As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country’s economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.

Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

Android operating system is widespread in today's smart-phone market due to its open source model, its easy functionality and huge number of Apps and App-Stores (the official and others). The coverage of some 90% of the market, it is also the place-holder for user's personal as well as sensitive information (IMEI, IMSI, etc.). There is a tendency of Apps user to trust on Android OS for securing his/her data but it has been proved that Android OS is more vulnerable to exploitation either for fun or for monetary purpose. Malware detection for Android OS has become an upcoming research problem of interest. Dynamic Taint Analysis is an efficient analysis in existing Android malware detection analysis. The aim of this paper is to work towards Dynamic Taint Analysis of Android malware using automatic tagging and with out modification in Android platform. In order to do this, this paper surveys exhaustively the available literature and works related to dynamic taint analysis. This paper proposes some novel ideas to improve the existing solution with more accuracy. There are two phases of our novel algorithm, the first phase is training phase for feature extraction and the second phase is the analysis phase for automatic tagging and tainting. We have developed a framework named AndroTaint, which is working on Dynamic Taint Analysis. AndroTaint follows a novel supervised and unsupervised Anomaly Detection technique with high precision, recall and harmonic mean. Our Dynamic Taint Analysis algorithm categorizes the App is risky, benign, malicious or aggressive as per their feature and behaviour. We have measured AndroTaint effectiveness on the basis of timeline for building dataset and 10-fold cross validation. AndroTaint covers 90% of malware and benign in analysis phase with less false positive and false negative.