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Optimizing Marketing Strategies: Predicting Customer Personality using Advanced machine Learning Models 2

Optimizing Marketing Strategies: Predicting Customer Persona...

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2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Aryan, Adla Sagar, Sada Preetham Varma, Nadimpalli Madana Kailash Manikanta, Rangisetti Rajashekar, Ramojula Arora, Gagan Deep Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501218 India

ISBN: (纸本)9798331541217

In the competition of modern marketing, it is highly important to foresee the correct personality profiles of customers because this may further improve the result of marketing campaigns. Therefore, in this research, three major models of machine learning are used, including Random Forest Classifier, Support Vector machine (SVM), and Gradient Boosting Classifier, to discuss the challenges of predicting customer personalities. This study is helpful in finding unique customer segments and appropriate marketing strategies. These algorithms are valid to use for classifying customer personalities, and then determine the efficiency of it using the context also proves that through the more complex applications of such approaches, the efficacy of targeted marketing might be enhanced appropriately. Therefore, even more advanced deep learning techniques would be used at the next stage of research to further improve the forecasting accuracy and applicability in various marketing scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial intelligence as a Tool for Graphic's Production Process 2

Artificial Intelligence as a Tool for Graphic's Production P...

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2nd international conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahul Kaur, Kamaljeet University Institute of Media Studies Chandigarh University Mohali India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519056

The integration of machine learning to the visual design process is known as artificial intelligence (AI). By assisting or even replacing a designer totally, AI tools streamline design work. Graphics technology and artificial intelligence (AI) are revolutionizing their respective fields and playing a crucial role in modern technology. However, when both components are combined, they produce extraordinary outcomes in the CGI system. There is a wide range of material accessible on AI plugins for Graphics, but there is a significant lack of information on the integration of AI with Graphics in a combined pipeline. This research focusses on the technological and artistic aspects of Graphics and AI. The individuals who have developed the plugins for Graphics are primarily from a technical background and may not possess knowledge in the artistic domain. This research can facilitate the development of innovative techniques to address the weaknesses of the CGI (Graphics) system using AI. This research has devised an algorithm to bridge the gap. This research utilised both qualitative and quantitative methodologies. This study involved conducting interviews with 5 specialists in Graphics and AI, as well as surveying 150 respondents to gather quantitative data. © 2024 IEEE.

关键词： Graphics Visualization Technological innovation Three-dimensional displays Pipelines Production machine learning Communications technology Artificial intelligence Interviews

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2024 IEEE 2nd international conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

2024 IEEE 2nd International Conference on Sensors, Electroni...

引用

2nd IEEE international conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

ISBN: (纸本)9798350373646

The proceedings contain 331 papers. The topics discussed include: research on computer aided French audiovisual intelligence system;feature extraction and intelligent generation of spatial environment design combined with deep learning;a federated learning algorithm based on combination of prototype and comparative learning;research on the optimization of wind-solar energy storage systems in park microgrids based on mixed integer linear programming and Monte Carlo simulation;deep learning in biometric recognition: applications and challenges;design and research of virtual human based on deep learning and paddle model;a fault diagnosis method based on TCN-LSTM-SE neural networks for distributed PV systems;and research on real-time image data transmission system of UAV in fault location of distribution network.

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A Review of Social Media Sentiment Analysis Using machine Learning To Enhance Stock Market Prediction 2

A Review of Social Media Sentiment Analysis Using Machine Le...

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2nd DMIHER international conference on Artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Raut, Rohini Saratkar, Saniya Thakre, Gayatri Thute, Trupti Chaudhari, Aarti Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Predicting stock prices is a critical financial activity that is frequently impacted by sentiment analysis, machine learning, and data mining approaches. This examination looks at a variety of stock price prediction methods, including artificial neural networks, graph-based approaches, time series analysis, and classic machine learning techniques such as Support Vector machine, K-Nearest Neighbor, and Random Forest. The article assesses the problems and efficacy of various methodologies, including a thorough examination of sentiment analysis in social media to enhance stock market predictions. By examining multiple research, this study provides a complete evaluation of various prediction algorithms and reveals their shortcomings, such as overfitting, non-stationary data, and the complexity of financial transactions. It helps academics and practitioners construct more accurate and dependable stock forecasting models by merging financial data with sentiment analysis. The study also discusses rising trends and new potential for further study, especially hybrid models and advanced deep learning approaches to improve the accuracy of stock forecasts. © 2024 IEEE.

关键词： Prediction models

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AI Powered Personalized Healthcare Recommender 2

AI Powered Personalized Healthcare Recommender

引用

2nd international conference on Artificial intelligence and machine Learning Applications, AIMLA 2024

作者： Hirushit, S. Raja, S. Suwetha, S. Yazhini, J. K S Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350349221

In the evolving landscape of healthcare, the integration of Artificial intelligence (AI) has introduced an innovative approach to personalized patient care. An extensive framework is introduced for an AI-powered healthcare recommender system, dedicated to providing customized health and wellness recommendations. Users create profiles to maintain health history, preferences, and real-time data, while recommendations are generated using advanced algorithms, regularly refined for accuracy. Real-time health monitoring, via wearable devices and health apps, ensures the latest health metrics inform recommendations. The AI-driven recommendation engine draws on the latest medical research to provide evidence-based advice. Privacy and data security are priorities, with stringent measures to protect sensitive health data and comply with data protection regulations. 0The user-friendly interface offers easy access to recommendations, progress monitoring, and health-related information. Continuous improvement is core, with user feedback and data analytics driving recommendation enhancements. This framework harnesses AI's potential in healthcare, emphasizing personalization, security, and ongoing optimization for individuals and the broader healthcare ecosystem. © 2024 IEEE.

关键词： Recommender systems

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A Review of machine Learning and Deep Learning algorithms for Chronic Kidney Disease Prediction 2

A Review of Machine Learning and Deep Learning algorithms fo...

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2nd DMIHER international conference on Artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Nagre, Pratik Verma, Prateek Kumar, Praveen Gavankar, Nitin L. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India Walchand College of Engineering Department of Computer Science and Engineering Maharashtra Sangli416415 India

ISBN: (纸本)9798331528713

Chronic Kidney Disease (CKD) is a common disease worldwide that is manifested by slow progressive loss of kidney function that can result in complications and kidney failure. That is why conventional approaches to CKD diagnosis and treatment have certain difficulties: late identification of the disease, inaccuracy of the diagnosis, and high costs for the therapy. The new approaches for solving these problems are opened with the help of machine Learning (ML) and deep learning (DL). These technologies can help existing information sets in large volumes in diagnosing, predicting, and in early detection of diseases to enhance the well-being of the patients. However, the outcome comes with a handful of constraints, including the quality of the data used in developing the models, the ability to interpret results from the generated model, and the complexity of the computation process. The following section describes the various ML and DL algorithms used for CKD, their strengths, weaknesses, and the possibility of implementing these technologies in clinical settings for better CKD outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Quantum intelligence: Merging AI and Quantum Computing for Unprecedented Power 2

Quantum Intelligence: Merging AI and Quantum Computing for U...

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2nd IEEE international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, Sonu Simran, S. Singh, Manjit Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350384277

The result of AI and quantum computing coming together is the novel idea known as Quantum intelligence (QI). This work explores the possible advantages of combining artificial intelligence (AI) and quantum computing to address difficult problems that have proven to be beyond the capabilities of conventional computer methods. This study offers a thorough grasp of the unique properties of qubits, superposition, and entanglement while examining the fundamental concepts of quantum computing. The current state of artificial intelligence is also examined, with a focus on the difficulties traditional computers have in managing the increasing complexity of computing tasks. The paper's main focus is on Quantum intelligence, providing an explanation of how quantum algorithms might enhance machine learning procedures and improve artificial intelligence algorithms. The paper also discusses Open Source Libraries, TensorFlow Quantum, and the necessity of quantum programming languages. This abstract discusses the advantages and challenges of integrating quantum computing into AI procedures, emphasizing the necessity for hybrid models and scalable algorithms. We explore the interdependent nature of quantum and classical machine learning models and show how quantum-enhanced algorithms may be able to outperform their classical counterparts in specific application domains. The conclusion of the abstract emphasizes how the development and use of open-source frameworks and tools will fuel the transformative potential of AI and quantum computing together. © 2024 IEEE.

关键词： Quantum computers

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Comprehensive machine Learning Approach for Early Detection and Classification of Hyperthyroidism and Hypothyroidism 2

Comprehensive Machine Learning Approach for Early Detection ...

引用

2nd IEEE international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Jayadharshini, P. Kumar, A Satheesh Sivasankar, R. Harsine, K.U. Geetha, P. Logu, B. Department of Artificial Intelligence Kongu Engineering College Perundurai Tamilnadu Erode India Department of Cse Nandha Engineering College Erode India Department of Cse Builders Engineering College Tirupur India

ISBN: (纸本)9798350384277

Thyroid disorders, including hyperthyroidism and hypothyroidism, pose significant health risks and necessitate early detection for effective management. In this study, we present a comprehensive approach employing various machine learning algorithms for the prediction of thyroid conditions. The algorithms investigated include Random Forest, Naive Bayes, Decision Tree, Gradient Boosting, Logistic Regression, Adaboost, Support Vector machines (SVM), and k-Nearest Neighbors (KNN). Using a dataset comprising diverse thyroid-related features, we conducted an extensive comparative analysis of these algorithms. Our findings reveal that Random Forest exhibited the highest predictive accuracy, reaching an impressive 98.46%. This outstanding performance positions Random Forest as a robust model for accurate thyroid disorder classification. The target classes encompass hyperthyroid, hypothyroid, and negative instances, providing a comprehensive framework for diagnosing thyroid conditions. The incorporation of multiple algorithms ensures the robustness of our predictive model, accommodating diverse data patterns associated with thyroid abnormalities. Our research provides a foundation for the development of an intelligent diagnostic system. As the field of machine learning progresses, there is a growing recognition of its capacity to advance healthcare diagnostics, specifically in the early identification of abnormalities related to the thyroid. This has the potential to significantly enhance patient outcomes and overall quality of life. © 2024 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Business intelligence: Harnessing AI and machine Learning for Strategic Insights and Competitive Advantage 2

Revolutionizing Business Intelligence: Harnessing AI and Mac...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Girimurugan, B. Parthiban, K. Saxena, Mudit Talasila, Gowtham Vamsi, N. Surya Sai, P Tharun Business School Koneru Lakshmaiah Education Foundation A.P. Vaddeswaram522302 India Department of Management Studies Erode638052 India College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9798350371055

Optimizing Performance Management through Data Analytics and Artificial intelligence in the Manufacturing Sector. There have been a lot of new breakthroughs and opportunities made possible as a result of the increased usage of machine learning and artificial intelligence in business intelligence. Because of these cutting-edge technological advancements, businesses are now able to perform data analysis, gain new insights, and make decisions that are superior to those made in the past. It important to note that predictive analytics are becoming increasingly popular. Using algorithms designed for machine learning, businesses are able to sort through mountains of data in order to make predictions about the future that are based on accurate information. Because of this, organisations have the opportunity to increase their efficiency, decrease risk, and anticipate the needs of their customers. With the help of business intelligence, companies are able to optimise their operations, find new chances for growth, and make decisions that are driven entirely by data, all of which have a direct influence on the bottom line of the firm. Another trend that is picking up steam is the employment of chatbots and digital assistants that are driven by Radial Basis Function (RBF). The possibilities that machine learning and artificial intelligence present in terms of business intelligence are extremely extensive. Automated data analysis, anomaly detection, demand forecasting, and dynamic pricing are examples of the types of technologies that assist businesses in streamlining processes, lowering expenses, and locating untapped sources of revenue. To summarise, there have been some fascinating new breakthroughs, as well as countless opportunities, in the area of using AI and machine learning to business intelligence. Businesses have the potential to acquire a competitive advantage, produce innovation, and unlock new levels of success in the digital world if they adopt these technologies a

关键词： Artificial intelligence (AI) Business intelligence (BI) RNN DL

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Enhancing Literary Analysis through Artificial intelligence and machine Learning: Insights from 'The Alchemist' 2

Enhancing Literary Analysis through Artificial Intelligence ...

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2nd IEEE international conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Vinodini, S. Adithya Pothan Raj, V. Sathyabama Institute of Science and Technology Faculty of Arts Dept. of English Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept. of CSE Chennai India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The rapid growth of digital literature has necessitated the advancement of analytical tools to extract valuable insights from extensive textual data. This paper investigates the application of Artificial intelligence (AI) and machine Learning (ML) in literary analysis, presenting innovative methods to understand and interpret complex narratives and themes in literary works. Traditional literary analysis relies on human interpretation, which, while insightful, is often subjective and labor-intensive. The advent of digital humanities has sparked interest in computational methods to enhance traditional literary analysis. AI and ML, especially through Natural Language Processing (NLP), offer powerful solutions to analyze literary themes. Techniques such as sentiment analysis, topic modeling, and named entity recognition can dissect thematic elements, emotional tones, and character dynamics in texts. For example, sentiment analysis reveals underlying themes related to mood and character psychology, while topic modeling uncovers hidden thematic structures. machine learning algorithms, through supervised and unsupervised learning, can recognize and classify themes across different literary works. Despite challenges like the complexity of human language and contextual nuances, AI and ML significantly enhance the speed and scale of literary analysis, uncovering subtle themes often overlooked by human readers. The Flair tool, developed by Zalando Research, exemplifies a robust NLP library that enhances literary analysis through pre-trained models for tasks such as Named Entity recognition (NER), sentiment analysis, text classification, and Part-of-Speech (POS) tagging. Applying Flair to Paulo Coelho's "The Alchemist"demonstrates its potential to analyze genres, themes, and stylistic categories, showcasing the transformative capabilities of AI and ML in the digital humanities. © 2024 IEEE.

关键词： Natural language processing systems

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