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Predicting Marketing Campaign Effectiveness through Consumer Personality Analysis using Machine Learning

Predicting Marketing Campaign Effectiveness through Consumer...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Singh, Yashvardhan Jena, Junali Jasmine Gourisaria, Mahendra Kumar Singh, Jay Prakash Kumar, Vivek Kumarbehera, Dayal KIIT Deemed to Be University School of Computer Engineering Odisha Bhubaneshwar India Manipal University Jaipur Dept of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350369175

Understanding customer behavior and personality is essential for marketing campaign. It might help huge corporations comprehend their purchasing behavior. Companies directed by their marketers can conduct product marketing activities that are specific to the customer's personality attributes. This study examines the use of various classification models to evaluate client personality attributes and predict marketing campaign performance by customer response after marketing campaign. Response of the customer was the target feature and it is a binary classification. Feature engineering has also been applied for optimal feature selection. Several machine learning classifier algorithms, including Gradient Boosting (GB), Ada Boost (AB), Cat Boost (CB), Voting Classifier (VC), Stacking Classifier (SC), K Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) were implemented after the dataset had been preprocessed and normalized. Several matrices were employed for comparing the performance of these models. The Stacking Classifier emerged to be the best performing model with accuracy of 0.8988. © 2024 IEEE.

关键词： Sales

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Comparing Developmental Approaches for Game-Based Learning in Cyber-Security Campaigns 1

Comparing Developmental Approaches for Game-Based Learning i...

引用

1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sreehari, S. Gokulapriya, R. Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Digital game-based learning (DGBL) has been viewed as an effective teaching strategy that encourages students to pick up and learn a subject. This paper explores its viability to help increase the reach and efficiency of the existing cybersecurity awareness spreading campaigns that find adolescent students as their demographic. This work intends to reinforce the benefits of multimedia learning in schools and universities with the use of video games and further find the ideal type and genre of game that can be developed to spread awareness about cybersecurity to students in grades 8th to 12th (tailored towards the Indian context). Game genres were compared on the basis of having a simple gameplay loop, being easy for instructors to train themselves in, being inclusive to special needs children, being able to be published as an independent title, and having very low hardware specification requirements. Ideally, the paper proposes that this game would be a single-player experience that would follow a game-based learning approach to maximize the game's reach. Once identified, the model of the game was assessed using already existing implementations. Finally, the ideal model, a single-player visual novel is proposed. A future iteration of the paper will implement the proposed model of game design and perform an analysis of the effects the video game had on the learning experience of the students surveyed. © 2023 IEEE.

关键词： Students

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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Das, Swatisipra Sahil, Mohammad Pandit, Naveen Kumar Priyadarshini, Rojalina Gochhayat, Sarada Prasad Fakir Mohan University Postgraduate Dept. of Computer Science Odisha India C.V. Raman Global University Dept. of Computer Science and Engineering Odisha India Indian Institute of Technology Jammu Dept. of Computer Science and Engineering J&K India

ISBN: (纸本)9798350348460

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user's details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management. © 2024 IEEE.

关键词： access management blockchain cloud identity management

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Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Establishing Dataset and Clustering by using Sequence and Image Similarity Comparisons 24

Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Es...

引用

7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Wang, Tzung-Wei Li, Ming-Kuang Lou, Der-Chyuan Lin, Chun-Yuan Dept. of Electronics Engineering Chang Gung University Taoyuan Taiwan Dept. of Computer Science and Information Engineering Asia University Taiwan Taichung Taiwan Dept. of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9798400717437

Based on the MPXV Clade IIb cases in Taiwan, 175 segments were identified from OPG001 to OPG210. The first focus of this paper is on sequence analysis of the MPXV cases in Taiwan, detailing the procedures, including semi-automated segmentation and dataset creation from a large number of MPXV Clade IIb nucleotide sequences found in the NCBI and GISAID databases. The second feature highlights the pioneering establishment of an MPXV Clade IIb RNA 2nd structure image dataset in Taiwan, as such a dataset does not yet exist globally. The third characteristic involves the use of unsupervised learning methods for the rapid clustering of MPXV Clade IIb RNA 2nd structure images. This approach allows not only for the quick detection and clustering of unknown MPXV Clade IIb cases but also for the rapid identification of new MPXV mutations and structural changes. Compared to traditional sequence analysis, this new hybrid systematic method significantly enhances overall efficiency, improving processing speed by at least 24 hours when handling the alignment of 10 MPXV sequences from Taiwan. Future plans include using this prototype analytical model and method to accelerate the analysis of critical lethal factors in MPXV Clade IIb, such as D14L, ultimately helping biologists reduce the time required for analysis and antiviral drug development. © 2024 Copyright held by the owner/author(s).

关键词： Nucleotides

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Authentication and Authorization in Zero Trust IoT: A Survey 35

Authentication and Authorization in Zero Trust IoT: A Survey

引用

35th Irish Systems and Signals Conference, ISSC 2024

作者： James, Meha Newe, Thomas O'Shea, Donna O'Mahony, George D. Munster Technological University Dept. Computer Science Cork Ireland University of Limerick Dept. Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350352986

A critical challenge in implementing Zero trust (ZT) in Internet of Things (IoT) environments is ensuring secure authentication and authorization mechanisms. The work in this article delves into the intricate realm of ZT by critically examining the state-of-the-art authentication and authorization processes within the ZT framework for IoT. Despite the wealth of work on ZT and Zero Trust Architecture (ZTA), notable research gaps persist, specifically in identity authentication and fine-grained access control. Given the vast and diverse ecosystem of IoT devices, device identification complexities arise from a lack of unique hardware identifiers, rendering traditional methods susceptible to cyber-attacks, such as, spoofing. For dynamic IoT environments, achieving the optimal implementation in access policies poses a significant challenge. Numerous ZTA methodologies, integrating access controls, blockchain frameworks, and advancements in Artificial Intelligence (AI) and Machine Learning (ML), have been implemented to tackle these challenges. However, research gaps remain, particularly in understanding the behavioral aspects within these frameworks. As a result, this article focuses on identifying how device behavioral aspects can improve authentication and authorization in ZT for IoT. Finally, the article concludes by highlighting how these challenges can serve as future research directions. © 2024 IEEE.

关键词： Authorization

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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases 5

A Novel Hybrid Deep Neural Network for Early Detection and C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Md Shihab Islam, Md Fakrul Rahman, Sharukh Foysal, Nasir Uddin Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350394290

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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'Precision Agriculture Farming' Enhancing Farming Efficiency through Technology Integration 1

'Precision Agriculture Farming' Enhancing Farming Efficiency...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Prashant Kumar, Pratyaksh Singh, Praveen Kumar Rana, Arun Kumar Galgotias College of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350356816

Precision agriculture, or precision farming, or smart farming, refers to a contemporary approach that combines technology and data analytics with agricultural practices. This research paper examines the concept of precision agriculture, its principles, advantages, disadvantages, and potential to transform farming practices. Through the use of various technologies including GPS, sensors, drones, and data analytics, precision agriculture seeks to maximize resource efficiency, enhance crop yields, reduce the environmental footprint, and boost overall profitability for the farm. The paper also addresses case studies and real-life implementations of precision agriculture and illustrates its effectiveness in various sectors of agriculture. It further identifies the challenges and constraints of adopting precision agriculture and proposes possible solutions to overcome these challenges. This paper generally offers insight into the revolutionary potential of precision agriculture in reshaping the future of farming. © 2024 IEEE.

关键词： Smart agriculture

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The Role of Medical Expert Systems in Diagnosis of Chronic Kidney Diseases 2

The Role of Medical Expert Systems in Diagnosis of Chronic K...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kumar, Kuchipudi Prasanth Bharati, K.F. Jawaharlal Nehru Technological University Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India Jntua College of Engineering Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India

ISBN: (纸本)9798350370188

The ability to forecast kidney diseases in real-time is critical for preserving patients' lives. Early detection of these diseases not only extends the patient's longevity but also reduces treatment costs. It is very advised to develop a reliable expertise system for detecting these problems before they worsen. These smart solutions have the potential to save many lives. This review study assesses the Dice score efficacy characteristics obtained from various methodologies utilized in previous studies for detecting renal abnormalities or chronic kidney diseases. It gives a complete overview of medical expert systems in proposed methodology and emphasizes the most effective methods for identifying chronic kidney disease (CKD). © 2024 IEEE.

关键词： Diagnosis

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Prediction and Modelling of Suicidal Mental Tendencies 8

Prediction and Modelling of Suicidal Mental Tendencies

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shreeraksha Shivani, K. Varsha, S.N. Yashoda, K. St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation. © 2024 IEEE.

关键词： Prediction models

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Predictive Models for Liver Disease: Unraveling Patterns with Machine Learning Algorithms 4

Predictive Models for Liver Disease: Unraveling Patterns wit...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Pandey, Priyam Jena, Junali Jasmine Singh, Jay Prakash Parui, Sricheta Kumar, Vivek Gourisaria, Mahendra Kumar School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneshwar India Manipal University Jaipur Dept of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798331528171

Liver Disease is a widespread problem around the globe. It can give rise to many problems such as hepatitis, jaundice, fatty liver etc. Poor lifestyle and genetic factors are both contributors to it. Annually around two million people die due to liver diseases. Part of this is caused due to a lack of early diagnosis. Machine Learning approach is a feasible solution to counter this problem. In this paper we trained various machine learning models like Logistic Regression, Random Forest, Cat Boost, ADA Boost, Light GBM etc. on a dataset of Liver Disease Patients and tested the performance of the models based on various classification metrics like Accuracy, Precision, Recall, F-Score, etc. The best result was shown by XG Boost with an accuracy score of 81 percent followed by Light GBM and Cat Boost with an accuracy score of 80.0 percent and 79.9 percent respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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