Forecasting stock price index is one of the major challenges in the trade market for investors. Time series data for prediction are difficult to manipulate, but can be focused as segments to discover interesting patterns. In this paper we use several functional link artificial neural networks to get such patterns for predicting stock indices. The novel architecture of functional link artificial neural network with working principle of different models are provided to achieve best forecasting and classification with increase in accuracy of prediction and decrease in training time. Various FLANN models with different polynomials are investigated using different Indian stock indices like IBM, BSE, Oracle, & RIL. The main absolute percentage error (MAPE), sum squared error (SSE) and the standard deviation error (SDE) have been considered to measure the performance of the different FLANN models. In this paper we have presented the result using Reliance Industries Limited (RIL) stock data between 22/12/1999 to 30/12/2011 on closed price of every trading day. Keywords-Artificial Neural Network (ANN); Functional Link ANN (FLANN); Power Functional Link ANN (PFLANN); Laguerre Functional Link ANN (LFLANN); Legendre Functional Link ANN (LeFLANN); Chebyshev Functional Link ANN (CFLANN); Absolute Percentage Error (MAPE); Sum of Squared Error (SSE); Standard Deviation of Error (SDE)I.978-1-4673-2272-0/12/$31.00

Hate speech on social media may spread quickly through online users and subsequently, may even escalate into local vile violence and heinous crimes. This paper proposes a hate speech detection model by means of machine learning and text mining feature extraction techniques. In this study, the authors collected the hate speech of English-Odia code mixed data from a Facebook public page and manually organized them into three classes. In order to build binary and ternary datasets, the data are further converted into binary classes. The modeling of hate speech employs the combination of a machine learning algorithm and features extraction. Support vector machine (SVM), naïve Bayes (NB) and random forest (RF) models were trained using the whole dataset, with the extracted feature based on word unigram, bigram, trigram, combined n-grams, term frequency-inverse document frequency (TF-IDF), combined n-grams weighted by TF-IDF and word2vec for both the datasets. Using the two datasets, we developed two kinds of models with each feature—binary models and ternary models. The models based on SVM with word2vec achieved better performance than the NB and RF models for both the binary and ternary categories. The result reveals that the ternary models achieved less confusion between hate and non-hate speech than the binary models.

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A low complexity Polynomial Functional link Artificial Recurrent Neural Network (PFLARNN) has been proposed for the prediction of financial time series data. Although different types of polynomial functions have been used for low complexity neural network architectures earlier for stock market prediction, a comparative study is needed to choose the optimal combinations of the nonlinear functions for a reasonably accurate forecast. Further a recurrent version of the Functional link neural network is used to model more accurately a chaotic time series like stock market indices with a lesser number of nonlinear basis functions. The proposed PFLARNN model when trained with the well known gradient descent algorithm produces reasonable accuracy with a choice of range of weight parameters of the network. However, to improve the accuracy of the forecast further, the weight parameters of the recurrent functional neural network are optimized using an evolutionary learning algorithm like the differential evolution (DE). A comparison with other well known neural architectures shows that the proposed low complexity neural model can provide significant prediction accuracy for one day advance and speed of convergence using the International Business Machines Corp. (IBM) stock market indices.