J. Mobile Multimedia最新文献_第3页

Optimal Double Layer Secret Sharing Scheme for Biometrics 生物特征识别的最优双层秘密共享方案

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1928

Elavarasi Gunasekaran, Vanitha Muthuraman

{"title":"Optimal Double Layer Secret Sharing Scheme for Biometrics","authors":"Elavarasi Gunasekaran, Vanitha Muthuraman","doi":"10.13052/jmm1550-4646.1928","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1928","url":null,"abstract":"Visual secret sharing (VSS) method is an encryption method to ensure the Security of secret data, that just performs the partitioning of ‘n’ shares and distributes among ‘n’ users in an ideal way so that exposing of less than ‘n’ shares is of no utilization. However, some dishonest members or hackers team up and tries to cheat other members in the group. Therefore, we have developed a dual layer secret sharing scheme based on universal share based secret sharing scheme. The dual layer is composed of threshold based secret sharing and then followed by the universal share based secret sharing. Here, the universal share is maintained by the trusted party avoids the contribution of dishonest participants. Moreover, to ensure additional security, the proposed approach employs Oppositional Artificial Fish Swarm Optimization (OAFSO) based Stream Cipher encryption technique to encrypt the shares, shows the novelty of the work. Furthermore, the confidentiality is enhanced with Biometric fingerprint authentication step, where the acknowledged users are alone allowed to get the decrypt shares with that the user can retrieve the secret data. The proposed fingerprint authentication method also makes use of Secure Hash Algorithm (SHA 1) to store the fingerprints and the matching is also done with hashed fingerprints only. So that the attackers cannot take and corrupt the stored fingerprints and the misuse of fingerprints is not possible. Finally the performance analysis is made with existing approaches in terms of PSNR and MSE. Maximum PSNR is 58.9802 and minimum MSE value is 0.6232, while the existing methods provide very less PSNR and greater MSE values than the proposed methods.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116443943","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

Optimal Machine Learning Based Intrusion Detection System in Wireless Sensor Networks for Surveillance Applications 基于最优机器学习的无线传感器网络入侵检测系统

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1924

Sibi Amaran, R. Mohan, R. Jebakumar

引用次数: 0

Research on Innovation and Entrepreneurship Approach in Universities Based on Large Data Innovation and Entrepreneurship 基于大数据的高校创新创业路径研究

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1929

Li Maoning

引用次数: 0

Fog-Enabled IoT Framework for Heart Disease Diagnosis Systems 用于心脏病诊断系统的雾支持物联网框架

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1922

Q. Minh, Do Thanh Thai, Phu H. Phung, P. N. Huu

{"title":"Fog-Enabled IoT Framework for Heart Disease Diagnosis Systems","authors":"Q. Minh, Do Thanh Thai, Phu H. Phung, P. N. Huu","doi":"10.13052/jmm1550-4646.1922","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1922","url":null,"abstract":"IoT technology has been recently adopted in healthcare systems to quickly detect abnormalities from patients, diagnose diseases and provide supports in time, even remotely. In the field of heart disease, timely diagnosis and prediction help to save people. This paper proposes a fog-based IoT approach to collect and analyze electrocardiogram (ECG) signals from patients to detect abnormalities or heart attacks with a short response time so that appropriate treatments can be provided. Commonly, ECG signals are transmitted to an eco-expert system deployed on the cloud to perform preliminary automatic diagnosis using a knowledge base built from medical experts. Although such an eco-expert system assists patients and supports physicians in performing treatment for their patients, there are several open technical challenges. First, noise in raw ECG signals makes the data imprecise and reduces the prediction accuracy. Second, involving data mining and machine learning on the cloud poses a significant latency since a huge amount of data needs to be transferred in the network. This paper proposes a novel framework that can provide the integrity of the ECG data by removing noise and then extract relevant knowledge for heart disease diagnosis at the network edge based on data mining techniques. Practical experiments demonstrate that the proposed framework not only guarantees the integrity of the data but also enhances the accuracy of the real-time detection compared with previous works.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122911514","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

Multimodal Driver Drowsiness Detection From Video Frames 基于视频帧的多模式驾驶员睡意检测

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.19210

Pritesh Kumar Singh, Archit Gupta, Mayank Upadhyay, Achin Jain, Manju Khari, Puneet Singh Lamba

引用次数: 2

Research on Visualization of Large-scale User Association Feature Data Based on Nonlinear Dimension Reduction Method 基于非线性降维方法的大规模用户关联特征数据可视化研究

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.19211

Yuchen Xie, Shehab Mohamed Beram, Baljinder Kaur, Rahul Neware, Manik Rakhra, D. Koundal

{"title":"Research on Visualization of Large-scale User Association Feature Data Based on Nonlinear Dimension Reduction Method","authors":"Yuchen Xie, Shehab Mohamed Beram, Baljinder Kaur, Rahul Neware, Manik Rakhra, D. Koundal","doi":"10.13052/jmm1550-4646.19211","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.19211","url":null,"abstract":"A high dimensional data visualization platform based on nonlinear dimension reduction approach was built and deployed in order to research the visualization of large-scale user linked feature data. The following test results were obtained through the implementation of a dimension reduction method and a functional module: The test set of the MNIST data set is given in CSV format, which may be represented as a 785*10000 matrix. The matrix is a representation of the handwritten picture that has been abstracted and transformed. The PCA approach provides the best dimensional-reduction impact on the dietary nutrient dataset, retaining 98.8 percent of the variation. The protein structure and function data set is not well served by any of the three dimensional-reduction techniques. Both T-SNE and Large Vis algorithms have better dimensional-reduction effects on MNIST data set, which reflects the nonlinear characteristics of the data set. Compared with T-SNE algorithm, Large Vis algorithm has no significant improvement in visualization effect, which is mainly reflected in time efficiency.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134412829","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

Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles 基于深度学习和无人机的实时交通流监测系统建模

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1926

Sachin Upadhye, S. Neelakandan, K. Thangaraj, D. Babu, N. Arulkumar, Kashif Qureshi

{"title":"Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles","authors":"Sachin Upadhye, S. Neelakandan, K. Thangaraj, D. Babu, N. Arulkumar, Kashif Qureshi","doi":"10.13052/jmm1550-4646.1926","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1926","url":null,"abstract":"Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116955501","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

An Improved Optimal Channel Sensing Algorithm in Cognitive Radio Networks Used for Video Surveillance 一种改进的用于视频监控的认知无线网络最优信道感知算法

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1927

Ranjita Joon, Parul Tomar

{"title":"An Improved Optimal Channel Sensing Algorithm in Cognitive Radio Networks Used for Video Surveillance","authors":"Ranjita Joon, Parul Tomar","doi":"10.13052/jmm1550-4646.1927","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1927","url":null,"abstract":"With a rapid rise in the number of wireless devices and gadgets, a shortage in the spectrum bands for wireless communications has been observed. To overcome this problem of shortage of spectrum bands, a new technology called Cognitive Radio Networks (CRNs) was adopted. CRNs help us utilize the spectrum bands which are currently being underutilized by opportunistically and intelligently switching to these underutilized white spaces. Thus, CRNs aim to use the frequency spectrum in an opportunistic manner by allowing different users to operate in available frequency bands without interference. In this paper, Double Q Learning (DQN) with prioritized experience relay approach has been used to study the throughput of the network at different parameters and to draw a relationship between throughput and Probability of Undetectable User Transmission. Double Q Learning (DQN) with prioritized experience relay is a reinforcement learning based method that adds backward exploration to the forward exploration of Q Learning method. Both forward and backward exploration are used to update the Q values. Since the sensor nodes in the cognitive environment have limited energy, and sensing the spectrum band involves energy consumption, so the technique for sensing should be energy efficient so that the sensor nodes can be effectively used for various operations of video surveillance.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123465060","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

Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications 鲁棒深度学习支持无人机监控应用的实时目标检测

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1925

C. Ranjith, B. Hardas, M. S. K. Mohideen, N. N. Raj, N. Robert, Prakash Mohan

{"title":"Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications","authors":"C. Ranjith, B. Hardas, M. S. K. Mohideen, N. N. Raj, N. Robert, Prakash Mohan","doi":"10.13052/jmm1550-4646.1925","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1925","url":null,"abstract":"Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133347252","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: 5

Securing of Cloud Storage Data Using Hybrid AES-ECC Cryptographic Approach 使用混合AES-ECC加密方法保护云存储数据

J. Mobile Multimedia Pub Date : 2022-11-15 DOI: 10.13052/jmm1550-4646.1921

Sunil Kumar, D. Kumar

{"title":"Securing of Cloud Storage Data Using Hybrid AES-ECC Cryptographic Approach","authors":"Sunil Kumar, D. Kumar","doi":"10.13052/jmm1550-4646.1921","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1921","url":null,"abstract":"Internet has revolutionized the world in a way no one could have ever imagined. It paved the way for various different technologies, that have transformed the world exceptionally. Internet enabled cloud technology which provides cost-effective, scalable, on-demand computing resources with little to no downtime. Cloud storage allows its users to store and access private data from anywhere in the world without needing any high-end computing system. Cloud storage isn’t always secure, but that doesn’t imply it isn’t. The security of a data depends on the security policies followed by the provider along with the security of the communication channel via which the data is being sent. Encryption is used to obfuscate the data so that it can only be viewed when correct credentials, known as encryption keys, are provided. Following study proposes an encryption technique using ECC (Elliptic Curve Cryptography) along with AES (Advance Encryption Standard) to provide data confidentiality in an efficient way for securing data on cloud and hence, protect the personal information of user from any adversary. This new method is more effective, and the results are superior as a consequence.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127544286","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