End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

Switched reluctance motor modeling with on-line parameteridentification

A nonlinear model with on-line parameter estimation using recursive identification for switched reluctance motors (SRMs) is presented. The model is robust toward parameter variations in the motor or any system disturbances. The parameters of the model are adjusted to account for errors in rotor position, which allows the use of crude inexpensive position sensors. The proposed modeling approach allows self-tuning of SRMs in a production unit. The simulations and experiments performed to test the model demonstrate the accuracy of estimation of the model     

Design, fabrication and control of a magnetic capsule-robot for the human esophagus

Much research on the development of a robotic capsule and micro robot for the diagnosis of gastrointestinal diseases has been carried out. The powering of these micro systems is becoming very challenging as the implementation of such systems is limited due to the existence of on-board power supplies. This paper presents a micro robotic system based on magnetic principles. The goal is to build a system in which a capsule-robot can be manipulated wirelessly inside an enclosed environment such as human??s body. A prototype of capsule-robot is built and tested, that can be remotely operated with three DOF in an enclosed environment by transferring magnetic energy and electromagnetic waves. A magnetic drive unit generates magnetic energy for the manipulation. Experimental results show the capsule-robot is manipulated and moved through a desired trajectory in a viscous fluid. The capsule-robot can be potentially used for endoscopy and colonoscopy.     

Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise

In the present paper, the identification and estimation problem of a single-input–single-output (SISO) fractional order state-space system will be addressed. A SISO state-space model is considered in which parameters and also state variables should be estimated. The canonical fractional order state-space system will be transformed into a regression equation by using a linear transformation and a shift operator that are appropriate for identification. The identification method provided in this paper is based on a recursive identification algorithm that has the capability of identifying the parameters of fractional order state-space system recursively. Another subject that will be addressed in this paper is a novel fractional order Kalman filter suitable for the systems with coloured measurement noise. The promising performance of the proposed methods is verified using two stable fractional order systems.     

SAPDS: self-healing attribute-based privacy aware data sharing in cloud

This paper addresses the issue of data governance in a cloud-based storage system. To achieve fine-grained access control over the outsourced data, we propose Self-Healing Attribute-based Privacy Aware Data Sharing in Cloud (SAPDS). The proposed system delegates the key distribution and management process to a cloud server without seeping out any confidential information. It facilitates data owner to restrain access of the user with whom data has been shared. User revocation is achieved by merely changing one attribute associated with the decryption policy, instead of modifying the entire access control policy. It enables authorized users to update their decryption keys followed by each user revocation, making it self-healing, without ever interacting with the data owner. Computation analysis of the proposed system shows that data owner can revoke n′ users with the complexity of O(n′). Besides this, legitimate users can update their decryption keys with the complexity of O(1).     

Sequence-dependent group scheduling problem on unrelated-parallel machines

In this research we address a sequence-dependent group scheduling problem on a set of unrelated-parallel machines where the run time of each job differs on different machines. To benefit both producer and customers we attempt to minimize a linear combination of total weighted completion time and total weighted tardiness. Since the problem is shown to be NP-hard, meta-heuristic algorithms based on tabu search are developed to find the optimal/near optimal solution. For some small size yet complex problems, the results from these algorithms are compared to the optimal solutions found by CPLEX. The result obtained in all of these problems is that the tabu search algorithms could find solutions at least as good as CPLEX but in drastically shorter computational time, thus signifying the high degree of efficiency and efficacy attained by the former.     

Evaluation of Antioxidant and Antimicrobial Activities of Essential Oils from Carum copticum Seed and Ferula assafoetida Latex

Carum copticum and Ferula assafoetida have several medicinal properties including antispasmodic, carminative, sedative, analgesic, and antiseptic. Reactive oxygen species (ROS), reactive nitrogen species (RNS), hydrogen peroxide (H₂O₂), and thiobarbituric acid reactive substances (TBARS) scavenging activities of Carum and Ferula oils along with their antibacterial and antifungal activities were examined. Thymol (40.25%), γ‐terpinene (38.7%) and p‐cymene (15.8%) were detected as the main components of Carum oil while, β‐pinene (47.1%), α‐pinene (21.36%), and 1, 2‐dithiolane (18.6%) were the main components of Ferula oil. Inhibitory concentrations (IC50) for total radical scavenging were between 40 and 60 and 130 and 160 μg/mL of Carum and Ferula oil, respectively. Minimal inhibitory concentration (MIC) for Salmonella typhi, Escherichia coli, Staphylococcus aureus, Bacillus subtilis, Aspergillus niger, and Candida albicans were 78 ± 8, 65 ± 7, 14 ± 3, 5 ± 2, 5.6 ± 1.3, and 8.8 ± 2.2 μg/mL of Carum oil, respectively. MIC for S. typhi, E. coli, S. aureus, B. subtilis, A. niger, and C. albicans were >200, >200, 125 ± 17, 80 ± 12, 85 ± 5, and 90 ± 11 μg/mL of Ferula oil, respectively. Accordingly, Carum and Ferula oils could be used as safe and effective natural antioxidants to improve the oxidative stability of fatty foods during storage and to preserve foods against food burn pathogens. Practical Application : This study clearly demonstrates the potential of Carum and Ferula oil especially Carum oil as natural antioxidant and antimicrobial agent. The chemical composition of essential oils was identified. Thus, identification of such compounds also helps to discover of new antioxidant, antibacterial and antifungal agents for potential applications in food safety and food preservation.