International Journal of Advanced Research in Science, Communication and Technology最新文献_第3页

Efficient Clothing Fashion Prediction using Machine Learning 利用机器学习进行高效服装时尚预测

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1231

M. Sanjai, Dr. C. Meenakshi

{"title":"Efficient Clothing Fashion Prediction using Machine Learning","authors":"M. Sanjai, Dr. C. Meenakshi","doi":"10.48175/ijetir-1231","DOIUrl":"https://doi.org/10.48175/ijetir-1231","url":null,"abstract":"Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from social media with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Machine Learning which takes advantage of the capability in modeling time series data. Moreover, it leverages internal and external knowledge in fashion domain that affects the time-series patterns of fashion element trends. Such incorporation of domain knowledge further enhances the deep learning model in capturing the patterns of specific fashion elements and predicting the future trends. Extensive experiments demonstrate that the proposed ML model can effectively capture the complicated patterns of objective fashion elements, therefore making preferable fashion trend forecast.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659983","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}

引用次数: 0

Machine Learning-Enhanced Probabilistic Validation Techniques with Minimal Data Exposure in Distributed Systems 分布式系统中数据暴露最小化的机器学习增强型概率验证技术

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1236

P. Udhayakumar, A.Poongodi

引用次数: 0

LifeSaver: A VaDE-Based Intelligent Ambulance Positioning System for Optimal Emergency Response and Alert System 救生员：基于 VaDE 的智能救护车定位系统，用于优化应急响应和警报系统

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1225

Ranjith Jayakumar S, Dr. Lipsa Nayak

引用次数: 0

AI based College Surveillance System for Class Skipper 基于人工智能的班长学院监控系统

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1249

Jasvanthram M, V. Sumalatha

{"title":"AI based College Surveillance System for Class Skipper","authors":"Jasvanthram M, V. Sumalatha","doi":"10.48175/ijetir-1249","DOIUrl":"https://doi.org/10.48175/ijetir-1249","url":null,"abstract":"In many of the educational institutions, managing attendance of students/candidates is tedious, as there would be large number of students in the class and keeping track of all is onerous. There are situations where student act as proxies for their friends even though they are not present. The presence of students repeatedly skipping classes and spending considerable time wandering on campus signals potential underlying issues, such as disengagement, personal challenges, or dissatisfaction with the educational experience. Traditional methods of monitoring attendance are often inadequate in addressing these nuanced challenges. Therefore, there is a need for an AI-based College Surveillance System using Faster R-CNN to accurately detect class skippers and provide insights into their behavioural patterns. In this system, a database containing the trained student’s face. A camera installed in the college campus captures the face of all the student in the classroom and other places too. This face image is processed using FRCNN algorithms to detect faces and to mark the attendance automatically in an excel sheet. The system records the entire class session and identifies when the students pay attention in the classroom, and then reports to the facilities and also this system can record violations of classroom, that is absence, roaming around the college campus during the class hours and send alert message to the H.O.D.This dynamic attendance system uses face recognition as an important aspect of taking attendance which saves time and proxy attendance and is avoided. The system identifies faces very fast needing only 100 milliseconds to one frame and obtaining a high accuracy. Our face recognition model has an accuracy rate of 98.87%..","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"19 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661842","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}

引用次数: 0

Exploring the Use of Iron Ore Tailings in Concrete by Partial Replacement of Fine Aggregates 通过部分替代细骨料探索铁矿尾矿在混凝土中的应用

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijarsct-19141

Madhuri Bore, Kishor Dhawade, Yash Dhamale, Mahima Kumari

{"title":"Exploring the Use of Iron Ore Tailings in Concrete by Partial Replacement of Fine Aggregates","authors":"Madhuri Bore, Kishor Dhawade, Yash Dhamale, Mahima Kumari","doi":"10.48175/ijarsct-19141","DOIUrl":"https://doi.org/10.48175/ijarsct-19141","url":null,"abstract":"Concrete is the most durable, resilient, available and affordable material in the built environment. The manufacture of large quantities of iron ore has created difficulties for the environment and disposal. The utilization of IOT as fine aggregate in building material production is relatively feasible. Experiments were conducted to determine the suitability of iron ore tailings as replacement of fine aggregates for concrete. The iron ore waste was collected from Gua Iron Mines, Singhbum. Mix Design was carried out for concrete of grade M35 using standard practice for selecting proportions for normal weight. Iron ore waste replaced with fine aggregates in mixes by 30% and 35% respectively. The materials used for M35 grade of concrete is coarse aggregate 10-20mm, fine aggregate is of Zone 2, cement of 53 grade. Tests were performed on materials, for cement and fine Aggregate Fineness test, Specific Gravity were performed. Water absorption test, specific gravity, Impact value these were performed for Coarse aggregate. It was observed that compression values of concrete for 30% for 3 days its 13.703 Mpa, For 7 days its 16.503 Mpa, and for 28 it was 28.033 Mpa","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"5 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662583","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}

引用次数: 0

Segmentand Classify Satellte Images to Analyze The Environment and Ensure Safety, Using Machine Learning 利用机器学习对卫星图像进行分割和分类，以分析环境并确保安全

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1232

D. Naveen, Dr. C. Meenakshi

{"title":"Segmentand Classify Satellte Images to Analyze The Environment and Ensure Safety, Using Machine Learning","authors":"D. Naveen, Dr. C. Meenakshi","doi":"10.48175/ijetir-1232","DOIUrl":"https://doi.org/10.48175/ijetir-1232","url":null,"abstract":"Image segmentation is a difficult task in computer vision. The process includes the classification of visual input to segments to simplify image analysis. There are many types of method for the image segmentation some of the common methods are edge detection based method region-based methods, clustering-based method, partial differential equation-based, watershed-based method, and neural network-based method. The proposed project work is mainly focused on image segmentation. Satellite images are given as the input of the proposed system. Machine learning techniques plan an important role in various domains. Here the remotely sensed data can be segmented by using the K-Means clustering method. Compared with other traditional methods this clustering technique yields better results. The system is implemented using the Matlab tool. Machine learning concepts drastically decrease the time needed to arrange an exact map. \u0000the project will be using K Nearest Neighbor (KNN) as existing and Support Vector Machine (SVM) as proposed system for classification and calculates results in terms of accuracy. From the results obtained its proved that proposed SVM works better than existing KNN.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"11 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661579","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}

引用次数: 0

Optimizing Production Stability while Safeguarding Information 在保护信息的同时优化生产稳定性

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1239

Eby TS, Dr. S. Prasanna

{"title":"Optimizing Production Stability while Safeguarding Information","authors":"Eby TS, Dr. S. Prasanna","doi":"10.48175/ijetir-1239","DOIUrl":"https://doi.org/10.48175/ijetir-1239","url":null,"abstract":"The goal of Finite Time Stabilization is to finish a particular thing in a fixed time. Regardless of the system's original state, finite-time stabilisation refers to the regulation of a system so that it reaches a desired equilibrium or setpoint in a finite length of time. Finite-time stabilisation is essential for providing quick and effective control over a variety of variables in industrial processes, such as temperature, pressure, flow rate, or composition. By layering materials based on a computer model, 3D printing, sometimes referred to as additive manufacturing, creates three-dimensional items. Even though 3D printing technology has advanced significantly in recent years, manufacturing them still presents a number of difficulties. Some of the typical difficulties include: Cost: Due to the intricate parts and high level of precision needed when manufacturing 3D printers, the cost might be high. Quality parts, such as motors, electronics, and extruders, can be expensive to source. As a result, manufacturers may find it difficult to strike a balance between price and performance. So, as part of our process, we analyse the data and forecast the pricing to make things simple for the client. To make this prediction, we employed logistic regression. More than that those client data is secured through fernet algorithm.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"12 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659916","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}

引用次数: 0

Sequestered Shepherded Long-Tailed Intercommunication Eradication using Restraint Adumbration 利用约束装置消除封闭式牧羊犬长尾互通信息

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1222

Sanjay S, Lipsa Nayak

{"title":"Sequestered Shepherded Long-Tailed Intercommunication Eradication using Restraint Adumbration","authors":"Sanjay S, Lipsa Nayak","doi":"10.48175/ijetir-1222","DOIUrl":"https://doi.org/10.48175/ijetir-1222","url":null,"abstract":"We address the critical task of identifying and rectifying flaws in aerospace machines to streamline the upgrading process. Leveraging Linear Regression algorithms, our model systematically detects defects in machines utilized in aerospace manufacturing, ensuring the continued precision and safety of aircraft, spacecraft, and related components. By analyzing maintenance histories and crucial parameters indicative of potential issues, such as fuselage damages or leakages, we employ linear regression to pinpoint defects. This integration of modern analysis techniques enables aerospace manufacturers to aggressively detect and address flaws in their equipment, thereby enhancing product quality, safety, and efficiency. Our project focuses on detecting machine defects in aerospace manufacturing by analyzing maintenance histories. By employing linear regression, we aim to identify defects based on various approaches and criteria, ensuring a comprehensive evaluation of machines used in aerospace manufacturing industries. Leveraging collected defect data from technicians, our system utilizes linear regression to identify and address machine defects effectively. However, Linear regression suitability for anomaly detection or defect identification in aerospace manufacturing machines may require adaptation","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"2 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662907","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}

引用次数: 0

Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions 智能交通管理：通过预测分析和人工智能驱动的解决方案提升城市流动性

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1243

Arshathkhan A, Priya. R

{"title":"Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions","authors":"Arshathkhan A, Priya. R","doi":"10.48175/ijetir-1243","DOIUrl":"https://doi.org/10.48175/ijetir-1243","url":null,"abstract":"In urban environments, the issue of unauthorized parking in designated no-parking zones persists, leading to traffic congestion and safety hazards. inaccurate license plate recognition, License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This leading to difficulties in identifying vehicle owners. To address this challenge, this project present TraceMe, a predictive system utilizing advanced machine learning algorithms. The system employs YOLOv8 for efficient object detection, focusing on identifying vehicles in no-parking zones, and Tesseract OCR for accurate license plate recognition. The extracted license plate information is then processed by a machine learning model trained to predict the owner of the vehicle. The proposed system involves collecting and annotating a diverse dataset, training YOLOv8 and LPRNet model for vehicle number plate detection, utilizing Tesseract OCR for license plate extraction, and implementing a machine learning model for owner identification. Real-time processing and integration with surveillance systems allow for immediate identification of unauthorized parking incidents. The system generates alerts or notifications, aiding law enforcement in enforcing parking regulations.TraceMe not only provides a technological solution to mitigate unauthorized parking but also contributes to improved traffic management and public safety.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660469","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}

引用次数: 0

Multi-Tenant IaaS Cloud Security Evaluation Model 多租户 IaaS 云安全评估模型

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-10 DOI: 10.48175/ijetir-1234

Jasmine Sharon S, S. Nagasundaram

引用次数: 0