Bimodal Biometric Verification Mechanism using fingerprint and face images(BBVMFF)

An increased demand of biometric authentication coupled with automation of systems is observed in the recent times. Generally biometric recognition systems currently used consider only a single biometric characteristic for verification or authentication. Researchers have proved the inefficiencies in unimodal biometric systems and propagated the adoption of multimodal biometric systems for verification. This paper introduces Bi-modal Biometric Verification Mechanism using Fingerprint and Face (BBVMFF). The BBVMFF considers the frontal face and fingerprint biometric characteristics of users for verification. The BBVMFF Considers both the Gabor phase and magnitude features as biometric trait definitions and simple lightweight feature level fusion algorithm. The fusion algorithm proposed enables the applicability of the proposed BBVMFF in unimodal and Bi-modal modes proved by the experimental results presented

Trust model genetic node recovery based on cloud theory for underwater acoustic sensor network

Underwater Acoustic Sensor Networks [UASNs] are becoming a very growing research topic in the field of WSNs. UASNs are harmful by many attacks such as Jamming attacks at the physical layer, Collision attacks at the data link layer and Dos attacks at the network layer. UASNs has a unique characteristic such as unreliable communication, mobility, and computation of underwater sensor network. Because of this the traditional security mechanism, eg cryptographic, encryption, authorization and authentications are not suitable for UASNs. Many trust mechanisms of TWSNs [Terrestrial Wireless Sensor Networks] had proposed to UASNs and failed to provide security for UASNs environment, due to dynamic network structure and weak link connection between sensors. In this paper, a novel Trust Model Genetic Algorithm based on Cloud Theory [TMC] for UASNs has been proposed. The TMC-GA suggested a genetic node recovery algorithm to improve the TMC network in terms of better network lifetime, residual energy and total energy consumption. Also ensures that sensor nodes are participating in the rerouting in the routing discovery and performs well in terms of successful packet delivery. Simulation result provides that the proposed TMC-Genetic node recovery algorithm outperforms compared to other related works in terms of the number of hops, end-to-end delay, total energy consumption, residual energy, routing overhead and network lifetime

RMSC: Robust Modeling of Subspace Clustering for high dimensional data

Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel technique called as Robust Modeling of Subspace Clustering (RMSC) presented to solve the above problem. An analytical research methodology is used to formulate two algorithms for computing outliers and for extracting elite subspace from the highdimensional data inflicted by different forms of noise. RMSC was found to offer higher accuracy and lower error rate both in presence of noise and absence of noise over high-dimensional data. © 2017 IEEE

Onto Collab: Strategic review oriented collaborative knowledge modeling using ontologies

Modeling efficient knowledge bases for improving the semantic property of the World Wide Web is mandatory for promoting innovations and developments in World Wide Web. There is a need for efficient and organized modeling of the knowledge bases. In this paper, a strategy Onto Collab is proposed for construction of knowledge bases using ontology modeling. Ontologies are visualized as the basic building blocks of the knowledge in the web. The cognitive bridge between the human conceptual understanding of real world data and the processable data by computing systems is represented by Ontologies. A domain is visualized as a collection of similar ontologies. A review based strategy is proposed over a secure messaging system to author ontologies and a platform for retracing the domain ontologies as individuals and as a team is proposed. Evaluations for ontologies constructed pertaining to a domain for non-wiki knowledge bases is carried out

EDSC: Efficient document subspace clustering technique for high-dimensional data

With the advancement in the pervasive technology, there is a spontaneous rise in the size of the data. Such data are generated from various forms of resources right from individual to organization level. Due to the characteristics of unstructured or semi-structuredness in data representation, the existing data analytics approaches are not directly applicable which leads to curse of dimensionality problem. Hence, this paper presents an Efficient Document Subspace Clustering (EDSC) technique for high-dimensional data that contributes to the existing system with respect to identification by eliminating the redundant data. The discrete segmentation of data points are used to explicitly expose the dimensionality of hidden subspaces in the clusters. The outcome of the proposed system was compared with existing system to find the effective document clustering process for high-dimensional data. The processing time of EDSC for subspace clustering is reduced by 50% as compared to the existing system

Trust model genetic node recovery based on cloud theory for underwater acoustic sensor network

Underwater Acoustic Sensor Networks [UASNs] are becoming a very growing research topic in the field of WSNs. UASNs are harmful by many attacks such as Jamming attacks at the physical layer, Collision attacks at the data link layer and Dos attacks at the network layer. UASNs has a unique characteristic such as unreliable communication, mobility, and computation of underwater sensor network. Because of this the traditional security mechanism, e.g. cryptographic, encryption, authorization and authentications are not suitable for UASNs. Many trust mechanisms of TWSNs [Terrestrial Wireless Sensor Networks] had proposed to UASNs and failed to provide security for UASNs environment, due to dynamic network structure and weak link connection between sensors. In this paper, a novel Trust Model Genetic Algorithm based on Cloud Theory [TMC] for UASNs has been proposed. The TMC-GA suggested a genetic node recovery algorithm to improve the TMC network in terms of better network lifetime, residual energy and total energy consumption. Also ensures that sensor nodes are participating in the rerouting in the routing discovery and performs well in terms of successful packet delivery. Simulation result provides that the proposed TMC-Genetic node recovery algorithm outperforms compared to other related works in terms of the number of hops, end-to-end delay, total energy consumption, residual energy, routing overhead, throughput and network lifetime

Efficient video transfer using LAN caching assisted by cloud computing

There is a good probability of accessing same video content multiple times from a cloud based Video Streaming Server by same peer or different peers of a given LAN, effectively increasing Internet bandwidth or data flow for same content from server to client, thereby over loading routers between server and client and also resulting in higher power consumption at routers. This proposed concept tries to avoid multiple streaming of high volume video files from Server by caching first successful streamed data on to LAN peer which is currently viewing the video data and subsequently the same LAN peer streaming the video to other desiring peers when demanded for. Proposed implementation model retains all other server activities with server except for allowing an available LAN peer copy of video to be streamed to another peer of the same LAN when requested for

The rising burden of chronic conditions among urban poor : a three-year follow-up survey in Bengaluru, India

Background: Chronic conditions are on rise globally and in India. Prevailing intra-urban inequities in access to healthcare services compounds the problems faced by urban poor. This paper reports the trends in self-reported prevalence of chronic conditions and health-seeking pattern among residents of a poor urban neighborhood in south India. Methods: A cross sectional survey of 1099 households (5340 individuals) was conducted using a structured questionnaire. The prevalence and health-seeking pattern for chronic conditions in general and for hypertension and diabetes in particular were assessed and compared with a survey conducted in the same community three years ago. The predictors of prevalence and health-seeking pattern were analyzed through a multivariable logistic regression analysis. Results: The overall self-reported prevalence of chronic conditions was 12 %, with hypertension (7 %) and diabetes (5.8 %) being the common conditions. The self-reported prevalence of chronic conditions increased by 3.8 percentage point over a period of three years (OR: 1.5). Older people, women and people living below the poverty line had greater odds of having chronic conditions across the two studies compared. Majority of patients (89.3 %) sought care from private health facilities indicating a decrease by 8.7 percentage points in use of government health facility compared to the earlier study (OR: 0.5). Patients seeking care from super specialty hospitals and those living below the poverty line were more likely to seek care from government health facilities. Conclusion: There is need to strengthen health services with a preferential focus on government services to assure affordable care for chronic conditions to urban poor

Cloud enabled 3D tablet design for medical applications

The prime objective of any technological innovation is to improve the life of people. Technological innovation in the field of medical devices directly touches the lives of millions of people; not just patients but doctors and other technicians as well. Serving these care givers is serving humanity. Growth of Mobil Devices and Cloud Computing has changed the way we live and work. We try to bring the benefits of these technological innovations to the medical field via equipment which can improve the working efficiencies and capabilities of the medical professionals and technicians. The improvements in the camera and image processing capabilities of the Mobile Devices coupled with their improved processing power and an infinite processing and storage offered by Cloud Computing infrastructure opens up a window of opportunity to use them in the specialized field like microsurgery. To enable microsurgery, surgeons use optical microscope to zoom into the working area to get better visibility and control. However, these devices suffer from various drawbacks and are not comfortable to use. We build a Tablet with large stereoscopic screen allowing glasses free 3D display enabled by cameras capable of capturing 3D video and enhanced by an image processing pipeline, greatly improves the visibility and viewing comfort of the surgeon. Moreover using the capabilities of Cloud computing, these surgeries can be recorded and streamed live for education, training and consultation. An expert sitting in a geographically remote location can guide the surgeon performing the surgery. All vital parameters of the patient undergoing surgery can be shown as an overlay on the Tablet screen so that the surgeon is alerted of any parameter going beyond limit. Developing this kind of complex device involves engineering skills in hardware and software and huge amount of investments in terms of time, resources and money. To accelerate the development, we make use of open source hardware and software and demonstrate how we can accelerate the development using these open source resources