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.     

Asymmetric Sharpless epoxidation of 13S‐hydroxy‐ 9Z, 11E‐octadecadienoic acid (13S‐HODE)

The asymmetric Sharpless epoxidation of methyl 13S‐hydroxy‐9Z, 11E‐octadeca‐dienoate (13S‐HODE, 1 ) with tert‐butyl hydroperoxide (TBHP) catalysed by titanium tetraisopropoxide {Ti(ⁱOPr)₄} in the presence of L(+)‐diisopropyl tartrate (L‐DIPT) gave methyl 13S‐hydroxy‐11S, 12S‐epoxy‐9Z‐octadecenoate 2 (erythro isomer) in 84% diastereomeric excess (de). The epoxidation of 1 with TBHP catalysed by Ti(ⁱOPr)₄ in the presence of D(‐)‐DIPT yielded methyl 13S‐hydroxy‐11RR12R‐epoxy‐9Z‐octadecenoate (threo isomer) 3 in 76% de.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

Graft Copolymerization of Acrylamide onto Oil Palm Empty Fruit Bunch (OPEFB) Fiber

Grafting of acrylamide (AAm) onto oil palm empty fruits bunch fiber using hydrogen peroxide as initiator and methyl acrylate as comonomer was investigated. The amount of comonomer needed to make grafting of acrylamide possible was determined. The percentage of poly(acrylamide) and the comonomer in the final graft copolymer was estimated by elemental analysis. Results obtained indicated that methyl acrylate facilitated the incorporation of acrylamide monomer onto OPEFB. The reactivity ratios for both monomers were determined by using Fineman–Ross plot. The effects of reaction temperature and period as well as amount of the initiator, solvent, monomer and comonomer on the percentage of grafting at fixed amount of comonomer (11 mmol) were studied. Maximum percentage of grafting was achieved when the amount of initiator and solvent 3.98×10⁻³ mol and 50 mL respectively. The optimum reaction temperature was 50 ^○C and the reaction period was 90 min. Highest percentage of grafting was 232% when 25.6 mmol of acrylamide was used under these optimum conditions. The presence of functional group in the grafted polymer is characterized by infrared spectroscopy and the surface morphology is observed by scanning electron microscopy. Thermoanalytic investigation on OPEFB and OPEFB-g-PAAM were carried out to evaluate the thermal stability and respective activation energy of the materials.     

Using a virtual environment to assess cognition in the elderly

Early diagnosis of Alzheimer’s disease (AD) is essential if treatments are to be administered at an earlier point in time before neurons degenerate to a stage beyond repair. In order for early detection to occur tools used to detect the disorder must be sensitive to the earliest of cognitive impairments. Virtual reality technology offers opportunities to provide products which attempt to mimic daily life situations, as much as is possible, within the computational environment. This may be useful for the detection of cognitive difficulties. We develop a virtual simulation designed to assess visuospatial memory in order to investigate cognitive function in a group of healthy elderly participants and those with a mild cognitive impairment (MCI). Participants were required to guide themselves along a virtual path to reach a virtual destination which they were required to remember. The preliminary results indicate that this virtual simulation has the potential to be used for detection of early AD since significant correlations of scores on the virtual environment with existing neuropsychological tests were found. Furthermore, the test discriminated between healthy elderly participants and those with a MCI.     

Eco-friendly electronics,based on nanocomposites of biopolyester reinforced with carbon nanotubes: a review

ABSTRACT

Fabrication of electronic materials from nanocomposite of biopolyesters reinforced with carbon nanotubes can be regarded as the effective alternative for conventional nanocomposites consisting of non-biodegradable polymers. Commercial availability of biopolyester-based nanocomposites is limited because of their high cost compared to other polymers, but the factor of their compostable nature is worthless for environmental protection. Such nanocomposites have potential applications in biodegradable sensors, EMI materials, etc. In this review, the current progress of biopolyester/CNTs nanocomposites in the field of biodegradable electronics is reviewed and also the impact of CNTs dispersion on electrical, thermal and mechanical properties of eco composites is stipulated.     

A free space frequency‐time‐domain technique for electromagnetic characterization of materials using reflection‐based measurement

In this report, a free space frequency‐time‐domain technique is presented for characterizing the electrical properties and thickness of the sample using multiple reflections and fabry‐perot resonance phenomenon. The retrieval of constitutive electromagnetic parameters of the sample has been carried out by comparing the measured reflection coefficient data from the sample at two different incident angles. The relative permittivity as well as relative permeability along with the thickness of different samples viz., beryllia, silicon, and plexiglass have been evaluated with high accuracy in the frequency range 1 to 15 GHz. The method is also experimentally validated by successfully reconstructing the unknown material properties of two different samples. The unique advantage of this method lies in non‐requirement of any prior knowledge of the sample's thickness for measuring the complex relative dielectric constant as well as relative permeability of the sample. To determine the electromagnetic properties of the sample, the sole knowledge of reflection coefficient data are needed. Moreover, the method does not involve any additional measurement for the reference calibration. The simple, cost‐effective proposed scheme is quite useful in many applications like accurate determination of signal strength in indoor wireless communication, through wall imaging, food industry, and so on.     

Color image segmentation using adaptive unsupervised clustering approach

This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches.     

维吾尔语三音节词韵律特征声学分析

本文从文本分析模块入手,利用“维吾尔语语音声学参数库”,选择了以开音节和闭音节结尾的333个三音节词的韵律参数,包括元音时长、音高和音强进行了统计分析,归纳了其元音时长、音高和音强分布模式,探讨了维吾尔语三音节词的韵律节奏模式与三音节词重音之间的关系问题,其目的是为了提高语音合成的自然度即更好的为自然语言处理服务。本项研究对维吾尔语语言乃至整个阿尔泰语系语言的韵律研究具有较高的参考价值。