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Customer-Based Market Segmentation Using Clustering in Data mining 2

Customer-Based Market Segmentation Using Clustering in Data ...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tressa, Neethu Asha, V. Kumar, Prashant Shree, Om Uday Kiran, M. Reddy, Vennapusa Venkata Sreenivasa New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

This research study aims to utilize predictive modelling and data mining to develop market and customer segments for effective marketing strategies. Market segmentation recognizes that customers have different interests, buying habits, and preferences. By creating specialized strategies for specific target groups, a company can enhance its resource management and sales. Customer segmentation involves clustering individuals with similar characteristics and behaviours, enhancing understanding of customers' demographics and dynamic behaviour. The RFM (Recency, Frequency, Monetary) approach is simple and efficient for dividing markets. RFM analysis examines how recently, frequently, and financially customers make purchases, providing insights into consumer behaviour. This study makes use of data mining techniques to categorize products based on recent sales, frequency of sales, and total amount spent. This study introduces a novel k-Means methodology for RFM analysis, aiming to improve customer segmentation and lower marketing expenses while raising customer satisfaction. The output of this study is compared with existing RFM models, assessing the efficiency of the suggested methodology. Overall, this research study focuses on leveraging predictive modelling and BD to create targeted marketing initiatives based on customer segmentation, ultimately enhancing sales efforts and resource allocation for companies, particularly in e- commerce platforms. © 2024 IEEE.

关键词： Centroid-based Clustering Data Normalization DBSCAN Density-based Clustering Dimensionality Reduction Elbow Hierarchical Clustering K-Means Clustering RFM Analysis Segmentation

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A Hybrid Reversible Digital Watermarking Algorithm Using Machine Learning for the Protection of Medical Images

A Hybrid Reversible Digital Watermarking Algorithm Using Mac...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Tripathi, Pradeep Kumar Varshney, Manoj Mangalayatan University Department of Computer Engineering & Applications Uttar Pradesh Aligarh India

ISBN: (纸本)9798350382723

There is a daily deluge of medical images created by medical institutes around the world using a wide variety of modalities. It is common practice to send these pictures to distant doctors online so that they can work together and get second opinions. We offer a medical image watermarking technology that incorporates Principal Component Analysis (PCA) to address concerns about the privacy and security of sensitive medical data during transmission. Firstly, we need to split the input medical image into two parts: the ROI and the RONI. Once these parts have been enhanced using PCA, they will be marked with two separate watermarks. In the PCA-enhanced ROI, a delicate binary design is incorporated, and in the PCA-transformed RONI, a robust composite watermark is applied. To achieve a perfect fit, the embedding procedure uses the LSB substitution technique. For experimental validation, a collection of CT scans and other medical pictures is used. While addressing issues with data authenticity and privacy in medical records, the simulation results show that the suggested PCA-enhanced method successfully increases the security of medical pictures. © 2024 IEEE.

关键词： Principal component analysis

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Machine Learning Based Sentiment Analysis of Pharmaceutical Reviews 5

Machine Learning Based Sentiment Analysis of Pharmaceutical ...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kumar, Anuj Gla University Department of Computer Engineering & Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350379945

In today's digital era, where the internet is a treasure trove of information, shaping various aspects of our life including the research field. Exploring the vast amount of information that can be accessed online, this research delves into the area of sentiment analysis, specifically concentrating on assessments of pharmaceutical sourced from multiple websites. This study incorporates various features like users' health condition, drug ratings and intake dates with the objective of interpreting drug users' sentiments through their feedback. We carefully analyzed the data using method like descriptive statistics and data visualization followed by testing various machine learning model such as Light GBM model, Random Forest Classifier, and LSTM Model and we found the accuracy ranging from 90% to 95.7%. We used the dataset from the UCI machine learning repository with over 161297 reviews and 7 features. Since the reviews didn't have sentiment labels we came up with the new method, we assigned sentiment labels on the ratings given by the user either positive or negative. This helped us uncover the hidden sentiment in the reviews. Overall, this research study highlights how people feel about drugs and its impact on experience and treatment outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating Cypress Testing with Web Interfaces in Cyber-Physical Systems 8

Integrating Cypress Testing with Web Interfaces in Cyber-Phy...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Jasmine, K.S. Savitha, R. Bhat, Prajna R. Rv College of Engineering Dept. of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798331505462

As cyber threats increasingly target critical infrastructure, safeguarding power systems has become a top priority. This paper investigates the integration of advanced simulation tools to enhance the resilience of power systems against evolving cyber threats. By leveraging the CYPRESS tool, which offers a comprehensive approach to simulating both power systems and cyber networks, the study demonstrates how these simulations can reveal vulnerabilities and inform mitigation strategies. The paper explores how real-time co-simulation of physical and cyber systems, combined with hardware-in-the-loop testing, provides valuable insights into the impact of cyber attacks on power system stability and security. © 2024 IEEE.

关键词： co-simulation cyber threats cybersecurity CYPRESS tool industrial hardware power system security real-time simulation

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Revolutionizing Facial Feature Extraction with AI: An Innovative Approach 5

Revolutionizing Facial Feature Extraction with AI: An Innova...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Lakshmi, B. Alekhya, Jandhyam Lakshmi Parish Venkata Kumar, K. Kishore, B. Kamala Nyni, K. Likhita, K. V.R. Siddhartha Engineering Dept. of Computer Applications KanuruVijayawada 7 India V.R. Siddhartha Engineering College Dept of Computer Science and Engineering KanuruVijayawada 7 India Department of Computer Applications India

ISBN: (纸本)9798350385649

This paper provides a technique for detecting human faces & extracting their features based on segmentation of skin tone & facial structure. The system goes through several stages. The method first searches for regions where faces might be present by segmenting skin tone. After performing various morphological operations on skin, it uses aspect ratio of a face to find viable face blocks. face blocks are then used to extract additional features. Therefore, one may identify and validate a person's image using their face features by using this biometric attribute. To enhance facial recognition, numerous researchers have employed Support Vector Machine (SVM), Principal Component Analysis (PCA), a combination of SVM and PCA, PCA with an Artificial Neural Network, and even the conventional PCA-SVM. Due to PCA-ANN's limited dataset limitation, PCA-SVM is superior to PCA-ANN. To extract features, relative location & size are used. If algorithm is unable to identify even one feature in a prospective face block, it rejects face block. © 2024 IEEE.

关键词： Feature extraction

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Illuminating the Future: A Smart Street Light Controlling and Monitoring System Using Internet of Things Enabled Smart Sensors 9

Illuminating the Future: A Smart Street Light Controlling an...

引用

9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Sujatha, V. Sridevi, V. Sumitha, S. Vidhya, N. Amirtha, M. Dakshinamoorthi, D. S.A. Engineering College Department of Master of Computer Applications Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365092

The Smart Street Light Controlling and Monitoring System using Internet of Things (IoT) presents an innovative approach to urban lighting management, aiming to enhance energy efficiency, improve safety, and streamline maintenance operations. This system integrates IoT technology with advanced sensors, a central control unit, and intelligent algorithms to automate street lighting based on real-time environmental conditions and user-defined parameters. Through a network of interconnected devices, the system continuously monitors ambient light levels and detects motion to dynamically adjust street lighting intensity. Additionally, remote accessibility enables authorities to monitor system performance, adjust lighting schedules, and receive alerts for maintenance needs or malfunctions. By optimizing energy consumption and improving visibility, this system contributes to sustainable urban development and enhances overall quality of life in urban areas. © 2024 IEEE.

关键词： Energy efficiency

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An Improved Digital Image Processing Based Driver Sleep Identification and Alert System Using Internet of Things with Smart Sensors Association 9

An Improved Digital Image Processing Based Driver Sleep Iden...

引用

9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Juliet, N. Prathap, C. Sujitha, S. Sandhiya, V. Surya Prakash, P. Mohana Priya, A. S.A. Engineering College Department of Master of Computer Applications Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365092

Employing computer vision and artificial intelligence techniques, this project aims to develop and deploy a system that can identify when a driver is sleepy and provide a real-time warning. The camera-acquired driver footage had its facial and ocular pictures segmented using a Viola-Jones detector. A single image was created by combining the images of the left and right eyes. The result was a two-eye minimum-dimensional picture. Using Gabor filters, we were able to extract features from these photos. Images with open and closed eyes were classified using these attributes. Major traffic accidents are caused by drivers who are too sleepy, according to studies on the topic. Nowadays, sleepiness is mostly caused by drivers who are too exhausted to operate heavy vehicles. The primary factor contributing to the current upsurge in traffic accidents is, unfortunately, sleepiness. This escalates into a global crisis that requires immediate attention. Improving the ability to identify sleepiness in real time is the main objective of all technologies. A wide variety of sleepiness detectors based on various AI algorithms are already on the market. A related area of our study is driver drowsiness detection, which involves detecting tiredness in drivers by facial recognition and eye tracking. The system compares the extracted eye picture to the dataset. In order to assess the efficacy of the suggested method, which is known as the Semantic Driver Sleep Identification Model (SDSIM), it is cross-validated with an existing model known as the Conventional Driver Drowsiness Detection Scheme (CDDDS). The proposed approach follows the logic of an innovative technology called Internet of Things (IoT) and dataset helped the system determine that the eyes needed to be open for the system to continue tracking, and that if they were closed for a specific amount of time, an alarm might be sounded to notify the driver. We established a score that went down while the eyes were open and up when

关键词： Semantics

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Thiyagarajan, Gomathi Swathi, Y. Cmr Institute of Technology Department of Computer Applications Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates. © 2025 IEEE.

关键词： Marketplaces

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Contemplating Cybersecurity Breaches in EdTech Products with a Focus on Student Privacy and Security 2

Contemplating Cybersecurity Breaches in EdTech Products with...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tressa, Neethu Asha, V. Mathew, Jincy C Ghorpade, Vijay R Senapati, Suman Ujwal, K. New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

This research study addresses the security and privacy issues with education technology (EdTech). It was based on an analysis of 0.5 million Reddit posts and 9 million Twitter postings showing customers' lacked knowledge about these topics. Teachers have concerns on students' privacy as much as their own. Threats to privacy and power disparities for people surrounding EdTech users were also noted. This research study showcases a detailed analysis on Twitter and Reddit datasets followed by listing out the important findings. The analysis was primarily done on the tweets and posts that convey the concerns related to privacy as well as security while using different EdTech tools. The analysis found that post pandemic, the concerns raised were increased when compared to the same before pandemic. The limitations of the study are also incorporated at the end along with key understandings. This study spotlights the new research problems and requirements for generating a solution to benefit researchers in the similar field. © 2024 IEEE.

关键词： EdTech Education Technology Privacy Reddit Surveillance Twitter User Perception

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Machine Learning-Based Phishing Website Detection A Comprehensive Approach for Cyber security 5

Machine Learning-Based Phishing Website Detection A Comprehe...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Sindhu Priya, K. Bala Chandrika, J. Prasanna Lakshmi, M. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India

ISBN: (纸本)9798350351378

In our contemporary lives where the internet plays a crucial role, the cybersecurity challenge posed by phishing attacks is noteworthy. This paper introduces an innovative strategy to tackle this issue by employing machine learning techniques for the identification of phishing websites. Harnessing advanced algorithms and feature extraction, our proposed model aims to differentiate between legitimate and malicious websites by analysing inherent patterns and characteristics of phishing attempts. Phishing remains a significant threat, causing substantial financial losses for internet users annually. This deceptive practice involves identity thieves using cunning strategies to trick individuals into disclosing confidential information. Phishers commonly employ tactics like forged emails and sophisticated phishing software to illegitimately obtain sensitive details, including usernames and passwords from financial accounts. This paper focuses on developing a robust system for identifying phishing websites, utilizing machine learning techniques. Specifically rooted in supervised learning, our methodology selects the Random Forest algorithm due to its exceptional classification performance. The primary objective of our study is to achieve the highest classification accuracy by thoroughly examining distinguishing features of both benign and phishing URLs. Through meticulous analysis, our goal is to identify the most effective combination of these features to train our classifier. As evidence of our efforts, we have achieved an impressive accuracy of 97% using the Random Forest algorithm, with the Logistic Regression method also demonstrating a commendable accuracy rate of 92%. We strongly recommend the adoption of our system as a sturdy defense against phishing attacks, ensuring the safety and security of internet users. © 2024 IEEE.

关键词： Cybersecurity

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