Search

The most frequent cause of natural disasters in India is tropical cyclones. Early warning, real-time monitoring, impact and damage assessment, and relief operations all depend on remote sensing and GIS. The Bay of Bengal is frequently the source of cyclones of various intensities. In this paper, the actual goal is to predicate the tropical cyclone in the tropical region of the Bay of Bengal, for that the main satellite data set is collected from the NASA data repository. The overall work is being done by using the tool MATLAB, where all machine learning classifications are applied to the data set and trained. As a result, which classifier has the highest accuracy will be considered as the best result. The primary goal of this research is to forecast cyclones using day-by-day satellite data of coastal regions and a variety of weather factors. The objective of this work is threefold. First to design a model to reduce the dependency on a single classification technique, secondly to avoid the over-fitting of data, as well as to improve the accuracy of the prediction. Two number of steps in collective learning: Multiple machine learning models created using the same or different machine learning algorithm. [ABSTRACT FROM AUTHOR]

Cardiovascular disease are leading causes of mortality worldwide, accounting for a substantial fatality rate in recent years, In the last several decades, it has been the number one cause of death, not just in India but globally. It is tough for medical practitioners to forecast because it is a complex undertaking that requires expertise and advanced understanding. In the field of healthcare data analysis, predicting heart disease may be difficult. Machine learning can provide a quick and accurate decision-making solution. The main purpose of the paper is to use ML algorithms to predict presence or absence of heart disease. Various ML algorithms have been analyzed such as RF, SVM, DT, LR, XGBoost, Naive Bayes and KNN. And, out of all of these algorithms, Logistic Regression and SVM had the highest accuracy of 81.97%. The maximum accuracy, recall, precision, and f1 score is achieved by Logistic Regression and SVM, whereas the lowest accuracy, recall, precision, and f1 score is achieved by KNN. [ABSTRACT FROM AUTHOR]

India is one of the hugest rice cultivating country in the world. For thousands of years rice is a most major food, consequently every mankind depends on it directly and indirectly. But the Main hindrance in rice production is rice leaf diseases. It greatly reduces the production rate. Rice leaf diseases have specific symptoms that cannot be easily identified by usual human's eye. Using one of the image processing techniques, the rice leaf diseases are detected efficiently. In order to detect the diseased image Median Filter, K-means Clustering, GLCM and SVM classifier algorithms were proposed. Accuracy, Precision and ROC of SVM classifier were calculated as performance metrices. Around 97±2 % accuracy was achieved in this method. This paper discusses the efficient methodology for rice leaf disease detection. By using proposed method diseased leaves are identified at the early stage, which can aid farmers in preventing further damage to their crops. [ABSTRACT FROM AUTHOR]

After a deep questioner among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies. [ABSTRACT FROM AUTHOR]

In India, climate change has a huge impact on the agricultural sector. It directly affects crop yield, nutritional quality and productivity. In this paper, a model is devised to correctly predict the crop yield only from past data including factors like rainfall so that appropriate measures can be taken in the future. Machine Learning is one such tool which can be used to solve this issue. Random Forest Algorithm, XGBoost and K-Nearest Neighbor (KNN) Algorithm are the machine learning algorithms used for crop yield prediction in this paper and the one with higher accuracy can be used for future work. Further discussion includes how much impact each factor related to crop yield has on the predicted value so an ordinary farmer can plant and take care of his crops according to it and use this model for his daily work. In doing this, modernization of the agricultural sector is made possible and a boost is given to crop yield in India. [ABSTRACT FROM AUTHOR]

Anemia disease is a common health problem in emerging countries and constitutes a challenge to public health in India as well. It affects persons of all age groups, especially women and children. India has the maximum total prevalence of anemia at 39.86%. According to WHO, about 32.4 million pregnant women suffer from anemia disease. The NFHS-4 provides crucial date related to anemic status in India. The prevalence percentage of anemia in pregnant women in India using the NFHS-4 survey remained 50.2%. Machine learning algorithms and data mining techniques open new doors of opportunities for precise prediction of Anemia disease. Still, the resourceful processing of such huge data is exciting, so we need a system that infers from the data. ML methods make systems learn itself. In this paper, we have presented a survey of ML algorithms used for prediction of anemia in pregnant women from NFHS-4. [ABSTRACT FROM AUTHOR]

"The best ever non– alcoholic drink which is consumed in India after water is tea ". India has privileged to stand in thetop listing World in tea production. In India, tea plantation contributes tremendously in the economic development. Nowadays,machine learning is playing significant role in the field of agriculture. With the help of machine learning in tea crop, which results in productivity enhancement and better classification of tea leaves, weather prediction, smart farming, crop disease detection, and to analyze the various effects of soil in future prediction. This paper aims at providing detailed information on various machine learning approaches proposed in the past five years by emphasizing the various techniques used in artificial intelligence in tea crop yield in various states of India and a broad where tea production has contributed in national income. [ABSTRACT FROM AUTHOR]

The main factor causing blindness in countries like India and others is Diabetic Retinopathy. Millions of people around the world are affected by DR, which causes vision loss and blindness. Detecting diabetes mellitus early plays an essential role in preventing the loss of vision that can result from diabetes mellitus being left untreated for a considerable amount of time. It is becoming increasingly more important to detect diabetic retinopathy early through automated systems instead of manual screening procedures like fluorescein angiography, optical coherence tomography etc., as it can lead to blindness among patients with uncontrolled diabetes. There are several published studies on machine learning and deep learning-based DR detection systems. In this paper, we explore the basics of advanced AI technologies used in DR analysis and early detection. The purpose of this study is to review DR detection methods are examined from a variety of perspectives. It includes fundus datasets, pre-processing of images, methodologies, machine learning and deep learning-based methodologies, and performance metrics. As well as presenting the review findings, it also presents the authors' observations. There were numerous public datasets for DR detection that are readily available. The Artificial Neural Network proved to be the better classifier over other machine learning methods based on shape, texture, and statistical features for DR detection. The purpose of this study is to propose the Convolutional Neural Network (CNN) through GoogLeNet for betterment based on the previous CNN model which was implemented using deep learning. Furthermore, the scientific community dedicated to developing automated DR investigative techniques. This review offers a thorough overview of DR detection methods. [ABSTRACT FROM AUTHOR]

The phrase "Fintech" (Financial Technology) refers to software and other cutting-edge technology utilized by companies that offer automated and enhanced financial services. It is a term used to describe a new technology designed to enhance and automate the provision of financial services. With 6,636 FinTech businesses, India has the highest rate of FinTech adoption internationally and is one of the fastest-growing Fintech industries. India has made significant progress in the area of digital payments, recording over 5.7 BN transactions each month in September of this year, amounting to about $2 TN (Total Digital Payments). India will surpass the combined total of the US, UK, and China in terms of real-time online transaction volume in 2020 with 25.5 Bn real-time payments transactions. This survey paper elaborates on different aspects of the Fintech sector in India, and various issues faced by many prominent fintech companies like unbanked population, regulation, technology integration, user experience, etc. It also describes technologies that underpin fintech business models, including blockchain technology, machine learning, artificial intelligence (AI), Cloud computing, the Internet of Things, etc. [ABSTRACT FROM AUTHOR]

This paper sheds light on the problems related database collection or selection in machine learning. It emphasizes on various aspects of database collection and issues one can get into while performing the collection procedure. It then discusses the second alternative method of using a premade database and how it could be an equally challenging issue, sometimes even worse. It concludes by a study of an air pollution database created in 2015 in.the state of Orissa, India which has over two thousand entries and applies different regression algorithms available analyzing the drawbacks of having data which is dense but limited to a particular location and time. This is followed by some methods through which the issues could be solved and emphasizes on the need for the issues to be solved and lack of databases in general. [ABSTRACT FROM AUTHOR]

Devanagari is an ancient script that is used to write Hindi, Nepali, Marathi, Maithili, Awadhi, Newari, and Bhojpuri, among other Indo-Aryan languages. Thousands of individuals in India use this script to write documents in Marathi and Hindi. Indian mythology is based on this script. Because of the script's prominence, handwritten Devanagari character identification has grown in popularity over time. Handwritten recognition of languages such as English has received a lot of attention, but Indian languages written in the Devanagari script are also a rich source of information. Most of the work on this problem statement has been done either using deep neural networks like CNN at its heart coupled with other machine learning techniques like SVM,Random Forest etc. In this paper we are utilising a recently introduced transformer model for computer vision known as Vision Transformer for the task of Devanagari Character Recognition. We have also compared our model with various pretrained CNN-based architectures like ResNet50,VGG16 and InceptionV3 and ViT has outperformed these models both on DHCD dataset and the modified slightly more complex version of it with accuracy scores of 99.68% on the original testing dataset of the DHCD dataset and accuracy score of 96.55% on the modified(blurred) slightly more complex version of the original testing dataset. The ViT model thus generalized better than standard CNN-based models on the problem of Devanagari Character recognition. [ABSTRACT FROM AUTHOR]

With Internet penetration expected to reach 639 million in India by the end of 2020 and social media usage booming to share opinions, firms are looking for social media monitoring to get the customers' pulse. This research paper investigates the impact of e-WOM on purchase behaviour and image by performing a sentiment analysis on 30,000 tweets using and Machine learning. It extracts real-time tweets of customers before the product launch of two brands, One Plus and OPPO, in the PAN-India market. It performs sentiment analysis to understand customer behaviour. This paper examined that tweets containing geopolitical situations like #Boycott China negatively influence purchase behaviour, addressed by sentiment analysis of tweets during OPPO's product launch during an India-China border spat. This paper has also found out that tweets from Influencers and Peer groups impact consumers' purchase behaviour. This paper also bridges the vital research gaps left in this domain, such as sentiment analysis on tweets containing emoticons, emojis, slang, local, regional languages, etc. Since social media conversations nowadays are mostly filled with emojis, emoticons slang, it is extremely important to consider it during sentiment analysis. This paper has also studied the significance of e-WOM factors on purchase behaviour and has provided a managerial perspective with a real-time example of decision making with sentiment analysis on product launch of two Chinese firms in India during a trending geopolitical situation. [ABSTRACT FROM AUTHOR]

In recent years, the human population has reached its peak in both the developing countries and developed countries like India and China. An increase in personnel density ended up being a hard task to maintain or manage people flow in socio-public places. However, people flow management is out of control for humans, which may also cause some safety damages. This paper aims to give an optimal solution for people flow with the help of machine learning, deep learning, and also AI-cameras are used for counting the number of people entering and leaving a user-defined place. Here, also the centroid tracking and centroid detecting algorithms are used to count the number of people together with their direction. This technique helps to replace manual security,in-store traffic management, and much more. [ABSTRACT FROM AUTHOR]

This paper mainly deals with the design and analysis of Agriculture data using Machine Learning (ML) techniques. Agriculture includes cultivating the soil and we can grow different types of crops. In India there are 28 states and 8 Union Territories. In each and every state people are farming and they are developing our agriculture in so many ways. Every state has its own farm for example: - Andhra Pradesh-Rice, Bazra, maize, ragi, pulses etc; the other name for agriculture is "Back Bone of India ". The sediment pressure is due to erosion and soil loss. Diffusion in nature occurs in the fine-grained sediments. Statistical analysis is being carried out for effective solution based on different parameters. This helps to get more information for farmers to make critical farming decisions. Researchers focused on agriculture and found an optimal option to increase production. Experimental data is analyzed and the production is optimized for agriculture such that it is more resilient to climatic change. [ABSTRACT FROM AUTHOR]

India is one of the largest countries of the world with a high population density. Being a developing country, this nation is facing enormous challenges in regulating its air quality. Population growth, vehicular ownership, urbanization, demand of energy and industrialization process are driving the pollution level to an alarming state in Delhi which is the capital of India. Accurate air quality index (AQI) forecasting of Delhi will assist Government in making strategic decisions so as to avoid catastrophic deterioration in the quality of healthy air. Motivated from this, in this paper, for the first time a systematic study is made to assess the effectiveness of four promising univariate statistical forecasting models and twelve machine learning (ML) models in predicting the AQI of Delhi. Four accuracy measures including "mean absolute scaled error (MASE), symmetric mean absolute percentage error (SMAPE), root means square error (RMSE) and mean absolute error (MAE)" are used to assess the performance of statistical and ML models. Extensive statistical analyses on obtained results using Wilcoxon Signed-Rank test illustrate the superiority of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models than other considered models in predicting the Delhi AQI. [ABSTRACT FROM AUTHOR]

With the ease of internet access, social media has proven as the mecca of connecting people worldwide, but as much as social media has established itself as a boon for people, it is a curse as well. Within a short time, social media's impact has brought the whole world together in one spot where individuals may express their thoughts and ideas. With India's user report standing at 518 million in 2020, the number might go up to 1.5 billion by 2040, while the worldwide social media users number stands at 4.55 billion people, which is more than a half-world now. Social media has undoubtedly proved a boon, opening doors towards ample opportunities for people worldwide, from allowing them to do business online to showcasing their talents which sometimes boosts their career once they get recognized. However, coming to the darker side, social media can be labelled as a curse as well in place of the boon, given the occurrence of cybercrimes, be it burglary or theft of personal data, spamming, impersonating by creating fake id using people's information present over social network handles, cyberbullying or sex crimes (especially paedophiles), and the list continues. The following study outlines the precautionary steps to safeguard oneself against cybercrimes and reviews the concerned research papers covering threat analysis of social media crimes using Artificial Intelligence. [ABSTRACT FROM AUTHOR]

India is an Agriculture based country and about 60% of the population depends on agriculture for their living. But there is a substantiable reduction in agricultural production has happened due to unstable climate conditions and global warming. Indian farmers are always lacking the support of advanced technologies for improving and maximizing their yield. If they get explicit information about soil type, nutrients, pH value, changes in climatic factors, previous year yield etc., they can optimize their work and yield. With the advancement of machine learning, big data analytics and cloud computing technologies, the climate and crop yield can be got predicted to the farmers. Prediction of yield in advance can help the farmers to take corrective decisions about fertilization, storage and marketing to increase their production and revenue. A huge number of studies have been conducted worldwide, on agriculture sector for the prediction of crop yield, plant diseases etc. This paper presents a brief study of different researches in India on crop yield prediction based on Machine Learning and deep learning. Most of the researchers used environmental parameters like temperature, rainfall and soil type as the main features for prediction. A number of machine learning algorithms are employed in this field and it is really a complex task to identify the best algorithm. The researchers tried to accurately predict the crop yield and thereby suggesting ways to improve production and efficient use of fertilizers. [ABSTRACT FROM AUTHOR]

Every year, tens of thousands of people are declared missing in India. A significant number of people who have gone missing have yet to be found. With the aid of facial recognition, this paper suggests a unique deep learning technique application for recognizing the identified missing person from the images of a large number of people available. Photographs of suspicious people may be uploaded to a shared portal, including location and timings. The snapshot will be linked to the missing person's images in the repository. The input individual picture is classified, and the picture with the good fit is chosen from a missing person database. Using the image reported by the public, an algorithm is learned such that we can accurately find missing person in archive with missing people's images generated. Face recognition is done using Convolution Neural Network (CNN) which is a common deep learning algorithm to process image-based applications. Face features are derived from pictures using a VGG(Visual Geometry Group)-Face deep architecture CNN model that has been pre-trained. Unlike other deep learning algorithms, ours only uses a convolutional network as a high feature attribute, with the qualified KNN classifier doing the individual recognition. When properly prepared, VGG-Face, the highest performing CNN algorithm for face detection, outputs a deep learning algorithm that is distortion, lighting, and person age and exceeds previous approaches in facial recognition-based missing person identification. This recognition device classification accuracy is 90%. [ABSTRACT FROM AUTHOR]

Indian economy is contributed heavily by agriculture. Most of the Indian farmers rely on their instincts to decide the crop to be sown at a particular time of year. They do not realize that the crop output is circumstantial, and depended heavily on the present-day weather and soil conditions. A single uninformed decision by the farmer can have undesirable consequences on the economic conditions of the region and also mental and financial impacts on the farmer himself. Applying systematic Machine Learning models will effectively help to alleviate this issue. The dataset used in the paper is built by adding up the datasets of India's rainfall, climate, and fertilizer. Machine learning models will be used on the dataset to get the highest accuracy model to recommend a crop for the farm's location. This recommendation will help the farmers in India to make learned decisions about the crops. The recommendation will take into account the parameters like the farm's location, sowing season, soil properties, and climate. [ABSTRACT FROM AUTHOR]

Farming is the major work which is considered as a culture instead like job and farming is the back bone of our economy as farming is the means which carried forth human advancement. India is nation which shows more interest towards farming and also grows all types of crops and its economy generally dependent on harvest profitability. Subsequently we can say that agriculture is major support for all business in our nation. Choosing of each harvest is significant in the choosing as each and every state in India grow various crop and the climate also varies from state to state. The choice of crop will depend on the various factors like, value of the crop, price given by the government, weather conditions and the price given by the private market buyers. Numerous progressions are needed in the field of agriculture to improve the benefit to Indian economy. We can improve agriculture by implementing AI mechanisms which can be same are defficiently on various cultivating areas. With all the advancements in the areas of machines and their improvements we can use them in cultivating the valuable and detailed data concerning various issues in addition to assuming the critical part in it. This paper helps use to getting an idea towards executing all the harvest based strategy with the ambitious techniques that helps in enchanting the maintenance of numerous agriculture and agriculture field issues. This helps the farmers to choose a best crop which helps them getting profit and also helps to increase our nation's economy. [ABSTRACT FROM AUTHOR]

Identification and classification of plant diseases is a major field of study, as most people in India depend on agriculture for their main source of income and food. This is one of the reasons why the identification of plant diseases plays a significant role in agriculture. It is useful to detect plant disease through any automated technique as it eliminates a significant amount of monitoring work in large crop farms and detects the symptoms of diseases at a very early stage, i.e. when they appear on plant leaves. The framework mainly involves different concepts related to image processing, such as image acquisition, image pre-processing, and extraction of features, database formation, and artificial neural network classification. This paper covers research on various methodologies for the use of neural networks to detect plant leaf diseases. [ABSTRACT FROM AUTHOR]

Currently, health management is a major concern all over the world. Particularly in India, due to population, the health issues are increasing rapidly. Diabetes has become a major issue that affects people all over India. With the advancement of Machine Learning approaches, many solutions have been developed to curb this serious issue. The goal of this paper is to design a model that can detect diabetes at preliminary stages with more accuracy. Therefore, various classification algorithms namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB) are used to solve this problem at earliest. Various performance metrics like accuracy, sensitivity, specificity, precision and recall are used for evaluation of the model. Evaluations are performed on PIMA Indian diabetes database, which is pulled from the Machine Learning cache. Results procured manifest that Random Forest outplay with the highest precision of 85.06%. The results are appropriately authenticated through a bar graph representation. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent crop prediction for farmland using real-time sensor data and machine learning. As an agricultural country, the majority of India's population and economy are dependent on agricultural yield growth, which is in turn reliant on soil variables as well as climate factors like temperature and precipitation. Crop production can be increased by technological advancements in agriculture like remote sensing systems and IoT. Further machine learning is used to also used in this project to predict the type of plant that is better suited to be cultivated by integrating sensors such as pH, moisture, rainfall, temperature, and humidity. [ABSTRACT FROM AUTHOR]

Balanced nutrition is the main source of energy. It is necessary for healthy life of people. Healthy nutrients enable cells to perform their regular activities at pace. Deficiency of proper nutrition while birth causes various complications in further life. These complications include wasting, stunting, edema, mental illness, low immune system, ridged or spoon-shaped nails, brittle, dry hair, and underweight etc. Malnutrition is a condition that occurs when a person consumes a diet that is deficient in one or more major nutrients, or has too many of them. Marasmus, kwashiorkor and intermediate states of marasmus-kwashiorkor are included in the term Protein-Energy Malnutrition (PEM) disorders. PEM is the cause of underweight (low weight for age), stunting (low height for age), and wasting (low weight for height). In India, stunting affects 48% of infants under five years age, wasting affects 20%, and underweight affects 43%. Most children suffering from undernutrition in mild to moderate forms are unnoticed in India, which affects their growth at early ages. Detecting malnutrition at early stage reduces further healthcare cost and improve health outcome. To alleviate the problem of malnutrition, this paper describes a decision tree model for classification of infants being between the ages of 0 and 59 months as normal, acute malnourished or severely malnourished for three categories: Stunting, Wasting and Underweight. In decision tree model, Gini index is adopted as an impurity measure. The accuracy obtained using decision tree for stunting is 82.22%, for wasting 72.23 % and underweight 78.35% using Gini index. [ABSTRACT FROM AUTHOR]

Milk adulteration becomes a great business in the dairy industry where its quality is compromised by adding certain chemicals or other substitutions. Milk is one of the most important nutritious food products that is consumed by humans irrespective of age from babies to older people. The consumption of adulterated milk can impair the functioning of various organs of the body, causing heart problems, cancer, and in extreme cases, even death. World Health Organization highlighted that if adulteration is not put to a stop, a lump of India's population would be suffering from serious and fatal diseases like cancer by the end of 2025. Hence there is a huge demand for a cutting-edge research to detect the adulterated milk. A comparative analysis of five machine learning algorithms i.e., Logistic Regression, Naive Bayes, Random Forest, Support Vector machine and Gradient Boosting Machine are presented in this paper to classify the pure and adulterated milk. [ABSTRACT FROM AUTHOR]