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The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting. [ABSTRACT FROM AUTHOR]

Malnutrition is generally considered as a state in which body does not receive proper nutritional elements required for its proper functioning. In Asian countries like Pakistan, India and Sri Lanka, micronutrient deficiency is one of the major causes of overall undernourishment among farmwomen. Therefore, this paper aims to identify the level of awareness about nutritional status and effects of malnutrition on women and their children in rural areas of Pakistan. Sample size comprised of 164 respondents who were selected by simple random sampling method by taking 41 respondents from each of the randomly selected villages. A pre-tested and well-structured interview schedule was developed for data collection. The results indicated that various diseases were present among rural families due to malnutrition. The diseases were lethargy, rapid heartbeat, underweight, weak muscles, kwashiorkor, rickets, depression, tiredness, iron deficiency, dizziness, headache, deficiency of protein and unusually pale skin. Pearson correlation coefficient shows significant and positive relation between nutritional awareness and proper diet P<0.05. Results also indicated that diseases and malnutrition were directly related with lack of awareness regarding balanced diet, which was also associated with insufficient micronutrient intake. Low-income, early age marriages, family size, family structure, education level, low access to health facilities were found to be causes of malnutrition among the respondents. There is dire need to take the situation as national nutritional emergency and should make mass level campaigns with the use of media as a tool of communication regarding awareness of coping strategies against malnutrition among rural masses. [ABSTRACT FROM AUTHOR]