A Multicriteria Weighted Vote‐Based Classifier Ensemble for Heart Disease Prediction

The availability of a large amount of medical data leads to the need of intelligent disease prediction and analysis tools to extract hidden information. A large number of data mining and statistical analysis tools are used for disease prediction. Single data‐mining techniques show acceptable level of accuracy for heart disease diagnosis. This article focuses on prediction and analysis of heart disease using weighted vote‐based classifier ensemble technique. The proposed ensemble model overcomes the limitations of conventional data‐mining techniques by employing the ensemble of five heterogeneous classifiers: naive Bayes, decision tree based on Gini index, decision tree based on information gain, instance‐based learner, and support vector machines. We have used five benchmark heart disease data sets taken from UCI repository. Each data set contains different set of feature space that ultimately leads to the prediction of heart disease. The effectiveness of proposed ensemble classifier is investigated by comparing the performance with different researchers' techniques. Tenfold cross‐validation is used to handle the class imbalance problem. Moreover, confusion matrices and analysis of variance statistics are used to show the prediction results of all classifiers. The experimental results verify that the proposed ensemble classifier can deal with all types of attributes and it has achieved the high diagnosis accuracy of 87.37%, sensitivity of 93.75%, specificity of 92.86%, and F‐measure of 82.17%. The F‐ratio higher than the F‐critical and p‐value less than 0.01 for a 95% confidence interval indicate that the results are statistically significant for all the data sets.     

An efficient numerical technique for solving one-dimensional batch crystallization models with size-dependent growth rates

In this work, an efficient numerical method is introduced for solving one-dimensional batch crystallization models with size-dependent growth rates. The proposed method consist of two parts. In the first part, a coupled system of ordinary differential equations (ODEs) for the moments and the solute concentration is numerically solved to obtain their discrete values in the time domain of interest. These discrete values are also used to get growth and nucleation rates in the same time domain. To overcome the issue of closure, a Gaussian quadrature method based on orthogonal polynomials is employed for approximating integrals appearing in the ODE system. In the second part, the discrete growth and nucleation rates along with the initial crystal size distribution (CSD) are used to construct the final CSD. The expression for CSD is obtained by applying the method of characteristics and Duhamel's principle on the given population balance model (PBM). The proposed method is efficient, accurate, and easy to implement in the computer. Several numerical test problems of batch crystallization processes are considered. For a validation, the results of the proposed technique are compared with those obtained using a high resolution finite volume scheme.     

XPS surface studies of injection-molded poly(phenylene ether)/nylon 6,6 and poly(phenylene ether)/HIPS blends

The surface compositions of a series of poly(phenylene ether)/nylon 6,6 blends (PPE/PA), and PPE/HIPS blends, prepared by melt compounding and injection molding, have been quantitatively measured using XPS. For PPE/PA blends, the surface is dominated by the PA component for blends containing more than 25 wt % PA in the bulk. The enrichment of the PA component, which is actually the component of highest surface free energy, is rationalized in terms of the bulk morphology that consists of PPE domains in a PA continuous phase. Blends prepared by reactive extrusion processes, which form compatibilizing PPE/PA copolymers, show a decrease in surface PA enrichment with increasing copolymer content in the final blend. PPE/HIPS blends have a surface composition equal to the formulated value over the entire composition range, for both molded and solvent cast blends. The addition of 5% PVME to a 60/40 PPE/HIPS blend results in a molded surface containing 35–40 wt % PVME. © 1992 John Wiley & Sons, Inc.     

The fatty acid composition of edible marine fish oils

The body oils of 13 species of marine edible fishes found around the Karachi-Makran coast were studied by gas-liquid chromatography (GLC) for their fatty acid composition. The analyses showed the presence of fatty acids with chain lengths from 10 to 24 carbon atoms and with zero to six double bonds. The oils were found to be rich in polyunsaturated acids, particularly the penta- and hexaenoic. Certain major fatty acids were found to vary widely among the species: myristic acid 2.3 to 13.7%; palmitic 11.6 to 41.2%; stearich 7.2 to 23.2%; oleic 6.9 to 29.6%; eicosapentaenoic 1.4 to 19.0%; docosapentaenoic zero to 10.2%; and docosahexaenoic zero to 36.4%. The linoleic and linolenic acids were present in small amounts in some of the fish oils, and arachidonic acid was present in all of them.     

Regression-based CVN–KIC Models for hot work tool steels

Dies and tools used in hot metal forming (extrusion, forging, rolling, etc.) are exposed to high pressures, elevated temperatures, and thermo-mechanical fatigue. The most common mode of in-service die failure is fatigue fracture (brittle failure through crack propagation). Reliable determination of fracture toughness of the die material is thus critically important. However, as die steels have a combination of high-hardness and high-strength, and are used at elevated temperatures, standard plane-strain fracture toughness (K_IC) testing methods become impracticable. Alternate testing procedures such as the Charpy impact energy (CVN), together with empirical/semi-empirical correlations of K_IC to other data, are then more viable and economical. Experimental data (values of K_IC, CVN, and HRC) of H13 steels have been collected through an exhaustive literature search. This data set has been augmented through in-house experimentation: samples variously heat treated (different tempering temperatures and times, and both air-cooling and oil-quenching), and tested at different working temperatures. Linear and quadratic models are proposed for determination of fracture toughness, based on experimental (in-house) and published values of Charpy impact energy (CVN) and Rockwell hardness (HRC), both at room and at elevated temperatures.     

New materials for micro-scale sensors and actuators: An engineering review

This paper provides a detailed overview of developments in transducer materials technology relating to their current and future applications in micro-scale devices. Recent advances in piezoelectric, magnetostrictive and shape-memory alloy systems are discussed and emerging transducer materials such as magnetic nanoparticles, expandable micro-spheres and conductive polymers are introduced. Materials properties, transducer mechanisms and end applications are described and the potential for integration of the materials with ancillary systems components is viewed as an essential consideration. The review concludes with a short discussion of structural polymers that are extending the range of micro-fabrication techniques available to designers and production engineers beyond the limitations of silicon fabrication technology.     

A forward only counter propagation network-based approach for contraceptive method choice classification task

This article proposes the forward only counter propagation network (FOCPN) for solving the contraceptive medical classification task. Contraceptive method choice (CMC) application is used for the medical classification and it is one of the challenging jobs in the field of the medicine. The experiments are performed on different radii of the neighbourhood and learning rate based on the size of the map. Experimental results show that FOCPN's convergence is faster and it gives the improved learning efficiency and reliable prediction performance. Also, the classification accuracy is much higher than the other models used for this purpose.     

A therapy-driven gamification framework for hand rehabilitation

Rehabilitative therapy is usually very expensive and confined to specialized rehabilitation centers or hospitals, leading to slower recovery times for corresponding patients. Therefore, there is a high demand for the development of technology-based personalized solutions to guide and encourage patients towards performing online rehabilitation program that can help them live independently at home. This paper introduces an innovative e-health framework that develops adaptive serious games for people with hand disabilities. The aim of this work is to provide a patient-adaptive environment for the gamification of hand therapies in order to facilitate and encourage rehabilitation issues. Theoretical foundations (i.e., therapy and patient models) and algorithms to match therapy-based hand gestures to navigational movements in 3D space within the serious game environment have been developed. A novel game generation module is introduced, which translates those movements into a 3D therapy-driven route on a real-world map and with different levels of difficulty based on the patient profile and capabilities. In order to enrich the user navigation experience, a 3D spatio-temporal validation region is also generated, which tracks and adjusts the patient movements throughout the session. The gaming environment also creates and adds semantics to different types of attractive and repellent objects in space depending on the difficulty level of the game. Relevant benchmarks to assess the patient interaction with the environment along with a usability and performance testing of our framework are introduced to ensure quantitative as well as qualitative improvements. Trial tests in one disability center were conducted with a total number of five subjects, having hand motor controls problems, who used our gamified physiotherapy solution to help us in measuring the usability and users’ satisfaction levels. The obtained results and feedback from therapists and patients are very encouraging.     

Dates Fruit Recognition: From Classical Fusion to Deep Learning

There are over 200 different varieties of dates fruit in the world. Interestingly, every single type has some very specific features that differ from the others. In recent years, sorting, separating, and arranging in automated industries, in fruits businesses, and more specifically in dates businesses have inspired many research dimensions. In this regard, this paper focuses on the detection and recognition of dates using computer vision and machine learning. Our experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates fruit. Classical machine learning includes the Bayesian network, Support Vector Machine, Random Forest, and Multi-Layer Perceptron (MLP), while the Convolutional Neural Network is used for the deep learning set. The feature set includes Color Layout features, Fuzzy Color and Texture Histogram, Gabor filtering, and the Pyramid Histogram of the Oriented Gradients. The fusion of various features is also extensively explored in this paper. The MLP achieves the highest detection performance with an F-measure of 0.938. Moreover, deep learning shows better accuracy than the classical machine learning algorithms. In fact, deep learning got 2% more accurate results as compared to the MLP and the Random forest. We also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.