Pub Date : 2024-05-18DOI: 10.32628/ijsrset24113100
Kajal, Kanchan Saini, Dr. Nikhat Akhtar, Prof. (Dr.) Devendra Agarwal, Ms. Sana Rabbani, Dr. Yusuf Perwej
An essential part of healthcare is disease prediction, which seeks to identify people who are at risk of getting certain diseases. Because of their superior capacity to sift through massive datasets in search of intricate patterns, machine learning algorithms have recently become useful instruments in the fight against illness prediction. The goal of this project is to make it easier for people to diagnose their own health problems using just their symptoms and precise vital signs. Due to excessive medical expenditures, many people put off taking care of their health, which can result in worsening symptoms or even death. Medical expenses can be overwhelming for people without health insurance. Using machine learning methods like ExtRa Trees, the suggested system provides a general illness forecast based on patients' symptoms. The algorithm provides a possible diagnosis based on the user's age, gender, and symptoms, suggesting that the user may be experiencing a certain illness. The system also suggests healthy eating and exercise routines to help lessen the impact of the condition, depending on how bad it is. Lastly, this article has shown a comparison examination of the suggested system using several algorithms including logistic regression, decision tree, and Naïve Bayes. The efficiency and accuracy of illness prediction are both enhanced by the suggested model.
疾病预测是医疗保健的重要组成部分，其目的是确定哪些人有患某些疾病的风险。由于机器学习算法具有筛选海量数据集以寻找复杂模式的超强能力，因此最近已成为疾病预测领域的有用工具。这个项目的目标是让人们更容易仅凭自己的症状和精确的生命体征来诊断自己的健康问题。由于医疗费用过高，许多人推迟了对健康的关注，这可能导致症状恶化甚至死亡。对于没有医疗保险的人来说，医疗费用可能会让他们不堪重负。建议的系统使用 ExtRa 树等机器学习方法，根据患者的症状提供一般疾病预测。该算法根据用户的年龄、性别和症状提供可能的诊断，提示用户可能正在经历某种疾病。系统还会根据病情的严重程度，建议健康的饮食和运动方式，以帮助减轻病情的影响。最后，本文对建议系统使用的几种算法进行了比较研究，包括逻辑回归、决策树和奈夫贝叶斯。建议的模型提高了疾病预测的效率和准确性。
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It is often difficult for a person to choose which mu- sic to listen to from a vast array of available options. Relatively, this paper focuses on building an efficient music recommendation system based on the user’s mood which determines the emotion of user using Facial Recognition technique. The model is build using the transfer learning approach for which MobileNet model and Cascade classifier are used. Analyzing the user’s face expression might help you better comprehend their current emotional or mental condition. Music and video are one area where there is a lot of potential to present clients with a variety of options depending on their interests and data. More than 60% of users anticipate that the number of songs in their music collection will grow to the point where they will be unable to find the song they need to play at some point in the future. The user would save time by not having to search for or look up tunes. The image of the user is captured using a webcam. Then, depending on the user’s mood, an appropriate song from the user’s playlist or a movie is shown.
{"title":"Predictive Music Based on Mood","authors":"Ganesh B. Regulwar, Nikhila Kathirisetty","doi":"10.32628/ijsrset2411310","DOIUrl":"https://doi.org/10.32628/ijsrset2411310","url":null,"abstract":"It is often difficult for a person to choose which mu- sic to listen to from a vast array of available options. Relatively, this paper focuses on building an efficient music recommendation system based on the user’s mood which determines the emotion of user using Facial Recognition technique. The model is build using the transfer learning approach for which MobileNet model and Cascade classifier are used. Analyzing the user’s face expression might help you better comprehend their current emotional or mental condition. Music and video are one area where there is a lot of potential to present clients with a variety of options depending on their interests and data. More than 60% of users anticipate that the number of songs in their music collection will grow to the point where they will be unable to find the song they need to play at some point in the future. The user would save time by not having to search for or look up tunes. The image of the user is captured using a webcam. Then, depending on the user’s mood, an appropriate song from the user’s playlist or a movie is shown.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"121 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125101","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}
Bones provide structural aid to the frame, and bone fracture recuperation is crucial for regaining functionality and mobility. The boundaries of traditional external fixators in bone fracture remedy, characterized by a loss of actual-time monitoring and capability complications, necessitate a paradigm shift. To clear up this trouble, a clever fixator design imbued with the transformative energy of Aware, Sensing, Smart, and Active (ASSA) technology has been developed. This fixator transcends its passive role, evolving into a wise IoT gateway. It continuously gathers and analyses facts from numerous incorporated sensors, supplying real-time insights into the tricky dance of fracture healing. Its analytical prowess fosters computerized identification of vital activities and milestones within the affected person's recuperation journey, empowering well-timed interventions and knowledgeable scientific selection-making. Furthermore, the fixator vigilantly monitors patient compliance, making sure adherence to prescribed behaviours and nipping non-compliance in the bud. However, its innovation extends beyond mere monitoring. Embedded within its smart framework lies a modern pain manipulation mechanism powered through a thermoelectric generator (TEG).
{"title":"Advanced Real Time Bone Fracture Monitoring with Pain Control Mechanism","authors":"Logasundari.T, Gopika.TP, Swathe. L, Saathvika. R","doi":"10.32628/ijsrset2411280","DOIUrl":"https://doi.org/10.32628/ijsrset2411280","url":null,"abstract":"Bones provide structural aid to the frame, and bone fracture recuperation is crucial for regaining functionality and mobility. The boundaries of traditional external fixators in bone fracture remedy, characterized by a loss of actual-time monitoring and capability complications, necessitate a paradigm shift. To clear up this trouble, a clever fixator design imbued with the transformative energy of Aware, Sensing, Smart, and Active (ASSA) technology has been developed. This fixator transcends its passive role, evolving into a wise IoT gateway. It continuously gathers and analyses facts from numerous incorporated sensors, supplying real-time insights into the tricky dance of fracture healing. Its analytical prowess fosters computerized identification of vital activities and milestones within the affected person's recuperation journey, empowering well-timed interventions and knowledgeable scientific selection-making. Furthermore, the fixator vigilantly monitors patient compliance, making sure adherence to prescribed behaviours and nipping non-compliance in the bud. However, its innovation extends beyond mere monitoring. Embedded within its smart framework lies a modern pain manipulation mechanism powered through a thermoelectric generator (TEG).","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"119 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125198","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}
Leonard K. Kirui, Samuel M. Mbuguah, Richard K. Ronoh
Web 4.0, also known as the next generation, intelligent internet, possesses the potential to become a widely and universally used communication medium for various types of information. However, its decentralized architecture lacks strong semantic support, resulting in an internet that is disorganized. The current system lacks the capacity to facilitate users' effective information discovery, extraction, and integration from multiple sources. Additionally, it fails to give consumers efficient tools for manipulating and turning acquired data into knowledge that is useful. Peer-to-peer (P2P) overlay technologies have recently come to light as a way to improve resource discovery on the internet. In dynamic and large-scale situations, these technologies provide a scalable framework for allocating, sharing, and gaining access to resources. The purpose of this research is to discuss on semantically enabled web architecture that makes use of P2P overlay technology. This architecture aims to facilitate structured and precise access to internet resources and promote knowledge sharing among community members who share similar interests. The paper examines the core elements of the semantic web architecture, which encompass the services and protocols responsible for resource advertising, discovery, and management, methods and material. It then delve into the hybrid peer-to-peer (P2P) overlay structure, specifically focusing on indexing and resource location, and explores the mechanisms necessary to facilitate scalable routing within a distributed environment.
Web 4.0 也被称为下一代智能互联网，具有成为广泛和普遍使用的各类信息通信媒介的潜力。然而，其分散式架构缺乏强有力的语义支持，导致互联网杂乱无章。当前的系统没有能力帮助用户有效地发现、提取和整合多种来源的信息。此外，它也无法为消费者提供有效的工具来操作获取的数据并将其转化为有用的知识。最近，点对点（P2P）叠加技术作为一种改进互联网资源发现的方法受到关注。在动态和大规模的情况下，这些技术为分配、共享和获取资源提供了一个可扩展的框架。本研究的目的是讨论利用 P2P 叠加技术的语义网络架构。该架构旨在促进对互联网资源的结构化和精确访问，并促进兴趣相投的社区成员之间的知识共享。本文探讨了语义网架构的核心要素，包括负责资源广告、发现和管理的服务和协议、方法和材料。然后，它深入探讨了混合点对点（P2P）覆盖结构，特别侧重于索引和资源定位，并探讨了在分布式环境中促进可扩展路由的必要机制。
{"title":"Leveraging P2P Architecture and Semantic Web for Enhanced Resource Discovery","authors":"Leonard K. Kirui, Samuel M. Mbuguah, Richard K. Ronoh","doi":"10.32628/ijsrset2411278","DOIUrl":"https://doi.org/10.32628/ijsrset2411278","url":null,"abstract":"Web 4.0, also known as the next generation, intelligent internet, possesses the potential to become a widely and universally used communication medium for various types of information. However, its decentralized architecture lacks strong semantic support, resulting in an internet that is disorganized. The current system lacks the capacity to facilitate users' effective information discovery, extraction, and integration from multiple sources. Additionally, it fails to give consumers efficient tools for manipulating and turning acquired data into knowledge that is useful. Peer-to-peer (P2P) overlay technologies have recently come to light as a way to improve resource discovery on the internet. In dynamic and large-scale situations, these technologies provide a scalable framework for allocating, sharing, and gaining access to resources. The purpose of this research is to discuss on semantically enabled web architecture that makes use of P2P overlay technology. This architecture aims to facilitate structured and precise access to internet resources and promote knowledge sharing among community members who share similar interests. The paper examines the core elements of the semantic web architecture, which encompass the services and protocols responsible for resource advertising, discovery, and management, methods and material. It then delve into the hybrid peer-to-peer (P2P) overlay structure, specifically focusing on indexing and resource location, and explores the mechanisms necessary to facilitate scalable routing within a distributed environment.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":" 59","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128256","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}
Yasodha K, M. Shabitha Sree, Krishna Sri. S, Kirubha Shakthi. J, Karthick. K, Kanishka. N
Cloud computing presents the concept of utility computing, which allows users to access computing, storage, and networking resources as needed, with a usage-based pricing model. However, consumers have limited control over network resources, and cloud-computing providers confront a number of issues when operating infrastructure as a service (IaaS) environments. This research investigates the networking difficulties and federation challenges inherent in IaaS, as well as unique software-defined networking (SDN) concepts that could provide efficient solutions for future deployments.
{"title":"Networking in Cloud Computing: Embracing Contests and Seizing Prospects","authors":"Yasodha K, M. Shabitha Sree, Krishna Sri. S, Kirubha Shakthi. J, Karthick. K, Kanishka. N","doi":"10.32628/ijsrset241138","DOIUrl":"https://doi.org/10.32628/ijsrset241138","url":null,"abstract":"Cloud computing presents the concept of utility computing, which allows users to access computing, storage, and networking resources as needed, with a usage-based pricing model. However, consumers have limited control over network resources, and cloud-computing providers confront a number of issues when operating infrastructure as a service (IaaS) environments. This research investigates the networking difficulties and federation challenges inherent in IaaS, as well as unique software-defined networking (SDN) concepts that could provide efficient solutions for future deployments.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"51 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973768","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 application of machine learning algorithms for anomaly detection in network traffic data is examined in this study. Using a collection of network flow records that includes attributes such as IP addresses, ports, protocols, and timestamps, the study makes use of correlation heatmaps, box plots, and data visualization to identify trends in numerical characteristics. After preprocessing, which includes timestamp conversion to Unix format, three machine learning models Support Vector Machine (SVM), Gaussian Naive Bayes, and Random Forest are used for anomaly identification. The Random Forest Classifier outperforms SVM and Naive Bayes classifiers with better precision and recall for anomaly diagnosis, achieving an accuracy of 87%. Confusion matrices and classification reports are used to evaluate the models, and they show that the Random Forest Classifier performs better than the other models in identifying abnormalities in network traffic. These results provide significant value to the field of cybersecurity by highlighting the effectiveness of machine learning models specifically, the Random Forest Classifier in boosting anomaly detection capacities for network environment security.
{"title":"Evaluating the Performance and Challenges of Machine Learning Models in Network Anomaly Detection","authors":"Sakshi Bakhare, Dr. Sudhir W. Mohod","doi":"10.32628/ijsrset5241134","DOIUrl":"https://doi.org/10.32628/ijsrset5241134","url":null,"abstract":"The application of machine learning algorithms for anomaly detection in network traffic data is examined in this study. Using a collection of network flow records that includes attributes such as IP addresses, ports, protocols, and timestamps, the study makes use of correlation heatmaps, box plots, and data visualization to identify trends in numerical characteristics. After preprocessing, which includes timestamp conversion to Unix format, three machine learning models Support Vector Machine (SVM), Gaussian Naive Bayes, and Random Forest are used for anomaly identification. The Random Forest Classifier outperforms SVM and Naive Bayes classifiers with better precision and recall for anomaly diagnosis, achieving an accuracy of 87%. Confusion matrices and classification reports are used to evaluate the models, and they show that the Random Forest Classifier performs better than the other models in identifying abnormalities in network traffic. These results provide significant value to the field of cybersecurity by highlighting the effectiveness of machine learning models specifically, the Random Forest Classifier in boosting anomaly detection capacities for network environment security.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"103 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140987232","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}
E. Thenmozhi, Bharath R. K., Gokulselvam R, Anbarasu K
Pneumatic systems, which transfer power through compressed air or gas, are used in pneumatic detection to identify certain events or situations. Pneumatic detection systems can benefit from the integration of deep learning, a kind of artificial intelligence, to increase their capabilities in a number of ways. Pneumatic data may be used to train deep learning algorithms to identify patterns. Through the examination of these departures from typical behaviour, anomalies that point to malfunctions or irregularities in pneumatic systems may be identified. Pneumatic data from the past may be used by deep learning algorithms to understand when parts are likely to break. This makes preventative maintenance possible, which lowers downtime and keeps expensive malfunctions at bay. By evaluating sensor data in real-time, deep learning algorithms are able to identify the underlying causes of pneumatic system malfunctions. This can enhance system performance and dependability by assisting professionals in promptly identifying and resolving problems. Pneumatic system characteristics may be optimised using deep learning approaches to increase effectiveness and performance. They are able to instantly adjust system settings to changing operating circumstances by evaluating data from several sensors. Pneumatic data may be analysed by deep learning models to guarantee product quality throughout production operations. They enable early intervention to uphold product standards by detecting flaws or variations from specifications. Huge X-ray image collections are gathered and classified as either normal or pneumonia-infected. To improve the variability of the training set, preprocessing operations may include augmentation methods, normalisation, and picture shrinking to a uniform size. Because CNNs can automatically extract hierarchical characteristics from pictures, they are commonly employed. Variants of VGG, ResNet, Inception, and AlexNet are examples of common designs. These architectures are frequently adjusted or changed to meet the particular needs of the job. Using supervised learning, the CNN model is trained on the labelled dataset. By modifying its parameters to minimise a loss function, usually cross-entropy loss, the model learns to map input X-ray pictures to their corresponding classes (normal or pneumonia-infected) during training.
气动系统通过压缩空气或气体传递动力，用于气动检测，以识别某些事件或情况。气动检测系统可以从深度学习（一种人工智能）的集成中获益，以多种方式提高其能力。气动数据可用于训练深度学习算法，以识别模式。通过检查这些与典型行为的偏差，可以识别出指向气动系统故障或异常的异常现象。深度学习算法可以利用过去的气动数据来了解部件何时可能损坏。这样就可以进行预防性维护，从而减少停机时间，避免发生昂贵的故障。通过实时评估传感器数据，深度学习算法能够识别气动系统故障的根本原因。这可以帮助专业人员及时发现并解决问题，从而提高系统性能和可靠性。可以使用深度学习方法优化气动系统特性，以提高效率和性能。通过评估来自多个传感器的数据，它们能够根据不断变化的操作环境即时调整系统设置。深度学习模型可对气动数据进行分析，以确保整个生产操作过程中的产品质量。它们可以通过检测缺陷或与规格的差异进行早期干预，以维护产品标准。收集大量 X 射线图像并将其分类为正常或肺炎感染。为了提高训练集的可变性，预处理操作可能包括增强方法、归一化和将图片缩小到统一尺寸。由于 CNN 可以自动从图片中提取分层特征，因此被普遍采用。VGG 的变体、ResNet、Inception 和 AlexNet 都是常见的设计实例。这些架构会经常调整或改变，以满足工作的特殊需要。使用监督学习，CNN 模型在标注数据集上进行训练。通过修改其参数以最小化损失函数（通常是交叉熵损失），该模型在训练过程中学会将输入的 X 光图片映射到相应的类别（正常或肺炎感染）。
{"title":"Convolutional Neural Network Technology and Deep Learning for X-ray Image-Based Pneumonia Identification","authors":"E. Thenmozhi, Bharath R. K., Gokulselvam R, Anbarasu K","doi":"10.32628/ijsrset241132","DOIUrl":"https://doi.org/10.32628/ijsrset241132","url":null,"abstract":"Pneumatic systems, which transfer power through compressed air or gas, are used in pneumatic detection to identify certain events or situations. Pneumatic detection systems can benefit from the integration of deep learning, a kind of artificial intelligence, to increase their capabilities in a number of ways. Pneumatic data may be used to train deep learning algorithms to identify patterns. Through the examination of these departures from typical behaviour, anomalies that point to malfunctions or irregularities in pneumatic systems may be identified. Pneumatic data from the past may be used by deep learning algorithms to understand when parts are likely to break. This makes preventative maintenance possible, which lowers downtime and keeps expensive malfunctions at bay. By evaluating sensor data in real-time, deep learning algorithms are able to identify the underlying causes of pneumatic system malfunctions. This can enhance system performance and dependability by assisting professionals in promptly identifying and resolving problems. Pneumatic system characteristics may be optimised using deep learning approaches to increase effectiveness and performance. They are able to instantly adjust system settings to changing operating circumstances by evaluating data from several sensors. Pneumatic data may be analysed by deep learning models to guarantee product quality throughout production operations. They enable early intervention to uphold product standards by detecting flaws or variations from specifications. Huge X-ray image collections are gathered and classified as either normal or pneumonia-infected. To improve the variability of the training set, preprocessing operations may include augmentation methods, normalisation, and picture shrinking to a uniform size. Because CNNs can automatically extract hierarchical characteristics from pictures, they are commonly employed. Variants of VGG, ResNet, Inception, and AlexNet are examples of common designs. These architectures are frequently adjusted or changed to meet the particular needs of the job. Using supervised learning, the CNN model is trained on the labelled dataset. By modifying its parameters to minimise a loss function, usually cross-entropy loss, the model learns to map input X-ray pictures to their corresponding classes (normal or pneumonia-infected) during training.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"322 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012496","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}
P. Karthik, P. Jayanth, K. Tharun Nayak, K. Anil Kumar
The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and elements associated with criminal behaviour and underscores the existing deficiencies and prospective avenues for advancing crime prediction precision. This thorough examination of the current research on crime forecasting through machine learning and deep learning serves as an essential resource for scholars in the domain. A more profound comprehension of these predictive methods will empower law enforcement to devise more effective prevention and response strategies against crime.
{"title":"Crime Prediction Using Machine Learning and Deep Learning","authors":"P. Karthik, P. Jayanth, K. Tharun Nayak, K. Anil Kumar","doi":"10.32628/ijsrset241134","DOIUrl":"https://doi.org/10.32628/ijsrset241134","url":null,"abstract":"The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and elements associated with criminal behaviour and underscores the existing deficiencies and prospective avenues for advancing crime prediction precision. This thorough examination of the current research on crime forecasting through machine learning and deep learning serves as an essential resource for scholars in the domain. A more profound comprehension of these predictive methods will empower law enforcement to devise more effective prevention and response strategies against crime.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"22 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141014282","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}
An important area of study that models the relationships between items is called graph theory. The shortest path between two objects is one of the most important concepts in graph theory. Many algorithms, such as Dijkstra's Algorithm, Prim’s Algorithm, Floyd Warshall Algorithm have been created for this purpose.
{"title":"Implementation of Shortest Path Algorithms","authors":"Amruta Amruta, Sneha Pawar, Bharati Bhamare","doi":"10.32628/ijsrset2411261","DOIUrl":"https://doi.org/10.32628/ijsrset2411261","url":null,"abstract":"An important area of study that models the relationships between items is called graph theory. The shortest path between two objects is one of the most important concepts in graph theory. Many algorithms, such as Dijkstra's Algorithm, Prim’s Algorithm, Floyd Warshall Algorithm have been created for this purpose.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"52 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140675630","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. Dr. V. S. Ubale, Kanawade Ravindra, Dongare Aniket, Gore Rohan
The greenhouse based modern agriculture industries are the recent requirement in every part of agriculture in India. In this technology, the humidity and temperature of plants are precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes may vary from place to place in large farmhouse, which makes very difficult to maintain the uniformity at all the places in the farmhouse manually. It is observed that for the first time an android phone-control the Irrigation system, which could give the facilities of maintaining uniform environmental conditions are proposed. The Android Software Development Kit provides the tools and Application Programmable Interface necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of human life serving multiple needs of humans. This application makes use of the GPRS [General Packet Radio Service] feature of mobile phone as a solution for irrigation control system. In India agricultural field play a crucial role in economic development. That is the way to concentrate on that point.
{"title":"3 Phase Motor Water Management System using GSM","authors":"Prof. Dr. V. S. Ubale, Kanawade Ravindra, Dongare Aniket, Gore Rohan","doi":"10.32628/ijsrset2411254","DOIUrl":"https://doi.org/10.32628/ijsrset2411254","url":null,"abstract":"The greenhouse based modern agriculture industries are the recent requirement in every part of agriculture in India. In this technology, the humidity and temperature of plants are precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes may vary from place to place in large farmhouse, which makes very difficult to maintain the uniformity at all the places in the farmhouse manually. It is observed that for the first time an android phone-control the Irrigation system, which could give the facilities of maintaining uniform environmental conditions are proposed. The Android Software Development Kit provides the tools and Application Programmable Interface necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of human life serving multiple needs of humans. This application makes use of the GPRS [General Packet Radio Service] feature of mobile phone as a solution for irrigation control system. In India agricultural field play a crucial role in economic development. That is the way to concentrate on that point.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140675375","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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