2023 8th International Conference on Communication and Electronics Systems (ICCES)最新文献_第10页

Image Display using FPGA with BRAM and VGA Interface for Multimedia Applications 带BRAM和VGA接口的FPGA多媒体图像显示

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192822

Navaneethan S, S. Nath, Udaya Krishnan M, Sakthekannan M S, Yogavignes B M, Lokesh Krishnaa M

引用次数: 0

Human Cognition and Artificial Intelligence: A Survey 人类认知与人工智能:综述

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192616

Vipasha Sharma, Navpreet Kaur Walia

引用次数: 0

Secure Color Image Encryption: An Innovative Algorithm based on 8D Hyperchaotic System and DNA Encoding Strategies 安全彩色图像加密:基于8D超混沌系统和DNA编码策略的创新算法

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192788

J. Anisha, Y. P. Arul Teen

{"title":"Secure Color Image Encryption: An Innovative Algorithm based on 8D Hyperchaotic System and DNA Encoding Strategies","authors":"J. Anisha, Y. P. Arul Teen","doi":"10.1109/ICCES57224.2023.10192788","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192788","url":null,"abstract":"In recent years, the increasing demand for secure image transmission and storage has necessitated the development of robust and efficient image encryption algorithms. This study presents a novel color image encryption algorithm based on an 8-dimensional (8D) hyperchaotic system and DNA encoding techniques. The proposed algorithm exploits the inherent complexity and unpredictability of the 8D hyperchaotic system to enhance security, while leveraging the unique properties of DNA sequences for encoding and data manipulation. The proposed encryption process consists of three main stages: (1) generation of chaotic sequences from the 8D hyperchaotic system, (2) DNA encoding of the color image using a set of predefined DNA encoding rules, and (3) an encryption process that combines chaotic sequences with DNA-encoded image data through a series of substitution and permutation operations. The decryption process reverses these stages to recover the original image. Comprehensive security analysis, including key space analysis, sensitivity analysis, and correlation analysis, demonstrates that the proposed algorithm provides a high level of security against various attacks. Additionally, performance evaluation based on image quality metrics, such as Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), confirms the efficiency and effectiveness of the proposed algorithm for color image encryption applications.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132170589","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

Development of an Automated Tool to download Youtube Audio/Video using Artificial Intelligence Techniques 使用人工智能技术下载Youtube音频/视频的自动化工具的开发

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192860

E. G, S. M, Amuthaguka. D, Sanjeevi Kumar. V, Gobala Krishnan. B, A. M

{"title":"Development of an Automated Tool to download Youtube Audio/Video using Artificial Intelligence Techniques","authors":"E. G, S. M, Amuthaguka. D, Sanjeevi Kumar. V, Gobala Krishnan. B, A. M","doi":"10.1109/ICCES57224.2023.10192860","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192860","url":null,"abstract":"The aim of this research study is to present a new YouTube downloader software tool that leverages the power of both artificial intelligence and the Python programming language. This innovative solution combines cutting-edge AI algorithms with the versatility of Python to offer a highly efficient and automated solution for downloading videos from YouTube. The software utilizes AI algorithms to analyze the YouTube video and determine the best possible download format and resolution. The user-friendly interface, built using Python, allows even novice users to easily search, select, and download videos from YouTube. The tool also supports batch downloading, enabling users to download multiple videos at once. Furthermore, the software employs AI-powered optimization techniques to enhance the speed and quality of the downloaded videos. The results of our experiments show that this software offers a faster and more efficient solution for downloading YouTube videos compared to traditional YouTube downloader tools. In conclusion, the developed YouTube downloader using Python and AI is a valuable tool for individuals and businesses who require a fast and reliable way to download and save YouTube videos for offline viewing.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133956294","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

Intelligent System for Landscape(ISI) Integrated with Optimized Scene Segmentation Algorithm 集成优化场景分割算法的景观智能系统

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192893

Ye Wang, Yanmin Li

引用次数: 0

Web based Diabetes Prediction System with ML and Probabilistic Risk Stratification: Evaluation and Analysis 基于网络的基于ML和概率风险分层的糖尿病预测系统:评价与分析

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192695

M.Mahesh Student, L. Priya, K.Niranjan Reddy, Dr.TVS Gowtham Prasad, L. .. Reddy

{"title":"Web based Diabetes Prediction System with ML and Probabilistic Risk Stratification: Evaluation and Analysis","authors":"M.Mahesh Student, L. Priya, K.Niranjan Reddy, Dr.TVS Gowtham Prasad, L. .. Reddy","doi":"10.1109/ICCES57224.2023.10192695","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192695","url":null,"abstract":"Type 1 diabetes, a metabolic condition marked by elevated blood sugar, has become much more prevalent among young individuals. Early identification is essential since it is a chronic illness with a protracted incubation period. The lack of clear beginning symptoms might cause therapy to be delayed. Chronic damage and malfunction of many tissues, including the eyes (Diabetic retinopathy), kidneys (Diabetic Nephropathy), heart(cardiovascular), blood vessels (peripheral arterial), and nerves (Diabetic Neuropathy), may result from long-term high blood sugar levels. It is crucial to diagnose diabetes early. To do this, a number of factors are examined, including age, pregnancy, glucose, blood pressure, body mass index (BMI), insulin, and skin thickness. A comparative analysis of different algorithms is conducted to determine the most accurate one for predicting diabetes. Important keywords include type 1 diabetes, chronic disease, early detection, high blood sugar, chronic damage, predictive algorithms, and attributes analysis. The field of machine learning is becoming increasingly important in data science, as it focuses on how machines can learn from experience. The objective of this research is to combine multiple machine learning approaches to develop a system that reliably predicts the early development of diabetes in individuals. To accomplish this, the model's accuracy is determined using techniques like distance-based algorithm, binary regression, Classification and Regression Tree(CART). The most accurate algorithm is then chosen for estimating the probability of diabetes. The main objective of the study is to increase the precision of diabetes prediction by using the capabilities of machine learning.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123883532","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

Design and Comparative Analysis of a User-Friendly Telegram Bot for Image Steganography using F5 and LSB Algorithms 基于F5和LSB算法的用户友好型电报机器人图像隐写设计与比较分析

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192875

Bishakha Gope, Sakshi Nawale, Sakshi Deo, Tanishka Chavan, P. Kumbharkar

{"title":"Design and Comparative Analysis of a User-Friendly Telegram Bot for Image Steganography using F5 and LSB Algorithms","authors":"Bishakha Gope, Sakshi Nawale, Sakshi Deo, Tanishka Chavan, P. Kumbharkar","doi":"10.1109/ICCES57224.2023.10192875","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192875","url":null,"abstract":"Secure communication has become increasingly important with the rise of online communication and data privacy concerns. Image steganography is a popular method for secure communication, but existing systems face several challenges. One of the primary challenges is maintaining image quality while hiding a significant amount of data within the image. Additionally, many existing systems are not user-friendly or system-independent, making them less accessible to a wider range of users. Security and detection of hidden messages are also challenges that need to be addressed. To address these challenges, user-friendly Telegram bot for image steganography that uses the F5 algorithm for JPEG images and the LSB algorithm for PNG images is presented in the study. The objective is to provide a secure and user-friendly platform for image steganography that is easily accessible and system-independent. The use of Telegram as the platform for the bot provides a familiar and widely-used interface that many users are comfortable with. Additionally, the use of encryption ensures that hidden messages are transmitted securely and cannot be easily intercepted or detected. This survey demonstrates that the proposed system provides high-quality steganography images that are difficult to detect, addressing the challenge of detection. Overall, the proposed system aims to overcome the challenges faced by existing image steganography systems and provide a reliable and effective method for secure communication.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123906170","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

Comparative Analysis of Machine-Learning Techniques for Insult Detection in Social Commentary 社会评论中侮辱检测的机器学习技术比较分析

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192796

Aakash K G, S. Juliet

{"title":"Comparative Analysis of Machine-Learning Techniques for Insult Detection in Social Commentary","authors":"Aakash K G, S. Juliet","doi":"10.1109/ICCES57224.2023.10192796","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192796","url":null,"abstract":"Social media users' immediate and long-term well-being is seriously threatened by cyberbullying. With an eye towards creating early warning systems for the anticipation of harmful comments vulnerable to attacks, we investigate the detection of cyberbullying in social networks. Online harassment disturbs by cyberbullying has grave repercussions. The ability to automatically detect cyberbullying and recognize the roles that individuals assume in social interaction is required to address this issue in online contexts. The use of technology as a bullying tool is known as cyberbullying. Cyberbullying is a problem that has arisen along with technology development and poses a risk to adolescents' psychological welfare. A framework is suggested to provide two distinct descriptions of cyberbullying. Cyberbullying is a significant issue on the internet that affects both adults and teenagers. Mistakes like despair and suicide have resulted from it. There is an increasing demand for content on social media platforms to be regulated. The following study builds a model based on the identification of cyberbullying in text data using a Naive Bayes classifier, utilizing data from Twitter and comments based on personal assaults from Wikipedia forums. The model offers accuracy levels above 90% for data from Tweets and accuracy levels above 80% for data from Wikipedia.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123911777","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

Design and Comparative Analysis of 10nm and 16nm Multichannel Nanosheet FinFET with Varying Doping Concentrations 不同掺杂浓度下10nm和16nm多通道FinFET的设计与比较分析

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192682

Veera Venkata Sai Amudalapalli, Hima Bindu Valiveti, Asisa Kumar Panigrahy

{"title":"Design and Comparative Analysis of 10nm and 16nm Multichannel Nanosheet FinFET with Varying Doping Concentrations","authors":"Veera Venkata Sai Amudalapalli, Hima Bindu Valiveti, Asisa Kumar Panigrahy","doi":"10.1109/ICCES57224.2023.10192682","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192682","url":null,"abstract":"Nanotechnology chip designing focuses on the Nano-electronics concepts and produces circuits that are built with the components sizing in nanometers (nm). Smaller channel lengths in the range of nm not only increase the speed of operation but also help in accommodating more transistors in the chip and also decreases power consumption. One of the major side effects of miniaturizing of the transistors are leakages. The current work emphasizes on design of 16nm and 10nm Nanosheet FinFET (NS-FET) with Gate All Around (GAA) technology and fully depleted Silicon on Insulator (SoI). The design of multi-channel NS-FET with uniform doping concentration level varying form 1e16 cm-3 to 1e18 cm-3 for the two channels of the NS-FET is presented for both 16nm and 10nm channel lengths. The impact of GAA nm-technology NS-FinFET devices are thoroughly investigated using Visual technology computer-aided design (Visual TCAD) simulator for parameters like (VGS-ID), transfer characteristics, Ion/Ioff ratio and Drain Induced Barrier Lowering (DIBL). Comparative results for 16nm and 10nm technology are presented under varying doping concentrations and 10nm technology. The 10nm technology exhibits better transfer characteristics in terms of threshold voltage, current ratio and DIBL.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121209510","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

Comparative Analysis of Machine Learning Techniques for Mental Health Prediction 心理健康预测的机器学习技术比较分析

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192763

Naveen Paul E, S. Juliet

{"title":"Comparative Analysis of Machine Learning Techniques for Mental Health Prediction","authors":"Naveen Paul E, S. Juliet","doi":"10.1109/ICCES57224.2023.10192763","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192763","url":null,"abstract":"The prevalence of mental health problems has prompted investigations into the use of machine learning to tackle the issues. Mental health is a crucial component of an individual’s overall well-being and can be detected and treated early on, significantly improving the quality of life for those affected. This study examines the use of machine learning algorithms to predict mental health disorders using a dataset of self-reported information. Four commonly used machine learning models K-nearest neighbor classifier, logistic regression, random forest and decision tree are compared in terms of their performance. The objective of this study is to compare the performance of these machine learning algorithms on a self-reported mental health dataset and identify the most suitable model for predicting mental health. The challenges faced by the system include the limited size and quality of the dataset, the need for ethical considerations in handling sensitive mental health information, and potential biases in the data .The results of the experiments identify the most suitable model for predicting mental health.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129243174","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