A sector element for thin plate flexure

This paper presents a sector shape of an element ideally suited for the analysis of sectorial and annular plates. A conforming function with twenty degrees of freedom is developed in polar co-ordinates to represent the deformation of the element in bending. The finite element method is applied to some plate bending problems to illustrate the power and efficiency of sector elements.     

Mass classification method in mammograms using correlated association rule mining

In this paper, we present an efficient computer-aided mass classification method in digitized mammograms using Association rule mining, which performs benign–malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. Association rule mining (ARM) exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Correlated association rule mining was proposed for classifying the marked regions into benign and malignant and 98.6% sensitivity and 97.4% specificity is achieved that is very much promising compare to the radiologist’s sensitivity 75%.     

Brain tumor detection and segmentation using mesh-free super-diffusive model

Multimedia Tools and Applications - Brain tumor detection and segmentation is a complex and challenging task in image processing. Most of the techniques used for brain tumor detection and...     

Comparative analysis on the application of neuro‐fuzzy models for complex engineered systems: Case study from a landfill and a boiler

This work aims at developing an explicit neuro‐fuzzy (NF) model to characterize complex engineered systems associated with high nonlinearity, uncertainties, and multivariable couplings. The NF model synergistically exploits the advantages of fuzzy belongingness of each input variable to all output variables and learning ability of neural networks. Owing to the inherent complexities associated with 2 complex engineered systems, a landfill and a boiler were selected to develop models that provide intelligent decisions for optimizing the operational parameters. Data compiled from field‐scale investigation/real plant operation involving various operating scenarios were used to develop the models. Predicting capability of the developed models was evaluated through the correlation coefficient and mean absolute percentage error values. Superiority of the proposed NF model to other similar models has been justified and demonstrated.     

Fuzzy logic techniques for navigation of several mobile robots

In this paper, navigation techniques for several mobile robots as many as one thousand robots using fuzzy logic are investigated in a totally unknown environment. Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of ultrasonic sensors for measuring the distances of obstacles around it and an infrared sensor for detecting the bearing of the target. These techniques have been demonstrated in various exercises, which depicts that the robots are able to avoid obstacles as well as negotiate the dead ends and reach the targets efficiently. Amongst the techniques developed, FLC having Gaussian membership function is found to be most efficient for mobile robots navigation.     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...     

A review of particle swarm optimization and its applications in Solar Photovoltaic system

Particle swarm optimization is a stochastic optimization, evolutionary and simulating algorithm derived from human behaviour and animal behaviour as well. Special property of particle swarm optimization is that it can be operated in continuous real number space directly, does not use gradient of an objective function similar to other algorithms. Particle swarm optimization has few parameters to adjust, is easy to implement and has special characteristic of memory. Paper presents extensive review of literature available on concept, development and modification of Particle swarm optimization. This paper is structured as first concept and development of PSO is discussed then modification with inertia weight and constriction factor is discussed. Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are also discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.     

Silver nanoparticle anchored with novel crosslinked beaded polystyrene/polyamidoxime sequential interpenetrating polymer networks (IPNs) and its antibacterial activity

The present article describes the synthesis of silver nanoparticles (SNPs) impregnatated crosslinked beaded polystyrene/polyamidoxime (PSD/PAMD) interpenetrating polymer networks (IPNs). The developed SNPs‐IPNs were tested for the antibacterial activity for water purification. Intially, the crosslinked beaded polystyrene/polyacrylonitrile (PSD/PAN) polymer was synthesized by radical sequential suspension polymerization method therafter, it has been converted into PSD/PAMD‐IPN comprising amidoxime group, which acts as capping agent to anchor the silver ions. The silver ions impregnated with PSD/PAMD‐IPN were converted into SNPs by chemical reduction methodology followed by stabilization without any deterioration or loss in polymer. IR and solid‐state ¹³C NMR spectra were use for the structural characterization of developed SNPs‐IPN, while morphological characterization was examined by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X‐ray diffraction (XRD). The size of the SNPs formed with the networks were found less than 5 nm. The disinfection potential of SNPs‐IPN was evaluated by different antibacterial test. The bactericidal test of SNPs‐IPN revealed the promissing antibacterial efficieny with complete microbial inhibition within 4–6 h. The reusability of the SNPs‐IPN beads was also tested for its repeated use for industrial application. POLYM. COMPOS., 70–80, 2016. © 2014 Society of Plastics Engineers