A Convolutional Neural Network-based Automatic Identification and Intervention Model for Health Surveillance Data during Postpartum Recovery Periods DOI Open Access
Y. F. Wang

Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract Women suffer great psychological pressure on the postpartum recovery period, which can cause certain diseases in long run if not paid attention to. Based research related to principle of health parameter detection and feature extraction method pulse wave data, study was conducted by extracting physiological signal features normal pulse, using improved support vector machine (OC-SVM) for abnormality detection, adding attention-based two-stage short-term memory network (DA-LSTM) AE, adaptively directs weights input sequences encoding/decoding stages, respectively allocation selecting hidden state encoder time step, respectively. Then, based experimental development monitoring system carried out from three major modules, namely, main control module, front-end acquisition processing auxiliary realize intervention recovery. Using this paper carry a three-month experiment women, it is found that group after each index value has decreased rate decrease large, somatization (1.26 ± 0.13) (1.09 0.58), compared with before significant difference (P < 0.05), help women recover their level more quickly childbirth.

Language: Английский

Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure DOI Creative Commons
Entesar Hamed I. Eliwa, Amr Mohamed El Koshiry, Tarek Abd El‐Hafeez

et al.

Advances in respiratory medicine, Journal Year: 2024, Volume and Issue: 92(5), P. 395 - 420

Published: Oct. 17, 2024

The global healthcare system faces challenges in diagnosing and managing lung colon cancers, which are significant health burdens. Traditional diagnostic methods inefficient prone to errors, while data privacy security concerns persist.

Language: Английский

Citations

8
Multi-view data representation via adaptive label propagation nonnegative matrix factorization DOI
Hui Li, Chengcai Leng, Jinye Peng

et al.

Information Sciences, Journal Year: 2025, Volume and Issue: 700, P. 121859 - 121859

Published: Jan. 6, 2025

Citations

0
A Bayesian regularization intelligent computing scheme for the fractional dengue virus model DOI Creative Commons
Manoj Gupta, Pattarasinee Bhattarakosol

Egyptian Informatics Journal, Journal Year: 2025, Volume and Issue: 29, P. 100606 - 100606

Published: Jan. 8, 2025

Language: Английский

Citations

0
Contraction ratio of multifidus and erector spinae muscles in unilateral sacroiliac joint pain: A cross-sectional trial DOI Creative Commons

Omar M. Mabrouk,

Khaled Ayad, Doaa A. Abdel Hady

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

Language: Английский

Citations

0
Novel approach for noninvasive pelvic floor muscle strength measurement using extracorporeal surface perineal pressure measurement and machine learning modeling DOI Creative Commons
Ui‐jae Hwang,

Sun-hee Ahn,

Hyeon-Ju Lee

et al.

Digital Health, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 1, 2025

Objective Accurate measurement of pelvic floor muscle (PFM) strength is crucial for the management disorders. However, current methods are invasive, uncomfortable, and lack standardization. This study aimed to introduce a novel noninvasive approach precise PFM quantification by leveraging extracorporeal surface perineal pressure (ESPP) measurements machine learning algorithms. Methods Twenty-one healthy women participated in this study. ESPP were obtained using 10 × array sensor during maximal voluntary contractions seated position. Simultaneously, transabdominal ultrasound was used measure bladder base displacement (mm) as reference contraction strength. Seven variables calculated based on data intra- inter-rater reliabilities assessed. Machine algorithms predicted from variables. Results The demonstrated good excellent intra-rater (ICC = 0.881) 0.967) reliability. Significant correlations observed between middle ( r .619, P < .001) front −.379, =.002) vectors. top-performing models predicting support vector [root mean square error (RMSE) 0.139, R2 0.542], random forest (RMSE 0.123, 0.367), AdaBoost 0.320) training set, 0.173, 0.537), 0.177, 0.512), 0.178, 0.508) test set. In displacement, Bland–Altman analysis revealed these had minimal systematic bias, with differences ranging −0.007 0.066, clinically acceptable limits agreement. Conclusion demonstrates potential reliable valid assessing quantifying directionality contractions, overcoming limitations traditional techniques.

Language: Английский

Citations

0
A Spatiotemporal Graph Transformer Network for real-time ball trajectory monitoring and prediction in dynamic sports environments DOI
Z. Li, Dan Yu

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 119, P. 246 - 258

Published: Feb. 5, 2025

Language: Английский

Citations

0
Multi-criteria decision making: Revealing Afinitor as the leading brain tumor drug Using CRITIC, CoCoSo, and MABAC methods combined with QSPR analysis via Banhatti indices DOI
Abid Mahboob,

Laiba Amin,

Muhammad Waheed Rasheed

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109820 - 109820

Published: Feb. 22, 2025

Language: Английский

Citations

0
NLP-Driven Integration of Electrophysiology and Traditional Chinese Medicine for Enhanced Diagnostics and Management of Postpartum Pain DOI Creative Commons
Yaning Wang

SLAS TECHNOLOGY, Journal Year: 2025, Volume and Issue: unknown, P. 100267 - 100267

Published: March 1, 2025

Postpartum pain encompasses a range of physical and emotional discomforts, often influenced by hormonal changes, recovery, individual psychological states. The complex interactions between the variables can make it difficult for traditional diagnostic techniques to fully capture, creating inadequacies inefficient management techniques. aims develop comprehensive framework postpartum integrating Natural Language Processing (NLP), electrophysiological data, Traditional Chinese Medicine (TCM) principles. seeks enhance accuracy diagnosis, uncover meaningful correlations TCM diagnoses physiological markers, optimize personalized treatment strategies. focuses on analyzing textual data from patient-reported symptoms, medical records, diagnosis notes. Data pre-processing involves text cleaning tokenization, followed feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) capture patterns. For diagnostics management, Refined Coyote Optimized Deep Recurrent Neural Network (RCO-DRNN) is employed analyze predict profiles, combining insights with markers. results highlight effectiveness RCO-DRNN in accurately diagnosing types offering holistic This approach represents significant advancement data-driven methodologies practices, providing more management. continuously beats other models after thorough evaluation metrics like MSE, MAE, R2, obtaining lowest MSE (0.005), smallest MAE (0.04), highest R2 (0.98).

Language: Английский

Citations

0
Strength prediction of recycled concrete using hybrid artificial intelligence models with Gaussian noise addition DOI
Yaqin Geng, Yongcheng Ji, Dayang Wang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 149, P. 110566 - 110566

Published: March 18, 2025

Language: Английский

Citations

0
A Convolutional Neural Network-based Automatic Identification and Intervention Model for Health Surveillance Data during Postpartum Recovery Periods DOI Open Access
Y. F. Wang

Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract Women suffer great psychological pressure on the postpartum recovery period, which can cause certain diseases in long run if not paid attention to. Based research related to principle of health parameter detection and feature extraction method pulse wave data, study was conducted by extracting physiological signal features normal pulse, using improved support vector machine (OC-SVM) for abnormality detection, adding attention-based two-stage short-term memory network (DA-LSTM) AE, adaptively directs weights input sequences encoding/decoding stages, respectively allocation selecting hidden state encoder time step, respectively. Then, based experimental development monitoring system carried out from three major modules, namely, main control module, front-end acquisition processing auxiliary realize intervention recovery. Using this paper carry a three-month experiment women, it is found that group after each index value has decreased rate decrease large, somatization (1.26 ± 0.13) (1.09 0.58), compared with before significant difference (P < 0.05), help women recover their level more quickly childbirth.

Language: Английский

Citations

0