2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)最新文献

Lung Cancer Detection using Ensemble of Machine Learning Models 基于机器学习模型集成的肺癌检测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734212

Basra Jehangir, S. Nayak, Sourav Shandilya

引用次数: 1

SleepQual and B.Health: Smartwatch and Smartphone based Behavioral Datasets of Youth 睡眠质量和健康:基于智能手表和智能手机的青少年行为数据集

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734122

Anshika Arora, P. Chakraborty, M. Bhatia

{"title":"SleepQual and B.Health: Smartwatch and Smartphone based Behavioral Datasets of Youth","authors":"Anshika Arora, P. Chakraborty, M. Bhatia","doi":"10.1109/confluence52989.2022.9734122","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734122","url":null,"abstract":"Wearable sensors recording different components of peoples’ daily activity are commonly used these days for collecting motion data. These provide objective measures of behavioral components like sleep and physical activity. Smartphones are being used increasingly contributing to users’ screen viewing and hence are capable of providing precise measures of users’ screen time. Disruption in the key lifestyle patterns like sleep, physical activity and screen viewing may result in health impairments as these correspond to the utmost engaged times during the day. This study presents a novel dataset containing attributes of physical activity, sleep, and smartphone-based screen viewing collected from users’ smartphones and smartwatches. The dataset contains real time objective measures of activities of 24 undergraduate students. A sleep quality indicator and a behavioral health indicator namely SleepQual and B.Health respectively are evaluated using the extracted features. The instances are labelled based on the scores of SleepQual and B.Health. We have made the dataset publicly available which opens avenues for researchers to employ various intelligent data analysis techniques in order to develop systems capable of automatically assessing sleep quality and behavioral health using digital data without the need of any self-reporting questionnaires and tools. To provide baseline performance four machine learning classifiers are implemented on the proposed dataset.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905660","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: 1

Multiclass Classification for Large Medical Data using Adaptive Random Forest and Improved Feature Selection Methods 基于自适应随机森林和改进特征选择方法的大型医疗数据多类分类

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734140

M. Ram, G. Suresh, Narasimha Swamy Biyappu

{"title":"Multiclass Classification for Large Medical Data using Adaptive Random Forest and Improved Feature Selection Methods","authors":"M. Ram, G. Suresh, Narasimha Swamy Biyappu","doi":"10.1109/Confluence52989.2022.9734140","DOIUrl":"https://doi.org/10.1109/Confluence52989.2022.9734140","url":null,"abstract":"A Classification method stands out as a reliable data mining technique applied in medical sciences. We observe multi-classification problem afflicting many recent applications that includes social network analysis, biology, anomaly detection and computer vision. However, such classification techniques usually struggle while dealing with features of data that is generated from multiple classes. Moreover, in case of large medical data, it is observed that the dimension of the data poses the biggest challenge while applying classification technique to them. In order to overcome such problems, we have proposed an adaptive random forest classifier method that uses ensemble feature selection technique for better information gain (IG), improved correlation (IC) and gain ratio (GR). Also, it seeks to solve the class imbalance problem by applying bootstrap resampling for medical data. The result of the proposed method proved that adaptive RF (Random Forest) classifier offers better accuracy, precision and F-score values than standard Random Forest and KNN classification algorithms. The overall performance of algorithms was tested over five real datasets. The result analysis shows performance of the proposed classifier is promising in all real datasets as compared to standard methods.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116921694","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

Designing of a Novel PCF Biosensor having Octagonal Core and based on SPR for Chemical and Heavy Metal Sensing 基于SPR的新型八角形核PCF生物传感器的设计及其在化学和重金属传感中的应用

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734120

Amita Shakya, Surinder Singh

引用次数: 6

Predictive analysis and supervised detection for fraudulent cases in healthcare 医疗保健欺诈案件的预测分析和监督检测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734195

Ayushi Bhardwaj, Sushant Kumar, Aishwarya Naidu

{"title":"Predictive analysis and supervised detection for fraudulent cases in healthcare","authors":"Ayushi Bhardwaj, Sushant Kumar, Aishwarya Naidu","doi":"10.1109/confluence52989.2022.9734195","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734195","url":null,"abstract":"Healthcare is perhaps one of the most crucial industry for humanity and it has been, and it will always be a growing industry. This increases the risk of being exploited. In our paper, we performed and analyzed different trends for the suspicious/fraudulent medical activities. We categorized different groups of patients involved and analyzed their distribution on the basis of multiple factors. Healthcare is a massive and widely distributed sector with a numerous entities and stakeholders involved. Limited connectivity within these distributed management create various loopholes that people try to exploit. We performed multiple analysis for suspicious and fraudulent activities in the healthcare/Medicare industry and tried to look for popular trends people opt to exploit the system. We also tried various supervised machine learning algorithms to see how they behave with our dataset and what is the accuracy of their detection. This can be helpful in choosing the best model while building a solution to deal with various use cases involved in fraudulent activity detection. Healthcare is one of the sectors that will always be relevant to living beings and should be taken special care of.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115023274","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

Automation of Water Distribution System by Prediction of Water Consumption and Leakage Detection Using Machine Learning and IoT 利用机器学习和物联网预测用水量和泄漏检测实现配水系统自动化

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734192

Sachin Aggarwal, Smriti Sehgal

{"title":"Automation of Water Distribution System by Prediction of Water Consumption and Leakage Detection Using Machine Learning and IoT","authors":"Sachin Aggarwal, Smriti Sehgal","doi":"10.1109/confluence52989.2022.9734192","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734192","url":null,"abstract":"The distribution of water can be a very challenging task and even a small mistake in consumption estimation can cause huge problem which includes shortage of water in some areas. Also, the leakage in water supply line is a huge problem as these supply lines are underground and it is very difficult to identify and repair if a leakage occurs in those pipelines. To solve these problems a lot of research have been done by different authors which includes the use of regression-based Machine learning algorithms for predicting the water consumption this includes Random Forest, Decision Tree and Support Vector Regression algorithms and for leakage detection they have used classification algorithms like deep autoencoder, hydroacoustic spectrograms and many more. Also, there are some models in which the researchers have used sensors like pressure sensor to use that data for prediction of leakage detection. This previous work will be further discussed in the second section of this paper. In this paper we have combined Machine Learning with IoT to create an automated model which can continuously monitor the date for leakage in water distribution line and predict the water consumption and this whole task is done in real-time. In this paper we have used classification variant of Artificial Neural Network algorithm for leakage detection and regression variant for water consumption prediction and for the performance evaluation of this model we have used R-squared and Adjusted R-Squared for water consumption prediction and confusion matrix, accuracy for leakage detection.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116119928","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

Enhancing the Security of Medical Images in Telemedicine Using Region-based Crypto-Watermarking Approach 基于区域的加密水印方法增强远程医疗中医学图像的安全性

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734148

U. Verma, Neelam Sharma

{"title":"Enhancing the Security of Medical Images in Telemedicine Using Region-based Crypto-Watermarking Approach","authors":"U. Verma, Neelam Sharma","doi":"10.1109/confluence52989.2022.9734148","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734148","url":null,"abstract":"Along with maintaining the confidentiality and privacy of patient information, the preservation and authentication of internal physiological features of tissues and organs present in any type of medical images is equally important while transmitting medical images in Telemedicine. To achieve it, this paper presents a region-based approach for Medical Image by integrating cryptography in the digital watermarking. To preserve the internal physiological features, medical image is separated into two regions – Region of Interest (ROI) and Not-a-Region of Interest (NROI). ROI contains the important internal physiological features of human body. Therefore, no information is embedded in ROI to preserve it. Cryptography is integrated only for generation and exchange of the secret key of ROI using Elliptic curve cryptography. Patient information and secret key is embedded into NROI using wavelet-based hybrid watermarking technique. The proposed work is tested on varieties of medical image dataset of MRI, CT scan and X-ray against various intentional and unintentional attacks and evaluated with performance metrics PSNR (Peak signal to Noise Ratio), SSIM (Structural Similarity Index), NC (Normalized Correlation) and BER (Bit Error Ratio). Secret key is matched at receiving end to authenticate the contents. The proposed approach not only enhances authenticity of medical images but also preserves the internal physiological features along with maintaining the confidentiality of patient information.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122719769","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

Improving the efficiency of Plant-Leaf Disease detection using Convolutional Neural Network optimizer-Adam Algorithm 利用卷积神经网络优化器- adam算法提高植物叶片病害检测效率

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734132

Ajay Kumar, Vikram Bali, Shubhangi Pandey

{"title":"Improving the efficiency of Plant-Leaf Disease detection using Convolutional Neural Network optimizer-Adam Algorithm","authors":"Ajay Kumar, Vikram Bali, Shubhangi Pandey","doi":"10.1109/confluence52989.2022.9734132","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734132","url":null,"abstract":"Crop diseases should be diagnosed and treated as early as possible in order to improve yield. With the growing demand for food, safe and diverse food to support a prosperous population and improve living standards, the management of plant diseases faces growing challenges. It has emerged the major problem in recent times. With the help of different technological implementations, many preventive measures are taken as detection and classification based on algorithms such as support vector machines and linear discriminant analysis, detection using image processing, recognition using pesticides etc. Since Convolutional neural networks (CNNs) have shown to be effective in the field of machine learning, (CNN) Adam optimization model is being used in this paper to detect and determine illnesses in plants based on their leaves The performance of the models was evaluated using various factors such as batch size, dropout, and the number of epochs. The accuracy of implemented model is 96.77% which is higher than the accuracy achieved from other models like SVM (Support Vector Machine) and basic CNN.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"495 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116200549","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

Next Word Prediction Using Deep Learning: A Comparative Study 使用深度学习的下一个单词预测:一个比较研究

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734151

Milind Soam, Sanjeev Thakur

引用次数: 7

BFLEdge: Blockchain based federated edge learning scheme in V2X underlying 6G communications BFLEdge: V2X底层6G通信中基于区块链的联邦边缘学习方案

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734213

Vishwani Patel, Pronaya Bhattacharya, S. Tanwar, N. Jadav, Rajesh Gupta

{"title":"BFLEdge: Blockchain based federated edge learning scheme in V2X underlying 6G communications","authors":"Vishwani Patel, Pronaya Bhattacharya, S. Tanwar, N. Jadav, Rajesh Gupta","doi":"10.1109/Confluence52989.2022.9734213","DOIUrl":"https://doi.org/10.1109/Confluence52989.2022.9734213","url":null,"abstract":"Sixth generation (6G) vehicle-to-anything (V2X) networks support intelligent edge computing that leverages data sensing, computation, and offloading among vehicular nodes (VN) with ultra-low latency. Data is heterogeneous with high complex interactions among V2X users and pass via open channels that induce privacy and security concerns. Thus, federated learning (FL) protects user privacy and fine-tunes the learning models at resource-constrained edge nodes to address security and computational concerns at the edge. However, to ensure reliability and trust, we propose a block-chain (BC) and FL-based edge scheme, BFLEdge. It also improves the overall learning rate of the FL model. The proposed scheme consists of three phases, where the first phase uses local machine learning (LML) to model the VN data and store it into the local BC network. The LML block updates are verified in the second phase through a proposed distributed consensus mechanism. Lastly, through 6G communication services, the channel dynamics are modelled as a Markov chain process to reduce end-to-end delay of local BC propagation updates at the edge that improves the V2X system throughput. Simulation and analytical results are proposed based on channel loss, block mining rate, edge latency, and FL-learning rate. The obtained results indicate the viability of the proposed framework against conventional state-of-the-art approaches.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129399958","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: 10