Comparative study of machine learning algorithms for anomaly detection in Cloud infrastructure

Cloud is one of the emerging technologies in the field of computer science and is extremely popular because of its use of elastic resources to provide optimized, cost-effective and on-demand services. As technology started to grow in scale and complexity, the need for automated anomaly detection and monitoring system has become important. Inappropriate exploitation of Cloud resources can often lead to faults like crashing of VMs, decreased efficiency of cloud system etc. thereby leading to violations of the Service Level Agreement (SLA). These faults are often preceded by anomalies in the behavior of the VMs. Hence, the anomalies can be used as indicators of faults which potentially violate the SLAs. We have created a system that will monitor the VMs, detect anomalies and warn the system administrator before any problem escalates. We present in this paper a comparative study of various machine learning algorithms used for detecting anomalies in cloud

Image Retrieval by using visual features and study of various Image Retrieval systems

Abstract Images have rich contents such as color, shape, texture etc. By using these contents it is possible to interactively find any kind of image from large collection of image database. We highlight various ideas from researchers about various methods in interactive search. We discuss visual features of an image like color, shape and texture. We also discuss several content based image retrieval systems (CBIR). This paper, explore Content Based Image Retrieval (CBIR) technique and various research applications of CBIR

Importance of Non-Selective Cation Channel TRPV4 Interaction with Cytoskeleton and Their Reciprocal Regulations in Cultured Cells

BACKGROUND: TRPV4 and the cellular cytoskeleton have each been reported to influence cellular mechanosensitive processes as well as the development of mechanical hyperalgesia. If and how TRPV4 interacts with the microtubule and actin cytoskeleton at a molecular and functional level is not known. METHODOLOGY AND PRINCIPAL FINDINGS: We investigated the interaction of TRPV4 with cytoskeletal components biochemically, cell biologically by observing morphological changes of DRG-neurons and DRG-neuron-derived F-11 cells, as well as functionally with calcium imaging. We find that TRPV4 physically interacts with tubulin, actin and neurofilament proteins as well as the nociceptive molecules PKCepsilon and CamKII. The C-terminus of TRPV4 is sufficient for the direct interaction with tubulin and actin, both with their soluble and their polymeric forms. Actin and tubulin compete for binding. The interaction with TRPV4 stabilizes microtubules even under depolymerizing conditions in vitro. Accordingly, in cellular systems TRPV4 colocalizes with actin and microtubules enriched structures at submembranous regions. Both expression and activation of TRPV4 induces striking morphological changes affecting lamellipodial, filopodial, growth cone, and neurite structures in non-neuronal cells, in DRG-neuron derived F11 cells, and also in IB4-positive DRG neurons. The functional interaction of TRPV4 and the cytoskeleton is mutual as Taxol, a microtubule stabilizer, reduces the Ca2+-influx via TRPV4. CONCLUSIONS AND SIGNIFICANCE: TRPV4 acts as a regulator for both, the microtubule and the actin. In turn, we describe that microtubule dynamics are an important regulator of TRPV4 activity. TRPV4 forms a supra-molecular complex containing cytoskeletal proteins and regulatory kinases. Thereby it can integrate signaling of various intracellular second messengers and signaling cascades, as well as cytoskeletal dynamics. This study points out the existence of cross-talks between non-selective cation channels and cytoskeleton at multiple levels. These cross talks may help us to understand the molecular basis of the Taxol-induced neuropathic pain development commonly observed in cancer patients