2023 3rd International Conference on Intelligent Technologies (CONIT)最新文献_第10页

A Comparative Study on Different Machine Learning Techniques in Diabetes Risk Assessment 不同机器学习技术在糖尿病风险评估中的比较研究

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205382

Mahnur Akther, Zahara Rahman Chowdhury, Anika Tabassum, Md. Saidur Rahman Kohinoor

{"title":"A Comparative Study on Different Machine Learning Techniques in Diabetes Risk Assessment","authors":"Mahnur Akther, Zahara Rahman Chowdhury, Anika Tabassum, Md. Saidur Rahman Kohinoor","doi":"10.1109/CONIT59222.2023.10205382","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205382","url":null,"abstract":"Diabetes is a chronic disease in which the body’s ability to process glucose is impaired due to insufficient insulin production or utilization. This can result in high blood sugar levels and other health issues. Since the majority of our country’s population are not conscious about their lifestyle and are unaware of the complications and casualties that diabetes can lead to if it is not examined timely, assessing their risk for diabetes is an important step in prevention and early detection. Assessing one’s diabetes risk prior to medical diagnosis is crucial to enable timely intervention and management of the disease. Our paper intends to provide such a solution that would help detect the disease risk beforehand. We selected two diabetes datasets: PIMA Indian and Sylhet dataset for evaluating ten different models, namely-Support Vector Machine, Random Forest, Naive Bayes, Decision Tree, K Nearest Neighbor, Logistic Regression, Adaboost, Gradient Boost, XGBoost, and Multilayer perceptron. We applied Grid Search and Stratified K-fold cross-validation to asses model’s performance. Our goal is to determine the best-performing model for predicting diabetes. From the analysis, Random Forest outperformed in PIMA Indian Dataset with 84.21% accuracy and Gradient Boost outperformed in Sylhet dataset with 98.85% accuracy. So, for the two different featured datasets Random Forest and Gradient Boost scored highest as the best-predicting models.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122405459","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 Novel Women Safety Analyis and Monitoring Sysetm over Social Media using Machine Learning 一种基于机器学习的新型社交媒体女性安全分析与监控系统

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205753

A. K, B. Rajalakshmi, Konapalli Sai Chaitanya Reddy, Geetha Priyanka Guggulla, S. B. V.

{"title":"A Novel Women Safety Analyis and Monitoring Sysetm over Social Media using Machine Learning","authors":"A. K, B. Rajalakshmi, Konapalli Sai Chaitanya Reddy, Geetha Priyanka Guggulla, S. B. V.","doi":"10.1109/CONIT59222.2023.10205753","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205753","url":null,"abstract":"In the current scenario, women community facing issues like gender discrimination, schooling, child marriage, sexual assault and harassment, and much more, not just from society but also from social media. Women are protected by organizations like the She Team, Disha Act, and many others in society, but these organizations are much less in social media. Social networking sites like Twitter, Instagram, Facebook, etc. cause problems for women. This paper focuses on the safety analysis and monitoring of women using various social media platforms in Indian cities. The posts on Facebook and Instagram, as well as tweets on Twitter, that abuse women are considered and show the percentage of threats that women face from social media, which aids in understanding by the youth of India who misuse the women’s safety and harass them in social medias via tweets, posts, and text should face strict action. People may grasp the threats to women with the help of this, and it demonstrates that women face challenges not only from society but also from social media platforms. The outcome is easily comprehended in the form of a graph and a pie chart. Algorithms such as Nave Bayes (NB) and XGBoost are used in the analysis of women’s safety on various social media sites. The goal is to use classification techniques to categorize or forecast the Type based on dataset properties. Using categorization algorithms, we can determine whether social media content is positive, negative, or neutral. It has been substantiated that Naive Bayes algorithm has proved better accuracy compared to random forest and decision tree algorithms.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658682","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 Real Time Conversion Model for Hand Gestures to Textual Content 手势到文本内容的实时转换模型

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205540

Anagha Bhardwaj, Akshita Singhal, Prakhar Mamgain, Utkarsh Joshi, Siddhant Thapliyal

引用次数: 0

Deep Learning Approach to Enhance Accuracy for Early Detection of Glaucoma 提高青光眼早期检测准确率的深度学习方法

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205533

P. N. Palsapure, Anu H A, Ashmitha G, A. B H, Mainak Jana

{"title":"Deep Learning Approach to Enhance Accuracy for Early Detection of Glaucoma","authors":"P. N. Palsapure, Anu H A, Ashmitha G, A. B H, Mainak Jana","doi":"10.1109/CONIT59222.2023.10205533","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205533","url":null,"abstract":"Diabetes is a medical disorder when the blood sugar (glucose) level cannot be controlled by the body. This can occur if the body can't properly use the insulin it produces or if the body doesn't produce enough insulin. Diabetes can lead to major health issues and increase your chance of developing a number of eye illnesses if it is not properly managed. The advancement of machine learning algorithms has made early detection of various eye illnesses using an automated method significantly more advantageous than manual detection. The ocular illness that lead to visual loss is Glaucoma which do not have any symptoms. Early detection can help to reduce disease-related vision loss. This study proposes a segmentation using UNet model (which is a U-shaped encoder-decoder network architecture, which consist of four encoder blocks and four decoder blocks that are connected via a bridge) on fundus images followed with data augmentation. The CNN (Convolution Neural Network) model is then trained using pre-processed fundus image. The proposed model was using IEEE dataset named REFUGE (Retinal Fundus Glaucoma Challenge). In an evaluation after 100 epochs, the accuracy is 98%. The proposed model outperforms existing deep learning model for early detection of glaucoma.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125233720","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

The Construction of Intelligent Classroom for Blended Teaching of Cognitive Load and College English Spoken Language Based on Data Mining 基于数据挖掘的认知负荷与大学英语口语混合教学智能课堂构建

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205736

Lu Liu, Yingying Wei, N. Peng

{"title":"The Construction of Intelligent Classroom for Blended Teaching of Cognitive Load and College English Spoken Language Based on Data Mining","authors":"Lu Liu, Yingying Wei, N. Peng","doi":"10.1109/CONIT59222.2023.10205736","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205736","url":null,"abstract":"Building an intelligent classroom that combines cognitive load and college English teaching is a key step in improving students' learning experience. Blended learning combines traditional face-to-face teaching with online learning, creating a more flexible and interactive learning environment. The use of technology in the classroom can help reduce cognitive load and enable students to focus on understanding complex concepts. Intelligent classrooms should be equipped with modern technologies such as interactive whiteboards, projectors, and audio-visual aids to facilitate the delivery of course content. In addition, the use of artificial intelligence can provide tailored feedback and suggestions based on the individual needs of each student, thereby helping them personalize their learning experience. Incorporating blended learning into college English courses can also provide opportunities for real language use through online communication tools, thereby improving language acquisition. This method can help students develop language skills in real life while reducing cognitive load. In short, building an intelligent classroom for blended learning is an important step in improving the quality of higher education. It provides an opportunity to integrate technology into traditional teaching methods, creating a more attractive and effective learning experience for students.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124133021","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

Privacy-Preserving of Edge Intelligence using Homomorphic Encryption 基于同态加密的边缘智能隐私保护

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205745

Ronaldo Jerang, Sumitra Nayak, Ganesh Kumar Mahato, Swarnendu Kumar Chakraborty

引用次数: 0

Image Encryption using Chaotic maps: State of the art 使用混沌映射的图像加密:最先进的技术

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205829

Lakshya Gupta, Prateek Jaiswal, Ishita Lather, R. Agarwal, Anita Thakur

{"title":"Image Encryption using Chaotic maps: State of the art","authors":"Lakshya Gupta, Prateek Jaiswal, Ishita Lather, R. Agarwal, Anita Thakur","doi":"10.1109/CONIT59222.2023.10205829","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205829","url":null,"abstract":"Due to the swift growth of the internet and the increasing flow of data across public networks, data security is becoming more and more crucial. One potential resolution to this issue is encrypting the information which might take the form of text, images, audio, video, etc. Today, photographs are used in the majority of multimedia applications. AES, DES, RSA, and other early picture encryption algorithms have low levels of security and poor anti-attack capabilities. By adopting chaos-based cryptography, this issue was solved. The chaotic systems have the potential to be used in image encryption because they are sensitive to the starting conditions and control variables. Many research endeavors have been focused on investigating image encryption. An overview of the components and methods of the image encryption design is attempted in this work. We used statistical analyses, such as entropy and pixel correlation, as well as differential analyses, such as unified averaged altered intensity (UACI) and number of changing pixel rate (NPCR), in order to assess the security against various cryptographic assaults. Analyses of key sensitivity and key space are also carried out to thwart brute force assaults. The randomness of the proposed encryption system was further examined through entropy and NIST randomness suit tests.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124422869","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

Facilitating Cryptocurrency Analysis and Watchlist Management with a Web-Based Platform 通过基于web的平台促进加密货币分析和监视列表管理

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205550

Nitin Shivsharan, Shardul Gajanan Kambli, Om Vaman Nikharge, Kedar Uttam Kudatarkar

{"title":"Facilitating Cryptocurrency Analysis and Watchlist Management with a Web-Based Platform","authors":"Nitin Shivsharan, Shardul Gajanan Kambli, Om Vaman Nikharge, Kedar Uttam Kudatarkar","doi":"10.1109/CONIT59222.2023.10205550","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205550","url":null,"abstract":"Cryptocurrencies are digital assets built on blockchain technology, known for their high volatility and lack of underlying assets to justify their intrinsic value. The trading and investing in cryptocurrencies heavily rely on price trends and a few supporting parameters. To facilitate cryptocurrency analysis, our platform provides a price chart that showcases the current short-term, medium-term, and long-term trends of each currency. This analysis is supported by additional parameters such as market capitalization, 24-hour percentage-wise price change, and concise information about each element.Moreover, with the wide array of cryptocurrencies available, individuals struggle to maintain a watchlist that reflects their prioritized selection. To address this challenge, we have developed a priority watchlist that assists users in both short-term trading and long-term investing. Our solution is web-based and utilizes various methodologies including an Application Programming Interface (API) for data retrieval, a library for displaying price charts, and an algorithm for implementing the priority watchlist functionality.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116571959","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

JPEG-XL based Compression of DICOM Images for Reduced Storage and Transmission Costs 基于JPEG-XL的DICOM图像压缩，降低存储和传输成本

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205928

Sam Devavaram Jebaraj, S. N

引用次数: 0

Unlocking the Potential of Machine Learning for Accurate Diagnosis of Breast Cancer 释放机器学习对乳腺癌准确诊断的潜力

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205897

Rinku Soni, Saeedah Zaina, Dr.Y.L Malathi Latha

{"title":"Unlocking the Potential of Machine Learning for Accurate Diagnosis of Breast Cancer","authors":"Rinku Soni, Saeedah Zaina, Dr.Y.L Malathi Latha","doi":"10.1109/CONIT59222.2023.10205897","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205897","url":null,"abstract":"Breast cancer is a major health concern affecting women globally, and early detection is crucial for successful treatment. A promising strategy for enhancing breast cancer diagnosis accuracy and lowering diagnostic mistakes is machine learning. This research aims to enhance the accuracy of breast cancer diagnosis by utilizing balanced data and comparing different machine learning algorithms for classification with and without the use of feature selection methods. In this study, we utilized the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, and to balance data both oversampling and undersampling techniques were utilized. We have used eight different classification models and five different feature selection techniques. We compared the performance of classifiers over undersampled and oversampled data, with and without feature selection. MLflow was utilized to monitor the effectiveness of algorithms and keep a record of their performance. Our results show that oversampling was more effective in improving the performance of our models compared to undersampling. When compared to other models, Logistic Regression achieved the highest accuracy on the oversampled data without feature selection. Our research showed that incorporating feature selection results in slightly lower accuracy compared to the base model which means that the results were not significant enough to compensate for the information loss caused by removing certain features. The study underscores the efficacy of machine learning in the diagnosis of breast cancer and draws attention to the potential of machine learning algorithms in enhancing the accuracy of cancer detection.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116784332","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