International Journal of Scientific Research in Computer Science, Engineering and Information Technology最新文献_第6页

Smart Traffic Management Using Transfer Learning Approach for Improve Urban Mobility 利用迁移学习法改善城市交通的智能交通管理

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-15 DOI: 10.32628/cseit2490217

Jenil Gohil, Yuvraj Chauhan, Dhaval Nimavat

引用次数: 0

A Survey on Relational Database Based Multi Relational Classification Algorithms 基于关系数据库的多关系分类算法概览

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-15 DOI: 10.32628/cseit2390656

Komal Shah, Kajal S Patel

引用次数: 0

Enhancing Campus Connectivity: A Smart Intra-Transit Strategy for Efficient Vehicle Throughput 加强校园连通性：高效车辆吞吐量的智能内部交通战略

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-15 DOI: 10.32628/cseit2490218

Varad Sham Kulkarni, Bhairo Amankumar Jaibir, Patel Rudra Nayneshkumar, Vraj Pujara, R. V. Chander, Himadri Vegad

引用次数: 0

Walking and Survival AI Using Reinforcement Learning - Simulation 使用强化学习的行走和生存人工智能 - 模拟

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-14 DOI: 10.32628/cseit2390629

Bharate Nandan Lahudeo, Makarand Vayadande, Rohit Malviya, Atharva Haldule

引用次数: 0

Contextual Sentence Similarity from News Articles 新闻文章中的上下文句子相似性

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-14 DOI: 10.32628/cseit2390628

Nikhil Chaturvedi, Jigyasu Dubey

{"title":"Contextual Sentence Similarity from News Articles","authors":"Nikhil Chaturvedi, Jigyasu Dubey","doi":"10.32628/cseit2390628","DOIUrl":"https://doi.org/10.32628/cseit2390628","url":null,"abstract":"An important topic in the field of natural language processing is the measurement of sentence similarity. It's important to precisely gauge how similar two sentences are. Existing methods for determining sentence similarity challenge two problems Because sentence level semantics are not explicitly modelled at training, labelled datasets are typically small, making them insufficient for training supervised neural models; and there is a training-test gap for unsupervised language modelling (LM) based models to compute semantic scores between sentences. As a result, this task is performed at a lower level. In this paper, we suggest a novel paradigm to handle these two concerns by robotics method framework. The suggested robotics framework is built on the essential premise that a sentence's meaning is determined by its context and that sentence similarity may be determined by comparing the probabilities of forming two phrases given the same context. In an unsupervised way, the proposed approach can create high-quality, large-scale datasets with semantic similarity scores between two sentences, bridging the train-test gap to a great extent. Extensive testing shows that the proposed framework does better than existing baselines on a wide range of datasets. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242879","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

SafePass : Reinventing Digital Access with Visual Cryptography, Steganography, and Multi-Factor Authentication 安全通行证：利用可视密码学、隐写术和多因素身份验证重塑数字访问方式

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-14 DOI: 10.32628/cseit2490214

Mansi Chauhan, Vraj Limbachiya, Naisargi Shah, Riya Shah, Yassir Farooqui

{"title":"SafePass : Reinventing Digital Access with Visual Cryptography, Steganography, and Multi-Factor Authentication","authors":"Mansi Chauhan, Vraj Limbachiya, Naisargi Shah, Riya Shah, Yassir Farooqui","doi":"10.32628/cseit2490214","DOIUrl":"https://doi.org/10.32628/cseit2490214","url":null,"abstract":"Safe-Pass presents a user-friendly and secure solution for simplifying digital access. With a downloadable application that operates seamlessly across your devices, it eliminates the inconvenience of traditional passwords. The process begins with accessing the Master password app through a distinctive image-based authentication. Operating inconspicuously in the background, the app not only enhances the strength of your existing passwords but also manages and facilitates automatic logins. This system offers adaptable security options, enabling swift access through a single factor or heightened security through the combination of multiple factors. Addressing the persistent threat of phishing, wherein sensitive user information is compromised, we introduce an innovative approach leveraging Visual Cryptography and Steganography for enhanced online security. Our method involves the application of Visual Cryptography to confidential credentials, generating two shares. One share is stored on the server, while the other is concealed within a reCAPTCHA image or a user-defined image through Steganography. During login attempts, users provide their username along with the reCAPTCHA image or chosen image. Successful authentication grants access, while repeated failed attempts trigger email notifications. Master Login prioritizes user privacy, safeguarding passwords as individual and exclusive data. Data sharing or selling is never practiced, ensuring the confidentiality of user information. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"16 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242888","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

Evaluation of the Effectiveness of Feature Selection Methods Combined with Regression Algorithms to Predict Particulate Matter (PM10) in Gandhinagar, Gujarat, India 评估结合回归算法的特征选择方法在预测印度古吉拉特邦甘地纳加尔的颗粒物质（PM10）方面的效果

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-14 DOI: 10.32628/cseit2390641

Zalak L. Thakker, Sanjay H. Buch

{"title":"Evaluation of the Effectiveness of Feature Selection Methods Combined with Regression Algorithms to Predict Particulate Matter (PM10) in Gandhinagar, Gujarat, India","authors":"Zalak L. Thakker, Sanjay H. Buch","doi":"10.32628/cseit2390641","DOIUrl":"https://doi.org/10.32628/cseit2390641","url":null,"abstract":"Feature selection is one of the important data pre-processing techniques that are used to increase the performance of machine learning models, to build faster and more cost-effective algorithms, and to make it easier to interpret the predictions made by the models. The main objective of this research work is to investigate the influence features to predict particulate matter (PM10). This research uses 24-hour average pollutant concentration data of 36 air quality monitoring stations provided by Gandhinagar Smart City Development Limited (GSCDL), Gandhinagar, Gujarat. Important features were identified using five feature selection techniques (correlation, forward selection, backward elimination, Exhaustive Feature Selection (EFS), and feature importance derived using Random Forest Regressor). With selected features six regression algorithms (Multiple Linear Regression, Random Forest, Decision Tree, K-nearest Neighbour, XGBoost, and Support Vector Regressor) were trained to predict PM10. Further, the models were compared based on the Root Mean Square Error (RMSE) and Coefficient of determination (R2) parameters to identify the model with good performance. This proposed model can be utilized as an early warning system, providing air quality information to local authorities to develop air-quality improvement initiatives.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"8 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242109","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

Justice : A Predicting Criminal Acts According To IPC Section 正义：根据《伊斯兰刑法典》条款预测犯罪行为

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-12 DOI: 10.32628/cseit2490215

Gaurav Varshney, Modi Manankumar R, Rajesh Maheshwari, Tirth Chhabhaiya Chhabhaiya, Bikram Kumar

{"title":"Justice : A Predicting Criminal Acts According To IPC Section","authors":"Gaurav Varshney, Modi Manankumar R, Rajesh Maheshwari, Tirth Chhabhaiya Chhabhaiya, Bikram Kumar","doi":"10.32628/cseit2490215","DOIUrl":"https://doi.org/10.32628/cseit2490215","url":null,"abstract":"The AI-driven IPC Section Prediction for Crime Classification project is a groundbreaking initiative with far- reaching implications for the legal and law enforcement sectors in India. Traditional crime classification and the assignment of the appropriate IPC section are often time-consuming and prone to human error. Our web application addresses these challenges by offering an efficient, accurate, and user-friendly solution. One of the key strengths of our application lies in its adaptability. It can process a wide range of crime descriptions, including those involving complex legal language or colloquial terms, ensuring its utility in diverse scenarios. Additionally, our system is designed to continuously learn and evolve. It adapts to changes in legal terminology, updates in the IPC, and emerging crime trends, thereby maintaining its relevance and precision over time. The social impact of this project cannot be overstated. By streamlining crime classification, it empowers law enforcement agencies to allocate resources more efficiently and prioritize cases based on severity and relevance. It also aids legal professionals by expediting case preparation and documentation. Moreover, it facilitates greater public engagement with the legal system, enabling citizens to better understand and navigate the complexities of the IPC. In conclusion, our AI-driven IPC Section Prediction web application is a pioneering tool that has the potential to revolutionize crime classification and legal processes. Its adaptability, continuous improvement, and positive societal impact make it an asset for law enforcement, legal practitioners, and the general public alike.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249759","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

Utilizing Deep Learning Techniques for the Classification of Spoken Languages in India 利用深度学习技术对印度口语进行分类

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-11 DOI: 10.32628/cseit2390556

Priyesha Patel, Ayushi Falke, Dipen Waghela, Shah Vishwa

引用次数: 0

Diabetes Prediction with Machine Learning with Python 用 Python 进行机器学习预测糖尿病

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-03-11 DOI: 10.32628/cseit2390651

S. R. Kumar, Kruthi. G, V. Supraja

引用次数: 0