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Machine learning (ML) algorithms can be applied to predict the suitability of soil for crop cultivation based on digital soil mapping. We used three distinct models viz. Multinomial Logistic Regression (MnLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to predict the suitability of wheat and pearl millet in the Barmer district of the Thar Desert. After the computation of crop suitability classes, ML techniques were used to develop suitability maps of wheat and pearl millet in the study area. The study found that the RF and XGBoost models worked well to classify crop suitability. The RF model showed that 11.9% of the total area was highly suitable, 1.6% was moderately suitable, 14.9% was marginally suitable, and 71.6% was not suitable for wheat crop. RF model for pearl millet showed that 15.5% of the area is highly suitable. Soil suitability mapping for wheat showed a Kappa index ranging from 0.23 to 0.57 and an overall accuracy ranging from 0.79 to 0.86, whereas the prediction of suitability for pearl millet showed a moderate range of Kappa index from 0.31 to 0.58 and accuracy from 0.63 to 0.77. The area under curve (AUC) for wheat crop was 0.72, 0.88, and 0.91 for MnLR, RF, and XGBoost models, respectively. Overall, the RF model performed better than the MnLR model, showing a 16% increase in accuracy. Therefore, the developed suitability maps using ML provide valuable details on agricultural potential in the Indian desert region while harmonizing its impact on the environment and the economy. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage using various novel machine learning (ML) algorithms. Individual interviews with diabetic patients were conducted over 6 months to acquire information regarding their experience with hypoglycemic episodes. Design: This information included warning signs of hypoglycemia, such as incoherent speech, exhaustion, weakness, and other clinically relevant cases of low blood sugar. Researchers used supervised, unsupervised, and hybrid techniques. In supervised techniques, researchers applied regression, while in hybrid classification ML techniques were used. In a 5‐fold cross‐validation approach, the prediction performance of seven models was examined using the area under the receiver operating characteristic curve (AUROC). We analyzed the data of 290 diabetic patients with low blood sugar episodes. Results: Our investigation discovered that gradient boosting and neural networks performed better in regression, with accuracies of 0.416 and 0.417, respectively. In classification models, gradient boosting, AdaBoost, and random forest performed better overall, with AUC scores of 0.821, 0.814, and 0.821, individually. Precision values were 0.779, 0.775, and 0.776 for gradient boosting, AdaBoost, and random forest, respectively. Conclusion: AdaBoost and Gradient Boosting models, in particular, outperformed all others in predicting the probability of clinically severe hypoglycemia. These techniques enable community health nurses to predict hypoglycemia at an early stage and provide the necessary therapies to patients to prevent complications resulting from hypoglycemia. [ABSTRACT FROM AUTHOR]

Objective The aim of this study was to compare the performance of four publicly available large language models (LLMs)—GPT-4o, GPT-4, Gemini, and Claude Opus—in translating radiology reports into simple Hindi. Materials and Methods In this retrospective study, 100 computed tomography (CT) scan report impressions were gathered from a tertiary care cancer center. Reference translations of these impressions into simple Hindi were done by a bilingual radiology staff in consultation with a radiologist. Two distinct prompts were used to assess the LLMs' ability to translate these report impressions into simple Hindi. Translated reports were assessed by a radiologist for instances of misinterpretation, omission, and addition of fictitious information. Translation quality was assessed using Bilingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit ORdering (METEOR), Translation Edit Rate (TER), and character F-score (CHRF) scores. Statistical analyses were performed to compare the LLM performance across prompts. Results Nine instances of misinterpretation and two instances of omission of information were found on radiologist evaluation of the total 800 LLM-generated translated report impressions. For prompt 1, Gemini outperformed others in BLEU (p < 0.001) and METEOR scores (p = 0.001), and was superior to GPT-4o and GPT-4 in TER and CHRF (p < 0.001), but comparable to Claude (p = 0.501 for TER and p = 0.90 for CHRF). For prompt 2, GPT-4o outperformed all others (p < 0.001) in all metrics. Prompt 2 yielded better BLEU, METEOR, and CHRF scores (p < 0.001), while prompt 1 had a better TER score (p < 0.001). Conclusion While each LLM's effectiveness varied with prompt wording, all models demonstrated potential in translating and simplifying radiology report impressions. [ABSTRACT FROM AUTHOR]

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries. [ABSTRACT FROM AUTHOR]

Objective: This study assessed predictors of stress, anxiety and depression during the COVID‐19 pandemic using a large number of demographic, COVID‐19 context and psychological variables. Methods: Data from 741 adults were drawn from the Boston College daily sleep and well‐being survey. Baseline demographics, the long version of the daily surveys and the round one assessment of the survey were utilized for the present study. A Gaussian graphical model (GGM) was estimated as a feature selection technique on a subset of ordinal/continuous variables. An ensemble Random Forest (RF) machine learning algorithm was used for prediction. Results: GGM was found to be an efficient feature selection method and supported the findings derived from the RF machine learning model. Psychological variables were significant predictors of stress, anxiety and depression, while demographic and COVID‐19‐related factors had minimal predictive value. The outcome variables were mutually predictive of each other, and negative affect and subjective sleep quality were the common predictors of these outcomes of stress, anxiety, and depression. Conclusion: The study identifies risk factors for adverse mental health outcomes during the pandemic and informs interventions to mitigate the impact on mental health. [ABSTRACT FROM AUTHOR]

Under the present Indian government initiative, allweather roads are being taken up for four pilgrimage locations in the Uttarakhand state of India. The Rishikesh to Gangotri road axis is a major road used by local citizens and tourists. Rainfall and numerous anthropogenic activities become the primary reasons for landslide hazards in the area. An accurate Landslide Susceptibility Map (LSM) for any area is of paramount importance for the decision makers of land-use planning. The present study gives a comparative analysis of recent advanced algorithms, i.e. CatBoost, LightGBM and deep neural network topology for generating the LSM by following pixel-based. Fourteen causative factors along with landslide inventory of 154 locations are used for the study. LSM are generated based on JENKS natural break criteria using all the algorithms and their performance comparison is evaluated. Overall accuracy for train and test data, prediction accuracy, area under receiver operating characteristics (AUROC) score for test data, and computational time for model fit on train data; are the criteria used for performance evaluation of each algorithm. In this study, it is observed that LSM can be generated at considerably fast pace if CatBoost or LightGBM is used while deep neural network-based topology gives marginally better results on all other performance measure. [ABSTRACT FROM AUTHOR]

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation. [ABSTRACT FROM AUTHOR]

Introduction: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensemble algorithm in predicting mortality. Methods: The study was a cohort of adults admitted to a medical unit of a tertiary care hospital with sepsis. The data were divided into a training data set (70%) and a test data set (30%). Boruta algorithm was used to identify important features. In the first phase of stacked ensemble model, weak learners such as random forest (RF), support vector machine (SVM), elastic net, and gradient boosting machine were trained. The SVM was used in phase 2 as meta learner to combine the results of all weak learners. All models were validated using test data. Results: In our cohort of 1,453 patients, the mortality rate was 27% (95% confidence interval [CI]: 25, 29). The Boruta algorithm identified inotrope use and assisted ventilation as the most important variables, which could predict mortality. The random forest outperforms (area under the curve [AUC]: 97.91%) the other algorithms. The AUCs for the other models are SVM (95.21%), GBM (93.67%), and GLM net (91.42%). However, the stacking of all the above models had an AUC of 92.14%. In the test data set, the accuracy of all methods including the RF method accuracy decreased (92.6 to 85.5%). Conclusions: The random forest showed high accuracy in train and moderate accuracy in the test data. We suggest more regional open-access intensive care databases that can aid making machine learning a bigger support for healthcare personnel. [ABSTRACT FROM AUTHOR]

Shear slowness data are crucial data in rock physics analysis and seismic reservoir characterization. In petrophysical formation evaluation, the use of sonic data is limited, and hence, sonic data, especially shear sonic, are not considered as critical. In many deep-water wells to save the cost of operations, shear sonic data are not recorded. In these scenarios for rock physics analysis, it becomes necessary to predict shear sonic data from other available datasets. Conventional techniques for shear slowness predictions rely on empirical relations and rock physics modeling. However, these approaches require extensive information as input and additionally carry assumptions and multiple prerequisites. Presently with the advancement of computing power Machine learning (ML) emerges as a robust and optimized technique for predicting precise DTS in quick time and with fewer input datasets. In this study, wells located in the deep-waters of the East Coast of India and penetrated siliciclastic reservoirs of both compacted sand and soft high porosity sands were used as input to train the ML algorithm. Random Forest machine learning algorithm is best used for both classification and regression tasks, and this algorithm is used here for the data prediction. As a comparison, the convolutional LSTM method is also used for data prediction. To comply with the geological variability in the prediction and to enhance the prediction accuracy, rock physics understandings were used as a guide in feature engineering. The RF prediction shows a good match of ~ 93%, and the LSTM model prediction shows ~ 94% correlation at validation well. Both the model predicted data show good agreement with the rock physics modeling interpretations at the target well. [ABSTRACT FROM AUTHOR]

The rapid growth in Biomedical Waste (BMW) generation poses significant challenges to effective BMW management, impacting the health and safety of healthcare and sanitation workers, patients, and the public. Segregating BMW at the source is critical to the BMW management system. However, in developing countries like India, less than half of the BMW is segregated at the source due to logistical challenges associated with manual segregation. This study addresses these challenges by developing a deep learning model for automated BMW segregation based on the Indian Government's Biomedical Waste Management Rules, 2016. To achieve this, the BMW-Seg-India dataset was created with images collected from a multi-specialty hospital in India and other publicly available datasets and organized into four classes: yellow, red, white, and blue, in line with the guidelines. A novel deep-learning model, BMWNet52, employing a modified ResNet50 architecture, was trained with transfer learning on the dataset. The model achieved an accuracy of 97.99% in classifying the four categories, demonstrating that this deep-learning classifier is an effective solution for automated BMW segregation. The findings could lead to a fully autonomous BMW management system, integrating automated segregation and disposal, minimizing exposure to hazardous waste, and enhancing efficiency worldwide. [ABSTRACT FROM AUTHOR]

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the 'very high' landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction. Research Highlights: A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier. Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide. The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95. The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022. [ABSTRACT FROM AUTHOR]

With the high demand for fossil fuels, exploring the frontier areas for hydrocarbon reserves has become imperative. The recent discoveries in Gojalia, Sonamura, Baramura, and Sundalbari fields emphasize the need to explore additional anticlinal structures in Tripura for hydrocarbon exploration. Tulamura anticline (the study area) produced gas from Upper Bhuban, establishing hydrocarbon prospectivity in the northern part, but the southern part remains largely unexplored. An electro-log interpretation revealed the presence of sand facies deposited in a fining upward sequence, suggesting channel deposition. An integrated geophysical approach using seismic inversion and machine learning techniques was performed to delineate and characterize the litho-facies dispersal patterns in the Tulamura field. Spectral decomposition (12, 20 and 28 Hz) of stacked seismic data were RGB (red-green-blue) blended, revealing the southward striking channel geometry of the Bhuban Formation at a depth of 2220 m. The 3D P-impedance and Vp/Vs ratio volumes were estimated using the model-based pre-stack seismic inversion. Inversion results help discriminate among sand, shale and siltstone litho-facies. Petrophysical property (effective porosity) was predicted by combining the post-stack seismic attributes and well-log data using neural network modelling. The identified sand facies within the channel geometry exhibit relatively moderate to low P-impedance (9800–10600 m/s * gm/cm3), low Vp/Vs ratio (1.68–1.76), and moderately high effective porosity (8–13%) from surroundings, indicating favourable conditions for hydrocarbon accumulations. Shale between channels and major faults can create favourable stratigraphic entrapment, while an upward fining sequence suggests an intact top seal. This study advocates an integrated approach involving geophysical inversion and machine learning to identify optimal conditions for hydrocarbon accumulation within sand facies, supported by structural and stratigraphic entrapment. [ABSTRACT FROM AUTHOR]

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India. [ABSTRACT FROM AUTHOR]

BACKGROUND: Quantitatively analyzing the impact of UV radiation and noise during welding operations is essential to assess the exposure, identify potential hazards, and develop targeted safety protocols to ensure worker safety and adherence to safety regulations, especially in developing countries with inadequate adherence to safety standards and resources. OBJECTIVES: This study employs machine learning for predicting ultraviolet radiation and noise levels during welding, emphasizing worker safety. The focus is on the Indian foundry sector to gauge actual exposure vis-á-vis safety standards. MATERIALS AND METHODS: Ultraviolet radiation and noise emitted during the welding of a ferrous alloy were collected from three foundries in Agra, India. Five machine learning (ML) algorithms were applied for data analysis and prediction of UV radiation and noise levels, and a relative performance comparison was carried out on the compiled data against safety standards. RESULTS: Out of all the ML algorithms applied, the Support Vector Machine regression algorithm (RMSE = 356.93) obtained the best performance on UV radiation data, and the Random Forest algorithm (RMSE = 11.4) was found to give the best results for the noise level prediction task. CONCLUSIONS: This work represents the first known application of machine learning techniques for predicting UV radiation and noise levels in arc welding processes. The results show the efficacy of algorithms such as SVM regression and Random Forest for the problem. Further, the datasets and ML algorithms implemented in the work will be made openly available to support further research endeavors in this and related areas. [ABSTRACT FROM AUTHOR]

The Indian west coast is under constant threat from climate change-induced hazards. Various social, economic, and infrastructural disparities along the coast cause significant variations in climate vulnerability. Current literature assesses vulnerability either over (1) a large area with poor spatial resolution or (2) a local area with better spatial resolution. The former assessments provide more comprehensive and broad insights into large spatial trends of vulnerability, while the latter provide more accurate and specific inputs needed by the local governments for effective intervention. However, there is a lack of studies that assess vulnerability simultaneously at a high-resolution and over a large geographic area, due to inadequacies in existing methodologies and difficulty in data management and analysis. This is a key gap that we address in our paper. We assess climate vulnerability of the entire Indian west coast at the village level, and propose a novel machine-learning based methodology tailored for high-resolution assessment over large geographic areas. This helped us produce the first high-resolution (i.e. village-level) climate vulnerability map of the entire Indian west coast. We found that the state of Maharashtra has the highest number of vulnerable villages and the state of Kerala has the least number of vulnerable villages. We collate and utilize a large dataset of 112 indicators describing socioeconomic characteristics, infrastructure and availability of financial services, among other aspects, to obtain a comprehensive picture of vulnerability. We analyze geospatial trends and attribute high vulnerability to specific indicators, which will help in effective decision-making at the village level. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is increasingly gaining importance in modern medicine. Yet, there is a dearth of knowledge on what the anesthesiologists know and think about AI in context. The objective of the study was to find conceptual, contextual, and translational aspects of AI among current practitioners of anesthesiology. Methods: This investigator-initiated, open-label, analytical, cross-sectional, noninterventional study was conducted from February 15, 2024, to March 21, 2024, with personal pursuance on consenting practitioners of anesthesiology using pretested partially open-ended questionnaire from across India. Results: Among 102 respondent anesthesiologists from diverse domains of anesthesiology and wide spectrum of experience and expertise, the majority were in the fifth decade of life, male and qualified professionals; the minority were faculty members and had a decade of practicing experience. Their concepts on techniques, applications, and safety of AI including levels and potentials of use were significant so far as predictive algorithms, assessing vital parameters and perioperative care were concerned. Their attitude on techniques and applications of AI was overall positive on innovation, integration, and employment of algorithms to reduce adverse events and on the incorporation of capacity building as a doable entity. The intended practice was overall significant on techniques and putting into effect an update and institutional practices of AI including predicting problems in real-life practice. However, the majority was apprehensive on the use of AI aligning it with a machine and expressed ethical concerns. Conclusion: The respondents felt that innovation, integration, and implementation of AI in anesthesia can heighten precision and safety in rural and remote areas of all surgical specialties. [ABSTRACT FROM AUTHOR]

Groundwater is under stress due to the growth of population, urbanization and industrialization, especially in developing countries. Consequently, there is a shortage of water in many parts of Indian cities. Thus, monitoring and assessing groundwater are crucial for effective management of water resources. The main objectives of our study is to explore the effectiveness of machine learning models for assessing groundwater potential in Lidder watershed in India. The site-specific factors influencing groundwater potential were selected for preparing groundwater potential zones in the study area. These factors were integrated for preparing the groundwater potential map using three models, namely logistic regression (LR), ensemble of random forest (LR-RF) and multilayer perceptron layer (LR-MLP). The area under curve of receiver operating characteristic (ROC) and several statistical performance measures were used to validate and compare the performance of the models. The validation revealed that the maximum value of AUC-ROC (0.946) was found for LR-RF for assessing groundwater potential than the LR and LR-MLP models. The results revealed low groundwater potential mostly found in the northern part of the watershed. High elevation, steep slope, highly alkaline and acidic soils, snow cover, barren land and hard basaltic rocks are attributed to low groundwater potential. Rainfall, vegetation, low elevation, younger alluvial plain, low drainage density and high lineament density have made groundwater potential high and very high in the southern part of the watershed. The central part of the watershed experienced moderate groundwater potential due to the piedmont alluvial plain, basal gravel, moderate rainfall, moderate drainage density, and scrub vegetation. We argue that the other geographical areas that are interested in properly managing groundwater resources can make use of the logistic regression and ensemble models of random forest and multilayer perceptron (MLP) for groundwater potential assessment. [ABSTRACT FROM AUTHOR]

It is paramount that solutions to questions of energy security for a developing nation be addressed through its internal resources. India, endowed with 23.8 billion tons of deep un-minable lignite, faces the challenge of economically sustainable extraction. This study presents a comprehensive assessment of lignite's suitability for underground coal gasification (UCG) compared to bituminous coal. Employing a multi-dimensional approach, combining single-step and distributed activation energy model of pyrolysis kinetic modeling with extensive physicochemical analysis (proximate and ultimate analyses, FTIR, SEM–EDX, XRD), revealed that lignite has a lower activation energy making it suitable for UCG. This finding, highlighted by kinetic modeling, is substantiated by the lignite's structural properties as identified in physicochemical analysis. This study leverages machine learning for higher heating value prediction, finding long short-term memory as the most effective model compared to five other models based on the R2 score and error values. Additionally, an XGBoost algorithm-based model predicts syngas heating value and yield while showcasing the application of machine learning in enhancing energy prediction accuracy. The economic analysis, applied for a 50 MW power plant framework, determines the unit costs for syngas and electricity production to be 7.49 and 6.71 $/GJ and 53.68 and 59.93 $/MWh for the samples of lignite and bituminous coal, respectively. The sensitivity analysis revealed that the energy content in syngas is the most significant parameter. These comprehensive findings validate lignite's potential for energy production in India, offering insights for similar resource optimization in other developing countries. [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]

Background: Population ageing represents a significant global challenge, particularly pronounced in countries like India. Aims: This study aims to explore how factors such as socio-economic status, behaviour, and health influence healthy ageing across the Indian older population. Methods: In this study, we utilized the Longitudinal Ageing Study in India – wave 1 dataset for analysis purposes. Scores were generated for five dimensions of healthy aging, including physical, functional, mental, cognitive, and social aspects and these scores were treated as the target variables. Multivariate Regression Trees analysis was employed to identify the behavioural and socio-demographic factors associated with each dimension of healthy ageing. Results: Years of education emerge as crucial across all dimensions, positively impacting cognitive health and mitigating age-related decline in healthy ageing. Marital status, engagement in household activities, spiritual practices, and living arrangements impacts the scores of different aspects of healthy ageing. Gender disparities in healthy aging are noticeable in the 60–74 age group, with women generally having lower scores. Safety of the living environment is a crucial determinant of the mental health of the elderly across all age groups.These findings highlight the complex interplay of factors in healthy ageing outcomes. Conclusion: Our study emphasizes the pivotal role of education in fostering healthy ageing in India. Factors such as environmental safety and social participation also influence well-being. Targeted interventions addressing education, gender equality, safety, and healthcare access are vital for enhancing the ageing experience and overall well-being of older adults. [ABSTRACT FROM AUTHOR]

Speech digit recognition research is growing decisively, and a bulk of digit recognition algorithms are used in European and a few Asian languages. Kui is a low-resourced tribal language locally used in several states of India. Despite its significance, there is not much research on Kui's speech. This research aims to present an in-depth analysis of novel Kui digit recognition using predefined machine learning (ML) techniques. For this purpose, we first gathered spoken numbers i.e. from 0 to 9 of eight different speakers containing a total of 200 words. Secondly, we choose the numbers: ଶନୂ (zero), ଏକ (one), ଦୁଇ (two), ତିନି (three), ସାରି (four), ପାସ (five), ସଅ (six), ସାତ (seven), ଆଟ (eight), ନଅ (nine). Meanwhile, we build nine different ML models to recognize Kui digits that take the Mel-frequency cepstral coefficients (MFCCs) method to extract the relevant features for model predictions. Finally, we compared the performance of ML models for both augmented and non-augmented Kui data. The result shows that the SVM+Augmentation method for Kui digit recognition combined obtained the highest accuracy of 83% than other methods. Moreover, the difficulties and potential prospects for Kui digit recognition are also highlighted in this work. [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]

Introduction: The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). Method: This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. Conclusion: The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas. [ABSTRACT FROM AUTHOR]

Gap/Lag acceptance is the primary basis for analysing uncontrolled intersections. Misjudgement in gap/lag acceptance imparts high risk to the drivers. The gap refers to the temporal difference between consecutive vehicles on a major road, whereas lag is a part of the available gap, occasionally coinciding with the first gap. Even though both are different in real scenarios, studies do not consider them separate. The lag acceptance behaviour of drivers must be studied thoroughly because the acceptance of shorter lags is more in developing countries due to the aggressive behaviour of drivers, which might lead to road crashes. However, such studies are very scarce compared to gap acceptance studies. A study of the lag acceptance process is essential for improved traffic safety and operational efficiency at skewed uncontrolled intersections. This study adopted machine-learning techniques to predict the lag acceptance decision of drivers to examine how it performs compared to commonly used methods. Data were collected at six intersections from various cities in Kerala, India, during peak hours. Artificial Neural Network (ANN), Logistic Regression (LR) and Support Vector Machine (SVM) models were developed, and their performance was compared. The occupancy time approach was used to determine the critical lag. The goodness of fit measures shows that the ANN model outperforms the LR and SVM models, with an accuracy of 93.6%. Furthermore, goodness-of-fit measures such as Fl score and R² values are 0.964 and 0.892, indicating that the prediction of the ANN model is excellent. Lag sizes of less than 2.7, 3.5, and 3.0 seconds were shown to be less safe, corresponding to right-turn from major, rightturn from minor and through from major roads. [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]

Background: Construction workers often experience work-related musculoskeletal disorders (MSDs) at a high rate. The poor performance of workers due to its presence is a serious concern to all the stakeholders and it is necessary to diagnose before it develops. The study aimed to ascertain the performance of machine learning (ML) classifiers and multi-layer perceptron (MLP) neural networks in predicting MSDs. Materials and Methods: The cross-sectional study utilized the data on potential MSD risk factors collected from 1040 construction workers on infrastructure projects across different states in India. The data was gathered through direct interactions with the construction workers and also, through the health records maintained by the safety department of the project sites. Stratified random sampling was the approach used for sampling. The prediction of the development of MSDs is based on nine features. In this study, Naive Bayes (NB), K-Nearest Neighbors (KNN), and XGBoost classifiers were applied to predict the presence of MSDs, and the results were compared with the MLP neural network based on the metrics. Results: In predicting the presence of MSDs, XGBoost's classifier, with 91% accuracy, was superior to NB, KNN, and MLP neural networks having 87%, 72%, and 85% accuracy, respectively. A powerful prediction tool has been developed to diagnose MSDs and effectively interpret the outcomes confidently. Conclusions: The performance metrics of the XGBoost classifier resulted in the best compared with the other classifiers. The prediction tool is useful to diagnose the prevalence of MSDs in the early stages. [ABSTRACT FROM AUTHOR]

Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability. [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]

Young generation of every country is the future of the country. The country with the highest GER in higher education will be more successful in all the terms (Keller, K.R.I., 2021). India's GER in higher education in 2018-19 was 26.3, and in 2019-20 is 27.1.It is observed from statistics that it is which is increased. Students are enrolling for higher education but many fails to complete it (Ministry of Education, Government of India, AISHE Report 2019-20). This leads to the need of identification of reasons of students' academic success or failure. If we predict students' academic success or failure at the initial stages of their graduation period will help to take preventive measures and increase passing percentage. Student academic success is one of the criteria for accessing quality of the educational institutions, and it is one of the crucial components. There are different aspects of students' academic success, such as exam-oriented, employment-oriented, and higher study-oriented. [ABSTRACT FROM AUTHOR]

Air pollution is a significant health concern in India. As a developing country, India has numerous issues such as air pollution and other issues. The aim of this study is to design and develop a system that is used to monitor and predict the air quality index (AQI) of the area using machine learning and IoT (Internet of Things). The system will generate AQI values and a line plot graph for future forecasting values. The proposed system will make it simple, convenient, and convenient for users to monitor air quality. It will also be used to forecast the Air Quality Index of the region using the Zhongli F1-AQI. The performance of the proposed system is evaluated using 5 different methods with and without imputation. [ABSTRACT FROM AUTHOR]

Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh. [ABSTRACT FROM AUTHOR]

Background: The role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance. Purpose: The purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features. Materials and Methods: Pretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation. Results: The average prediction accuracy was found to be 0.65 (95% CI: 0.60– 0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively. Conclusion: Our study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients. [ABSTRACT FROM AUTHOR]

This project introduces a comprehensive agricultural decision support system for India, combining machine learning models and real-time data sources to predict the optimal crop selection for a given state, district, and season. Leveraging meteorological data, local soil conditions, and historical crop performance, the system provides precise recommendations for farmers, enhancing crop yield and resource utilization. In addition, it offers real-time news feeds relevant to the agricultural sector and current meteorological weather updates for farmers, empowering them with the information needed to make informed decisions and adapt to changing conditions. This innovative solution contributes to sustainable farming practices and economic viability while addressing the dynamic challenges of Indian agriculture. [ABSTRACT FROM AUTHOR]

India's agricultural sector has been grappling with the adverse effects of climate change over the last two decades, resulting in the diminished performance of various crops. Predicting crop yields well in advance of harvest holdsimmense significance, aiding policymakers and farmers in making informed decisions regarding marketing and storage. This project endeavors to provide farmers with a tool that offers predictive insights into crop yields before they embark on the cultivation process. The proposed solution involves thedevelopment of a credit card firm's need to be on the lookout for a fraudulent prototype for an interactive prediction system, featuring a user-friendly web-based graphical interface and employing machine learning algorithms. The prediction results are readily accessible to farmers, enabling them to plan and strategize effectively. In the face of challenges posed by climatic variables such as weather, temperature, humidity, rainfall, and moisture, this project harnesses various data analytics techniques and algorithms to enhance crop yield predictions. The Random Forest algorithm, a robust and widely-used supervised machine learning method capable of both classification and regression tasks, is leveraged for this purpose. [ABSTRACT FROM AUTHOR]

In today’s time we can see that everything is depend on the Information superhighway means that many people use their instant on the web. India is also known as a DigitalIndia. Many of the activities conducted online, therefore here is a possibility tomake the data corrupt or add any miscellaneous activity to hack our information. Hackers try to hack that data which is generally used by us like education websites, shopping websites etc. they add or do minor change in the URL of any website to hack ourdata which is harm for us, that’s why we are going to implement that type of model whichwill help to detect any miscellaneous URL of any websites. We will use ML algorithms likeRandom Forest, XG Boost, Light GBM model to easily detection. As a result, these ML algorithms give us the best accuracy. [ABSTRACT FROM AUTHOR]

Farmers in India are threatened by animal damage, which results in lower yields. It is critical to safeguard crops against animal damage. To overcome the fore mentioned issues and achieve our goal, machines are used. The entry of the animals is detected using the deep learning model, and an alert is notified to the farmer. Depending on the requirements, the automatic irrigation system feeds or drains water. Water would be sprayed automatically in the event of a fire disaster. Smart irrigation and crop protection makes the protection in the agricultural field where it protects from various crop diseases and makes the crop from the protection to the disease. The crop in the field system makes the use of deep learning algorithms which makes the process of protection and detection. The crop protection process involves the operation with the system which detects ad prevents from the operation of the system from the crops where it makes the use of various operation. The smart irrigation process involves the operation of the system with the various operation where it makes the detection of the system with the deep learning algorithms and makes the detection technique in the system. The crop in the fields are affected with the various operation methods which makes the operation in the system with the use of Arduino with ESP8266 WiFi module. The various sensors used in the system makes the sensing unit in the external environment which makes the use of motor driver which controls the crop water irrigation control in the system. The motor is controlled with the relay unit where it is connected to the sprayer which sprays the water in the field evenly to the crops. [ABSTRACT FROM AUTHOR]

In India, Agriculture plays a pivotal role in the Indian economy, contributing approximately 17% to the total GDP and employing over 60% of the population. Not only in India, agriculture sector is one of the most important sectors in all over the world. In the past few years many machine learning algorithms are being used to recommend the best crop for farmers under the different conditions. It is important to recommend the best crop to the farmers not only for the better growth but also for the decrease the risk of farmers. Tn this paper we are doing the review of different machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine, Extra Trees, Decision Tree, Randam Forest and many more. And then we find out the best algorithms based on their accuracy rate and used that algorithm in our project to recommend the crop. It also requires the dataset to train our system. So, we take the dataset of values like N, P, K, temperature, humidity, ph, and rainfall. [ABSTRACT FROM AUTHOR]

Every year, India loses nearly 1.5 million citizens due to road accidents. In this work, we try to implement a two pronged approach to reduce the number road accidents in India. Firstly, we create a Machine learning model to predict road accidents subject to different road categories in India, using data from publicly available government dataset. Parallelly, we have a web application that we can use to decentralize the mapping of accident hot-spots via OpenStreetMaps. The hot spot markers vary in their intensity depending upon the accidents occurring in that locality. We also categorize the accidents into National highways, State highways and Other roads for more accurate measures so that various institutions can use them to make policies to reduce the mortality of road accidents in India. The results derived from the model are also presented that demonstrates the high efficiency of the model. [ABSTRACT FROM AUTHOR]

Air pollution is a considerable issue to human health and requires prompt reduction in anthropogenic zones like industrial plants. Every day, harmful pollutants including CO, CO2, particulate matter, NO2, SO2, and NH3 are discharged into our environment. The mining industry stands out as a key source of air pollution, particularly due to the substantial generation of Particulate Matter. This leads to an unhealthy atmosphere for both mine workers and neighboring communities. This study focuses on monitoring air quality in Jharkhand, known as India's largest coal-producing state in India. The state hosts 38 opencast coal mines and 5 underground coal mines. Accurate prediction of air quality is crucial. Traditional measurement methods often yield imprecise results and require complex mathematical computations. A variety of supervised algorithms used for prediction logistic regression with 0.7482, decision tree classifier with 0.9989, random forest with 0.9989, and KNN with 0.9898 accuracy. [ABSTRACT FROM AUTHOR]

The study critically reviews the existing literature on inflation forecasting in India, emphasizing the limitations of traditional econometric models in capturing intricate relationships among macroeconomic variables influencing inflation. In response, Machine Learning (ML) and Deep Learning (DL) models are proposed for inflation forecasting, leveraging a dataset comprising Indian macroeconomic variables. The devised model undergoes training and testing through various ML and DL techniques, encompassing LR, K-NN, DT, SVM (Linear Kernel), SVM (RBF Kernel), NN, RF, GB, AdaBoost, and XGBoost. Comparative analysis against traditional econometric models demonstrates the superior predictive performance of the ML and DL model. Notably, the convolutional neural network model stands out as the most accurate forecasting techniques. This study also identifies key variables influencing inflation rates in India, including interest rates, exchange rates, and fuel prices. The results underscore the potential of ML and DL methods in enhancing inflation forecasting in India, utilizing the latest dataset collected through survey methods across diverse Indian cities. Furthermore, XGBoost emerges as the optimal classifier for predicting inflation in India. These findings hold significant implications for policymakers and central banks, offering valuable insights for informed decision-making and the formulation of effective monetary policies in the context of developing countries. [ABSTRACT FROM AUTHOR]

This website proposes comprehensive information about educational scope and possibilities in the Arts, Science, and Commerce streams. This platform serves as a dynamic hub where students can explore various academic options, empowering them to make well–informed decisions about their educational journey.It mainly aims toward career scope in Arts, Science, and Commerce, Entrance exams that are available for engineering degrees, and different engineering courses available in India. Prospective students can easily search for institutions based on factors such as location, field of study, admission criteria, and more. Additionally, information about scholarships, entrance exams, and application procedures streamlines the often complex and overwhelming process of applying to colleges and universities. It is an education based website with college prediction using machine learning algorithm like linear regression and random forest with Tkinter library. EduVerse main aim is to help students to get information about the courses, entrance exam, scholarships and colleges’ admission criteria and all this thing at one place is the main motive of this website. Our application “EduVerse” is aimed to help students understand and know all the career opportunities and to make confident choices about their educational pursuits. [ABSTRACT FROM AUTHOR]

Sericulture is the process of cultivating silkworm cocoons for the production of silks. The quality silk production requires quality seed production which in turn requires accurate classification of male and female pupa in grainage centers. The challenges in the current methods of silkworm cocoon sex classification using manual observation lie in the time-consuming nature of the process, potential human error, and difficulties in accurately discerning subtle morphological differences between male and female cocoons. FC1 and FC2 single hybrid variety breed pupa are commonly used in south India for the production of high yielding double hybrid bivoltine silkworm seeds. In this study, 1579 FC1 and 1669 FC2 variety samples were used for the classification process. To overcome the challenges of present physical observation by expert employees, camera images of FC1 and FC2 cocoons were used in this study for sex classification. The proposed model used Histogram Oriented Gradient (HOG) feature descriptor of cocoon samples. Linear Discriminant Analysis (LDA) was applied on the feature vector to reduce the dimension and this feature matrix was given to the classical machine learning algorithms support vector machine (SVM), k-nearest neighbors (kNN), and gaussian naïve bayes for classification with stratified 10-fold cross validation. The results showed that for FC1 data HOG + LDA + Naïve Bayes performed better with a mean accuracy of 95.3% and for FC2 data HOG + LDA + KNN attained a mean accuracy of 96.2%. Our results suggest that this camera imaging method can be used efficiently in the classification based on the cocoon size and shape of different breeds. [ABSTRACT FROM AUTHOR]

Crop yield predictions are important for crop monitoring and agronomic management. The traditional methods for yield predictions are complicated and resource consuming. With the availability of affordable handheld imaging and computing devices, the image processing-based yield prediction methods are gaining popularity. In this work, RGB images of rice panicles are captured using DSLR camera with simple background and processed to determine the panicle area in terms of number of pixels. A machine learning-based model is developed to make predictions for rice yield. The model is trained to make predictions on unseen data. Various machine learning-based regression algorithms including decision tree, random forest, support vector machine, and convolution neural network are tested. The experiments are performed on a publically available dataset from China as well as on a self-acquired dataset in India. The results have shown that image processing and machine learning-based methods can make yield predictions satisfactorily as evident from the coefficient of determination ( R 2 ) that ranges 0.80–0.97 for different cultivars. The prediction error is determined in terms of root mean square error (RMSE) and mean absolute error (MAE). RMSE for different methods lies between 0.14 and 0.40, whereas MAE varies from 0.11 to 0.30. Among the tested algorithms, linear regression achieved the best precision with R 2 = 0.97, RMSE = 0.14, and MAE = 0.11. [ABSTRACT FROM AUTHOR]