Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively

HASBE access control model with Secure Key Distribution and Efficient Domain Hierarchy for cloud computing

Cloud computing refers to the application and service that run on a distributed system using virtualized resources and access by common internet protocol and networking standard. Cloud computing virtualizes system by pooling and sharing resources. System and resources can be monitored from central infrastructure as needed. It requires high security because now day’s companies are placing more essential and huge amount of data on cloud. Hence traditional access control models are not sufficient for cloud computing applications. So encryption based on Attribute (“ABE”-“Attribute based encryption”) has been offered for access control of subcontracted data in cloud computing with complex access control policies. Traditional HASBE provides Flexibility, scalability and fine-grained access control but does not support hierarchical domain structure. In this paper, we had enhanced “Hierarchical attribute-set-based encryption” (“HASBE”) access control with a hierarchical assembly of users, with flexible domain Hierarchy structure and Secure key distribution with predefined polic

Integrated biomechanical model of cells embedded in extracellular matrix

Nature encourages diversity in life forms (morphologies). The study of morphogenesis deals with understanding those processes that arise during the embryonic development of an organism. These processes control the organized spatial distribution of cells, which in turn gives rise to the characteristic form for the organism. Morphogenesis is a multi-scale modeling problem that can be studied at the molecular, cellular, and tissue levels. Here, we study the problem of morphogenesis at the cellular level by introducing an integrated biomechanical model of cells embedded in the extracellular matrix. The fundamental aspects of mechanobiology essential for studying morphogenesis at the cellular level are the cytoskeleton, extracellular matrix (ECM), and cell adhesion. Cells are modeled using tensegrity architecture. Our simulations demonstrate cellular events, such as differentiation, migration, and division using an extended tensegrity architecture that supports dynamic polymerization of the micro-filaments of the cell. Thus, our simulations add further support to the cellular tensegrity model. Viscoelastic behavior of extracellular matrix is modeled by extending one-dimensional mechanical models (by Maxwell and by Voigt) to three dimensions using finite element methods. The cell adhesion is modeled as a general Velcro-type model. We integrated the mechanics and dynamics of cell, ECM, and cell adhesion with a geometric model to create an integrated biomechanical model. In addition, the thesis discusses various computational issues, including generating the finite element mesh, mesh refinement, re-meshing, and solution mapping. As is known from a molecular level perspective, the genetic regulatory network of the organism controls this spatial distribution of cells along with some environmental factors modulating the process. The integrated biomechanical model presented here, besides generating interesting morphologies, can serve as a mesoscopic-scale platform upon which future work can correlate with the underlying genetic network

Association of Etonogestrel-Releasing Contraceptive Implant with Reduced Weight Gain in an Exclusively Breastfed Infant: Report and Literature Review

Background: Studies have not found that hormonal contraceptive implants adversely affect breastfeeding, but theoretical concerns exist

Empirical study on Microsoft malware classification

A malware is a computer program which causes harm to software. Cybercriminals use malware to gain access to sensitive information that will be exchanged via software infected by it. The important task of protecting a computer system from a malware attack is to identify whether given software is a malware. Tech giants like Microsoft are engaged in developing anti-malware products. Microsoft's anti-malware products are installed on over 160M computers worldwide and examine over 700M computers monthly. This generates huge amount of data points that can be analyzed as potential malware. Microsoft has launched a challenge on coding competition platform Kaggle.com, to predict the probability of a computer system, installed with windows operating system getting affected by a malware, given features of the windows machine. The dataset provided by Microsoft consists of 10,868 instances with 81 features, classified into nine classes. These features correspond to files of type asm (data with assembly language code) as well as binary format. In this work, we build a multi class classification model to classify which class a malware belongs to. We use K-Nearest Neighbors, Logistic Regression, Random Forest Algorithm and XgBoost in a multi class environment. As some of the features are categorical, we use hot encoding to make them suitable to the classifiers. The prediction performance is evaluated using log loss. We analyze the accuracy using only asm features, binary features and finally both. xGBoost provide a better log-loss value of 0.078 when only asm features are considered, a value of 0.048 when only binary features are used and a final log loss of 0.03 when all features are used, over other classifiers

Use and perceived effectiveness of non-analgesic medical therapies for chronic pancreatitis in the United States

Aliment Pharmacol Ther 2011; 33: 149–159Effectiveness of medical therapies in chronic pancreatitis has been described in small studies of selected patients.To describe frequency and perceived effectiveness of non-analgesic medical therapies in chronic pancreatitis patients evaluated at US referral centres.Using data on 516 chronic pancreatitis patients enrolled prospectively in the NAPS2 Study, we evaluated how often medical therapies [pancreatic enzyme replacement therapy (PERT), vitamins/antioxidants (AO), octreotide, coeliac plexus block (CPB)] were utilized and considered useful by physicians.Oral PERT was commonly used (70%), more frequently in the presence of exocrine insufficiency (EI) (88% vs. 61%, P  < 0.001) and pain (74% vs. 59%, P  < 0.002). On multivariable analyses, predictors of PERT usage were EI (OR 5.14, 95% CI 2.87–9.18), constant (OR 3.42, 95% CI 1.93–6.04) or intermittent pain (OR 1.98, 95% CI 1.14–3.45). Efficacy of PERT was predicted only by EI (OR 2.16, 95% CI 1.36–3.42). AO were tried less often (14%) and were more effective in idiopathic and obstructive vs. alcoholic chronic pancreatitis (25% vs. 4%, P  = 0.03). Other therapies were infrequently used (CPB – 5%, octreotide – 7%) with efficacy generally <50%.Pancreatic enzyme replacement therapy is commonly utilized, but is considered useful in only subsets of chronic pancreatitis patients. Other medical therapies are used infrequently and have limited efficacy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79114/1/j.1365-2036.2010.04491.x.pd

The progestational and androgenic properties of medroxyprogesterone acetate: gene regulatory overlap with dihydrotestosterone in breast cancer cells

INTRODUCTION: Medroxyprogesterone acetate (MPA), the major progestin used for oral contraception and hormone replacement therapy, has been implicated in increased breast cancer risk. Is this risk due to its progestational or androgenic properties? To address this, we assessed the transcriptional effects of MPA as compared with those of progesterone and dihydrotestosterone (DHT) in human breast cancer cells. METHOD: A new progesterone receptor-negative, androgen receptor-positive human breast cancer cell line, designated Y-AR, was engineered and characterized. Transcription assays using a synthetic promoter/reporter construct, as well as endogenous gene expression profiling comparing progesterone, MPA and DHT, were performed in cells either lacking or containing progesterone receptor and/or androgen receptor. RESULTS: In progesterone receptor-positive cells, MPA was found to be an effective progestin through both progesterone receptor isoforms in transient transcription assays. Interestingly, DHT signaled through progesterone receptor type B. Expression profiling of endogenous progesterone receptor-regulated genes comparing progesterone and MPA suggested that although MPA may be a somewhat more potent progestin than progesterone, it is qualitatively similar to progesterone. To address effects of MPA through androgen receptor, expression profiling was performed comparing progesterone, MPA and DHT using Y-AR cells. These studies showed extensive gene regulatory overlap between DHT and MPA through androgen receptor and none with progesterone. Interestingly, there was no difference between pharmacological MPA and physiological MPA, suggesting that high-dose therapeutic MPA may be superfluous. CONCLUSION: Our comparison of the gene regulatory profiles of MPA and progesterone suggests that, for physiologic hormone replacement therapy, the actions of MPA do not mimic those of endogenous progesterone alone. Clinically, the complex pharmacology of MPA not only influences its side-effect profile; but it is also possible that the increased breast cancer risk and/or the therapeutic efficacy of MPA in cancer treatment is in part mediated by androgen receptor