International Journal of Advanced Research in Science, Communication and Technology最新文献_第8页

Anticipating Trends and Innovations for A Sustainable Automotive Industrial Revolution 预测趋势和创新，实现可持续的汽车工业革命

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18407

Krasini S, H. Jayamangala

{"title":"Anticipating Trends and Innovations for A Sustainable Automotive Industrial Revolution","authors":"Krasini S, H. Jayamangala","doi":"10.48175/ijarsct-18407","DOIUrl":"https://doi.org/10.48175/ijarsct-18407","url":null,"abstract":"Graph classification aims to predict the label associated with a graph and is an important graph analytic task with widespread applications. Recently, graph neural networks (GNNs) have achieved state-of-the-art results on purely supervised graph classification by virtue of the powerful representation ability of neural networks. However, almost all of them ignore the fact that graph classification usually lacks reasonably sufficient labelled data in practical scenarios due to the inherent labelling difficulty caused by the high complexity of graph data. The existing semi-supervised GNNs typically focus on the task of node classification and are incapable of dealing with graph classification. To tackle the challenging but practically useful scenario, we propose a novel and general semi-supervised GNN framework for graph classification, which takes full advantage of a slight amount of labelled graphs and abundant unlabelled graph data. In our framework, we train two GNNs as complementary views for collaboratively learning high-quality classifiers using both labelled and unlabelled graphs. To further exploit the view itself, we constantly select pseudo-labelled graph examples with high confidence from its own view for enlarging the labelled graph dataset and enhancing predictions on graphs. Furthermore, the proposed framework is investigated on two specific implementation regimes with a few labelled graphs and the extremely few labelled graphs, respectively. Extensive experimental results demonstrate the effectiveness of our proposed semi-supervised GNN framework for graph classification on several benchmark datasets","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"85 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101542","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

CRM Model for Smart Kid’z Preschool Smart Kid'z 幼儿园的客户关系管理模式

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18462

Rutuja Pandharpatte, Sakshi Nanavare, Preeti Dhavale, Sukanya Ghodake, Dr. C. S. Shinde

引用次数: 0

One-Stop Flair Slot Booking Platform for All Needs 满足所有需求的一站式 Flair 插槽预订平台

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18416

Aarthi. R, Priya. R

{"title":"One-Stop Flair Slot Booking Platform for All Needs","authors":"Aarthi. R, Priya. R","doi":"10.48175/ijarsct-18416","DOIUrl":"https://doi.org/10.48175/ijarsct-18416","url":null,"abstract":"Pick Your Slot (PYS) is a versatile booking and scheduling web-based application that caters to a single platform for all your needs. PYS offers services ranging from sports such as football, cricket and badminton, personal grooming services like salons, fitness facilities such as gyms, entertainment options like a dance studio, rage room, and car wash services. The platform, built using React.js and React Native for web and mobile respectively, utilizes a SQL database backend supported by Java. It is an open-source application accessible to both customers and vendors. The app facilitates slot booking according to customers’ flexible time, PYS not only accommodates flexible scheduling but also utilizes geolocation API to detect customer locations. This feature enables the app to suggest nearby services tailored to the customer's requirements, leveraging real-time availability updates and offering on-demand services. Real-time availability in our app ensures instant slot booking, adapting to users' dynamic schedules for hassle-free planning. Users can quickly secure appointments, saving time and reducing frustration. Security is paramount, with authentication mechanisms for both vendors and customers. With its blend of intuitive design and advanced functionality, PYS offers a comprehensive solution for booking needs, making it a reliable choice for users seeking convenience and peace of mind. In a world where time is precious, our app offers swift slot bookings, recognizing the impatience of modern life. With just a few taps, users can secure their slots, bypassing unnecessary delays in the nearest location. Designed for speed and convenience, we ensure no one waits longer than necessary.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"4 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100811","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

Leveraging Face Recognition Technology for Secure ATM Transaction 利用人脸识别技术实现 ATM 安全交易

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18406

Mehfooz Ur Rehman, H. Jayamangala

{"title":"Leveraging Face Recognition Technology for Secure ATM Transaction","authors":"Mehfooz Ur Rehman, H. Jayamangala","doi":"10.48175/ijarsct-18406","DOIUrl":"https://doi.org/10.48175/ijarsct-18406","url":null,"abstract":"ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transactions with ATMs is increasing day by day. ATMs are a very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including fingerprinting, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holders to verify the identity of unauthorized users through some dedicated artificial intelligent agents, for remote certification. However, it is obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone to have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"38 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102325","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

Authorized Keyword Search over Outsourced Encrypted Data in Cloud Environment 在云环境中对外包加密数据进行授权关键词搜索

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18468

Manikanta K. M, A. G Akshay, Aftab Ahmed, Gavi Siddhana Gowda, Prof. Shivakeshi Choupiri

引用次数: 0

Diabetes Prediction using Machine Learning 利用机器学习预测糖尿病

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18485

Aryan Sodhi, Dnyaneshwari Chaugule, Divya Patankar, Dr, Bhausaheb Shinde, Prof. Palak Desai

{"title":"Diabetes Prediction using Machine Learning","authors":"Aryan Sodhi, Dnyaneshwari Chaugule, Divya Patankar, Dr, Bhausaheb Shinde, Prof. Palak Desai","doi":"10.48175/ijarsct-18485","DOIUrl":"https://doi.org/10.48175/ijarsct-18485","url":null,"abstract":"This study explores the application of machine learning for diabetes prediction. Leveraging a dataset of relevant features such as glucose levels, BMI, and family history, various algorithms are employed to develop predictive models. The goal is to enhance early detection and management of diabetes, contributing to more effective healthcare interventions. Results indicate promising accuracy and potential for real-world implementation in preventive healthcare systems. This presents an approach for predicting diabetes using machine learning techniques. With the increasing prevalence of diabetes worldwide, early detection and effective management are crucial for mitigating its impact on public health. Leveraging machine learning algorithms, such as decision trees, support vector machines, and neural networks, this research aims to develop predictive models based on various patient attributes and medical history data. The dataset used for model training and evaluation comprises demographic information, clinical measurements, and lifestyle factors collected from diabetic patients. Through extensive experimentation and evaluation, the performance of different machine learning algorithms is compared in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The results demonstrate the efficacy of the proposed approach in accurately predicting diabetes risk, thereby offering valuable insights for preventive healthcare strategies and personalized treatment plans","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"2 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100118","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

Control Flow Integrity: Embedded System Code Reuse Defence 控制流完整性：嵌入式系统代码重用防御

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18467

Chethan C V, Pavan Sai, Deepak U, Chirag S

{"title":"Control Flow Integrity: Embedded System Code Reuse Defence","authors":"Chethan C V, Pavan Sai, Deepak U, Chirag S","doi":"10.48175/ijarsct-18467","DOIUrl":"https://doi.org/10.48175/ijarsct-18467","url":null,"abstract":"Hardware-based program Control Flow Integrity (CFI) components, centering on the state-of- the-art usage innovations. Control Stream Keenness may be a significant security degree pointed at moderating control- flow capturing assaults, such as Return Oriented Programming (ROP) and Jump Oriented Programming (JOP). Whereas software-based CFI arrangements have been compelling to a few degree, their restrictions have impelled the improvement of hardware-based approaches for improved security. This survey analyzes unmistakable innovations in this space, counting Intel CET (Control-flow Requirement Innovation), ARM Pointer Verification, AMD SEV-SNP (Secure Settled Paging), and RISC-V CFI Expansions. Each technology's highlights, usage strategies, and contributions to fortifying cyber security are analyzed to supply bits of knowledge into the current scene of hardware-based computer program CFI. By leveraging hardware-level protections, these progressions offer strong security against advanced control-flow capturing assaults, subsequently reinforcing the security pose of computing frameworks. As cyber dangers proceed to advance, the require for vigorous security components to ensure against control-flow capturing assaults gets to be progressively basic. This audit digs into the state-of-the-art execution innovations for hardware-based computer program Control Stream Astuteness","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"17 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100189","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

Adaptive Critic Design for Event-Triggered Tracking Control in Discrete-Time on Linear Systems with Observers 带观测器线性系统离散时间事件触发跟踪控制的自适应批判设计

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18410

Mohamed Faizal. M, Dr. Krithika. D. R.

{"title":"Adaptive Critic Design for Event-Triggered Tracking Control in Discrete-Time on Linear Systems with Observers","authors":"Mohamed Faizal. M, Dr. Krithika. D. R.","doi":"10.48175/ijarsct-18410","DOIUrl":"https://doi.org/10.48175/ijarsct-18410","url":null,"abstract":"This research pioneers an innovative methodology for the bespoke manufacturing of SquidSkin safety products, meticulously tailored to the distinct requisites of diverse industries encompassing medical, military, and defense sectors. The triple DES algorithm is a three instance of DES on same plain text in blocks of 64 bits and converts them to cipher text using keys of 168 bits. The triple DES encryption process creates cipher text, which is an unreadable, effectively indecipherable conversion of plaintext data, the version of information that humans can read and understand. The output of the encryption process, the triple DES cipher text, cannot be read until a secret triple DES key is used to decrypt it. The delineation of industry- specific manufacturing parameters mandates a nuanced approach to material composition and production processes, culminating in a paradigmatic advancement in safety product customization. Integral to this methodology is a rigorous analysis of industry-specific requirements, guiding the selection of optimal material compositions. This selection is underpinned by a meticulously designed material testing phase, ensuring adherence to exacting industry standards and performance benchmarks. Augmenting this paradigm is an imperative emphasis on fortifying the security framework underpinning the handling of proprietary manufacturing data. The proposal introduces an advanced cryptographic architecture, featuring encryption and decryption protocols. Approach assures heightened efficiency, unwavering reliability, and the preservation of proprietary information across varied industry sectors.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"8 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100593","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

PHISHWIPER: Real Time Scam Website Detection and Blocking using Predictive Attention Model PHISHWIPER：利用预测注意力模型实时检测和阻止诈骗网站

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18413

M. K. Siva Prakash, A. Poongodi

{"title":"PHISHWIPER: Real Time Scam Website Detection and Blocking using Predictive Attention Model","authors":"M. K. Siva Prakash, A. Poongodi","doi":"10.48175/ijarsct-18413","DOIUrl":"https://doi.org/10.48175/ijarsct-18413","url":null,"abstract":"A data breach is a security event, where sensitive data is accessed without any permission from a website or an organization. An information breach will be considered as the purposeful or accidental gathering of secure or personal data from an organization. A breach can be an accession of a data without any permission, these kinds of regulations should be provided with safe and secured framework but this is not happening in many corporations. So, by analyzing the previous attempts (successful or unsuccessful attacks), the proposed model can be trained to adapt to new scenarios and predict the next breach. Further, this research work has designed a model by using machine learning to defend a website from security breaches. The primary aim of this research work is to create a machine learning model, which trains in Real-time and monitors the website or a system and trains from the state-of-art attacks. The proposed model has created a web application, which takes the data from multiple sources such as Amazon, Flipkart, Snapdeal, and Shop clues, which shows the data that is safe to obtain from the website.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101327","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

A CNN-Based Framework for Video Analysis and Accident Detection 基于 CNN 的视频分析和事故检测框架

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-24 DOI: 10.48175/ijarsct-18486

Arnav Tatewar, Sakshi Kothurkar, Shreyas Jadhav, Venktesh Mahajan, Mr. Digambar Jadhav, Mrs. Savita Jadhav

{"title":"A CNN-Based Framework for Video Analysis and Accident Detection","authors":"Arnav Tatewar, Sakshi Kothurkar, Shreyas Jadhav, Venktesh Mahajan, Mr. Digambar Jadhav, Mrs. Savita Jadhav","doi":"10.48175/ijarsct-18486","DOIUrl":"https://doi.org/10.48175/ijarsct-18486","url":null,"abstract":"This research investigates the development and deployment of a Convolutional Neural Network (CNN) model for automatic accident detection in CCTV footage. The ever-increasing reliance on video surveillance necessitates efficient and accurate methods for accident identification. CNNs, with their inherent ability to learn complex spatial relationships within images, are particularly well-suited for this task. This study proposes a CNN architecture that utilizes a pre-trained MobileNetV2 base for feature extraction, followed by a custom classification head tailored to the specific task of accident vs. no accident classification. The model is trained on a dataset of grayscale video frames, achieving an impressive accuracy of 92% on the testing set. This high level of accuracy suggests that CNNs hold significant promise for real-world accident detection applications. Furthermore, to bridge the gap between research and practical implementation, the model is converted to a TensorFlow Lite (TFLite) format for deployment on resource-constrained devices. Additionally, a user-friendly frontend application is developed, empowering users to interact with the model and analyze both images and videos. This user-centric approach broadens the model's accessibility and paves the way for potential improvements in road safety through real-time accident detection.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"92 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101413","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