Search results for: neural activity

Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Fadwa Haraka, Ahmad Elouatouati, Mourad Taha Janan

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In this study, we propose a neural network based method in order to calculate the heat source temperature of isotropic plate with lower surface forced cooling. To validate the proposed model, the heat source temperatures values will be compared to the analytical method -variables separation- and finite element model. The mathematical simulation is done through 3D numerical simulation by COMSOL software considering three different materials: Aluminum, Copper, and Graphite. The proposed method will lead to a formulation of the heat source temperature based on the thermal and geometric properties of the base plate.

Keywords: thermal model, thermal resistance, finite element simulation, neural network

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Authors: J. M. Hung, J. S. Chan, M. I. Kuo, D. S. Chan, C. P. Lu

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Wheat germ is a by-product obtained from wheat milling and it contains highly concentrated nutrients. Due to highly lipase and lipoxygenase activities, wheat germ products can easily turn into rancid flavor and cause a short life. The objective of this study is to control moisture content and retard lipid hydrolysis by fluidized-bed drying. The raw wheat germ of 2 kg was dried with a vertical batch fluidized bed with the following varying conditions, inlet air temperature of 50, 80 and 120°C, inlet air velocity of 3.62 m/s. The experiment was designed to obtain a final product at around 40°C with water activity of 0.3 ± 0.1. Changes in the moisture content, water activity, enzyme activity of dried wheat germ during storage were measured. Results showed the fluidized-bed drying was found to reduce moisture content, water activity and lipase activity of raw wheat germ. After drying wheat germ, moisture content and water activity were between 5.8% to 7.2% and 0.28 to 0.40 respectively during 12 weeks of storage. The variation range of water activity indicated to retard lipid oxidation. All drying treatments displayed inactivation of lipase, except for drying condition of 50°C which showed relative high enzyme activity. During storage, lipase activity increased slowly during the first 6 weeks of storage and reached a plateau for another 6 weeks. As a result, using a fluidized-bed dryer was found to be effective drying technique in improving storage stability of wheat germ.

Keywords: wheat germ, fluidized-bed dryer, storage, lipase, stability

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Authors: Shyam Sajan, Kamal Bijlani

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The widespread effects of digital media help people to explore the world more and get entertained with no effort. People became fond of these kind of sedentary life style. The increase in sedentary time and a decrease in physical activities has negative impacts on human health. Even though the addiction to video games has been exploited in exergames, to make people exercise and enjoy game challenges, the contribution is restricted only to physical wellness. This paper proposes creation and implementation of a game with the help of digital media in a virtual environment. The game is designed by collaborating ideas from neural linguistic programming and Stroop effect that can also be used to identify a person’s mental state, to improve concentration and to eliminate various phobias. The multiplayer game is played in a virtual environment created with Kinect sensor, to make the game more motivating and interactive.

Keywords: exergaming, Kinect Sensor, Neural Linguistic Programming, Stroop Effect

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Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

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In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt

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Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

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This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: Supatra Karnjanapratum, Soottawat Benjakul

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Gelatin hydrolysate, especially from marine resource, has been known to possess antioxidative activity. Nevertheless, the activity is still lower in comparison with the commercially available antioxidant. Maillard reactions can be use to increase antioxidative activity of gelatin hydrolysate, in which the numerous amino group could be involved in glycation. In the present study, gelatin hydrolysate (GH) from unicorn leatherjacket skin prepared using glycyl endopeptidase with prior autolysis assisted process was used for preparation of Maillard reaction products (MRPs) under dry condition. The impacts of different factors including, types of saccharides, GH to saccharide ratio, incubation temperatures, relative humidity (RH) and times on antioxidative activity of MRPs were investigated. MRPs prepared using the mixture of GH and galactose showed the highest antioxidative activity as determined by both ABTS radical scavenging activity and ferric reducing antioxidant power during heating (0-48 h) at 60 °C with 65% RH, compared with those derived from other saccharide tested. GH to galactose ratio at 2:1 (w/w) yielded the MRPs with the highest antioxidative activity, followed by the ratios of 1:1 and 1:2, respectively. When the effects of incubation temperatures (50, 60, 70 °C) and RH (55, 65, 75%) were examined, the highest browning index and the absorbance at 280 nm were found at 70 °C, regardless of RH. The pH and free amino group content of MRPs were decreased with the concomitant increase in antioxidative activity as the reaction time increased. Antioxidative activity of MRPs generally increased with increasing temperature and the highest antioxidative activity was found when RH of 55% was used. Based on electrophoresis of MRP, the polymerization along with the formation of high molecular weight material was observed. The optimal condition for preparing antioxidative MRPs was heating the mixture of GH and galactose (2:1) at 70 °C and 55% RH for 36 h. Therefore, antioxidative activity of GH was improved by Maillard reaction and the resulting MRP could be used as natural antioxidant in food products.

Keywords: antioxidative activity, gelatin hydrolysate, maillard reaction, unicorn leatherjacket

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Authors: Abhijit Das, Runu Chakraborty

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Tea is the most widely consumed beverage aside from water; it is grown in about 30 countries with a per capita worldwide consumption of approximately 0.12 liter per year. Green tea is of growing importance with its antioxidant contents associated with its health benefits. The various extraction methods can influence the polyphenol concentrations of green tea. The purpose of the study was to quantify the polyphenols, flavonoid and antioxidant activity of both caffeinated and decaffeinated form of tea manufactured commercially in Assam, North Eastern part of India. The results display that phenolic/flavonoid content well correlated with antioxidant activity which was performed by DPPH (2,2-diphenyl-1-picrylhydrazyl) and FRAP (Ferric reducing ability of plasma) assay. After decaffeination there is a decrease in the polyphenols concentration which also affects the antioxidant activity of green tea. For the enrichment of antioxidant activity of decaffeinated tea a herbal plant extract is used which shows a synergistic effect between green tea and herbal plant phenolic compounds.

Keywords: antioxidant activity, decaffeination, green tea, flavonoid content, phenolic content, plant extract

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Authors: Marlene Perez Villalpando, Kelly Joel Gurubel Tun

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In this paper, we propose an optimal control strategy for a hydroelectric power plant subject to input disturbances like meteorological phenomena. The engineering characteristics of the system are described by a nonlinear model. The random availability of renewable sources is predicted by a high-order neural network trained with an extended Kalman filter, whereas the power generation is regulated by the optimal control law. The main advantage of the system is the stabilization of the amount of power generated in the plant. A control supervisor maintains stability and availability in hydropower reservoirs water levels for power generation. The proposed approach demonstrated a good performance to stabilize the reservoir level and the power generation along their desired trajectories in the presence of disturbances.

Keywords: hydropower, high order neural network, Kalman filter, optimal control

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Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

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Authors: Giselle Maggie Fer Castañeda, Diego Iván González

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The objective of this work was to study the autoshaped choice behavior in the Donahoe, Burgos and Palmer (DBP) neural network model and analyze it under the matching law. Autoshaped choice can be viewed as a form of economic behavior defined as the preference between alternatives according to their relative outcomes. The Donahoe, Burgos and Palmer (DBP) model is a connectionist proposal that unifies operant and Pavlovian conditioning. This model has been used for more than three decades as a neurobehavioral explanation of conditioning phenomena, as well as a generator of predictions suitable for experimental testing with non-human animals and humans. The study consisted of different simulations in which, in each one, a ratio of reinforcement was established for two alternatives, and the responses (i.e., activations) in each of them were measured. Choice studies with animals have demonstrated that the data generally conform closely to the generalized matching law equation, which states that the response ratio equals proportionally to the reinforcement ratio; therefore, it was expected to find similar results with the neural networks of the Donahoe, Burgos and Palmer (DBP) model since these networks have simulated and predicted various conditioning phenomena. The results were analyzed by the generalized matching law equation, and it was observed that under some contingencies, the data from the networks adjusted approximately to what was established by the equation. Implications and limitations are discussed.

Keywords: matching law, neural networks, computational models, behavioral sciences

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Authors: Maria Medrano, Cristina Cadenas-Sanchez, Maddi Oses, Lide Arenaza, Maria Amasene, Idoia Labayen

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The COVID-19 outbreak began in December 2019 in China and was rapidly expanded globally. Emergency measures, such as social distance or home confinement, were adopted by many country governments to prevent its transmission. In Spain, one of the most affected countries, the schools were closed, and one of the most severe mandatory home confinement was established for children from 14th March to 26th April 2020. The hypothesis of this study was that the measures adopted during the COVID-19 pandemic may have negatively affected physical activity and screen time of children. However, few studies have examined the effects of COVID-19 pandemic on lifestyle behaviours. Thus, the aim of the current work was to analyse the effects of the COVID-19 confinement on physical activity and screen time in Spanish children. For the current purpose, a total of 113 children and adolescents (12.0 ± 2.6 yr., 51.3% boys, 24.0% with overweight/obesity according to the World Obesity Federation) of the MUGI project were included in the analyses. Physical activity and screen time were longitudinally assessed by 'The Youth Activity Profile' questionnaire (YAP). Differences in physical activity and screen time before and during the confinement were assessed by dependent t-test. Before the confinement, 60% did not meet physical activity recommendations ( ≥ 60/min/day of moderate to vigorous physical activity), and 61% used screens ≥ 2 h/day. During the COVID-19 confinement, children decreased their physical activity levels (-91 ± 55 min/day, p < 0.001) and increased screen time ( ± 2.6 h/day, p < 0.001). The prevalence of children that worsened physical activity and screen time during the COVID-19 confinement were 95.2% and 69.8%, respectively. The current study evidence the negative effects of the COVID-19 confinement on physical activity and screen time in Spanish children. These findings should be taken into account to develop and implement future public health strategies for preserving children's lifestyle behaviours and health during and after the COVID-19 pandemic.

Keywords: COVID-19, lifestyle changes, paediatric, physical activity, screen time

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Authors: Harshada Vaidya-Kannur, Dattatraya Naik

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Propolis, one of the beehive products also referred as bee-glue is sticky dark coloured complex mixture of compounds. The volatile oil can be isolated from the propolis by hydrodistillation. The mark that is left behind after the removal of volatile oil is referred as the de-oiled propolis. Antioxidant as well as anti-inflammatory properties of total ethanolic extract of de-oiled propolis (TEEDP) was investigated. Another lot of deoiled propolis was successively exacted with hexane, ethyl acetate and ethanol. Activities of these fractions were also determined. Antioxidant activity was determined by studying ABTS, DPPH and NO radical scavenging. Determination of anti-inflammatory activity was carried out by topical TPA induced mouse ear oedema model. It is noteworthy that ethyl acetate fraction of deoiled propolis (EAFDP) exhibited 49.45 % TEAC activity at the concentration 0.2 mg/ml which is equivalent to the activity of trolox at the concentration 0.2 mg/ml. Its DPPH scavenging activity (72.56%) was closely comparable to that of trolox (75%). However its NO scavenging activity was comparatively low. From IC50 values it could be concluded that the efficiency of scavenging ABTS radicals by the de-oiled propolis was more pronounced as compared to scavenging of other radicals. Studies by TPA induced mouse ear inflammation model indicated that the de-oiled propolis of Indian origin had significant topical anti-inflammatory activity. The EAFDP was found to be the most active fraction for this activity also. The purification of EAFP yielded six pure crystalline compounds. These compounds were identified by their physical data and spectral data.

Keywords: anti-inflammatory activity, anti-oxidant activity, column chromatography, de-oiled propolis

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Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: TDNN, neural networks, noise, speech recognition

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Authors: Avyarthana Dey, Naren P. Rao, Arpitha Jacob, Chaitra V. Hiremath, Shivarama Varambally, Ganesan Venkatasubramanian, Rose Dawn Bharath, Bangalore N. Gangadhar

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Objective: Impaired facial emotion recognition is widely reported in schizophrenia. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. However, its effect on facial emotion recognition deficits seen in schizophrenia is not well explored. In this study, we examined the effect of intranasal OXT on processing facial emotions and its neural correlates in patients with schizophrenia. Method: 12 male patients (age= 31.08±7.61 years, education= 14.50±2.20 years) participated in this single-blind, counterbalanced functional magnetic resonance imaging (fMRI) study. All participants underwent three fMRI scans; one at baseline, one each after single dose 24IU intranasal OXT and intranasal placebo. The order of administration of OXT and placebo were counterbalanced and subject was blind to the drug administered. Participants performed a facial emotion recognition task presented in a block design with six alternating blocks of faces and shapes. The faces depicted happy, angry or fearful emotions. The images were preprocessed and analyzed using SPM 12. First level contrasts comparing recognition of emotions and shapes were modelled at individual subject level. A group level analysis was performed using the contrasts generated at the first level to compare the effects of intranasal OXT and placebo. The results were thresholded at uncorrected p < 0.001 with a cluster size of 6 voxels. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. Results: Compared to placebo, intranasal OXT attenuated activity in inferior temporal, fusiform and parahippocampal gyri (BA 20), premotor cortex (BA 6), middle frontal gyrus (BA 10) and anterior cingulate gyrus (BA 24) and enhanced activity in the middle occipital gyrus (BA 18), inferior occipital gyrus (BA 19), and superior temporal gyrus (BA 22). There were no significant differences between the conditions on the accuracy scores of emotion recognition between baseline (77.3±18.38), oxytocin (82.63 ± 10.92) or Placebo (76.62 ± 22.67). Conclusion: Our results provide further evidence to the modulatory effect of oxytocin in patients with schizophrenia. Single dose oxytocin resulted in significant changes in activity of brain regions involved in emotion processing. Future studies need to examine the effectiveness of long-term treatment with OXT for emotion recognition deficits in patients with schizophrenia.

Keywords: recognition, functional connectivity, oxytocin, schizophrenia, social cognition

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Authors: Maryam Kiani

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Non-precious electrocatalysts play a crucial role in the oxygen reduction reaction (ORR) for regenerative fuel cells and rechargeable metal-air batteries. To enhance ORR activity, La (a less active element) is added to modify the activity of Ni. This addition increases the surface contents of Ni2+, N, and S species in LaNi/N-S-C, while still maintaining a substantial specific surface area and hierarchical porosity. Therefore, the additional La is essential for the successful ORR process.In addition, the presence of extra La in the LaNi/N-S-C electrocatalyst enhances the efficiency of charge transfer and improves the surface acid-base characteristics, facilitating the adsorption of oxygen molecules during the ORR process. As a result, this superior and desirable electrocatalyst exhibits significantly enhanced ORR bifunctional activity. In fact, its ORR activity is comparable to that of the 20 wt% Pt/C.

Keywords: fuel cells, batteries, dual-doped carbon black, ORR

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Authors: Khadidja Bouzid, Fouzia Benali Toumi, Mohamed Bouzouina

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We made a comparison between the total phenolic content, concentrations of flavonoids and antioxidant activity of four different extracts (butanol, ethyl acetate, chloroform, water) of Arbutus unedo L. fruit (Ericacea) of El Marsa and Terni area. The total phenolic content in the extracts was determined using the Folin-Ciocalteu reagent and it ranged between 26.57 and 48.23 gallic acid equivalents mg/g of dry weight of extract. The concentrations of flavonoids in plant extracts varied from 17.98 to 56.84 catechin equivalents mg/g. The antioxidant activity was analyzed in vitro using the DPPH reagent; among all extracts, ethyl acetate fraction from El Marsa area showed the highest antioxidant activity.

Keywords: antioxidant activity, Arbutus unedo L., fruit flavonoids, phenols, Western Algeria

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Authors: Nadia Chagtmi, Nejla Rejab, Noureddine Boulejfen

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This paper presents a calibration approach based on artificial neural networks (ANN) to determine the envelop signal (I+jQ) of a six-port based receiver (SPR). The memory effects called also dynamic behavior and the nonlinearity brought by diode based power detector have been taken into consideration by the ANN. Experimental set-up has been performed to validate the efficiency of this method. The efficiency of this approach has been confirmed by the obtained results in terms of waveforms. Moreover, the obtained error vector magnitude (EVM) and the mean absolute error (MAE) have been calculated in order to confirm and to test the ANN’s performance to achieve I/Q recovery using the output voltage detected by the power based detector. The baseband signal has been recovered using ANN with EVMs no higher than 1 % and an MAE no higher than 17, 26 for the SPR excited different type of signals such QAM (quadrature amplitude modulation) and LTE (Long Term Evolution).

Keywords: six-port based receiver; calibration, nonlinearity, memory effect, artificial neural network

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Authors: Md. Aftabuzzaman, Md. Kamruzzaman

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Road-related expenditure and building construction activity are two significant drivers of the Victorian economy. This paper investigates the relationship between building construction activity and road-related expenditure. Data for construction activities were collected from Victorian Building Authority, and road-related expenditure data were explored by the Bureau of Infrastructure and Transport Research Economics. The trend between these two sectors was compared. The analysis found a strong relationship between road-related expenditure and the volume of construction activity, i.e., the more construction activities, the greater the requirement of road-related expenditure, or vice-versa. The road-related expenditure has a two-year lag period, suggesting that the road sector requires two years to respond to the growth in the building sector.

Keywords: building construction activity, infrastructure, road expenditure, Victorian Building Authority

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Authors: Zohreh Bayat, Dariush Minai-Tehrani

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Lipases constitute one of the most important groups of industrial enzymes that catalyze the hydrolysis of triacylglycerol to glycerol and fatty acids. Muscarinic antagonist relieves smooth muscle spasm of the gastrointestinal tract and effect on the cardiovascular system. In this research, the effect of a muscarinic antagonist on the lipase activity of Pseudomonas aeruginosa was studied. Lineweaver–Burk plot showed that the drug inhibited the enzyme by competitive inhibition. The IC50 value (60 uM) and Ki (30 uM) of the drug revealed the drug bound to the enzyme with high affinity. Determination of enzyme activity in various pH and temperature showed that the maximum activity of lipase was at pH 8 and 60°C both in presence and absence of the drug.

Keywords: bacteria, inhibition, kinetics, lipase

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Authors: Wei Zhang

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With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: deep learning, field programmable gate array, FPGA, hardware accelerator, convolutional neural networks, CNN

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Sabir Messalti, Bilal Boudjellal, Azouz Said

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In this paper, power system Voltage improvement using wind turbine is presented. Two controllers are used: a PI controller and Artificial Neural Networks (ANN) controllers are studied to control of the power flow exchanged between the wind turbine and the power system in order to improve the bus voltage. The wind turbine is based on a doubly-fed induction generator (DFIG) controlled by field-oriented control. Indirect control is used to control of the reactive power flow exchanged between the DFIG and the power system. The proposed controllers are tested on power system for large voltage disturbances.

Keywords: artificial neural networks controller, DFIG, field-oriented control, PI controller, power system voltage improvement

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Authors: Cheng-Yen Lu, Chin-Yuan Hsu

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Ambient temperature reduction (ATR) can extend the lifespan of an annual fish (Nothobranchius rachovii), but the underlying mechanism is unknown. In this study, the expression, concentration, and activity of cellular-degraded molecules were evaluated in the muscle of N. rachovii reared under high (30 °C), moderate (25 °C), and low (20 °C) ambient temperatures by biochemical techniques. The results showed that (i) the activity of the 20S proteasome, the expression of microtubule-associated protein 1 light chain 3-II (LC3-II), the expression of lysosome-associated membrane protein type 2a (Lamp 2a), and lysosome activity increased with ATR; (ii) the expression of the 70 kD heat shock cognate protein (Hsc 70) decreased with ATR; (iii) the expression of the 20S proteasome, the expression of lysosome-associated membrane protein type 1 (Lamp 1), the expression of molecular target of rapamycin (mTOR), the expression of phosphorylated mTOR (p-mTOR), and the p-mTOR/mTOR ratio did not change with ATR. These findings indicated that ATR activated the activity of proteasome, macroautophagy, and chaperone-mediated autophagy. Taken together these data reveal that ATR likely activates cellular degradation activity to extend the lifespan of N. rachovii.

Keywords: ambient temperature reduction, autophagy, degradation activity, lifespan, proteasome

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

Abstract:

2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Khadija Refouh

Abstract:

Culture bound-expressions have been a bottleneck for Natural Language Processing (NLP) and comprehension, especially in the case of machine translation (MT). In the last decade, the field of machine translation has greatly advanced. Neural machine translation NMT has recently achieved considerable development in the quality of translation that outperformed previous traditional translation systems in many language pairs. Neural machine translation NMT is an Artificial Intelligence AI and deep neural networks applied to language processing. Despite this development, there remain some serious challenges that face neural machine translation NMT when translating culture bounded-expressions, especially for low resources language pairs such as Arabic-English and Arabic-French, which is not the case with well-established language pairs such as English-French. Machine translation of opaque idioms from English into French are likely to be more accurate than translating them from English into Arabic. For example, Google Translate Application translated the sentence “What a bad weather! It runs cats and dogs.” to “يا له من طقس سيء! تمطر القطط والكلاب” into the target language Arabic which is an inaccurate literal translation. The translation of the same sentence into the target language French was “Quel mauvais temps! Il pleut des cordes.” where Google Translate Application used the accurate French corresponding idioms. This paper aims to perform NMT experiments towards better translation of opaque idioms using high quality clean multilingual corpus. This Corpus will be collected analytically from human generated idiom translation. AutoML translation, a Google Neural Machine Translation Platform, is used as a custom translation model to improve the translation of opaque idioms. The automatic evaluation of the custom model will be compared to the Google NMT using Bilingual Evaluation Understudy Score BLEU. BLEU is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Human evaluation is integrated to test the reliability of the Blue Score. The researcher will examine syntactical, lexical, and semantic features using Halliday's functional theory.

Keywords: multilingual corpora, natural language processing (NLP), neural machine translation (NMT), opaque idioms

Procedia PDF Downloads 149