Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments

The main objective of online anomaly detection is to identify abnormal/unusual behavior such as network intrusions, malware infections, over utilized system resources due to design defects etc from real time data stream. Terrabytes of performance data generated in cloud data centers is a well accepted example of such data stream in real time. In this paper, we propose an online anomaly detection framework using non-parametric statistical technique in cloud data center. In order to determine the accuracy of the proposed work, we experiments it to data collected from RUBis cloud testbed and Yahoo Cloud Serving Benchmark (YCSB). Our experimental results shows the greater accuracy in terms of True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR) and False Negative Rate (FNR)

Evaluation of central neuropathy in type 2 diabetes mellitus: a case control study

Background: Diabetes mellitus (DM) is a global pandemic affecting almost every organ in the body. Peripheral nervous system involvement in diabetes is well known but there are not many studies on central nervous system involvement. Visual evoked potential (VEP) is a sensitive, non-invasive test to detect central demyelination of optic nerve. The objective was to compare the visual evoked potentials in type-2 DM patients with that of healthy controls and to find out if any correlation is there with the duration and glycaemic control of the disease and to compare incidence of peripheral and central neuropathy in DM patients.Methods: Author included 50 DM patients and 50 age and sex matched controls. Patients with previous stroke, demyelination, diabetic retinopathy and other ophthalmological disorders were excluded. VEP was recorded using pattern reversal stimulation with EMG RMS MARK II machine and p100 latency was measured.Results: P100 latencies (ms) was significantly prolonged in diabetics with mean±SD of (111.24±5.28 ms) as compared to controls (101.30±1.66 ms) with p value <0.003. Also, there was significant correlation between duration of DM and P100 latency prolongation, but no significant correlation was present when compared with glycaemic control.Conclusions: Central neuropathy is very common in DM. It is related to duration of DM and not HbA1c unlike PNP which is related to both. Central neuropathy occurs even prior to development of retinopathy or PNP. Hence, VEP is a non-invasive and sensitive screening tool for early neurological involvement in DM

Taxonomical Keys for Morphological Identification of Coral-Associated Polychaetes from Great Nicobar Islands

The present study illustrates the insufficient taxonomy records and highlights the use of microscopic diagnostic tool in polychaete taxonomy. It leads to a better understanding of coral-associated polychaete taxonomy in Great Nicobar Islands, India. A total of 24 species under 14 genera, 7 orders, and 11 families were identified, in spite of 3 species of Phyllocidae, 8 species of Nereidae, 5 species of Eunicidae, 2 species of Spionidae, and 1 species of Opheliidae, Sabellariidae, Terebellidae, Polynoidae, Amphinomidae, and Sabellidae. The current status of taxonomic information varies greatly among taxa and among geographic areas within taxa. The problems encountered included nomenclature, diagnoses, and determination of taxonomic relationships. We provide examples of a variety of these problems. Each species has distinct features of the particular families, and taxonomic section to assist the polychaete identification that is necessary to assess the biodiversity and taxonomy at any level. This chapter considers the importance of monitoring biological diversity, current morphological taxonomy of polychaetes and describes the approach developed for protected areas in Great Nicobar Islands

An Efficient Hierarchical Multi-Authority Attribute Based Encryption Scheme for Profile Matching using a Fast Ate Pairing in Cloud Environment

In cloud environment, profile matching is a key technique in applications such as health care and social networks. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable technique for data sharing in such environments. In this paper, we propose an asymmetric pairing based Hierarchical Multi-Authority CP-ABE (HM-CP-ABE) construction for profile matching. We utilize the fast Ate pairing to make the proposed HM-CP-ABE scheme efficient. The performance analysis of the proposed scheme shows improved efficiency in terms of computational costs for initialization, key generation and encryption using ELiPS library when compared with existing works

Secure Data Communication using File Hierarchy Attribute Based Encryption in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) play an important role in healthcare system by enabling medical experts to guide patients without measuring face-to-face. The unauthorized access of medical data from WBAN controller as well as the unreliable data communication may leads to risk for patients life. Currently, Chunqiang Hu et al., [1] proposed a data communication protocol by using Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for a single file. The major limitation of Chunqiang Hu et al., [1] is that as the number of files increases, CP-ABE will suffer from parameters such as message size, energy consumption and computation cost. This paper proposes a more secure and efficient data communication protocol for WBANs by using an efficient File Hierarchy CP-ABE (FH-CP-ABE). We propose a more efficient Secure and Efficient data communication protocol for WBANs by using an efficient File Hierarchy CP-ABE (FH-CP-ABE). We use the integrated access structure which is a combination of two or more access structures and with the hierarchical files are encrypted. We evaluate the performance analysis of the proposed data communication protocol in terms of message size, energy consumption, computation cost and compared with Chunqiang Hu et al., [1] while the number of files increases

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes

MODELING AND ANNOTATING THE EXPRESSIVE SEMANTICS OF DANCE VIDEOS

Abstract: Dance videos are interesting and semantics-intensive. At the same time, they are the complex type of videos compared to all other types such as sports, news and movie videos. In fact, dance video is the one which is less explored by the researchers across the globe. Dance videos exhibit rich semantics such as macro features and micro features and can be classified into several types. Hence, the conceptual modeling of the expressive semantics of the dance videos is very crucial and complex. This paper presents a generic Dance Video Semantics Model (DVSM) in order to represent the semantics of the dance videos at different granularity levels, identified by the components of the accompanying song. This model incorporates both syntactic and semantic features of the videos and introduces a new entity type called, Agent, to specify the micro features of the dance videos. The instantiations of the model are expressed as graphs. The model is implemented as a tool using J2SE and JMF to annotate the macro and micro features of the dance videos. Finally examples and evaluation result

An Efficient Tate Pairing Algorithm for a Decentralized Key-Policy Attribute Based Encryption Scheme in Cloud Environments

Attribute-based encryption (ABE) is used for achieving data confidentiality and access control in cloud environments. Most often ABE schemes are constructed using bilinear pairing which has a higher computational complexity, making algorithms inefficient to some extent. The motivation of this paper is on achieving user privacy during the interaction with attribute authorities by improving the efficiency of ABE schemes in terms of computational complexity. As a result the aim of this paper is two-fold; firstly, to propose an efficient Tate pairing algorithm based on multi-base number representation system using point halving (TP-MBNR-PH) with bases 1/2, 3, and 5 to reduce the cost of bilinear pairing operations and, secondly, the TP-MBNR-PH algorithm is applied in decentralized KP-ABE to compare its computational costs for encryption and decryption with existing schemes