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artificial neural network

Asim Waris, Imran Khan Niazi, Mohsin Jamil, Kevin Englehart, Winnie Jensen, Ernest Nlandu Kamavuako
Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for Electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days...
August 8, 2018: IEEE Journal of Biomedical and Health Informatics
Xiaomao Fan, Qihang Yao, Yunpeng Cai, Fen Miao, Fangmin Sun, Ye Li
Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity in stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection of AF is necessary for averting the possibility of disability or mortality. However, AF detection remains problematic due to its episodic pattern. In this paper, a multi-scaled fusion of deep convolutional neural network (MSCNN) is proposed to screen out AF recordings from single lead short ECG recordings...
August 7, 2018: IEEE Journal of Biomedical and Health Informatics
Mariano Gallo, Giuseppina De Luca
This paper proposes a method for estimating traffic flows on some links of a road network knowing the data on other links that are monitored with sensors. In this way, it is possible to obtain more information on traffic conditions without increasing the number of monitored links. The proposed method is based on artificial neural networks (ANNs), wherein the input data are the traffic flows on some monitored road links and the output data are the traffic flows on some unmonitored links. We have implemented and tested several single-layer feed-forward ANNs that differ in the number of neurons and the method of generating datasets for training...
August 12, 2018: Sensors
Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak
The automated detection of arrhythmia in a Holter ECG signal is a challenging task due to its complex clinical content and data quantity. It is also challenging due to the fact that Holter ECG is usually affected by noise. Such noise may be the result of the regular activity of patients using the Holter ECG - partially unplugged electrodes, short-time disconnections due to movement, or disturbances caused by electric devices or infrastructure. Furthermore, regular patient activities such as movement also affect the ECG signals and, in connection with artificial noise, may render the ECG non-readable or may lead to misinterpretation of the ECG...
August 13, 2018: Physiological Measurement
Wenhao Shen, Feini Huang, Xuewen Zhang, Yuefei Zhu, Xiaoquan Chen, Nishonov Akbarjon
Chemical oxygen demand (COD), an important indicative measure of the amount of oxidizable pollutants in wastewater, is often analyzed off-line due to the expensive sensor required for on-line analysis. However, its off-line analysis is time-consuming. An on-line COD estimation method was developed with photoelectrocatalytic (PEC) technology. Based on the on-line data of the oxidation-reduction potential (ORP), dissolved oxygen (DO) and pH of wastewater, four different artificial neural network methods were applied to develop working models for COD estimation...
August 2018: Water Science and Technology: a Journal of the International Association on Water Pollution Research
Michael S C Thomas
From the genetic side, giftedness in cognitive development is the result of contribution of many common genetic variants of small effect size, so called polygenicity (Spain et al., 2016). From the environmental side, educationalists have argued for the importance of the environment for sustaining early potential in children, showing that bright poor children are held back in their subsequent development (Feinstein, 2003a). Such correlational data need to be complemented by mechanistic models showing how gifted development results from the respective genetic and environmental influences...
July 2018: Intelligence
Naweed I Chowdhury, Timothy L Smith, Rakesh K Chandra, Justin H Turner
BACKGROUND: Convolutional neural networks (CNNs) are advanced artificial intelligence algorithms well suited to image classification tasks with variable features. These have been used to great effect in various real-world applications including handwriting recognition, face detection, image search, and fraud prevention. We sought to retrain a robust CNN with coronal computed tomography (CT) images to classify osteomeatal complex (OMC) occlusion and assess the performance of this technology with rhinologic data...
August 11, 2018: International Forum of Allergy & Rhinology
Dat Tien Nguyen, Tuyen Danh Pham, Young Won Lee, Kang Ryoung Park
Iris recognition systems have been used in high-security-level applications because of their high recognition rate and the distinctiveness of iris patterns. However, as reported by recent studies, an iris recognition system can be fooled by the use of artificial iris patterns and lead to a reduction in its security level. The accuracies of previous presentation attack detection research are limited because they used only features extracted from global iris region image. To overcome this problem, we propose a new presentation attack detection method for iris recognition by combining features extracted from both local and global iris regions, using convolutional neural networks and support vector machines based on a near-infrared (NIR) light camera sensor...
August 8, 2018: Sensors
Mathilde Legrand, Manelle Merad, Etienne de Montalivet, Agnès Roby-Brami, Nathanaël Jarrassé
Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements...
2018: Frontiers in Neurorobotics
Piergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek, Matthias Hullin, Catherine F Higham, Robert Henderson, Roderick Murray-Smith, Daniele Faccio
Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with a deep convolutional artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N = 3)...
August 9, 2018: Scientific Reports
Mauro Carrara, Eleonora Massari, Alessandro Cicchetti, Tommaso Giandini, Barbara Avuzzi, Federica Palorini, Claudio Stucchi, Giovanni Fellin, Pietro Gabriele, Vittorio Vavassori, Claudio Degli Esposti, Cesare Cozzarini, Emanuele Pignoli, Claudio Fiorino, Tiziana Rancati, Riccardo Valdagni
OBJECTIVES: To apply artificial neural network (ANN) classification methods for the prediction of late fecal incontinence (LFI) following high-dose prostate cancer radiotherapy (RT) and to develop a "ready to use" graphical-tool. MATERIAL & METHODS: 598 men recruited in two national multicentre trials were analyzed. Information was recorded on comorbidity, previous abdominal surgery, use of drugs and dose distribution. Fecal incontinence was prospectively evaluated through self-reported questionnaires...
August 6, 2018: International Journal of Radiation Oncology, Biology, Physics
Defang Fan, Hongbin Yang, Fuxing Li, Lixia Sun, Peiwen Di, Weihua Li, Yun Tang, Guixia Liu
Genotoxicity tests can detect compounds that have an adverse effect on the process of heredity. The in vivo micronucleus assay, a genotoxicity test method, has been widely used to evaluate the presence and extent of chromosomal damage in human beings. Due to the high cost and laboriousness of experimental tests, computational approaches for predicting genotoxicity based on chemical structures and properties are recognized as an alternative. In this study, a dataset containing 641 diverse chemicals was collected and the molecules were represented by both fingerprints and molecular descriptors...
March 1, 2018: Toxicology Research
Fuxing Li, Defang Fan, Hao Wang, Hongbin Yang, Weihua Li, Yun Tang, Guixia Liu
Aquatic toxicity is an important issue in pesticide development. In this study, using nine molecular fingerprints to describe pesticides, binary and ternary classification models were constructed to predict aquatic toxicity of pesticides via six machine learning methods: Naïve Bayes (NB), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Classification Tree (CT), Random Forest (RF) and Support Vector Machine (SVM). For the binary models, local models were obtained with 829 pesticides on rainbow trout (RT) and 151 pesticides on lepomis (LP), and global models were constructed on the basis of 1258 diverse pesticides on RT and LP and 278 on other fish species...
November 1, 2017: Toxicology Research
Salvador Gutiérrez, Juan Fernández-Novales, Maria P Diago, Javier Tardaguila
Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine ( Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h...
2018: Frontiers in Plant Science
Hailong Li, Nehal A Parikh, Lili He
Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence...
2018: Frontiers in Neuroscience
Neda O Đorđević, Nevena Todorović, Irena T Novaković, Lato L Pezo, Boris Pejin, Vesna Maraš, Vele V Tešević, Snežana B Pajović
Screens of antioxidant activity (AA) of various natural products have been a focus of the research community worldwide. This work aimed to differentiate selected samples of Merlot wines originated from Montenegro, with regard to phenolic profile and antioxidant capacity studied by survival rate, total sulfhydryl groups and activities of glutathione peroxidase (GPx), glutathione reductase and catalase in H₂O₂⁻stressed Saccharomyces cerevisiae cells. In this study, DPPH assay was also performed. Higher total phenolic content leads to an enhanced AA under both conditions...
August 7, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
R Patcas, D A J Bernini, A Volokitin, E Agustsson, R Rothe, R Timofte
This observational study aimed to use artificial intelligence to describe the impact of orthognathic treatment on facial attractiveness and age appearance. Pre- and post-treatment photographs (n=2164) of 146 consecutive orthognathic patients were collected for this longitudinal retrospective single-centre study. Every image was annotated with patient-related data (age; sex; malocclusion; performed surgery). For every image, facial attractiveness (score: 0-100) and apparent age were established with dedicated convolutional neural networks trained on >0...
August 4, 2018: International Journal of Oral and Maxillofacial Surgery
Marilia Lira, Fernanda Naomi Pantaleão, Carolina Gudin de Souza Ramos, Paulo S Boggio
The body ownership induced by the rubber hand illusion (RHI) has been related to a neural network involving a frontal-parietal circuit. Previous functional neuroimaging studies have demonstrated neural activation in the parietal area relative to the multisensory integration processing and to the recalibration of the felt position of body while a ventral premotor cortex activation has been linked to bodily self-attribution during the RHI. Our study aimed to investigate the effects of transcranial direct current stimulation (tDCS) on the posterior parietal cortex (PPC) or on the premotor cortex (PMv) during RHI to address the specific roles of these two brain areas in the illusion...
August 6, 2018: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
Andrzej Bak, Violetta Kozik, Malgorzata Walczak, Justyna Fraczyk, Zbigniew Kaminski, Beata Kolesinska, Adam Smolinski, Josef Jampilek
The pharmacophore properties of a new series of potential purinoreceptor (P2X) inhibitors determined using a coupled neural network and the partial least squares method with iterative variable elimination (IVE-PLS) are presented in a ligand-based comparative study of the molecular surface by comparative molecular surface analysis (CoMSA). Moreover, we focused on the interpretation of noticeable variations in the potential selectiveness of interactions of individual inhibitor-receptors due to their physicochemical properties; therefore, the library of artificial dipeptide receptors (ADP) was designed and examined...
August 6, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
Chris Reed
Using artificial intelligence (AI) technology to replace human decision-making will inevitably create new risks whose consequences are unforeseeable. This naturally leads to calls for regulation, but I argue that it is too early to attempt a general system of AI regulation. Instead, we should work incrementally within the existing legal and regulatory schemes which allocate responsibility, and therefore liability, to persons. Where AI clearly creates risks which current law and regulation cannot deal with adequately, then new regulation will be needed...
September 13, 2018: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
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