An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

Particle distribution control in cast aluminium alloy-mica composites

A suspension of mica particles (40m diameter and 3.7 thick) obtained in a mechanically stirred Al-4 wt % Cu-1.5 wt % Mg melt was poured and solidified in a variety of moulds under different heat flow configurations. The resulting cast structure showed a non-uniform distribution of dispersed mica particles with mica-depleted and segregated zones due to their flotation before and during solidification. The experimentally observed profiles of mica-free regions deviate significantly from those computed on the basis of Stokes's law and freezing-time computations. In relatively thick castings, segregation of mica could be minimized by using low pouring temperatures and/or side as well as bottom chilling. It was found, however, that thin castings (12.5 mm) could easily be produced with a homogeneous distribution of mica particles.     

Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas

Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient’s immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them.     

GPU accelerated novel particle filtering method

In this paper, a graphics processor unit (GPU) accelerated particle filtering algorithm is presented with an introduction to a novel resampling technique. The aim remains in the mitigation of particle impoverishment as well as computational burden, problems which are commonly associated with classical (systematic) resampled particle filtering. The proposed algorithm employs a priori-space dependent distribution in addition to the likelihood, and hence is christened as dual distribution dependent (D3) resampling method. Simulation results exhibit lesser values for root mean square error (RMSE) in comparison to that for systematic resampling. D3 resampling is shown to improve particle diversity after each iteration, thereby affecting the overall quality of estimation. However, computational burden is significantly increased owing to few excessive computations within the newly formulated resampling framework. With a view to obtaining parallel speedup we introduce a CUDA version of the proposed method for necessary acceleration by GPU. The GPU programming model is detailed in the context of this paper. Implementation issues are discussed along with illustration of empirical computational efficiency, as obtained by executing the CUDA code on Quadro 2000 GPU. The GPU enabled code has a speedup of 3 and 4 over the sequential executions of systematic and D3 resampling methods respectively. Performance both in terms of RMSE and running time have been elaborated with respect to different selections for threads per block towards effective implementations. It is in this context that, we further introduce a cost to performance metric (CPM) for assessing the algorithmic efficiency of the estimator, involving both quality of estimation and running time as comparative factors, transformed into a unified parameter for assessment. CPM values for estimators obtained from all such different choices for threads per block have been determined and a final value for the chosen parameter is resolved for generation of a holistic effective estimator.     

Limits of compensation in a failed antenna array

In large antenna arrays, the possibility of occurrence of faults in some of the radiating elements cannot be precluded at all times. In such situations, the radiation pattern of the array gets distorted, mostly with an increase in sidelobe level and decrease in gain. Although it is not possible to restore the pattern fully by rearranging the excitations of the functioning elements, compensation methods have been reported in the literature for restoring one performance parameter of the array and making a trade‐off on some other parameter. In this article, we have made a study on the tolerance level of this compensation process. One part of the study deals with the thinning in the failed array, that is, to find a limit on the minimum number of functioning elements of the array that can restore the digital beamforming of the failed array. The second part of study deals with finding the maximum number of element failures that can be compensated. The study was carried out by optimizing the amplitude excitations of the failed array. Instead of classical optimization techniques, particle swarm optimization was used for the compensation process. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:635–645, 2014.     

Studies on the reaction of bis(diisopropyl) thiophosphoryl disulfide with silica in the vulcanization of NR

The reaction between bis(diisopropyl) thiophosphoryl disulfide (DIPDIS) and silica has been investigated. The study reveals that DIPDIS can be used as a coupling agent for silica. The chemical bond between the silanol groups of silica and DIPDIS has been established through the isolation and characterization of isopropyl alcohol that is eliminated from the reaction. The chemical nature of the bond is also supported by the IR analysis of the reaction products of silica and DIPDIS obtained both in the presence and absence of rubber. From the exploratory studies it is indicated that DIPDIS can also react with NR even during mixing of ingredients. When used with 2-mercaptobenzothiazole (MBT) or N-oxydiethylene-2-benzothiazole sulfenamide (OBTS) in the presence of silica, DIPDIS exhibits notable activation of cure and enhances the torque, modulus, tensile strength, and the scorch safety.     

The dielectric behavior of natural resin shellac

The Cole-Cole method has been applied for an analysis of the dielectric relaxation data of the natural resin shellac, obtained by Bhattacharya in the temperature range 20–110°C. Three relaxation processes were observed for temperatures 20–60°C. At 70°C, two relaxation processes were noticed, whereas only one was observed by Bhattacharya between 20 and 110°C. The relaxations at 80°C and above represented typical Cole-Cole type. The variation of the static dielectric constant with temperature revealed three distinct slopes, which indicated different phases of the resin during its thermal transition from solid to liquid state. Two “transformation points,” around 40 and 70°C were noted, i.e., the glass transition temperature and melting point of the resin, respectively. Significant changes in static dielectric constant, dielectric increment, relaxation time, and Cole-Cole distribution parameter were observed during a rise of temperature from 80 to 110°C. The results of the present study indicate that some rearrangement in the teriary disposition of the resin molecules takes place during the rise of temperature from 20 to 110°C.     

Topography of the flank wear surface

In many applications, topography represents the main external features of a surface. This paper describes the topography of the flank wear surface and also presents the relationship between the maximum flank wear and the topography parameters (roughness parameters) of the flank wear surface during the turning operation. A modern CNC lathe machine (Okuma LH35-N) was used for the machine turning operation. Three-dimensional surface roughness parameters of the flank wear surface were measured by a surface texture instrument (from Talysurf series) using surface topography software (Talymap). Based on the resulting experimental data, it is found that as the flank wear increases, the roughness parameters (sR_a, sR_q, and sR_t) on the flank surface increase significantly. The greater the roughness value of the flank wear surface, the higher the friction of the tool on the workpiece and the greater the heat generation that will occur, thus ultimately causing tool failure. On the other hand, positive skewness (sR_sk) indicates the presence of a small number of spikes on the flank surface of the cutting tool, which could quickly wear off during the machining process.     

Reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured P2P networks

Free riding is a major problem in peer-to-peer networks. Reputation management systems are generally employed to overcome this problem. In this paper, a new reputation based scheme called probabilistic resource allocation is proposed. This strategy probabilistically decide whether to provide the resource to requesting peer or not. Aforesaid method gives selection preference to higher reputation peers and at the same time provides some finite probability of interaction between those peers who don’t have good reputation about each other. This avoids disconnection between the aforesaid peers. The proposed scheme also introduces a new mechanism for resource distribution which not only allocates resources based on peers’ reputation but simultaneously maximizes network utility also. Algorithm for formation of interest groups based upon both similarity of interests and reputation between peers is also presented.