2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)最新文献_第8页

Stacked Autoencoders Based Deep Learning Approach for Automatic Epileptic Seizure Detection 基于堆叠自编码器的深度学习方法用于癫痫发作自动检测

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703357

Kuldeep Singh, J. Malhotra

{"title":"Stacked Autoencoders Based Deep Learning Approach for Automatic Epileptic Seizure Detection","authors":"Kuldeep Singh, J. Malhotra","doi":"10.1109/ICSCCC.2018.8703357","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703357","url":null,"abstract":"Epilepsy is one of the major chronic nervous disorders, which affects the lives of millions of patients per annum globally, because of occurrence of sudden death or major injuries occurred during walk, driving or working in hazardous work environment. Its prognosis through modern technologies is the need of the day, which is attaining worldwide attention in research community with the use of latest technologies like internet of things, machine learning and cloud computing. This paper presents a model of automatic epileptic seizure detection model using Stacked Autoencoders based deep learning approach, which is an advanced form of machine leaning, employed for effectively handling the problem of big data with reduced complexity and processing time and to make this process more real time compatible with least delays. This model processes the sensed EEG signals by breaking it into short duration segments. Then, these EEG segments are fed to Stacked Autoencoders for its classification into different epileptic seizure stages like normal, preictal and ictal. The performance of this model has been compared with other existing models consisting of higher order spectral analysis based feature extraction and classification using traditional machine learning algorithms like Bayes Net, Naïve Bayes, Multilayer Perceptron, Radial basis function neural networks and C4.5 decision tree classifier. The analysis of performance through simulation results reveal that Stacked Autoencoders based deep learning approach is an efficient model for real time automatic epileptic seizures detection at early stage with classification accuracy 88.8%, sensitivity 89.44%, specificity 93.77% values and least value of processing time, which is approximately 23 times lesser than that of models utilizing traditional higher order statistics feature extraction and machine learning based classification approaches.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133470613","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: 15

Local Binary Pattem Variants: A Review 局部二进制模式变体:综述

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703326

Anterpreet Kaur Bedi, R. K. Sunkaria, Simarjot Kaur Randhawa

引用次数: 1

Opinion Mining and Classification of Music Lyrics Using Supervised Learning Algorithms 使用监督学习算法的意见挖掘和音乐歌词分类

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703292

M. Ahuja, A. L. Sangal

引用次数: 3

Design and Performance Analysis of Reference Point Group Mobility Model for Mobile Ad hoc Network 移动自组网参考点群移动模型的设计与性能分析

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703317

P. Dorge, Samiksha L. Meshram

引用次数: 8

Smart Automatic Control and Monitor Water Purification Using Wireless Sensor System 智能自动控制和监测用水净化无线传感器系统

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703291

Bhupesh B. Lonkar, R. Nakhate, M. R. Sayankar

引用次数: 1

Economic Analysis of Reactive Power Services in Deregulated Power Market Integrated with Doubly Fed Induction Generator Wind Unit 与双馈风力发电机组相结合的无功市场经济分析

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703281

Aditi Gupta, Y. P. Verma, A. Chauhan

引用次数: 0

A Color-Based Approach for Melanoma Skin Cancer Detection 基于颜色的黑色素瘤皮肤癌检测方法

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703309

Shalu, A. Kamboj

{"title":"A Color-Based Approach for Melanoma Skin Cancer Detection","authors":"Shalu, A. Kamboj","doi":"10.1109/ICSCCC.2018.8703309","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703309","url":null,"abstract":"Skin cancer cases are continuously arising from the past few years. Broadly skin cancer is of three types: Basal Cell Carcinoma, Squamous Cell Carcinoma, and Melanoma. Among all its types, melanoma is the dangerous form of skin cancer whose treatment is possible only if it is detected in early stages. Early detection of melanoma is really challenging. Therefore, various systems were developed to automate the process of melanoma skin cancer diagnosis. Features used to characterize the disease play a very important role in the diagnosis. It is also very important to find the correct combination of features and the machine learning techniques for classification. Here, a system for the melanoma skin cancer detection is developed by using a MED-NODE dataset of digital images. Raw images from the dataset contain various artifacts so firstly preprocessing is applied to remove these artifacts. Then to extract the region of interest Active Contour segmentation method is used. Various color features were extracted from the segmented part and the system performance is checked by using three classifiers (Naïve Bayes, Decision Tree, and KNN). The system achieves an accuracy of 82.35% on Decision Tree which is greater than other classifiers.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121179181","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: 32

Statistical Analysis and Forecasting Models for Stock Market 股票市场的统计分析与预测模型

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703324

Sourabh Yadav, K. P. Sharma

引用次数: 3

Self-Organizing Sustainable Spectrum Management Methodology in Cognitive Radio Vehicular Adhoc Network (CRAVENET) Environment: A Reinforcement Learning Approach 认知无线电车载自组织网络环境下的自组织可持续频谱管理方法:一种强化学习方法

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703268

K. Ghanshala, Sachin Sharma, S. Mohan, Lata Nautiyal, P. Mishra, R. Joshi

{"title":"Self-Organizing Sustainable Spectrum Management Methodology in Cognitive Radio Vehicular Adhoc Network (CRAVENET) Environment: A Reinforcement Learning Approach","authors":"K. Ghanshala, Sachin Sharma, S. Mohan, Lata Nautiyal, P. Mishra, R. Joshi","doi":"10.1109/ICSCCC.2018.8703268","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703268","url":null,"abstract":"The era of new and emerging technologies demand that the new challenges they bring about to be effectively tackled and resolved. One such key challenge is spectrum management, especially in Cognitive Radio Vehicular Adhoc Network (CRAVENET) environment. The large-scale deployment of multimedia and Internet of Things (IoT) applications generate the need to establish an efficient spectrum allocation mechanism. This paper proposes a centralized self-organizing spectrum management in the context of economic and social sustainability using reinforcement learning technique. The objective of the proposed approach facilitates economic and social justice. The social economic justice architecture is developed through a user demand level concepts. The spectrum management methodology has been developed in a CRAVENET environment for better quality of service (QoS) with low average latency. The proposed methodology is expected to be highly effective for its economic feasibility, social impact, user comfort, efficiency, and communication latency minimization requirements.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126006799","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: 6

Vertical Tunnel-FET Analysis for Excessive Low Power Digital Applications 过低功耗数字应用的垂直隧道-场效应管分析

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI: 10.1109/ICSCCC.2018.8703312

Shailendra Singh, B. Raj

引用次数: 16