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"Artificial neural network"

Noemí León-Roque, Mohamed Abderrahim, Luis Nuñez-Alejos, Silvia M Arribas, Luis Condezo-Hoyos
Several procedures are currently used to assess fermentation index (FI) of cocoa beans (Theobroma cacao L.) for quality control. However, all of them present several drawbacks. The aim of the present work was to develop and validate a simple image based quantitative procedure, using color measurement and artificial neural network (ANNs). ANN models based on color measurements were tested to predict fermentation index (FI) of fermented cocoa beans. The RGB values were measured from surface and center region of fermented beans in images obtained by camera and desktop scanner...
December 1, 2016: Talanta
Regina Aroca-Santos, John C Cancilla, Enrique S Pariente, José S Torrecilla
The identification and quantification of binary blends of refined olive oil with four different extra virgin olive oil (EVOO) varietals (Picual, Cornicabra, Hojiblanca and Arbequina) was carried out with a simple method based on combining visible spectroscopy and non-linear artificial neural networks (ANNs). The data obtained from the spectroscopic analysis was treated and prepared to be used as independent variables for a multilayer perceptron (MLP) model. The model was able to perfectly classify the EVOO varietal (100% identification rate), whereas the error for the quantification of EVOO in the mixtures containing between 0% and 20% of refined olive oil, in terms of the mean prediction error (MPE), was 2...
December 1, 2016: Talanta
Xiaoran Duan, Yongli Yang, Shanjuan Tan, Sihua Wang, Xiaolei Feng, Liuxin Cui, Feifei Feng, Songcheng Yu, Wei Wang, Yongjun Wu
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length...
October 20, 2016: Medical & Biological Engineering & Computing
Abdul Akbar, Ananya Kuanar, Raj K Joshi, I S Sandeep, Sujata Mohanty, Pradeep K Naik, Antaryami Mishra, Sanghamitra Nayak
The drug yielding potential of turmeric (Curcuma longa L.) is largely due to the presence of phyto-constituent 'curcumin.' Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors...
2016: Frontiers in Plant Science
Giulio Binetti, Laura Del Coco, Rosa Ragone, Samanta Zelasco, Enzo Perri, Cinzia Montemurro, Raffaele Valentini, David Naso, Francesco Paolo Fanizzi, Francesco Paolo Schena
The development of an efficient and accurate method for extra-virgin olive oils cultivar and origin authentication is complicated by the broad range of variables (e.g., multiplicity of varieties, pedo-climatic aspects, production and storage conditions) influencing their properties. In this study, artificial neural networks (ANNs) were applied on several analytical datasets, namely standard merceological parameters, near-infra red data and (1)H nuclear magnetic resonance (NMR) fingerprints, obtained on mono-cultivar olive oils of four representative Apulian varieties (Coratina, Ogliarola, Cima di Mola, Peranzana)...
March 15, 2017: Food Chemistry
Osman Aydoğan, Ali Öter, Kerim Güney, M Kemal Kıymık, Deniz Tuncel
Obstructive sleep apnea is a sleep disorder which may lead to various results. While some studies used real-time systems, there are also numerous studies which focus on diagnosing Obstructive Sleep Apnea via signals obtained by polysomnography from apnea patients who spend the night in sleep laboratory. The mean, frequency and power of signals obtained from patients are frequently used. Obstructive Sleep Apnea of 74 patients were scored in this study. A visual-scoring based algorithm and a morphological filter via Artificial Neural Networks were used in order to diagnose Obstructive Sleep Apnea...
December 2016: Journal of Medical Systems
Naushad Shaik Mohammad, P Sai Shruti, Venkat Bharathi, Chintakindi Krishna Prasad, Tajamul Hussain, Salman A Alrokayan, Usha Naik, Akella Radha Rama Devi
BACKGROUND: The rationale of the current study was to test the clinical utility of the folate pathway genetic polymorphisms in predicting the risk for autism spectrum disorders (ASD) and to address the inconsistencies in the association of MTHFR C677T and hyperhomocysteinemia with ASD. PATIENTS AND METHODS: An artificial neural network (ANN) model was developed from the data of 138 autistic and 138 nonautistic children using GCPII C1561T, SHMT1 C1420T, MTHFR C677T, MTR A2756G, and MTRR A66G as the predictors of autism risk...
October 17, 2016: Psychiatric Genetics
Jaya Sanyal, Shiek S S J Ahmed, Hon Keung Tony Ng, Tufan Naiya, Epsita Ghosh, Tapas Kumar Banerjee, Jaya Lakshmi, Gautam Guha, Vadlamudi Raghavendra Rao
Parkinson's disease (PD) is a neurodegenerative disease with the absence of markers for diagnosis. Several studies on PD reported the elements imbalance in biofluids as biomarkers. However, their results remained inconclusive. This study integrates metallomics, multivariate and artificial neural network (ANN) to understand element variations in CSF and serum of PD patients from the largest cohort of Indian population to solve the inconsistent results of previous studies. Also, this study is aimed to (1) ascertain a common element signature between CSF and serum...
October 18, 2016: Scientific Reports
Negisa Darajeh, Azni Idris, Hamid Reza Fard Masoumi, Abolfazl Nourani, Paul Truong, Shahabaldin Rezania
Artificial neural networks (ANN) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study ANN was applied for modeling of COD and BOD removal from Palm Oil Mill Secondary Effluent (POMSE) by Vetiver system. The independent variable including POMSE concentration, Vetiver slips density, and removal time has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output...
October 17, 2016: International Journal of Phytoremediation
Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen
In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective. Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks. We also compare the reported performance of many recently published dementia identification algorithms...
March 2016: Brain Informatics
C Vijayakumar, M Ramesh, A Murugesan, N Panneerselvam, D Subramaniam, M Bharathiraja
The modern scenario reveals that the world is facing energy crisis due to the dwindling sources of fossil fuels. Environment protection agencies are more concerned about the atmospheric pollution due to the burning of fossil fuels. Alternative fuel research is getting augmented because of the above reasons. Plant seed oils (vegetable oils) are cleaner, sustainable, and renewable. So, it can be the most suitable alternative fuel for compression ignition (CI) engines. This paper reviews the availability of different types of plant seed oils, several methods for production of biodiesel from vegetable oils, and its properties...
October 15, 2016: Environmental Science and Pollution Research International
Ilaria Bergese, Simona Frigerio, Marco Clari, Emanuele Castagno, Antonietta De Clemente, Elena Ponticelli, Enrica Scavino, Paola Berchialla
OBJECTIVES: Return visit (RV) to the emergency department (ED) is considered a benchmarking clinical indicator for health care quality. The purpose of this study was to develop a predictive model for early readmission risk in pediatric EDs comparing the performances of 2 learning machine algorithms. METHODS: A retrospective study based on all children younger than 15 years spontaneously returning within 120 hours after discharge was conducted in an Italian university children's hospital between October 2012 and April 2013...
October 6, 2016: Pediatric Emergency Care
Xiang Li, Ling Peng, Yuan Hu, Jing Shao, Tianhe Chi
With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed...
October 13, 2016: Environmental Science and Pollution Research International
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, Demis Hassabis
Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer...
October 12, 2016: Nature
Darko Ivanović, Aleksandar Kupusinac, Edita Stokić, Rade Doroslovački, Dragan Ivetić
The diagnosis of metabolic syndrome (MetS) has a leading role in the early prevention of chronic disease, such as cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease. It would be very greatful that MetS diagnosis can be predicted in everyday clinical practice. This paper presents artificial neural network (ANN) prediction of the diagnosis of MetS that includes solely non-invasive, low-cost and easily-obtained diagnostic methods. This solution can extract the risky persons and suggests complete tests only on them by saving money and time...
December 2016: Journal of Medical Systems
R M G E van de Goor, N Leunis, M R A van Hooren, E Francisca, A Masclee, B Kremer, K W Kross
Electronic nose (e-nose) technology has the potential to detect cancer at an early stage and can differentiate between cancer origins. Our objective was to compare patients who had head and neck squamous cell carcinoma (HNSCC) with patients who had colon or bladder cancer to determine the distinctive diagnostic characteristics of the e-nose. Feasibility study An e-nose device was used to collect samples of exhaled breath from patients who had HNSCC and those who had bladder or colon cancer, after which the samples were analyzed and compared...
October 11, 2016: European Archives of Oto-rhino-laryngology
Cătălin Buiu, Mihai V Putz, Speranta Avram
The dependency between the primary structure of HIV envelope glycoproteins (ENV) and the neutralization data for given antibodies is very complicated and depends on a large number of factors, such as the binding affinity of a given antibody for a given ENV protein, and the intrinsic infection kinetics of the viral strain. This paper presents a first approach to learning these dependencies using an artificial feedforward neural network which is trained to learn from experimental data. The results presented here demonstrate that the trained neural network is able to generalize on new viral strains and to predict reliable values of neutralizing activities of given antibodies against HIV-1...
October 11, 2016: International Journal of Molecular Sciences
Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, Keum-Shik Hong
We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks...
2016: Computational Intelligence and Neuroscience
Meijun Sun, Dong Zhang, Li Liu, Zheng Wang
Hyperspectral imaging (HSI) in the near-infrared (NIR) region (900-1700nm) was used for non-intrusive quality measurements (of sweetness and texture) in melons. First, HSI data from melon samples were acquired to extract the spectral signatures. The corresponding sample sweetness and hardness values were recorded using traditional intrusive methods. Partial least squares regression (PLSR), principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN) models were created to predict melon sweetness and hardness values from the hyperspectral data...
March 1, 2017: Food Chemistry
Kadir Sabanci, Ahmet Kayabasi, Abdurrahim Toktas
BACKGROUND: A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying the wheat grains into bread or durum is presented. The images of 100 bread wheat grains and 100 durum wheat grains are taken via a high resolution camera and they are subjected to a pre-processing. The main visual features of 4 dimensions, 3 colours and 5 texture are acquired using image processing techniques (IPTs). A total number of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model...
October 8, 2016: Journal of the Science of Food and Agriculture
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