Showing papers in "Intelligent Automation and Soft Computing in 2021"

A big data approach to black Friday sales

Abstract: Retail companies recognize the need to analyze and predict their sales and customer behavior against their products and product categories Our study aims to help retail companies create personalized deals and promotions for their customers, even during the COVID-19 pandemic, through a big data framework that allows them to handle massive sales volumes with more efficient models In this paper, we used Black Friday sales data taken from a dataset on the Kaggle website, which contains nearly 550,000 observations analyzed with 10 features: Qualitative and quantitative The class label is purchases and sales (in U S dollars) Because the predictor label is continuous, regression models are suited in this case Using the Apache Spark big data framework, which uses the MLlib machine learning library, we trained two machine learning models: Linear regression and random forest These machine learning algorithms were used to predict future pricing and sales We first implemented a linear regression model and a random forest model without using the Spark framework and achieved accuracies of 68% and 74%, respectively Then, we trained these models on the Spark machine learning big data framework where we achieved an accuracy of 72% for the linear regression model and 81% for the random forest model © 2021, Tech Science Press All rights reserved

Roughsets-based Approach for Predicting Battery Life in IoT

Abstract: Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water. Firstly, the missing values in the data are replaced with the attribute mean. Later, one-hot encoding technique is applied for achieving data homogeneity followed by the standard scalar technique to normalize the data. Then, rough set theory is used for feature extraction, and the resultant data is fed into a Deep Neural Network (DNN) model for the optimized prediction results. The proposed model is then compared with the state of the art machine learning models and the results justify its superiority on the basis of performance metrics such as Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and Test Variance Score.

Predicting COVID-19 based on environmental factorswith machine learning

Abstract: The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases and weather variables such as temperature, humidity, wind and precipitation are developed. This study aims to identify the best classification model to classify COVID-19 by using significant weather features chosen by Principle Component Analysis (PCA) feature selection method. The DS4C COVID-19 dataset is used to train and validate each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by PCA. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized CNN classifier with seven weather variables selected by PCA achieved the highest performance among all the techniques. The experimental results obtained show that the weather variables are more relevant in predicting the confirmed cases as compared to the other variables. Thus, from this result, it is evident that temperature, humidity, wind and precipitation are important features for predicting COVID-19 confirmed cases. © 2021, Tech Science Press. All rights reserved.

Machine Learning-based Detection and Classification of Walnut Fungi Diseases

Abstract: Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using intensity adjustment and histogram equalization. After that, the detected infected area of the leaf is segmented using the Otsu thresholding algorithm. The proposed method extracts color and shape features from the leaf’s segmented area using the gray level co-occurrence matrix (GLCM) and color moments. Finally, the extracted features are provided to the back-propagation neural network (BPNN) classifier to detect and classify walnut leaf diseases. Experimental results demonstrate that the proposed method’s detection accuracy is 95.3%, which is significantly higher than those of the state-of-the-art techniques. The proposed method assists farmers in detecting diseases affecting walnut trees and thus enables them to generate more revenue by improving the productivity and quality of their walnuts.

A Smart Comparative Analysis for Secure Electronic Websites

Abstract: Online banking is an ideal method for conducting financial transactions such as e-commerce, e-banking, and e-payments. The growing popularity of online payment services and payroll systems, however, has opened new pathways for hackers to steal consumers’ information and money, a risk which poses significant danger to the users of e-commerce and e-banking websites. This study uses the selection method of the entire e-commerce and e-banking website dataset (Chi-Squared, Gini index, and main learning algorithm). The results of the analysis suggest the identification and comparison of machine learning and deep learning algorithm performance on binary category labels (legal, fraudulent) between similar datasets, and understanding which function plays a vital role in predicting safe e-banking and e-commerce website datasets. The e-commerce and e-banking website dataset was compiled from the UCI machine learning library. We obtained 11,056 entries based on 30 unique website attributes. We used the machine learning algorithms support vector machine (SVM), k-nearest neighbors, random forest (RF), decision tree (DT), and the multilayer perceptron (MLP) deep learning algorithm to analyze the datasets of e-commerce and e-banking websites and found the best algorithms based on accuracy, precision, recall, and F1-measure. MLP had the highest precision at 97%. With this procedure we can now accurately test websites to assist in the early prediction of secure e-banking e-commerce transactions.