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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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AI-Powered Smart Energy Solutions: Combating Global Warming with Innovation

AI-Powered Smart Energy Solutions: Combating Global Warming ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sinha, Aashna Pramanik, Atreyi Iqbal, Mohammed Ismail Aluvala, Srinivas School of Applied and Life Science Uttaranchal University Division of Research & Innovation Dehradun India University of Technology and Applied Sciences Nizwa Oman School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350357769

One of the most critical issues of our day is global warming, which is advancing relentlessly and has serious repercussions for the environment and economy. Individual nations are dealing with different effects of global warming at the national level, such as rising temperatures, changing weather patterns, and extreme weather occurrences. There has never been a time when it has been more important to fight climate change, and at this critical juncture, the fusion of cutting-edge technology and visionary innovation is positioned to play a crucial role in our joint efforts. The powerful potential of artificial intelligence (AI) lies at the center of this confluence. AI provides smart energy solutions that have the ability to fundamentally alter how we create, distribute, and use energy while also reducing the effects of global warming. This article provides a glimpse into the comprehensive plan of 'AI-Powered Smart Energy Solutions' in combating climate change through innovative and astute energy sector applications. © 2023 IEEE.

关键词： Artificial intelligence Energy storage Global warming Renewable energy forecasting Smart buildings

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Constructing the ChatGPT for PDF Files with Langchain - AI 7

Constructing the ChatGPT for PDF Files with Langchain - AI

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prem Jacob, T. Bizotto, Beatriz Lucia Salvador Sathiyanarayanan, Mithileysh Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India University Center Unifacvest Department of Social And Applied Sciences Lages Brazil Research & Innovation MIT Square London United Kingdom

ISBN: (纸本)9798350359299

Queries in PDFs can be time-consuming and labor-intensive because of the unstructured nature of the PDF document type and the need for accurate and relevant search results. By applying cutting-edge algorithms for natural language processing to examine PDF documents and extract relevant data, LangChain solves these difficulties. It makes use of an easy search interface, adjustable filters, and efficient indexing and retrieval mechanisms to enhance the search experience. To efficiently retrieve relevant information from PDF documents, users can annotate critical portions, store queries, and create bookmarks with LangChain. The characteristics of LangChain improve overall productivity and greatly simplify PDF querying. Semantic search, driven by the latest Transformer language models, represents a significant evolution in information retrieval systems. This research work explores the capabilities of semantic search to efficiently retrieve documents from large collections in response to natural language queries. Unlike traditional keyword-based approaches, semantic search connects the power of Transformer models to discern meaning, providing users with more contextually relevant and accurate results within seconds. This technology not only enhances the user experience by delivering superior matches from document collections but also lays the foundation for tackling more intricate tasks, like text summarization and question-answering. The research investigates the impact of semantic search on information retrieval efficiency and accuracy, comparing its performance with conventional methods. The findings presented herein not only showcase the immediate benefits of semantic search but also open paths for future research and development in natural language processing and its applications. © 2024 IEEE.

关键词： Information retrieval systems

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Wavelet-Based Denoising Diffusion Models for ECG Signal Enhancement 12

Wavelet-Based Denoising Diffusion Models for ECG Signal Enha...

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12th IEEE International Symposium on Signal, Image, Video, and Communications, ISIVC 2024

作者： Bouny, Lahcen El Zouidine, Mohamed Fakhar, Khalid Khalil, Mohammed University Hassan Ii of Casablanca Laboratory of Computer Science Mathematics and Applications Faculty of Science and Technology of Mohammedia Morocco Berrechid Hassan First University Lamsad Research Laboratory National School of Applied Sciences Morocco

ISBN: (纸本)9798350385267

Diffusion models are emerging as a robust solution for achieving high-fidelity data denoising, often surpassing classical deep learning methods in quality under various conditions. Nonetheless, their slow training and inference speeds present a significant limitation, preventing their practical use in realtime applications. The objective of this paper is to improve the denoising performances of an existing diffusion model by introducing a new denoising diffusion scheme based on wavelets. In our scheme, we leverage the Discrete Wavelet Transform (DWT) to reduce the dimensionality of the input signals, before applying the denoising diffusion model. The proposed waveletdenoising diffusion model exhibits the capability to reconstruct clean ECG signals from corrupted ones. Also, the number of model parameters to be estimated has been reduced using the improved approach. The proposed improved model has been tested in a medical scenario using Electrocardiogram (ECG) data from the QT database, corrupted with real noises that are sourced from the MIT-BIH Noise Stress Test database. To assess the denoising performance, we employ metrics such as the Sum of the Square of Distances (SSD), the Absolute Maximum Distance (MAD), the Percentage Root-Mean-Square Difference (PRD), and the Cosine Similarity (Cosine Sim). The results of this work demonstrate the encouraging denoising capabilities of our proposed method, showcasing lower SSD, MAD, and PRD, along with higher Cosine Sim scores compared to the state-of-the-art methods. © 2024 IEEE.

关键词： Discrete wavelet transforms

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An investigation into time-varying characteristics of multivariate time series in Grassmann classification

An investigation into time-varying characteristics of multiv...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Nuriye, Bezawit Habtamu Oh, Beomseok Gyeongsang National University Department of Computer Science Jinju Korea Republic of Seoul National University of Science and Technology Department of Applied Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350320213

Since multivariate time series (MTS), which lie in a non-Euclidean space, exhibit temporal evolution and correlation characteristics, its classification is considered a non-trivial task. To mitigate the impact of the time-varying characteristics and thus enhance the classification accuracy, in this paper, we propose to model MTS data using a time-varying linear dynamical system followed by a neural network-based classification on the Grassmannian manifold. Our experiments on publicly available MTS datasets show promising classification results. © 2023 IEEE.

关键词： Grassmannian manifold Linear Dynamical System Multivariate Time Series

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3D Gaze Estimation via Binocular Disparity and Feature Refinement 10

3D Gaze Estimation via Binocular Disparity and Feature Refin...

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10th International Conference on Big Data Computing and Communications, BIGCOM 2024

作者： Wang, Chengxin Chen, Qiuxia Tian, Ying Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798331509538

The eyes are the windows to the soul and are crucial for studying human behavior. Therefore, gaze estimation has attracted much attention in the field of computer vision. In recent years, convolutional neural networks have driven significant progress in gaze estimation research. Despite the remarkable achievements of gaze technology based on full-face images, the inability to obtain full-face images in specific scenarios makes research based on eye images particularly important. This paper proposes a three-dimensional gaze estimation model that takes binocular images as input, using VGG16 as the backbone network for feature extraction. To enhance the feature extraction capability, we have embedded channel attention and spatial attention mechanisms. Meanwhile, to improve the stability of gaze estimation, we classify real gaze angles. First, we perform a preliminary screening of gaze direction through a classification task, and then perform linear regression with the true angle. Finally, we optimize gaze features using binocular vision differences to finetune the gaze estimator. Experiments have shown that our method performs well on three public datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Data-Driven Bandwidth Selection in Kernel Density Estimation via Unsupervised Learning 8th

Data-Driven Bandwidth Selection in Kernel Density Estimatio...

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8th International Symposium on Modelling and Implementation of Complex Systems, MISC 2024

作者： Guidoum, Arsalane Chouaib Henider, Amel Department of Mathematics and Computer Science Faculty of Sciences and Technology University of Tamanghasset Tamanghasset11000 Algeria Department of Finance and Actuarial National Higher School of Statistics and Applied Economics Kolea42003 Algeria

ISBN: (纸本)9783031821110

The selection of optimal bandwidth bw in Kernel Density Estimation (KDE) is crucial for accurate density estimation. Traditional methods, such as unbiased and biased cross-validation and their variants, often suffer from performance and computational limitations. This study presents a novel, data-driven approach to bandwidth selection using unsupervised machine learning techniques. Synthetic samples are generated from the original data, a comprehensive set of statistical parameters is calculated, and bandwidth is estimated using a rule-of-thumb [2]. Feature importance is assessed through random forests (RF) to identify the most influential factors in bandwidth selection. Various unsupervised learning models are then applied to predict the optimal bandwidth for the original sample bwos. The results demonstrate that this machine learning-based approach achieves superior accuracy and computational efficiency compared to traditional cross-validation methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Blockchain-based federated learning with checksums to increase security in Internet of Things solutions

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4685-4694页

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Lin, Jerry Chun-Wei Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland Dept. of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Western Norway University of Applied Sciences Bergen Norway Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon

Federated learning is becoming a practical solution for machine learning (ML) in industry. This is due to the possibility of implementing artificial intelligence (AI) systems and training its models on private data sets. However, this is not an ideal solution as it is possible to manipulate or even intercept the model during its transmission between the server and workers. In this paper, we propose a solution to ensure the security of the model transmitted between units in FL. The given model is encrypted with AES, DES, RSA algorithms, then a checksum is determined. This checksum with a private key is stored as a transaction on a blockchain. In the case of sending the model and its modification, the recipient can easily verify whether it is correct. The proposed solution has been described, tested, and compared to indicate its advantages and disadvantages. Conducted experiments were based on analyzing the communication time between participants, the accuracy of machine learning models, and attack detection. In terms of attack detection on the blockchain, we reached 81% thanks to the checksum mechanism. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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Facial Emotion Recognition Based on Textural Pattern and Histogram of Oriented Gradient 1

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Mukhopadhyay, Moutan Dey, Aniruddha Ghosh, Ankush Shaw, Rabindra Nath Department of Computer Science Engineering MAKAUT Kolkata India Department of Information Technology MAKAUT Kolkata India School of Engineering and Applied Science Neotia University Kolkata India

ISBN: (数字)9783031250880

ISBN: (纸本)9783031250873

In the past few years Emotion detection from images has become very popular due to its immense applications fields in day-to-day life. Various computer visions techniques are applied to detect facial emotions but it’s a very challenging task in real time scenario. Most of real-life images are taken in the poor illumination condition and it fails to achieve good recognition accuracy. LBP and LTP both the textural feature descriptor come into the picture to overcome such condition. Histogram of Oriented Gradient (HOG) detects the edges and corners from the images very efficiently. Textural feature descriptor captures the local pattern. HOG and textural feature descriptor capture different types of information of the image. In the proposed method textural image and HOG image fusion is performed to increase the accuracy for recognizing the facial expression from images. The performance of proposed method is validated on CK+ dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Emotion Recognition

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Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network 8

Optimized Smart Agricultural Production Systems using Enhanc...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, S. Gomathi Goyal, Himanshu Rai Lavanya Muthukumar, E. Megha, Mamtaben Kamleshbhai Parikh, Swapnil Peri College of Arts and Science Pg Department of Computer Science and Computer Applications Tamil Nadu Mannivakkam600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Viruthunagar626126 India Nehru College of Management Department of Mba Coimbatore Tamil Nadu Thirumalayampalayam641105 India Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of Cvm University Department of Computer Science Gujarat Vallabh Vidyanagar388120 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India

ISBN: (纸本)9798350376425

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). This system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced Parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. The EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). These results highlight the efficiency of the approach and its potential for further advancement in smart agriculture. © 2024 IEEE.

关键词： Emperor Penguin Optimizer (EPO) Enhanced Parallel Convolutional Neural Network (EPar-CNN) Green Anaconda Optimization (GAO) Internet of Things (IoT) Smart Agricultural Systems

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