2022 OITS International Conference on Information Technology (OCIT)最新文献_第9页

Autoencoders Learning Sparse Representation 学习稀疏表示的自动编码器

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00017

Abhinav Sharma, Ruchir Gupta

引用次数: 0

Systematic Survey on security issues in Cognitive Wireless Sensor Networks (CWSN) 认知无线传感器网络(CWSN)安全问题系统研究

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00088

Roshan Kumar Chatei, Rudrashish Das, P. S. Chatterjee

引用次数: 0

HybridSSCN: Analysis Of Hierarchical Feature Learning Architecture Using Blended Conv3D And DepthwiseConv2D For Hyperspectral Image Classification HybridSSCN:混合Conv3D和DepthwiseConv2D用于高光谱图像分类的分层特征学习架构分析

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00019

Pradeep Kumar Ladi, Murali Gopal Kakita, Ratnakar Dash, Sandeep Kumar Ladi

引用次数: 0

Classification of Agriculture Crops Using Transfer Learning 基于迁移学习的农作物分类

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00058

Silky Goel, Snigdha Markanday, Shlok Mohanty

引用次数: 0

A Novel Optimization Strategy For Computation Offloading in the UAV-assisted Edge Computing 无人机辅助边缘计算中一种新的计算卸载优化策略

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00092

Khatsuria Yash Vijaybhai, K. Venkateswararao, Tejas M. Modi, Pravati Swain

{"title":"A Novel Optimization Strategy For Computation Offloading in the UAV-assisted Edge Computing","authors":"Khatsuria Yash Vijaybhai, K. Venkateswararao, Tejas M. Modi, Pravati Swain","doi":"10.1109/OCIT56763.2022.00092","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00092","url":null,"abstract":"Unmanned aerial vehicles (UAVs) capture real-time aerial data. However, onboard computational resources and battery life in a UAV device is limited. In both academic and industrial sectors, solutions based on the mobile edge computing paradigm have been extensively discussed. In this paper, a group of small UAVs are exposed to a range of computing tasks. Some of these tasks call for the execution of difficult computations and complicated algorithms which are computationally-heavy task, and offloading to a powerful computational device is required. In contrary, some tasks are data-heavy, and offloading these tasks lead to a considerable transmission delay. Thus, the performance of the system depends on whether a task is offloaded or computed locally. The UAVs are having three options for a given task, i.e., locally complete the computation task, transfer the task to a surrogate UAV (medium/Iarge UAV) through the wireless local access network, or transfer to a edge server through the cellular network. To solve this optimization problem, a heuristic approach is purposed where each UAV device takes a decentralized offloading decision based on the nature of the task (computationally-heavy or data-heavy) for minimizing the total overhead, i.e., computation delay, transmission delay and monetary cost. The performance of the proposed approach is compared with three models, i.e., local computation, offloading all the tasks to the surrogate UAV, and offloading all the tasks to the edge server. It is observed that the proposed model achieved on average 25–30% less global overhead.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127675121","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

Radial Basis Neural Networks for Class Discovery 类发现的径向基神经网络

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00069

A. K. Behera, J. P. Mohanty, C. S. K. Dash, S. Dehuri

{"title":"Radial Basis Neural Networks for Class Discovery","authors":"A. K. Behera, J. P. Mohanty, C. S. K. Dash, S. Dehuri","doi":"10.1109/OCIT56763.2022.00069","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00069","url":null,"abstract":"The non-iterative structure of radial basis function neural networks makes them more appealing for classification tasks decade after decade. In line with the growth of the training data set, the pattern layer also grows. The unprecedented growth of data with no class labels is becoming a challenge for researchers who are working in data science, big data analysis, etc. Although there is literature witnessed for many algorithms to handle data with no class label. However, neural network-based algorithms with a special characteristic of radial basis neural networks for uncovering class labels are very rare. In this paper, we propose a novel class discovery algorithm that combines the best features of radial basis function neural networks (RBFN) and self-organizing feature map (SOFM). We have taken a few datasets with class label for our experimental work. In the training phase of the network, the training instances are used without class labels. In the test phase, they are validated by combining the predicted class labels with their actual class label. The result shows that the proposed algorithm can be treated as an alternative method for class discovery.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131513836","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

Using of Machine Learning Techniques to detect Credit Card Frauds 使用机器学习技术检测信用卡欺诈

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00033

Yogesh Gupta

引用次数: 0

Distributed Self Intermittent Fault outlier identification technique for WSN s 分布式自间断故障离群点识别技术

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00064

B. S. Gouda, Sudhakar Das, T. Panigrahi

{"title":"Distributed Self Intermittent Fault outlier identification technique for WSN s","authors":"B. S. Gouda, Sudhakar Das, T. Panigrahi","doi":"10.1109/OCIT56763.2022.00064","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00064","url":null,"abstract":"In this research paper, we provide a distributed K-mean strategy based on distributed Self Intermittent fault outer identification (DISF) algorithm for identifying the fault due to outlier. It deals with locating problematic nodes by using intermittent faults and clustering mechanisms in the sensor network. The described model, sensor node, takes into account the average of all the cluster regions by using the median-based K-mean approach to gather the data from nearby sensors within the specified environment. The proposed method is rigorously tested, with the cluster head presumed to be the trustworthy node that reliably supplies the right data. The correctness is determined after taking into account the data from the dispersed cluster heads. With regard to the different parameters are to use for predicting the accuracy of data, fault positive rate and fault alarm ratio over the data transmission. This proposed model is contrasted with other ones already in use. The outcomes of the statistical analysis show that the proposed methodology produces an accurate result as compared to the traditional or existing approaches.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114148744","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

A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach 基于方面的意见挖掘方法的客户评论分析框架

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00052

S. Dasgupta, Jaydip Sen

引用次数: 2

Device Fingerprinting in Wireless Networks using Deep Learning 使用深度学习的无线网络设备指纹识别

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00081

A. K. Dalai, B. Sahoo

引用次数: 0