Secure efficient and verifiable large linear equations solve outsourcing computing scheme by Xing-lan ZHANG, Xiang LIU

“…A secure,efficient and verifiable outsourcing computation scheme was proposed based on the current problems of leaking users information and tampering with the calculating results,which were encountered while solving large-scale linear equations in outsourcing computation.A new linear equation with similarity solutions was constructed based on the constant transformation between random permutation and linear equations.It avoids the problem that the current data camouflage scheme is easily attacked by solving common factor method.It also improves the verification effciency and reduces the complexity of space.The performance analysis shows that the scheme is highly efficient.…”
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Bounds for the mean square error of reliability estimation from gamma distribution in presence of an outlier observation by M. E. Ghitany, W. H. Laverty

“…A semi-Bayesian approach is discussed when the outlier effect on σ is treated as a random variable having a prior distribution of beta type. …”
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Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities by Haiyan Wang, Peidi Xu, Jinghua Zhao

“…To solve the problems, this paper first proposes a PK-means++ algorithm, which can better ensure the stability of a random experiment. Then, based on it and spherical region division, an improved KNNPK+ is proposed. …”
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AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism by Lucindah N. Fry-Nartey, Cyril Akafia, Ursula S. Nkonu, Spencer B. Baiden, Ignatus Nunana Dorvi, Kwasi Agyenkwa-Mawuli, Odame Agyapong, Claude Fiifi Hayford, Michael D. Wilson, Whelton A. Miller, Samuel K. Kwofie

“…To handle imbalance within the training data, resampling techniques such as random under-sampling, synthetic minority oversampling technique, and the random selection of 5000 instances of the majority class were employed. …”
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Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis by Feras Al-Obeidat, Wael Hafez, Wael Hafez, Asrar Rashid, Mahir Khalil Jallo, Munier Gador, Ivan Cherrez-Ojeda, Ivan Cherrez-Ojeda, Daniel Simancas-Racines

“…The common and random effects models had high sensitivity values of 1.0000 and 0.8581, respectively, indicating that the machine learning models in this study can accurately detect true-positive leukemia cases. …”
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Effectiveness of interventions for middle-aged and ageing population with neck pain: a systematic review and network meta-analysis protocol by Alison B Rushton, Nicola R Heneghan, Taweewat Wiangkham, Uchukarn Boonyapo, Piyameth Dilokthornsakul, Nattawan Phungwattanakul

“…Traditional pairwise meta-analysis using random-effect model will be performed for all direct comparisons, and NMA using a frequentist random-effect model then performed based on NP classification where possible. …”
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Faktor-Faktor yang Berhubungan dengan Usia Menarche Siswi SMP Adabiah by Fitrah Umi Mutasya, Edison ., Hasnar Hasyim Hasyim

“…Jumlah sampel sebanyak 72 siswi yang diambil secara <em>Simple Random Sampling. </em>Data dikumpulkan dengan angket dan pengukuran tinggi dan berat badan responden. …”
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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…The algorithm was trained using 400 randomly selected devices and then tested using 100 unique random devices. …”
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Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes by Junwei Peng, Xiaoyujie Geng, Yiyue Zhao, Zhijin Hou, Xin Tian, Xinyi Liu, Yuanyuan Xiao, Yang Liu

“…Four machine learning (ML) algorithms including random forest, extreme gradient boosting, light gradient boosting machine and binary logistic regression were used to construct prediction models. …”
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A Pilot Study on Cognitive Processing Characteristics and Cerebral Mechanisms of Native Chinese-Speaking Savant Calendar Calculators with Autism Spectrum Disorder by CHEN Jian, XU Shutian, WU Yuwei, CHEN Songmei, SHAN Chunlei

“…The patient with SCC was examined with crainial MRI and compared with healthy subjects to explore the organic brain damage.Results(1) Calendar calculation ability test: the correct rate of random normal calendar calculation in the SCC patient was 80%, and the average time of answering each question was 11.8 s, while the average correct rate of random normal calendar calculation in the healthy subjects was only 16%. (2) Calendar memorizing ability test: the correct rate of the adjusted new calendar calculation in the SCC patient was only 20%. (3) Numerical calculation ability: the correct rate of addition and division in the SCC patient was 100%, and the average time was 10.67 s and 58.19 s respectively. …”
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PENGARUH MODEL PEMBELAJARAN TEAM ASSISTED INDIVIDUALIZATION TERHADAP PEMAHAMAN KONSEP DAN KETERAMPILAN GENERIK SAINS SISWA by Jeni Rahmayanti, Connie Connie, Iwan Setiawan

“…Sampel penelitian diambil dengan teknik simple random sampling dengan kelas eksperimen menggunakan model Team Assisted Individualization  sedangkan kelas kontrol menggunakan model direct instruction. …”
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Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis by Shahd M. Noman, Youssef M. Fadel, Mayar T. Henedak, Nada A. Attia, Malak Essam, Sarah Elmaasarawii, Fayrouz A. Fouad, Esraa G. Eltasawi, Walid Al-Atabany

“…LightGBM, XGBoost, and Random Forest models were employed and validated against data from the Baheya Foundation. …”
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Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity by Hongguo Zhao, Peng Liu, Fei Chen, Mengjuan Wang, Jiaxi Liu, Xiling Fu, Hang Yu, Manman Nai, Lei Li, Xinbin Li

“…In total, 1184 women with VL have been randomly selected and categorized into groups using the finger measurement method. …”
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Prognostic and diagnostic accuracy of intracoronary electrocardiogram recorded during percutaneous coronary intervention: a meta-analysis by Jun Fan, Weijie Li, Yizhi Pan, Jialin He, Jiankai Huang, Pingan Chen

“…Summary estimates of clinical outcomes were obtained using a random effects model. Summary diagnostic accuracy was obtained by using a Bayesian bivariate random effects model.Results Of the 12 included studies, 7 studies reported the clinical outcomes (821 patients) and 6 studies reported the diagnostic accuracy (485 patients) of IC-ECG. …”
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Fructosamine measurement for diabetes mellitus diagnosis and monitoring: a systematic review and meta-analysis protocol by Andre Pascal Kengne, Eugene Sobngwi, Jean Jacques N Noubiap, Jobert Richie N Nansseu, Joël Fokom-Domgue, Eric V Balti

“…Absolute accuracy measures (sensitivity, specificity) will be pooled in a bivariate random-effects model, allowing for intersetting variability. …”
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Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia by O. A. Storonova, N. I. Kanevskii, A. S. Trukhmanov, V. T. Ivashkin

“…The model with the best results was integrated into a web application.Results. The RandomForestClassifier was chosen as the best performing model to compare. …”
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Right ventricular function in athletes engaged in endurance exercise using speckle tracking echocardiography: a meta-analysis by Chenzan Guo, Hebin Zhang, Cunxin Yang, Peipei Hu, Hui Ma, Ying Ma, Feng Gao

“…In addition, a random-effects meta-analysis revealed significant impairments in RV function when assessed by comparing RV GLS pre-endurance versus post endurance exercise WMD = 2.51, 95% CI (1.634 ~ 3.40), p < 0. 001. …”
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A Panpsychist Interpretation of Evolutionary Theory by Ferhat Onur

“… If there is a questionable element in the theory of evolution, it is likely the randomness of mutations, which is seen as the primary source of evolutionary change. …”
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A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi

“…Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. …”
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Identifikasi dan Penentuan Kadar Klorin pada Beras yang Dijual di Pasar Raya Padang by Suci Aulia Yude, Yuniar Lestari, Endrinaldi Endrinaldi

“…Identifikasi dan penentuan kadar klorin dilakukan terhadap 34 sampel beras yang diambil secara random. Metoda yang digunakan adalah metoda iodometri dan menggunakan larutan titrasi Na2S2O3 0,01 N. …”
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