An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F‐score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.     

Effects of Using Alternative Extreme Pressure (EP) and Anti-Wear (AW) Additives with Oxy-Nitrided Samples

Oxy-nitriding is a widely used industrial process aiming to improve the tribological properties and performance of components. Previous studies have shown the effectiveness of the treatment with friction and wear performance, but very few have focussed on optimising this behaviour. The lubrication properties of several EP and AW additives were examined to investigate their effectiveness in improving the tribological properties of the layers formed after treatment. Previous studies showed the presence of an oxide layer on the sample could improve the effectiveness of the sulphurised olefin (SO) and tricresyl phosphate (TCP) additives. The friction and wear behaviour of oxy-nitrided samples were analysed using a tribometer and surface profiler. Scanning electron microscope, energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy were employed to identify the morphologies and chemical compositions of the treated surface before and after testing. No real effect on friction was observed when using the SO or TCP additives, mostly due to lack of interaction with the less reactive iron nitride layer and their roles as anti-wear additives. However, when the zinc dialkyldithiophosphate-containing lubricant was used, a higher friction coefficient was observed. Greater improvements in anti-wear properties with the presence of additives in comparison with only using base oil were reported, with the TCP additive producing the lowest wear rates. The study effectively demonstrated that the additive package type used could impact the tribological and tribochemical properties of oxy-nitrided surfaces.     

An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN. The proposed BatLFBP is applied to the problem of insulin DNA sequence classification of healthy homosapien. For classification performance, the proposed models such as Bat levy flight Artificial Neural Network (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) are compared with the other state-of-the-art algorithms like Bat Artificial Neural Network (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distribution back propagation (BatGDBP), in-terms of means squared error (MSE) and accuracy. From the perspective of simulations results, it is show that the proposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185, and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5. While on WL10 the proposed BatLFANN achieved 99.89899% accuracy with MSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of 0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853% accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracy with MSE of 0.006738 which achieve better accuracy as compared to the other hybrid models.

     

A review on graphene and its derivatives as the forerunner of the two-dimensional material family for the future

Journal of Materials Science - In 2004, Geim and Novoselov discovered two-dimensional graphene comprised of sp2 carbon atoms. Graphene is a thin layer of carbon consisting of excellent surface...     

A First-Principle Study of Monolayer Transition Metal Carbon Trichalcogenides

Journal of Superconductivity and Novel Magnetism - Monolayer MnCX3 metal–carbon trichalcogenides have been investigated by using the first-principle calculations. The compounds show...     

Introduction,production, characterization and applications of defects in graphene

Journal of Materials Science: Materials in Electronics - The successful preparation of graphene in research laboratory has brought a revolutionary progress in almost all the research fields...     

Detection and tracking of rotated and scaled targets by use of Hilbert-wavelet transform

In a recent work, we demonstrated the usefulness of the Hilbert transform in identifying the in-plane rotation angle between two objects. Here we use the Hilbert-wavelet bases instead of the Hilbert transform in the determination of the exact angle of rotation. We describe the design of the two-dimensional Hilbert-wavelet filter based on the spectral-factorization method to generate a Hilbert-transform pair of orthogonal wavelet bases. We compare the relative performance of the Hilbert transform and the Hilbert wavelet to identify both in-plane and out-of-plane rotation angles. We demonstrate that the Hilbert wavelet offers better rotation-angle determination than the Hilbert transform. We present correlation based rotated and scaled object identification and tracking using Hilbert or Hilbert-wavelet transformed infrared image sequences. We also demonstrate reduced data handling and improved tracking of distorted objects using the Hilbert-wavelet transform.     

Quantification of low dose signal in EPR tooth dosimetry--a novel approach

For radiation exposures below 100 mGy, the dosimetric signal in tooth enamel is too small to be measured by using the traditional dose reconstruction procedure. This is because low amplitude zero-added-dose signal can not be identified in an EPR spectrometer. A technique is presented wherein, zero-added-dose signal. when amplified by a proper known dose, can be measured in the EPR spectrometer. Mathematically, the accidental dose x is modified by a known amount of exposure, y (large enough so that the signal is now visible), and total exposure becomes x' = x + y, which is the modified-zero-added dose. The exposure x' is then quantified using the conventional backward extrapolation method and the accidental dose can be measured. In a laboratory controlled experiment, the feasibility of dose reconstruction in the 100 mGy range has been demonstrated. This may enable measurements of dose even due to suspected low exposure in tooth enamel.     

Experimental analysis and numerical modelling of the forming process of polypropylene replicas of micro-cavities using hot embossing

Numerical modelling of the deformation of a polymer using the finite elements method in axisymetrical mode was performed using the LsDyna^® software to describe the filling of micro-cavities during the forming process of the material using the hot embossing. These simulations firstly allow verifying whether the chosen forming process conditions promote or not an optimized filling of the superficial cavities in order to achieve precise replicas which best reproduce the superficial topography of the mould. The simulations were carried out to evaluate the filling of the cavities taking into account the mechanical behaviour of the selected polymer into the model. Moreover, these models were developed to verify the effect of the distribution of the mould cavities on their filling. The influence of the mobility of non deformable rigid plates on the filling of the cavities represents an auxiliary variable. In the approach presented, the compression plates are assumed to be parallel and non deformable, whereas the polymer disk follows a rubbery behaviour around a temperature equal to 140°C. Globally the modelling results are satisfactory for they are rather close to the experimental observations conducted. In summary, the effect of the normal stress as also the distribution of micro-cavities at the mould surface seem to prevail in the case of the forming process by hot embossing.     

Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation

With growing interest in extending GIS to support multi-criteria decision-making (MCDM) methods, enhancing GIS-based MCDM with sensitivity analysis (SA) procedures is crucial to understand the model behavior and its limitations. This paper presents a novel approach of examining multi-criteria weight sensitivity of a GIS-based MCDM model. It explores the dependency of model output on the weights of input parameters, identifying criteria that are especially sensitive to weight changes and to show the impacts of changing criteria weights on the model outcomes in spatial dimension. A methodology was developed to perform simulations where the weights associated with all criteria used for suitability modelling were varied one-at-a-time (OAT) to investigate their relative impacts on the final evaluation results. A tool which incorporates the OAT method with the Analytical Hierarchy Process (AHP) within the ArcGIS environment was implemented. It permits a range of user defined simulations to be performed to quantitatively evaluate model dynamic changes, measures the stability of results with respect to the variation of different parameter weights, and displays spatial change dynamics. A case study of irrigated cropland suitability assessment addressing the application of the new GIS-based AHP-SA tool is described. It demonstrates that the tool is spatial, simple and flexible.