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Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector Machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. [ABSTRACT FROM AUTHOR]

Total electron data (TEC) from GPS nowadays can be used as a tool for understanding the space weather phenomena. The development of prediction model for TEC is quiet crucial and challenging due to the dynamic behavior of the ionosphere, since it depends on different factors such as seasonal, diurnal and spatial variations, solar geomagnetic conditions etc. In this paper, an attempt is made for predicting the GPS derived TEC values for different GNSS stations over India using a hybrid method based on Ensemble empirical mode decomposition (EEMD) and Long Short-Term Memory (LSTM) deep learning method. The daily TEC time series data from the IISc Bangalore (Latitude 13.021, Longitude 77.570), Lucknow (Latitude 26.912, Longitude 80.956) and Hyderabad (Latitude 17.417, Longitude 78.551) stations over India during the period 2008 to 2015 of solar cycle 23 and 24 is used for analysis. The assessment of model performance for testing predicted output compared with LSTM and EMD-LSTM models, and their comparison results show that the hybrid EEMD-LSTM model presents better than the other models. [ABSTRACT FROM AUTHOR]

Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved. [ABSTRACT FROM AUTHOR]

Agriculture is a significant contributor to India's economic growth. The rising population of country and constantly changing climatic conditions have an impact on crop production and food security. A variety of factors influence crop selection, including market price, production rate, soil type, rainfall, temperature, government policies, etc. Many changes are required in the agricultural sector in order to enhance the Indian economy. In this research work authors have implemented various machine learning techniques to estimate the crop yield in Rajasthan state of India on five identified crops. The results indicate that among all the applied algorithms; Random Forest, SVM, Gradient Descent, long short-term memory, and Lasso regression techniques; the random forest performed better than others with 0.963 R2, 0.035 RMSE, and 0.0251 MAE. The results were validated using R2, root mean squared error, and the mean absolute error to cross-validation techniques. This paper intends to put the crop selection method into practice to help farmers solve crop yield problems. [ABSTRACT FROM AUTHOR]

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners. • A novel Hidden Markov based Deep Learning (DL) models for accurate agricultural commodity price forecasting. • Addresses DL model generalization challenges by integrating sequential dependencies and hidden states obtained from HMMs. • Proposed models outperform baseline models - RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU for price predictions of agriculture commodities. • Evaluated using RMSE, MAPE, MAE and Diebold Mariano test, for accuracy and reliability of the proposed approach. • Offers precision in commodity price forecasts, benefiting stakeholders such as farmers, planners and policymakers. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) are playing a vital role nowadays in every aspect of computer vision applications. In this paper we have used the state of the art CNN in recognizing handwritten Tamil characters in offline mode. CNNs differ from traditional approach of Handwritten Tamil Character Recognition (HTCR) in extracting the features automatically. We have used an isolated handwritten Tamil character dataset developed by HP Labs India. We have developed a CNN model from scratch by training the model with the Tamil characters in offline mode and have achieved good recognition results on both the training and testing datasets. This work is an attempt to set a benchmark for offline HTCR using deep learning techniques. This work have produced a training accuracy of 95.16% which is far better compared to the traditional approaches. [ABSTRACT FROM AUTHOR]

Early rainfall prediction is very important to ensure the economic balance of any agriculture-dominated country, such as India. Deep learning has recently received considerable attention for predicting rainfall using historical data. However, rainfall depends on multiple factors like ElNino, rising sea levels, warming of the Atlantic Ocean, etc. The changing climate has also significantly increased rainfall variability. The current deep-learning approaches are either based on a limited set of predictors or do not utilize the time-series properties for the recurrence of similar climate conditions every 12 months. Besides, they perform poorly in predicting accurate rainfall estimates for the diverse areas of India due to limited data availability across various regions. Therefore, this paper presents a groundbreaking solution to address the challenge of limited data by introducing RfGan (Rainfall GAN), a novel variant of generative adversarial networks. RfGan aims to fulfill the crucial need for abundant data to ensure the effectiveness of model training. Moreover, a novel rainfall prediction method, RfGanNet, has been introduced that leverages the strengths of the proposed RfGan for data augmentation, long-short-term memory, and convolutional neural networks for rainfall prediction. The performance of RfGanNet has been validated for India and its clustered regions in terms of nine parameters: precision, recall, specificity, Matthews correlation coefficient, accuracy, F1-score, mean absolute error, mean square error and computation time. The experimental results of RfGan are compared against original and synthetic datasets generated from state-of-the-art methods, namely tabular gan (TGAN), Wasserstein gan (WGAN), and conditional tabular gan (CTGAN). The experimental results indicate that the proposed RfNet model, when trained using RfGan-generated data, surpassed all the other considered methods by achieving 97.22 % accuracy. Moreover, RfNet has been compared with other related deep Learning approaches to validate its efficacy. Additionally, an ablation study investigating various LSTM–CNN combinations has been conducted. The findings of the study indicate that oceanic and climate-related predictors have a noteworthy influence on Indian rainfall. Moreover, the study highlights the suitability of combining Bidirectional LSTM, CNN, and multiple predictors for accurate rainfall prediction. • Novel variant of generative adversarial network- RfGan is proposed. • Different combinations of LSTM and CNN for rainfall prediction are tested. • Novel classifier — RfNet is tested on different augmented datasets. • K-means clustered regions of India have been identified. [ABSTRACT FROM AUTHOR]

Artificial intelligence has evolved into a mechanism that can predict environmental changes and investigate climatic conditions. In recent days, extreme precipitation and flooding have resulted in huge damage to the society of human beings and farmers. In real-life, the climatic conditions were predicted using huge collected data. However, the collected data are not utilized properly due to the presence of uncertainty. Most of the research was carried out in time series data for classification and prediction. However uncertain data leads to erroneous predictions. Thus, an integrated model is required to overcome the uncertainty and to improve the prediction accuracy. The paper aims in integrating the Rough Set on Fuzzy Approximation Space (RSFAS) with a Deep Learning (DL) techniques to predict the precipitation level in the southern coastal areas of India in a seasonal way. The proposed model can handle uncertainty using an RSFAS whereas the DL technique is utilized for classification and prediction. The developed model is estimated based on various evaluation metrics and optimizers, benchmarked with existing Deep Learning and Machine Learning (ML) techniques to demonstrate the prediction accuracies obtained with minimized error rate. Consequently, the proposed model assists in understanding the climatic conditions in extreme events to provide a prior warning about heavy rainfall to the people who live near the coastal areas. • Towards the development of a low-cost solution for seasonal precipitation prediction. • Rough Set on Fuzzy Approximation Space with LSTM used for precipitation prediction. • High accuracy as impact of tuning hyperparameters with adam optimizer for prediction. [ABSTRACT FROM AUTHOR]

• A systematic approach for optimal portfolio construction based on sector analysis is presented. • The concepts of probability theory, econometrics, and deep learning are integrated. • Evidence theory and Granger causal network are unified to select strong and diverse economic sectors. • Deep recurrent neural network is used for optimal ratio allocation to portfolio securities. • The proposed model is designed for investment in National Stock exchange of India. With the emerging areas of economy, the diverse sector-based investment portfolios are considered more significant. This paper presents an integrated approach of portfolio construction based on sector analysis for investment in National Stock Exchange of India. The model is developed in four stages: First, multiple evidences are collected using historical data to analyze the performance of different sectors, and ranked using Dempster-Shafer evidence theory. The top-ranked sectors are considered as the strong sectors. Second, Granger causality test is employed for determining interdependencies between the strong sectors using the past closing price time series. Then, the strong sectors with no causal relationships are selected to diversify the investment. Third, leading stocks of the selected sectors are picked on the basis on their performance in previous years. Thus, the portfolio of best stocks from strong and diverse sectors is constructed. Fourth, for optimizing of the constructed portfolio or to obtain optimal ratio allocations of stocks in the portfolio, an optimization function is constructed and simulated using a deep recurrent neural network. The results obtained by the proposed portfolio are found very significant which confirms its effectiveness. The applicability of the proposed model is verified by empirical study over the existing models. [ABSTRACT FROM AUTHOR]

Individual identification contributes significantly towards investigating behavioral mechanisms of animals and understanding underlying ecological principles. Most studies employ invasive procedures for individually identifying organisms. In recent times, computer-vision techniques have served as an alternative to invasive methods. However, these studies primarily rely on user input data collected from captivity or from individuals under partially restrained conditions. Challenges in collecting data from free-ranging individuals are higher when compared to captive populations. However, the former is a far more important priority for real-world applications. In this paper, we used Unmanned Aerial Vehicle (UAV) to collect data from free-ranging mugger crocodiles, Crocodylus palustris. We applied convolutional neural networks (CNNs) to individually identify muggers based on their dorsal scute patterns. The CNN model was trained on a data set of 88,000 images focusing on the mugger's dorsal body. The data was collected from 143 individuals across 19 different locations along the western part of India. We trained two CNN models, one with an annotated bounding box approach, the YOLO-v5l, and another without annotations, the Inception-v3. We used two parameters, True Positive Rate (TPR) and True Negative Rate (TNR), to validate the efficiency of the trained models. Using YOLO-v5l, TPR (re-identification of trained muggers) and TNR (differentiating untrained muggers as 'unknown') values at the 0.84 decision threshold were 88.8% and 89.6%, respectively. The trained model showed 100% TNR for the non-mugger species, the Gharial, Gavialis gangeticus , and the Saltwater crocodile, Crocodylus porosus. The performance of the CNN model was reliable and accurate while using only 125 images per individual for training purposes. Inception-v3 underperformed for both the parameters, thus, showing that a bounding box approach (YOLO-v5l model) with background elimination is a promising method to individually identify free-ranging mugger crocodiles. Our manuscript demonstrates that UAV imagery appears to be a promising tool for non-invasive collection of data from free-ranging populations. It can be used to train open-source algorithms for individual identification. Further, the identification method is entirely based upon dorsal scute patterns, which can be applied to different crocodilian species, as well. • Creation of annotated dataset for free-ranging mugger crocodiles using UAV. • Trained CNN models (YOLO-v5l and Inception-v3) for individual identification. • YOLO-v5l showed robust identification and re-identification of muggers. • Accurate detection of untrained, muggers and non-mugger species, as 'unknowns'. • Pre-trained CNN model will help ecologists to non-invasively tag focal muggers. [ABSTRACT FROM AUTHOR]

Multimodality has shown to be helpful in several natural language processing tasks. Thus, adding multiple modalities to the traditional sentiment analysis has also proven to be useful. However, multimodality in a low resource setting for sentiment analysis is yet to be explored for several resource-constrained languages. Assamese is a low-resource language spoken mainly in the state of Assam in India. This paper presents an Assamese multimodal dataset comprising of 16,000 articles from the news domain as a benchmark resource. Secondly, we present a multi-stage multimodal sentiment analysis framework that concurrently exploits textual and visual cues to determine the sentiment. The proposed architecture encodes the news content collaboratively. The text branch encodes semantic content information by considering the semantic information of the news. At the same time, the visual branch encodes the visual appearance information from the news image. Then, an intermediate fusion-based multimodal framework is proposed to exploit the internal correlation between textual and visual features for joint sentiment classification. Finally, a decision-level fusion mechanism is employed on the three models to integrate cross-modal information effectively for final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance (89.3%) than the best unimodal features (85.6%). • A multimodal and multi-purpose Assamese news dataset is presented. • Two models are proposed for visual and textual modality for sentiment classification. • A multi-stage fusion approach is employed for multimodal sentiment classification. • Qualitative and quantitative analysis are carried out of the models. [ABSTRACT FROM AUTHOR]

To combat the recent coronavirus disease 2019 (COVID-19), academician and clinician are in search of new approaches to predict the COVID-19 outbreak dynamic trends that may slow down or stop the pandemic. Epidemiological models like Susceptible–Infected–Recovered (SIR) and its variants are helpful to understand the dynamics trend of pandemic that may be used in decision making to optimize possible controls from the infectious disease. But these epidemiological models based on mathematical assumptions may not predict the real pandemic situation. Recently the new machine learning approaches are being used to understand the dynamic trend of COVID-19 spread. In this paper, we designed the recurrent and convolutional neural network models: vanilla LSTM, stacked LSTM, ED_LSTM, BiLSTM, CNN, and hybrid CNN+LSTM model to capture the complex trend of COVID-19 outbreak and perform the forecasting of COVID-19 daily confirmed cases of 7, 14, 21 days for India and its four most affected states (Maharashtra, Kerala, Karnataka, and Tamil Nadu). The root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metric are computed on the testing data to demonstrate the relative performance of these models. The results show that the stacked LSTM and hybrid CNN+LSTM models perform best relative to other models. • Various variants of LSTM and CNN deep learning architectures have been designed. • Predicted COVID-19 confirmed cases for India and its four most affected states. • RMSE and MAPE are computed to compare the performance of the designed models. • Experimental results show that predicted cases are very close to actual cases. [ABSTRACT FROM AUTHOR]