The fast development of mobile networks and compact mobile devices bring attention to the users for wireless mobile communication. But providing security and protecting the privacy of users are the main challenges of wireless mobile communication. Recently, Authenticated Key Agreement protocols are used for secure and reliable communication in wireless mobile communication. However, the User-to-User Mutual Authentication and Key Agreement scheme is based on bilinear pairings, which involves relatively high computation cost when compared to elliptic curve scalar point multiplication. Hence, in this paper, we propose a Pairing-Free Identity-Based Mutual Authentication and Key Agreement protocol for wireless mobile communications. It consists of the following phases: Setup and Key generation, Mobility Management Entity authentication and Proxy Signature generation, Proxy Signature verification and User-to-User authentication. Experimental results show that Pairing-Free Identity-Based Mutual Authentication and Key Agreement attains significantly less computational and communication cost when compared to User-to-User Mutual Authentication and Key Agreement scheme.
Cloud computing is associate inclusive new approach on however computing services square measure made and utilized. Cloud computing is associate accomplishment of assorted styles of services that has attracted several users in today's state of affairs. The foremost enticing service of cloud computing is information outsourcing, because of this the information homeowners will host any size of information on the cloud server and users will access the information from cloud server once needed. A dynamic outsourced auditing theme that cannot solely defend against any dishonest entity and collision, however conjointly support verifiable dynamic updates to outsourced information. The new epitome of information outsourcing conjointly faces the new security challenges. However, users might not totally trust the cloud service suppliers (CSPs) as a result of typically they may be dishonest. It's tough to work out whether or not the CSPs meet the customer's expectations for information security. Therefore, to with success maintain the integrity of cloud information, several auditing schemes are projected. Some existing integrity ways will solely serve for statically archived information and a few auditing techniques is used for the dynamically updated information. The analyzed numerous existing information integrity auditing schemes together with their consequences.
Lung cancer is one of the leading causes of cancer related deaths. It is due to the complexity of early detection of nodules. In clinical practice, radiologists find it difficult to determine whether a condition is normal or abnormal by manually analysing CT scan or X-ray images for nodule identification. Currently, various deep learning techniques have been developed to identify lung nodules as benign or malignant, but each technique has its own advantages and drawbacks. This work presents a thorough analysis based on segmentation techniques, Related features-based detection, multi-step detection, automatic detection, and deep convolutional neural network techniques. Performance comparison was conducted on a selected works based on performance measures. A potential research direction for the recognition of lung nodules is given at the end of this study.
In Wireless Mobile Networks (WMN), the proliferation of mobile devices and smart phones stimulates an array of personalized information services that exploits the user's personal data for processing. So, it is very significant to preserve the data privacy and protect the integrity of data of mobile users. However, as the WMN devices are heterogeneous and highly independent, it is challenging to achieve privacy protection and efficient authentication in better levels. With those concerns, this paper illustrates a new model called Enhanced Privacy Preserving-Anonymity Authentication (EPPAA) for protecting the user's personal information. Further, the model incorporates the effectiveness of Quantum-behaved Particle Swarm Optimization (QPSO) for selecting the node at middle of neighbours that are closer to the Serving Base Station (seBS). The ticket based anonymity authentication has been employed and the algorithm has been designed and implemented predominantly. For providing confidentiality over the communication, the query message is encrypted, by that way; the anonymous users could not claim the private data of the mobile users. Moreover, the proposed model is implemented and evaluated using the NS2 simulator. The experimentation has been analyzed with the parameters such communication overhead, authentication delay, success ratio, packet delay and compared with some existing privacy preserving models such as Kerberos based Authentication for Inter-domain Roaming (KAIR), Privacy Preserving Nearest Neighbor Queries(PPNNQ) and Efficient Mobile Authentication Scheme (EMAS). The results of the proposed EPPAA show that the model outperforms the traditional methodologies and provides better authentication and security to the user information on WMN.
Parkinson disease is a rigorous neurodegenerative disorder characterized by the cognitive behavior ending with disability problems. Especially, the elderly people should be given more care and spend more time duration to diagnose when they are at risk. It is more important to identify and diagnose Parkinson disease at an earlier stage rather than spending too much of cost later stages. Different ways of diagnosing the disease ranging from gene analysis to gait behavior, speech, writing test and olfactory models were used in the conventional testing process. In order to increase the patient’s quality of life and minimize the cost of healthcare utilization, an Onboard Cloud-Enabled Parkinson Disease Identification System (OCPDIS) is proposed. An enhanced grey wolf optimization is explored along with the differential evolution techniques to form an effective hybrid feature selection method. Using this feature selection method in the enhanced k-Nearest Neighbor (k-NN) classifier model could substantially improve the prediction time and prediction accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.