International Journal of Advanced Research in Science, Communication and Technology最新文献

{"title":"Management of Dairy Farm using PHP and MySQL","authors":"Harris Charles F, H. Jayamangala","doi":"10.48175/ijarsct-18405","DOIUrl":"https://doi.org/10.48175/ijarsct-18405","url":null,"abstract":"A survey was prepared by the Dairy farming Working Group together with invited milk recording organizations. This paper is one part of this project and focuses on management and organizational questions. The management of recording organizations in the current climate of growing competition is more challenging than ever. The main part of this approach is how to develop a clear relationship with customers and how to provide value to farmers in regard to collected data and samples. New tools of analysis are already very common in some countries, while other participants are now focusing on maximizing increased efficiency in data capturing and processing. In those countries whose workflow is technician-based, training and certification are major components in improving human resources. The reporting of results back to farmers is also a very challenging area. The use of paper and pdf-reports is very common, but new online technologies and smartphone usage now provide new opportunities for farmers to manage information. Real value is created by additional analyses from identified milk samples. The goal was to develop a program that is flexible enough to be useful in a wide variety of management systems by providing reports suited to the individual producer. The existing program already collects farmers, employees, deliveries and their databases respectively other maintenance procedures and performance records","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"4 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099662","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}

{"title":"Enterprise Data Breach: Causes, Challenges, Prevention, and Future Directions","authors":"Naveenraj D, S Anu Priya","doi":"10.48175/ijarsct-18403","DOIUrl":"https://doi.org/10.48175/ijarsct-18403","url":null,"abstract":"Cloud data is such a valuable and necessary resource, security is a major worry. Despite the widespread misperception that hackers are the source of security lapses, insiders are primarily responsible for data theft. In practically dispersed settings, critical data is routinely moved from the distributor to trustworthy parties. The stability and security of the services must be guaranteed in light of the increasing volume of user requests. When a client discloses important information, the client should be held accountable as soon as possible. Therefore, it's necessary to keep an eye on the data as it moves from the distributor to the agents. In the context of cloud computing, the project identifies data leakage detection, which examines data tampering and concludes that the information leak was caused by a particular employee in the organization","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099971","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}

{"title":"Supply Chain Optimization in the Package Industry through Machine Learning Analysis","authors":"Ashok. R, Priya. R","doi":"10.48175/ijarsct-18418","DOIUrl":"https://doi.org/10.48175/ijarsct-18418","url":null,"abstract":"The package industry relies heavily on efficient supply chain management to meet customer demands and maintain profitability. However, managing complex supply chains involving multiple suppliers, transportation networks, and distribution channels poses significant challenges. This research proposes a machine learning-based approach to optimize supply chain operations in the package industry. By analysing historical data on supply chain activities, including procurement, inventory management, and distribution, our system aims to identify patterns and trends to improve decision-making processes. Machine learning algorithms such as support vector machine, naive Bayes, and logistic regression are utilized to forecast demand, optimize inventory levels, and streamline logistics operations. Experimental results demonstrate the effectiveness of the proposed approach in enhancing supply chain efficiency and reducing operational costs in the package industry.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"12 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099500","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}

{"title":"A Proxy Re-Encryption Approach to Secure Data Sharing in the Internet of Things Based on Blockchain","authors":"Mrs. R. Jhansi, Rani Nidiginti, Siva Prasad, Shaik Mubarak","doi":"10.48175/ijarsct-18404","DOIUrl":"https://doi.org/10.48175/ijarsct-18404","url":null,"abstract":"The Internet of Things has seen data sharing as one of its most useful applications in cloud computing. As eye-catching as this technology has been, data security remains one of the obstacles it faces since the wrongful use of data leads to several damages. In this article, we propose a proxy re-encryption approach to secure data sharing in cloud environments. Data owners can outsource their encrypted data to the cloud using identity-based encryption, while proxy re-encryption construction will grant legitimate users access to the data. With the Internet of Things devices being resource-constrained, an edge device acts as a proxy server to handle intensive computations. Also, we make use of the features of information-centric networking to deliver cached content in the proxy effectively, thus improving the quality of service and making good use of the network bandwidth. Further, our system model is based on blockchain, a disruptive technology that enables decentralization in data sharing. It mitigates the bottlenecks in centralized systems and achieves fine-grained access control to data. The security analysis and evaluation of our scheme show the promise of our approach in ensuring data confidentiality, integrity, and security","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"23 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099901","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}

{"title":"Design and Development of an Autonomous Rover Application using A Rocker-Bogie Mechanism In Agriculture","authors":"Rutvik Sandbhor, Sanket Kachale, Aditya Bhosale, Prof. Kundlik Kshirsagar, Prof. Pravin Sharma","doi":"10.48175/ijarsct-18487","DOIUrl":"https://doi.org/10.48175/ijarsct-18487","url":null,"abstract":"The crucial topic of upgrading the rover over its earlier designs is covered in the project work \"STUDY ON ROCKER ROVER AND ITS IMPLEMENTATION IN THE FIELD OF AGRICULTURE.\" The ROCKER rover was intended for use on similar excursions that require it to function in challenging conditions, such as the Moon's surface. However, the use of the rocker rover can be expanded even further in fields of employment where the land has to be used for operations, such as agricultural farming. Our research focuses on how the rocker rover can be modified for use in farming, greatly increasing the automation of the agricultural sector. The rover's body is entirely composed of PVC to boost.\u0000With the introduction of cutting-edge technologies, agricultural practices are changing with the goal of enhancing sustainability, accuracy, and efficiency. The design and construction of an autonomous rover with a rocker-bogie suspension system specifically intended for agricultural applications is the main goal of this study.\u0000 When doing in-situ scientific investigation of goals that are separated by several metres to tens of km, rocker bogie are crucial. The complicated mobility designs of today use numerous wheels or legs. They are vulnerable to mechanical failure brought on by Mars' hostile atmosphere. a four-wheeled rover with a high degree of mobility suspension system that is effective in navigating uneven terrain. The main mechanical characteristic of the rocker bogie design is how simple its drive train is—it only requires two motors to move. Because both motors are housed inside the body, where heat variance is minimised, dependability and efficiency are raised. Because there aren't many barriers on natural terrain that call for the rover to use all of its front wheels, four wheels are used","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"11 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141098638","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}

{"title":"Skin Disease Detection","authors":"Mrs. R Soundharya, Akanksha Shettigar, Ananya Prasad, Ashwith R Poojary, Deepa Naik","doi":"10.48175/ijarsct-18465","DOIUrl":"https://doi.org/10.48175/ijarsct-18465","url":null,"abstract":"The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time-consuming. To address these challenges, there is a critical need to develop advanced diagnostic methods utilizing machine learning techniques to enhance accuracy cross various skin diseases. Machine learning algorithms have proven valuable in medical applications, leveraging image feature values to facilitate decision-making. The diagnostic process involves three key stages: feature extraction, training, and testing. By employing machine learning technology, these algorithms learn from a diverse set of skin images o enhance their diagnostic capabilities. The primary goal is to significantly improved the accuracy of skin disease detection. This study focuses on utilizing color and texture features for the classification of skin diseases. The distinctive color of healthy skin differs from that affected by disease, while texture features effectively discern smoothness, coarseness, and regularity in images. Key features such as texture, color, and shape phyla pivotal role in image classification. The incorporation of convolution neural networks (CNN) further augments the capabilities of image classification in the realm of skin disease diagnosis","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"53 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102071","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}

{"title":"Identify Cricket Shots using Machine Learning","authors":"Prof. Bhakti Puranik, Swayam Nikam, Piyush Sandhan, Vedant Sadgir, Avishkar Jadhav","doi":"10.48175/ijarsct-18453","DOIUrl":"https://doi.org/10.48175/ijarsct-18453","url":null,"abstract":"Cricket shot detection is a game-changing technology that offers deep insights into player performance and match data, completely changing the way the sport is played. The main elements and importance of cricket shot detection systems are explored in this abstract. Using computer vision and machine learning techniques, the system examines video footage of cricket matches to accurately detect and classify every shot made by batsmen. Shot types (such as cover drive, pull, or leg glance), shot trajectories, and success rates are among the important data it retrieves. Numerous stakeholders in the cricketing ecosystem find diverse uses for cricket shot detection. It provides coaches and professional athletes with an unmatched post-match analysis tool that helps with strategic planning and performance enhancement.The method is used by team analysts to create winning strategies by gaining insight into opponents' shot patterns. Shot detection provides compelling visualizations and real-time shot labels in the broadcasting domain, enhancing the viewing experience. While talent scouts and cricket organizations use technology to find players and nurture talent, cricket enthusiasts profit from comprehensive shot data. This abstract highlight cricket shot detection's potential and adaptability, highlighting how it can revolutionize the cricket industry. Technology keeps improving the game, empowering players, and enthralling spectators with a deeper comprehension of the sport. Several sports have received a lot of attention and popularity recently. Many people were pining for live sports action during the height of the recent outbreak because there were no sporting activities. With millions of devoted fans who watch the games with emotion, cricket is one of the most respected sports in India. Enticed by the game, fans frequently conduct in-depth evaluations of certain players, focusing on their skills and shot choices. A greater number of people are interested in assessing players' performances in order to make wise choices for fantasy teams, especially in light of the popularity of fantasy leagues and related services. Automation presents a potential solution to the significantly time-consuming and manual process of detecting cricket batters' shots. In order to accomplish its goals, this study uses deep learning in the form of Convolutional Neural Networks (CNNs) to present an efficient method for evaluating cricket strokes","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102288","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}

{"title":"Stameering Speech Signal Segmentation and Classification using Machine Learning","authors":"V. Naveen, Dr. S. Nagasundaram","doi":"10.48175/ijarsct-18411","DOIUrl":"https://doi.org/10.48175/ijarsct-18411","url":null,"abstract":"Stuttering or Stammering is a speech defect within which sounds, syllables, or words are rehashed or delayed, disrupting the traditional flow of speech. Stuttering can make it hard to speak with other individuals, which regularly have an effect on an individual's quality of life. Automatic Speech Recognition (ASR) system is a technology that converts audio speech signal into corresponding text. Presently ASR systems play a major role in controlling or providing inputs to the various applications. Such an ASR system and Machine Translation Application suffers a lot due to stuttering (speech dysfluency). Dysfluencies will affect the phrase consciousness accuracy of an ASR, with the aid of increasing word addition, substitution and dismissal rates. In this work we focused on detecting and removing the prolongation, silent pauses and repetition to generate proper text sequence for the given stuttered speech signal. The stuttered speech recognition consists of two stages namely classification using ANN and testing in ASR. The major phases of classification system are Re-sampling, Segmentation, Pre Emphasis, Epoch Extraction and Classification. The current work is carried out in UCLASS Stuttering dataset using MATLAB with 4% to 6% increase in accuracy by ANN.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"23 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102506","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}

{"title":"Criminal Identification Web App Utilizes Facial Recognition to Identify and Track Criminals","authors":"V. Shrey Jain, A. Poongodi","doi":"10.48175/ijarsct-18415","DOIUrl":"https://doi.org/10.48175/ijarsct-18415","url":null,"abstract":"Criminal record generally contains all the information both personal and criminal with the photograph of the person. In order to recognize Criminal, identification of some sort is required, designated by eyewitnesses. In most cases the resolution or/and quality of the recorded image sections is unsatisfactory and is difficult to recognize the face. Recognition can be achieved in various different ways like DNA, eyes, finger print, etc. One of the ways is face identification. Since facial recognition technology is powered by artificial intelligence, it can provide excellent results in identifying criminals. Even considering that most people, when committing an illicit activity, try to hide their identity: hiding their faces or covering their faces with scarves, masks, etc. In such cases, AI uses deep learning methods to identify the individual. In this project, proposed a CrimeNet an automatic criminal identification system for Police Department to enhance and upgrade the criminal classification into a more effective and efficient approach using Convolutional neural network algorithms.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"63 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101963","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}

{"title":"Semisupervised Machine Learning Approach for Ddos Detection","authors":"Sanjeevi. J, Dr. Krithika. D. R.","doi":"10.48175/ijarsct-18409","DOIUrl":"https://doi.org/10.48175/ijarsct-18409","url":null,"abstract":"Analyzing cyber incident data sets is an important method for deepening our understanding of the evolution of the threat situation. In present generation we come to know about many cyber breaches and hacking taking place. In this project work, we research about the various cyber- attacks and breaches and study the way these attacks are done and find an alternative for the same. We show that rather than by distributing these attacks as because they exhibit autocorrelations, we should model by stochastic process both the hacking breach incident inter- arrival times and breach sizes. We draw a set of cyber securities insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency. In our project we will be using the algorithms such as Convolution Neural Network (CNN) as existing and Recurrent Neural Network (RNN) as proposed for analyzing our results. From the results obtained its proved that proposed RNN works better than existing CNN.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"6 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099547","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}