An efficient recommender system algorithm using trust data

The Journal of Supercomputing - Smart services are a concept that provides services to the citizens in an efficient manner. The online shopping and recommender system can play an important role for...     

Theoretical Investigations of Magnetic Properties and Mechanical Stability of Quaternary Heusler Compounds FeYCrZ (Z = Al,Ga, Ge,and Si): a Spin Gapless Semiconductor

Journal of Superconductivity and Novel Magnetism - In this work, full potential linearized augmented plane wave (FP-LAPW) method is used to determine the electronic, magnetic, and mechanical...     

Fruits and Vegetables Freshness Categorization Using Deep Learning

The nutritional value of perishable food items, such as fruits and vegetables, depends on their freshness levels. The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only. We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories: pure-fresh, medium-fresh, and rotten. We gathered a dataset comprising of 60K images of 11 fruits and vegetables, each is further divided into three categories of freshness, using hand-held cameras. The recognition and categorization of fruits and vegetables are performed through two deep learning models: Visual Geometry Group (VGG-16) and You Only Look Once (YOLO), and their results are compared. VGG-16 classifies fruits and vegetables and categorizes their freshness, while YOLO also localizes them within the image. Furthermore, we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree. A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset. The dataset is publicly available for further evaluation by the research community.     

Computation of Structural,Mechanical, Thermal,and Magnetic Characteristics of Newly Designed Quaternary Heusler Alloys CoNbCrZ (Z = Al,Ga, Si,Ge)

Journal of Superconductivity and Novel Magnetism - First-principles calculations accomplished to investigate structural, mechanical, thermal and magnetic properties of newly designed CoNbCrZ...     

Increasing the efficiency of a diesel engine running on biodiesel through butanol fumigation

A single-cylinder, direct-injection diesel engine was tested with regular diesel oil, neat Jatropha biodiesel, and biodiesel with butanol injection into the inlet manifold. An engine fueled with neat biodiesel showed 5% reduction of the fuel conversion efficiency and 17% increase in specific fuel consumption relative to diesel oil. With butanol injection at a rate of 25% of the total fuel consumption, the efficiency was equivalent to that of diesel oil and specific fuel consumption was less than that of neat biodiesel. Engine emissions with biofuel were improved except for carbon monoxide and unburned hydrocarbons. It was concluded that the diesel engine can operate on the neat biofuel (biodiesel plus alcohol) with the fuel conversion efficiency equivalent to that of the diesel oil.     

Neuro-wavelet based intelligent medical image fusion

Imaging based sensitive clinical diagnosis is critically depending on image quality. In this article, the problem of enhancing fundus images is addressed by a novel fusion technique. The proposed approach utilizes the representation capability of wavelet transform and the learning ability of artificial neural networks. In this approach, input images are decomposed into wavelet transform followed by appropriate feature extraction for training of neural networks to obtain fused image. To ensure homogeneity, it employs consistency verification for minimizing the fusion artifacts. The visual and quantitative performance of the proposed approach is assessed using a number of experiments performed on the standard datasets of DRIVE and DRION-DB. The experimental results demonstrate that the proposed fusion technique offers high average structural similarity of “0.99.” The proposed approach outperforms state-of-the-art techniques which validates its effectiveness. The developed approach might be applied by the clinical diagnosis system for fundus related diseases.     

Structural and magnetic studies of Ce-Zn doped M-type SrFe12O19 hexagonal ferrite synthesized by sol-gel auto-combustion method

In the present work, the M-type Sr-hexagonal ferrites having chemical composition Sr_1-xCe_xFe_12-yZn_yO₁₉ (x?=?0.000, 0.025, 0.050, 0.075, 0.10, y?=?0.00, 0.25, 0.50, 0.75, 1.0) are prepared via sol-gel autocombustion technique. The Structural and magnetic properties of M-type Sr-hexagonal ferrites are studied and discussed thoroughly. The structural, micro graphical and magnetic particularities of the samples are calculated through X-Ray diffraction (XRD), scanning electron microscopy (SEM), and vibrating sample magnetometer (VSM) respectively. X-ray diffraction assured that all the synthesized composites possess a pure M-type hexaferrite structure and basic crystalline configuration of Sr-hexaferrite which does not alter by Ce-Zn substitution. It is observed that the Ce-Zn substitution has significant effect on its magnetic properties. VSM results reveal that pure sample has coercivity of 4.49?kOe, which shows the hard nature of the samples. It is perceived that values of remanence (M_r) and saturation magnetization (M_s) decline with increasing the Ce-Zn ions substitution. The reason behind the reduction in magnetic saturation (M_s) and remanence (M_r) might be spin canting and dilution phenomena with increasing the rare earth substituted ions. The large coercivity magnets may be valuable for permanent (stable) magnet applications. The prepared composites could be useful for applications in microwave absorbing materials.     

ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimental models have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network (ANN). The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples. As the result shows in ten-fold cross-validation, a brilliant performance is achieved through the predictor model with an accuracy of 99.88%, and also achieved 0.9976 as the highest value of MCC. So, the predictor model is a very useful and helpful tool for lipoylation sites prediction. Some of the residues around lysine lipoylation sites play a vital part in prediction, as demonstrated during feature analysis. The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.     

Formation and transport of secondary air pollutants: ozone and aerosols in the St. Louis urban plume

Emissions from metropolitan St. Louis caused reduced visibilities and concentrations of ozone in excess of the federal ambient standard (0.08 part per million) 160 kilometers or more downwind of the city on 18 July 1975. Atmospheric production of ozone and visibility-reducing aerosols continues long after their primary precursors have been diluted to low concentrations.