并将其作为具有物联网概念的网络服务器。网络作为系统的界面，允许用户监测和控制 NFT 水培农业。NFT 水培网络界面管理系统使用了响应式网络框架，如用于前端的 Bootstrap、JQuery 和 JavaScript 库。结果表明，该系统能帮助农民提高监测和控制 NFT 水培农场的效率和效果。
{"title":"IoT Empowered Harvest: Advancing NFT Hydroponics with Smart Agricultural Automation","authors":"Trupti Ghate, Kalpana Malpe","doi":"10.32628/cseit241025","DOIUrl":"https://doi.org/10.32628/cseit241025","url":null,"abstract":"For every nation, farmers play the most essential role and that is to feed the population. In the urban areas there is lack of open green space for farming and even if the land is available it is infertile for plants to grow on them. Problems faced in urban areas farms are due to the toxic elements let in the soil. The sources of toxic metals and effluents in urban soils are mainly from emissions from industries, automobiles, industrial as well as domestic sewage. In urban areas, people are busy in their work which leads them to buy pesticide and chemically treated food which in injurious to health and they are unable to grow organic vegetable at home due to deficit of space, time and un-fertile soil. \u0000Hydroponics is the method of cultivating plants without soil. Water with oxygen and required minerals acts as the cultivation medthod. Smart Hydroponic Farming using the NFT Method helps the farmer to stay connected to their farm anytime and anywhere. This hydroponic system requires special attention to several parameters such as the water temperature, water level, acidity (pH), and the concentration of the nutrient (EC/PPM). We first monitor and collect information from NFT Hydroponic farmer and then systematically evaluate and analyze them. Unfortunately, it is still controlled by using the conventional way (human), for example in controlling the concentrations of nutrient has to be done at least once a day, so much time is wasted. In addressing these issues, we need a system that can be applied and used easily. \u0000We built a hydroponic monitoring and automation system that can monitored using sensors connected to the Arduino Uno microcontrollerm, Wi-Fi module ESP8266 and Raspberry Pi 2 Model B microcomputers as the webserver with the concept Internet of Things, in which each block hydroponic farming can communicate with the webserver (broker). Web used as the interface of the system that allows user to monitor and control the NFT hydroponic farming. The NFT hydroponic web interface management systems using a responsive web framework, such as Bootstrap for the front-end, JQuery and JavaScript libraries. The result shows that this system helps farmers to increase the effectivity and efficiency on monitoring and controlling NFT Hydroponic Farm.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"190 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140235710","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}
Ensuring safety, in cities is a focus in the development of urban areas requiring new and creative methods for categorizing and managing accidents. Traditional approaches often face challenges in evaluating accident seriousness within changing city environments. This research utilizes Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to create a system that categorizes accidents into three severity levels; minor, moderate and severe. By leveraging learning capabilities, our method boosts the precision and efficiency of safety protocols in cities. The outcomes exhibit promising results in categorizing accident severity offering a tool for enhancing urban safety infrastructure. Through empowering cities to handle accidents, our model establishes a foundation for safety initiatives. In essence, this study contributes to enhancing safety standards in cities promoting resilience and sustainability, within settings.
{"title":"Road Accident Severity Detection In Smart Cities","authors":"Deeksha K, Kavya S, Nikita J, E. R. C, E. R. C","doi":"10.32628/cseit241024","DOIUrl":"https://doi.org/10.32628/cseit241024","url":null,"abstract":"Ensuring safety, in cities is a focus in the development of urban areas requiring new and creative methods for categorizing and managing accidents. Traditional approaches often face challenges in evaluating accident seriousness within changing city environments. This research utilizes Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to create a system that categorizes accidents into three severity levels; minor, moderate and severe. By leveraging learning capabilities, our method boosts the precision and efficiency of safety protocols in cities. The outcomes exhibit promising results in categorizing accident severity offering a tool for enhancing urban safety infrastructure. Through empowering cities to handle accidents, our model establishes a foundation for safety initiatives. In essence, this study contributes to enhancing safety standards in cities promoting resilience and sustainability, within settings.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"122 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140235997","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 implementation is for a computer vision application that detects individuals and verifies their compliance with safety gear regulations, such as safety jackets and hard-hats. The system counts the number of individuals violating safety standards and keeps track of the total number of individuals detected. The system uses advanced image processing techniques, including object detection and classification, to accurately identify the presence or absence of safety gear. The user interface provides real-time analysis of the data, with the option to alert the user of any violations. This implementation is a valuable tool for organizations looking to ensure the safety of their employees and customers, providing a comprehensive solution for monitoring compliance with safety regulations. It can also be used to analyze trends and identify areas for improvement, making it an essential tool for safety professionals and facilities managers.
{"title":"Safety Measure Detection Using Deep Learning","authors":"Tejas Bagthaliya, Vaidehi Shah, Shubham Shelke, Devang Shukla, Yatin Shukla","doi":"10.32628/cseit2490216","DOIUrl":"https://doi.org/10.32628/cseit2490216","url":null,"abstract":"This implementation is for a computer vision application that detects individuals and verifies their compliance with safety gear regulations, such as safety jackets and hard-hats. The system counts the number of individuals violating safety standards and keeps track of the total number of individuals detected. The system uses advanced image processing techniques, including object detection and classification, to accurately identify the presence or absence of safety gear. The user interface provides real-time analysis of the data, with the option to alert the user of any violations. This implementation is a valuable tool for organizations looking to ensure the safety of their employees and customers, providing a comprehensive solution for monitoring compliance with safety regulations. It can also be used to analyze trends and identify areas for improvement, making it an essential tool for safety professionals and facilities managers.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"15 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140239220","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}
YaatriAssist is a revolutionary travel application that reimagines global adventures with unmatched convenience and sophistication. This innovative app integrates essential functionalities seamlessly, offering real-time GPS navigation for confident exploration and integrated weather updates for preparedness in diverse climates. It features a comprehensive travel log for capturing and cherishing memories, along with an optimized scheduler to maximize trip enjoyment. The curated news hub keeps travelers informed about local events and global developments, while Travel mate fosters connections between fellow explorers, enhancing journey richness. Language barriers are effortlessly overcome with translation and OCR functionalities. Customizable settings ensure personalized experiences, evolving with individual travel needs. Facilitating bookings for flights, accommodations, and activities directly through the app streamlines trip management, offering unparalleled convenience. In summary, YaatriAssist stands as the epitome of travel convenience, catering to both seasoned globetrotters and business travelers, empowering users to navigate, explore, and engage with the world confidently and effortlessly.
{"title":"YAATRIASSIST : Passenger Facilitation Using AI and ML","authors":"Ankit Dilip Nihalchandani, Shivang Deepak Kulshrestha, Raj Kirit Lakhani, Deepak Pandagre, Rachit Adhvaryu","doi":"10.32628/cseit2490219","DOIUrl":"https://doi.org/10.32628/cseit2490219","url":null,"abstract":"YaatriAssist is a revolutionary travel application that reimagines global adventures with unmatched convenience and sophistication. This innovative app integrates essential functionalities seamlessly, offering real-time GPS navigation for confident exploration and integrated weather updates for preparedness in diverse climates. It features a comprehensive travel log for capturing and cherishing memories, along with an optimized scheduler to maximize trip enjoyment. The curated news hub keeps travelers informed about local events and global developments, while Travel mate fosters connections between fellow explorers, enhancing journey richness. Language barriers are effortlessly overcome with translation and OCR functionalities. Customizable settings ensure personalized experiences, evolving with individual travel needs. Facilitating bookings for flights, accommodations, and activities directly through the app streamlines trip management, offering unparalleled convenience. In summary, YaatriAssist stands as the epitome of travel convenience, catering to both seasoned globetrotters and business travelers, empowering users to navigate, explore, and engage with the world confidently and effortlessly.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"15 59","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237641","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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