Search results for: feed forward network

Authors: Frankie Burgos, Emely Munar, Conrado Basa

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This paper aimed at developing an artificial neural network (ANN) model specifically for the Philippine Stock Exchange index closing value. The inputs to the ANN are US Dollar and Philippine Peso(USD-PHP) exchange rate, GDP growth of the country, quarterly inflation rate, 10-year bond yield, credit rating of the country, previous open, high, low, close values and volume of trade of the Philippine Stock Exchange Index (PSEi), gold price of the previous day, National Association of Securities Dealers Automated Quotations (NASDAQ), Standard and Poor’s 500 (S & P 500) and the iShares MSCI Philippines ETF (EPHE) previous closing value. The target is composed of the closing value of the PSEi during the 627 trading days from November 3, 2011, to May 30, 2014. MATLAB’s Neural Network toolbox was employed to create, train and simulate the network using multi-layer feed forward neural network with back-propagation algorithm. The results satisfactorily show that the neural network developed has the ability to model the PSEi, which is affected by both internal and external economic factors. It was found out that the inputs used are the main factors that influence the movement of the PSEi closing value.

Keywords: artificial neural networks, artificial intelligence, philippine stocks exchange index, stocks trading

Procedia PDF Downloads 298

Authors: R. Poonkuzhali, M. Y. Sanavullah, M. R. Gurupriya

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Opportunistic routing is used, where the network has the features like dynamic topology changes and intermittent network connectivity. In Delay Tolerant network or Disruption tolerant network opportunistic forwarding technique is widely used. The key idea of opportunistic routing is selecting forwarding nodes to forward data and coordination among these nodes to avoid duplicate transmissions. This paper gives the analysis of pros and cons of various opportunistic routing techniques used in MANET.

Keywords: ETX, opportunistic routing, PSR, throughput

Procedia PDF Downloads 495

Authors: Lea Lenhardt, Ivana Zeković, Tatjana Dramićanin, Miroslav D. Dramićanin

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Fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) and artificial neural networks (ANN) were used for characterization and classification of honey. Excitation emission spectra were obtained for 95 honey samples of different botanical origin (acacia, sunflower, linden, meadow, and fake honey) by recording emission from 270 to 640 nm with excitation in the range of 240-500 nm. Fluorescence spectra were described with a six-component PARAFAC model, and PARAFAC scores were further processed with two types of ANN’s (feed-forward network and self-organizing maps) to obtain algorithms for classification of honey on the basis of their botanical origin. Both ANN’s detected fake honey samples with 100% sensitivity and specificity.

Keywords: honey, fluorescence, PARAFAC, artificial neural networks

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Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

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Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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Authors: Robyn Augustine, Irena Petrinic, Claus Helix-Nielsen, Marshall S. Sheldon

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This study examined the performance of two commercial forward osmosis (FO) membranes; an aquaporin (AQP) based biomimetic membrane, and cellulose triacetate (CTA) membrane in a fertiliser is drawn forward osmosis (FDFO) system for the reclamation of water from synthetic winery wastewater (SWW) operated over 24 hr. Straight, 1 M KCl and 1 M NH₄NO₃ fertiliser solutions were evaluated as draw solutions in the FDFO system. The performance of the AQP-based biomimetic and CTA FO membranes were evaluated in terms of permeate water flux (Jw), reverse solute flux (Js) and percentage water recovery (Re). The average water flux and reverse solute flux when using 1 M KCl as a draw solution against controlled feed solution, deionised (DI) water, was 11.65 L/m²h and 3.98 g/m²h (AQP) and 6.24 L/m²h and 2.89 g/m²h (CTA), respectively. Using 1 M NH₄NO₃ as a draw solution yielded average water fluxes and reverse solute fluxes of 10.73 L/m²h and 1.31 g/m²h (AQP) and 5.84 L/m²h and 1.39 g/m²h (CTA), respectively. When using SWW as the feed solution and 1 M KCl and 1 M NH₄NO₃ as draw solutions, respectively, the average water fluxes observed were 8.15 and 9.66 L/m²h (AQP) and 5.02 and 5.65 L/m²h (CTA). Membrane water flux decline was the result of a combined decrease in the effective driving force of the FDFO system, reverse solute flux and organic fouling. Permeate water flux recoveries of between 84-98%, and 83-89% were observed for the AQP-based biomimetic and CTA membrane, respectively after physical cleaning by flushing was employed. The highest water recovery rate of 49% was observed for the 1 M KCl fertiliser draw solution with AQP-based biomimetic membrane and proved superior in the reclamation of water from SWW.

Keywords: aquaporin biomimetic membrane, cellulose triacetate membrane, forward osmosis, reverse solute flux, synthetic winery wastewater and water flux

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Authors: Clyde Haruzivi, Olusegun Oyebade Ikusika, Thando Conference Mpendulo

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Feed scarcity, increasing demand for animal products due to the growing human population, competition for conventional feed resources for humans and animal production, and ever-increasing prices of these feed resources are major constraints to the livestock industry in Sub-Saharan Africa. As a result, the industry is suffering immensely as the cost of production is high, hence the reduced returns. Most affected are the communal and resource-limited farmers who cannot afford the cost of conventional feed resources to supplement feeds, especially in arid and semi-arid areas where the available feed resources are not adequate for maintenance and production. This has tasked researchers and animal scientists to focus on the potential of non-conventional feed resources (NCFRs). Non-conventional feed resources could fill the gap through reduced competition, cost of feed, increased supply, increased profits, and independency as farmers will be utilizing locally available feed resources. Identifying available non-conventional feed resources is vital as it creates possibilities for novel feed industries and markets and implements methods of using these feedstuffs to improve livestock production and livelihoods in Sub-Saharan Africa. Hence, this research work analyses the progress, challenges, and prospects of some non-conventional feed resources in Sub-Saharan Africa.

Keywords: non-conventional, feed resources, livestock production, food security, Sub-Saharan

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Authors: Osung Im, Neha Yadav, Eui Hoon Lee, Joong Hoon Kim

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Artificial Neural Network (ANN) approach is commonly used in lots of fields for forecasting. In water resources engineering, forecast of water level or inflow of reservoir is useful for various kind of purposes. Due to advantages of ANN, many papers were written for inflow prediction in river networks, but in this study, ANN is used in urban sewer networks. The growth of severe rain storm in Korea has increased flood damage severely, and the precipitation distribution is getting more erratic. Therefore, effective pump operation in pump station is an essential task for the reduction in urban area. If real time inflow of pump station reservoir can be predicted, it is possible to operate pump effectively for reducing the flood damage. This study used ANN model for pump station reservoir inflow prediction using upstream sewer depth data. For this study, rainfall events, sewer depth, and inflow into Banpo pump station reservoir between years of 2013-2014 were considered. Feed – Forward Back Propagation (FFBF), Cascade – Forward Back Propagation (CFBP), Elman Back Propagation (EBP) and Nonlinear Autoregressive Exogenous (NARX) were used as ANN model for prediction. A comparison of results with ANN model suggests that ANN is a powerful tool for inflow prediction using the sewer depth data.

Keywords: artificial neural network, forecasting, reservoir inflow, sewer depth

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Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Yuan-Jye Tseng, Yi-Shiuan Chen

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In this paper, a new concept of closed-loop design model is presented. The closed-loop design model is developed by integrating forward design and reverse design. Based on this new concept, a closed-loop design model for sustainable manufacturing by integrated evaluation of forward design, reverse design, and green manufacturing using a fuzzy analytic network process is developed. In the design stage of a product, with a given product requirement and objective, there can be different ways to design the detailed components and specifications. Therefore, there can be different design cases to achieve the same product requirement and objective. Thus, in the design evaluation stage, it is required to analyze and evaluate the different design cases. The purpose of this research is to develop a model for evaluating the design cases by integrated evaluation of forward design, reverse design, and green manufacturing models. A fuzzy analytic network process model is presented for integrated evaluation of the criteria in the three models. The comparison matrices for evaluating the criteria in the three groups are established. The total relational values among the three groups represent the total relational effects. In application, a super matrix can be created and the total relational values can be used to evaluate the design cases for decision-making to select the final design case. An example product is demonstrated in this presentation. It shows that the model is useful for integrated evaluation of forward design, reverse design, and green manufacturing to achieve a closed-loop design for sustainable manufacturing objective.

Keywords: design evaluation, forward design, reverse design, closed-loop design, supply chain management, closed-loop supply chain, fuzzy analytic network process

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Authors: Redhouane Benfares, Kamel Boudjemaa, Affaf Kord, Sonia Messis, Linda Farai, Belkacem Guenachi, Kherarba Maha, Jaroslava ŠVarc-Gajić

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In recent years valorization of biowaste has attracted a lot of attention worldwide owing to its high nutritional value and low price. In this work, biowaste of animal (sardines) and plant (tomato) biowaste was used to formulate a new feed for red tilapia that showed to be competitive in its price, and zootechnical performance in comparison to commercially available tilapia feeds. Mathematical modelling was used to formulate optimal feed composition with favorable chemical composition and the lowest price. Formulated feed had high protein content (40.76%) and an energy value of 279.6 Kcal/100 g. Optimised feed was manufactured and compared to commercially available reference feed with respect to feeding intake, feed efficiency, the specific growth rate of fingerlings of Tilapia sp, and, most important, zootechnical parameters. With a fish survival rate of 100% calculated feed conversion index for the formulated feed was 2.7.

Keywords: conversion index, fish waste, formulated feed, tomato waste

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Authors: Yansong Zhang, Xueyuan Li, Junjie Zhou, Xufeng Yin, Shihua Yuan, Shuxian Liu

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Aiming at the verification of control algorithms for skid-steering vehicles, a vehicle simulation model of 6×6 electric skid-steering unmanned vehicle was established based on Trucksim and Simulink. The original transmission and steering mechanism of Trucksim are removed, and the electric skid-steering model and a closed-loop controller for the vehicle speed and yaw rate are built in Simulink. The simulation results are compared with the ones got by theoretical formulas. The results show that the predicted tire mechanics and vehicle kinematics of Trucksim-Simulink simulation model are closed to the theoretical results. Therefore, it can be used as an effective approach to study the dynamic performance and control algorithm of skid-steering vehicle. In this paper, a method of motion control based on feed forward control is also designed. The simulation results show that the feed forward control strategy can make the vehicle follow the target yaw rate more quickly and accurately, which makes the vehicle have more maneuverability.

Keywords: skid-steering, Trucksim-Simulink, feedforward control, dynamics

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Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Silvius Stanciu

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Romanian food products sold on European markets have been accused of several non-conformities of quality and safety. Most products incriminated last period were those of animal origin, especially meat and meat products. The study proposed an analysis of the notifications made by network members through Rapid Alert System for Food and Feed on products originating in Romania. As a source of information, the Rapid Alert System portal and the official communications of the National Sanitary Veterinary and Food Safety Authority were used. The research results showed that nearly a quarter of network notifications were rejected and were withdrawn by the European Authority. Although national authorities present these issues as success stories of national quality policies, the large number of notifications related to the volume of exported products is worrying. The paper is of practical and applicative importance for both the business environment and the academic environment, laying the basis for a wider research on the quality differences between Romanian and imported products.

Keywords: food, quality, RASFF, Rapid Alert System for Food and Feed, Romania

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Authors: Danijela Vuković, Radovan Čobanović, Milorad Plačkić

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The expansion of population imposes increase in usage of animal meat, on whose quality directly affects the quality of the feed that the animals are fed with. The selection of raw materials, hygiene during the technological process, various hydrothermal treatments, methods of mixing etc. have an influence on the quality of feed. Monitoring of the feed is very important to obtain information about the quality of feed and the possible prevention of animal diseases which can lead to different human diseases outbreaks. In this study parameters of feed safety were monitored. According to the mentioned, the goal of this study was to evaluate microbiological safety of feed (feedstuffs and complete mixtures). Total number of analyzed samples was 4399. Analyzed feed samples were collected in various retail shops and feed factories during the period of 44 months (from January 2013 untill September 2017). Samples were analyzed on Salmonella spp. and Clostridium perfringens in quantity of 50g according to Serbian regulation. All microorganisms were tested according to ISO methodology: Salmonella spp. ISO 6579:2002 and Clostridium perfringens ISO 7937:2004. Out of 4399 analyzed feed samples 97,5% were satisfactory and 2,5% unsatisfactory concerning Salmonella spp. As far as Clostridium perfringens is concerned 100% of analyzed samples were satisfactory. The obtained results suggest that technological processing of feed in Serbia is at high level when it comes to safety and hygiene of the products, but there are still possibilities for progress and improvement which only can be reached trough the permanent monitoring of feed.

Keywords: microbiology, safety, hygiene, feed

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Authors: Ahmad Saadiq, Neeraj Sahu

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Sediment, which comprises of solid particles of mineral and organic material are transported by water. In river systems, the amount of sediment transported is controlled by both the transport capacity of the flow and the supply of sediment. The transport of sediment in rivers is important with respect to pollution, channel navigability, reservoir ageing, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. The sediment load transported in a river is a very complex hydrological phenomenon. Hence, sediment transport has attracted the attention of engineers from various aspects, and different methods have been used for its estimation. So, several experimental equations have been submitted by experts. Though the results of these methods have considerable differences with each other and with experimental observations, because the sediment measures have some limits, these equations can be used in estimating sediment load. In this present study, two black box models namely, an SRC (Sediment Rating Curve) and ANN (Artificial Neural Network) are used in the simulation of the suspended sediment load. The study is carried out for Seonath subbasin. Seonath is the biggest tributary of Mahanadi river, and it carries a vast amount of sediment. The data is collected for Jondhra hydrological observation station from India-WRIS (Water Resources Information System) and IMD (Indian Meteorological Department). These data include the discharge, sediment concentration and rainfall for 10 years. In this study, sediment load is estimated from the input parameters (discharge, rainfall, and past sediment) in various combination of simulations. A sediment rating curve used the water discharge to estimate the sediment concentration. This estimated sediment concentration is converted to sediment load. Likewise, for the application of these data in ANN, they are normalised first and then fed in various combinations to yield the sediment load. RMSE (root mean square error) and R² (coefficient of determination) between the observed load and the estimated load are used as evaluating criteria. For an ideal model, RMSE is zero and R² is 1. However, as the models used in this study are black box models, they don’t carry the exact representation of the factors which causes sedimentation. Hence, a model which gives the lowest RMSE and highest R² is the best model in this study. The lowest values of RMSE (based on normalised data) for sediment rating curve, feed forward back propagation, cascade forward back propagation and neural network fitting are 0.043425, 0.00679781, 0.0050089 and 0.0043727 respectively. The corresponding values of R² are 0.8258, 0.9941, 0.9968 and 0.9976. This implies that a neural network fitting model is superior to the other models used in this study. However, a drawback of neural network fitting is that it produces few negative estimates, which is not at all tolerable in the field of estimation of sediment load, and hence this model can’t be crowned as the best model among others, based on this study. A cascade forward back propagation produces results much closer to a neural network model and hence this model is the best model based on the present study.

Keywords: artificial neural network, Root mean squared error, sediment, sediment rating curve

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Authors: Yuan-Lin Chen

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This paper presents an approaching forward collision probability index (AFCPI) for alerting and assisting driver in keeping safety distance to avoid the forward collision accident in highway driving. The time to collision (TTC) and time headway (TH) are used to evaluate the TTC forward collision probability index (TFCPI) and the TH forward collision probability index (HFCPI), respectively. The Mamdani fuzzy inference algorithm is presented combining TFCPI and HFCPI to calculate the approaching collision probability index of the vehicle. The AFCPI is easier to understand for the driver who did not even have any professional knowledge in vehicle professional field. At the same time, the driver’s behavior is taken into account for suiting each driver. For the approaching index, the value 0 is indicating the 0% probability of forward collision, and the values 0.5 and 1 are indicating the 50% and 100% probabilities of forward collision, respectively. The AFCPI is useful and easy-to-understand for alerting driver to avoid the forward collision accidents when driving in highway.

Keywords: approaching index, forward collision probability, time to collision, time headway

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Authors: Amit R. Bhende, G. K. Awari

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Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling.

Keywords: bearing degradation data, remaining useful life (RUL), back propagation, prognosis

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Desierin Rodrin, Magdalena Alcantara, Cristina Olo

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The increase in environmental temperatures brought about by climate change impacts negatively the growth performance of broilers that may be solved by manipulating the diet of the animals. Hence, this study was conducted to evaluate the effects of different vitamin supplements on the growth performance of broiler chickens subjected to ambient (31°C) and heat stress (34°C) temperatures. The treatments were: I- Control (no vitamin supplement), II- Vitamin A (4.5 mg/kg of feed), III- Vitamin C (250 mg/kg of feed), IV- Vitamin E (250 mg/kg of feed), V- Vitamin C and E (250 mg/kg of feed and 250 mg/kg of feed), VI- Vitamin A and E (4.5 mg/kg of feed and 250 mg/kg of feed), VII- Vitamin A and C (4.5 mg/kg of feed and 250 mg/kg of feed), and VIII- Vitamin A, C and E (4.5 mg/kg of feed, 250 mg/kg of feed and 250 mg/kg of feed). The birds (n=240) were distributed randomly into eight treatments replicated three times, with each replicates having five birds. Ambient temperature was maintained using a 25 watts bulb for every 20 birds, while heat stress condition was sustained at 34°C for about 9 hours daily by using a 50 watts bulb per 5 birds. The interaction of vitamin supplements and temperatures did not significantly (P>0.05) affected body weight, average daily gain, feed consumption and feed conversion efficiency throughout the growing period. Similarly, supplementation of different vitamins did not improve (P>0.05) the overall production performance of birds throughout the rearing period. Birds raised in heat stress (34°C) condition had significantly lower ((P<0.05) body weight, average daily gain, and feed consumption compared to birds raised in ambient temperature at weeks 3, 4 and 5 of rearing. Supplementation of vitamins A, C, and E in the diet of broilers did not alleviate the effect of heat stress in the growth performance of broilers.

Keywords: broiler growth performance, heat stress, vitamin supplementation, vitamin A, vitamin C, vitamin E

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: Pawan Kumar Sharma, J. Stephan Sampath Kumar, S. Anand, Chandana B. L.

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Protein is the major component for the success in culture of shrimp and fishes. Apparently, excess dietary protein is undesirable, as it not only enhances the production cost but also leads to water quality deterioration. A field survey was conducted with aqua farmers of Kerala, India, a leading state in coastal aquaculture, to assess the role of protein component in feed that can be efficiently and effectively managed for sustainable aquaculture. The study showed an average feed amount of 13.55 ± 2.16 tonnes per hectare was being used by the farmers of Kerala. The average feed cost percentage of Rs. 57.76 ± 13.46 /kg was invested for an average protein level of 36.26 % ± 0.082 in the feed and Rs.78.95 ± 3.086 per kilogram of feed was being paid by the farmers. Study revealed that replacement of fish meal and fish oil within shrimp aquafeeds with alternative protein, and lipid sources can only be achieved if changes are made in the basic shrimp culturing practices, such as closed farming system through water recycling or zero-water exchange, and by maximizing in-situ, floc and natural food production within the culture system. The upshot of such production systems is that imports of high-quality feed ingredients and aqua feeds can eventually be eliminated, and the utilization of locally available feed ingredients from agricultural by-products can be greatly improved and maximized. The promotion of closed shrimp production systems would also greatly reduce water use and increase shrimp production per unit area but would necessitate the continuous provision of electricity for aeration during production. Alternative energy sources such as solar power might be used, and resource poor farming communities should also explore wind energy for use. The study concluded that farm made feed and closed farming systems are essential for the sustainability and profitability of the aquaculture industry.

Keywords: aqua feeds, floc, fish meal, protein, zero-water exchange

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Authors: Zainal A. Muchlisin, Muhammad Iqbal, Abdullah A. Muhammadar

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The objective of the present study was to determine the optimum dose of papain enzyme in the diet for growing, survival rate and feed efficacy of climbing perch (Anabas testudineus). The study was conducted at the Laboratory of Aquatic of Faculty of Veterinary, Syiah Kuala University from January to March 2016. The completely randomized design was used in this study. Six dosages level of papain enzyme were tested with 4 replications i.e. 0 g kg-1 of feed, 20.0 g kg-1 feed, 22.5 g kg-1 of feed, 25.0 g kg-1 of feed, 27.5 g kg-1 of feed, and 30.0 g kg-1 of feed. The experimental fish fed twice a day at feeding level of 5% for 60 days. The results showed that weight gain ranged from 2.41g to 7.37g, total length gain ranged from 0.67cm to 3.17cm, specific growth rate ranged from 1.46 % day to 3.41% day, daily growth rate ranged from 0.04 g day to 0.13 g day, feed conversion ratio ranged from 1.94 to 3.59, feed efficiency ranged from 27.99% to 51.37%, protein retention ranged from 3.38% to 28.28%, protein digestibility ranged from 50.63% to 90.38%, and survival rate ranged from 88.89% to 100%. The highest rate for all parameters was found in the dosage of 3.00% papain enzyme kg feed. The ANOVA test showed that enzyme papain gave a significant effect on the weight gain, total length gain, daily growth rate, specific growth rate, feed conversion ratio, feed efficiency, protein retention, protein digestibility, and survival rate of the climbing perch (Anabas testudieus). The best enzyme papain dosage was 3.0%.

Keywords: betok, feed conversion ratio, freshwater fish, nutrition, feeding

Procedia PDF Downloads 237

Authors: Waode Nurmayani, Nikmatul Riswanda

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Broilers are chickens which are kept with the most efficient time and hoped get a good body weight. All things are done, for example with the improvement of feed and use antibiotics. Feed cost is the most cost to be spent. Nearly 80% of the cost is spent just for buy feed. Earthworm (Lumbricus rubellus) is a good choice to reduce the cost of feed protein source. The Earthworm has a high crude protein content of about 48.5%-61.9%, rich with proline amino acid about 15% of the 62 amino acids. Not only about protein, this earthworm also has a role in disease prevention. Prevention of disease in livestock usual with use feed supplement. Earthworm (Lumbricus rubellus) is one of the natural materials used as feed. In addition, several types of earthworms that have been known to contain active substances about antibacterial pathogens namely Lumbricus rubellus. The earthworm could be used as an antibiotic because it contain the antibody of Lumbricine active substance. So that, this animal feed from Lumbricus rubellus could improve the performance of broilers. Bioactive of anti-bacterial is called Lumbricine able to inhibit the growth of pathogenic bacteria in the intestinal wall so that the population of pathogenic bacteria is reduced. The method of write in this scientific writing is divided into 3 techniques, namely data completion, data analysis, and thinking pan from various literature about earthworm (Lumbricus rubellus) as broiler feed. It is expected that innovation of feed material of earthworm (Lumbricus rubellus) could reduce the cost of protein feed and the use of chemical antibiotics.

Keywords: earthworm, broiler, protein, antibiotic

Procedia PDF Downloads 158

Authors: Gülşah Kanbur, Veysel Ayhan

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Feed oil samples which are used as mixed feed raw material were taken from six different feed factories in March, May and July. All factories make production in Konya, Turkey and all of the samples were which taken are crude soybean oil. Some physical and chemical analysis, free radical scavenger effect and total phenol content were determined on these oil samples. Moisture content was found between 0.10-22.23 %, saponification number was determined 143.13 to 167.93 KOH/kg, free fatty acidity was varied 0.73 to 35.00 % , peroxide value was found between 1.53 and 28.43 meq/kg , unsaponifiable matter was determined from 0.40 to 17.10 % , viscosity was found between 34.30 and 625.67 mPas, sediment amount was determined between 0.60-18.16 % , free radical scavenger effect was varied 20.7 to 43.04 % inhibition of the extract and total phenol content was found between 1.20 and 2.69 mg/L extract. Different results were found between months and factories.

Keywords: crude soybean oil, feed oils, mixed feed, Konya

Procedia PDF Downloads 313

Authors: Bhavini Pandya

Abstract:

This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

Procedia PDF Downloads 189

Authors: John Michael Russo

Abstract:

Honeybees are important to our food system and continue to suffer from high rates of colony loss. Precision feed has brought many benefits to livestock cultivation and these should transfer to apiculture. However, apiculture has unique challenges. The objective of this research is to understand how principles of precision agriculture, applied to apiculture and feed specifically, might effectively improve state-of-the-art cultivation. The methodology surveys apicultural practice to build a model for assessment. First, a review of apicultural motivators is made. Feed method is then evaluated. Finally, precision feed methods are examined as accelerants with potential to advance the effectiveness of feed practice. Six important motivators emerge: colony loss, disease, climate change, site variance, operational costs, and competition. Feed practice itself is used to compensate for environmental variables. The research finds that the current state-of-the-art in apiculture feed focuses on critical challenges in the management of feed schedules which satisfy requirements of the bees, preserve potency, optimize environmental variables, and manage costs. Many of the challenges are most acute when feed is used to dispense medication. Technology such as RNA treatments have even more rigorous demands. Precision feed solutions focus on strategies which accommodate specific needs of individual livestock. A major component is data; they integrate precise data with methods that respond to individual needs. There is enormous opportunity for precision feed to improve apiculture through the integration of precision data with policies to translate data into optimized action in the apiary, particularly through automation.

Keywords: precision agriculture, precision feed, apiculture, honeybees

Procedia PDF Downloads 79

Authors: M. Soltani, K. Pakzad, A. Haghigh-Khiyabani, M. Alavi, R. Naderi, M. Castex

Abstract:

Effect of probiotic Bactocell (Pediococcus acidilactici) as a supplementary diet was studied on post-larvae 12-15 of white leg shrimp (Litopenaeus vannamei) (150000 PL/0.5 h pond, average body weight=0.02 g) growth performance under farm condition for 102 days at water quality parameters consisting of temperature at 30.5-36οC, dissolved oxygen 4.1-6.6 mg/l, salinity 40-54 g/l, turbidity 35-110 cm, ammonia 0.1-0.8 mg/l and nitrite 0.1-0.9 mg/l. Also, the resistance level of the treated shrimps was assessed against a virulent strain of Vibrio paraheamolyticus as intramuscular injection route at 1.4 x 106 cells/shrimp. Significantly higher growth rate (11.3±1.54 g) and lower feed conversion ratio (1.1) were obtained in shrimps fed diets supplemented with Bactocell at 350 g/ tone feed compared to other treatments of 250 g Bactocell/ton feed (10.8±2 g, 1.3), 500 g Bactocell/ton feed (10.3±1.7 g, 1.3) and untreated control (10.1±2 g, 1.4). Also, thermal growth coefficient (0.057%) and protein efficiency ratio (2.13) were significantly improved in shrimps fed diets supplemented with Bactocell at 350 g/ton feed compare to other groups. Shrimps fed diet supplemented with Bactocell at 350 g/tone feed showed significantly higher protein content (72.56%) in their carcass composition than treatments of 250 g/ton feed (65.9%), 500 g/ton feed (67.5%) and control group (65.9%), while the carcass contents of moisture, lipid and ash in all shrimp groups were not significantly affected by different concentrations of Bactocell. No mortality occurred in the experimentally infected shrimps fed with Bactocell at 500 g/tone feed after 7 hours post-challenge with V. parahemolyticus. The mortality levels of 100%, 40%, 50% and 70% were obtained in shrimps fed with 0.0, 500 g/tone feed, 350 g/ton feed and 250 g/ton feed, respectively 14 hours post-infection. Also, the cumulative mortalities were achieved in 100%, 92% and 81% in shrimps few with Bactocell at 500 g/ton feed, 250 g/ton feed and 350 g/ton feed, respectively.

Keywords: litopenaeus vannamei, vibrio paraheamolyticus, pediococcus acidilactici, growth performance, bactocell

Procedia PDF Downloads 678