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A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy. [ABSTRACT FROM AUTHOR]

Handwritten script recognition is a vital application of the machine-learning domain. Applications like automatic license plate detection, pin-code detection, and historical document management increases attention toward handwritten script recognition. English is the most widely spoken language in India; hence, there has been a lot of research into identifying a script using a machine. Devanagari is a popular script that is used by a large number of people on the Indian subcontinent. In this paper, a level-wised efficient transfer-learning approach is presented on the VGG16 model of a convolutional neural network (CNN) for identifying isolated Devanagari handwritten characters. In this work, a new dataset of Devanagari characters is presented and made accessible to the public. This newly created dataset is comprised of 5800 samples for 12 vowels, 36 consonants, and 10 digits. Initially, a simple CNN is implemented and trained on this new small dataset. During the next stage, a transferlearning approach is implemented on the VGG16 model, and during the last stage, the efficient fine-tuned VGG16 model is implemented. The obtained accuracy of the fine-tuned model's training and testing came to 98.16 and 96.47%, respectively. [ABSTRACT FROM AUTHOR]

Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. The occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers' income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model's performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi. [ABSTRACT FROM AUTHOR]

Summary: The importance of monsoon rainfall cannot be ignored as it affects round‐the‐year activities ranging from agricultural to industrial. Due to the nonlinear and dynamic nature of monsoon rainfall, its accurate forecast becomes a challenging task. In this paper, we propose a novel deep and wide neural network model to predict summer monsoon rainfall using historical time‐series data of 118 years. We have considered Rajasthan as our area of study, which is the largest state of India with varying atmospheric regions. Two different types of datasets are used: gridded rainfall dataset from the Indian Meteorological Department (IMD) and station rainfall data from Water Resources Department (WRD). Results obtained on both the datasets demonstrate that a single model works well in efficiently predicting summer monsoon rainfall in different geographical regions of Rajasthan, ranging from plains and plateaus to deserts and hills. We compare the results with various advanced deep learning algorithms such as 1‐dimensional convolutional neural network, long short term memory, and multilayer perceptron. The comparison results show that the proposed method clearly outperforms these algorithms in terms of its forecasting ability, which is evaluated using root mean square error and mean absolute error. [ABSTRACT FROM AUTHOR]

Medicinal plants are used to cure different common and chronic diseases in different Asian countries including India. The easy availability and planting possibility make them popular resources for alternative medicinal practices like Ayurveda. These medicinal plants possess proven healing potential without causing any side effects. The biochemical constituents of the medicinal leaves are the fundamental reasons of their healing power which considerably vary with maturity. The existing practices of maturity detection are largely based on chemical analysis of leaves through different instruments which are expensive, invasive in nature and time consuming. This paper reports a computer vision-based system to classify the medicinal leaves along with the corresponding maturity level. The process reported is advantageous in terms of comparatively easier, faster, less expensive and non-invasive operations. The presented system captures the leaf images in controlled illumination and processed leaf images are fed to the convolutional neural network (CNN) architecture-based system for classification of both the type of leaves and maturity stage. The paper also presents application of binary particle swarm optimization ((BPSO) for arriving the values of the CNN hyperparameters. The potential of the system has been assessed with three popular species of medicinal plants, namely Neem, Tulsi and Kalmegh. The classification results were verified with repeated test runs and different standard metrics including tenfold cross-validation method. The paper shows that the presented CNN-driven computer vision framework can provide about 99% classification accuracy for simultaneous prediction of leaf specie and maturity stage. Such significant performance of the presented method can be considered as a potential addition to the existing chemical and instrumental methods. [ABSTRACT FROM AUTHOR]

The availability of appropriate research corpora is a fundamental concern in music information retrieval research. This paper addresses the design, development, and evaluation of a poetic corpus, POMET, for the meter estimation task. Poems, which communicate through rhythm and apparent meaning, have a vital role in many literary traditions. Metrical rhythm generally involves periodic arrangements of sequences of stressed and unstressed syllables in each line of poems. It has already been proved that poetry's aesthetic and emotional perception can be studied well using poetic meter analysis. A corpus with eight meters is designed and recorded in a studio environment for Malayalam, one of the prominent languages in South India. Using deep neural network architectures, a pilot evaluation is performed with musical texture features and spectrograms. We hope that the corpus can be used as a benchmark dataset for poetic meter estimation, rhythmic analysis, and corpus-based prosody analysis. [ABSTRACT FROM AUTHOR]

In India, nearly 12 million visually impaired people had difficulty in identifying the currency notes. There is a need to develop an application that can recognize the currency note and provide a vocal message. In this paper, a novel lightweight Convolutional Neural Network (CNN) model is developed for efficient web and mobile applications to recognize the Indian currency notes. A new dataset for Indian currency notes has been created to train, validate, and test the CNN model. This CNN based web and mobile applications will provide a text and audio output based on the recognized currency note. The proposed model is developed using TensorFlow and improved by selection of optimal hyperparameter value, and compared with existing well known CNN architectures using transfer learning. Based on the results it has been observed that proposed model perform well over six widely used existing architectures in terms of training and testing accuracy. [ABSTRACT FROM AUTHOR]

COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed "Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)". The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection. [ABSTRACT FROM AUTHOR]

As India is an Agricultural based country, its economic stability depends on agriculture. Now a days due to technological advances in the digital world, production of crops has also increased, and along with this disease infection in crops has also grown. Manual detection of pests using plants parts like leaves, stem, roots is time consuming and non-availability of timely help from experts which is time consuming and costly. Also spraying the pesticides cannot be the all-time solution as pesticides leave behind many adverse effects, such as reduction in the fertility of soil as well as health issues to the workers. To overcome these problems has led to the early detection of pests using an expert system is need of an hour. In this paper researcher has presented review of research articles based on pest detection using Image Processing, machine learning and deep learning techniques such as Convolution Neural Networks which is best suitable to extract features from the diseased leaves images and classify them. [ABSTRACT FROM AUTHOR]

India is among the largest cultivators and consumers of wheat grains leading to apparent demand for identifying the quality and varietal distribution of wheat to fulfill the specific requirements of food industries. Moreover, with the variations in prices of distinct varieties in different parts of the country, it becomes a vital need for the customers as well as for the cultivators to identify and classify the grains based upon specific end products, demand, and prices of individual variety. The growth of Machine Learning and Computer Vision in agriculture, facilitate the development of such techniques that can successfully identify the classes based on visual features and representation. In this paper, a model has been developed from scratch for the classification of fifteen different varieties of wheat consists of 15000 images based on their visual traits using Convolutional Neural Network. The model has been produced under a different set of hyper-parameters tuned to develop the best model that can classify the varieties of wheat grains with high accuracy and minimum loss. The performance of the different models are compared in terms of classification accuracy and categorical cross-entropy loss. The model which is found best, successfully classifies the wheat varieties with 94.88% training accuracy and 97.53% test accuracy while on the other side reduces loss to 15% for training and 8% for the test set. Hence, the developed model can be deployed for the classification of different grain varieties, plant diseases, plant varieties, and several other fields under agriculture. [ABSTRACT FROM AUTHOR]

Human-Elephant Conflict (HEC) and its mitigation have always been a serious conservation issue in India. It occurs mainly due to the encroachment of forests by humans as part of societal development. Consequently, these human settlements are highly affected by the intrusion of wild elephants as they cause extensive crop-raiding, injuries and even death in many cases. HEC is a growing problem in rural areas of India which shares a border with forests and other elephant habitats. Based on the studies, it is very explicit that HEC is an important conservation issue which affects the peaceful co-existence of both humans and elephants near the forest areas. The desirable solution for this problem would be to facilitate co-existence among humans and elephants, but this often fails because of technical difficulties. Hence, this paper presents an end-to-end technological solution to facilitate smoother coexistence of humans and elephants. The proposed work deploys a live video surveillance system along with deep learning strategies to effectively detect the presence of elephants. From the numerical analysis, it is revealed that the post-training accuracy of the deep learning model used in the proposed approach is evaluated at 98.7% and outperforms an an out-of-the-box image detector. The layered approach used in the proposed work improves resource management which is a major bottleneck in real-time deployment scenarios. [ABSTRACT FROM AUTHOR]

This paper dispenses an exhaustive review on deep learning techniques used in the prognosis of eight different neuropsychiatric and neurological disorders such as stroke, alzheimer, parkinson's, epilepsy, autism, migraine, cerebral palsy, and multiple sclerosis. These diseases are critical, life-threatening and in most of the cases may lead to other precarious human disorders. Deep learning techniques are emerging soft computing technique which has been lucratively used to unravel different real-life problems such as pattern recognition (Face, Emotion, and Speech), traffic management, drug discovery, disease diagnosis, and network intrusion detection. This study confers the discipline, frameworks, and methodologies used by different deep learning techniques to diagnose different human neurological disorders. Here, one hundred and thirty-six different articles related to neurological and neuropsychiatric disorders diagnosed using different deep learning techniques are studied. The morbidity and mortality rate of major neuropsychiatric and neurological disorders has also been delineated. The performance and publication trend of different deep learning techniques employed in the investigation of these diseases has been examined and analyzed. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. The research implication, challenges and the future directions related to the study have also been highlighted. Eventually, the research breaches are identified and it is witnessed that there is more scope in the diagnosis of migraine, cerebral palsy and stroke using different deep learning models. Likewise, there is a potential opportunity to use and explore the performance of Restricted Boltzmann Machine, Deep Boltzmann Machine and Deep Belief Network for diagnosis of different human neuropsychiatric and neurological disorders. [ABSTRACT FROM AUTHOR]

Western countries entertain speech recognition-based applications. It does not happen in a similar magnitude in East Asia. Language complexity could potentially be one of the primary reasons behind this lag. Besides, multilingual countries like India need to be considered so that language identification (words and phrases) can be possible through speech signals. Unlike the previous works, in this paper, we propose to use speech signal patterns for spoken language identification, where image-based features are used. The concept is primarily inspired from the fact that speech signal can be read/visualized. In our experiment, we use spectrograms (for image data) and deep learning for spoken language classification. Using the IIIT-H Indic speech database for Indic languages, we achieve the highest accuracy of 99.96%, which outperforms the state-of-the-art reported results. Furthermore, for a relative decrease of 4018.60% in the signal-to-noise ratio, a decrease of only 0.50% in accuracy tells us the fact that our concept is fairly robust. [ABSTRACT FROM AUTHOR]

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Diabetic retinopathy (DR) is an eye disease, which affects the people who are all having the diabetes for more than 10 years. The ophthalmologist identifies when the dilated eye exam causes severe in any one of the following deviations in the retina: changes in blood vessels, leaking blood vessels, newly grown blood vessels, swelling of the macula, changes in the lens, and damages to the nerve tissue. It can eventually lead to vision loss. The early detection of DR prevents the cause of blindness. In this paper, we propose the retinal image segmentation and extraction of blood vessels by morphological processing, thresholding, edge detection, and adaptive histogram equalization. For the automatic diagnosis of DR from the fundus image, we also developed a network with the convolutional neural network architecture for accurately classifying its severity. By using high-end graphical processor unit (GPU), we trained this network on the publicly available dataset such as DRIVE, DIARETDB0, and DIARETDB1_v1, and the images collected from the Aravind Eye Hospital, Coimbatore, India. Our proposed CNN achieves a sensitivity of 98%, a specificity of 93%, and an accuracy of 96.9% containing a database of 854 images. [ABSTRACT FROM AUTHOR]

Abstract The exact measure of mitotic count is one of the crucial parameters in breast cancer grading and prognosis. Detection of mitosis in standard H & E stained histopathology images is challenging due to diffused intensities along object boundaries and shape variation in different stages of mitosis. This paper explores the feasibility of transfer learning for mitosis detection. A pre-trained Convolutional Neural Network is transformed by coupling random forest classifier with the initial fully connected layers to extract discriminant features from nuclei patches and to precisely prognosticate the class label of cell nuclei. The modified Convolutional Neural Network accurately classify the detected cell nuclei with limited training data. The designed framework accomplishes higher classification accuracy by carefully fine tuning the pre-trained model and pre-processing the extracted features. Moreover, proposed method is evaluated on MITOS dataset provided for the MITOS-ATYPIA contest 2014 and clinical data set from Regional Cancer Centre, Thiruvananthapuram, India. Significance of Convolutional Neural Network based method is justified by comparing with recently reported works including a Multi Classifier System based on Deep Belief Network. Experiments show that the pre-trained Convolutional Neural Network model outperforms conventionally used detection systems and provides at least 15% improvement in F-score on other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Mysuru City is a unique place in India due to its culture, green cover, historical places, and pleasant weather. In the last few decades, the city was witnessed rapid urban growth. This present work is conducted to assess the decadal changes in Mysuru City vegetation cover using multispectral remotely sensed data of 2009 and 2019 within Mysuru City Corporation (MCC). The main objective of this work is to assess the vegetation cover of the city and generate the land use and land cover classes (LULC) map using the deep learning model. Therefore, convolutional neural network (CNN)-based multiple training round (CNN-MTR) deep learning model is proposed and used for the classification of remote sensing images. The classified results were analyzed to assess the vegetation cover changes in the city over one decade. Vegetation cover within the Mysuru City Corporation area was estimated in 2019 to be 39.09% as compared to 43.32% in 2009. These results indicate that over a decade vegetation cover of Mysuru City is decreased by 3.43%. The overall classification accuracy of the proposed CNN-MTR model was estimated to be 95.20% for 2009 and 94.17% for 2019 respectively. [ABSTRACT FROM AUTHOR]

Minimum support price (MSP) is an advisory price signal, which is a component of a huge collection of agricultural policies in parts of India. The agricultural commodities evaluate the derivatives by noticing the climate changes regarding external factors like economic and weather conditions. The severe changes in these factors result in significant price changes. Here, it may experience more cost-efficiency in analyzing the agricultural species. The problem arises because the mixed species in the single pixel increases the pixel size. The high-dimensional problem of input and outputs occurs in the hyper-spectral data. Hence, this research implements a new MSP method in Agronomics or Agriculture through deep learning algorithms. In the species prediction phase, the input remote sensing images are gathered and processed in pre-processing phase using median filtering and contrast limited adaptive histogram equalization. Here, the pre-processed images are fed to feature extraction using the gray-level co-occurrence matrix (GLCM) and spatial feature extraction techniques. Further, the deep feature extraction is analyzed using convolutional neural network (CNN) by considering the input as the pre-processed images and extracting GLCM and spatial features. These deep features are forwarded to the enhanced recurrent neural-long short-term memory (ERN-LSTM), where the parameters of RNN and LSTM are tuned by self-adaptive dingo optimizer (SA-DOX). Finally, the species prediction outcomes are attained by enhanced RNN + LSTM. Secondly, in the MSP prediction phase, the major aim is to predict the MSP based on the species detected. Here, the gathered price data, along with the extracted CNN-based deep features, are processed to select the significant optimal features and are carried out by the same improved DOX. The selected features are given to enhanced RNN + LSTM for predicting the MSP price related to the crop type. Thirdly, the predicted prices are split into four shares. Fourthly, profit suggestion is carried out by training the location and regional crop data, and thus, the enhanced RNN + LSTM model gives the best profitable harvests. Through the experimental results, the accuracy of species classification using SA-DOX-based ERN-LSTM was 10.9, 8.3, 6.85, and 4.7%, accordingly advanced than SVM, LSTM, RNN, and RNN-LSTM. From the given findings, the better accuracy rate of the given designed method is 96.75%. Accordingly, the better sensitivity and precision rates are 95.9 and 95.5%. Finally, this study explores competitive performance through the experimental results relative to the traditional approaches. [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]

Nowadays in the era of automation, the postal automation system is one of the major research areas. Developing a postal automation system for a nation like India is much troublesome than other nations because of India's multi-script and multi-lingual behavior. This proposed work will be helpful in the postal automation of district names of Punjab (state) written in Gurmukhi script, which is the official language of the state in North India. For this, a holistic approach i.e. a segmentation-free technique has been used with the help of Convolutional Neural Network (CNN) and Deep learning (DL). For the purpose of recognition, a database of 22 000 images (samples) which are handwritten in Gurmukhi script for all the 22 districts of Punjab is prepared. Each sample is written two times by 500 different writers generating 1000 samples for each district name. Two CNN models are proposed which are named as ConvNetGuru and ConvNetGuruMod for the purpose of recognition. Maximum validation accuracy achieved by ConvNetGuru is 90% and ConvNetGuruMod is 98%. [ABSTRACT FROM AUTHOR]

Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit's surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%. [ABSTRACT FROM AUTHOR]

This research aims to generate a landslide susceptibility map (LSM) for the Bhagirathi river basin located in the Tehri Garhwal district of Uttarakhand state in India. For this study, we incorporated and utilized machine learning novel ensemble models, namely: Generalize Linear Model (GLM), Random Forest (RF), Convolutional Neural Network (CNN), GLM-RF, CNN-RF, CNN-GLM and CNN-GLM-RF. The above-specified ensemble models were incorporated for the preparation of LSM. A total of 171 landslide locations were chosen and studied for the preparation of landslide inventory. From the landslide inventory, 70% of landslides were utilized for the training purpose, whereas the rest, 30%, were used for the validation purpose. In the ongoing research study, a total of 17 landslide conditioning factors (LCFs) were utilized, and the analysis for multi-collinearity was performed by tolerance (TOL) as well as variance inflation factor (VIF) methods. These LSM were then validated through receiver operating characteristic curve (ROC), mean absolute error (MAE), root mean square error (RMSE) and Chi-Square methods. Finally, the outcomes of the analysis, as well as validation models, show that the CNN model proves to be the most effective for predicting landslide susceptibility in the study area. The study found that the methods incorporated for the area under consideration can be applied to the regions having similar geo-environmental factors globally. [ABSTRACT FROM AUTHOR]

The encouraging trend of usage of human machine interfaces in diverse areas has driven the evolution of Automatic Speech Recognition (ASR) systems during last two decades. Lately, the inclination has been towards the use of machine learning techniques for under-resourced human languages primarily to focus on designing of voice activated digital tool for a sizable portion of computer illiterate speakers. A vast majority of the works in this field have employed shallow models like conventional Artificial Neural Network and Hidden Markov Model in combination with Mel Frequency Cepstral Coefficients and other relevant features for the applications of speech recognition systems. Although these shallow models are found effective, but to minimize human intervention from the approach and also to yield the better system performance, recent research has focused to incorporate deep learning models for ASR applications especially for under-resourced languages. Sylheti language, a member of Indo-Aryan language group, is an under resourced language which has more than 10 million Sylheti speakers living across the world mostly in India and Bangladesh. Focusing on the need of an ASR model for Sylheti, this work aims to design a robust ASR model for an under resourced language Sylheti by employing state-of-the-art deep learning technique Convolutional Neural Network (CNN). To find out the best and suitable ASR model for Sylheti, certain ASR approaches are formulated and trained by Sylheti isolated and connected words. The specially configured ASR model based on CNN is trained with clean, and noisy speech data which are necessary for training and making the system robust. Thereafter, a comparative analysis is presented by configuring the ASR model by some shallow models like Feed-forward neural network, Recurrent neural Network, Hidden Markov model and Time Delay neural Network. Experimental results indicate that the proposed CNN based ASR system works well for Sylheti language and the performance accuracy obtained by the system is found to be satisfactory despite the system demonstrating certain training latency. [ABSTRACT FROM AUTHOR]

Mango leaf diseases have a negative impact on mango quality and yield. It is difficult to make an accurate diagnosis of mango leaf disease diagnosis with the naked eye. A lot of computer-aided and machine learning techniques have recently been used by researchers for the classification of mango leaf diseases. However, it has been reported that these approaches have some limitations to their performance which can be attributed to problems due to higher feature dimensionality, overfitting, computational complexity, and lack of feature qualities. To overcome these issues, we proposed a novel framework for mango leaves disease classification. The images were taken from Andhra Pradesh, the largest mango cultivating land in India. The proposed framework is categorized into four stages: data preparation stage, feature selection stage, learning and classification stage, and the performance evaluation stage. We selected 380 images from the categories of healthy and diseased (Mango Anthracnose, Bacterial black spot, and Sooty mold). Different data augmentation techniques are applied to prevent overfitting and improve generalization. Next, a convolutional neural network with crossover-based levy flight distribution is applied for better feature selection. Further, the pre-trained MobileNetV2 model is used for the learning stage and leaves diseases classification is done via support vector machine at the final stage of the MobileNetV2 model. The experimental results demonstrate superior classification performances over other state-of-art methods. [ABSTRACT FROM AUTHOR]

Seasonal dynamics of snow cover is an essential area of research for hydrological modelling and water resource management. With the increased availability of remote sensing data, the timely information of the spatiotemporal distribution of snow cover is feasible at regular intervals. The primary objective of this study is to assess the effect of pansharpening in the accuracy of snow cover change detection in mountainous regions using freely available Landsat-8 multispectral data. In mountainous regions at the medium resolution, the changes at the mountain ridges are seldom identified. The incorporation of pansharpening in the change detection framework facilitates an improvement in the snow cover change detection at the ridges. For pansharpening, the PanNet architecture based on convolutional neural networks was adopted. A study area around Dhundi in the state of Himachal Pradesh in India was selected for the analysis. The experiments were carried out using a subset of Landsat-8 multispectral data acquired in the autumn and the winter seasons of 2017 and 2018, respectively. An improvement of 0.184 and 0.267 in the kappa coefficient was observed for the overall changes in the snow cover and at the ridges, respectively, based on the results from the proposed approach. [ABSTRACT FROM AUTHOR]

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique's effectiveness is confirmed by a fair comparison to existing procedures. [ABSTRACT FROM AUTHOR]

The ever-growing interest in and requirement for green energy have led to an increased focus on research related to forecasting solar irradiance recently. This study aims to develop forecast models based on deep learning (DL) methodologies and multiple-site data to predict the daily solar irradiance in two locations of India based on the daily solar radiation data obtained from NASA's POWER project repository over 36 years (1983–2019). The forecast modeling of solar irradiance data is performed for extracting and learning the symmetry latent in data patterns and relationships by the machine learning models and utilizing it to predict future solar data. The goodness of fit and model performance are compared with rolling window evaluation using mean squared error, root-mean-square error and coefficient of determination ( R 2 ) for evaluation. The contributions of this study can be summarized as follows: (i) time series models based on deep learning methodologies were implemented to forecast the daily solar irradiance of two locations in India in consideration of the historical data collected by NASA; (ii) the models were developed on the basis of single-location univariate data as well as multiple-location data; (iii) the accuracy, performance and reliability of the model were investigated on the basis of standard performance evaluation metrics and rolling window evaluation; (iv) the feature importance of the nearby locations with respect to forecasting target location solar irradiance was analyzed and compared based on the solar irradiance data obtained from NASA over 36 years. The results indicate that the bidirectional long short-term memory (LSTM) and attention-based LSTM models can be used for forecasting daily solar irradiance data. According to the findings, the multiple-site data with solar irradiance historical data improve upon the forecast performance of single-location univariate solar data. [ABSTRACT FROM AUTHOR]