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This paper focuses on training the mathematical models for prediction of optimal livestock feed blend for animal. In this paper, three functions are trained to find the most optimal percentage of price, nutrients and water. For this purpose three objective functions has been prepared using corresponding values of feed ingredients and an algorithm is developed using ANN. The objective functions are trained with 5000 random values to find the optimized results and prediction ability is compared with existing model. Developed algorithm shows superiority over the existing programming models. [ABSTRACT FROM AUTHOR]

Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural network to further improve the accuracy of the approach while minimizing the cost of crowdsourcing. The paper also discusses the data pre-processing steps used for training the convolutional neural network. Finally it describes the airplane sensor dataset which is used for demonstration of this approach and then shows the experimental results achieved using convolutional neural network. [ABSTRACT FROM AUTHOR]

Abstract This paper provides a starting point for the development of metacognition in a common model of cognition. It identifies significant theoretical work on metacognition from multiple disciplines that the authors believe worthy of consideration. After first defining cognition and metacognition, we outline three general categories of metacognition, provide an initial list of its main components, consider the more difficult problem of consciousness, and present examples of prominent artificial systems that have implemented metacognitive components. Finally, we identify pressing design issues for the future. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel divide-and-conquer model for CPI predictionwith the existing compilation method of the Consumer Price Index (CPI) in China. Historical national CPI time series is preliminary divided into eight sub-indexes includingfood, articles for smoking and drinking, clothing, household facilities, articles and maintenance services, health care and personal articles, transportation and communication, recreation, education and culture articles and services, and residence. Three models including back propagation neural network (BPNN) model, grey forecasting model (GM (1, 1)) and autoregressive integrated moving average (ARIMA) model are established to predict each sub-index, respectively.Then the best predicting result among the three models’ for each sub-index is identified. To further improve the performance, special modification in predicting method is done to sub-CPIs whose forecasting results are not satisfying enough. After improvement and error adjustment, we get the advanced predicting results of the sub-CPIs.Eventually, the best predicting results of each sub-index are integrated to form the forecasting results of the national CPI. Empirical analysis demonstrates that the accuracy and stability of the introduced method in this paper is better than many commonly adopted forecasting methods, which indicates the proposed method is an effective and alternative one for national CPI prediction in China. [Copyright &y& Elsevier]

Since the air monitoring stations do not provide the relation between other toxic gas and meteorological parameters with the particulate matter up to 10 micrometer or PM-10. The influence of meteorological as well as correlation with other toxic gas is investigated and used them to forecast PM-10 in the case of Chiang Mai province of Thailand. In this paper an attempt to develop hybrid models of an Autoregressive Integrated Moving Average (ARIMA) model with other exogenous variables (ARIMAX) and Neural Networks (NNs), the two hybrid models, i.e. hybrid ARIMAX-NNs model and hybrid NNs-ARIMAX model were implemented to forecast PM-10 for highly season during January-April of Chiang Mai Province. Simulation results of hybrid model are compared with the results of ARIMA, ARIMAX and NNs model. The experimental results demonstrated that the hybrid NNs-ARIMAX model outperformed best over the hybrid ARIMAX-NNs model, ARIMAX model, NNs model, and ARIMA model respectively. In this case study and maybe other cases, it has proved that the NNs model should be priori captured and filtered the non-stationary non-linear component while the fully linearly stationary residuals were accurately predicted by ARIMAX model later. [ABSTRACT FROM AUTHOR]

Even though the number and variety of fuel consumption models projected in the literature are common, studies on their validation using real-life data is not only limited but also does not fit well with the real-time data. In this paper, three statistical models namely Support Vector Machine (SVM), Artificial Neural Network and Multiple Linear Regression are used in term of prediction of total and instant fuel consumption. The models are compared against data collected in real-time from three different passenger vehicles on three routes by causal drive, using a mobile phone application. Our outcomes reveal that, the results obtained by the models vary depending on the total consumption and instant consumption correlation. Support Vector Machine model of fuel consumption expose comparatively better correlation than the other statistical fuel consumption models. [ABSTRACT FROM AUTHOR]

Supply Chain consists of various components like supplier, manufacturer, factories, warehouses, distribution agents, customers, etc. Supply Chain Management encompasses all the activities from moving goods from sourcing to consumption. Sales forecasting is a part of downstream activity of supply chain and is the process of predicting future sales of the product. It helps in making informed business decisions. In this paper a study of various sales forecasting algorithms is done and results of sales of oral-care products are calculated using Back-Propagation Neural Network Model. The error rate for different products is also calculated. [ABSTRACT FROM AUTHOR]

The electrical short term load forecasting has been emerged as one of the most essential field of research for efficient and reliable operation of power system in last few decades. It plays very significant role in the field of scheduling, contingency analysis, load flow analysis, planning and maintenance of power system. This paper addresses a review on recently published research work on different variants of artificial neural network in the field of short term load forecasting. In particular, the hybrid networks which is a combination of neural network with stochastic learning techniques such as genetic algorithm(GA), particle swarm optimization (PSO) etc. which has been successfully applied for short term load forecasting (STLF) is discussed thoroughly. [ABSTRACT FROM AUTHOR]

Echo state networks (ESN) or reservoirs, are random, recurrent neural network topologies that integrate temporal data over short time windows by operating on the edge of chaos. Recently, there is a significant effort on the mathematical modeling and software topologies of the reservoirs. However, hardware reservoir fabrics are essential to deploy in energy constrained environments. In this paper, we investigate a hardware reservoir with bi-stable memristive synapses. In particular, we demonstrate a scalable hardware model for detecting real-time epileptic seizures in human models. The performance of the proposed reservoir hardware is evaluated for absent seizure signals with 85% accuracy. [ABSTRACT FROM AUTHOR]

This paper discusses the development and evaluation of distribution models for predicting alluvial mineral potential mapping. A number of existing models includes Weight of Evidence, Knowledge-driven Fuzzy, Data-driven Fuzzy, Neural-Network, Bayesian Classifier and Geostatistical Kriging. We offer classification models developed in our laboratory, where point pattern analysis was used to identify presence or absence of a known secondary alluvial (cassiterite) deposits in the Nigerian Younger Granite Region (NYGR) and the model performance assessed. We focused on the training and testing data split using longitudinal spatial data splitting (strips and halves) to ensure predictive attribute's independence. The spatial data split runs counter to the traditional random sample data selection as a procedure for checking overfitting of models mainly due to spatial data autocorrelation. Specifically, we used classification algorithms such as; Naive Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree Bagging and Discriminant Analysis algorithms for training and testing. We analysed the model's performance results using model predictive accuracy and ROC curve values in two different approaches that improve spatial data independence among predictive attributes to give a meaningful model performance. [ABSTRACT FROM AUTHOR]

Abstract: One of the important issues in the design of fuzzy classifiers is the formation of fuzzy if-then rules and the membership functions. This paper presents a hybrid Particle Swarm Optimization based approach for fuzzy classifier design which incorporates the concept of mutation from evolutionary computations. The proposed MutPSO develops the fuzzy classifier system by encoding and evolving both the membership functions and rule set as particles simultaneously. Non-uniform mutation is applied to the membership functions which are represented as real numbers. Uniform mutation is applied to the rule set which is represented as discrete numbers. In the classification problem under consideration, the objective is to maximize the correctly classified data and minimize the number of rules. This objective is formulated as a fitness function to guide the search procedure to select an appropriate fuzzy classification system so that the number of fuzzy rules and the number of incorrectly classified patterns are simultaneously minimized. The performance of the proposed MutPSO approach is demonstrated through development of fuzzy classifiers for iris data available in UCI machine learning repository. Simulation results show the suitability of the proposed approach for developing the fuzzy system. [ABSTRACT FROM AUTHOR]