BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI

Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.&nbsp

OPTIMAL SLIDING MODE CONTROLLER DESIGN BASED ON WHALE OPTIMIZATION ALGORITHM FOR LOWER LIMB REHABILITATION ROBOT

The Sliding Mode Controllers (SMCs) are considered among the most common stabilizer and controllers used with robotic systems due to their robust nonlinear scheme designed to control nonlinear systems. SMCs are insensitive to external disturbance and system parameters variations. Although the SMC is an adaptive and model-based controller, some of its values need to be determined precisely. In this paper, an Optimal Sliding Mode Controller (OSMC) is suggested based on Whale Optimization Algorithm (WOA) to control a two-link lower limb rehabilitation robot. This controller has two parts, the equivalent part, and the supervisory controller part. The stability assurance of the controlled rehabilitation robot is analyzed based on Lyapunov stability. The WO algorithm is used to determine optimal parameters for the suggested SMC. Simulation results of two tested trajectories (linear step signal and nonlinear sine signal) demonstrate the effectiveness of the suggested OSMC with fast response, very small overshoot, and minimum steady-state error

Implementation of national cryptocurrency using ethereum development platform

While Ethereum run in public networks which make the blockchain size large and transaction run time longer than time for private or national network, that led to continuous worries over the expanding size of Ethereum Blockchain, which certainly reduce Cryptocurrency's effectiveness. The estimations were on increase and believed it would cross the node limit of 1 TB terribly shortly. If new consumer a full node enters to that blockchain and cryptocurrency world, a node is a computer software cum database of the blockchain, which a full node client must download on their personal computers to become a full node in the blockchain. in this way, the client can be verifying transaction on the network with the help of other nodes on the system. We proposed to implement national cryptocurrency which developed using Ethereum as a development platform that could serve national or regional people that has limited or slow internet connections like Iraq, in addition payments in countries with unstable fiat currencies, although cryptocurrencies are suffering from unstable exchange rates against fiat currencies, the use of national cryptocurrency instead of the native fiat cash could even be a far better alternative for individuals in certain countries like Iraq, Iran and Syria, with high rate of inflation. The reminder of this paper is arranging as following: an introduction, the advantages and drawbacks of cryptocurrencies, Background on Blockchain and Ethereum, implementation, results and conclusion

On analysing deformable (moving) objects

Performing a high level vision is usually based on features extracted at low and intermediate levels of the process of perception of a visual scene.Segmentation and matching are instrumental tasks in producing comparable features in applications such as medical imaging, mining and oil extraction, gaming consoles, face, ear and gait biometrics, and etc.The ultimate goal of this study is to develop a fully functional prior aided segmentation framework to extract deformable shapes over a sequence of frames. This thesis acknowledges the demand by these applications for a robust and flexible approach which is particularly designed to extract deformable timely shape sequences. It is also recognised that existing methods are either too general, and thus inaccurate, or too specific, thereby limited in usability.This thesis suggests a learning model for gait synthesis with the ability to extrapolate to novel data. It involves computing comparable features from multiple sources. We show that these features which we formulate as continuous functions can be modelled by linear PCA.This thesis also proposes a new fast and robust shape registration algorithm to match shapes from different sources in the proposed framework. This algorithm is based on linear orthogonal transformations and shape moments. The registration parameters are computed directly by analysing the signed distance functions of the shapes. This is in-line with the level sets based prior shape segmentation framework adopted here. The segmentation is performed in a balanced framework between the data in the given images on one hand and the prior induced by the shape model and the registration algorithm proposed here on the other hand. This configuration ensures more control for the shape force over the overall shape geometry. Thus, favouring shapes familiar to the learned knowledge

Detection of epileptic seizures in EEG by using machine learning techniques

In this research a public dataset of recordings of EEG signals of healthy subjects and epileptic patients was used to build three simple classifiers with low time complexity, these are decision tree, random forest and AdaBoost algorithm. The data was initially preprocessed to extract short waves of electrical signals representing brain activity. The signals are then used for the selected models. Experimental results showed that random forest achieved the best accuracy of detection of the presence/absence of epileptic seizure in the EEG signals at 97.23% followed by decision tree with accuracy of 96.93%. The least performing algorithm was the AdaBoost scoring accuracy of 87.23%. Further, the AUC scores were 99% for decision tree, 99.9% for random forest and 95.6% for AdaBoost. These results are comparable to state-of-the-art classifiers which have higher time complexity