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The article provides a quantitative and qualitative description of machine learning research in India using measures such as growth rate, global publication share, national-level share of international collaborative papers, citations per paper, and highly cited papers. The data for the study was sourced from Scopus database covering 12-year period 2006-17. The study finds that the United States is the world leader in the machine learning research, followed by China, United Kingdom, Germany, and India. India registered 52.46% annual growth in the subject, more than twice that of the world (22.03%). India's citation impact was 5.34 citations per paper and its national share of highly cited papers was 0.68% (27 papers). The leading Indian research organizations in machine learning research include Vellore Institute of Technology, Anna University, Chennai, Indian Institute of Technology, New Delhi, Indian Institute of Science (IIS), Bangalore, and so on. The top 15 journals that accounted for 31.46% of India's output include International Journal of Machine Learning and Cybernetics (with 82 papers), International Journal of Applied Engineering Research (77 papers), Journal of Machine Learning Research (46 papers), Neurocomputing (44 papers), and so on. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [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]

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction. [ABSTRACT FROM AUTHOR]

Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE = ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs. [ABSTRACT FROM AUTHOR]

Numerous studies emphasize accuracy in machine learning regression models, yet scalability and execution efficiency are often overlooked, critical for large datasets or extensive computations. This paper introduces a scalable, distributed Spark MLlib regression model through the best subset selection approach to predict Covid-19 statistics in India, demonstrating high accuracy, scalability, and execution efficiency. Notably, limited research focuses on tree-based regression, particularly gradient boost regression, in the context of the Covid-19 dataset. The proposed work optimizes regression models for accuracy and execution time on Spark clusters of varying sizes using the best subset selection approach. Evaluation encompasses Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R2 Error for accuracy, and execution time analysis. Results indicate superior prediction accuracy in tree-based regression, with Gradient Boosted Tree Regression (GBTR) leading, and Random Forest Regression (RFR) surpassing Decision Tree Regression (DTR). Accuracy remains consistent across Python library, Spark MLlib on a single machine, and clusters of varying sizes, with Spark MLlib displaying lower execution times than Python's machine learning library on a single machine. Furthermore, execution times decrease substantially within Spark clusters, particularly for the iterative GBTR. This research uncovers scalability and execution efficiency aspects, highlighting tree-based regression's accuracy and advocating for Spark MLlib's efficacy in enhancing execution efficiency, especially across multi-node clusters. [ABSTRACT FROM AUTHOR]

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic induced hazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India. [ABSTRACT FROM AUTHOR]

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model's performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model. [ABSTRACT FROM AUTHOR]

The COVID-19 has affected and threatened the world health system very critically throughout the globe. In order to take preventive actions by the agencies in dealing with such a pandemic situation, it becomes very necessary to develop a system to analyze the impact of environmental parameters on the spread of this virus. Machine learning algorithms and artificial Intelligence may play an important role in the detection and analysis of the spread of COVID-19. This paper proposed a twinned gradient boosting machine (GBM) to analyze the impact of environmental parameters on the spread, recovery, and mortality rate of this virus in India. The proposed paper exploited the four weather parameters (temperature, humidity, atmospheric pressure, and wind speed) and two air pollution parameters (PM2.5 and PM10) as input to predict the infection, recovery, and mortality rate of its spread. The algorithm of the GBM model has been optimized in its four distributions for best performance by tuning its parameters. The performance of the GBM is reported as excellent (where R2 = 0.99) in training for the combined dataset comprises all three outcomes i.e. infection, recovery and mortality rates. The proposed approach achieved the best prediction results for the state, which is worst affected and highest variation in the atmospheric factors and air pollution level. [ABSTRACT FROM AUTHOR]

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

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

Early-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel 'FeatureBoostThyro' (FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of model selection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset. [ABSTRACT FROM AUTHOR]

At the heart of India's livelihood lies agriculture, a foundational and ever-evolving sector. The realm of agriculture faces modern challenges, encompassing unpredictable climate fluctuations, insufficient irrigation infrastructure, and erratic weather conditions. Amidst these challenges, machine learning has emerged as a valuable tool for evaluating soil fertility and Crop assessment in agriculture. Ensemble machine learning methodologies have garnered attention for their potential to enhance predictive capabilities. These techniques involve constructing meta-classifiers that collectively contribute to improved predictive accuracy. This paper centers on the analysis of soil data acquired from testing laboratories, to predict fertility based on a comprehensive dataset. The study employs prominent ensemble machine learning algorithms, specifically focusing on boosting techniques, to elevate predictive accuracy and ensure heightened consistency. The assessment of soil fertility categories involved the examination of 12 carefully chosen attributes. Diverse soil parameters were measured to facilitate the prediction of soil fertility levels. The outcomes of the experimentation revealed that the application of the boosting technique using the Xgboost algorithm yielded superior accuracy compared to alternative ensemble classifiers, achieving a remarkable 96% accuracy rate. [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]

The rampant adoption of digital technologies made momentous changes in all economic sectors. The agriculture sector cannot abstain from the digital revolution. Agriculture and farming are one of the oldest and most important professions in India. The sector remains the backbone of the Indian rural economy, which desperately demands technological impetus for the socio-economic development of rural areas. Smart agriculture is a revolution in the agriculture industry which helps to guide the actions that are required to modify and reorient the agricultural systems. The paper made an extensive survey of various technologies proposed for the agriculture sector. We have surveyed various smart agricultural applications developed and proposed a taxonomy for classifying them. Network infrastructure and connectivity remains the major challenge for rural areas. The paper explores the viability of deploying IoT-based technologies in agricultural sectors along machine learning techniques to optimize resource utilization, planning and cultivation, marketing, pesticide selection, price prediction, etc. An in-depth coverage of recent research works is also mentioned which will help the future researchers to address specific challenge and adopt suitable technology to help the farmers to improve their productivity and better decision making in cultivation. Apart from listing the applications, we also propose an architecture for smart agriculture and implemented smart price prediction model for crops like cotton and cardamom along with a prototype for smart irrigation. [ABSTRACT FROM AUTHOR]

The emergent technology has been increasingly incorporated with the tourism industry. In the digital world, social media plays an important role in identifying the most visited tourist places. More than 80% of people commonly used the social media platform called 'Twitter'. By using tweet reviews, tourists around the world know about the feelings, suggestions and opinions of most visited tourist places in the selected region. This paper recommends an approach for the development of trip planning in South India based on tourist's preferences. For the research, we use Twitter Application Programming Interface to collect information about the most visited tourist places in South India by using tweets reviews and store it as South India Tourism Tweet reviews database. This paper mainly focuses on analyzing Twitter reviews, which are very helpful to locate the most visited tourist place. In the tweets where reviews are mostly unstructured and heterogeneous, which are then classified into positive tweets, negative tweets and neutral tweets by using the preprocessing technique and machine learning algorithm called support vector machine. Performance measures are calculated by using the classification results. Finally, the results are plotted by using the classification results and performance measures as a wordcloud. This can be very useful for tourists to select the most visited tourist locations. The proposed system can satisfy individual tourist needs. [ABSTRACT FROM AUTHOR]

Globally, the rapid loss of mangrove forests often creates long-lasting environmental damage alongside coastlines, mudflats, and river banks. All-weather, physical monitoring of such areas is almost impossible because of inaccessibility to swampy areas and a hostile substrate. Subsequently, conventional field surveys are relatively unavailable to monitor human encroachment in coastal areas like the Mumbai mangrove region of India. In this context, the polarimetric synthetic aperture radar (PolSAR) remote sensing tool becomes a potential candidate for mangrove conservation and management. Since the Mumbai mangrove region of India exists over the extensive land cover, the large-scale classification can articulate the continuous encroachment because of the location of human settlements or activities across this metropolitan coastal city. For this, the traditional algorithms need the essential improvisation to apply for large-scale data analysis, aiming simultaneously at getting the highest decisive and time efficiency. Here, we introduce a shallow learning model of MapReduce-based Multi-Layer Perception (MLP) algorithm to classify the hybrid compact polarimetric (CP) and fully polarimetric (FP) feature space. Even though a shallow learning model of the automated method is easily scalable, it requires a distinctive shallow learnable feature set for better land cover classification. In this effort, this paper investigates the efficacy of derived feature space compared to direct polarimetric measurements of sensors and shows that the shallow learnable feature set is more effective with both CP & FP observations. Simultaneously, the relevancy of the proposed distributed model of MLP is also justified compared to distributed extreme learning machine (DELM) algorithm and provides a practically implementable scaled-MLP algorithm of shallow learning model. Ultimately, this paper comes up with a better polarimetric signature of land types for both the CP and FP datasets, which can be used in an alternative manner as per data availability for a multi-sensor data analysis. [ABSTRACT FROM AUTHOR]

Background: This research adds to the growing body of work demonstrating the vital role of image categorization in the medical sector. The efficiency of illness diagnosis is being greatly enhanced using image classification. A brain tumor is a collection of abnormal cells in the brain. Diagnostic precision is required when looking for a tumor in the brain because even the smallest error in human judgement can have disastrous results. To get around this problem, the medical community has implemented an automated brain tumor segmentation system. A variety of methods are employed to segment a brain tumor, but the results are inconsistent. To improve the quality of MRI images, we present our findings in this paper. Deep learning methods for image segmentation and classification are discussed in this paper. Methods: In this paper we a very robust deep learning method for image segmentation and classification. For image classification, we are employing the enhanced faster R-CNN method. The ResNet50 model is used to differentiate between tumor and non-tumor images. The next step in this framework is to use Deep Residual UNET for segmentation. RESUNET is an FCNN that maximizes efficiency. The proposed method works well in terms of its ability to find and classify things accurately. Results: The accuracy rate for identifying tumours in MRI scans using the proposed technique is 94.23%. Using transfer learning with Resnet50 as the base model and the use of discriminative learning rates, our model achieved superior results than other recent models. Conclusion: Within the scope of this study, we have integrated the residual networks with the U-Net to make the network stronger. This strategy improves the efficiency of classification and segmentation tools. To achieve a higher level of accuracy, the model may be trained further, or additional data may be applied in the training process. [ABSTRACT FROM AUTHOR]

Introduction: Accurate estimation of dementia prevalence is essential for making effective public and social care policy to support individuals and families suffering from the disease. The purpose of this paper is to estimate the prevalence of dementia in India using a semi-supervised machine learning approach based on a large nationally representative sample. Methods: The sample of this study is adults 60 years or older in the wave 1 (2017–2019) of the Longitudinal Aging Study in India (LASI). A subsample in LASI received extensive cognitive assessment and clinical consensus ratings and therefore has diagnoses of dementia. A semi-supervised machine learning model was developed to predict the status of dementia for LASI participants without diagnoses. After obtaining the predictions, sampling weights and age standardization to the World Health Organization (WHO) standard population were applied to generate the estimate for prevalence of dementia in India. Results: The prevalence of dementia for those aged 60 years and older in India was 8.44% (95% CI: 7.89%–9.01%). The age-standardized prevalence was estimated to be 8.94% (95% CI: 8.36%–9.55%). The prevalence of dementia was greater for those who were older, were females, received no education, and lived in rural areas. Discussion: The prevalence of dementia in India may be higher than prior estimates derived from local studies. These prevalence estimates provide the information necessary for making long-term planning of public and social care policy. The semi-supervised machine learning approach adopted in this paper may also be useful for other large population aging studies that have a similar data structure. [ABSTRACT FROM AUTHOR]

The road is the most commonly used means of transportation and serves as a country's arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance. [ABSTRACT FROM AUTHOR]

Over the past decade, extensive research on stock market prediction using machine learning models has been conducted. In this framework, different approaches for data standardisation methods have been used for financial time series analysis and to assess the impact of data standardisation on the final prediction outcome. The paper uses the feature-level optimal rolling-window batch data standardisation method to improve the machine learning model's predictive power significantly. Along with the standardisation method, the paper explores the performance of the automated feature interactions learner (Deep Cross Networks) effect on a plethora of technical indicators aiming at predicting the movements of the NIFTY50 index in India, as these predicted changes are reflected in options contracts. Feature-level optimal rolling window data standardisation can identify the optimal window of time such that the correlation between features and the response variable is maximized, with most features correlating at 0.7. In the experiment, 48% of important technical indicators negatively correlated with the response variable. The Deep Cross Network regression model combined with the optimal rolling window batch data standardisation method outperformed all other model configurations at weekly and monthly data frequency. It achieved a directional hit rate of 69.52% (weekly) and 79.17% (monthly) and root mean square error of 2.82 (weekly) and 5.01 (monthly), generating a profit 5.5 times (weekly) and 2.85 times (monthly) greater than the benchmark buy-and-hold strategy providing opposing evidence to the sub-martingale model. [ABSTRACT FROM AUTHOR]

Pregnancy and fetus development is an incredibly intricate biological process that can go wrong when the health of fetus or the mother gets worse. Currently, India has the highest number of stillbirths, with an estimated 5,92,100 deaths per year, and a WHO estimated rate of 22 per 1000 total births. Cardiotocography is one procedure used to check whether the fetus is growing as expected. This diagnostic procedure measures the mother's uterine contractions and the fetus' heartbeat, usually in the third trimester of pregnancy when the fetus heart is fully developed. This paper aims to classify the results into one of three states (physiological, suspicious or abnormal) based on cardiotocographic data using machine learning algorithms and compare the accuracies of different ML classification models such as logistic regression, k-nearest neighbor, decision tree, random forest classifier and support vector machine. [ABSTRACT FROM AUTHOR]

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ML target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ML algorithm proposed to improve the revenue of agriculture field. [ABSTRACT FROM AUTHOR]

The future of agriculture may be fully realized using knowledge of artificial intelligence (AI) accumulated by human expertise. With increasing climate change and population pressure challenges, new technologies, such as AI and RS in vital sectors like agriculture is urgently required to assess agricultural suitability, optimize productivity, and then support sustainable development. This paper presents bibliometric and review analyses on AI and geospatial analysis applied to the agricultural field. Based on the prestigious database, Scopus, thousands of documents were retrieved in the period 1992–2021 and processed using Scopus online tool and VOSviewer software. The results show a growing trend in the number of publications during this period. Earth and Planetary Sciences is the top domain, remote sensing (RS) is the leading source, while the United States and India are the most influential countries. RS, machine learning (ML), GIS, land-use, decision-making, decision trees, and satellite imagery are the most occurring topics in this search. Otherwise, research in this field is turning toward ML and deep learning techniques, whereas agricultural robots and antennas are the top trending tools. The current study provides specific information about the connections between artificial intelligence, geospatial analysis, and agriculture in the period 1992–2021, the fruitful period where the AI coupled to geospatial technologies is in full development. [ABSTRACT FROM AUTHOR]

According to researches, there are around 188 million unemployed people around the globe. We may find many job vacancies on job portals and across the internet to help the job seekers. India alone has more than a hundred job portals. One major issue people face here is that the job seekers are not sure if the employer is real or fake. Most of these portals do not have a system that could check if the employer, posting a job is real or fake. Scammers are making use of this opportunity to post fake job offers which might look genuine to the job seekers applying for it. This way the poor job seekers might lose a large amount of money and time. A best possible solution for this problem would be that the job portal itself being able to identify if the job being posted is real or fake. This paper suggests using a machine learning model to achieve this goal. The idea here is to use natural language processing to understand and analyze the job posting and then making use of a machine learning model to predict if the job posting is real or fake. The first step is to import a dataset which has real life real and fake job posting. In this project, Employment Scam Aegean Dataset provided by University of Aegean Laboratory of Information and Communication system Security is being used. This dataset contains 18000 samples containing real life job postings. Various text cleaning techniques like lemmatization, stop words removal and special characters and punctuation removal is done on the data. Once the text data is processed, various algorithms like Random Forest, Linear SVC, Gradient Boosting Classifier, Gaussian naïve Bayes classifier and XGB classifier is used to test the performance of the model. The best two algorithms with respect to the percentage of accuracy with which the models could classify real and fake job posting was taken into consideration. Random Forest and Linear SVC could give accuracy close to 98%. Both of these algorithms were tuned using GridSearchCV, a library function which is a part of sklearn's model selection package. After tuning, the performance of both these algorithms increased and Linear SVC gave a better accuracy score of 99%. Hence Linear SVC is being used in this project for predicting real and fake job posting on a job portal.. [ABSTRACT FROM AUTHOR]

Agricultural product costs play a significant part in the horticultural market. In India, vegetables, for example, tomatoes have the biggest supply and price variances among farming items. As tomatoes are grown around the year, outdoor and indoor, their yields change because of various factors, it is hard to settle tomatoes' inventory and costs. Although the Government puts numerous efforts to balance out the supply and costs of vegetables, continuous meteorological changes have prompted unstable supply and price fluctuations of vegetables. Accordingly, the right anticipating of vegetable costs is a significant issue. To oblige these, in this paper, an attempt has been made to dissect the costs and yield of tomatoes in India by utilizing a Machine Learning approach. This will unquestionably help the farmers and the Government if the anticipated costs are getting higher in the forthcoming months, then appropriate strategies can be made to diminish the costs of tomatoes. [ABSTRACT FROM AUTHOR]

Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. In terms of technical implementations and satisfaction analysis, results found acceptable with above average score. Proposed framework is easy to use by caregivers with even having little knowledge. Support individual with deficit to learn social verbal communication skill to survive in society. Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits. Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry. Design/methodology/approach: The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India. Findings: The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data. Originality/value: This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers. [ABSTRACT FROM AUTHOR]

Accurate prediction of rainfall can save farmers from crop damage as erratic rains in India have caused agricultural losses. Due to continuous climate change, rainfall and weather prediction has become essential. Rain causes damage to people and property. So, rainfall forecasting is crucial for crop as our country’s economy is still heavily dependent on agriculture. Using machine learning (ML) and deep learning (DL) models, we can train these models for the rainfall dataset and make predictions. In this paper, we estimate rainfall over the Karnataka region using a stacking ensemble model on a rainfall dataset collected between 1901 and 2015. [ABSTRACT FROM AUTHOR]

Purpose: In India, one of the prime focuses of a post-graduate management program is to prepare students and make them job-ready. Masters in Business Management (MBA) program helps students to imbibe theoretical and practical skills which are required by the industry, which can make them hit the ground running from the day they start their career. Many students (almost 40–50%) get pre-placement offers based on their performance in summer internship. The selection for summer interns by the corporate happens within a few months of the student joining the MBA program. Signaling theory in education indicates that the level of productivity of an individual is independent of education, but the educational qualification acts as a testimony for higher ability. However, this theory does not explain the reason for the mismatch between "education and work" or "education and the disparity in salary" between individuals who earn differently but have the same qualification. The paper aims to explore three attributes namely – "employability"– the chance of being employable; "pre-placement offers" – the chance of securing a job offer based on the performance in internship and "salary" – the chance of bagging a good job offer with a high salary. Design/methodology/approach: The authors have used longitudinal data consisting of 1,202 students who graduated from reputable business schools (B-Schools) in India. In the study, the authors have used predictive analytics on six years data set that have been gathered. The authors have considered 24 attributes including educational background at the graduate level (BE, B Tech, B Com, BSc, BBA and others), score secured in class ten (high, medium and low), score secured in class twelve (high, medium and low), score secured in graduation (high, medium and low), competency in soft skills (high, medium and low), participation in co-curricular activities (high, medium and low) and social engagement status (high, medium and low). Findings: The findings of the study contradict the signaling theory in education. The findings suggest that the educational qualification alone cannot be the predictor of the employability and the salary offered to the student. The authors note that the better performance at a lower level of qualification (class 12) is the strong predictor in comparison to the student performance at their graduation and post-graduation level. The authors further observed at the post-graduate management education level that soft skills and participation in co-curricular activities are the major deciding factors to predict employability and pre-placement job opportunity and marks secured in class 12 is one more factor that gets added to this list to predict salary. The paper can immensely help management graduates to focus on key aspects that can help to hone appropriate skills and also can help management institutions to select the right students for management programs. Research limitations/implications: The analysis and the predictive model may apply to Indian B-Schools wherein the quality of students are almost the same or better. Predictive analytics has been used to explain the employability of management graduates alone and not any other. Practical implications: The authors' study might be useful for those students who often fail to understand "what" skills are the most important predictors of their performance in the pre-placement and final-placement interviews. Moreover, the study may serve as a useful guide to those organizations that often face dilemmas to understand "how" to select an ideal candidate for the particular job profile from a campus. Originality/value: The authors believe that the current study is one of the few studies that have attempted to examine the employability of management graduates using predictive analytics. The study further contradicts that the signaling theory in education does not help better explain the employability of the students in extremely high-paced business environments. [ABSTRACT FROM AUTHOR]

The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle. [ABSTRACT FROM AUTHOR]

This paper explores the magnitude of changes that is happening in the Indian banking sector in terms of the technology used, human resource deployed and change in the customer experience. The emergence of gig workforce has made banks rework their strategy. Here, we will describe how Indian banking sector is battling legacy systems and trying to adopt to latest technologies. The gig economy which has been playing a major role in many other sectors have not made a significant impact in the banking industry. The paper discusses the reasons this and gives a first-hand perspective of how the banking sector is getting disrupted. [ABSTRACT FROM AUTHOR]

The government of India is the final authority for country-wide policy making and policy implementation in the country. Recent times have witnessed increasing use of data-driven approaches in policy and decision making. This is true for public policy formulation as well. This paper is an effort to highlight how data science and artificial intelligence have the potential to revolutionize the field of public policy in India. The paper discusses some such applications and lists down the likely challenges. An extensive review of the research activities in this field has been undertaken. Cases of data science and artificial intelligence reviewed in this paper span over various sectors of public policy such as- policy making process, health, education, environment, agriculture, economy, and security. This paper, thus, aims to facilitate collaboration in the fields of public policy and data technology, in order to enable higher levels of public service in India. [ABSTRACT FROM AUTHOR]

This paper attempts to evaluate the predictive ability of four machine learning models: logit, decision tree, random forest and 2-class support vector machine and to identify the key predictors of default. The models are applied on a dataset of 57 companies under the Insolvency and Bankruptcy Code (IBC) in India and a matched sample of 55 solvent companies spanning over ten years from FY06-FY 2016. The solvent companies are matched on size (log of total assets) and sector and are rated 'AAA' and 'AA'. Thirty-one explanatory variables are identified by us for the study which include (i) financial ratios, (ii) size and age of the company, (iii) ownership pattern and (iv) market ratios. The empirical findings reveal that random forest strongly outperforms all other models in their predictive ability, followed by SVM, DT and logit model. The findings also confirm relevance of size and age of the firm, market ratios and ownership pattern as predictors of default in addition to financial ratios. We conclude that both parametric (logit) and non-parametric models are useful in the study of default prediction as reflected in the robustness of all models with accuracy of over 75%. These models can help banks in strategising their lending decisions based on credit quality of borrower firms. Our contribution is that to the best of our knowledge this is the first paper that is using the database of companies that are legally defined as insolvent and bankrupt and also taking a balanced sample to avoid biasness and inaccuracy from data imbalance. Also, this study has gone beyond traditional financial statements in identifying key default drivers. [ABSTRACT FROM AUTHOR]

Code-switching is a very common occurrence in social media communication, predominantly found in multilingual countries like India. Using more than one language in communication is known as codeswitching or code-mixing. Some of the important applications of codeswitch are machine translation (MT), shallow parsing, dialog systems, and semantic parsing. Identifying code-switch and monolingual information is useful for better communication in online networking websites. In this paper, we performed a character level n-gram approach to identify monolingual and code-switch information from English-Kannada social media data. We paralleled various machine learning techniques such as naïve Bayes (NB), support vector classifier (SVC), logistic regression (LR) and neural network (NN) on English-Kannada code-switch (EKCS) data. From the proposed approach, it is observed that the character level n-gram approach provides 1.8% to 4.1% of improvement in terms of Accuracy and 1.6% to 3.8% of improvement in F1-score. Also observed that SVC and NN techniques are outperformed in terms of accuracy (97.9%) and F1-score (98%) with character level n-gram. [ABSTRACT FROM AUTHOR]

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing "crop management", "soil management", and "water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management. [ABSTRACT FROM AUTHOR]

Escalated groundwater contamination around the world developed the idea of aquifer vulnerability, which has widely been employed globally to safeguard groundwater from contamination. Several extensive studies have been done to evaluate the degree of groundwater vulnerability using various techniques. However, a comprehensive assessment of existing literature in order to identify major research gaps associated with aquifer vulnerability is still required. The current study provides a critical overview of several groundwater risk assessment methodologies, including the GIS-based DRASTIC and modified DRASTIC methods, application of Artificial Intelligence (AI) and Machine Learning techniques, and integrated approach for both intrinsic and specific vulnerability assessment. The study discusses validation of DRASTIC model and its comparison with a few other methodologies as well. The paper also highlights the international approach (time dimension in groundwater vulnerability, integrated process based and isotopic studies, etc.) that needs to be practiced rigorously on a larger scale in India. A descriptive summary of the literature indicates that a multidisciplinary and integrated framework such as understanding contaminant type, uncertainties in vadose zone need to be implemented in a much more comprehensive way, especially in India. A conceptual framework that is both reasonably adaptable and scientifically sound must be created for specific vulnerability assessment. [ABSTRACT FROM AUTHOR]

Agriculture is the main source of income, food, employment, and livelihood for most rural people in India. Several crops can be destroyed yearly due to a lack of technical skills and changing weather patterns such as rainfall, temperature, and other atmospheric parameters that play an enormous role in determining crop yield and profit. Therefore, selecting a suitable crop to increase crop yield is an essential aspect of improving real-life farming scenarios. Anticipating crop yield is one of the major concerns in agriculture and plays a critical role in global, regional, and field decision-making. Crop yield forecasting is based on crop parameters and meteorological, atmospheric, and soil conditions. This paper introduces a crop recommendation and yield prediction system using a Hybrid Moth Flame Optimization with Machine Learning (HMFO-ML) model. The presented HMFO-ML method effectively recommends crops and forecasts crop yield accurately and promptly. The proposed model used a Probabilistic Neural Network (PNN) for crop recommendation and the Extreme Learning Machine (ELM) method for the crop yield forecasting process. The HMFO algorithm was used to improve the forecasting rate of the ELM approach. A wide-ranging simulation analysis was carried out to evaluate the HMFO-ML model, showing its advantages over other models, as it exhibited a maximum R² score of 98.82% and an accuracy of 99.67%. [ABSTRACT FROM AUTHOR]

In a survey conducted in the year 2020, we came to know that India's around 50% of population includes young people of the age group of 25 and students. Guiding this young mass in the right way and strengthening their future is a huge responsibility put over the head of the elder citizens of India such as their parents teachers and professors. This paper aims to build a model that can predict the students' competency level and the risk factors or the fields where he needs to put their effort to improve themselves, and this model also helps the parents, professors and Educational institutes to know about their children's and students in which zone they stand, are they ready to compete with others. This analysis is done by using different ML bifurcation algorithms. Also we aim to find the best classifier which can emerge with the highest predicting accuracy among all other classifiers to the above-said problem. The accuracy of 88.5% is achieved through the proposed machine learning algorithm for particular education datasets which have been taken into consideration. [ABSTRACT FROM AUTHOR]

Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN. [ABSTRACT FROM AUTHOR]

Aim/Purpose This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendations for Researchers Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the dataset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India. [ABSTRACT FROM AUTHOR]

India is developing at a fast speed as is the utilization of innovation in the developing areas of the country. A significant mass of the populace is as yet reliant and rehearsing horticulture as its essential type of revenue. India has been in a persistent tryst with its cultivating infra, rehearses and related networks since freedom. Price Prediction, these days, has turned into a vital agrarian issue which is to be addressed just in light of the accessible information. The point of this paper is to anticipate the yield cost for the following revolution. This work depends on observing appropriate information models that aides in accomplishing high exactness and over-simplification for price prediction, Yield prediction and fertilizer recommendation. For taking care of this issue, different Machine learning strategies were assessed on various information sets. This work presents a framework which involves information examination strategies to foresee the cost of the yield. The proposed framework will apply AI calculations and foresee the cost of the harvest in view of different factors like Area collected, Area planted and so on. This furnishes a rancher with knowledge of what the future cost of the yield that he will gather. Along these lines, the paper fosters a framework by incorporating information from different sources, information investigation, expectation examination which can assist with foreseeing the objective cost of the yield and increment the benefit edges of rancher helping them over a more drawn-out run. The total examination comes up to a resolution that XGBoost and Gradient boosting algorithm is the reasonable strategy for our task. [ABSTRACT FROM AUTHOR]

In India and over the world, Cancer has become a deadly disease that is adversely affecting women from different age groups. According to a survey, one in every 30 women suffers from this deadly disease in their lifetime. This project was first thought of because of the increase in cases of breast cancer and if we can detect it at an early stage then there is an increased chance of it getting cured. This paper lays a foundation in making the detection of cancer automated from histopathological images using deep learning algorithms so that more and more people can get it diagnosed at an early stage and subsequently get cured. To enhance the capability of histopathology image classification, a powerful deep learning algorithm and a sizable, varied dataset are required. In this paper, we have proposed an automatic and precise histopathological image analytical method, to detect breast cancer at an early stage. Deep learning techniques have recently made significant strides and produced outstanding results in the fields of computer vision and image processing, which has encouraged many researchers to use this method for the categorization of histopathology images. [ABSTRACT FROM AUTHOR]

A court of a country is the main source of justice, to the respective country's citizens. Every individual approaches courts to seek justice when his/her rights are violated. The main power house of these courts is the supreme court and each country has its own respective supreme court, in a country like India with a huge population every day hundreds and hundreds of new and appeal cases have been filed to the supreme court and seek justice. The objective of this paper is to classify the cases and to predict the behavior of the court (whether the appeal case will be accepted or dismissed by the Supreme Court). This paper gives the individual an ideology about how the case will be taken and treated by the court. The model considers past 20 years of cases from the Supreme Court of India database containing all types of cases to predict leave granted and Disposed off parameters. The proposed model is tested as three modules (i) classification module (ii) prediction module and (iii) baseline module and also compares various ML algorithms using Accuracy and F-measure as performance metrics. [ABSTRACT FROM AUTHOR]

The outburst of the pandemic COVID-19 in 2019 from the land of China to the entire world has brought a major change in people's living style. The severity of the disease is so much that more than 20 million people are affected by it worldwide. The recommended preventive measure to avoid the spread of this fatal disease is to maintain social distance leading to a significant economic disruption that could submerge the economies globally. However in order to keep the economies moving, the work from culture is adopted globally wherein the employees are connected via the internet and the work continues to happen. This paper aims to discover how people adopt such an attempt in the working culture. Also, the purpose of the paper is to find out its bearing on the employees' working capacity and whether they would like to continue with this arrangement in the future. The research employs primary data collected from 400 employees across various industries who are currently working from home. The research proposes advantages and challenges in working from home with a majority of employees preferring the method. [ABSTRACT FROM AUTHOR]

Diabetes is one of the most prevalent non-communicable diseases and is the 6th leading cause of death worldwide. It's a chronic metabolic disorder which has no cure, however, it is a highly treatable condition, if diagnosed and managed on time to avoid its complications. This paper explores and compares various machine learning (ML) approaches that can help in determining the risk of diabetes at an early stage and aid in improving the medical diagnosis of diabetes. The paper considers two real-world datasets one is a diabetic clinical dataset (DCA) collected from a medical practitioner in the state of Assam, India during the year 2017–2018 and other is public PIMA Indian diabetic dataset. To analyze the various machine learning techniques on DCA and PIMA Indian diabetic datasets for the classification of diabetic and non-diabetic patients, different classifiers like perceptron, Gaussian process, linear discriminant analysis, quadratic discriminant analysis, statistical gradient descent, ridge regression classifier, support vector machines, k-nearest neighbors, decision tree, naïve Bayes, logistic regression, random forest and ELM for multiquadric, RBF, sigmoid activation functions are used. The results of numerical experiments suggested that logistic regression yields better performance in comparison to the other techniques. [ABSTRACT FROM AUTHOR]

Background: Silicosis has been one of the most serious occupational public health problems worldwide for many decades. The global burden of silicosis is largely unknown, although it is thought to be more prevalent in low and medium-income countries. Individual studies among workers exposed to silica dust in various industries, however, reveal a high prevalence of silicosis in India. This paper is an updated review of the novel challenges and opportunities for silicosis prevention and control in India. Main body: The unregulated informal sector employs workers on contractual appointment thereby insulating the employers from legislative provisions. Due to a lack of awareness of the serious health risks and low-income levels, symptomatic workers tend to disregard the symptoms and continue working in dusty environments. To prevent any future dust exposure, the workers must be moved to an alternative job in the same factory where they will not be exposed to silica dust. Government regulatory bodies, on the other hand, must guarantee that factory owners relocate workers to another vocation as soon as they exhibit signs of silicosis. Technological advances such as artificial intelligence and machine learning might assist industries in implementing effective and cost-saving dust control measures. A surveillance system needs to be established for the early detection and tracking of all patients with silicosis. A pneumoconiosis elimination program encompassing health promotion, personal protection, diagnostic criteria, preventive measures, symptomatic management, prevention of silica dust exposure, treatment, and rehabilitation is felt important for wider adoption. Conclusion: Silica dust exposure and its consequences are fully preventable, with the benefits of prevention considerably outweighing the benefits of treating patients with silicosis. A comprehensive national health program on silicosis within the public health system would strengthen surveillance, notification, and management of workers exposed to silica dust in India. [ABSTRACT FROM AUTHOR]

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier's importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy. [ABSTRACT FROM AUTHOR]