Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique of the created approach includes four stages, such as pre-processing, segmentation, highlight extraction, and the order. From the outset, the Computerized Tomography (CT) image of the lung is taken care of to the division. When the division is done, the highlights are extricated through morphological factors for feature observation. By getting the features are analysed and the characterization is done dependent on the Deep Belief Network (DBN) which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm (CSCA) which distinguish the lung tumour, giving two classes in particular, knob or non-knob. The proposed system produce high performance as well compared to the other system. The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity, precision, affectability, and the explicitness.     

Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system.     

海陆区域观——雅加达大都市地区水上住区发展的新范式

不同的角度会导致对区域概念的不同理解，这就影响到我们如何对待一个地区．这一点通常表现在对一个区域的规划编制、设计手法以及应用怎样的适宜的技术。本文将对雅加达大都市地区“内部”水上岛屿住区(water based islands settlement)的未来发展进行详尽阐述，探讨并举例论证在有关这片岛屿住区的规划和设计中，将雅加达大都市区的海洋地区视为一个“内部”(inside)区域和将岛屿地区作为一个统一的“海陆”(sealand)区域的原因和途径。     

Oxidative Transformations of 3,4-Dihydroxyphenylacetaldehyde Generate Potential Reactive Intermediates as Causative Agents for Its Neurotoxicity

Neurogenerative diseases, such as Parkinson’s disease, are associated, not only with the selective loss of dopamine (DA), but also with the accumulation of reactive catechol-aldehyde, 3,4-dihydroxyphenylacetaldehyde (DOPAL), which is formed as the immediate oxidation product of cytoplasmic DA by monoamine oxidase. DOPAL is well known to exhibit toxic effects on neuronal cells. Both catecholic and aldehyde groups seem to be associated with the neurotoxicity of DOPAL. However, the exact cause of toxicity caused by this compound remains unknown. Since the reactivity of DOPAL could be attributed to its immediate oxidation product, DOPAL-quinone, we examined the potential reactions of this toxic metabolite. The oxidation of DOPAL by mushroom tyrosinase at pH 5.3 produced conventional DOPAL-quinone, but oxidation at pH 7.4 produced the tautomeric quinone-methide, which gave rise to 3,4-dihydroxyphenylglycolaldehyde and 3,4-dihydroxybenzaldehyde as products through a series of reactions. When the oxidation reaction was performed in the presence of ascorbic acid, two additional products were detected, which were tentatively identified as the cyclized products, 5,6-dihydroxybenzofuran and 3,5,6-trihydroxybenzofuran. Physiological concentrations of Cu(II) ions could also cause the oxidation of DOPAL to DOPAL-quinone. DOPAL-quinone exhibited reactivity towards the cysteine residues of serum albumin. DOPAL-oligomer, the oxidation product of DOPAL, exhibited pro-oxidant activity oxidizing GSH to GSSG and producing hydrogen peroxide. These results indicate that DOPAL-quinone generates several toxic compounds that could augment the neurotoxicity of DOPAL.     

Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump

Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machines becomes essential in order to reduce the unnecessary break downs. Vibration based approaches are widely used to carry out the condition monitoring tasks. Decision tree, fuzzy logic, support vector machine and artificial neural networks are some of the classification algorithms employed for condition monitoring and fault diagnosis. In the present study, fault discriminating capability of wavelets in its continuous form with the application of J48 algorithm is analyzed. Vibration signals are extracted from the experimental setup. The continuous wavelet transform (CWT) is calculated for different families and at different levels which form the feature set. The features are then fed as an input to the classifier (J48 algorithm, a WEKA implementation) and the classification accuracies are calculated. Then, the results are validated to find classification capability of CWT features for monoblock centrifugal pump. The different faults considered for this study are cavitation (CAV), impeller fault, bearing fault (FB) and both bearing and impeller fault.     

Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features

Hydraulic brakes are the most important components in automobile. It requires advanced supervision and fault diagnosis to improve the safety of passenger, reliability and economy. Condition monitoring is one of the major division through which the reliability of such components could be monitored. The condition of the brake components can be monitored by using the vibration characteristics which will reveal the condition of the brake systems. In this paper machine learning algorithm using vibration monitoring is proposed as a possible solution to this problem. From the hydraulic brake test set up, the vibration signals were acquired by using a piezoelectric transducer and data acquisition system. C4.5 decision tree algorithm was used to extract statistical features from vibration signals. Feature selection was also carried out. Since no much of methodologies are available to find the effective number of features for a given problem, a detailed study is needed to find the best possible number of features. Hence the effect of number of features was studied by using decision tree. The selected features were classified using C4.5 decision tree algorithm and Best first decision tree algorithm with pre pruning and post pruning techniques. The results are discussed and conclusions of the study are presented.     

Cytotoxic potentials of biologically fabricated platinum nanoparticles from Streptomyces sp. on MCF‐7 breast cancer cells

Biosynthesis of novel therapeutic nano‐scale materials for biomedical and pharmaceutical applications has been enormously developed, since last decade. Herein, the authors report an ecological way of synthesising the platinum nanoparticles (PtNPs) using Streptomyces sp. for the first time. The produced PtNPs exhibited the face centred cubic system. The fourier transform infrared spectrum revealed the existence of amino acids in proteins which serves as an essential reductant for the formation of PtNPs. The spherical morphology of the PtNPs with an average size of 20–50 nm was observed from topographical images of atomic force microscopy and field emission scanning electron microscopy. The X‐ray fluorescence spectrum confirms the presence of PtNPs with higher purity. The PtNPs size was further confirmed with transmission electron microscopy analysis and the particles were found to exist in the same size regime. Additionally, PtNPs showed the characteristic surface plasmon resonance peak at 262 nm. Dynamic light scattering studies report that 97.2% of particles were <100 nm, with an average particle diameter of about 45 nm. Furthermore, 3‐(4, 5‐dimethyl‐2‐thiazolyl)‐2, 5‐diphenyl‐tetrazolium assay based in vitro cytotoxicity analysis was conducted for the PtNPs, which showed the inhibitory concentration (IC₅₀) at 31.2 µg/ml against Michigan Cancer Foundation‐7 breast cancer cells.Inspec keywords: biomedical materials, materials preparation, nanoparticles, nanomedicine, nanofabrication, cellular biophysics, microorganisms, cancer, platinum, Fourier transform infrared spectra, proteins, atomic force microscopy, scanning electron microscopy, fluorescence, transmission electron microscopy, surface plasmon resonance, light scatteringOther keywords: cytotoxic potentials, biologically fabricated platinum nanoparticles, Streptomyces sp, MCF‐7 breast cancer cells, biosynthesis, therapeutic nanoscale materials, biomedical applications, pharmaceutical applications, Fourier transform infrared spectrum, amino acids, spherical morphology, topographical images, atomic force microscopy, field emission scanning electron microscopy, X‐ray fluorescence spectrum, transmission electron microscopy analysis, surface plasmon resonance, dynamic light scattering, 3‐(4, 5‐dimethyl‐2‐thiazolyl)‐2, 5‐diphenyl‐tetrazolium assay, cytotoxicity analysis, Pt     

Development of superlattice CrN/NbN coatings for joint replacements deposited by high power impulse magnetron sputtering

The demand for reliable coating on medical implants is ever growing. In this research, enhanced performance of medical implants was achieved by a CrN/NbN coating, utilising nanoscale multilayer/superlattice structure. The advantages of the novel high power impulse magnetron sputtering technology, namely, its unique highly ionised plasma, were exploited to deposit dense and strongly adherent coatings on CoCr implants. Transmission electron microscopy analysis revealed coating superlattice structure with bi-layer thickness of 3.5?nm. CrN/NbN deposited on CoCr samples showed exceptionally high adhesion, critical load values of L_C2?=?50?N in scratch adhesion tests. Nanoindentation tests showed high hardness of 34?GPa and Young’s modulus of 447?GPa. Low coefficient of friction (μ) 0.49 and coating wear coefficient (K _C)?=?4.94?×?10^?16?m³?N^?1?m^?1 were recorded in dry sliding tests. Metal ion release studies showed a reduction in Co, Cr and Mo release at physiological and elevated temperatures (70?°C) to almost undetectable levels (<1?ppb). Rotating beam fatigue testing showed a significant increase in fatigue strength from 349?±?59?MPa (uncoated) to 539?±?59?MPa (coated). In vitro biological testing has been performed in order to assess the safety of the coating in biological environment; cytotoxicity, genotoxicity and sensitisation testing have been performed, all showing no adverse effects.     

Web personalization for user acceptance of technology: An empirical investigation of E-government services

E-Commerce firms have adopted Web Personalization techniques extensively in the form of recommender systems for influencing user behavior for customer retention. Although there are numerous studies in this area, academic research addressing the role of Web Personalization in user acceptance of technology is very scant. Further, owing to the potential of recommender systems to attract and retain customers, most studies in web personalization have been done in E-Commerce setting. In this research, the ‘Consumer Acceptance and Use of Information Technology’ theory proposed in previous research has been extended to include web personalization as a moderator and has been tested in an E-Government context. Data collection involved conducting a laboratory experiment with the treatment group receiving personalized web forms for requesting an E-Government service. Our analyses show that personalizing the Web by self-reference and content relevance has a significant moderator role in influencing the relationship between determinants of intention to use and behavioral intention in certain cases.     

Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool

The studies on tool condition monitoring along with digital signal processing can be used to prevent damages on cutting tools and workpieces when the tool conditions become faulty. These studies have become more relevant in today’s context where the order realization dates are crunched and deadlines are to be met in order to catch up with the competition. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is extensively used to probe into structural health of the tool and the process. This paper discusses condition monitoring of carbide tipped tool using Support Vector Machine and compares the classification efficiency between C-SVC and ν-SVC. It further analyses the results with other classifiers like Decision Tree and Naïve Bayes and Bayes Net. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better features-classifier combination.