Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

The human liver disorder is a genetic problem due to the habituality of alcohol or effect by the virus. It can lead to liver failure or liver cancer, if not been detected in initial stage. The aim of the proposed method is to detect the liver disorder in initial stage using liver function test dataset. The problem with many real-world datasets including liver disease diagnosis data is class imbalanced. The word imbalance refers to the conditions that the number of observations belongs to one class having more or less than the other class(es). Traditional K- Nearest Neighbor (KNN) or Fuzzy KNN classifier does not work well on the imbalanced dataset because they treat the neighbor equally. The weighted variant of Fuzzy KNN assign a large weight for the neighbor belongs to the minority class data and relatively small weight for the neighbor belongs to the majority class to resolve the issues with data imbalance. In this paper, Variable- Neighbor Weighted Fuzzy K Nearest Neighbor Approach (Variable-NWFKNN) is proposed, which is an improved variant of Fuzzy-NWKNN. The proposed Variable-NWFKNN method is implemented on three real-world imbalance liver function test datasets BUPA, ILPD from UCI and MPRLPD. The Variable-NWFKNN is compared with existing NWKNN and Fuzzy-NWKKNN methods and found accuracy 73.91% (BUPA Dataset), 77.59% (ILPD Dataset) and 87.01% (MPRLPD Dataset). Further, TL_RUS method is used for preprocessing and it improved the accuracy as 78.46% (BUPA Dataset), 78.46% (ILPD Dataset) and 95.79% (MPRLPD Dataset).

     

Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society

Innovations in Systems and Software Engineering - Recommender system is a computer-based intelligent technique which facilitates the customers to fulfill their purchase requirements. In addition to...     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

A comparative study on the metaheuristic-based optimization of skew composite laminates

Engineering with Computers - Plate structures are the integral parts of any maritime engineering platform. With the recent focus on composite structures, the need for optimizing their design and...     

A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classi f ier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thor oughly tested and compared with other methods for various faults on HVDC transmission lines.     

The synthesis of pixelated metamaterial cross polarizer using binary wind driven optimization algorithm

Microsystem Technologies - In this paper synthesis of two wideband Metamaterial Cross Polarizer (MCPs) is proposed. The synthesis of proposed MCPs is done by using Binary Wind Driven Optimization...     

An overview on microwave processing of material: A special emphasis on glass melting

Material processing adopting microwave heating has emerged as an alternative tool owing to faster processing, a cleaner environment, and several other advantages. This review provides a summary of recent reports of microwave synthesis of materials. This study reviews the use of microwave energy for application in several material processing technologies apart from food processing. A special emphasis has been made in the processing of glass adopting microwave energy. Melting of glass comprising SiO₂, P₂O₅, B₂O₃ as the main building block has been discussed. It has been revealed that silica, a microwave transparent material as reported earlier, can be heated under microwave heating directly. Microwave absorption of raw materials and different glass system has been discussed. Dielectric properties, particularly loss tangent or loss factor, are presented for some glass composition. Less evaporation of ingredient and low contamination from the crucible wall are noticed during glass melting using microwave heating. Enhanced iron redox ratio (Fe⁺²/∑Fe) in microwave processing may be considered an advantage in the preparation of heat absorbing filter glass. Small-scale glass melting using the microwave heating has a significant impact on energy and time saving. However, the challenges associated with the upscaling glass melting with microwave heating and future scope have been talked about.     

Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization

Laser micro-marking is an efficient technique for permanent marking and logo printing on materials. This study details the selection of an optimal parametric combination for laser micro-marking. In this work, markings were performed on Gallium Nitride (GaN) with varying the levels of marking parameters. The parameters considered in the present work are current (A), pulse frequency (Hz), and scanning speed (mm/sec). This experiment was designed using a “central composite design,” grounded in the response surface methodology. Mark intensity, which is a prominent response in laser marking, was considered the output response. The data interpretation involved analysis of variance (ANOVA) and mathematical modelling between the input parameters. It is essential to determine the relationship and significance of input-output variation. The interaction effect of various input parameters on mark intensity was also studied. Finally, two techniques, namely genetic algorithm (GA) and particle swarm optimization (PSO), were applied, and the optimal settings of input constraints were predicted.     

LDA + U Study of Induced Half Metallicity in Cr-Doped GaN

Half-metallic ferromagnetism in the Ga_{1 ? x}Cr _x N compound at different concentrations, x = 25, 12.5 and 6.25 %, have been investigated using density functional theory as implemented in code Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA) using LDA + U as exchange-correlation (XC) potential, to find out the possibility of new diluted DMSs. The outcomes reveal that transition metal atom (Cr) doping in GaN induces ferromagnetism. The 3d levels of the TM ion originate a half-metallic gap at the Fermi level in the majority spin channel for all concentrations. Moreover, diluted magnetic semiconductor compounds retain the half-metallic nature at all concentrations, i.e., x = 0.25, 0.125 and 0.0625, with 100 % spin polarization at the Fermi level (E _F). The total magnetic moment of these compounds is due to Cr-3d states, and the existence of a small magnetic moment on Ga and N, non-magnetic atoms, for all doping concentrations is a consequence of p-d hybridization of Cr-d and N-p states. The calculated values of s-d exchange constant N α and p-d exchange constant N βconfirm the ferromagnetic character of the Cr-doped GaN compound.     

Hydrostatic Pressure Effect on Ga<Subscript>0.75</Subscript>Cr<Subscript>0.25</Subscript>As DMS: DFT Study

This paper studies the first-principles calculations of the effect of hydrostatic pressure on the structural, electronic and magnetic properties of Ga_0.75Cr_0.25As dilute magnetic semiconductor in zb (B3) phase. High-pressure behaviour of Ga_0.75Cr_0.25As has been investigated between 0 and 100 GPa. The calculations have been performed using DFT as implemented in code SIESTA using LDA + U as an exchange-correlation (XC) potential. The study of band structures shows half-metallic ferromagnetic nature with 100% spin polarization. Under application of external pressure, the valence band and conduction band are shifted from their positions which lead to modification of electronic structure.