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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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A peristaltic soft, wearable robot for compression and massage therapy

arXiv

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arXiv 2022年

作者： Zhu, Mengjia Ferstera, Adrian Dinulescu, Stejara Kastor, Nikolas Linnander, Max Hawkes, Elliot W. Visell, Yon Media Arts and Technology Program Department of Electrical and Computer Engineering Department of Mechanical Engineering California NanoSystems Institute University of California Santa Barbara United States Mechanical Engineering Program Department of Mechanical Engineering ETH Zurich Switzerland

Soft robotics is attractive for wearable applications that require conformal interactions with the human body. Soft wearable robotic garments hold promise for supplying dynamic compression or massage therapies, such as are applied for disorders affecting lymphatic and blood circulation. In this paper, we present a wearable robot capable of supplying dynamic compression and massage therapy via peristaltic motion of finger-sized soft, fluidic actuators. We show that this peristaltic wearable robot can supply dynamic compression pressures exceeding 22 kPa at frequencies of 14 Hz or more, meeting requirements for compression and massage therapy. A large variety of software-programmable compression wave patterns can be generated by varying frequency, amplitude, phase delay, and duration parameters. We first demonstrate the utility of this peristaltic wearable robot for compression therapy, showing fluid transport in a laboratory model of the upper limb. We theoretically and empirically identify driving regimes that optimize fluid transport. We second demonstrate the utility of this garment for dynamic massage therapy. These findings show the potential of such a wearable robot for the treatment of several health disorders associated with lymphatic and blood circulation, such as lymphedema and blood clots. © 2022, CC BY.

关键词： Blood

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Enhancement of Co-located Shared VR Experiences: Representing Non-HMD Observers on Both HMD and 2D Screens 23

Enhancement of Co-located Shared VR Experiences: Representin...

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23rd IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2024

作者： Guo, Zixuan Xu, Wenge Wang, Hongyu Wan, Tingjie Baghaei, Nilufar Lo, Cheng-Hung Liang, Hai-Ning Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China The University of Liverpool Department of Computer Science Liverpool United Kingdom Birmingham City University College of Computing Birmingham United Kingdom The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia Xi'an Jiaotong-Liverpool University Department of Industrial Design Suzhou China Computational Media and Arts Thrust Guangzhou China

ISBN: (纸本)9798331516475

Virtual reality (VR) not only allows head-mounted display (HMD) users to immerse themselves in virtual worlds but also to share them with others. When designed correctly, this shared experience can be enjoyable. However, in typical scenarios, HMD users are isolated by their devices, and non-HMD observers lack connection with the virtual world. To address this, our research investigates visually representing observers on both HMD and 2D screens to enhance shared experiences. The study, including five representation conditions, reveals that incorporating observer representation positively impacts both HMD users and observers. For how to design and represent them, our work shows that HMD users prefer methods displaying real-world visuals, while observers exhibit diverse preferences regarding being represented with real or virtual images. We provide design guidelines tailored to both displays, offering valuable insights to enhance co-located shared VR experiences for HMD users and non-HMD observers. © 2024 IEEE.

关键词： Virtual environments

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Preliminary Design for Reference tracking feedback for Medical Cyber-Physical Pharmacokinetic Systems 26

Preliminary Design for Reference tracking feedback for Medic...

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2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Oliveira, Pedro M. Bustos, Daniel Lobo, Rodolfo Lacerda, Marcio J. Palma, Jonathan M. Federal University of Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Universidad Católica Del Maule Dept. of Translational Medicine Talca Chile Universidad Diego Portales Ingenieria en Telecomunicaciones e Informática Santiago Chile London Metropolitan University Centre for Communications Technology School of Computing and Digital Media London United Kingdom Universidad de Talca Faculty of Engineering Curicó Chile

ISBN: (纸本)9798350378115

This paper addresses the reference tracking control problem for Medical Cyber-Physical Systems (MCPS). The control theory is employed to guarantee the suitable concentration of drugs in the body of patients to guarantee a safe treatment. The MCPS is modeled as a switched system, and the modes consider the different scenarios for the problem. A discrete-time model is utilized for the pharmacokinetic process, and the zero input control strategy is employed to design state-feedback controllers with a guaranteed exponential convergence rate. A numerical experiment is presented to illustrate the validity and effectiveness of our method. © 2024 IEEE.

关键词： State feedback

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Intra-class patch swap for self-distillation

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Neurocomputing 2025年 645卷

作者： Choi, Hongjun Jeon, Eun Som Shukla, Ankita Turaga, Pavan Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Geometric Media Lab School of Arts Media and Engineering and School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre-trained high-capacity teacher networks, which introduce significant challenges such as increased memory/storage requirements, additional training costs, and ambiguity in selecting an appropriate teacher for a given student model. Although a teacher-free distillation (self-distillation) has emerged as a promising alternative, many existing approaches still rely on architectural modifications or complex training procedures, which limit their generality and efficiency. To address these limitations, we propose a novel framework based on teacher-free distillation that operates using a single student network without any auxiliary components, architectural modifications, or additional learnable parameters. Our approach is built on a simple yet highly effective augmentation, called intra-class patch swap augmentation. This augmentation simulates a teacher–student dynamic within a single model by generating pairs of intra-class samples with varying confidence levels, and then applying instance-to-instance distillation to align their predictive distributions. Our method is conceptually simple, model-agnostic, and easy to implement, requiring only a single augmentation function. Extensive experiments across image classification, semantic segmentation, and object detection show that our method consistently outperforms both existing self-distillation baselines and conventional teacher-based KD approaches. These results suggest that the success of self-distillation could hinge on the design of the augmentation itself. Our codes are available at https://***/hchoi71/Intra-class-Patch-Swap. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks

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Computers, Materials & Continua 2022年第7期72卷 1627-1644页

作者： Zulqurnain Sabir Manoj Gupta Muhammad Asif Zahoor Raja N.Seshagiri Rao Muhammad Mubashar Hussain Faisal Alanazi Orawit Thinnukool Pattaraporn Khuwuthyakorn Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Electronics and Communication Engineering JECRC UniversityJaipur(Rajasthan)303905India Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan Department of Applied Mathematics School of Applied Natural SciencesAdama Science and Technology UniversityAdamaEthiopia Department of Mathematics University of PunjabJhelum CampusPakistan Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model(NSM)using the supervised neural networks(SNNs)along with the novel features of Levenberg-Marquardt backpropagation technique(LMBT),i.e.,*** SNNs-LMBT is implemented with three different types of sample data,authentication,testing and *** ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75%for training,15%for validation and 10%for testing,*** the numerical measures of the nonlinear dynamics of the NSM,the Runge-Kutta scheme is implemented to form the reference *** attained numerical form of the nonlinear dynamics of the NSM through the SNNs-LMBT is implemented in the reduction of the mean square error(MSE).For the exactness,competence,reliability and efficiency of the proposed SNNs-LMBT,the numerical actions are capable using the proportional arrangements through the features of the MSE results,error histograms(EHs),regression and correlation.

关键词： Nonlinear dynamics nervous stomach system reference dataset levenberg-marquardt backpropagation technique numerical outcomes

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A Dynamic Model for Pauses in the Synthesized Speech of Mathematical Expressions in MathML 22

A Dynamic Model for Pauses in the Synthesized Speech of Math...

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Proceedings of the 14th International Conference on Education technology and Computers

作者： Adriana Souza Diamantino Freitas Bahia Federal Institute of Education Science and Technology Brazil and Doctoral Program in Digital Media Faculty of Engineering Faculty of Engineering of University of Porto Portugal Department of Electrical and Computer Engineering Faculty of Engineering of University of Porto Portugal

ISBN: (纸本)9781450397766

Voice synthesizers still present several challenges in the speech of mathematical content, as spoken mathematics has quite peculiar rules. In the synthesized speech, pauses help blind and visually impaired students identify the limits of mathematical operators and subexpressions. However, most studies on pauses define them uniformly or use simple punctuation marks to force the synthesizer to introduce pauses in certain parts of the expression. Speech is a dynamic process and pauses in expressions also need to be dynamic to make the synthetic speech of expressions more natural, as this can help in memorizing this type of content. This work proposed a dynamic model of pauses for mathematical expressions. Collected math expressions spoken by teachers were used to create the model. These expressions were useful for identifying patterns and creating a linear regression model. Blind and visually impaired students evaluated the model. Some improvements were observed when we compared the synthesized mathematical expressions with the model and Audiomath, the parameter tool used in this study.

关键词： Accessibility Synthesized Speech Mathematics Visual Impairment

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Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method

arXiv

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arXiv 2025年

作者： Zhang, Ruichen Zhao, Changyuan Du, Hongyang Niyato, Dusit Wang, Jiacheng Sawadsitang, Suttinee Shen, Xuemin Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering City University of Hong Kong Pok Fu Lam Hong Kong The College of Arts Media and Technology Chiang Mai University Suthep 50200 Thailand The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper investigates adaptive transmission strategies in embodied AI-enhanced vehicular networks by integrating large language models (LLMs) for semantic information extraction and deep reinforcement learning (DRL) for decision-making. The proposed framework aims to optimize both data transmission efficiency and decision accuracy by formulating an optimization problem that incorporates the Weber-Fechner law, serving as a metric for balancing bandwidth utilization and quality of experience (QoE). Specifically, we employ the large language and vision assistant (LLAVA) model to extract critical semantic information from raw image data captured by embodied AI agents (i.e., vehicles), reducing transmission data size by approximately more than 90% while retaining essential content for vehicular communication and decision-making. In the dynamic vehicular environment, we employ a generalized advantage estimation-based proximal policy optimization (GAE-PPO) method to stabilize decision-making under uncertainty. Simulation results show that attention maps from LLAVA highlight the model’s focus on relevant image regions, enhancing semantic representation accuracy. Additionally, our proposed transmission strategy improves QoE by up to 36% compared to DDPG and accelerates convergence by reducing required steps by up to 47% compared to pure PPO. Further analysis indicates that adapting semantic symbol length provides an effective trade-off between transmission quality and bandwidth, achieving up to a 61.4% improvement in QoE when scaling from 4 to 8 vehicles. © 2025, CC BY.

关键词： Deep reinforcement learning

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Deep Learning-Based Automated Segmentation of Fiber Breaks in Advanced Composites 38

Deep Learning-Based Automated Segmentation of Fiber Breaks i...

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38th Technical Conference of the American Society for Composites, ASC 2023

作者： Jain, Sudhir Li, Carina Xiaochen Vuong, Daniel McGllick, Matthew Saifaee, Saeed Furtado, Carolina Kopp, Reed Joseph, Joshua Adjodah, Dhaval Rosini, Sebastian Sinclair, Ian Spearing, S. Mark Wardle, Brian L. Integrated Design and Management Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States MIT Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering University of Southampton SouthamptonSO17 1BJ United Kingdom Departments of Aeronautics and Astronautics and Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781605956916

The heterogeneity of the microstructure and the anisotropy of the mechanical properties of carbon fiber reinforced polymer composites (CFRP) are associated with complex damage mechanisms that make failure prediction highly challenging. Typical damage mechanisms for CFRP include: (i) interlaminar cracking (delamination), (ii) matrix cracking, and (iii) fiber breakage. X-ray computed tomography (CT) has been utilized recently to identify the complex damage in progressive failure of CFRP in 3D and 4D (3D spatial and 1D temporal). However, these scans contain a large amount of data (≈10 GB/mm3), which makes it challenging to extract mechanistic insights in a reasonable time frame. In previous work, we presented the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale matrix damage in CFRP. The trained CNN segments complex and sparse composite matrix damage mechanism to ≈ 99.99% agreement, with nearly 100% of human time eliminated, providing higher accuracy compared to human segmentation. In this work, we developed CNN-based method for the automated segmentation of fiber breaks using synchrotron radiation-based CT (SRCT) scans that have been human-labeled. The model employed a U-Net full CNN as the high-level encoder (feature extraction)-decoder (feature localization) structure, and VGG16 as the deep CNN backbone via the publicly available Segmentation Models GitHub repository. An unbiased evaluation was performed comparing the machine vs. the human-labeled ground truth. Finally, active learning was presented for the machine-assisted relabeling of datasets containing fiber breaks. The deep learning-based automated segmentation method demonstrated accurate identification (> 90%) of fiber breakage for CFRP. © 2023 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.

关键词： Complex networks

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A Face Image Processing System Based on Object Tracking technology

A Face Image Processing System Based on Object Tracking Tech...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Pandi Mao Xiaowen Liao Daolian Xu School of Electrical Engineering and Intelligent Manufacturing Chongqing Metropolitan College of Science and Technology Yongchuan hongqing China School of Arts and Media Chongqing Metropolitan College of Science and Technology Yongchuan Chongqing China College of Optoelectronic Engineering Chongqing University Shapingba Chongqing China

In order to meet the current training, employment and regulatory needs, the Hong Kong government will launch a comprehensive training program. The system consists of the main arm controller, sensor module, adjustable camera and image acquisition and processing system. The system has the advantages of high precision and reasonable price, and has broad market prospects. In this paper, the problem of autonomous motion control for tracking target is studied, and an active tracking control method for serial action output target is proposed. The learning strategy utilizes visual perception and depth improvement to make decisions, using consecutive frames’ visual image as continuous control model for input state and output action. The system has the advantages of high precision and reasonable price, and has broad market prospects. Target tracking is an important application in video surveillance and a new research field in image processing. Video surveillance uses cameras to identify moving or multiple objects, widely used in human-computer interaction, security, traffic control, airports, subway stations, and mass activities. As a result, MUSIC obtained 96% of accuracy, 95% of precision, 94% of f-measure, and 91% of recall.

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