2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)最新文献_第10页

Reference framework “HOGO” for cybersecurity in SMEs based on ISO 27002 and 27032 基于ISO 27002和27032的中小型企业网络安全参考框架“HOGO”

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734116

Carlos F. Cruzado, Liset S. Rodriguez-Baca, Lizeth G. Huanca-Lopez, Erika I. Acuna-Salinas

引用次数: 3

COVID-19 Fake News Detection System COVID-19假新闻检测系统

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734144

R. Malhotra, Anushree Mahur, Achint

{"title":"COVID-19 Fake News Detection System","authors":"R. Malhotra, Anushree Mahur, Achint","doi":"10.1109/confluence52989.2022.9734144","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734144","url":null,"abstract":"This article deals with the problem of the rapidly increasing COVID-19 infodemic in the world. Thus, there is a need for an effective framework of detecting fake information or misleading news related to COVID-19 virus/disease. To resolve this, we have used a dataset obtained from ConstraintAI'21. The dataset consists of 10,700 tweets and online posts of fake and real news concerning COVID-19. Machine Learning (ML) algorithms compared in this paper to classify the given news or tweet into real or fake are Logistic Regression (LR), K-Nearest Neighbor (KNN), Linear Support Vector Machine (LSVM), Random Forest Classifier (RFC), Decision Tree (DT), Naive Bayes (NB) and Stochastic Gradient Descent (SGD) algorithm. Two feature extraction techniques were used count vectorization and TF-IDF. Deep Learning (DL) algorithms implemented using Adam optimizer are Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The best testing accuracy was achieved with the LSVM model using TF-IDF feature extraction method followed by Stochastic Gradient Descent classifier with TF-IDF feature extraction technique. LR, DT, and RFC performed better with the Count vectorization feature extraction technique, whereas LSVM, KNN, NB and SGD had better accuracy with TF-IDF feature extraction technique. The LSTM model performed slightly better among the DL algorithms.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"63 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":"122896148","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

Prediction of heart disease using machine learning: State of the art and future direction 使用机器学习预测心脏病:现状和未来方向

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734165

Saloni Mittal, Nirbhay Kashyap, Rahul Verma

{"title":"Prediction of heart disease using machine learning: State of the art and future direction","authors":"Saloni Mittal, Nirbhay Kashyap, Rahul Verma","doi":"10.1109/Confluence52989.2022.9734165","DOIUrl":"https://doi.org/10.1109/Confluence52989.2022.9734165","url":null,"abstract":"Heart disease detection is done here using a number of sample data taken from various sources. We have to use different machine learning technique to detect whether a given data is cancer infected or not. An ingenious technique that allows many technologies to learn from themselves. It is an instance of artificial learning that enables computers to function like humans. Machine learning aims for computers to learn on their own without any human interruption. When united with IoT, it has a high capability to grasp things. It has ability to change the mortgage market. It has accurate data analysis and has very sharp business intelligence. Machine learning has four fundamental steps to create a model. First, a training dataset is selected and prepared, then an algorithm has to be selectedto apply to the training dataset. After this, the algorithm is trained to create the model and, lastly using and improving the model. Machine learning consists of various techniques like supervised learning algorithms, unsupervised learning algorithms, reinforcementmachine learning, and semi-supervised machine learning. To create any machine learning model there are few python libraries that are always needed. They are pandas, NumPy, skleam, and matplotlib. If there is a need to evaluate the performance of a machine learning algorithm, the train test split technique can be used. To create a graph/plot, pyplot which is a matplotlib module comes in handy. It can help in creating bar graphs, pie charts, histograms, scatter plots,and 3D plotting. In the model, we are using a few functions like standardscaler () function, classification report, and confusion matrix. In the end, we are getting a required plot that will show us the accuracy of our model. Results are shown at last and conclusions are derived.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"189 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":"117322160","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

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734220

Harshitha Batta, Anjana V Murthy, S. Savitri

引用次数: 1

A Survey on Resource Allocation Schemes in Device-to-Device Communication 设备对设备通信中资源分配方案综述

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734183

Sucheta Gupta, Rajan Patel, Rajesh Gupta, S. Tanwar, Nimisha Patel

{"title":"A Survey on Resource Allocation Schemes in Device-to-Device Communication","authors":"Sucheta Gupta, Rajan Patel, Rajesh Gupta, S. Tanwar, Nimisha Patel","doi":"10.1109/confluence52989.2022.9734183","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734183","url":null,"abstract":"Device-to-device (D2D) communication is a breakthrough technology of fifth-generation (5G) and beyond networks. It offers direct communication between D2D devices without communicating via a centralized base station (BS). It works either in an overlay or underlay communication mode. The underlay mode significantly improves spectral efficiency, communication delay, energy efficiency, and overall sum rate. But, it induces huge interference, i.e., inter and intra-cell interferences. To overcome the aforementioned interference issues, researchers across the globe have given various game theory, graph theory, heuristic, deep reinforcement learning (DRL), and machine learning (ML)-based efficient resource management schemes for D2D communication. But, as per the literature explored, there is no such survey that sums up all such solutions and techniques and their comparative analysis. Motivated from this, we present a brief survey on resource allocation schemes, which helps researchers working in this field. We also highlight various open issues and research challenges pertaining to resource allocation in D2D communication.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"94 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":"127306874","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

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734137

Madhulika Bhatia, Saru Dhir, Poonam Tanwar, Amit Khan

引用次数: 0

Poisoning attack detection using client historical similarity in non-iid environments 在非id环境中使用客户端历史相似性进行投毒攻击检测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734158

Xintong You, Zhengqi Liu, Xu Yang, Xuyang Ding

{"title":"Poisoning attack detection using client historical similarity in non-iid environments","authors":"Xintong You, Zhengqi Liu, Xu Yang, Xuyang Ding","doi":"10.1109/Confluence52989.2022.9734158","DOIUrl":"https://doi.org/10.1109/Confluence52989.2022.9734158","url":null,"abstract":"Federated learning has drawn widespread attention as privacy-preserving solution, which has a protective effect on data security and privacy. It has unique distributed machine learning mechanism, namely model sharing instead of data sharing. However, the mechanism also leads to the fact that malicious clients can easily train local model based on poisoned data and upload it to the server for contaminating the global model, thus severely hampering the development of federated learning. In this paper, we build a federated learning system and simulate heterogeneous data on each client for training. Although we cannot directly differentiate malicious customers by the uploaded model in a heterogeneous data environment, by experiments we found some features that are used to distinguish malicious customers from benign customers during training. Given above, we propose a federated learning poisoning attack detection method for detecting malicious clients and ensuring aggregation quality. The method can filter out anomaly models by comparing the similarity of the historical changes of clients and gradually identifying attacker clients through reputation mechanism. We experimentally demonstrate that the method significantly improves the performance of the global model even when the proportion of malicious clients is as high as one-third.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"132 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":"121400056","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

A Constructive approach to Numerical Mapping scheme of Nucleotides for Preprocessing in Machine Learning 一种用于机器学习预处理的核苷酸数值映射方案的建设性方法

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734186

C. Saravanakumar, N. Usha Bhanu

{"title":"A Constructive approach to Numerical Mapping scheme of Nucleotides for Preprocessing in Machine Learning","authors":"C. Saravanakumar, N. Usha Bhanu","doi":"10.1109/confluence52989.2022.9734186","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734186","url":null,"abstract":"One of the major issues in the Bioinformatics discipline is to construct a method by which the precise protein-coding region can be identified in the intended nucleotide series. The exact spotting of protein coding regions in a nucleotide is valuable in numerous entities. For an instance, it aids in describing unique proteins, develop drugs, and furthermore in uncovering the developmental foundation of a specific living being. Digital Signal Processing (DSP) rooted technique is quite popular for identifying protein coding regions. The main fundamental stage of the DSP oriented prediction of exon, is to direct the nucleotide base to the numeric values. Choosing a numerical mapping configuration influences the characteristics of the DNA sequence, helping them to pinpoint the precise area of the exon. Over the most recent twenty years, a number of methods to map the nucleotides have been effectively utilized as a preprocessing stage for exon prediction. The proposed method of mapping a sequence outerforms other schemes in predicting the region of exons.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"20 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":"114766591","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

Supervision of Video Game Car Steering Implementing HORCNN Network 基于HORCNN网络的电子游戏汽车转向监管

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734198

Clay Motupalli, Rp Mohinth, Shivam Gaur, Vishesh Mittal, S. Prakash

引用次数: 1

Distance measures of Pythagorean Fuzzy Sets based on sine function in property selection under TOPSIS approach TOPSIS方法下基于正弦函数的毕达哥拉斯模糊集属性选择的距离度量

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734130

M. Bhatia, H. Arora, Anjali Naithani, Surbhi Gupta

引用次数: 1