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This study is based on temperature prediction in the capital of India (New Delhi). We have adopted different ML models such as (MPR and DNN) which are designed and implemented for temperature prediction. The MPR models are varied on the degree of the polynomial, whereas the DNN models differ in the number of input parameters. DNNM‐1 takes date, time, and temperature as input, and DNNM‐2 receives date, time, temperature, pressure, humidity, and dew point as input parameters, whereas DNNM‐3, is a complex model that takes date, time, temperature, pressure, humidity, dew point, and 32 weather conditions as input. To evaluate the accuracy of the predictions, a comparison of the predicted temperature and the actual recorded temperature is done, and the performance and accuracy of the models are examined. The MPR models work well in case of fewer input features, but as the number of input features grows, the DNN model outperforms the MPR models. The DNN model (DNNM‐3) outperformed the other models with better accuracy as compared to past evidence. [ABSTRACT FROM AUTHOR]

Discharge prediction with novel machine learning techniques are highly encouraged for its effective response in flood forecasting. This study establishes discharge forecasting models based on artificial neural networks (ANN) and long short-term memory (LSTM) networks at three hydrological stations, Teesta Bazaar, Domohani, and Mekhliganj, within the Teesta river basin, India, for different lead times. Wavelet transform was linked with ANN and LSTM to develop two hybrid models: the wavelet-based artificial neural network (WANN) and the wavelet-based long short-term memory (WLSTM) models. The selection of input variables for the WANN model was carried out through cross-correlation statistics of the discharge data from 2001 to 2017, available only for the monsoon season. Wavelet decomposition of the discharge data culminated in various details and approximation components, which were arranged in specific combinations to generate multiple sub-series. These sub-series were employed to formulate the WANN and WLSTM models. Both LSTM and WLSTM models applied Encoder-Decoder-LSTM (En-De-LSTM) architecture for those stations with multivariate inputs. Furthermore, the Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) have been utilized to assess the accuracy of WANN, LSTM, and WLSTM models and compare them. The LSTM networks are characterized by long-term dependencies, memorizing the necessary information, and discarding redundant data. This research highlights that WANN and WLSTM models are superior alternatives for long-term discharge prediction, which can assist the strategy makers in mitigating flood. [ABSTRACT FROM AUTHOR]

Different soil characteristics in different parts of India affect agriculture growth. Crop growth and crop production are significantly impacted by healthy soil. Soil enzymes mediate almost all biochemical reactions in the soil. Understanding the biological processes of soil carbon and nitrogen cycling requires defining the significance of prospective elements at the play of soil enzymes and evaluating their activities. A combination of Multiple Linear Regression (MLR), Random Forest (RF) models, and Artificial Neural Networks (ANN) was employed in this study to assess soil enzyme activity, including amylase and urease activity, soil physical properties, such as sand, silt, clay, and soil chemical properties, including organic matter (SOM), nitrogen (N), phosphorus (P), soil organic carbon (SOC), pH, and fertility level. Compared to other methods for estimating soil phosphatase, cellulose, and urease activity, the RF model significantly outperforms the MLR model. In addition, due to its ability to manage dynamic and hierarchical relationships between enzyme activities, the RF model outperforms other models in evaluating soil enzyme activity. This study collected 3972 soil samples from 25 villages in the Bhandara district of Maharashtra, India, with chemical, physical, and biological parameters. Overall, 99% accuracy was achieved for cellulase enzyme activity and 94% for N-acetyl-glucosaminidase enzyme activity using the Random Forest model. Crops have been suggested based on the best performance accuracy algorithms and evaluation performance metrics. [ABSTRACT FROM AUTHOR]

The globe and more particularly the economically developed regions of the world are currently in the era of the Fourth Industrial Revolution (4IR). Conversely, the economically developing regions in the world (and more particularly the African continent) have not yet even fully passed through the Third Industrial Revolution (3IR) wave, and Africa's economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth in the global human population, which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet the steep food demand and stabilize the economies of the African continent and countries such as India that are dependent on the agricultural sector to some extent. Technological advances in precision agriculture are already improving farm yields, although several opportunities for further improvement still exist. This study evaluated plant disease detection models (in particular, those over the past two decades) while aiming to gauge the status of the research in this area and identify the opportunities for further research. This study realized that little literature has discussed the real-time monitoring of the onset signs of diseases before they spread throughout the whole plant. There was also substantially less focus on real-time mitigation measures such as actuation operations, spraying pesticides, spraying fertilizers, etc., once a disease was identified. Very little research has focused on the combination of monitoring and phenotyping functions into one model capable of multiple tasks. Hence, this study highlighted a few opportunities for further focus. [ABSTRACT FROM AUTHOR]

The landslide susceptibility (LS) of any mountainous region is significantly affected by the land-use land-cover (LULC) change. Recently, LULC change effects on landslides have been investigated by many researchers. However, the future prediction of the LS using these LULC changes has not been quantified. The main objective of this study is to predict the future LS map considering the future LULC change scenario for the Tehri region, India. To achieve this objective, we first prepared a geospatial database of past landslide events. These events data were clustered into three major temporal categories, 2005–2010, 2010–2015, and 2015–2020. Second, the artificial neural network (ANN) approach was adopted to prepare LS maps for the years 2010, 2015, and 2020. Then, for the same years, LULC maps were also developed. Third, the future scenario of LULC for the year 2030 was simulated using the ANN-cellular automata model, and the future LULC changes were derived using the change detection technique. Finally, the future LS map for 2030 was projected using derived future LULC changes. The LULC change results reveal that the region is expected to see a significant growth in the built-up area by 34.1%, water body by 6.3%, and agriculture land by 1%. Further, a shrink in dense forest area by 2.4% and sparse forest area by 0.9% is expected in the future. Additionally, the projected LS results reveal a 33% increment in the very high LS zone. This information about the increase in future LS due to rapid urban growth in the mountains can help the various government agencies to scientifically plan the various developmental activities. [ABSTRACT FROM AUTHOR]

In the present work, the scatterometer satellite (SCATSAT-1) has been implemented and validated to provide the near-real-time estimation of snow cover area (SCA) in the Western Himalayas, India. The SCA derived from standalone SCATSAT-1 L4 (Level-4 India) products, i.e. sigma-nought (σ°), and gamma-nought (γ°) has been validated with different MODIS products individually, i.e. MOD02 L1B (calibrated-radiances) derived NDSI (normalized difference snow index), MOD10A1 L3 (daily composite snow cover), and MOD10A2 L3 (8-Day composite snow cover). The experimental outcomes confirm the potential of SCATSAT-1 in estimating the SCA with respect to other MODIS products and also, suggested the utilization of the different MODIS products for referencing/validation in different scenarios. The findings of this paper suggest that SCATSAT-1 offers the near real-time mapping and monitoring of large-scale snow extent at the global level even under cloudy conditions. [ABSTRACT FROM AUTHOR]

The aim of the study is to analyze the past and future land use and land cover change (LULCC) in Betwa River Basin (BRB), central India. The LULC maps were derived from Landsat satellite images using Maximum Likelihood Classifier (MLC). The artificial neural network (ANN) embedded with Land Change Modeler (LCM) was trained with driver variables. The model prediction accuracy has been accessed by evaluating Receiver Operating Characteristic (ROC) values. The study reveals that during the period 1990-2020 agriculture land, open forest, and built-up land area have increased significantly. Further, the LULCC prediction for the period 2030-2050 suggests that expansion in open forest and the built-up land area will continue while agriculture land area will keep back in the future. This research provides up-to-date LULCC information of BRB, which would be useful for all the stakeholders governing river basin management and land resource planning in this region. [ABSTRACT FROM AUTHOR]

In India, the manufacturing industry is beset by issues. Lack of innovation and machine capabilities, scarcity of skilled labour, power supply shortages, inadequate R&D conduct, and the cost of basic facilities are just a few of the major issues. At present, the manufacturing firms are mostly concentrating on the enhancements of the production process, and thereby developing innovations for enhancing the products' quality. A lean and sustainable integrated Quality Management System is proposed to enhance the quality in addition to manufacturing performance. For this study, small and medium enterprises (SME) are deemed. Here, the artificial neural networks (ANN) computational prediction technique is implemented to envisage the optimum manufacturing parameters. For adjusting the weight in the neural networks (NN) for prediction, the ANN optimization is performed by utilizing an imperialist competitive algorithm (ICA) which is basically a competitive algorithm for verifying the firm's performance with reference to manufacturing cost. The proposed effort is executed in the Mat Lab, which is an analyzing software platform that models the ANN-ICA algorithm relating to manufacturing cost in addition to lead time of SMEs. The outcomes established that the proposed work is an effective method for envisaging the chief parameters that influence the manufacturing enterprises. [ABSTRACT FROM AUTHOR]

In India, coal is one of the prime sources of energy used in the power generation and metallurgy sector. The processing of coal below 3 mm is not successfully carried out in India. The quality of coal below 3 mm can be improved by decreasing the coal's particle size, which reduces the ash percentage of coal. Screening is one of the significant beneficiation techniques used to reduce the size fraction of coal. The difficult to process coal of size −3 + 1 mm was selected in the present work. In this work, an attempt has been made to screen the coal of size −2 + 1 mm from −3 + 1 mm using a 2 mm screen mesh in the vibrating screen generated at different moisture content, angle, and frequency of the deck. The performance of the vibrating screen was evaluated using screening efficiency. Furthermore, prediction using a feed backward artificial neural network (ANN) model was developed on the experimental results for ten different neuron conditions. From the results, it was clear that the prediction results obtained from the ANN model were in good correlation with the experimental results. [ABSTRACT FROM AUTHOR]

The present study evaluates and compares performance of three supervised classification algorithms namely Maximum Likelihood (MXL), Artificial Neural Network (ANN) and Support Vector Machine (SVM), using very high resolution WorldView-2 satellite imagery for vegetation type/land cover (VT/LC) mapping in Keoladeo National Park (KNP), India. We mapped 16 (8 gregarious VT and 8 LC) classes, and used Bootstrap (with 100 iterations) method for accuracy assessment. All three algorithms produced high overall accuracy (OA) (67–85%) and kappa (K) (65–83) values. Visual comparison of the predictions revealed that SVM (OA = 85.12% (K = 83.9) with 3.85% width of confidence interval) performed the best followed by ANN (69.72% (67.32) with 4.43%) and MXL (67.37% (65.22) with 4.33%). This research provides insight for selection of classification algorithm for detailed VT/LC mapping of wetland associated systems using very high resolution satellite data. The findings of this research are useful for environmental management, restoration and conservation planning of KNP, India. The database will be of high value for future development and sustainability issues in the park. [ABSTRACT FROM AUTHOR]

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Fluoride plays an essential role in terms of the health of human beings. Persistent exposure to fluoride, which is present in drinking water mainly, may result in dental, skeletal, and non-skeletal fluorosis. However, drinking water, being consumed presently, is not sufficient to indicate the degree of exposure to fluoride. The existing literature indicates that nails can be used as indicative (biomarkers) of not only to exposure of fluoride but also the degree of the same. However, because of differential metabolism rate depending on a number of factors like age, gender, nutritional status, water characteristics, etc., exposure to fluoride is not easily detectable in human beings by just testing the fluoride content in nails. Moreover, due to sensitive chemical analysis and lack of facilities, it is difficult to identify the exact concentration of fluoride in nails. The objective of this study is to identify the significant parameters that affect the fluoride content in nail samples. Apart from laboratories test, the application of different artificial intelligence (AI) methods are used for the prediction of fluoride in nails, which will help to identify the degree of fluoride exposure to children, females, and males. The field study covers a collection of 2401 nail samples from eight districts of Rajasthan, India. The samples were taken of different age groups and genders. 1024 water samples were also collected from the same households from where the nails samples were collected. These water and nail samples were tested in laboratories at Birla Institute of Technology and Science, Pilani, India. This data set was used to train, test, and validate the results using AI techniques. The proposed hybrid model (HM) combines Principal Component Analysis (PCA), Firefly Algorithm (FA), and Artificial Neural Network (ANN) to predict fluoride concentration in nails. The model performance was assessed by using different errors and coefficient of relationship. A comparative analysis of the proposed HM was done against ANN and PCA–ANN models. Sensitivity analysis was performed to study the importance of input parameters on the output. Results show that the PCA–FA–ANN–AFpE model having four input parameters (age, fluoride, pH, and electrical conductivity), displayed the best performance with high R2 values of 0.97. The study generates a novel methodology for predicting fluoride concentration in nails, which can be used for the prediction of hotspots in terms of continuous exposure to fluoride. [ABSTRACT FROM AUTHOR]

This study aims at identifying, mapping the distribution of vulnerable debris flow sites, and generating a corresponding susceptible map of the study area using Remote Sensing, Geographic Information system (GIS), and Artificial Neural Network (ANN). The above sites are identified using visualization technique in Google Earth to be later utilized in ANN-based model setup. The model is constructed, optimized, and validated using identified sites. The controlling factors of debris flow: Topographic Position Index (TPI), Normalized Vegetation Index (NDVI), Slope, Aspect, Topographic Wetness Index (TWI), Stream Power Index (SPI), Distance to Drainage and Digital Elevation Model (DEM) derived from Remote Sensing and observed datasets are utilized for generation of a best-fit model (Area Under Curve (AUC) - 0.77). The observations analyzed from generated susceptibility map include 1) area along the river with less vegetation, high slopes, barren land are more susceptible 2) consistent field validation results made this technique potentially reliable. [ABSTRACT FROM AUTHOR]

By studying the spectral reflectance features of different land cover types and leveraging information of primarily “BLUE” band along with “RED” and “NIR” bands, this article seeks to introduce a new built-up index such as powered B1 built-up index (PB1BI). The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas from Landsat7 satellite images efficiently. For Landsat7 satellite imagery, classification performances of the proposed index along with support vector machine (SVM), artificial neural network (ANN), and three existing built-up indices have been examined for three study sites of 1° Latitude $\times 1^\circ $ Longitude ($\approx 12\,100~{\mathrm {sq}}\cdot {\mathrm {km}}$) area from three diverse geographical regions in India. The computed value of the M-Statistics for PB1BI is consistently greater than 1.80, indicating a better spectral separability between built-up and nonbuilt-up classes by the index. In order to improve the performance of the built-up indices, this article has suggested a bootstrapping method for threshold estimation in addition to the existing Otsu’s method for the same. It has been found that using bootstrapping method instead of Otsu’s method for threshold estimation has helped to improve the classification performance of built-up indices up to 17.75% and 40.49% in terms of overall accuracy and kappa ($\kappa $) coefficient, respectively. It has been observed that for the validation set, average overall accuracy (97.45%) and kappa ($\kappa $) coefficient (0.907) of PB1BI for considered study sites are not only significantly higher than existing indices but also comparable with the same of SVM (99.10% and 0.942) and ANN (87.24% and 0.450). This article has also shown that the proposed index provides a stable performance for multitemporal analysis of the study sites and is able to capture growth in built-up region in time horizon. The classification performance of PB1BI has also been verified for Landsat8 imagery across 11 study sites from different continents around the globe, and the results show overall accuracy and $\kappa $ to be consistently more than 90% and 0.75, respectively. For considered study sites, the reported values of average accuracy and $\kappa $ of PB1BI for built-up classification using Landsat8 satellite data are 95.7151% and 0.8843, respectively. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to develop a family of GARCH models, combining them with popular Recurrent Neural Network (RNN) models, which can capture the high nonlinear relationships between past return innovations and conditional variance, which is overlooked by standard GARCH models. The next objective is to apply Markov Switching GARCH model and see the differences in the predictive accuracy (according to AIC and BIG criteria and in two viewpoints: MSB and MAD) between the standard GARCH models, GJRGARCH model, RNN-GARCH models and Markov Switching GARCH model by comparing their out-of-sample forecasts. The dataset consists of a series of daily returns obtained from the National Stock Exchange (NSE) for the Indian Equity Markets. The results indicated that the proposed RNN-GARCH model and RNN-Markov Switching models are accurate and quick prediction methods. [ABSTRACT FROM AUTHOR]

Phytoplankton acts as carbon sinks due to photosynthetic efficacy and their diversity is expressed by SWDI (Shannon-Weaver Diversity Index), which depends on water quality parameters. The coastal water of Diu was studied for three seasons, and the relationship between different parameters and SWDI was established. Subsequently, an attempt was made to build up a prediction model of SWDI based on multilayer perceptron Artificial neural network (ANN) using the R programme. Analysis shows interrelationship between the water quality parameters and phytoplankton diversity is same in linear principal component analysis (PCA) and neural network model. Variations of different parameters depend on seasonal changes. The ANN model shows that ammonia and phosphate are key parameters that influence the SWDI of phytoplankton. Seasonal variation in SWDI is related to variation in water quality parameters, as explained by both ANN and PCA. Hence, the ANN model can be an important tool for coastal environmental interaction study., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagan script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved. [ABSTRACT FROM AUTHOR]

The focus of the present work aims at the forecast of formation of thunderstorm using the effective 20 parameters at Kolkata (22.53°N, 88.33°E), India during the pre-monsoon season (March-May). One hidden layer Artificial Neural Network (ANN) with variable learning rate back propagation algorithm is utilized for the purpose. In the first phase of the work, in the region of active convective development, that is, from surface to the 500 hPa pressure level, the parameters selected are derived from wind speed, pressure, temperature, dew-point temperature, pressure at lifting condensation level of radio-sonde observations of 12 years (1985-1996). Thereafter, thunderstorm or otherwise days are predicted with ANN model with the observations of 3 years (1997- 1999). In the second phase of the analysis, the skill scores such as Probability of detection (POD), Heidke skill score (HSS), False alarm ratio (FAR) etc. are computed and the scores endorses the efficiency of the model. The squared errors are observed as 10-5. A 60-70% success rate is achieved in the analysis. [ABSTRACT FROM AUTHOR]

In this paper an attempt has been made to determine efficiency of semi transparent hybrid photovoltaic thermal double pass air collector for different PV technology and compare it with single pass air collector using artificial neural network (ANN) technique for New Delhi weather station of India. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for determination of thermal, electrical, overall thermal and overall exergy efficiency of the system. The ANN model uses ambient air temperature, number of sunshine hours, number of clear days, temperature coefficient, cell efficiency, global and diffuse radiation as input parameters. The transfer function, neural network configuration and learning parameters have been selected based on highest convergence during training and testing of network. About 2000 sets of data from four weather stations (Bangalore, Mumbai, Srinagar and Jodhpur) have been given as input for training and data of the fifth weather station (New Delhi) has been used for testing purpose. It has been observed that the best transfer function for a given configuration is logsig. The feed forward back-propagation algorithm has been used in this analysis. Further the results of ANN model have been compared with analytical values on the basis of root mean square error. [ABSTRACT FROM AUTHOR]

Artificial Neural Network (ANN) models have been proved to be powerful predictive tools, where a variable is explained by a set of explanatory variables without assuming any structural or linear relationship among the variables. In the field of finance, a large number of models, especially those derived from the field of econometrics, are used for forecasting stock returns. This paper intends to test the forecasting ability of ANN models using Nifty index data from Indian stock market. Daily time series data of the index of National Stock Exchange is analyzed using a three-layer architecture of the ANN. The results of the study reveal that ANN models could efficiently predict daily returns of Nifty index for a given period under investigation. The results of this study are significant value addition to the trading decisions in the stock index futures with special reference to Indian stock market. [ABSTRACT FROM AUTHOR]

This article discusses the use of an artificial neural network and a genetic algorithm to model streamflow in the Mahanadi River in India. According to the authors, the interaction between the El Niño-southern oscillation (ENSO) and the equatorial Indian Ocean oscillation (EQUINOO) can improve the forecast of monthly basin-scale streamflow. They argue that to model the complex non-linear relationship between large-scale atmospheric circulation information and basin-scale streamflow measurements requires efficient optimization of an artificial neural network architecture using an evolutionary optimizer.

Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics. [ABSTRACT FROM AUTHOR]

Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time. It is found that the proposed ML based sentence extraction techniques and the composite feature set used with RNN as classifier outperform all other approaches. By using ANN in FF form as feature extractor, the performance of the system is evaluated and a comparison is made. Experimental results show that the application of big data samples has enhanced the learning of the ASR system. Further, the ANN based sample and feature extraction techniques are found to be efficient enough to enable application of ML techniques in big data aspects as part of ASR systems., (Copyright © 2015 Elsevier Ltd. All rights reserved.)