2022 IEEE International Conference on Data Science and Information System (ICDSIS)最新文献

Comparative Analysis on Brain Tumor Classification using Deep Learning Models 基于深度学习模型的脑肿瘤分类比较分析

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915947

M. B. Sahaai, G. Jothilakshmi, R. Selva Kumar, S. Praveen Kumar

引用次数: 2

A Robust Blockchain Architecture for Electronic Health Data using Efficient Lightweight Encryption Model with Re-Encryption Scheme 使用高效轻量级加密模型和重加密方案的电子健康数据健壮的区块链体系结构

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915902

A. G. Chandini, P. I. Basarkod

{"title":"A Robust Blockchain Architecture for Electronic Health Data using Efficient Lightweight Encryption Model with Re-Encryption Scheme","authors":"A. G. Chandini, P. I. Basarkod","doi":"10.1109/ICDSIS55133.2022.9915902","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915902","url":null,"abstract":"The handling of electronic health records (EHRs) from the Internet of Medical Things (IoMT) is one of the most challenging research areas as it consists of sensitive information which is a target for attackers. Also, it is highly complex and expensive to deal with modern healthcare systems as it requires a lot of secured storage space. However, these problems can be mitigated with the improvement in health record management using blockchain technology. To improve data security, patient privacy, and scalability, the proposed work develops a scalable lightweight framework based on blockchain technology. Initially, the COVID-19 related data records are hashed by using an enhanced Merkle tree (EMT) data structure. The hashed values are encrypted by lattice-based cryptography with a Homomorphic Proxy Re-Encryption scheme (LBC-HPRS) in which the input data are secured. After the completion of the encryption process, the blockchain uses IPFS to store secured information. Finally, the Proof of Work (PoW) concept is utilized to verify and validate the security of the input COVID-based data records. The experimental setup of the proposed work is performed by using a python tool and the performance metrics like encryption time, re-encryption time, decryption time, overall processing time and latency prove the efficacy of the proposed schemes.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115301289","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: 2

Grading Severity of Pterygium using Fuzzy Reasoning 模糊推理对翼状胬肉严重程度的分级

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9916017

H. Kumar, M. Jayaram

引用次数: 1

RSSI Based Improved Weighted Centroid Localization Method Using Indirect Transmission and Error Estimation 基于RSSI的间接传输和误差估计改进加权质心定位方法

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915992

S. Vinay Prasad, Somnath Sinha

引用次数: 0

Design and Analysis of Low Power High Gain Amplifiers for DAC Application 用于DAC的低功率高增益放大器的设计与分析

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915853

S. Surabhi, Deepa

引用次数: 1

Introspecting diverse IoT-traffic analysis methods in Smart Environments and Prospects 智能环境下多种物联网流量分析方法的反思与展望

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915825

Manish Snehi, A. Bhandari

{"title":"Introspecting diverse IoT-traffic analysis methods in Smart Environments and Prospects","authors":"Manish Snehi, A. Bhandari","doi":"10.1109/ICDSIS55133.2022.9915825","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915825","url":null,"abstract":"The intelligent Universally Interacting IoT Objects (UIO) ecosystem is deployed at the perception layer in smart systems. Furthermore, application of IoT devices in virtually all domains has outfitted the path for implementing efficient and sustainable intelligent systems. The digital wave heralded the IoT as the globe’s most technical revolution. Cyber-physical systems, Smart ecosystems, digital technologies, and organizations constantly redesign and accept IoT devices. However, the advent of IoT devices has incubated cyber security issues. Researchers have invested efforts in understanding IoT-traffic behavior to defend against such attacks. This paper outlines the attributes of IoT traffic, performs a comparative analysis of existing traffic classification solutions, and recommends future cyber defense solutions based on IoT-traffic characteristics in Smart Environments. The paper also discusses the characteristics of a resilient classification and defense framework. It emphasizes the vital performance metrics and proposes a distributed, resilient, and scalable framework based on intelligent learning approaches.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122366109","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

Prediction of Alopecia Areata using Machine Learning Techniques 使用机器学习技术预测斑秃

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915804

S. Aditya, Sanah Sidhu, M. Kanchana

{"title":"Prediction of Alopecia Areata using Machine Learning Techniques","authors":"S. Aditya, Sanah Sidhu, M. Kanchana","doi":"10.1109/ICDSIS55133.2022.9915804","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915804","url":null,"abstract":"Alopecia areata (AA) is a chronic, autoimmune condition that attacks anagen hair follicles, causing sudden hair loss, resulting in circular bald patches. The disorder may be developed in both adults as well as children and the prevalence of the same is approximated to be 1 in every 1000 people, putting around 2% of the broad population at a risk of developing this disease at some point in their lives. Existing techniques to assess the disorder are heavily supported by naked-eye examination and thus have demonstrated nominal accuracy. In recent years, Machine Learning has paved the way for enhanced diagnosis of diseases in various fields of healthcare, including Dermatology. This study postulates a significant role of computer-aided diagnosis of AA to equip medical practitioners with a more accurate form of prediction and classification. Our proposed framework is in relevance to the categorization of healthy hair and hair with symptoms that indicate AA. For the dataset, a 1000 images of healthy hair were collected via web scraping and the Figaro1k dataset. Over 500 AA images were web scraped along with some of the same from the Dermnet dataset. To improve the dataset further, the images were preprocessed by performing image enhancement, segmentation and data augmentation. This study aims to elevate the quality of diagnosis carried out for accurate prediction of Alopecia areata in the field of Dermatology by conducting a comparative study for classification by implementing SVM, KNN, Random Forest Classifier, Gaussian Naive Bayes and CNN algorithms which gave 85%, 78%, 88%, 80% and 92% accuracy respectively.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128235672","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: 3

Computer Vision System in Identifying the Ripening Stages of Mango – Alphonso Cultivar 计算机视觉系统在芒果阿方索品种成熟阶段识别中的应用

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915811

A. Prabhu, C. Mamatha

引用次数: 0

Car Crash Detection System using Machine Learning and Deep Learning Algorithm 基于机器学习和深度学习算法的汽车碰撞检测系统

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915889

S. S, Sahana. P. Shankar, Himanshu Jain B J, Lisha L Narayana, Nikhita Gumalla

{"title":"Car Crash Detection System using Machine Learning and Deep Learning Algorithm","authors":"S. S, Sahana. P. Shankar, Himanshu Jain B J, Lisha L Narayana, Nikhita Gumalla","doi":"10.1109/ICDSIS55133.2022.9915889","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915889","url":null,"abstract":"Over 80% of mishaps are caused by a lack of identifying the accident on time, as well as failure to arrive in time to provide emergency care for the victim. The point is to distinguish and utilize machine learning to decide the best means of detecting car crash with light of the live transfer of dash cam data in the vehicle. The thought is to take every pixel and run it with a deep learning model prepared to recognize video outlines into mishap or non-mishap. Essentially, in Artificial Intelligence (AI), informational indexes are collected. Gathered information bases will be refined and given to AI calculations to prepare for image recognition with the help of computer vision. After fruitful preparation, AI calculations will be tried and the outcomes will be recorded for examination purposes. In the proposed vehicle crash location framework, the impact identification is performed utilizing the Convolutional Neural Network (CNN), taking a bunch of pictures as information, the framework distinguishes the crash, the effect of the vehicle and the greatness of the mishap. Based on the performance of machine learning algorithms, comparative analysis is performed and the results will be tabulated. Two machine learning algorithms are considered i.e. Random Forest Classifier and Logistic Regression which enables the output regarding the generated index from the CNN and running through the indices with location impact and severity of the damage.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134155211","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: 3

Waste Segregation into Biodegradable & Non-Biodegradable using Transfer Learning 利用迁移学习将垃圾分类为可生物降解和不可生物降解

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915984

Shubh Nisar, Yash Jhaveri, Tanay Gandhi, Tanay Naik, Sanket J. Shah, Pratik Kanani

{"title":"Waste Segregation into Biodegradable & Non-Biodegradable using Transfer Learning","authors":"Shubh Nisar, Yash Jhaveri, Tanay Gandhi, Tanay Naik, Sanket J. Shah, Pratik Kanani","doi":"10.1109/ICDSIS55133.2022.9915984","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915984","url":null,"abstract":"Garbage generation, inadequate waste collection, transportation, treatment, and disposal are serious environmental issues around the world. Because of rapid urbanisation and population growth, global annual waste generation is expected to increase from 2.01 billion tonnes to 3.4 billion tonnes over the next 30 years. In 2016, the world produced 242 million tonnes of plastic waste, accounting for 12% of all solid waste. The amount of waste generated in India is increasing remarkably that the current systems cannot cope with it due to the increase in urban population, and this impacts on the environment and public health. This paper proposes a smart bin concept using modern Artificial Intelligence techniques on a microcontroller-based platform. The primitive idea is to segregate waste after the waste is dumped, but the proposed system’s basic idea is to segregate the waste while being dumped. The software is designed in such a manner that it opens the corresponding bin on recognizing the type of waste using contemporary transfer learning methods. Once these smart bins are implemented on a larger scale, replacing the conventional bins today allows waste to be managed efficiently, thereby steering off dump yards.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134215228","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