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An Effective Customer Segmentation Approach to Improve E-Commerce Platform Quality Based on Intensive learning Methodology

An Effective Customer Segmentation Approach to Improve E-Com...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suresh, G.R. Vinoparkavi, D. Govindasamy, Suresh Rohini, S. Giri, Jayant Sabri, M. Omar Dhanalakshmi Srinivasan College of Engineering and Technology Department of Ece Mamallapuram Chennai India Nandha Engineering College Department of Cse Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India R.M.K Engineering College Department of Computer Science Engineering Kavaraipettai Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

To accomplish company goals and reap advantages while satisfying and meeting consumer expectations, segmentation is an essential facilitator. Identifying subgroups of the target market that share common traits, requirements, and priorities is customer segmentation's primary goal. Various dataset dimensionalities are also considered, as are changes across time. In this study, we use time-based segmentation and clustering approaches to the e-commerce industry to conduct predictive neural network analysis using factors such as product reviews, goods, buying trend, watching pattern, and more. In this research, Intensive Neural Classification learning (INCL) is presented as a novel deep learning technique. To assess its efficacy, it is cross-validated with the traditional Neural Network (NN) learning algorithm. We have evaluated unigrams, bigrams, and trigrams as part of the feature extraction process and identified the unique brands per consumer. In addition, the customer's chosen brands may be determined using a neural network, and the projected data can then be utilized for statistical analysis. We all know that sentiment analysis and categorization are huge help when trying to guess how customers feel about certain companies and items. Lastly, using the provided set of input data, we will choose the top brands by assessing the accuracy of the forecasts. © 2024 IEEE.

关键词： Sales

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EST: Integrating Explainable AI and Semi-Supervised Ensemble learning for Social Media Stress Detection

EST: Integrating Explainable AI and Semi-Supervised Ensemble...

引用

2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Tasnia, Naima Hosen, Md.Hamid Uddin, Altaf Foysal, Md. Fahim Symon, Nurul Asgar, Safa Chowdhury, Linkon East Delta University Computer Science and Engineering Chittagong Bangladesh International Islamic University Chittagong Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Social media platforms have become potential sites for analyzing people's mental health trends since they provide real-time data on users' thoughts and feelings. Automated stress detection using machine learning techniques can assist in identifying at-risk individuals, facilitating early intervention, and contributing to broader public health monitoring. This paper introduces an Ensemble Self-Training (EST) model for stress detection, combining multiple machine learning algorithms and a self-training mechanism, and provides a comprehensive interpretation of results by explainable artificial intelligence (XAI) techniques. The study utilized four distinct datasets comprising posts from popular social media sites like Reddit and Twitter. Different NLP tools, including Bigrams and Word2Vec word embeddings, created meaningful data for enhanced detection. Four machine learning classifiers- Logistic Regression, Support Vector Machine, Random Forest, and Naïve Bayes were evaluated and compared with the EST model. The EST model outperformed other models, achieving an impressive accuracy of 91.67% on the Dreaddit dataset and nearly 89% accuracy on the different datasets. Locally Interpretable Model Agnostic Explanations (LIME) enhanced the model's trustworthiness to ensure transparency and clarity of the model's predictions. © 2024 IEEE.

关键词： Self-supervised learning

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Topological Quantum computing for Engineers 4th

Topological Quantum Computing for Engineers

引用

4th international conference on Artificial Intelligence and Knowledge Processing

作者： Ganguly, Srinjoy Devendrababu, Shalini Mustafa, Hasan Jain, Prateek Ayala Bertel, Luis Gerardo Fractal Analyt QuantumAI Lab Gurugram India Univ Cartegena Cartegena Colombia

ISBN: (纸本)9783031734762;9783031734779

This didactic paper explores topological superconductivity, an essential idea for enhancing topological quantum computing by utilizing Majorana Fermions (Majorana Zero Modes). Topological quantum computing can enable fault-tolerant qubits, which are essential for implementing quantum AI algorithms and related techniques. This paper discusses the basic principles of topological superconductors, their experimental implementations, and the crucial importance of Majorana Fermions in facilitating error-resistant quantum computing. The paper also examines the integration of quantum AI, emphasizing how the stability of topological qubits enhances the performance of quantum machine learning algorithms. It discusses the obstacles and future possibilities of using topological states for quantum technologies, focusing on engineering applications and recent technical advancements. The study highlights the potential of topological superconductivity to transform quantum computing by providing insight into the creation of reliable quantum systems and scalable quantum circuits.

关键词： Topological Superconductivity Quantum computing Majorana Fermions Kitaev Chain

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Conventional Feature engineering and Deep learning Approaches to Facial Expression Recognition: A Brief Overview 1

引用

2nd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Agrwal, Shubh Lakshmi Sharma, Sudheer Kumar Kant, Vibhor Manipal Univ Jaipur Jaipur Rajasthan India LNM Inst Informat Technol Jaipur Rajasthan India Banaras Hindu Univ RGSC Varanasi Uttar Pradesh India

ISBN: (数字)9783031281839

ISBN: (纸本)9783031281822;9783031281839

Facial expression recognition (FER) is vital in pattern recognition, artificial intelligence, and computer vision. It has diverse applications, including operator fatigue detection, automated tutoring systems, music for mood, mental state identification, and security. Image data collection, feature engineering, and classification are vital stages of FER. A comprehensive critical review of benchmarking datasets and feature engineering techniques used for FER is presented in this paper. Further, this paper critically analyzes the various conventional learning and deep learning methods for FER. It provides a baseline to other researchers about future aspects with the pros and cons of techniques developed so far.

关键词： Facial expression recognition Feature engineering Conventional learning Deep learning Face expression dataset

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Comparison of Deep learning Methods for Detecting Brain Tumor 14

Comparison of Deep Learning Methods for Detecting Brain Tumo...

引用

14th international conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Bedi, Kiran Preet Jadon, Jitendra Singh Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Noida India

ISBN: (纸本)9798350344837

The intricate nature of brain tumors and their resemblance to normal brain tissues present significant challenges in effectively distinguishing and categorizing them through traditional medical image processing methods. Brain tumors pose a considerable global health risk, often leading to inaccurate diagnoses and prognoses, even with human assistance. Recent advancements in deep learning have significantly enhanced the accuracy of brain tumor detection and classification using magnetic resonance imaging (MRI) of the brain. In this research, we provide an illustrative approach for the classification of brain tumors, employing transfer learning and convolutional neural networks (CNNs). We explore several CNN architectures, including MobileNet-V2, ResNet, and Xception. Notably, MobileNet-V2 emerged as the most effective model, achieving an impressive Fl score of 98.45%. The accuracy of brain tumor categorization is pivotal for the effective evaluation of tumors and the formulation of appropriate treatment strategies. MRI is a preferred imaging modality due to its superior image quality and safety, as it does not involve ionizing radiation, unlike other imaging techniques. The integration of computational intelligence, particularly deep learning (DL), has revolutionized the diagnosis and categorization of complex clinical conditions like brain tumors. Deep learning has consistently demonstrated outstanding performance in the partitioning and classification of brain tumors. In this paper, we introduce an AI-driven method for the classification of brain tumors into benign and malignant categories using deep learning algorithms. To conduct this study, publicly available datasets comprising brain images are utilized, subject to in-depth analysis and assessment to enhance the accuracy of brain tumor classification © 2024 IEEE.

关键词： Image classification

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Deep learning Assisted Readout of In Situ Frequency Tunable Superconducting Qubits 4

Deep Learning Assisted Readout of In Situ Frequency Tunable ...

引用

4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Wang, Wei University of Macau Institute of Applied Physics and Materials Engineering 999078 China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534622

Dispersive readout of superconducting qubits is one of the most critical components of superconducting quantum computing systems. Traditional dispersive readout schemes typically employ digital signal processing technique to demodulate multiple measurements into clouds in the IQ plane, determining the qubit state through projection and threshold analysis. However, when addressing tunable qubits, this method is limited to single-frequency measurements;attempting to measure at different frequencies without adjusting the readout frequency accordingly can compromise readout fidelity. To address this, this paper proposes a deep learning-based readout scheme that can directly train and predict the readout signals corresponding to the ground and excited states of the same superconducting qubit at different frequencies under a fixed readout frequency. A supervised learning approach is utilized to construct a deep learning model based on the multilayer perceptron. The trained model can directly predict the qubit state at different frequencies within a large frequency range. Moreover, the deep learning model can not only distinguish between the ground and excited states of the qubit but also leverage its capability to handle regression problems to test any superposition state of the qubit, which has been verified through Rabi oscillation tests. Experimental results indicate that this approach can effectively discern the state within a 300 MHz tuning range of the qubit frequency, achieving a readout fidelity exceeding 92% under various magnetic flux bias and a Rabi measurement fidelity above 96%. The findings of this study underscore the efficacy of the deep learning scheme in augmenting the convenience of qubit readout processes, concurrently preserving high fidelity, which is imperative for the advancement of quantum computing technologies. © 2024 IEEE.

关键词： Qubits

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Smart Wearable Devices for Prediction Using Machine learning Techniques: Depth Exploration 7

Smart Wearable Devices for Prediction Using Machine Learning...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mary, L. William Antony Raj, S. Albert SRM Institute of Science and Technology Department of Computer Applications India

ISBN: (纸本)9798350372816

The emergence of the COVID-19 epidemic has emphasized the continuous need for innovative and discreet methods to monitor and evaluate the progression of the disease. Wearable technology, equipped with an array of sensors and powered by advanced machine learning algorithms, presents a highly promising solution. These devices make use of sensors like heart rate monitors, pulse oximeters, temperature sensors, and accelerometers to continuously gather real-time data. This research explores how wearables, including fitness trackers and smartwatches, can be used to continuously monitor the COVID-19 disease and capture the data. It also explores applications in assessing COVID-19 severity through the utilization of machine learning techniques. Machine learning techniques including decision trees, support vector machines, and deep neural networks are used to process this data. This includes the early detection of warning signs, evaluating disease progression, and distinguishing between mild, moderate, and severe cases. The integration of patient-reported symptoms with wearable data further enhances the precision of assessing COVID-19 severity. © 2024 IEEE.

关键词： COVID-19

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Enhancing the Ethical and Truthful Dimension of Hate Speech Detection Through Machine learning Methods 1

Enhancing the Ethical and Truthful Dimension of Hate Speech ...

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Khan, Zaid Riyaz Punia, Sanjeev Kumar Galgotias University School of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350356816

With the technological developments past decade, there is an increase seen in the usage of social media applications as more people have come over on such platforms to show their thoughts. We look for the problems faced by applications for processing hate speech in text on their platforms. A few of the difficulties faced are because of the reason of so many languages, the way of using the words making the sentence have different meanings, and the shortage of proper data for testing and training. We derive a way for making such task simpler by the use of support vector machine which is giving results better than a neural network model. At the end we also go through the technical issues which still persists till now. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved Alzheimer's Disease Early Prediction using Machine learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier 4

Improved Alzheimer's Disease Early Prediction using Machine ...

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4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Munawar, Madiha Monika Singh, T. Kishor Kumar Reddy, C. Hanafiah, Marlia Mohd Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Universiti Kebangsaan Malaysia Faculty of Science and Technology Malaysia

ISBN: (纸本)9798331515720

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. The obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). The Random Forest classifier also achieves higher Time Taken that is 483.654 sec. © 2024 IEEE.

关键词： Alzheimer's Disease Decision Trees Deep learning Dementia learning Approaches Machine learning Random Forest

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Prediction of Bevel-Tip Needle Deflection Using CART Based Decision Tree 3

Prediction of Bevel-Tip Needle Deflection Using CART Based D...

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3rd international conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Behera, Bulbul Orlando, M. Felix Anand, R.S. Indian Institute of Technology Roorkee Department of Electrical Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798350349207

Minimal-invasive surgery has gained popularity due to reduced blood loss, minimal patient discomfort, lower costs, and faster recovery. However, its success relies heavily on the needle tip's precision. The interaction between the needle and tissue notably influences this accuracy. Achieving a significant degree of accuracy is challenging because of the uncertain mechanics and the varying stiffness of heterogeneous tissue. The foremost factor influencing the misplacement of a needle is deflection. Therefore, this research paper introduces a machine learning approach for predicting needle deflection. The machine learning method such as a decision tree, is utilized and trained using collected experimental data. Decisions, such as determining which feature contributes more to deflection, are made without depending on any mathematical model. © 2024 IEEE.

关键词： Forecasting

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