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Agriculture plays a pivotal role in the economy of most countries, serving as a primary source of livelihood and sustenance. In the case of India, it occupies a substantial portion of the nation's land. This article proposes the integration of IoT (Internet of Things) and an automated irrigation system with ML/DL (Machine Learning and Deep Learning) to revolutionize agriculture. The implementation of crop monitoring through sensors not only eases the burden on farmers but also enhances crop productivity. The system, at its core, monitors crucial field parameters such as soil moisture, temperature, and humidity. Given the increasing importance of efficient water management in agriculture, this study outlines an automated irrigation system that leverages cloud computing and IoT to curtail water consumption. Its primary objective is to gather and consolidate data from diverse sources, including data generated by sensors and IoT devices. This centralized data storage approach facilitates seamless data integration from various locations and devices. Through the application of algorithms and dataset analysis, the study determines that the cultivation of "Spider" plants is more favorable when compared to other plant species. Notably, the Random Forest classifier emerged as the most accurate, achieving an impressive 94.77% accuracy rate in this project. In essence, this research endeavors to propel agriculture into a technology-driven and sustainable future, optimizing water usage and improving crop yield. [ABSTRACT FROM AUTHOR]

Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. However, current DL models for breast mammogram mass segmentation face several limitations, including false positives (FPs), false negatives (FNs), and challenges with the end‐to‐end approach. This paper presents a novel two‐stage end‐to‐end cascaded breast mass segmentation framework that incorporates a saliency map of potential mass regions to guide the DL models for breast mass segmentation. The first‐stage segmentation model of the cascade framework is used to generate a saliency map to establish a coarse region of interest (ROI), effectively narrowing the focus to probable mass regions. The proposed presegmenter attention (PSA) blocks are introduced in the second‐stage segmentation model to enable dynamic adaptation to the most informative regions within the mammogram images based on the generated saliency map. Comparative analysis of the Attention U‐net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. Experimental results consistently demonstrate enhanced breast mass segmentation performance by the proposed cascade framework across all three datasets: INbreast, CSAW‐S, and DMID. The cascade framework shows superior segmentation performance by improving the dice score by about 6% for the INbreast dataset, 3% for the CSAW‐S dataset, and 2% for the DMID dataset. Similarly, the FN outcomes were reduced by 10% for the INbreast dataset, 19% for the CSAW‐S dataset, and 4% for the DMID dataset. Moreover, the proposed cascade framework's performance is validated with varying state‐of‐the‐art segmentation models such as DeepLabV3+ and Swin transformer U‐net. The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model. [ABSTRACT FROM AUTHOR]

In modern horticulture, the grape industry across the globe has been coping with the issue of grape crop diseases. The detection of grape leaf diseases using automated methods can greatly assist farmers in mitigating yield losses and ensuring sustainability. However, existing systems face hurdles while handling grape leaf images at the farm level, and these models fail to generalize well on un-seen images. This study proposes the development of a well-curated real-time dataset of grape leaf images assimilated through field visits in the study area in India. This designed dataset is further used to train convolutional neural network models to accurately identify and classify grape leaves as either diseased or healthy. The potential of transfer learning using CNN models like VGG, ResNet, Inception, and Xception is assessed on the curated dataset. Our findings indicate that ResNet50V2 outperformed the other models in accurately identifying and classifying grape leaf diseases. Using transfer learning, existing weights (pretrained) and learned features were utilized for further training and fine-tuning the CNN models on our curated dataset. The results of the proposed approach are compared with existing automated grape leaf disease identification techniques. It is observed that the proposed approach, which is on a real-time grape leaf image dataset, provides the highest accuracy among others. Further, this study provides a wellcurated dataset of on-field grape leaf images in the Indian context, which can serve as a benchmark for future research. This study shows that deep learning techniques can aid farmers in identifying grape leaf diseases early. [ABSTRACT FROM AUTHOR]

OBJECTIVE: In India, agriculture is the backbone of economic sectors because of the increasing demand for agricultural products. However, agricultural production has been affected due to the presence of pests in crops. Several methods were developed to solve the crop pest detection issue, but they failed to achieve better results. Therefore, the proposed study used a new hybrid deep learning mechanism for segmenting and detecting pests in crops. METHOD: Image collection, pre‐processing, segmentation, and detection are the steps involved in the proposed study. There are three steps involved in pre‐processing: image rescaling, equalized joint histogram based contrast enhancement (Eq‐JH‐CE), and bendlet transform based De‐noising (BT‐D). Next, the pre‐processed images are segmented using the DenseNet‐77 UNet model. In this section, the complexity of the conventional UNet model is mitigated by hybridizing it with the DenseNet‐77 model. Once the segmentation is done with an improved model, the crop pests are detected and classified by proposing a novel Convolutional Slice‐Attention based Gated Recurrent Unit (CS‐AGRU) model. The proposed model is the combination of a convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). In order to achieve better accuracy outcomes, the proposed study hybridized these models due to their great efficiency. Also, the slice attention mechanism is applied over the proposed model for fetching relevant feature information and thereby enhancing the computational efficiency. So, pests in the crop are finally detected using the proposed method. RESULT: The Python programming language is utilized for implementation. The proposed approach shows a better accuracy range of 99.52%, IoU of 99.1%, precision of 98.88%, recall of 99.53%, F1‐score of 99.35%, and FNR of 0.011 compared to existing techniques. DISCUSSION: Identifying and classifying pests helps farmers anticipate potential threats to their crops. By knowing which pests are prevalent in their region or are likely to infest certain crops, farmers can implement preventive measures to protect their crops, such as planting pest‐resistant varieties, using crop rotation, or deploying traps and barriers. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India. [ABSTRACT FROM AUTHOR]

Predicting rainfall is crucial across multiple sectors and activities, impacting agriculture, water management and disaster preparedness. In this study, the Particle Swarm Optimization (PSO) algorithm is used to optimize the hyperparameters of hybrid deep learning and machine learning models such as Bidirectional Long Short-Term Memory (BiLSTM), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Artificial Neural Network (ANN) and Support Vector Regression (SVR). The performances of the PSO-optimized models are compared using the monthly rainfall dataset of Aizawl Weather Station and the all-India monthly average rainfall dataset. For the all-India rainfall datasets, the results of the PSO models are also compared with models from previous studies. The results show that, for the all-India rainfall dataset, the hybrid model PSO-BiLSTM IV achieved an RMSE of 225.12 and outperformed an existing RNN model by 14% and an existing single-cell LSTM, Vanilla LSTM and stacked LSTM by 11%, 10% and 8% respectively. In the Aizawl Weather Station dataset, the hybrid model PSO-BiLSTM II achieved the best result with an RMSE of 76.6, a benchmark result for this dataset. Overall, the hybrid PSO-BiLSTM models have the lowest RMSE score and the SVR models achieve the lowest performance for both datasets. [ABSTRACT FROM AUTHOR]

The fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing. [ABSTRACT FROM AUTHOR]

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection’s severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures. [ABSTRACT FROM AUTHOR]

Kidney cancer is the most common type of cancer, and designing an automated system to accurately classify the cancer grade is of paramount importance for a better prognosis of the disease from histopathological kidney cancer images. Application of deep learning neural networks (DLNNs) for histopathological image classification is thriving and implementation of these networks on edge devices has been gaining the ground correspondingly due to high computational power and low latency requirements. This paper designs an automated system that classifies histopathological kidney cancer images. For experimentation, we have collected Kidney histopathological images of Non-cancerous, cancerous, and their respective grade of Renal Cell Carcinoma (RCC) from Kasturba Medical College (KMC), Mangalore, Karnataka, India. We have implemented and analyzed performances of deep learning architectures on a Field Programmable Gate Array (FPGA) board. Results yield that the Inception-V3 network provides better accuracy for kidney cancer detection as compared to other deep learning models on Kidney histopathological images. Further, the DenseNet-169 network provides better accuracy for kidney cancer grading as compared to other existing deep learning architecture on the FPGA board. [ABSTRACT FROM AUTHOR]

Introduction: With increasing demand for food and changing environmental conditions, a better understanding of the factors impacting wheat yield is essential for ensuring food security and sustainable agriculture. By analyzing the effect of multiple factors on wheat yield, the presented research provides novel insights into the potential impacts of climate change on wheat production in India. In the present study, datasets consisting of countrywide environmental and agronomic factors and wheat yield were collected. In addition, the study also analyzes the effect of information demand of farmers on production. Methodology: The study employs a regional analysis approach by dividing the country into five zonal clusters: Northern Hills, Central India, Indo-Gangetic Plains, North-Eastern India, and Peninsular India. Correlation and Principal Component Analysis (PCA) were performed to uncover the month-wise key factors affecting wheat yield in each zone. Furthermore, four Machine Learning/Deep Learning-based models, including XGBoost, Multi-layer Perceptron (MLP), Gated Recurrent Unit (GRU), and 1-D Convolutional Neural Network (CNN), were developed to estimate wheat yield. This study estimated partial derivatives for all factors using Newton's Quotient Technique, a numerical method-based approach. Results: The analysis focused on applying this technique to the best-performing wheat yield estimation model, which was the GRU-based model (with RMSE and MAE of 0.60 t/ha and 0.46 t/ha, respectively). Discussion: In the later sections of the article, multiple policy recommendations are communicated based on the extracted insights. The results of the presented research help inform decision-making regarding the development of strategies and policies to mitigate the impacts of climate change on wheat production in India. [ABSTRACT FROM AUTHOR]

Soil erosion poses a significant threat to sustainable land management and agricultural productivity. Addressing this issue requires advanced predictive models that can accurately identify areas at risk and inform soil conservation strategies. This study focuses on the development and interpretation of well-optimized deep learning (DL) models to predict soil erosion probability, aiming to enhance decision-making in land management. Utilizing the Revised Universal Soil Loss Equation (RUSLE) in conjunction with ground-truthing, we identified critical erosion-prone areas. To predict soil erosion probability, we employed Bayesian optimization to fine-tune Deep Neural Network (DNN), Convolutional Neural Network (CNN), Fully Connected Neural Network (FCNN), and DNN-CNN hybrid models. These DL models were verified using a set of metrics. SHAP value analysis as a means of explainable artificial intelligence (XAI) was used to interpret these DL models for better decision-making. RUSLE estimations and ground truthing highlight that Soil erosion rates in the northeastern and northwestern regions are nearing the highest observed at 25 tonnes per hectare annually, largely due to steep slopes and limited vegetation. In contrast, the southern and southeastern areas have lower erosion rates, due to denser vegetation and gentler slopes. Deep learning models, optimized using Bayesian methods, demonstrate high performance in spatially modeling soil erosion probability. The DNN model achieved an accuracy of 0.93, a precision of 0.92, and an F1-score of 0.94, identifying 222.73 sq. km as highly susceptible to erosion, which indicates its strong ability to detect true erosion events. The CNN model identified 49.68% of the study area (503.30 sq. km) as high-risk, with an accuracy of 0.90 and a precision of 0.91. The FCNN model showed a balanced risk distribution, indicating 37.04% of the land (375.25 sq. km) as very low risk and 36.43% (369.11 sq. km) as very high risk, with an accuracy of 0.91. The DNN-CNN hybrid model highlighted 41.58% of the area (421.20 sq. km) as high risk, demonstrating its effectiveness in capturing spatial patterns of erosion susceptibility. SHAP value analysis indicates that land use and soil type (LULC and K-factor) are crucial in erosion predictions, with LULC having a significant predictive influence in the DNN model. These insights facilitate the prioritization of soil conservation measures, enabling decision-makers to focus on the most impactful factors for mitigating soil erosion. [ABSTRACT FROM AUTHOR]

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF)), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC(2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape. [ABSTRACT FROM AUTHOR]

Speech Emotion Recognition (SER) is very crucial in enriching next generation human machine interaction (HMI) with emotional intelligence capabilities by extracting the emotions from words and voice. However, current SER techniques are developed within the experimental boundaries and faces major challenges such as lack of robustness across languages, cultures, age gaps and gender of speakers. Very little work is carried out for SER for Indian corpus which has higher diversity, large number of dialects, vast changes due to regional and geographical aspects. India is one of the largest customers of HMI systems, social networking sites and internet users, therefore it is crucial for SER that focuses on Indian corpuses. This paper presents, cross corpus SER (CCSER) for Indian corpus using multiple acoustic features (MAF) and deep convolution neural network (DCNN) to improve the robustness of the SER. TheMAF consists of various spectral, temporal and voice quality features. Further, Fire Hawk based optimization (FHO) technique is utilized for the salient feature selection. The FHO selects the important features from MAF to minimize the computational complexity and improve feature distinctiveness based in inter class and inter class variance of the features. The DCNN algorithm provides the better correlation, higher feature representation, better description of variation in timbre, intonation and pitch, superior connectivity in global and local features of the speech signal to characterize the corpus. The outcomes of suggested DCNN based SER is evaluated on Indo-Aryan language family (Hindi and Urdu) and Dravidian Language family (Telugu and Kannada). The proposed scheme results in improved accuracy for the various cross corpus and multilingual SER and out performs the traditional techniques. It provides an accuracy of 58.83%, 61.75%, 69.75% and 45.51% for Hindi, Urdu, Telugu and Kannada language for multilingual training. [ABSTRACT FROM AUTHOR]

Food security is a crucial requirement in today's world to meet the dietary needs of individuals. As the population continues to grow, the demand for food will increase by 70 to 100 percent by 2050. Therefore, there is an urgent need to develop an approach that can assist farmers in predicting crop yield accurately and in a timely manner before crop harvesting. In this direction, the present study proposed a nature-inspired optimized hybrid convolutional neural network with bidirectional long short-term memory to extract the nonlinear complex relationships among crop attributes. This hybrid deep learning model was optimized using a particle swarm optimization approach to automate the selection of appropriate hyperparameters for wheat yield prediction. The proposed model was developed to estimate wheat yield in a major wheat-producing state in India from 2000 to 2018 using the temporal and spatial characteristics of wheat crops. The experiment was conducted by integrating the historical yield, meteorological data, remote sensing-derived indices, and soil parameters from the October to April season. Furthermore, we also evaluated the performance of the proposed model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE) with publicly available datasets such as Agro_data, Soybean_data, and FAO_data. The experimental results showed that the proposed PSO-CNN-Bi-LSTM method achieved an MAE of 0.39, MSE of 0.18, and RMSE of 0.42 (tonnes/ha) and outperformed existing CNN, LSTM, CNN-LSTM, CNN-Bi-LSTM, CNN-LSTM-PSO, CNN-Bi-LSTM-BO-GO, and neural network methods. Our findings demonstrated that the proposed methodology could be a promising alternative for predicting crop yield. [ABSTRACT FROM AUTHOR]

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis. [ABSTRACT FROM AUTHOR]

Electronic waste (e-waste) is considered a major issue that our world is tackling nowadays. This electronic waste causes various health issues to animals as well as human beings which further results in environmental pollution in developing countries like India. To overcome these issues, proper e-waste collection is proposed by using the dynamic sine cosine-based neural network optimization (DSCNN) approach. The major objective of this approach involves collecting waste from the individual, hence handling the widespread adoption and use of smartphones. To enhance waste planning collection, residents upload a photograph of their waste to the waste collection company's server, which mechanically recognizes and categorizes the image. A new classification and detection scheme using the DSCNN approach is proposed for efficient e-waste collection planning and correctly detects the type and quantity of waste components in images. The identification and classification accuracy of the uploaded images is very accurate; this method describes the e-waste collection process in various streets and buildings in Maharashtra, India. Experimental results describe that the proposed approach readily achieves the proper allocation of vehicle collection, vehicle routing plan, and household e-waste collection, resulting in reduced collection costs. Moreover, the proposed DSCNN method is compared to various other methods like random forest algorithm (RFA), fractional henry gas optimization (FHGO), behavior-based swarm model by the fuzzy controller (BSFC), and deep learning convolutional neural network (DL-CNN). The DSCNN approach yielded an e-waste collection detection accuracy of 97%. The accuracy rates of 94%, 95%, 93%, and 92.15% are obtained from the DL-CNN, FHGO, BSFC, and RFA. [ABSTRACT FROM AUTHOR]

Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned "gamucha" from India's northeast, from counterfeit powerloom imitations. Our study's objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures—VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201—were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven "gamucha"s and counterfeit powerloom imitations—a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products. [ABSTRACT FROM AUTHOR]

The video camera is essential for reliable activity monitoring, and a robust analysis helps in efficient interpretation. The systematic assessment of classroom activity through videos can help understand engagement levels from the perspective of both students and teachers. This practice can also help in robot-assistive classroom monitoring in the context of human–robot interaction. Therefore, we propose a novel algorithm for student–teacher activity recognition using 3D CNN (STAR-3D). The experiment is carried out using India's indigenously developed supercomputer PARAM Shivay by the Centre for Development of Advanced Computing (C-DAC), Pune, India, under the National Supercomputing Mission (NSM), with a peak performance of 837 TeraFlops. The EduNet dataset (registered under the trademark of the DRSTATM dataset), a self-developed video dataset for classroom activities with 20 action classes, is used to train the model. Due to the unavailability of similar datasets containing both students' and teachers' actions, training, testing, and validation are only carried out on the EduNet dataset with 83.5% accuracy. To the best of our knowledge, this is the first attempt to develop an end-to-end algorithm that recognises both the students' and teachers' activities in the classroom environment, and it mainly focuses on school levels (K-12). In addition, a comparison with other approaches in the same domain shows our work's novelty. This novel algorithm will also influence the researcher in exploring research on the "Convergence of High-Performance Computing and Artificial Intelligence". We also present future research directions to integrate the STAR-3D algorithm with robots for classroom monitoring. [ABSTRACT FROM AUTHOR]

India being an agrarian economy, rainfall is an essential component that directly or indirectly influences agricultural produce. With the increasing impacts of the changing climate scenario, it is anticipated that in the near future, frequent and extreme rainfall episodes will trigger events like severe floods, landslides, etc. Therefore, it is extremely important to make precise predictions so that the intensity of the impacts on life and property can be reduced. In recent times, with the advancement of AI/ML applications, it has become popular in weather and climate sciences. The current work uses 121 years of rainfall data for analysis and prediction purposes, where deep learning (DL) approaches like LSTM (Long Short Term Memory), BiLSTM (Bi-directional LSTM) and GRU (Gated Recurrent Unit) have been adopted. The long-term rainfall analysis and prediction over selected smart cities of India is based on their location in the homogenous monsoon regions. The results obtained from the models indicated that for univariate forecasting, the overall performance of BiLSTM is better than others for most cities considered, while GRU predicted well for places with higher rainfall variability. In the multivariate approach, LSTM's performance is superior. Therefore, a combination of BiLSTM and GRU might offer a better result in the univariate approach, or an advanced version of LSTM might enrich the outcomes in the multivariate approach for rainfall analysis and forecasting. [ABSTRACT FROM AUTHOR]

The mental health of the human population, particularly in India during and after the COVID-19 pandemic is a major concern. All age groups have undergone mental stress during and after COVID-19, especially college students in urban areas and individuals belonging to the age group from 16 to 25. Early detection of mental stress among urban students will help in the resolution of major related issues that may hurt one's career. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enabled the prediction of mental health status. Numerous studies have been conducted using various approaches, but there is still no agreement on how to predict mental symptoms across age groups. In the current study, proposed DL, Long Short-Term Memory (LSTM), and ML models, namely Support Vector Machine (SVM), ADA Boost, Random Forest (RF), K-Nearest Neighbor (K-NN), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) are trained and tested on a real-world dataset. The DL LSTM model outperformed the conventional ML models with an accuracy of 100%. [ABSTRACT FROM AUTHOR]

Temporal gaps in the Gravity Recovery and Climate Experiment (GRACE) and GRACE–FO missions pose difficulties in analyzing spatiotemporal variations of terrestrial water storage (TWS) anomalies over Indian river basins. In this study, we developed a deep learning-based CNN–LSTM model to address these temporal gaps by integrating GRACE–TWS with other meteorological variables. The model achieved a strong Pearson's correlation coefficient (median of 0.96 in training, 0.92 in testing), high Nash–Sutcliffe efficiency (0.91 in training, 0.85 in testing) and low normalized root-mean-squared error (0.064 in training, 0.098 in testing) when compared to the original GRACE–TWS. Moreover, a comparison with two publicly available global datasets suggests the superior performance of CNN–LSTM in predicting TWSA (terrestrial water storage anomalies). The study also highlighted potential biases up to 2 cm/yr in long-term TWSA trends due to temporal gaps in GRACE/GRACE–FO. Additionally, the estimated TWSA matched well with in situ wells across majority of the Indian river basins. The findings revealed significant groundwater depletion in the northern and northwestern river basins but positive trends in the central and southern basins in India. Overall, the estimated TWSA product developed in this study provides continuous data records and valuable insights into long-term trends in groundwater storage, making it useful for groundwater monitoring. [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]

Language identification has become a critical challenge in NLP, particularly in multilingual countries like India. This study addresses the identification of closely related Indo-Aryan languages, proposing a robust deep-learning ensemble model with data augmentation. Pre-trained models (Indic BERT, Indic Transformer, DevBERT, L3Cube-HindBERT) and various embedding techniques (word2vec, Indic transformer, Keras embedding) are explored with LSTM, Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM deep learning models. Keras embedding performs better as compared to other embedding methods. The four best deep learning models are selected, and ensemble methods (soft voting, hard voting, stacking with meta models Random Forest and SVM classifier) are employed. When utilizing data augmentation with the training data and fitting the models Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM with Keras embedding, the best performance, a 93% F1-score, was achieved by their soft voting classifier as compared to the other models. The performance of the proposed model was compared with the existing baseline models on the same dataset. For the comparison of model performance, the macro F1 score, accuracy, recall, and precision were used. [ABSTRACT FROM AUTHOR]

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]

Text classification involves organizing textual information into predefined classes, a task which is particularly useful in domains like sentiment analysis, spam detection, and content labeling. In India, where a massive amount of information is generated daily through newspapers and social media, Hindi is one of the most widely used and spoken languages. However, there is limited research on Hindi text classification and, particularly, regarding Hindi news classification. This paper presents a research study to classify Hindi news articles published in Hindi-language newspapers in India by using and comparing various Machine Learning (ML) and Deep Learning (DL) algorithms. To prepare the textual news data for classification, pre-processing and feature engineering techniques, such as count vectorizer, Tf-Idf vectorizer and Doc2Vec, were used and applied to convert texts into vectors. This pre-processing step on the textual data was very challenging due to the presence of multimodal words, conjunctions, punctuation, and special characters in Hindi texts. The study considered Hindi news headlines from predetermined categories (Science, Sports, Entertainment and Business) and, among the different ML and DL models tested and evaluated, Linear Regression with Doc2Vec vectorizer and SGD classifier with Tf-Idf vectorizer produced best accuracies of 97.04% and 96.59%, respectively. The best performing DL model was found to be the Bi-LSTM with an accuracy of approximately 97% on the testing data. [ABSTRACT FROM AUTHOR]

With the rising concerns over climate change and global warming, reducing carbon dioxide (CO2) emissions has become crucial, especially in rapidly industrializing and urbanizing countries like India. This study aims to predict India's harmful CO2 emissions for the next two decades using a combination of statistical and deep learning models based on univariate time series data. The research employs an Auto Regressive Integrated Moving Average (ARIMA) model alongside four deep learning models, including Recurrent Neural Network (RNN), Artificial Neural Network (ANN), BiLSTM-DNN, and ResNet models. Utilizing the Climate Analysis Indicators Tool (CAIT) dataset, covering India's CO2 emissions from 1990 to 2019, the study forecasts emissions from 2023 to 2042. The predictive models' accuracy is analyzed and compared using various metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R2 Score, and Mean Absolute Percentage Error (MAPE). The research identifies the most accurate model, which can serve as an effective tool for predicting India's CO2 emissions. The findings reveal that the ResNet and BiLSTM-DNN models outperform other models, exhibiting high R2 Scores and low MSE, MAPE, and MAE values, thereby holding promise for applications in the automotive industry and other sectors to mitigate air pollution and combat climate change. [ABSTRACT FROM AUTHOR]

As the world's population grows, the agricultural sectors are destined to increase crop production and security. Improving crop yield by using advanced technologies gives remarkable growth to the economy of the country. Agriculture provides over 20% of India's GDP. Using machine learning algorithms, the crop yield can be predicted which is useful to the farmers to plan the cultivation beforehand. In this work, various machine learning (ML) algorithms are applied to predict the yield of 'rice and sorghum (jowar)' and a novel weighted feature approach with a combination of Support Vector Machine (SVM) and Random Forest (RF) is proposed for two Indian seasons. RF is used to select training data at random, and the learning rate approach from deep learning concepts is implemented to add random weights to each parameter; the SVM model is then trained using the weighted training data. The best weights are again applied for the whole data to implement the SVM and RF algorithms. The weighted feature hybrid model is compared with SVM, RF, Decision tree, Naive Bayes, and k-Nearest Neighbor algorithms. RF-based regression method is also implemented and its ability to predict the crop yield has been discussed based on its performance metrics. The results show that the proposed weighted feature hybrid SVM-RF model gives the best accuracy of 90% when compared with the traditional algorithms. Also, the performances of various ML algorithms for crop yield prediction are analysed and cross-validation of the models is performed and compared, which improved the accuracy by 8-10%. [ABSTRACT FROM AUTHOR]

In countries like India, diseases in plants are a major concern in the agricultural sector. Crop loss due to diseases led to reduction in the quality and quantity of agricultural products. This also leads to economic losses. Hence, timely monitoring of plants is necessary. But monitoring diseases in large fields is a difficult task. To overcome this problem, effective management strategies should be taken to control diseases in plants. It can be done by acquiring data for disease identification and automation of the recognition of diseases. Deep learning is of great use in this area using the principles of object detection. In this paper, we are using YOLOv3 (you only look once) to determine the diseases in plants, based on the symptoms seen on the leaves. The advantage of using YOLOv3 is that multiple diseases can be detected on the image of a single leaf. An important feature of YOLOv3 is that it can detect small disease spots seen on the leaves. Here, we are concentrating on the bacterial spot disease seen on the bell pepper plant. The identification results show a mean average precision of 90%. [ABSTRACT FROM AUTHOR]

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country's economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language. [ABSTRACT FROM AUTHOR]

Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learning, and stochastic model have been proposed. The denoised series are fitted with various benchmark models, including long short-term memory (LSTM), support vector regression (SVR), artificial neural network (ANN), and autoregressive integrated moving average (ARIMA) models. The effectiveness of a wavelet-based denoising approach was investigated on monthly wholesale price data for three major spices (turmeric, coriander, and cumin) for various markets in India. The predictive performance of these models is assessed using root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The wavelet LSTM model with Haar filter at level 6 emerged as a robust choice for accurate price predictions across all spices. It was found that the wavelet LSTM model had a significant gain in accuracy than the LSTM model by more than 30% across all accuracy metrics. The results clearly highlighted the efficacy of a wavelet-based denoising approach in enhancing the accuracy of price forecasting. [ABSTRACT FROM AUTHOR]

Natural disasters and floods brought on by heavy rainfall pose serious threats to human health and lives every year on a global scale. The intricacy of meteorological data makes it difficult to provide accurate rainfall predictions, despite their critical importance in nations like India where agriculture is the primary occupation. Rainfall forecasting has recently benefited from Artificial Intelligence (AI) developments such as Deep Learning (DL) and Machine Learning (ML) techniques. This article provides a comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year. The findings show that DL approaches are more effective than traditional ML methods and shallow neural network models. This research is important as it has significant impacts on agriculture, disaster preparedness, and water resource management. Finally, it outlines future research directions for further advancements in rainfall prediction through AI methodologies. [ABSTRACT FROM AUTHOR]

Maize is ranked as the third most important food crop in India after rice and wheat. The cultivation of this particular crop and its associated agricultural practices have faced many challenges over the centuries. Maize is susceptible to Northern Leaf Blight (NLB), a highly contagious fungal foliar disease. The primary reason for the reduction in yield resulting from NLB is the loss of photosynthetic leaf area. Therefore, identifying diseases at an early stage is critical to ensure that the treatment process is carried out correctly and that the quality of results is maintained. This research uses a deep learning-based Attention U-Net model to explore a real-time, effective approach for segmentation and detecting NLB diseases in maize crops. Data augmentation is performed after image annotation to increase the model's effectiveness, and the model is trained from scratch. The model was trained for a maximum of 50 epochs using an initial learning rate of 0.0001 with Adam as an optimizer, and its performance was tested on the test dataset. This study shows that the Attention U-Net model outperformed other image segmentation methods, such as Res U-Net and Plain U-Net, and showed better results with an Intersection over Union (IoU) of 72.41%, 70.91% and 51.95%, respectively. The proposed model achieves an average pixel-wise F1 score of 85.23%. The diseased segmentation accuracy clenched to 98.97%, and the Dice coefficient (DC) of disease spot segmentation is 81.39%. Adding an attention mechanism to the U-Net architecture improves its ability to express local features, resulting in improved NLB disease image segmentation performance. [ABSTRACT FROM AUTHOR]

Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model's ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model's predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves. [ABSTRACT FROM AUTHOR]

Script identification of text in scene images has attracted massive attention recently. However, the existing techniques primarily emphasize on scripts where data are available abundantly, such as English, European, or East Asian. Although these methods are robust in dealing with high‐resource data, how these techniques will work on low‐resource scripts has yet to be discovered. For example, in India, there is a disparity among the text scripts across the country's demographic. To bridge the research gap for resource‐constraint script identification, we present a few‐shot learning network called the TextScriptFSLNet. This network does not require huge training data while achieving state‐of‐the‐art performance on benchmark datasets. Our proposed method acts in accordance with a C$$ C $$‐way K$$ K $$‐shot paradigm by splitting the train set as support and query set, respectively. The support set learns representative knowledge of each class and creates its prototypes. We use multi‐kernel spatial attention fused 2‐layer convolutional neural network and averaging technique to generate the prototype of each class. This spatial attention aids in grasping important information in an image and enriches the feature representation. To the best of our knowledge, the proposed work is the first of its kind in the scene text understanding domain. Additionally, we created a dataset called Indic‐FSL2023 comprising 10 of the 22 officially recognized Indian scripts. The proposed method achieves the highest accuracy among the tested methods on the newly created Indic‐FSL2023. Experiments are also conducted on MLe2e to demonstrate its versatility. Furthermore, we also showed how our proposed model performed concerning illumination changes and blur on scene text script images. [ABSTRACT FROM AUTHOR]

Fresh water is vital for all living creatures and maintains the hydrological cycle. Surface water bodies conserve freshwater and exhibit dynamic changes yearly due to high/low rainfall and over/underutilization. Therefore, extracting water bodies and determining their extent is imperative for effective water resource management. Water body extraction using Water Indices (WI) and Machine Learning (ML) face threshold selection and feature optimization challenges, respectively. This paper proposes a Lightweight Attention-based Multiscale Neural Network (LWAMNNet) for surface water body extraction from Linear Imaging Self Scanning‒III (LISS-III) remote sensing images. The LWAMNNet is an encoder-decoder architecture designed using a modified residual block in both the encoder and decoder to extract high-level features. Feature Extraction Module (FEM) is sandwiched between encoder and decoder to extract global contextual features. The LWAMNNet replaces convolutions with depthwise separable convolutions to reduce computation complexity. In the decoder, the attention module is incorporated to provide attention to fused features (i.e., combined deep features with spatial encoder features) at different scales. The LWAMNNet effectively extracts different-sized water bodies with non-linear boundaries. The proposed LWAMNNet qualitatively and quantitatively outperforms other DL models in performance metrics (accuracy of 99.5%) and computation complexity (in terms of trainable parameters and time). Additionally, the water extent of five major reservoirs in south India was determined annually from 2016 to 2019. Also, the reason for water dynamics is analyzed with the help of rainfall and water availability data provided by the Indian Metrological Department (IMD). [ABSTRACT FROM AUTHOR]

The foundation of the Indian economy is agriculture, the amount of land available for agricultural activities has decreased due to numerous factors. To fulfill the demands of the expanding population, the maximum yield must be produced on the least amount of land that is accessible. To overcome the challenges of agriculture, many researches have been carried out to adopt technology into agriculture. As India is one of the world's top producers of Mangoes and has a vast market, and has encouraged extensive Mango farm development. Automatic yield estimation of Mangoes in the early stage is important to improve the quality and quantity of production which improves both domestic and export markets. The work proposes a fast region (FR) based convolutional neural network (CNN) residual network (ResNet)-50 model for efficient deep learning-based Mango crop yield estimation system to count the Mango fruit from the images of individual trees. A temporal Mango fruit database is used to estimate the yield of on tree Mango fruits, and a framework is provided to estimate Mango fruit yield in red, green, and blue (RGB) image. This experiment shows that the suggested FRCNN ResNet-50 model attained a better accuracy of 98.20% on the proposed dataset. [ABSTRACT FROM AUTHOR]

The Indian solid waste management sector is experiencing fast growth and transition as a result of rising urbanization and industrial activity. This research study examines the landscape of solid waste management in India, focusing on the informal sector's involvement, market size, and trends. It also looks into the possibility of creating an online solid waste marketplace with a progressive web app (PWA) with the goal of optimizing waste management processes, increasing resource efficiency, and contributing to environmental sustainability. The purpose of this study is to create a new waste management system that uses the YOLO v7 item recognition model andThe project concentrated on crafting design prototypes for a progressive web app (PWA) and an administrative web interface for a solid waste marketplace in India. Utilizing Figma, these designs aim to streamline waste management processes, engage users effectively, and advocate for recycling initiatives. This effort signifies a pivotal move towards closing the gap in India's waste management infrastructure. It highlights technology's role in promoting environmental sustainability and responsible waste management practices nationwide. [ABSTRACT FROM AUTHOR]

Farmers, notably in India, face challenges such as inadequate expertise in crop selection and crop failures due to disease. Deep Learning's untapped potential in agriculture, limited by data quality and processing constraints, presents an opportunity for enhancement. The objective is to give an open-source online application is developed using Flask to aid farmers in informed decision-making. Utilize Precision Agriculture principles to recommend crops based on soil types, features, and data-driven insights to boost productivity. The various methods based on a machine learning-based ensemble model is employed to suggest suitable crops based on soil data, focusing on accuracy and efficiency. The outcome of the developed system shows promise in enhancing input-output efficiency, improving decision-making, and reducing unsuitable crop choices, thereby boosting agricultural productivity and positively impacting India's economy. This approach demonstrates practical potential in resolving farming challenges by leveraging machine learning for crop recommendations, potentially revolutionizing agriculture in India and beyond. [ABSTRACT FROM AUTHOR]

Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR]

The objective of paper is to study a deep learning strategy for recognizing Indian Sign Language (ISL) using convolutional neural networks CNN. Sign language recognition has become a crucial tool for enhancing communication and accessibility for the deaf in India. ISL is a visual-gestural language used by the deaf population in India, and it is a visual-gestural language used by the deaf community in India. Gesture based communication Application is a characteristic language that involves various methods for articulation for correspondence in day-to-day existence. This paper presents a Programmed interpretation framework for token of manual letters in order in English communication via gestures. It manages pictures of uncovered hands, which permits the client to cooperate with the framework in a characteristic manner. In proposed system a deep learning model using CNN with ISL (Indian Sign Language) Dataset is used. The model consists of preprocessing the dataset to extract features from the image, then train a CNN to recognize the signs and gestures with audio. Experimental results show that the proposed approach is capable of achieving accuracy in recognizing ISL signs and gestures with accuracy of 0.868, recall 0.856, precision 0.863, and F-score is 0.884 respectively. [ABSTRACT FROM AUTHOR]

Solar energy based green hydrogen production is dependent on energy produced from Photovoltaic (PV) panels that is in turn dependent on Global Horizontal Irradiance (GHI), which has stochastic and intermittent nature. The intermittent power output from PV panels may interfere with the steady production of electricity required for electrolysis process to synthesis the green hydrogen thereby; reducing the effectiveness and scalability of green hydrogen generation. Ultimate goal of this work will be to effectively forecast and develop the atlas map for solar based green hydrogen production potential using the proposed algorithm i.e. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Bidirectional Long Short-Term Memory (CEEMDAN-BiDLSTM). In order to suggest a reliable and precise forecasting methodology that will support India's low-carbon economy goals, the proposed method is evaluated on GHI datasets acquired via national portal of National Institute of Wind Energy (NIWE) for two Indian sites i.e. Bhadla and Fatehgarh. • Use of decomposition & deep learning algorithms for solar irradiance prediction. • Highly accurate results of GHI forecasting on statistical parameters are obtained. • Building atlas map for solar based green hydrogen production potential of India. • Significant potential for solar-powered green hydrogen production observed in India. • The study will benefit businesses/policy makers/institutions etc. Significantly. [ABSTRACT FROM AUTHOR]

Rainfall forecasting is essential because heavy and irregular rainfall creates many impacts like destruction of crops and farms. Here, the occurrence of rainfall is highly related to atmospheric parameters. Thus, a better forecasting model is essential for an early warning that can minimize risks and manage the agricultural farms in a better way. In this manuscript, Deep Neural Network (DNN) optimized with Flamingo Search Optimization Algorithm (FSOA) is proposed for Long-term and Short-term Rainfall forecasting. Here, the rainfall data is obtained from the standard dataset as Sudheerachary India Rainfall Analysis (IRA). Moreover, the Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) approach is utilized for pre-processing process. Also, the deep neural network is utilized for performing rainfall prediction and classification. Additionally, the parameters of the DNN model is optimizing by Flamingo Search Optimization Algorithm. Finally, the proposed MFEEWT-DNN- FSOA approach has effectively predict the rainfall in different locations around India. The proposed model is implemented in Python tool and the performance metrics are calculated. The proposed MFEEWT-DNN- FSOA approach has achieved 25%, 26%, 25.5% high accuracy and 35.8%, 24.7%, 15.9% lower error rate for forecasting rainfall in Cannur at Kerala than the existing Map-Reduce based Exponential Smoothing Technology for rainfall prediction (MR-EST-RP), modular artificial neural networks with support vector regression for rainfall prediction (MANN-SVR-RP), and biogeography-based extreme learning machine (BBO-ELM) (BBO-ELM-RP) methods respectively. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]