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...     

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.     

Effect of air temperature and velocity on moisture diffusivity in relation to physical and sensory quality of dried pumpkin seeds

Understanding the effect of drying process parameters on food quality is helpful in process optimization and control. The objective of this work is to understand the effect of mild and harsh effective moisture diffusivity (D_eff), varied by air temperature and velocity, of drying processes on the physical and sensory quality of flat food products. Pumpkin seeds were selected as a food representative. It was found that increments of air temperature and velocity resulted in increased D_eff and brown color on seed hull surfaces and embryos, but decreased hardness of seed embryos. Changes in taste and aroma of seed embryos were able to be sensed. Indicating that D_eff is related to seed physical quality. Similar phenomena occurred with both tray and fluidized bed drying. Air temperature, velocity, and D_eff should be controlled to ensure the best dried flat food products. Mild drying conditions are potentially preferred for good physical and sensory quality.     

Multi‐agent‐based sharing power economy for a smart community

In this paper, a multi‐agent‐based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small‐scale battery ESSs (each owned by community users), which are connected together to form a unified‐ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified‐ESS. This decision is based on the price of electricity at ‘time of use’ and ‘state of charge’ of unified‐ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of ‘no profit, no loss’. The objective of the proposed PDH is to limit the purchase of electricity on ‘high priced’ hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi‐agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on‐peak, off‐peak, and mid‐level (mid‐peak) prices of a real‐time price signal during 24 h of a day. Results clearly show vital financial benefits of ‘sharing power economy’ for end users and efficient use of power within the smart community. Copyright © 2017 John Wiley & Sons, Ltd.