Dynamic ridge polynomial neural network with Lyapunov function for time series forecasting

The ability to model the behaviour of arbitrary dynamic system is one of the most useful properties of recurrent networks. Dynamic ridge polynomial neural network (DRPNN) is a recurrent neural network used for time series forecasting. Despite the potential and capability of the DRPNN, stability problems could occur in the DRPNN due to the existence of the recurrent feedback. Therefore, in this study, a su cient condition based on an approach that uses adaptive learning rate is developed by introducing a Lyapunov function. To compare the performance of the proposed solution with the existing solution, which is derived based on the stability theorem for a feedback network, we used six time series, namely Darwin sea level pressure, monthly smoothed sunspot numbers, Lorenz, Santa Fe laser, daily Euro/Dollar exchange rate and Mackey-Glass time-delay di erential equation. Simulation results proved the stability of the proposed solution and showed an average 21.45% improvement in Root Mean Square Error (RMSE) with respect to the existing solution. Furthermore, the proposed solution is faster than the existing solution. This is due to the fact that the proposed solution solves network size restriction found in the existing solution and takes advantage of the calculated dynamic system variable to check the stability, unlike the existing solution that needs more calculation steps

Radiosensitivity of human breast cancer cell lines expressing the breast tumor kinase (Brk)

The breast tumor kinase (Brk) is over-expressed in 80% of all breast cancers and we sought to determine the influence of this oncogene on radiation sensitivity in breast cancer cell lines. Since radiotherapy is a routinely used method of treatment for early and intermediate stage breast cancer, the alteration of clinical radiosensitivity in breast cancer by an oncogene over-expression would have important implications in radiotherapy management. To address this question, we conducted an in vitro study of radiation sensitivity in two breast cancer cell lines, MDA-MB-157 and MDA-MB-468 transfected with cDNA constructs to over-express the following genes: Brk wild type (WT); Brk with an inactivating mutation in the kinase domain (KM) and vector only. Gamma H2AX foci assays by imaging flow cytometry were used to measure DNA double strand break (DSB) repair after radiation exposure. Total ataxia telangiectasia (ATM) and activated phospho794-ATM protein was measured by imaging flow cytometry. In all cell lines tested there was a proportionate decline in cell survival following gamma radiation exposure. Radiation sensitivity of the cell lines in clonogenic assays and repair of DNA double strand breaks were similar and independent of Brk expression status. We conclude that over-expression of the Brk proto-oncogene in breast cancer cell lines does not appear to influence radiation sensitivity or affect DNA DSB repair.This work was supported by a grant from The Bart’s Charity (Grant Number: 419/2071), 12 Cock Lane, London EC1A 9BU

601 metal-on-metal total hip replacements with 36 mm heads a 5 minimum year follow up: Levels of ARMD remain low despite a comprehensive screening program

BACKGROUND: We conducted a retrospective study to assess the clinical outcome, failure rate, and reason for failure of a large consecutive series of 36 mm MoM Corail/Pinnacle total hip replacements (THRs). METHODS: Between 2006 and 2011, 601 consecutive 36 mm MoM THRs were performed (585 patients). Patients were followed according to the UK Medicines and Healthcare Products Regulatory Agency (MHRA) guidelines. All patients were accounted for and 469 patients (78%) were clinically and radiographically assessed. 328 females and 141 males with a median age of 73 (range 36-94 years) and a median follow up of 7.2 years (range 5.2-9.7 years) were followed. Clinical data included blood cobalt and chromium, Oxford Hip Score (OHS), plain radiograph, ultrasound of hip and intra-operative findings in those patients who had revision surgery. RESULTS: 56 patients died of causes unrelated to their hip replacement. The mean survivorship of the implant was 92.8% (range 91.6-94%, 95% CI) at a median time to follow up of 84 months (62-113 months). The functional outcome was good with a median OHS of 38 out of 48 (23-44). The dislocation rate was 0.99%, with all these 6 cases requiring revision. 476 patients had blood tests. 100 patients (21%) had elevated levels of either cobalt above MHRA guidelines of 7 parts per billion (120 and 135 nmol/L respectively for cobalt and chromium). Cobalt was elevated independently of chromium in 75% of the cases (but never vice versa). The mean cup inclination angle was 42°. Each incremental stem size increase resulted in a decrease in cobalt by 11 nmol/L. The most common reason for revision was adverse reaction to metal debris (ARMD) (12 cases). CONCLUSION: This paper is the largest and longest follow up of 36 mm MoM THRs. Using the MHRA guidelines for follow up, the revision rates of this cohort has remained low compared to other studies, but unacceptably higher than that of other bearing surfaces. LEVEL OF EVIDENCE: III

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy

Human Body Posture Recognition Approaches

Human body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body’s posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential ‎ hardware technologies are ‎used in posture recognition systems‎. These systems capture and collect datasets through ‎accelerometer sensors or computer vision. In addition, this paper presents a comparison ‎study with state-of-the-art in terms of accuracy. We also present the advantages and ‎limitations of each system and suggest promising future ideas that can increase the ‎efficiency of the existing posture recognition system. Finally, the most common datasets ‎applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 202

Human Fall Down Recognition Using Coordinates Key Points Skeleton

Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly· where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however· these solutions have specific privacy· maintenance· and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However· in real-world scenarios· falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates· the critical points of the shoulder coordi-nates· and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective· with a classification accuracy of 97.7%

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

IoT-Enabled flood severity prediction via ensemble machine learning models

© 2013 IEEE. River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively

A Data Science and Machine Learning Approach to Measure and Monitor Physical Activity in Children

Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments. The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases