Enhance Crawler For Efficiently Harvesting Deep Web Interfaces

Scenario in web is varying quickly and size of web resources is rising, efficiency has become a challenging problem for crawling such data. The hidden web content is the data that cannot be indexed by search engines as they always stay behind searchable web interfaces. The proposed system purposes to develop a framework for focused crawler for efficient gathering hidden web interfaces. Firstly Crawler performs site-based searching for getting center pages with the help of web search tools to avoid from visiting additional number of pages. To get more specific results for a focused crawler, projected crawler ranks websites by giving high priority to more related ones for a given search. Crawler accomplishes fast in-site searching via watching for more relevant links with an adaptive link ranking. Here we have incorporated spell checker for giving correct input and apply reverse searching with incremental site prioritizing for wide-ranging coverage of hidden web sites

Privacy Preserving Public Auditing and Data Integrity for Secure Cloud Storage Using Third Party Auditor

Using cloud services, anyone can remotely store their data and can have the on-demand high quality applications and services from a shared pool of computing resources, without the burden of local data storage and maintenance. Cloud is a commonplace for storing data as well as sharing of that data. However, preserving the privacy and maintaining integrity of data during public auditing remains to be an open challenge. In this paper, we introducing a third party auditor (TPA), which will keep track of all the files along with their integrity. The task of TPA is to verify the data, so that the user will be worry-free. Verification of data is done on the aggregate authenticators sent by the user and Cloud Service Provider (CSP). For this, we propose a secure cloud storage system which supports privacy-preserving public auditing and blockless data verification over the cloud

An Enhanced Multi-layered Cryptosystem Based Secure and Authorized De-duplicaton Model in Cloud Storage System

Data de-duplication is one of the essential data compression techniques for eliminating duplicate copies of repeating data, and it has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To protect the privacy of sensitive data while supporting de-duplication, the salt encryption technique has been proposed to encrypt the data before its outsourcing. To protect the data security in a better way, this paper makes the first attempt to formally address the problem of authorized data de-duplication. Different from traditional de-duplication systems, the derivative privileges of users are further considered in duplicate check besides the data itself. We also present various new de-duplication constructions which supports the authorized duplicate check in hybrid cloud architecture. Security analysis demonstrates that the scheme which we used is secure in terms of the definitions specified in the proposed security model. We enhance our system in security. Specially, we present a forward-looking scheme to support a stronger security by encrypting file with differential privilege keys. We show that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations

Alzheimer Detection System Using Hybrid Deep Convolutional Neural Network

Alzheimer’s disease of the sixth leading causes of death in the United States of America is projected to grow to the third place of all causes of death for the elderly soon to cancer and heart decease. Timely detection and prevention are crucial to it. AD detection is based on multiple medical examinations which all lead to extensive multivariate heterogeneous data. This factor makes manual comparison, evaluation, and analysis hardly possible. The hereby study proposes a new approach to the detection of AD at the earliest stage hybrid deep learning algorithms. Several feature extraction and selection draw possible features. The method involves InceptionV3 and DenseNet for both pre-processing and classification tasks, while MobileNet enables data pre-processing and object detection. Experimental results with 100 epochs and 15 hidden layers show InceptionV3 has an accuracy of 98%, which outperforms other models available. The comparative analysis with other CNN models endorses the proposed method, achieving the highest performance across the board from our system

Explaining Suicide in an Urban Slum of Mumbai, India

Background: Health demographic mortality studies use verbal autopsies to identify suicide as a cause of death. Psychological autopsies focus almost exclusively on associated high-risk psychiatric disorders. New approaches considering contextual factors are needed for preventing suicide and promoting mental health. Aims: This study examined explanations of suicide reported by surviving family members or close friends with reference to social, cultural, and environmental conditions as well as the challenges of life in the Malavani slum of Mumbai. Methods: An EMIC (Explanatory Model Interview Catalog) interview based on a cultural epidemiological framework considered underlying problems, perceived causes, and sociocultural contexts. It was administered to survivors of 76 people who had died by suicide (56.6% women). Results: Accounts of underlying problems typically referred to various aspects of tension (73.7%). Perceived causes often identified multiple factors. The sociocultural contexts of suicide included the victimization of women, the personal and social impact of problem drinking, marital problems, physical health problems, mental tension, possession and sorcery. Women were particularly vulnerable to the impact of problem drinking by a spouse or father. Conclusions: This study demonstrates the value of an approach to sociocultural autopsy examining local contexts and explanations of suicide. Findings highlight needs for both mental health services and culturally sensitive social interventions

Contextualizing mental health: gendered experiences in a Mumbai slum

Urban mental health programmes in developing countries remain in their infancy. To serve low-income communities, research needs to consider the impact of common life experience in slums, including poverty, bad living conditions, unemployment, and crowding. Our study in the Malavani slum of Mumbai examines afflictions of the city affecting the emotional well-being and mental health of women and men with respect to gender. This is a topic for which mental health studies have been lacking, and for which psychiatric assumptions based on middle-class clinical experience may be most tenuous. This study employs ethnographic methods to show how environmental and social contexts interact in shaping local experience with reference to common mental health problems. Focusing on the social and environmental context of the mental health of communities, rather than psychiatric disorders affecting individuals, findings are broadly applicable and sorely needed to guide the development of locally appropriate community mental health programmes. Identified afflictions affecting mental health include not only access to health care, but also sanitation, addictions, criminality, domestic violence, and the so-called bar-girl culture. Although effective clinical interventions are required for mental health services to treat psychiatric disorders, they cannot directly affect the conditions of urban slums that impair mental health

Innovation of System Biological Approach in Computational Drug Discovery

Computational methods like classification and network-based algorithms can be used to understand the mode of action and the efficacy of a given compound and to help elucidating the patho-physiology of a disease. In the pharmacological industry there has already been a shift from symptomatic oriented drugs that can relieve the symptoms but not the cause of the disease to pathology-based drugs whose targets are the genes and proteins involved in the etiology of the disease. Drugs targeting the affected pathway have thus the potential to become therapeutic. A network approach to drug design would examine the effect of drugs in the context of a network of relevant protein regulatory metabolic interactions resulting in the development of a drug that would hit multiple targets selected in such a way as to decrease network integrity and so completely disrupt the functioning of the network. The screening of a compound to quickly identify the proteins it interacts with gives us all the necessary tools to identify and repair the deregulated biological pathway causing the disease

An Automated Solution to Training and Placement Cell Activities

The paper is aimed at developing an application as an ?An Automated Solution To Training And Placement Cell Activities. It can be accessed and effectively used throughout the organization with proper login enabled. This application can be used for the Placement Officers in the college to manage the student information with regard to placement. Student logging should be able to upload their personal and educational information in the form of a resume. The key feature of this project is that it is one time registration enabled. Our project provides the facility of maintaining the details of the students. The management of Training and Placement is supported by databases, spreadsheets and E-mail communications. It also provides a requested list of candidates to recruit the students based on given query. Administrator logging may also search any information put up by the students. It intends to help fast access procedures in placement related activities and ensures to maintain the details of the students

Ciliogenesis Mechanisms Mediated by PAK2-ARL13B Signaling in Brain Endothelial Cells is Responsible for Vascular Stability

In the developing vasculature, cilia, microtubule-based organelles that project from the apical surface of endothelial cells (ECs), have been identified to function cell autonomously to promote vascular integrity and prevent hemorrhage. To date, the underlying mechanisms of endothelial cilia formation (ciliogenesis) are not fully understood. Understanding these mechanisms is likely to open new avenues for targeting EC-cilia to promote vascular stability. Here, we hypothesized that brain ECs ciliogenesis and the underlying mechanisms that control this process are critical for brain vascular stability. To investigate this hypothesis, we utilized multiple approaches including developmental zebrafish model system and primary cell culture systems. In the p21 activated kinase 2 (pak2a) zebrafish vascular stability mutant [redhead (rhd)] that shows cerebral hemorrhage, we observed significant decrease in cilia-inducing protein ADP Ribosylation Factor Like GTPase 13B (Arl13b), and a 4-fold decrease in cilia numbers. Overexpressing ARL13B-GFP fusion mRNA rescues the cilia numbers (1–2-fold) in brain vessels, and the cerebral hemorrhage phenotype. Further, this phenotypic rescue occurs at a critical time in development (24 h post fertilization), prior to initiation of blood flow to the brain vessels. Extensive biochemical mechanistic studies in primary human brain microvascular ECs implicate ligands platelet-derived growth factor-BB (PDGF-BB), and vascular endothelial growth factor-A (VEGF-A) trigger PAK2-ARL13B ciliogenesis and signal through cell surface VEGFR-2 receptor. Thus, collectively, we have implicated a critical brain ECs ciliogenesis signal that converges on PAK2-ARL13B proteins to promote vascular stability