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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Jambukeshwar Pujari Javed Wasim Aprna Tripathi Department of Computer Engineering and Applications Mangalayatan University Aligarh India Department of Data Science Engineering Manipal University Jaipur India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective.

关键词： Accuracy Surveillance Urban planning Land surface Satellite images Scattering parameters Convolutional neural networks Environmental monitoring Optimization Geographic information systems

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Analyzing User Satisfaction with Mobile Internet Service Providers through Technical Performance and User Experience Evaluation

Analyzing User Satisfaction with Mobile Internet Service Pro...

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International Research Conference on Smart Computing and Systems Engineering (SCSE)

作者： Prasanga Hewa Pothuwilage Pansujee Dissanayaka Uditha Prabhath Liyanage Department of Mathematics Faculty of Engineering University of Moratuwa Moratuwa Sri Lanka Department of Statistics & Computer Science Faculty of Science University of Kelaniya Kelaniya Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

In today's digital era, the Internet holds a fundamental position in daily life, particularly in Sri Lanka. This study addresses two primary objectives: first, to quantify the technical performance of internet services, and second, to assess customer satisfaction and its alignment with service provider performance. Conducted in the Narthanagala Grama Niladhari Division, specifically the 616C division in Horana, the study utilizes a structured questionnaire to gather customer experience data. Technical data are collected using the "Open Signal" and "Network Cell Info" mobile applications. Statistical techniques are employed to achieve the objectives. Principal Component Analysis reduces the initial 9 dimensions of internet service quality to a singular dimension, with the primary principal component explaining 84.40% of variance. This reduction is validated through the composite index of "Application Based Technical Performance of Mobile data Service (AppBased_TecPerf)". Factor Analysis and simple linear regression unveil two influential latent factors: Customer-Perceived Technical Performance of Mobile data Service (Customer_TecPerf) and Customer Service Satisfaction (Customer_Service), collectively accounting for 60.66% of variability across six dimensions. These dimensions include Video Quality Experience, Gaming Experience, Voice & Video Quality Experience on Social Media Apps, Client Rating for Internet Speed at Residence, Client Rating for Satisfaction with Price Fairness, and Client Rating for Customer Service Quality. Reliability analysis confirms the effectiveness of these factors in measuring the Application Based Technical Performance of Mobile data Service. Regression analysis confirms that the index, AppBased_TecPerf, effectively captures Customer_TecPerf.

关键词： Customer services Web and internet services Linear regression Customer satisfaction Streaming media Quality assessment Internet

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Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms

arXiv

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arXiv 2025年

作者： Wang, Xiaoyu Kasprzak, Mikolaj J. Negrea, Jeffrey Bourguin, Solesne Huggins, Jonathan H. Department of Mathematics & Statistics Boston University United States Department of Information Systems Data Analytics & Operations ESSEC Business School France Department of Statistics & Actuarial Science University of Waterloo Canada Faculty of Computing & Data Sciences Boston University United States

Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error in, and characterized the uncertainty of, these approximations. One common approach has been to use scaling limit analyses to relate the distribution of algorithm sample paths to a continuous-time stochastic process approximation, particularly in asymptotic setups. Focusing on the univariate setting, in this paper, we build on previous work to derive non-asymptotic functional approximation error bounds between the algorithm sample paths and the Ornstein-Uhlenbeck approximation using an infinite-dimensional version of Stein's method of exchangeable pairs. We show that this bound implies weak convergence under modest additional assumptions and leads to a bound on the error of the variance of the iterate averages of the algorithm. Furthermore, we use our main result to construct error bounds in terms of two common metrics: the Lévy-Prokhorov and bounded Wasserstein distances. Our results provide a foundation for developing similar error bounds for the multivariate setting and for more sophisticated stochastic approximation *** Codes 60F17 (Primary) 60J60, 62-08, 68T05, 62E17 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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A UNIFIED MODEL FOR ZERO-SHOT SINGING VOICE CONVERSION AND SYNTHESIS 23

A UNIFIED MODEL FOR ZERO-SHOT SINGING VOICE CONVERSION AND S...

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23rd International Society for Music Information Retrieval Conference, ISMIR 2022

作者： Wu, Jui-Te Wang, Jun-You Jang, Jyh-Shing Roger Su, Li NTU-AS Data Science Degree Program National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9781732729926

Recent advances in deep learning not only facilitate the implementation of zero-shot singing voice synthesis (SVS) and singing voice conversion (SVC) tasks but also provide the opportunity to unify these two tasks into one generalized model. In this paper, we propose such a model that generate the singing voice of any target singer from any source singing content in either text or audio format. The model incorporates self-supervised joint training of the phonetic encoder and the acoustic encoder, with an audio-to-phoneme alignment process in each training step, such that these encoders map the audio and text data respectively into a shared, temporally aligned, and singer-agnostic latent space. The target singer’s latent representations encoded at different granularity levels are all trained to match the source latent representations sequentially with the attention mechanisms in the decoding stage. This enables the model to generate unseen target singer’s voice with fine-grained resolution from either text or audio sources. Both objective and subjective experiments confirmed that the proposed model is competitive with the state-of-the-art SVC and SVS methods. © J.-T. Wu, J.-Y. Wang, J.-S. R. Jang and L. Su.

关键词： Deep learning

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Handling of Simultaneous Morphology of Sign Languages: Concerns for Cross-modal Machine Translation of Marathi to Indian Sign Language

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SN computer science 2023年第5期4卷 629页

作者： Bhagwat, Suvarna R. Bhavsar, R.P. Pawar, B.V. Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune 411037 India School of Computer Sciences KBC North Maharashtra University Umavi Nagar Maharashtra Jalgaon 425001 India

Effective communication can be challenging when individuals are unfamiliar with each other's spoken language. This problem becomes more complex when the languages involved differ in modality such as spoken language and sign language. Machine translation has emerged as a valuable tool for bridging this communication gap in various scenarios. This research addresses the unique challenges presented by cross-modal machine translation systems, specifically in translating from spoken language to sign language. While spoken languages typically exhibit linear linguistic characteristics, sign languages employ simultaneous expressions through manual, non-manual, and spatial features. This divergence in delivery mechanisms necessitates careful consideration in the development of a translation system. The primary objective of this research is to develop a framework that translates simple Marathi sentences into Indian sign language, incorporating computational techniques to account for the simultaneous nature of sign language grammar. A comprehensive linguistic analysis is conducted to identify the specific divergences between Marathi and Indian sign language. The system is evaluated using a database comprising vocabulary relevant to daily communication. This paper provides a detailed account of the system architecture, experimental setup, results, and evaluation. The development of a cross-modal translation system holds significant potential in bridging the communication gap between individuals using spoken languages and those who rely on sign languages as their primary means of communication. The system-generated ISL can facilitate communication for hearing-impaired persons and, thus, it contributes to the inclusivity of hearing-impaired communities in the mainstream. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cross-modal machine translation Inclusion of hearing-impaired people Indian sign language Marathi Phrase-based machine translation Simultaneous morphology

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Synergizing Roughness Penalization and Basis Selection in Bayesian Spline Regression

arXiv

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arXiv 2023年

作者： Lim, Sunwoo Jeong, Seonghyun Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Applied Statistics Yonsei University Seoul Korea Republic of

Bayesian P-splines and basis determination through Bayesian model selection are both commonly employed strategies for nonparametric regression using spline basis expansions within the Bayesian framework. Although both methods are widely employed, they each have particular limitations that may introduce potential estimation bias depending on the nature of the target function. To overcome the limitations associated with each method while capitalizing on their respective strengths, we propose a new prior distribution that integrates the essentials of both approaches. The proposed prior distribution assesses the complexity of the spline model based on a penalty term formed by a convex combination of the penalties from both methods. The proposed method exhibits adaptability to the unknown level of smoothness while achieving the minimax-optimal posterior contraction rate up to a logarithmic factor. We provide an efficient Markov chain Monte Carlo algorithm for implementing the proposed approach. Our extensive simulation study reveals that the proposed method outperforms other competitors in terms of performance metrics or model complexity. An application to a real dataset substantiates the validity of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Regression analysis

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Regression with cost-based rejection 23

Regression with cost-based rejection

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xin Cheng Yuzhou Cao Haobo Wang Hongxin Wei Bo An Lei Feng College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous studies on cost-based rejection only focused on the classification setting, which cannot handle the continuous and infinite target space in the regression setting. In this paper, we investigate a novel regression problem called regression with cost-based rejection, where the model can reject to make predictions on some examples given certain rejection costs. To solve this problem, we first formulate the expected risk for this problem and then derive the Bayes optimal solution, which shows that the optimal model should reject to make predictions on the examples whose variance is larger than the rejection cost when the mean squared error is used as the evaluation metric. Furthermore, we propose to train the model by a surrogate loss function that considers rejection as binary classification and we provide conditions for the model consistency, which implies that the Bayes optimal solution can be recovered by our proposed surrogate loss. Extensive experiments demonstrate the effectiveness of our proposed method.

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The exact risks of reference panel-based regularized estimators

arXiv

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arXiv 2024年

作者： Su, Buxin Sun, Qiang Yang, Xiaochen Zhao, Bingxin Department of Mathematics University of Pennsylvania United States Department of Statistical Sciences University of Toronto Canada Department of Statistics Purdue University United States Department of Statistics and Data Science University of Pennsylvania United States

Reference panel-based estimators have become widely used in genetic prediction of complex traits due to their ability to address data privacy concerns and reduce computational and communication costs. These estimators estimate the covariance matrix of predictors using an external reference panel, instead of relying solely on the original training data. In this paper, we investigate the performance of reference panel-based L1 and L2 regularized estimators within a unified framework based on approximate message passing (AMP). We uncover several key factors that influence the accuracy of reference panel-based estimators, including the sample sizes of the training data and reference panels, the signal-to-noise ratio, the underlying sparsity of the signal, and the covariance matrix among predictors. Our findings reveal that, even when the sample size of the reference panel matches that of the training data, reference panel-based estimators tend to exhibit lower accuracy compared to traditional regularized estimators. Furthermore, we observe that this performance gap widens as the amount of training data increases, highlighting the importance of constructing large-scale reference panels to mitigate this issue. To support our theoretical analysis, we develop a novel non-separable matrix AMP framework capable of handling the complexities introduced by a general covariance matrix and the additional randomness associated with a reference panel. We validate our theoretical results through extensive simulation studies and real data analyses using the UK Biobank database. © 2024, CC BY-NC-ND.

关键词： Forecasting

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Enhancing Chronic Kidney Disease Prediction Through data Preprocessing Optimization and Machine Learning Techniques

Enhancing Chronic Kidney Disease Prediction Through Data Pre...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Ekwe, Stephen Obono Oladejo, Sunday Oladayo University of South Africa Cset Department of Compt. Science & Electrical Engineering Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

ISBN: (纸本)9798350327816

Health care is the improvement of health through the avoidance and cure of diseases. Chronic kidney disease (CKD) poses a substantial health issue, ranking as the eighth leading cause of death and the tenth leading cause of disability-adjusted life years for both genders. This research aims to utilize machine learning (ML) algorithms to accurately predict the presence of CKD, which affects people worldwide. Timely identification of CKD is crucial to prevent it from progressing to its final and most critical phase. Machine learning algorithms, such as Decision Tree (DT), Naïve Bayes (NB), and others, are used in this study for the prediction of CKD. Various health parameters such as age, albumin level, and more are used as input into each ML algorithm to predict the presence of CKD. data preprocessing is carried out to improve the quality of the data for more accurate prediction. The data is analyzed to uncover the connections between CKD health parameters and their impact on the occurrence of CKD. This research further examines the influence of different train and test data ratios on the accuracy of the employed algorithms. Evaluation metrics such as accuracy, sensitivity, and more are employed to gauge the effectiveness of each algorithm in making predictions. Random Forest stood out as the most accurate algorithm, with a score of 96.25%. Naive Bayes and XGBoost demonstrated the highest sensitivity, achieving a perfect score of 100%. © 2023 IEEE.

关键词： Chronic Kidney Disease (CKD) data Analysis data Preprocessing Health Care Machine Learning

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NOMA Attack Prediction in Large-Scale Networks Using Non-Linear Chaotic Belief Mechanism

NOMA Attack Prediction in Large-Scale Networks Using Non-Lin...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： K. Sangeetha M. Suguna Jebakumar Immanuel D SA Suje Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Non-Orthogonal Multiple Access (NOMA) systems are becoming relevant in the fast-expanding terrain of large-scale networks because of their efficiency in concurrently managing many users. This is true since NOMA systems let numerous users concurrently be managed. On the other hand, the intricacy of these networks leaves them vulnerable to a wide spectrum of attacks, including the more advanced and erratic NOMA attacks on the network. These strikes could produce major disturbances that would compromise the quality of service and cast questions regarding the general network security. It has been demonstrated that the effective projection of these hazards is limited by standard linear and probabilistic techniques. This is true as contemporary methods fail to adequately capture the basic non-linear dynamics of these large-scale networks. This article offers a novel method for NOMA attack prediction by means of a non-linear chaotic belief process. The results are shown here. To recreate the uncertainty and intricate interactions inside the network, the proposed method which is the logistic map which in turn generates the sequences for ensuring the accurate iterative updates which in turn provides better scalability and precision. This integrates belief networks with chaos theory. More exactly, we capture the random and non-linear aspect of network dynamics by building belief values indicating the likelihood of an attack by use of a chaotic map. After that, the belief values proliferate across the network in search of defects and project the probability of NOMA attacks. Effectiveness of the proposed method is demonstrated by test results on a simulated large-scale network simulation. With a prediction accuracy of 92.7%, the chaotic belief mechanism obtained much above the average accuracy of 78.4% of traditional linear prediction systems. Moreover, the proposed approach lowered the false positive rate to 5.3%, substantially below the rate of 12.8% applied in the standard a

关键词： Training NOMA Accuracy Uncertainty Chaotic communication Prevention and mitigation Network security Iterative methods computer crime Standards

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