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Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

computer Systems science & engineering 2022年第12期43卷 1255-1270页

作者： Nasebah Almufadi Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer ScienceNational University of Sciences and Technology(NUST)IslamabadPakistan

Currently,mobile communication is one of the widely used means of ***,it is quite challenging for a telecommunication company to attract new *** recent concept of mobile number portability has also aggravated the problem of customer *** need to identify beforehand the customers,who could potentially churn out to the *** the telecommunication industry,such identification could be done based on call detail *** research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn *** use the mobile telephony churn prediction dataset obtained from ***,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned *** imbalanced data are handled using undersampling and *** accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,***,DNN got 99%for ROC.

关键词： Deep learning machine learning churn prediction convolutional neural network recurrent neural network

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Monitoring energy losses of a residential building through thermographic assessments 17

Monitoring energy losses of a residential building through t...

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17th International Conference on Development and Application Systems, DAS 2024

作者： Eugen, Iavorschi Laurențiu, Milici Dan Faculty of Electrical Engineering and Computer Science Department of Electrical Engineering "Stefan cel Mare" University of Suceava Suceava Romania

ISBN: (纸本)9798350349290

One of the significant opportunities for reducing energy waste and carbon emissions is the renovation, modernization, and rehabilitation of existing buildings. Replacing the windows of existing residential buildings with new energy-efficient ones is an easy way to upgrade an existing home into a nearly zero-energy building, NZEB (nearly zero-energy building). There is a wide variety of methods to discover the thermal weak points of a building, and the continuous innovation of construction materials provides solutions to maximize energy savings and minimize heat transfer to the exterior. This document presents a methodology for visualizing and recording the temperature distribution on the investigated surface of the building envelope to identify and locate vulnerable points where heat losses to the exterior are substantial. The objective was to highlight the need for upgrading the insulation system and replacing the existing windows of the analyzed building based on a multi-objective approach to maximize the building's energy performance and increase thermal comfort indoors. Using a Fluke Ti300+ Thermal Camera, energy losses were visualized through thermographic assessments, which could provide an adequate picture for accurately assessing the building's condition before renovation, to evaluate the need for partial renovation. For the case study, a residential building with one floor and mansard roof located in a region with hot summers and cold winters in Romania was chosen. The results showed that there are several options for significantly reducing the building's energy consumption. Thus, multiple energy efficiency solutions were proposed for the building to reduce heat losses. ©2024 IEEE.

关键词： Heat transfer

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METAPHYSICA: IMPROVING OOD ROBUSTNESS IN PHYSICS-INFORMED MACHINE LEARNING 12

METAPHYSICA: IMPROVING OOD ROBUSTNESS IN PHYSICS-INFORMED MA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Mouli, S. Chandra Alam, Muhammad Ashraful Ribeiro, Bruno Department of Computer Science Purdue University Indiana United States Department of Electrical and Computer Engineering Purdue University Indiana United States

A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require learning-to-learn from observations of the same (ODE) dynamical system with different unknown ODE parameters, and demand accurate forecasts even under out-of-support initial conditions and out-of-support ODE parameters. In this work we propose to improve the OOD robustness of PIML via a meta-learning procedure for causal structure discovery. Using three different OOD tasks, we empirically observe that the proposed approach significantly outperforms existing state-of-the-art PIML and deep learning methods (with 2× to 28× lower OOD errors). © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep learning

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Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN 46

Data Quality Matters: Suicide Intention Detection on Social ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Lin, Emily Sun, Jian Chen, Hsingyu Mahoor, Mohammad H. University of Denver Department of Electrical and Computer Engineering Denver United States University of Denver Department of Computer Science Denver United States

ISBN: (纸本)9798350371499

Suicide remains a pressing global health concern, necessitating innovative approaches for early detection and intervention. This paper focuses on identifying suicidal intentions in posts from the SuicideWatch subreddit by proposing a novel deep-learning approach that utilizes the state-of-the-art RoBERTa-CNN model. The robustly Optimized BERT Pretraining Approach (RoBERTa) excels at capturing textual nuances and forming semantic relationships within the text. The remaining Convolutional Neural Network (CNN) head enhances RoBERTa's capacity to discern critical patterns from extensive datasets. To evaluate RoBERTa-CNN, we conducted experiments on the Suicide and Depression Detection dataset, yielding promising results. For instance, RoBERTa-CNN achieves a mean accuracy of 98% with a standard deviation (STD) of 0.0009. Additionally, we found that data quality significantly impacts the training of a robust model. To improve data quality, we removed noise from the text data while preserving its contextual content through either manually cleaning or utilizing the OpenAI API. © 2024 IEEE.

关键词： Convolutional neural networks

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Alzheimer's Disease Stage Classification using Blood Data 2

Alzheimer's Disease Stage Classification using Blood Data

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rasheed, Asif Fadlullah, Zubair Md Fouda, Mostafa M. Lakehead University Department of Computer Science Thunder Bay Canada Western University Department of Computer Science London Canada Idaho State University Department of Electrical and Computer Engineering Pocatello United States

ISBN: (纸本)9798331529529

In this work, we aim to evaluate the performance of Machine Learning models in the classification of Alzheimer's patients into disease stages using two feature selection methods proposed in our previous work. The first method identifies relevant features by ranking them according to their dependency on the diagnosis, which is measured using metrics such as Mutual Information, Symmetric Uncertainty and Cramer's V. The second method filters relevant features by applying a threshold to the Euclidean distance between the class means for each feature. Three new approaches are proposed to accommodate multiple classes in the Euclidean distance-based feature selection method. These approaches involve scoring each feature using the minimum, maximum and average of the Euclidean distances between the class means. Using two datasets, we extensively tested all possible combinations of relevant features identified using the two methods, ensuring minimal correlation among features in each panel. The panels achieved accuracies as high as 75.17% and 85.46% on the first and second datasets, respectively. The performances achieved in this work demonstrate the feasibility of using blood-based biomarkers in Alzheimer's disease stage classification and could help in developing accurate low-cost and non-invasive early detection tools that could aid in Alzheimer's disease diagnosis. © 2024 IEEE.

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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A Speech Enhancement Algorithm Combining Wavelet Transform and Adaptive Filters 6

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Joorabchi, Marjan Panahi, Issa Firouzeh, Fereshteh Fakhar The University of Texas at Tyler Department of Electrical and Computer Engineering TX United States Sharif University of Technology School of Science and Engineering Iran The University of Texas at Dallas Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331506520

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines Wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with Wavelet Transform significantly improves noise reduction, outperforming the use of Wavelet Transform alone. © 2025 IEEE.

关键词： Adaptive filtering

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Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

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Complex System Modeling and Simulation 2025年第1期5卷 68-85页

作者： Xuewu Wang Jianing Zhang Yi Hua Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and *** paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion *** address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is *** NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution *** dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two *** addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is *** local search operators based on ideal point are proposed to find a better local *** improve the global exploration ability of the algorithm,a dual population restart mechanism is *** tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.

关键词： limited automatic guided vehicle(AGV)scheduling problem nondominated sorting genetic algorithm II(NSGA-II) dual population co-evolution matrix manufacturing workshop

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Keep in Balance: Runtime-reconfigurable Intermittent Deep Inference

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ACM Transactions on Embedded Computing Systems 2023年第5 s期22卷 1-25页

作者： Yen, Chih-Hsuan Mendis, Hashan Roshantha Kuo, Tei-Wei Hsiu, Pi-Cheng Department of Computer Science and Information Engineering National Taiwan University Taiwan Academia Sinica Taiwan Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates College of Electrical Engineering and Computer Science National Taiwan University Taiwan Department of Computer Science and Information Engineering National Chi Nan University Taiwan

Intermittent deep neural network (DNN) inference is a promising technique to enable intelligent applications on tiny devices powered by ambient energy sources. Nonetheless, intermittent execution presents inherent challenges, primarily involving accumulating progress across power cycles and having to refetch volatile data lost due to power loss in each power cycle. Existing approaches typically optimize the inference configuration to maximize data reuse. However, we observe that such a fixed configuration may be significantly inefficient due to the fluctuating balance point between data reuse and data refetch caused by the dynamic nature of ambient *** work proposes DynBal, an approach to dynamically reconfigure the inference engine at runtime. DynBal is realized as a middleware plugin that improves inference performance by exploring the interplay between data reuse and data refetch to maintain their balance with respect to the changing level of intermittency. An indirect metric is developed to easily evaluate an inference configuration considering the variability in intermittency, and a lightweight reconfiguration algorithm is employed to efficiently optimize the configuration at runtime. We evaluate the improvement brought by integrating DynBal into a recent intermittent inference approach that uses a fixed configuration. Evaluations were conducted on a Texas Instruments device with various network models and under varied intermittent power strengths. Our experimental results demonstrate that DynBal can speed up intermittent inference by 3.26 times, achieving a greater improvement for a large network under high intermittency and a large gap between memory and computation performance. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep neural networks

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