Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

Enhancing power transfer capability through flexible AC transmission system devices:a review

Global demand for power has significantly increased, but power generation and transmission capacities have not increased proportionally with this demand. As a result, power consumers suffer from variou...     

Design of Scale Model Mechanism in Low Speed Wind Tunnel

Motion control can be considered as the synergistic collaboration of mechanical and electrical engineering, computer science and information technology to apply a controlled force to achieve useful motion in fluid or soiled electromechanical systems. With the development of computer, electronics, and automatic control theory, motion control comes to a new stage. Great applications are based on the characteristics, stemming from advantages of electronics and modern control technology achievements, having a v...     

Design of backpropagated neurocomputing paradigm for Stuxnet virus dynamics in control infrastructure

Neural Computing and Applications - In the present study, a novel application of backpropagated neurocomputing heuristics (BNCH) is presented for epidemic virus model that portrays the Stuxnet...     

Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD. Untreated myeloma causes by increasing the OC and reducing the osteoblasts, resulting in net bone waste the tumor growth. The solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation (LVMBP) neural networks (NN), i.e., LVMBPNN. The mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the LVMPNN. The static structural performances are 82% for investigation and 9% for both learning and certification. The performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton mechanism. To accomplish the capability, steadiness, accuracy, and ability of the LVMBPNN, the performances of the error histograms (EHs), mean square error (MSE), recurrence, and state transitions (STs) will be provided.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

Re-estimation of absolute gamma ray intensities of ^56Mn using k0-standardization

The thermal neutron capture gamma ray facility at Pakistan Research Reactor （PARR-1） is being used for the re-estimation of various properties like capture cross-sections, resonance integral, absolute gamma intensities, etc. of different isotopes. The data for gamma ray transitions from the capture of thermal neutrons by ^55Mn are not in good agreement specifically below 2 MeV. So there is a need to re-estimate its intensities with better accuracy. Analytical grade MnCl2 powder and high purity Mn metal pieces were used in this study. Standard ^152Eu and ^60Co radioactive sources as well as thermal neutron capture γ-rays in chlorine were chosen for efficiency calibration. The k0 standardization technique was applied for these measurements to eliminate systematic errors in efficiencies. Chlorine also acted as a comparator in k0-factor calculations. The results have been tabulated for the main gamma rays from ^56Mn in the low as well as in the medium energy regions. The absolute intensities are in good agreement with most of the reported values.