An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

The Cloud system shows its growing functionalities in various industrial applications. The safety towards data transfer seems to be a threat where Network Intrusion Detection System (NIDS) is measured as an essential element to fulfill security. Recently, Machine Learning (ML) approaches have been used for the construction of intellectual IDS. Most IDS are based on ML techniques either as unsupervised or supervised. In supervised learning, NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns. Similarly, the unsupervised model fails to provide a satisfactory outcome. Hence, to boost the functionality of unsupervised learning, an effectual auto-encoder is applied for feature selection to select good features. Finally, the Naïve Bayes classifier is used for classification purposes. This approach exposes the finest generalization ability to train the data. The unlabelled data is also used for adoption towards data analysis. Here, redundant and noisy samples over the dataset are eliminated. To validate the robustness and efficiency of NIDS, the anticipated model is tested over the NSL-KDD dataset. The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%, which is higher compared to J48, AB tree, Random Forest (RF), Regression Tree (RT), Multi-Layer Perceptrons (MLP), Support Vector Machine (SVM), and Fuzzy. Similarly, False Alarm Rate (FAR) and True Positive Rate (TPR) of Naive Bayes (NB) is 0.3 and 0.99, respectively. When compared to prevailing techniques, the anticipated approach also delivers promising outcomes.     

Achromatic phase-shifting for white-light interferometry

The geometric (Pancharatnam) phase provides a method of introducing a variable phase shift that is almost independent of the wavelength and opens up new possibilities in broadband interferometry.     

Secondary grain growth in ammonium nitrate crystallization

Crystallization and ageing of pure ammonium nitrate crystals was studied by scanning electron microscopy. Ageing the crystals beyond 1 h led to the growth of secondary grains along the grain boundaries of primary grains (140–200 m). After 6 h ageing secondary growth along grain boundaries was not observed; instead, distinct clusters with morphologies similar to the secondary grains were formed. The ancillary growth obeys approximately the parabolic relation L=(Kt)^1/n+1 where n=1 justifies grain growth in the pure crystals, and its formation is ascribed to the dissolution of dendrites, edges and corners. The high interfacial activity and interparticle voids can affect the storage and mechanical properties of the material.     

Classification of speech dysfluencies with MFCC and LPCC features

The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech samples from UCLASS are used for our analysis. The stuttered events are identified through manual segmentation and used for feature extraction. Two simple classifiers are used for testing the proposed features. Conventional validation method is used for testing the reliability of the classifier. The experimental investigation elucidates MFCC and LPCC features which can be used for identifying the stuttered events and LPCC features were slightly outperformed than MFCC features.     

Composite polymer electrolyte based on (PEO)6:NaPO3 dispersed with BaTiO3

BACKGROUND: Polymer electrolytes have attracted considerable attention as regards portable solid‐state electrochemical device applications. The present investigation is focused on the characterization of a new Na⁺ ion conducting polymer electrolyte (PEO)₆:NaPO₃ dispersed with 3–10 wt% BaTiO₃ (0.7 µm) fillers. The composite polymer electrolytes (CPEs) were prepared by a solution‐casting method and characterized using various physical measurement techniques. RESULTS: Differential scanning calorimetry results indicate a maximum reduction in the degree of crystallinity of the polymer from 62.6% for uncomplexed poly(ethylene oxide) (PEO) to 27.6% for the CPE with 6 wt% BaTiO₃. This substantiates an enhancement in the amorphous phase of the polymer inferred from X‐ray diffraction and optical micrographs. The CPE dispersed with 6 wt% BaTiO₃ is found to be the best composition exhibiting a maximum ionic conductivity of 1.2 × 10^?6 S cm^?1 at 345 K with cationic transport number (t) of 0.33. CONCLUSIONS: An enhancement in the ionic conductivity of about two orders of magnitude is achieved for the composite electrolytes when compared to filler‐free solid polymer electrolyte. Correlation of the temperature‐dependent conductivity, activation energy for ion migration and transport number enables an understanding of the role played by the fillers in conduction characteristics of the CPEs. Copyright © 2007 Society of Chemical Industry     

A proximity effect model for superconductivity in titanium-transition metal alloys

We present a unified model for the variation in superconducting transition temperature T _c of quenched titanium-transition metal alloys with intermediate-temperature aging. Experimental results on T _c and the low-temperature resistivity of -quenched Ti-27 at % Nb solid solution, a representative system, measured as a function of annealing are given. It is shown that the enrichment of the matrix in Nb as a result of precipitation of a Ti-rich phase is too small to be of any consequence in influencing T _c. Our resistivity data give, for the first time, evidence for the formation of athermal phase in this system. In the light of the present investigation a clear picture has emerged for the interpretation of T _c in terms of varying extent of superconducting proximity effect, which is shown to be intimately related to the microstructural evolution. The result is shown to be general, applicable to any Ti-transition metal alloy system.     

A stacked ensemble learning model for intrusion detection in wireless network

Neural Computing and Applications - Intrusion detection pretended to be a major technique for revealing the attacks and guarantee the security on the network. As the data increases tremendously...     

Analysis of the urine proteome in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) accounts for over 213?000 deaths worldwide each year, largely due to late diagnosis. One of the risk factors for the development of PDAC is chronic pancreatitis (CP); the intense desmoplastic reaction makes differentiation between the two conditions extremely difficult. In order to identify biomarkers for noninvasive diagnosis, we performed 2-D DIGE analysis of urine samples from healthy individuals and patients with PDAC and CP. Despite considerable intersample heterogeneity, a total of 127 statistically valid (p<0.05), differentially expressed protein spots were detected, 101 of which were identified using MALDI-TOF MS. A number of these, including annexin A2, gelsolin and CD59 have already been associated with PDAC, however, their validation using immunoblotting proved challenging. This is probably due to extensive PTMs and processing thus indicating the need for raising specific antibodies for urinary proteins. Despite this, our study clearly demonstrates that urine is a valid source of noninvasive biomarkers in patients with pancreatic diseases.