Auto Upload and Chi-Square Test on Application Software as a Service for Cloud Computing Environment
Authors: Sampada Kembhavi, Gajendra Singh
Abstract
The cloud delivery model has been suggested as the panacea for complex as well as inflexible IT systems that can be ill afforded by small and large enterprises alike. However the lack of mature operational models for Application software as a Service (Asaas) meant that the cloud capabilities have not yet been developed and researched to a level that allows their exploitation to a full degree. In this paper we provide a survey for the cloud computing and suggest a novel framework where data is auto upload after a proper authentication and processing their task (gathering and sharing) in the cloud environment. Then we apply Chi-Square test, to test the hypothesis for correctness. Then we check the program capability under three parameters; first is F-measure (FM), second is odds ratio (OR) and third is power (PO). Based on the three parameters we can find the better application software as a service approach. Based on the above phenomena we can compare the cost and the time.
Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier
Authors: Jogendra Kushwah, Divakar Singh
Abstract
The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.
Solubility Enhancement of Poorly Ater Soluble Drug Aceclofenac
Authors: Girish C. Soni, PD Chaudhary, PK Sharma
Abstract
Solid dispersion was aim for increasing solubility and bioavailability of poorly aqueous soluble drug aceclofenac (ACE). Solid dispersion of drug aceclofenac (NSAID) was prepared by using different polymers i.e. Polaxomer 188, PVP k30, PEG 6000, in different ratios 1:1, 1:2, 1:3, by different methods of solid dispersion preparation i.e. Physical method, solvent evaporation method, melt method. The prepared formulations was characterised by Differential scanning calorimetry (DSC), Scanning electron microscopy (SEM), X-ray diffraction (XRD), In vitro dissolution studies, drug content estimation, saturation solubility. It was concluded that there is change in the crystalline form of drug into amorphous state during formation of solid dispersion. Polaxomer 188 in ratio 1:3 through melt method, it shows maximum drug release which is 99.04% in 40 minutes. It was concluded that solid dispersion formation of aceclofenac drug by using polaxomer 188 is promising approach in enhancement of its absorption rate increase bioavailability and minimising its side effects.
Monitoring Greenhouse using Wireless Sensor Network
Authors: Rohit. K. Nikhade, S. L. Nalbalwar
Abstract
Recent advances in science and technology have been led to facilitate monitoring the environment, collecting data, processing the sensed data, threshold-decision making process and lastly performing of suitable actions by using of distributed wireless sensor networks and actor networks. Wireless sensor actor networks (WSAN) is a combination of at least one coordinator node with sensors and actor nodes that communicate wirelessly to perform some specified tasks of sensing, monitoring and actuation. This paper summarizes an idea that can be carried out to provide an efficient control mechanism of microclimate into greenhouses through the implementation of an infrastructure of Wireless Sensors Network to control environmental parameters.
Design and Evaluation of Fast Dissolving Oral Films of Zolpidem by Solvent Casting Method
Authors: Dasari Nirmala, Swapna Nandhini, M. Sudhakar
Abstract
Present research was aimed with the objective of formulation of Zolpidem fast dissolving oral films, for rapid dissolution of drug and absorption, which may produce the rapid on set off action. The fast dissolving oral films were prepared by solvent casting method using various polymers like HPMC E5, HPMC E15, HPMC K15, microcrystalline cellulose and poly vinyl alcohol. Glycerol used as plasticizer. Prepared films were evaluated for various physicochemical parameters like film thickness, tensile strength, folding endurance, content uniformity, estimation of moisture content, disintegration studies and dissolution studies. As polymer concentration increases thickness increases. Thickness directly proportion to tensile strength so as thickness increases tensile strength also increases. Folding endurance increases as polymer concentration increases. The moisture uptake studies revealed that, the film take up very little moisture (1-2%) in high humid environment, hence this may favors the stability as well as compatability. In vitro drug release studies reveals that among all formulations F7 formulation highest amount of drug release compared to other formulations, hence F7 formulation was taken as optimized formulation for further studies. Short term stability studies were conducted for optimized formulation up to 3 months at 40±2ċ 75% R.H. after stability studies films were transparent all physical parameters were favorable and drug release was satisfactory.
Random Particle Swarm Optimization (RPSO) based Intrusion Detection System
Authors: Ranjna Patel, Deepa Bakhshi, Tripti Arjariya
Abstract
Intrusion detection is a challenging area of research. As now there are several research work are already done and the result improvement is in progress. In this paper a hybrid combination of association rule mining and random particle swarm optimization (RPSO) has been applied. This approach is applied on NSL-KDD dataset. A limit set is provided by our framework which will be adapted as per the user choice to select the set of data for use. Our approach successfully differentiates the normal and attack node. Then we have applied a recheck frame for the normal node for finding the suspicious node. Then by the help of association rule associated values are passed for the next procedure. Then we apply RPSO to check the boundary value for the possible type of intrusion detection. If it is passed the velocity value then it will be listed in the attack type. Finally based on the attack category of Denial of Service (DoS),User to Root (U2R),Remote to User (R2L) and Probing (Probe) attacks are classified. The results of our method shows improvement in detection different type of attack in comparison to the previous method.