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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Accelerating multiscale electronic stopping power predictions with timedependent density functional theory and machine learning

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npj Computational Materials 2024年第1期10卷 993-1002页

作者： Logan Ward Ben Blaiszik Cheng-Wei Lee Troy Martin Ian Foster AndréSchleife Data Science and Learning Division Argonne National LaboratoryLemontIL60437USA Department of Computer Science The University of ChicagoChicagoIL60637USA Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaUrbanaIL61801USA

Knowing the rate at which particle radiation releases energy in a material,the“stopping power,”is key to designing nuclear reactors,medical treatments,semiconductor and quantum materials,and many other *** the nuclear contribution to stopping power,i.e.,elastic scattering between atoms,is well understood in the literature,the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions,including that materials are *** establish a method that combines time-dependent density functional theory(TDDFT)and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic *** approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer *** demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition,the“Bragg Peak,”varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical *** lack of any experimental information requirement makes our method applicable to most materials,and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation *** prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science.

关键词： electronic quantum directions

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Aggregate Plant Species Categorization with Deep CNN Based Positional Encoded Cross Attentional Mechanism

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SN computer science 2024年第7期5卷 912页

作者： Pravin, A. Deepa, C. Department of Computer Science Sri Ramakrishna College of Arts and Science Coimbatore India Department of Computer Science (Artificial Intelligence and Data Science) Sri Ramakrishna College of Arts and Science Coimbatore India

Plants plays a major role in the life of humans. It offers food, medicines, fibers, wood, spices, perfume, oil, and paper. Besides, it minimizes soil erosion and prevents air pollution. Particularly, the piper plant is used around the world for various applications. It comprises of several species, which is useful for humans in enormous ways like medicine, food, ornaments, etc. For that reason, the classification of plants is necessary to utilize the exact plants for appropriate applications. The traditional way of plant classification are time consuming, less accurate and require skilled professionals. To resolve this, several conventional researches utilized technology like Artificial Intelligence (AI) to classify plant species but lacks in few limitations such as accuracy and speed. Therefore, the proposed system employ Positional Encoded Cross Attention Mechanism-Deep Convolutional Neural Network (PECAM-DCNN) system for the classification of all plants and piper plant species. The DCNN is used for the capability of handling larger datasets and accuracy. Though, it is the effective classifier, it still have few limitations like noise, speed and over-fitting of data. To resolve this, the respective method used Adaptable normalization with positional encoded cross attention mechanism to enhance the speed, accuracy, denoise task and prevent over fitting of data. The positional encoding in the cross attentional mechanism capture significant features at abundant scales and minimize loss in spatial resolution. Further, the efficiency of the projected system is calculated using performance metrics to evaluate the efficacy of the system. Moreover, the efficiency of the proposed model is revealed through the comparative analysis of respective system with conventional methods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial Intelligence Attention mechanism Deep convolutional neural network Deep learning Plant classification

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Authors’ reply to the Discussion of ‘Estimating means of bounded random variables by betting’

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Journal of the Royal Statistical Society. Series B: Statistical Methodology 2024年第1期86卷 53-61页

作者： Waudby-Smith, Ian Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA United States

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SEIR Mathematical Model for Influenza-Corona Co-Infection with Treatment and Hospitalization Compartments and Optimal Control Strategies

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computer Modeling in Engineering & sciences 2025年第2期142卷 1899-1931页

作者： Muhammad Imran Brett McKinney Azhar Iqbal Kashif Butt Tandy School of Computer Science University of TulsaTulsaOK 74104USA Department of Mathematics and Statistics College of ScienceKing Faisal UniversityAl-Ahsa31982Saudi Arabia

The co-infection of corona and influenza viruses has emerged as a significant threat to global public health due to their shared modes of transmission and overlapping clinical *** article presents a novel mathematical model that addresses the dynamics of this co-infection by extending the SEIR(Susceptible-Exposed-Infectious-Recovered)framework to incorporate treatment and hospitalization *** population is divided into eight compartments,with infectious individuals further categorized into influenza infectious,corona infectious,and co-infection *** proposed mathematical model is constrained to adhere to fundamental epidemiological properties,such as non-negativity and boundedness within a feasible ***,the model is demonstrated to be well-posed with a unique *** points,including the disease-free and endemic equilibria,are identified,and various properties related to these equilibrium points,such as the basic reproduction number,are *** and global sensitivity analyses are performed to identify the parameters that highly influence disease dynamics and the reproduction *** the most influential parameters is crucial for understanding their impact on the co-infection’s spread and ***,an optimal control problem is defined to minimize disease transmission and to control strategy *** purpose of our study is to identify the most effective(optimal)control strategies for mitigating the spread of the co-infection with minimum cost of the *** results illustrate the effectiveness of the implemented control strategies in managing the co-infection’s impact on the population’s *** mathematical modeling and control strategy framework provides valuable tools for understanding and combating the dual threat of corona and influenza co-infection,helping public health authorities and policymakers make informed decisions in the face of these intertwined epidemics.

关键词： Influenza-corona co-infection stability analysis sensitivity analysis treatment self-precaution optimal control

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Blockchain with drug traceability in medical supply chain with Henry gas solubility optimisation algorithm

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 135-148页

作者： Devi, P. Yamini Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai India Department of Data Science Saveetha School of Engineering SIMATS Tamil Nadu Chennai India

The drug traceability model is used for ensuring drug quality and its safety for customers in the medical supply chain. The healthcare supply chain is a complex network, which is susceptible to failures and leakage of information because of cyber security attacks. Hence, this research aims to design a blockchain to perform traceability of drugs using Henry Gas Solubility Optimisation (HGSO). The entities include distributors, wholesalers, retailers, blockchain and patients. This model comprises initialisation, registration, key generation, authentication, drug distribution with traceability and key generation phases. Any exchange of medicines needs the approval of both sender and receiver. The drugs are registered in the blockchain, which then generates a secret key that is obtained using HGSO. The verification and authentication of drugs are performed after the authentication process. The implemented model outperformed with the response time, memory, normalised variance, and privacy of 32.678 sec, 84 MB, 0.958 and 0.958. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： authentication blockchain drug traceability key generation medical supply chain

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Segmented Linear Regression Trees

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Acta Mathematica Sinica,English Series 2025年第2期41卷 498-521页

作者： Xiangyu Zheng Songxi Chen JD Technology Beijing 100176P.R.China Department of Statistics and Data Science Tsinghua UniversityBeijing 100084P.R.China

Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-tree method and propose the segmented linear regression trees(SLRT) model that replaces the traditional constant leaf model with linear ones. From the parametric view, SLRT can be employed as a recursive change point detect procedure for segmented linear regression(SLR) models,which is much more efficient and flexible than the traditional grid search method. Along this way,we propose to use the conditional Kendall's τ correlation coefficient to select the underlying change points. From the non-parametric view, we propose an efficient greedy splitting method that selects the splits by analyzing the association between residuals and each candidate split variable. Further, with the SLRT as a single-tree predictor, we propose a linear random forest approach that aggregates the SLRTs by a weighted average. Both simulation and empirical studies showed significant improvements than the CART trees and even the random forest.

关键词： Regression trees segmented linear regression split selection tree pruning

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ChainApparel: A Trustworthy Blockchain and IoT-Based Traceability Framework for Apparel Industry 4.0

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computers, Materials & Continua 2023年第11期77卷 1837-1854页

作者： Muhammad Shakeel Faridi Saqib Ali Guojun Wang Salman Afsar Awan Muhammad Zafar Iqbal Department of Computer Science University of AgricultureFaisalabad38000Pakistan School of Computer Science Guangzhou UniversityGuangzhou510006China Department of Mathematics and Statistics University of AgricultureFaisalabad38000Pakistan

Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers,manufacturers,suppliers,and *** stakeholder has implemented different technology-based systems to record and track product ***,these systems work in silos,and there is no intra-system communication,leading to a lack of complete supply chain traceability for all apparel ***,apparel stakeholders are reluctant to share their business information with business competitors;thus,they involve third-party auditors to ensure the quality of the final ***,the apparel manufacturing industry faces challenges with counterfeit products,making it difficult for consumers to determine the authenticity of the ***,in this paper,a trustworthy apparel product traceability framework called ChainApparel is developed using the Internet of Things(IoT)and blockchain to address these challenges of authenticity and traceability of apparel ***,multiple smart contracts are designed and developed for registration,process execution,audit,fault,and product traceability to authorize,validate,and trace every business transaction among the apparel ***,the real-time performance analysis of ChainApparel is carried out regarding transaction throughput and latency by deploying the compute nodes at different geographical locations using Hyperledger *** results conclude that ChainApparel accomplished significant performance under diverse workloads while ensuring complete traceability along the complex supply chain of the apparel ***,the ChainApparel framework helps make the apparel product more trustworthy and transparent in the market while safeguarding trust among the industry stakeholders.

关键词： Blockchain smart contracts hyperledger fabric Internet of Things traceability apparel industry 4.0

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Sentiment Analysis of Sinhala News Comments Using Transformers 1

Sentiment Analysis of Sinhala News Comments Using Transforme...

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1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Bandaranayake, Isuru Usoof, Hakim Department of Statistics & Computer Science Faculty of Science University of Peradeniya Peradeniya Sri Lanka

ISBN: (纸本)9798891762145

Sentiment analysis has witnessed significant advancements with the emergence of deep learning models such as transformer models. Transformer models adopt the mechanism of self-attention and have achieved state-of-the-art performance across various natural language processing (NLP) tasks, including sentiment analysis. However, limited studies are exploring the application of these recent advancements in sentiment analysis of Sinhala text. This study addresses this research gap by employing transformer models such as BERT, DistilBERT, RoBERTa, and XLM-RoBERTa (XLM-R) for sentiment analysis of Sinhala news comments. This study was conducted for 4 classes: positive, negative, neutral, and conflict, as well as for 3 classes: positive, negative, and neutral. It revealed that the XLM-R-large model outperformed the other four models, and the transformer models used in previous studies for the Sinhala language. The XLM-R-large model achieved an accuracy of 65.84% and a macro-F1 score of 62.04% for sentiment analysis with four classes and an accuracy of 75.90% and a macro-F1 score of 72.31% for three classes. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Probabilistic time series forecasting with deep non-linear state space models

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 3-13页

作者： Heming Du Shouguo Du Wen Li School of Computer Science College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralian Capital TerritoryAustralia Shanghai Municipal Big Data Center ShanghaiChina Department of Economic Statistics School of Statistics and InformationShanghai University of International Business and EconomicsShanghaiChina

Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series *** is a widespread challenge in various tasks,such as risk management and decision *** investigate temporal patterns in time series data and predict subsequent probabilities,the state space model(SSM)provides a general *** of SSM achieve considerable success in many fields,such as engineering and ***,since underlying processes in real-world scenarios are usually unknown and complicated,actual time series observations are always irregular and ***,it is very difficult to determinate an SSM for classical statistical *** this paper,a general time series forecasting framework,called Deep Nonlinear State Space Model(DNLSSM),is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series *** fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series *** particular,the unscented Kalman filter(UKF)is adopted to calculate marginal likelihoods and update distributions recursively for non-linear *** that,a non-linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive ***,the authors enhance the tolerance of UKF to round-off errors and manage to combine UKF and deep neural *** this manner,the DNLSSM effectively models non-linear correlations between observed time series data and underlying dynamic *** in both synthetic and real-world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.

关键词： Artificial Intelligence machine learning time series

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