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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]

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]