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The increase in data from modern sources, the heterogeneous nature of data, ambiguous and unstructured data, and the so-called Big Data with all of its five v's characteristics, indicate a growing need to use approaches that provide assistance in modeling and processing these data, provide additional automated data processing, and so on. The majority of all these studies involve one or more big data sets that may be used in various applications. Most of these selected papers use one or more distributed frameworks (such as MapReduce, Spark, or HDFS Hadoop distributed file systems). Furthermore, more than one strategy of Web mining is dealt with (such as Naive Bayes, Logistic Regression, and Random Forest, as examples) more than recommender system types (collaborative filtering, or/and content-based filtering). Various data sets were used (such as Movie Lens data set, LDOSCoMoDa data set, real bank loans dataset, using Facebook and Twitter for collecting data, data was taken across different companies, thousands of movies were used as exemplary data sets, or data sets used a sample data from different schools of Central Board of Secondary Education (CBSE) across India). Testing accuracy on the dataset obtained is required in the accuracy used (precision and recall). The best precision was 0.9886 and the best recall was 0.9835 in these studies. We have displayed some studies that have high precision and low recall and some other studies that have low precision and high recall. This paper introduces a literature survey about recommender systems that deal with big data through web mining techniques. [ABSTRACT FROM AUTHOR]

Nowadays in India, due to the increase in the number of accidents vehicles are being automated using a variety of Computer vision algorithms. This paper focuses on the detection of tail signals of cars under different illumination circumstances. This system is implemented using FAST and SIFT algorithm which helps to extract features from the images. The obtained features were optimized by using K-Means Clustering Algorithm. This huge feature vector is converted into 8 clusters. These optimized feature vectors were trained on five different classifiers as Decision Tree, SVM(RBF), Random Forest, and Voting Classifier. The trained data set used in this algorithm contains around 9052 images. The obtained accuracy results of different classifiers are as follows, Decision tress has 76.36%, SVM(RBF) has 77.91%, Random Forest has 87.52%, KNN has 89.72%, the Voting classifier has 86.52%. It is observed that KNN gives the highest accuracy among the five used classifiers. This has, to the best of the authors' knowledge, not been presented in literature before. [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]

This paper is a study about COVID-19 cases in India to analyze and visualize the spread of COVID-19 cases in INDIA. For the analysis, the concept Exploratory Data Analysis (EDA) and some well know prediction models like Prophet Model, ARIMA Light GBM, Random Forest Regressor, XGBoost Regressor have been used to get the correct analyzed result. Furthermore, it has been observed that these prediction models and tools are helpful to analyze and visualize the COVID-19 situation in India and reason to spread it in optimal ways. Here, Matplotlib library has also been used to show the proper output in the form of graphs and charts. Analysis has been done on different Age/Gender Group. The Spike of Cases in India, State-wise Insights, the reason for the spread of COVID 19 cases. So, the main objective of this paper is to do analysis to know the reasonforCOVID-19 cases in India, State-wise Insight and State-Wise Testing, Prediction. To understand the optimal result of growth factors, the concept of Prophet Model ARIMA, LightGBM, Random Forest Regressor, XGBoost Regressor have been used on the data set. In addition, the data set includes the parameters of Age Group, Spike of Cases and Testing in India. From the literature survey, it has been observed that authors have used two and three techniques to analyze the result, but they have not used the methods we stated earlier for optimal result. This paper will provide the State-wise Insight and State-Wise Testing going in India with reason for spreading of COVID-19 cases. [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]

On Instagram, the quantity of devotees is a typical achievement marker. Consequently, adherents selling administrations become an enormous piece of the market. Influencers become assaulted with counterfeit supporters and this makes an entrepreneur pay more than they ought to for a brand underwriting. Distinguishing counterfeit supporters gets imperative to decide the credibility of an influencer. This examination intends to recognize counterfeit clients' conduct and proposes administered AI models to order credible and counterfeit clients. With the appearance of the Internet and online media, while many individuals have profited by the immense wellsprings of data accessible, there has been a huge expansion in the ascent of digital wrongdoings, especially focused towards ladies. As indicated by a 2019 report in the [1] Economics Times, India has seen a 457% ascent in cybercrime in the long term length somewhere in the range of 2011 and 2016. Most estimate that this is because of effect of online media like Facebook, Instagram and Twitter on our everyday lives. This paper reveals insight into the job of phony characters in cutting edge determined dangers and covers the referenced methodologies of distinguishing counterfeit online media profiles. To make a pertinent forecast of phony or real profiles, we will survey the effect of three regulated AI calculations: Random Forest Algorithm, Logistic Regression [ABSTRACT FROM AUTHOR]

The goal of this paper is to develop an assistive device for the visually impaired that will allow them to move more freely in unfamiliar indoor situations by recognizing household objects that may hinder their route. This solution focuses on safe navigation in unfamiliar interior environments. Human-assisted mobility and assistive aids are expensive, especially for people residing in developing countries like India. These are not affordable for the population which has a per capita GDP of 1500$. This solution aims to provide cues for movement in real-time which can be initiated by a simple obstacle detection. The basic feature of obstacle detection is to give a better experience and also be pocket-friendly. This system implements KNN, Random Forest, Decision Trees, and SVM classifiers. The proposed system analyzed that the Random Forest classifier gave the best performance accuracy for indoor obstacle detection. This system has implemented a simple guidance system that gives a tactile impulse to the user for moving in the right direction. The real-time assistive solution demonstrated 98.3% accuracy for object detection and reliable execution on a low-power device. [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]