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Artificial neural networks

A Mohammed Abubakar, Himmet Karadal, Steven W Bayighomog, Ethem Merdan
This article proposes and tests a model of the interaction effect of organizational safety climate and behaviors on workplace injuries. Using artificial neural network and survey data from 306 metal casting industry employees in central Anatolia, we found that organizational safety climate mitigates workplace injuries, and safety behaviors enforces the strength of the negative impact of safety climate on workplace injuries. The results suggest a complex relationship between organizational safety climate, safety behavior, and workplace injuries...
March 19, 2018: International Journal of Occupational Safety and Ergonomics: JOSE
Federica Cavalera, Mario Zanoni, Valeria Merico, Thi Thu Hien Bui, Martina Belli, Lorenzo Fassina, Silvia Garagna, Maurizio Zuccotti
Infertility clinics would benefit from the ability to select developmentally competent vs. incompetent oocytes using non-invasive procedures, thus improving the overall pregnancy outcome. We recently developed a classification method based on microscopic live observations of mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage, followed by the analysis of the cytoplasmic movements occurring during this time-lapse period. Here, we present detailed protocols of this procedure...
March 3, 2018: Journal of Visualized Experiments: JoVE
Cecília M Costa, Ittalo S Silva, Rafael D de Sousa, Renato A Hortegal, Carlos Danilo M Regis
Myocardial infarction is one of the leading causes of death worldwide. As it is life threatening, it requires an immediate and precise treatment. Due to this, a growing number of research and innovations in the field of biomedical signal processing is in high demand. This paper proposes the association of Reconstructed Phase Space and Artificial Neural Networks for Vectorcardiography Myocardial Infarction Recognition. The algorithm promotes better results for the box size 10 × 10 and the combination of four parameters: box counting (Vx), box counting (Vz), self-similarity method (Vx) and self-similarity method (Vy) with sensitivity = 92%, specificity = 96% and accuracy = 94%...
February 8, 2018: Journal of Electrocardiology
Patrick McAllister, Huiru Zheng, Raymond Bond, Anne Moorhead
Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101...
February 17, 2018: Computers in Biology and Medicine
A Yakubu, O I A Oluremi, E I Ekpo
There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to forecast heat stress index (HSI) in Sasso laying hens. One hundred and sixty seven records on the thermo-physiological parameters of the birds were utilized. They were reared on deep litter and battery cage systems. Data were collected when the birds were 42- and 52-week of age. The independent variables fitted were housing system, age of birds, rectal temperature (RT), pulse rate (PR), and respiratory rate (RR)...
March 17, 2018: International Journal of Biometeorology
Li Zhang, Jiasheng Chen, Chunming Gao, Chuanmiao Liu, Kuihua Xu
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics...
March 16, 2018: Medical & Biological Engineering & Computing
Simone Franceschini, Emanuele Gandola, Marco Martinoli, Lorenzo Tancioni, Michele Scardi
Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artificial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fish species occurrence estimates and a case study for Leucos aula in NE Italy is presented as a demonstration case. Potentially useful biotic information (i...
March 15, 2018: Scientific Reports
Hythem Sidky, Jonathan K Whitmer
Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes...
March 14, 2018: Journal of Chemical Physics
Rensheng Cao, Mingyi Fan, Jiwei Hu, Wenqian Ruan, Xianliang Wu, Xionghui Wei
Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training...
March 15, 2018: Materials
Alfonso González-Briones, Javier Prieto, Fernando De La Prieta, Enrique Herrera-Viedma, Juan M Corchado
At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption...
March 15, 2018: Sensors
François Ancien, Fabrizio Pucci, Maxime Godfroid, Marianne Rooman
The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing...
March 14, 2018: Scientific Reports
Alfonso González-Briones, Pablo Chamoso, Hyun Yoe, Juan M Corchado
The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of this are nuclear, thermal and carbon power plants, which lose a large amount of energy that could otherwise be used for different purposes, such as heating greenhouses. The role of GreenVMAS is to maintain the required temperature level in greenhouses by using the waste energy generated by power plants...
March 14, 2018: Sensors
Maurizio Giordano, Kumar Parijat Tripathi, Mario Rosario Guarracino
BACKGROUND: System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models...
March 8, 2018: BMC Bioinformatics
Guijie Liu, Mengmeng Wang, Anyi Wang, Shirui Wang, Tingting Yang, Reza Malekian, Zhixiong Li
In nature, the lateral line of fish is a peculiar and important organ for sensing the surrounding hydrodynamic environment, preying, escaping from predators and schooling. In this paper, by imitating the mechanism of fish lateral canal neuromasts, we developed an artificial lateral line system composed of micro-pressure sensors. Through hydrodynamic simulations, an optimized sensor structure was obtained and the pressure distribution models of the lateral surface were established in uniform flow and turbulent flow...
March 11, 2018: Sensors
Fiona M Z van den Heiligenberg, Tanya Orlov, Scott N Macdonald, Eugene P Duff, David Henderson Slater, Christian F Beckmann, Heidi Johansen-Berg, Jody C Culham, Tamar R Makin
The human brain contains multiple hand-selective areas, in both the sensorimotor and visual systems. Could our brain repurpose neural resources, originally developed for supporting hand function, to represent and control artificial limbs? We studied individuals with congenital or acquired hand-loss (hereafter one-handers) using functional MRI. We show that the more one-handers use an artificial limb (prosthesis) in their everyday life, the stronger visual hand-selective areas in the lateral occipitotemporal cortex respond to prosthesis images...
March 9, 2018: Brain: a Journal of Neurology
Shiva Borzouei, Ali Reza Soltanian
Objectives: Identify the most important of demographic risk factors to the diagnosis of Type 2 Diabetes Mellitus (T2DM) using the neural network model. Methods: In this study was conducted on 234 samples, and data were collected from individuals referring to diabetes center in Hamadan city (west of Iran) from 27 November to 15 March 2016. Diagnosis of normal people and diabetics was performed by HbA1c measures. Multilayer perceptron artificial neural network used to identify demographic risk factors on T2DM and their importance rate...
March 10, 2018: Epidemiology and Health
Ali Abdollahi Gharbali, Shirin Najdi, José Manuel Fonseca
OBJECTIVE: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. METHODS: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3 -A2 ), Left EOG, Chin EMG and ECG signals...
March 7, 2018: Computers in Biology and Medicine
Peristera-Maria Toziou, Panagiotis Barmpalexis, Paraskevi Boukouvala, Susan Verghese, Ioannis Nikolakakis
Since culture-based methods are costly and time consuming, alternative methods are investigated for the quantification of probiotics in commercial products. In this work ATR- FTIR vibration spectroscopy was applied for the differentiation and quantification of live Lactobacillus (La 5) in mixed populations of live and killed La 5, in the absence and in the presence of enteric polymer Eudragit® L 100-55. Suspensions of live (La 5_L) and killed in acidic environment bacillus (La 5_K) were prepared and binary mixtures of different percentages were used to grow cell cultures for colony counting and spectral analysis...
March 5, 2018: Journal of Pharmaceutical and Biomedical Analysis
Sonja Cerar, Kim Mezga, Gorazd Žibret, Janko Urbanc, Marko Komac
Groundwater is the most important source of drinking water in the world. Therefore, information on the quality and quantity is important, as is new information related to the characteristics of the aquifer and the recharge area. In the present study we focused on the isotope composition of oxygen (δ18 O) in groundwater, which is a natural tracer and provides a better understanding of the water cycle, in terms of origin, dynamics and interaction. The groundwater δ18 O at 83 locations over the entire Slovenian territory was studied...
March 9, 2018: Science of the Total Environment
Ivan Grbatinić, Nebojsa Milosevic, Dusica Maric
The aim of this study is to determine whether the dentate neurons can be translaminary neuromorphotopologically classified as ventrolateral or dorsomedial type. Adult human dentate 2D binary interneuron images are used for the purposes of the analysis. The analysis is performed on the real and the virtual neuron sample. The total of 29 parameters is used. They can be divided into the classes: neuron surface, shape, length, branching and complexity. The clustering is performed through the algorithm consisted from of the steps of predictor extraction (matrix attractor analysis/non-negative matrix factorization and cluster analysis of predictor factors, separate unifactor analysis/Student's t-test and MANOVA) and multivariate cluster analysis set (cluster analysis, principal component analysis, factor analysis with pro/varimax rotation, Fisher's linear discriminant analysis and feed-forward backpropagation artificial neural networks)...
2018: Journal of Integrative Neuroscience
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