Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these suffer from low contrast, complex background, variations lesion shape and color, which affect the accuracy their segmentation subsequent classification. This research proposes an automated system for detection classification ulcers WCE images, based on state-of-the-art deep learning networks. Deep techniques, particular, convolutional neural networks (CNNs), have recently become...

Advances in stretchable and flexible sensors are responding to the emerging demand of wearable portable smart electronics. A core component these electronics tactile sensing devices which detect external stimuli obtain in-time information from surroundings. fusion electronics, physics materials science, have great potential robots, biomedicine, interactive devices, several other applications. By integrating with a polymer matrix conductive (nanometals, carbon nanomaterials, conducting...

Cloud computing is extensively used in a variety of applications and domains, however task resource scheduling remains an area that requires improvement. Put simply, heterogeneous system, algorithms, which allow the transfer incoming tasks to machines, are needed satisfy high performance data mapping requirements. The appropriate between resources reduces makespan maximises utilisation. In this contribution, we present novel algorithm using Directed Acyclic Graph (DAG) based on Prediction...

Early detection of retinal diseases is one the most important means preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system proposed for multiple diseases, using fundus images collected from variety sources. First, new disease dataset, MuReD constructed, number publicly available datasets classification. Next, sequence post-processing steps applied to ensure quality image data and range present dataset. For first time classification,...

Modern cars are equipped with autonomous systems to assist the driver and improve driving experience. Driving system (DAS) is one of most significant components a self-driving vehicle (SDV), used overcome non-autonomous challenges. However, conventional not DAS, high-cost required equip these vehicles DAS. Moreover, design DAS very complex outside industry while it requires going through Electronic Control Unit (ECU), which has high level security. Therefore, basic needs be installed in...

Forecasting of oil production plays a vital role in petroleum engineering and contributes to supporting engineers the management reservoirs. However, reliable forecasting is difficult achieve, particularly view increase digital big data. Although significant amount work has been reported literature relation use machine learning gas domain, traditional approaches have limited potential terms representing complex features time series More specifically, high-dimensional nonlinear multivariate...

Vascular structures in the retina contain important information for detection and analysis of ocular diseases, including age-related macular degeneration, diabetic retinopathy glaucoma. Commonly used modalities diagnosis these diseases are fundus photography, scanning laser ophthalmoscope (SLO) fluorescein angiography (FA). Typically, retinal vessel segmentation is carried out either manually or interactively, which makes it time consuming prone to human errors. In this research, we propose...

River flooding is a natural phenomenon that can have devastating effect on human life and economic losses. There been various approaches in studying river flooding; however, insufficient understanding limited knowledge about conditions hinder the development of prevention control measures for this phenomenon. This paper entails new approach prediction water level association with flood severity using ensemble model. Our leverages latest developments Internet Things (IoT) machine learning...

Massive open online courses (MOOCs) have been experiencing increasing use and popularity in highly ranked universities recent years. The opportunity of accessing high quality courseware content within such platforms, while eliminating the burden educational, financial, geographical obstacles has led to a rapid growth participant numbers. number diversity participating learners opened up new horizons research community for investigation effective learning environments. Learning Analytics used...

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in it cannot provide spatially resolved temperature measurements non-homogeneous fields. The aim of this research to explore use data-driven models measuring distributions a manner using data. Two categories methods are analyzed: (i) Feature engineering and classical machine learning algorithms, (ii) end-to-end convolutional neural networks (CNN). In total, combinations fifteen feature...

Machine learning (ML) techniques are popular in many parameter estimation tasks; however, they face challenges the real-world deployment due to lack of robustness errors. ML estimators not able ascertain performance presence noise, variations data distribution, and anomalies test samples. This work proposes a novel framework, surrogate-based physical error correction (SPEC), which addresses unmet need for measurement reliability self-correction under process uncertainty, by bringing together...

This study aimed at investigating the effect of myocardial motion on pulsating blood flow distribution left anterior descending coronary artery in presence atheromatous stenosis. The moving 3D arterial tree geometry has been obtained from conventional x-ray angiograms during heart cycle and includes a number major branches. reconstruction model validated against projection data virtual phantom as well with CT-based for same patient investigated. Reconstructions have temporal points while...

Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility teaching environment, number participants is rapidly growing. However, extensive research reports that high attrition rate low completion are major concerns. In this paper, early identification students who at risk withdrew failure provided. Therefore, two models constructed namely at-risk student...

Counterfeit medicines represent a public health threat that results in treatment failure and may even have lethal effects the worst-case scenario. Near-infrared Chemical Imaging (NIR-CI) offers an informative in-depth tool for several applications pharmaceutical industry, particularly medicine authentication. The current study aimed to authenticate antibiotic tablets using NIR-CI. These were measured non-destructively near-infrared microscope within their blister packaging, without blisters,...

This paper proposes a new nonparametric regression method, based on the combination of generalized neural networks (GRNNs), density-dependent multiple kernel bandwidths, and regularization. The presented model is generic substitutes very large number bandwidths with much smaller trainable weights that control model. It depends sets extracted data density features which reflect properties distribution irregularities training sets. We provide an efficient initialization scheme second-order...