The main objective of this paper is at developing an effective machine learning techniques for cancer classification, which could provide reliable cancer classification with better accuracy. The work comprises of two steps. In the first step, important genes are chosen using ANalysis Of VAriance (ANOVA) ranking scheme. The second step involves the classification task using an efficient classifier. Here we use the three efficient classifiers such as Fast Support Vector Machine Learning (FSVML), Fast Extreme Learning Machine Learning (FELML) and Relevance Vector Machine Learning (RVMMLML). The experimental values are computed using three datasets namely Lymphoma, Leukemia and SRBCT. The results are interpreted in terms of Testing Accuracy and Training Time. From the experimental results, it is observed that the proposed RVMMLML approach provides better classification accuracy results compared to other classification on the datasets considered.

This paper carves out a way to video coding that is motivated by the recent advancement in video inpainting. In the proposed video coding approach, the regions are mainly divided into two types namely, Local Motion Region and Global Motion Region. The regions are removed by using block based motion estimation in Local Motion Regions and the method of texture synthesis is used for obtaining the removed regions. Shearlet - Laplacian based Partial Differential Equation (PDE) inpainting is carried out to recover the removed regions in Global Motion Regions. In both regions, Exemplar Selection (Region Removal) Process is based on the edges extracted from the input region. Wavelets like curvelets and contourlets are not very efficient when dealing with edges in multidimensional signals. In this paper, a Discrete Shearlet Transform (DST) is used for edge detection. Using DST helps to improve the overall compression performance together with the Shearlet - Laplacian based inpainting and texture synthesis Schemes. The Scheme has been integrated into H.264/AVC and achieves better bit rate saving.

Data mining is an emanating area not only in the field of computer science but also contributes more to clinical data research. This analysis mainly focuses on medical diagnosis of both patients and drugs through classification and prediction techniques. Nowadays, liver disease is increasing dramatically among alcoholic consumer, smoker and person infected by hepatitis A, hepatitis C and Cirrhosis. This research work focused more on diagnosing patient with liver disorders. In this work, two different datasets namely, Bupa liver dataset and Indian liver patient disorder dataset are taken from UCI machine learning repository which contains 345 records and 745 records respectively for experimental study. Both these liver patient datasets is subjected to ReliefF filtering and stepwise discriminate feature relevance ranking algorithms before applying various classification algorithms. The comparative study on all these datasets is done to evolve the best classifier for diagnosing liver disease. The experimental results showed that Random tree algorithm, C4.5 and k-NN delivered accurate classification rules for identifying liver disorders on all these datasets The performance evaluation is done based on accuracy, precision, sensitivity and specificity for the above three selected classifiers revealed that Random tree classifier is the best classifier for diagnoses liver diseases.

This paper gives a brief introduction to a novel visual attention detection method, which combines features of luminance, color contrast and structural dissimilarity to detect salient regions in images. The method simultaneously considers regional contrast and spatial coherence information and outputs full resolution saliency maps. The main purpose of this paper is to improve robustness of salient object detection for complex texture images, highlight uniformly salient object and boost the overall precision and recall for saliency detection. The main contributions of this paper include: 1) Identifying structural dissimilarity based on internal regions of image to measure structure contrast, which is conducive to recognize salient objects from texture complex images. 2) A nonlinear fusion model, which effectively combines structural dissimilarity and color contrast to distinguish saliency of each image regions. 3) A salient objects segmentation method which can automatically extract salient objects from images based on saliency maps. The proposed method is compared with nine state-of-the-art salient region detection methods on fixed threshold segmentation experiments and on adaptive salient object segmentation experiments. The evaluated results demonstrate that our method has outstanding performances both on acquiring higher precision and better recall, especially improves saliency detection effect for complex texture image.

The performance of a person with DCD (Developmental Coordination Disorder) is lower than the person’s chronological age and measured intelligence regarding the daily activities. It may be manifested by delays in achieving motor milestones (such as walking, crawling and sitting), dropping things, clumsiness, poor performance in sports, and poor handwriting. The main goal of research is to assess the developmental coordination disorder. Descriptive and analytical methods are used in research through library studies to get the results. It was mainly resulted of the research that the pre-school years (ages 3 to 6) are the most ideal time to start rehabilitation to overcome the problems and disorders.

Glaucoma is the eye disorder in which the optic never suffers damage, leading to vision defect in the affected eye(s) and leading to complete blindness if untreated. It is frequently associated with increased pressure in the aqueous humour of the eye. Glaucoma often goes undetected until significant damage to the subject’s visual field has occurred. As glaucoma progresses, neural tissues die, the nerve fiber layer thins, and the cup-to-disc ratio increases. The conventional techniques typically used for this measurement are unreliable and creates intricacies while measuring considerably small changes in the nerve head geometry. In this paper spatially weighted fuzzy c-mean clustering is used to find the CDR from the colour fundus image and to determine pathological process of glaucoma. The blood vessels in the optic disc regions are segmented by using local entropy thresholding approach.The proposed method can be used to automatically segment the neuroretinal rim.We have extracted features such as cup to disc ratio,ratio of the blood vessels area in inferior-superior side to area blood vessels in the nasal-temporal side and the ratio of neuroretinal rim area in imferior-superior side to area of neuroretinal rim in the nasal-temporal side.These features are validated by classifying the normal and glaucoma images using Support vector machine, and neural network classifier.A batch of 300 retinal is used to assess the performance of proposed system and a classification rate of 96% is achieved

Abstract \nManufacturing and productive service industries such as urban recreation and leisure industry have contributed to capital generation during past several decades. Modernism era (19th to middle of 20th centuries) had many conceptual and symbolic differences compared to present atmosphere known as postmodern era. \nMetropolitan areas were historically recognized by the “brands” of their manufacturing industries up to the middle of 20th century. Former city functions have been replaced by new ones and urban spaces are now organized based on social and economic changes that occurred during postmodern era. Among spaces that extensively increase public consumption, create employment, and promote urban living are spaces created to provide recreational, leisure, training, and tourism services. \nPresent amusement parks are multifunctional and provide cultural, economic, and social functions. These parks provide pre and post relations various industries that supply product and services to Metropolitan areas including large industrial producers of amusement park equipments, consulting and construction companies, transport industry, and other productive service industries providing design, consulting, insurance, banking, travel, and other similar services.\nAmusement parks produce extensive social and cultural impacts. Large theme parks have turned into “global villages” that act as a crossroad for different social strata, civilizations, and nations. \nIran 1404 Outlook Document has put priority on improvement of social justice in all economic, social, and cultural programs. This document has highlighted infrastructure development for macro programming. In compliance with the directives of this document, Tehran Municipality has undertaken to construct one of the largest social and cultural projects with the participation of private sector. This huge project is named “Hezar-o-Yek Shahr” recreational complex. This complex is planned over 150,000 hectares and incorporates Islamic and Iranian cultural and architectural characteristics in its construction. “Hezar O Yek Shahr” is intended to become the focal point and serve as a model for optimization of economic, social, and cultural functions for residents of Tehran metropolitan area. \nPresent study is a practical research based on its objectives. Research methodology is analytic-descriptive. The findings of this research confirmed the effectiveness of social oriented projects in improving living quality and enrichment of leisure time subject to effective management and monitoring.

Background-AIMS: Little is known about the prognostic significance of elevated serum relaxin in acute myocardial infarction (AMI) patients.The present study is designed to investigate the potential association between serum relaxin levels and the risk of AMI. MATERIALS AND METHODS:We measured circulating relaxin levels in 80 patients (median age 62.3 years) who presented with first-time AMI 8 hours after the incident.The circulating relaxin-2 levels in 80 healthy volunteers (median age 61.5 years) was also measured. Relaxin-2 was detected using enzyme immunoassay in both groups. RESULTS: Serum relaxin levels were significantly higher in patients with AMI (27.4±6.3ng/ml) compared to controls (9.2±2.3 ng/ml)(P<0.01).We found that a relaxin level>13.8 ng/ml had a sensitivity of 79% and a specificity of 86% for predicting AMI.Relaxin revealed the higher sensitivity and specificity for diagnosing AMI. CONCLUSIONS: Elevated Relaxin in plasma may be a novel biomarker for early detection of AMI

Objective\nThe purpose of this study was to assess the efficacy and outcomes of preoperative prophylactic metoclopramide and dimenhydrinate use in elective cesarean births.\nMaterial and Methods\nParticipants (n=84) scheduled for elective cesarean births were randomized equally into placebo (10cc 0.9% NaCl), 10mg metoclopramide or 50 mg dimenhydrinate groups. \nOral alimentation was prohibited eight hours before the surgery; however patients continued drinking water until four hours before surgery. Placebo and anti-emetics were administered 1 hour before the anticipated procedure in a 5 ml syringe. In metoclopramide and dimenhydrinate group an ampoule of the agents were completed to 5 ml by adding 0.9% NaCl. In the control group 5 ml of 0.9% NaCl was used. All prophylactic agents were administered intramuscularly. All patients received a general anesthesia.\nThe placebo group (control group) was compared with the metoclopramide and dimenhydrinate groups.\nResults\nDemographic data including maternal age, height, weight, body mass index, gravidity, parity, miscarriage, induced abortion, the number of offspring, and the medical history did not show significant differences among the three groups.\nPostoperative nausea, vomiting and the use of rescue medication ratios were significantly lower in metoclopramide and dimenhydrinate groups compared with the placebo group (p<0.05), however the difference between the metoclopramide and dimenhydrinate groups was not significant (p>0.05).\nConclusions\nDimenhydrinate and metoclopramide significantly decrease postoperative nausea, vomiting and the need for rescue anti emetic medication. Both agents have similar efficacy and may be used as an alternative to each other.

Recent developments in Cardiac surgery have led to increase in number of patients between the age group of 25 and 69 years of age who have survived Myocardial Infarction. Regular and continuous monitoring of these patients becomes essential as this age group of people is the real asset to a family. Hence this paper aims in design and development of low cost, light weight, portable Telecardiac system for remote monitoring of cardiac patients. The proposed system employs a Field Programmable Gate Array(FPGA), a reconfigurable platform that performs online analysis of the acquired ECG signal continuously . FPGA based Arrhythmia detector, implemented on a DE2 Altera Cyclone II board is interfaced with GSM/GPS module to alert the physician on a mobile phone about the imminent fatal condition and location of the patient. If the portable system detects any arrhythmia, it automatically sends an alarm condition to the patient’s care taker and ambulance centre by sending an SMS that has information about the abnormal condition and location of the patient.

Providing security and efficient access control for video streaming in cloud computing is one of the challenging ongoing research works ,because the data owners outsource their multimedia data to the cloud service provider to make it available for the user at anytime , anywhere and by using any devices. Existing solutions for secure video streaming uses standard cryptographic techniques. But it suffers from heavy computational cost, both in the data owner and in the user sides. For example if we use AES for encryption and decryption for large video files, the processing time in terms of encryption and decryption will increase even though it provides higher level of security. Also AES kind of security is not needed for simple media files and e-learning type of applications. This paper addresses this challenging problem by proposing MBBXOR Encryption Algorithm – a computational efficient encryp-tion/decryption algorithm for multimedia streaming in cloud computing. The simulation runs and analysis shows that the proposed approach is highly efficient and provides adequate security.