Search

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]

In India, in metropolitan cities, a common problem seen is traffic jams or slowdown on traffic due to potholes. Most of the road accidents are due to potholes. In this paper, the authors have tried to ensure that the road surface quality and the potholes should be monitored continuously and must be repaired as necessary. The best possible distribution of resources could be attained when the real-time data is gathered from the mounted sensory system and displayed and visualized in a way (Google Maps) which is comprehensive and empirical. Different sensors would be mounted onto the public transport vehicles. The method mentioned in this paper uses the ultrasonic sensor and the accelerometer to send the data regarding the intensity of pothole straightaway to the cloud data storage, along with the location coordinates of the potholes and the time stamp. Different databases such as traffic density of the city, rain intensity of different areas is used to train different machine learning models to predict the intensity of potholes at the particular locations and get the best possible accuracy. The best-case accuracy attained was 35.5%. The pothole intensity data is visualized through a heat map application. This system helps in reducing the frequency of traffic jams in any city of India. The method suggested in the paper would help in reducing the number of accidents happening in India due to the potholes. [ABSTRACT FROM AUTHOR]

When it comes to cancer in Indian women, breast cancer is one of the most common and frequently occurring cancer types. In most of the developing countries like India, the overhead charges for breast cancer are very high which leads to a poor mortality rate when compared to western countries. As of now, large numbers of researches have been carried out based on deep learning and machine learning prediction of Breast cancer. This paper reviews some of the existing machine learning algorithms like Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machines (SVMs) in linear (SVML), polynomial (SVM-P), and radial basis function (SVM-RBF) on the Breast Cancer Wisconsin (BCW) data set using Python. An experimental result shows that SVM-L outperforms all the above algorithms with 96.5% accuracy. [ABSTRACT FROM AUTHOR]

"Novel Coronavirus", also known as COVID-19 has spread globally which is sparked in Wuhan, the capital city of Hubei in China. It is a big problem for the world's health organization as the number of affected cases as well as deaths increases in each country. Several Machine Learning models are designed to predict and forecast the COVID-19 outbreak. In this paper, we are designed to understand the current scenario of coronavirus, and predicting and forecasting the total number of confirmed cases of novel COVID-19 in India using linear regression, polynomial regression, and support vector machine regression models. The decision-making process of an organization depends on the data analysis and it is presenting with interactive visual representation. It is not possible to predict without having necessary information from the past and the present. Trends over time tell us where we were and where we can go with our model. The model is predicting the number of confirmed cases from 30th May 2021 to 15th June 2021on the data available from 22 January 2020 to 29 May 2021. Forecasting the future of confirmed cases can help health organizations to plan medical facilities. Moreover, the time series forecasting technique is being engaged to forecast the overall total of confirmed cases in the upcoming. [ABSTRACT FROM AUTHOR]

This research suggests utilizing natural language processing to summarize the YouTube video transcript in a way that preserves important details. Around one-third of YouTube users in India who view videos on their mobile devices spend more than 48 hours each month on the platform, according to a Google study. It has gotten tougher to sit through these films, some of which may go longer than they should. Our efforts can even be in naught if we can't find the information we need in them. It can take a lot of time and effort to find movies that provide the information we are really seeking for. There are a lot of online videos, for instance, where the speaker wants to capture the audience's attention, but we won't understand the message unless we watch the entire video. The built-in model accepts links to YouTube videos and creates summaries for each one. It also gives the user the ability to choose the algorithm and the percentage of summaries they want to create. Another feature that the user has access to is the ability to transform the generated summary from text to speech and to translate it into the Hindi language. [ABSTRACT FROM AUTHOR]

The infrequent and inadequate medical facilities for the people residing in the remote villages of India due to the lack of medical experts is a glaring medical issue facing the country. The proposed cross language application aims to solve the aforementioned problem with the use of technology by utilizing Translation techniques and Machine Learning models. With the usage of the proposed application, the users can vocally input their experienced symptoms into the application in their local language and get as output, the disease they possibly might be suffering from within seconds. To increase the accuracy of the results, an ensemble approach is followed in the paper which is a combination of five Machine Learning Models and a majority decision technique is applied to get the most accurate results. The proposed application is able to predict the disease with an accuracy of 90% in return of the symptoms provided by the user. [ABSTRACT FROM AUTHOR]

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

Global Warming has become an emerging threat to mankind. Sudden changes caused in the environment are making survival, a difficult task. The flora and fauna, not only of the country India but also the whole world is disturbed; hence, the ecosystems are not easily adapting to the changes. Many hands-on methods adopted by the people do not contribute to the solution needed. To resolve this issue, prediction of the extent of Global Warming to name the solutions is needed and the proposed system suggests the same. The system proposed uses the technique of Machine Learning and based upon the hypothesis proposed, the prediction of global warming for an upcoming decade is made. Also, the formula to calculate the extent of global warming is derived. [ABSTRACT FROM AUTHOR]

Land use and land cover changes have greatly contributed to global warming and increased urbanization during the last two decades. It is crucial to track alterations of the globe's surface's usage of land as well as cover in order to obtain accurate information about the study region for all development planning scenarios. Land cover categorization is critical for identifying changes, managing development, and tracking and assessing the ground's surface area. Along with RS and GIS methodologies, machine learning algorithms have recently gained popularity in land use and land cover change detection studies. This present study was conducted to detect land use and land cover (LULC) in a heterogeneous landscape of Pauri Garhwal district of Uttarakhand, India by using different machine learning classifiers, i.e. (i) Random forest. (ii) SmileCart (iii) SmileGradientTreeBoost and (iv) SVM. Overall accuracy and kappa coefficient found for RF, SVM, CART and GTB were (0.98, 0.96), (0.97, 0.95), (0.80, 0.75) and (0.89, 0.80) respectively. Results show that Random Forest (RF) provided highest classification accuracy among all four classifier. [ABSTRACT FROM AUTHOR]

Tomatoes, which are extensively grown in Indian agricultural fields, are among the commonly cultivated vegetable crops. The tropical climate in India provides favorable conditions for tomato cultivation. However, various factors, including climatic conditions and other elements, can adversely affect the normal growth of tomato plants. Plant diseases present a significant risk to crop production and can lead to economic losses, adding to the challenges posed by climate conditions and natural disasters. Unfortunately, traditional methods of detecting diseases in tomato crops have proven to be ineffective and time-consuming. This study primarily aims to accurately identify tomato plant leaf diseases that are found using image analysis. Various methods have been applied for extracting the features that have been used to improve the accuracy of classification. Alex Net, InceptionV3, and a CNN (Conv Net) algorithm have been utilized to classify many types of tomato plant diseases. When comparing the results, it is notable that the CNN (Conv Net) classifier outperforms the other two models. The findings demonstrate the practical applicability of the model in real-life scenarios. [ABSTRACT FROM AUTHOR]

In an agricultural country like India farmer's contribution is very vital. Though a lot of work is going on to increase the yield quality, security of the crops is also the question on the front because if the crops are destroyed or damaged due to animals or thefts the farmers are helpless. Hence we propose a system to help a farmer to identify where an animal or thief has entered the field using Fuzzy Classification. In machine learning many classifiers are there like Neural network, Convolutional neural network, Probabilistic approach, K-nearest neighbor, SVM, etc. But little literature is found on Fuzzy classifiers. Hence we introduce here an approach to classify the images of farm at fencing with and without animals using Fuzzy systems. [ABSTRACT FROM AUTHOR]