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LRFM model for customer purchase behaviour using K-Means algorithm

IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C Jamunadevi S Tamil Selvan M Govindarajan C Saravanan B R Janaki Raman Assistant Professor Department of CT-PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India Associate Professor Department of Computer Applications Velalar College of Engineering and Technology Erode India Assistant Professor Department of Information Technology Velalar College of Engineering and Technology Erode India UG Scholar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

The COVID-19 pandemics have a major collision on every aspect of life, including how people shop for their requirements. As the pandemic has reshaped life as we know, it's also initiated many trends – but the biggest of these trends may be online shopping. The shift toward online shopping was happening before the pandemic, but according to new statistics from IBM, the COIVD-19 has accelerated consumers shift toward online shopping by 5 years. The chief idea of the article is to inspect if the situation is approaching people to purchase things online and the continuation of shopping things online even after the end of pandemic. The information for the article has been gathered by circulating the survey on social networks. The questionnaire is comprised of 12 different questions, and 615 people responded to it. This work is based on LRFM (Length, Recency, Frequency, and Monetary) replica and separation of data based on the questionnaire using K-Means algorithm. Silhouette analysis helps to decide the extent of division among clusters. The results of the survey has a termination that people are fond of purchasing products online through the lockdown and people too agreed that the rate of online shopping will increase in the future when this pandemic is over.

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Data dimensional reduction and principal components analysis

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Procedia computer science 2019年 163卷 292-299页

作者： Nema Salem Sahar Hussein Assistant Professor Electrical and Computer Engineering Department Effat University Jeddah 21478 KSA Assistant Professor National Science Mathematics and Technology Unit Effat University Jeddah 21478 KSA

Research in the fields of machine learning and intelligent systems addresses essential problem of developing computer algorithms that can deal with huge amounts of data and then utilize this data in an intellectual way to solve a variety of real-world problems. In many applications, to interpret data with a large number of variables in a meaningful way, it is essential to reduce the number of variables and interpret linear combinations of the data. Principal Component Analysis (PCA) is an unsupervised learning technique that uses sophisticated mathematical principles to reduce the dimensionality of large datasets. The goal of this paper is to provide a complete understanding of the sophisticated PCA in the fields of machine learning and data dimensional reduction. It explains its mathematical aspect and describes its relationship with Singular Value Decomposition (SVD) when PCA is calculated using the covariance matrix. In addition, with the use of MATLAB, the paper shows the usefulness of PCA in representing and visualizing Iris dataset using a smaller number of variables.

关键词： PCA Data dimension reduction Iris dataset SVD

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Classification of Malignant Melanoma using Convolutional Neural Networks

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： V Vijeya Kaveri V Meenakshi K A Saran Karthik S Shri Raam Professor Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Assistant Professor Department of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology India UG Student Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Human cancer is one of the world's most deadly diseases caused due to genetic disruption of skin cells and several molecular mutations. Skin cancer remains the most predominant form of cancer in human beings. The major goal is to detect skin cancer in early stages by research and analyse it using various techniques such as segmentation and feature extraction. The diagnosis of malignant melanoma skin cancer is done by dermatologist by examining skin and physical biopsy to determine the accurate stage of melanoma. It is developed because of high accumulation of melanin in the dermis layer of the skin. ABCD law is used in along with dermoscopy technology to detect malignant melanoma skin cancer. Image Acquisition Technique, pre-processing, segmentation, distinguishing function for skin Feature Selection, which specifies lesion characterization and classification methods are all conducted in this project for melanoma skin lesion characterization. We used symmetry detection, border detection, colour and diameter detection, as well as feature extraction to remove texture based features using a digital image processing technique. The deep Neural Network was proposed here to characterize the benign or malignant stage.

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An empirical research on consumer online buying behaviour during the COVID-19 pandemic

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C Jamunadevi S Deepa K T Kalaiselvi R Suguna A Dharshini Assistant Professor Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India Associate Professor Department of Management Studies Erode Sengunthar Engineering College Erode India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Student Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

The extraordinary episode of the 2019 novel Corona-virus named as COVID-19 by the World Health Organization (WHO) has put various governments around the globe in a shaky position. The shortage of health care facilities to bear the COVID-19 has forced the countries to go with the hand of complete or partial lock down. According to the World Health Organization (WHO) report 32.6 million cases has been recorded as on 26 September 2020. Corona-virus have its major influence on consumer buying behaviour which is going to change the consumers' future hopping habits. This article highlights the change in consumer behaviour from physical store to online shopping. The present study was conducted during the month of July- August 2020, where questionnaires' are formed and data collected using Google forms. Analysis of the data is made through Simple percentage method, ANOVA test, Chi square test and ranking method. After analysis it found that the majority holding 21-30 age group of customers prefer shopping while free shipping and their cart behaviour is also analysed for the same.

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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis

Results in Engineering

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Results in engineering 2025年 26卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Krunal Vaghela Gowrishankar J Prabhat Kumar Sahu Kamred Udham Singh R. Sivaranjani Rohit Chauhan Siya Singla Samim Sherzod Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of Pharmacy The Islamic University Najaf Iraq College of Pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi University Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Information Technology Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India School of Computing Graphic Era Hill University Dehradun India Adjunct Professor Centre for Promotion of Research Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of CSE Raghu Engineering College Visakhapatnam Andhra Pradesh 531162 India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali Punjab 140307 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Faculty of Engineering Nangarhar University Nangarhar Afghanistan

Biochar, a pivotal product of biomass pyrolysis, is crucial in sustainable agriculture, carbon sequestration, and renewable energy systems. This study employs a comprehensive data-driven approach to predict biochar yield using machine learning algorithms. The dataset, comprising 14 chemical, physical, and reaction parameters collected from reputable studies, was processed using outlier detection via the Monte Carlo Outlier Detection (MCOD) algorithm and hyperparameter tuning to optimize model performance. Among the evaluated models, the Decision Tree algorithm emerged as the most robust predictor, achieving an R² value of 0.771, a mean square error (MSE) of 16.182, and an average absolute relative error percentage (AARE%) of 11.848 % in testing. The SHAP analysis identified residence time, pyrolysis temperature, and ash content as the most influential predictors of biochar yield. Simplified explanations and interpretability offered by these models support their practical application. These findings underline the potential of machine learning to optimize biochar production, enhance agricultural systems, and create scalable methods for sustainable waste management, directly contributing to renewable energy and environmental remediation industries.

关键词： Biochar yield Machine learning Biomass pyrolysis Decision tree SHAP analysis

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A deep learning approach for Indian sign language gestures classification with different backgrounds

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Journal of Physics: Conference Series 2021年第1期1950卷

作者： R. Dhiman G. Joshi C. Rama Krishna PG Scholar Department of Computer Science & Engineering NITTTR Chandigarh INDIA. Assistant Professor Department of Electronics & Communication Engineering UIET Panjab University Chandigarh INDIA Professor & Head Department of Computer Science & Engineering NITTTR Chandigarh INDIA.

The sign language recognition system recently has drawn the attention of various researchers as there is no universal sign language, moreover, it consists of many patterns and postures. Many methods for extracting features and classifying sign language have been proposed in the literature, most of them are based on machine learning techniques. In this article, a deep learning method has been adopted by designing a Convolution Neural Network (CNN) model to extract the sign language features where for classification softmax layer is used. All alphabets in simple as well as complex backgrounds have been considered, where data is collected from 100 subjects in different lighting conditions. The effect of various optimization techniques (Adam, Sgdm, RMSProp), activation functions (ReLU and Leaky ReLU) for generalization ability is also observed. The proposed approach has succeeded in attaining the testing accuracy of 99.10%, 92.69%, and 95.95% on the Indian sign language dataset with simple, complex backgrounds and on a mixed background scenarios, respectively. The model is also tested on NUS dataset-I, NUS dataset-II, their combination, and achieved the accuracy of 100%, 95.95%, and 97.22% respectively.

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An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT

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IOP Conference Series: Materials science and engineering 2021年第1期1131卷

作者： Sanwta Ram Dogiwal Pankaj Dadheech Ankit Kumar Linesh Raja Abhishek Kumar Mahender Kumar Beniwal Associate Professor Department of Information Technology Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Associate Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Applications Manipal University Jaipur Rajasthan India Assistant Professor Department of Computer Science & IT Jain (Deemed to be University) Bangalore India

AI Solution for Farmers is an agricultural based project, created to help farmers and to help them in increasing their productivity. Since the technologies are running the world, why agriculture must be deprived of it. Agriculture is one of the most important areas that have major impacts on the economy and society of a country. Technological developments serve as tools to share knowledge and practices of agricultural products and make more satisfactory lives for farmers, traders, policymakers, and the overall society. It is evident that knowledge has become a crucial component in production, society, food security, health, poverty, and other millennium development goals. The use of technology may provide a better approach to solve the problems arising from sowing the seed to harvest the crop. The new technologies like Machine Learning and Data science may be great help to have an eye on the deciding factors to the growth of crop.

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Real time Machine Learning Approach for a Smart Door Unlocking Using Face recognition System

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IOP Conference Series: Materials science and engineering 2021年第1期1130卷

作者： P Rayavel A Sivakumar P V Gopirajan S Surenderanath P Rathnavel Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India Assistant Professor Department of Information Technology Sri Sairam Engineering College Chennai-600044 Tamilnadu India Assistant Profesor Department of Computer Science and Engineering Saveetha Engineering College Chennai-602105 Tamilnadu India Assistant Professor Department of Electrical and Electronics Engioneering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India

This proposed framework goes about as a home security framework for both Person location and gives security to entryway access control by utilizing facial acknowledgment for the home climate. The more peculiar's face is distinguished inside a home climate accomplished by catching a picture from a web camera and handling will be done on caught pictures. The web camera catches the arrangement of pictures when the individual comes before the entryway. The caught picture is analyzed utilizing our face acknowledgment calculation model. In the event that the individual is the approved one, at that point the entryway is opened. Then again, if the individual isn't the approved one. The picture of the individual is shipped off the power in the event that he perceives the individual and wants to open the entryway. He can distantly get to the entryway and permit the individual in.

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Renewable Energy Sources Efficient Detection in Triangulation for Wireless Sensor Networks

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： B Barani Sundaram Nunu Srinivas N Kannaiya Raja Manish Kumar Mishra Dhanabal Thirumoorthy NR Reddy Professor & Associate Dean-ICT Department of Computer Science BuleHora University Bulehora Ethiopia Assistant Professor School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Professor Department of Computer Science School of Informatics and Electrical Engineering Ambo University IOT Campus Ambo Ethiopia Department of Computer Science University of Gondar Gondar Ethiopia Lecturer BuleHora University Bulehora Ethiopia Assistant Professor Department of Computer Science Mekelle University Mekelle Ethiopia

It is important for wireless sensor networks (WSNs) to determine whether hubs can be restricted, called localizability recognition. This progression is important for confining hubs, achieving minimal organizations of effort and distinguishing critical in field-based applications. Because of their high calculation and communication costs, centralized graph algorithms are meaningless to an asset-restricted WSN, whereas distributed methodologies can skip a large number of hypothetically locatable hubs in a resource-limited WSN. In this document we are proposing a efficient and successful distributed methodology to solve that address of issue. In addition, we demonstrate our calculation accuracy and analyze the reasons why our calculation will discover increasingly localizable hubs while requiring less established area hubs. The paper introduces a new RSSI-based localization approach triangle as well as centroid placement, utilizing triangle as well as centroid approach to minimize RSSI measurement error. Modelling simulations show that this algorithm can significantly increase the precision of the location compared to the trilateration.

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Critical Comparative Study of Robot Path Planning in Grid-Based Environment

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Journal of Physics: Conference Series 2021年第1期1804卷

作者： R D Dhaniya K M Umamaheswari Research Scholar School of Computing Department of Computer science and Engineering SRM Institute of Science and Technology Kattankulathur-603203 Assistant Professor School of Computing Department of Computer science and Engineering SRM Institute of Science and Technology Kattankulathur-603203

The path planning problem under two-dimensional maps is a fundamental issue in mobile robotics and other applications (unmanned vehicles, and so forth). The traditional algorithms (graph searching, artificial potential field and so forth) are used to find the shortest path that rely on grid-by-grid searching. The main disadvantage of the traditional methods is robot size is not considered and has much redundant in sharp curves which is difficult to guide a robot movement. In this paper, we compared the five baseline approaches of heuristic methods to find the optimal path which reduces the number of turns and time costs to attain a target. The experimental analysis shows that the second-order x derivative has an advantage over other baseline approaches that the search time and the number of turns are reduced.

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