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您的检索式：作者名="Mohanad Omar"
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| 1 | Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling显示文摘Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD. | Omar M.El-Habbak Abdelrahman M.Abdelalim Nour H.Mohamed Habiba M.Abd-Elaty Mostafa A.Hammouda Yasmeen Y.Mohamed Mohanad A.Taifor Ali W.Mohamed | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 2 | Effects of 12 weeks of back-squat training program on jump performances and bone markers in female students显示文摘Background:The training program promoted improvements of jump abilities throughout the musculoskeletal system including bone markers.The aim of this study is to examine both the acute and chronic response of bone markers to resistance training program.Methods:Ten female students(age:18±0.7 years,body mass:63±3.6 kg;height:164±5.2 cm)participated in this study.They were recruited for a back-squat training program for 12 weeks,two days/week.The full-back squat protocol consisted of 3–5 sets×3–8 repetitions at 45–55%one repetition maximum.Testing sessions included a 5 jump test(5JT),standing long jump(SLJ),drop jump(DJ),and vertical jump(VJ).Results:Substantial improvements in all testing jumps(5JT:∆10%;P=0.000;ES=1.72;SLJ:∆7%;P=0.000;ES=1.33;DJ:∆11%;P=0.000;ES=0.72;VJ:∆20%;P=0.000;ES=1.84)were found during post program in comparison to pre-program results.Moreover,a significant change(P≤0.05)of bone markers during post-exercise compared to pre-exercise either before or after the training program.Only collagen type I carboxy-terminal peptide(CICP)levels elevated after the training program(pre-exercise only)compared to former levels.Conclusion:12 weeks of back-squat training program resulted greater acute improvements of jump abilities with adaptation in all musculoskeletal system including bone formation. | Badiea Sharaif Nidhal Jebabli Saber Abdellaoui Jed Mohamed Tijani Jihen Khalfoun Mohanad Omar Abderraouf Ben Abderrahman | 2022 | TMR Non-Drug Therapy2022，5，3： | 0 |
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