A novel solution of deep learning for sleep apnea detection: enhancement of SC and elimination of GVICS

Multimedia Tools and Applications - Convolutional neural network (CNN) classification has not achieved a medically-satisfied level of accuracy in sleep apnea detection due to the negative effect of...     

A novel augmented reality for hidden organs visualisation in surgery: enhanced super-pixel with sub sampling and variance adaptive algorithm

In recent years, Augmented Reality (AR) has gained more attention as an effective tool in medical surgeries. The potentials of using AR in the medical field can change conventional medical procedures. However, the technology still facing fundamental challenges, especially hidden organs, for example, the organs behind the bowel and liver. The surgeries in these areas lack accuracy in the visualization of the soft tissues behind the bowel and liver like the uterus and gall bladder. This research aims to improve the accuracy of visualisation and the processing time of the augmented video. The proposed system consists of an enhanced super-pixel algorithm with variance weight adaptation and subsampling method. The simulation studies show significant improvements in visualization accuracy and a reduction in processing time. The results show reduced visualisation error by 0.23 mm. It provides better accuracy of the video in terms of visualization error from 1.58?~?1.83 mm to 1.35?~?1.60 mm, and the processing time decreases from 50?~?58 ms/frames to 40?~?48 ms/frames. The proposed system \ focused on the pixel refinement for the 3d reconstruction of the soft tissue, which helps solve the issue of visualising the bowel and liver in an augmented video.

     

Augmented reality for dental implant surgery: enhanced ICP

Augmented reality surgery has not been successfully implemented in dental implant surgery due to the negative impact of an incorrect implant placement. This research aimed to improve the convergence between computed tomography derived teeth model and real-time stereo view of patient’s teeth to provide high registration accuracy. Enhanced iterative closest point algorithm is proposed to reduce the error caused due to matching wrong points. Weighting mechanism and median value are used to reduce alignment error caused due to matching wrong points. In addition, random sample consensus (RANSAC) algorithm is used to detect and remove the outlier. Furthermore, the current solution for dental implants did not provide the position and orientation of the surgical tool, and without this information, there is a risk of damaging adjacent structure, dental nerves, and root canals. Optical tracking device is used in the proposed solution to address this information and ensure that nerve does not get damaged during the dental implant placement surgery. While the state-of-the-art solution provided 0.44 mm registration accuracy, the proposed solution was improving it by providing 0.33 mm registration accuracy. Additionally, the proposed system can produce good results despite not having a good initialization. The processing time improved to 14 fps in comparison to the 9-fps given by state-of-the-art solution. The proposed system improved the accuracy of convergence and the processing time compared to the globally optimal-ICP algorithm. We also employed RANSAC algorithm to detect and remove the outlier on the estimation and reduce the influence of extreme points.

     

A novel solution of using deep learning for prostate cancer segmentation: enhanced batch normalization

Multimedia Tools and Applications - Deep learning has not been successfully implemented in the past with accurate segmentation of prostate on Magnetic Resonance (MR) image in nerve sparing prostate...     

A Novel Enhanced Naïve Bayes Posterior Probability (ENBPP) Using Machine Learning: Cyber Threat Analysis

Neural Processing Letters - Machine learning techniques, that are based on semantic analysis of behavioural attack patterns, have not been successfully implemented in cyber threat intelligence....     

A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL)

Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.

     

Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 s and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.