2022 International Conference on Innovative Trends in Information Technology (ICITIIT)最新文献_第5页

Auto-Encoder LSTM for learning dependency of traffic flow by sequencing spatial-temporal traffic flow rate: A speed up technique for routing vehicles between origin and destination 基于时序交通流速率学习交通流相关性的自编码器LSTM:一种车辆始发地与目的地之间路由的加速技术

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744139

Jayanthi Ganapathy, Thanushraam Sureshkumar, Medha Raghavendra Prasad, Cheekireddy Dhamini

{"title":"Auto-Encoder LSTM for learning dependency of traffic flow by sequencing spatial-temporal traffic flow rate: A speed up technique for routing vehicles between origin and destination","authors":"Jayanthi Ganapathy, Thanushraam Sureshkumar, Medha Raghavendra Prasad, Cheekireddy Dhamini","doi":"10.1109/ICITIIT54346.2022.9744139","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744139","url":null,"abstract":"Urban transport system is a time varying network. The variation in travel time and delay in travel faced by commuters is the adverse effect of traffic congestion. Traffic information in preceding time instances contributes in analyzing traffic in succeeding instances and spatial information of traffic is required for traffic flow assessment on highways. Sequencing spatial and temporal traffic information in preceding time instance helps in estimating traffic flow in sequence in successive time instances by formalizing Sequence Convolution based auto-encoder Long Short term Memory (SCAE-LSTM) network. The objective of this work is to estimate traffic flow on highways for different origin-destination (OD) pair based on spatial-temporal traffic sequences. Hence, Spatial-TemporAl Reconnect (STAR) algorithm is proposed. The performance of STAR is investigated by conducting extensive experimentation on real traffic network of Chennai Metropolitan City. The computational complexity of the algorithm is empirically analyzed. The proposed STAR algorithm is found to estimate traffic flow during peak hour traffic with reduced complexity in computation compared to other baseline methods in short term traffic flow predictions like LSTM, ConvLSTM and GRNN. Finally, conclusions on results are presented with directions for future research.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115496090","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

Parallelizing CPU-GPU Network Processing Flows 并行化CPU-GPU网络处理流

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744209

Anup Nair, Amit M. Joshi

引用次数: 0

HomeID: Home Visitors Recognition using Internet of Things and Deep Learning Algorithms HomeID:使用物联网和深度学习算法的家庭访客识别

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744223

K. Guravaiah, Gorinka Rithika, S. Raju

引用次数: 2

Cross-Project Change-Proneness Prediction with Selected Source Project 使用选定源项目进行跨项目变更倾向预测

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744186

A. Bansal, Viraj Madaan, Rahul Gaur, Ritesh Shakya

{"title":"Cross-Project Change-Proneness Prediction with Selected Source Project","authors":"A. Bansal, Viraj Madaan, Rahul Gaur, Ritesh Shakya","doi":"10.1109/ICITIIT54346.2022.9744186","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744186","url":null,"abstract":"Software change-proneness prediction aims to identity change prone parts of a software where focused attention is required by the managers and other stakeholders. This reduces development and maintenance costs by highlighting the classes which may change and work with the class in such a manner that prevents further changes from occurring often. Prediction requires training data which is generally obtained from historical data of the projects. However, this may not be the case for new projects which have limited or no historical data available. Cross-project change prediction helps solve this issue by using another project as training data to create a prediction model. With the vast number of candidate projects that can be used as a source to train the classifier, the problem of how to select an appropriate source project which can return a decent prediction accuracy with a model trained with it arises in cross-project change prediction.Through this paper, we propose an algorithm to select a source project which can be used to determine change prone classes in a target project with high accuracy. The source project is selected from a pool of 8 open-source projects. Three strategies are used to identity a suitable source project. The results of the three strategies are compared with one another and with a related change-proneness model proposed by Malhotra and Bansal known as the Random Cross-Project Prediction (RCP). Out of the three strategies in the proposed algorithm, the first two strategies performed better in comparison to the prediction performance of the random cross project prediction model with improvements in terms of AUC (1.04% and 1.27%), F-Measure (5.83% and 3.82%), and MCC (14.14% and 7.77%).","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129853867","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: 4

Numerical Methods for Solving High-Order Mathematical Problems using Quantum Linear System Algorithm on IBM QISKit Platform 基于IBM QISKit平台的量子线性系统算法求解高阶数学问题的数值方法

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744239

Simran Jakhodia, Babita Jajodia

{"title":"Numerical Methods for Solving High-Order Mathematical Problems using Quantum Linear System Algorithm on IBM QISKit Platform","authors":"Simran Jakhodia, Babita Jajodia","doi":"10.1109/ICITIIT54346.2022.9744239","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744239","url":null,"abstract":"Researchers are currently working on computational solutions based on quantum systems to accelerate the speed of complex mathematical models. This work presented how to formulate complex computational problems as a quantum system of linear equations and find solutions using Quantum Linear System Algorithm (QLSA), also called Quantum Harrow-Hassidim-Lloyd (HHL) algorithm. This paper showed experimental evaluation of multiple problem statements (curve-fitting functions, interpolating polynomials) as a quantum system of linear equations that involve computation of Vandermonde matrices as co-efficient matrices on IBM Quantum Information Software Kit for Quantum Computation (QISKit) platform. Along with a few examples demonstrating its evaluation on diagonal, Hermitian, and Non-Hermitian matrices as co-efficient matrices. The fidelity is used as a measure of performance for comparing the accuracy of quantum results with respect to existing classical solutions on IBM QISKit and drawing conclusions from the experimental results. Experimental evaluation shows that the fidelity depends on the sparsity of the input matrices and therefore the results vary depending on those matrices.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130089913","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

Estimated Decoder for Polar Codes Based on Belief Propagation 基于信念传播的极坐标码估计解码器

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744146

H. Prasad, M. Samson, J. Jebanazer

引用次数: 0

Prediction of growth in COVID-19 Cases in India based on Machine Learning Techniques 基于机器学习技术的印度COVID-19病例增长预测

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744141

Aindrila Saha, Vartika Mishra, S. K. Rath

{"title":"Prediction of growth in COVID-19 Cases in India based on Machine Learning Techniques","authors":"Aindrila Saha, Vartika Mishra, S. K. Rath","doi":"10.1109/ICITIIT54346.2022.9744141","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744141","url":null,"abstract":"One of the biggest health challenges that the world has faced in recent times is the pandemic due to coronavirus disease known as SARS-CoV-2, or Covid-19 as officially named by the World Health Organization (WHO). To plan medical facilities in a certain location in order to combat the disease in near future, public health policy makers expect reliable prediction of the number of Covid-19 positive cases in that location. The requirement of reliable prediction gives rise to the need for studying growth in the number of Covid-19 positive cases in the past and predicting the growth in the number in near future. In this study, the growth in the number of Covid-19 positive cases have been modelled using several machine learning based regression techniques viz., Multiple Linear Regression, Decision Tree Regression and Support Vector Regression. Further, different feature selection techniques based on Filter and Wrapper methods have been applied to select the suitable features based on which prediction is to be done. This study proposes the best observed method for modelling the pattern of growth in number of Covid-19 cases in the near future for a locality and also the best selection method that can be employed for obtaining the optimal feature set. It has been observed that unregularized Multiple Linear regression model yields promising results on the test data set, compared to the other regression models, for predicting the future number of Covid-19 cases and Backward Elimination feature selection method performs better than other feature selection methods.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134096986","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

Blockvoting:An Online Voting System Using Block Chain 区块投票:使用区块链的在线投票系统

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744132

D. K, U. K

引用次数: 4

Wireless Communication Technologies with IoT-Based Cloud-Enabled Service for Smart Agriculture Monitoring System 基于物联网云服务的智能农业监测系统无线通信技术

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744230

Lova Raju K, V. V

引用次数: 0

Machine Learning Approaches for Type-2 Diabetes Software Predictor 2型糖尿病预测软件的机器学习方法

2022 International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2022-02-12 DOI: 10.1109/ICITIIT54346.2022.9744229

Shubham Mishra, Vinod A, Kala S

引用次数: 0