Search results for: gamma neural oscillation

Authors: Hwayeon Song

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The long-term purpose of research and development (R&D) programs is to strengthen national competitiveness by developing new knowledge and technologies. Thus, it is important to determine a proper budget for government programs to maintain the vigor of R&D when the total funding is tight due to the national deficit. In this regard, a ripple effect analysis for the budgetary changes in R&D programs is necessary as well as an investigation of the current status. This study proposes a new approach using Artificial Neural Networks (ANN) for both tasks. It particularly focuses on R&D programs related to Construction and Transportation (C&T) technology in Korea. First, key factors in C&T technology are explored to draw impact indicators in three areas: economy, society, and science and technology (S&T). Simultaneously, ANN is employed to evaluate the relationship between data variables. From this process, four major components in R&D including research personnel, expenses, management, and equipment are assessed. Then the ripple effect analysis is performed to see the changes in the hypothetical future by modifying current data. Any research findings can offer an alternative strategy about R&D programs as well as a new analysis tool.

Keywords: Artificial Neural Networks, construction and transportation technology, Government Research and Development, Ripple Effect

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Authors: Hamd Rezaeifar, Hamid Reza Sahriari

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Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data.

Keywords: accident, data mining, neural network, GIS

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Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

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Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

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Authors: Oktay Nusratov, Ramin Rzaev, Aydin Goyushov

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Fuzzy inference method based approach to the forming of modular intellectual system of assessment the quality of communication services is proposed. Developed under this approach the basic fuzzy estimation model takes into account the recommendations of the International Telecommunication Union in respect of the operation of packet switching networks based on IP-protocol. To implement the main features and functions of the fuzzy control system of quality telecommunication services it is used multilayer feedforward neural network.

Keywords: quality of communication, IP-telephony, fuzzy set, fuzzy implication, neural network

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Authors: M. Boudjerda, B. Touaibia, M. K. Mihoubi

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In Algeria agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. Economic development in the last decade has weighed heavily on water resources which are relatively limited and gradually decreasing to the detriment of agriculture. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Foum El-Gherza dam’s reservoir system in south of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 12.32% to 55%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.

Keywords: water management, agricultural demand, dam and reservoir operation, Foum el-Gherza dam, dynamic programming, artificial neural network

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Tiago do Carmo Nogueira, Cássio Dener Noronha Vinhal, Gélson da Cruz Júnior, Matheus Rudolfo Diedrich Ullmann

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Generating automatic image descriptions through natural language is a challenging task. Image captioning is a task that consistently describes an image by combining computer vision and natural language processing techniques. To accomplish this task, cutting-edge models use encoder-decoder structures. Thus, Convolutional Neural Networks (CNN) are used to extract the characteristics of the images, and Recurrent Neural Networks (RNN) generate the descriptive sentences of the images. However, cutting-edge approaches still suffer from problems of generating incorrect captions and accumulating errors in the decoders. To solve this problem, we propose a model based on the encoder-decoder structure, introducing a module that generates the weights according to the importance of the word to form the sentence, using the part-of-speech (PoS). Thus, the results demonstrate that our model surpasses state-of-the-art models.

Keywords: gated recurrent units, caption generation, convolutional neural network, part-of-speech

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Authors: Nelvin Joseph, K. Krishna Milan Rao, Praveen Dwarakanath

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The explosive growth of Smart phones around the world has led to the shift of the primary engagement tool for entertainment from traditional consoles and music players to an all integrated device. Augmented Reality is the next big shift in bringing in a new dimension to the play. The paper explores the construct and working of the community engine in Delta T – an Augmented Reality game that allows users to evolve rules in the game basis collective bargaining mirroring democracy even in a gaming world.

Keywords: augmented reality, artificial neural networks, mobile application, human computer interaction, community engine

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Authors: Alexander Chinyere Nwankpa, Nneka Ngozi Nwankpa

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In Nigeria, the Federal government is trying to make sure that everyone has access to enough food that is nutritiously adequate and safe. But in the southwest of Nigeria, notably in Ondo State, the most valuable minerals such as oil and gas, bitumen, kaolin, limestone talc, columbite, tin, gold, coal, and phosphate are abundant. Therefore, some regions of Ondo State are now linked to large quantities of natural radioactivity as a result of the mineral presence. In this work, the baseline radioactivity levels in some of the most important food crops in Ondo State were analyzed, allowing for the prediction of probable radiological health impacts. To this effect, maize (Zea mays), yam (Dioscorea alata) and cassava (Manihot esculenta) tubers were collected from the farmlands in the State because they make up the majority of food's nutritional needs. Ondo State was divided into eight zones in order to provide comprehensive coverage of the research region. At room temperature, the maize (Zea mays), yam (Dioscorea alata), and cassava (Manihot esculenta) samples were dried until they reached a consistent weight. They were pulverized, homogenized, and 250 g packed in a 1-liter Marinelli beaker and kept for 28 days to achieve secular equilibrium. The activity concentrations of Radium-226 (Ra-226), Thorium-232 (Th-232), and Potassium-40 (K-40) were determined in the food samples using Gamma-ray spectrometry. Firstly, the Hyper Pure Germanium detector was calibrated using standard radioactive sources. The gamma counting, which lasted for 36000s for each sample, was carried out in the Centre for Energy Research and Development, Obafemi Awolowo University, Ile-Ife, Nigeria. The mean activity concentration of Ra-226, Th-232 and K-40 for yam were 1.91 ± 0.10 Bq/kg, 2.34 ± 0.21 Bq/kg and 48.84 ± 3.14 Bq/kg, respectively. The content of the radionuclides in maize gave a mean value of 2.83 ± 0.21 Bq/kg for Ra-226, 2.19 ± 0.07 Bq/kg for Th-232 and 41.11 ± 2.16 Bq/kg for K-40. The mean activity concentrations in cassava were 2.52 ± 0.31 Bq/kg for Ra-226, 1.94 ± 0.21 Bq/kg for Th-232 and 45.12 ± 3.31 Bq/kg for K-40. The average committed effective doses in zones 6-8 were 0.55 µSv/y for the consumption of yam, 0.39 µSv/y for maize, and 0.49 µSv/y for cassava. These values are higher than the annual dose guideline of 0.35 µSv/y for the general public. Therefore, the values obtained in this work show that there is radiological contamination of some foodstuffs consumed in some parts of Ondo State. However, we recommend that systematic and appropriate methods also need to be established for the measurement of gamma-emitting radionuclides since these constitute important contributors to the internal exposure of man through ingestion, inhalation, or wound on the body.

Keywords: contamination, environment, radioactivity, radionuclides

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Sun Zhe, Ruggero Micheletto

Abstract:

The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

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Authors: Jihan Abdulazeez Ahmed, Adnan Mohsin Abdulazeez Brifcani

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This paper suggests a new internal architecture of holon based on feature selection model using the combination of Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is used to generate features while ANN is used as a classifier to evaluate the produced features. Proposed system is applied on the Wine data set, the statistical result proves that the proposed system is effective and has the ability to choose informative features with high accuracy.

Keywords: artificial neural network, bees algorithm, feature selection, Holon

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Authors: Aghbalou Nihad, Charki Abderafi, Saida Rahali, Reklaoui Kamal

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Maximize the benefit from the wind energy potential is the most interest of the wind power stakeholders. As a result, the wind tower size is radically increasing. Nevertheless, choosing an appropriate wind turbine for a selected site require an accurate estimate of vertical wind profile. It is also imperative from cost and maintenance strategy point of view. Then, installing tall towers or even more expensive devices such as LIDAR or SODAR raises the costs of a wind power project. Various models were developed coming within this framework. However, they suffer from complexity, generalization and lacks accuracy. In this work, we aim to investigate the ability of neural network trained using the Bayesian Regularization technique to estimate wind speed profile up to height of 100 m based on knowledge of wind speed lower heights. Results show that the proposed approach can achieve satisfactory predictions and proof the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights.

Keywords: bayesian regularization, neural network, wind shear, accuracy

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Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

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Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

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Authors: Amin Sedighfar, M. R. Moniri

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Determination of state of charge (SOC) in today’s world becomes an increasingly important issue in all the applications that include a battery. In fact, estimation of the SOC is a fundamental need for the battery, which is the most important energy storage in Hybrid Electric Vehicles (HEVs), smart grid systems, drones, UPS and so on. Regarding those applications, the SOC estimation algorithm is expected to be precise and easy to implement. This paper presents an online method for the estimation of the SOC of Valve-Regulated Lead Acid (VRLA) batteries. The proposed method uses the well-known Kalman Filter (KF), and Neural Networks (NNs) and all of the simulations have been done with MATLAB software. The NN is trained offline using the data collected from the battery discharging process. A generic cell model is used, and the underlying dynamic behavior of the model has used two capacitors (bulk and surface) and three resistors (terminal, surface, and end), where the SOC determined from the voltage represents the bulk capacitor. The aim of this work is to compare the performance of conventional integration-based SOC estimation methods with a mixed algorithm. Moreover, by containing the effect of temperature, the final result becomes more accurate. 

Keywords: Kalman filter, neural networks, state-of-charge, VRLA battery

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Authors: Amir Alani, Mojtaba Mahmoodian, Anna Romanova, Asaad Faramarzi

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Pipelines are extensively used engineering structures which convey fluid from one place to another. Most of the time, pipelines are placed underground and are encumbered by soil weight and traffic loads. Corrosion of pipe material is the most common form of pipeline deterioration and should be considered in both the strength and serviceability analysis of pipes. The study in this research focuses on concrete pipes in sewage systems (concrete sewers). This research firstly investigates how to involve the effect of corrosion as a time dependent process of deterioration in the structural and failure analysis of this type of pipe. Then three probabilistic time dependent reliability analysis methods including the first passage probability theory, the gamma distributed degradation model and the Monte Carlo simulation technique are discussed and developed. Sensitivity analysis indexes which can be used to identify the most important parameters that affect pipe failure are also discussed. The reliability analysis methods developed in this paper contribute as rational tools for decision makers with regard to the strengthening and rehabilitation of existing pipelines. The results can be used to obtain a cost-effective strategy for the management of the sewer system.

Keywords: reliability analysis, service life prediction, Monte Carlo simulation method, first passage probability theory, gamma distributed degradation model

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Authors: V. R. Ahire, A. Kumar, B. N. Pandey, K. P. Mishra, G. R. Kulkarni

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Radiation therapy is an effective vital strategy used globally in the treatment of cervical cancer. However, radiation efficacy principally depends on the radiosensitivity of the tumor, and not all patient exhibit significant response to irradiation. A radiosensitive tumor is easier to cure than a radioresistant tumor which later advances to local recurrence and metastasis. Herbal polyphenols are gaining attention for exhibiting radiosensitization through various signaling. Current work focuses to study the radiosensitization effect of ellagic acid (EA), on HeLa cells. EA intermediated radiosensitization of HeLa cells was due to the induction γ-H2AX foci formation, G1 phase cell cycle arrest, and loss of reproductive potential, growth inhibition, drop in the mitochondrial membrane potential and protein expression studies that eventually induced apoptosis. Irradiation of HeLa in presence of EA (10 μM) to doses of 2 and 4 Gy γ-radiation produced marked tumor cytotoxicity. EA also demonstrated radio-protective effect on normal cell, NIH3T3 and aided recovery from the radiation damage. Our results advocate EA to be an effective adjuvant for improving cancer radiotherapy as it displays striking tumor cytotoxicity and reduced normal cell damage instigated by irradiation.

Keywords: apoptotic radiosensitivity, ellagic acid, mitochondrial potential, cell-cycle arrest

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Authors: Neil Erick Q. Madariaga, Noel B. Linsangan

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Body Mass Index (BMI) is one of the different ways to monitor the health of a person. It is based on the height and weight of the person. This study aims to compute for the BMI using an Android tablet by obtaining the height of the person by using a camera and measuring the weight of the person by using a weighing scale or load cell. The height of the person was estimated by applying background subtraction to the image captured and applying different processes such as getting the vanishing point and applying Artificial Neural Network. The weight was measured by using Wheatstone bridge load cell configuration and sending the value to the computer by using Gizduino microcontroller and Bluetooth technology after the amplification using AD620 instrumentation amplifier. The application will process the images and read the measured values and show the BMI of the person. The study met all the objectives needed and further studies will be needed to improve the design project.

Keywords: body mass index, artificial neural network, vanishing point, bluetooth, wheatstone bridge load cell

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

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Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Irving Barragán-Vite

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Wind speed forecasting is an important issue for planning wind power generation facilities. The accuracy in the wind speed prediction allows a good performance of wind turbines for electricity generation. A model based on artificial neural networks is presented in this work. A dataset with atmospheric information about air temperature, atmospheric pressure, wind direction, and wind speed in Pachuca, Hidalgo, México, was used to train the artificial neural network. The data was downloaded from the web page of the National Meteorological Service of the Mexican government. The records were gathered for three months, with time intervals of ten minutes. This dataset was used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The model with the best performance contains three hidden layers and 9, 6, and 5 neurons, respectively; and the coefficient of determination obtained was r²=0.9414, and the Root Mean Squared Error is 1.0559. In summary, the ANN approach is suitable to predict the wind speed in Pachuca City because the r² value denotes a good fitting of gathered records, and the obtained ANN model can be used in the planning of wind power generation grids.

Keywords: wind power generation, artificial neural networks, wind speed, coefficient of determination

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Authors: Yudhajit Datta

Abstract:

Will is a phenomenon that has puzzled humanity for a long time. It is a belief that Will Power of an individual affects the success achieved by an individual in life. It is thought that a person endowed with great will power can overcome even the most crippling setbacks of life while a person with a weak will cannot make the most of life even the greatest assets. Behavioral aspects of the human experience such as will are rarely subjected to quantitative study owing to the numerous uncontrollable parameters involved. This work is an attempt to subject the phenomena of will to the test of an artificial neural network. The claim being tested is that will power of an individual largely determines success achieved in life. In the study, an attempt is made to incorporate the behavioral phenomenon of will into a computational model using data pertaining to the success of individuals obtained from an experiment. A neural network is to be trained using data based upon part of the model, and subsequently used to make predictions regarding will corresponding to data points of success. If the prediction is in agreement with the model values, the model is to be retained as a candidate. Ultimately, the best-fit model from among the many different candidates is to be selected, and used for studying the correlation between success and will.

Keywords: will power, will, success, apathy factor, random factor, characteristic function, life story

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Authors: Amit Verma, Simranjeet Kaur, Sandeep Kaur

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Test Case Prioritization (TCP) has gained wide spread acceptance as it often results in good quality software free from defects. Due to the increase in rate of faults in software traditional techniques for prioritization results in increased cost and time. Main challenge in TCP is difficulty in manually validate the priorities of different test cases due to large size of test suites and no more emphasis are made to make the TCP process automate. The objective of this paper is to detect the priorities of different test cases using an artificial neural network which helps to predict the correct priorities with the help of back propagation algorithm. In our proposed work one such method is implemented in which priorities are assigned to different test cases based on their frequency. After assigning the priorities ANN predicts whether correct priority is assigned to every test case or not otherwise it generates the interrupt when wrong priority is assigned. In order to classify the different priority test cases classifiers are used. Proposed algorithm is very effective as it reduces the complexity with robust efficiency and makes the process automated to prioritize the test cases.

Keywords: test case prioritization, classification, artificial neural networks, TF-IDF

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Authors: Ahmed M. Ashteyat

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Artificial Neural Network (ANN) models have been widely used in material modeling, inter-correlations, as well as behavior and trend predictions when the nonlinear relationship between system parameters cannot be quantified explicitly and mathematically. In this paper, ANN was used to predict the residual modulus of elasticity (RME) of self compacted concrete (SCC) damaged by heat. The ANN model was built, trained, tested and validated using a total of 112 experimental data sets, gathered from available literature. The data used in model development included temperature, relative humidity conditions, mix proportions, filler types, and fiber type. The result of ANN training, testing, and validation indicated that the RME of SCC, exposed to different temperature and relative humidity levels, could be predicted accurately with ANN techniques. The reliability between the predicated outputs and the actual experimental data was 99%. This show that ANN has strong potential as a feasible tool for predicting residual elastic modulus of SCC damaged by heat within the range of input parameter. The ANN model could be used to estimate the RME of SCC, as a rapid inexpensive substitute for the much more complicated and time consuming direct measurement of the RME of SCC.

Keywords: residual modulus of elasticity, artificial neural networks, self compacted-concrete, material modeling

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

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Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Yalçın Bozkurt

Abstract:

Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

Procedia PDF Downloads 348

Authors: Leila Keshavarz Afshar, Hedieh Sajedi

Abstract:

Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: Redhouane Ghernaout, Amira Fredj, Boualem Remini

Abstract:

Suspended sediment transport is a serious problem worldwide, but it is much more worrying in certain regions of the world, as is the case in the Maghreb and more particularly in Algeria. It continues to take disturbing proportions in Northern Algeria due to the variability of rains in time and in space and constant deterioration of vegetation. Its prediction is essential in order to identify its intensity and define the necessary actions for its reduction. The purpose of this study is to analyze the concentration data of suspended sediment measured at Wadi El-Abtal Hydrometric Station. It also aims to find and highlight regressive power relationships, which can explain the suspended solid flow by the measured liquid flow. The study strives to find models of artificial neural networks linking the flow, month and precipitation parameters with solid flow. The obtained results show that the power function of the solid transport rating curve and the models of artificial neural networks are appropriate methods for analysing and estimating suspended sediment transport in Wadi Mina at Wadi El-Abtal Hydrometric Station. They made it possible to identify in a fairly conclusive manner the model of neural networks with four input parameters: the liquid flow Q, the month and the daily precipitation measured at the representative stations (Frenda 013002 and Ain El-Hadid 013004 ) of the watershed. The model thus obtained makes it possible to estimate the daily solid flows (interpolate and extrapolate) even beyond the period of observation of solid flows (1985/86 to 1999/00), given the availability of the average daily liquid flows and daily precipitation since 1953/1954.

Keywords: suspended sediment, concentration, regression, liquid flow, solid flow, artificial neural network, modeling, mina, algeria

Procedia PDF Downloads 77