A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective by Dung Nguyen Tien, Huong Thi Thu Bui, Tram Hoang Thi Ngoc, Thuy Thi Pham, Dac Trung Nguyen, Huyen Nguyen Thi Thu, Thi Thu Hang Vu, Thi Lan Anh Luong, Lan Thu Hoang, Ho Cam Tu, Nina Körber, Tanja Bauer, Lam Khanh Ho

“…MethodsThis study used decision tree algorithms—ID3 and CART—on a data set of 60 hepatitis B surface antigen (HBsAg)–positive pregnant women. …”
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OPTIMIZATION OF AUTOMOBILE TRANSPORTATION COSTS IN INDUSTRIAL ENTERPRISE by V. M. Kurganov, V. N. Mukayev, M. V. Gryaznov

“…The economic-mathematical model and the algorithm of decisions development on optimization of the automobile transportations costs in the industrial enterprise, which provide balance of interests of the customer to lower costs of motor transportation services and of the carrier to increase business profitability are offered. …”
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Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning by Chunyi Zhan, Hongyi Mao, Rongsheng Fan, Tanggui He, Rui Qing, Wenliang Zhang, Yi Lin, Kunyu Li, Lei Wang, Tie’en Xia, Youli Wu, Zhiliang Kang

“…Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). …”
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Image Based Detection of Coating Wear on Cutting Tools with Machine Learning by Jan Wolf, Nithin Kumar Bandaru, Martin Dienwiebel, Hans-Christian Möhring

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Predicting survival in patients with SARS-CoV-2 based on cytokines and soluble immune checkpoint regulators by Nuri Lee, Kibum Jeon, Min-Jeong Park, Wonkeun Song, Seri Jeong

“…The impact of these markers on overall survival were analyzed using a machine learning algorithm.ResultssICs, including sCD27, sCD40, herpes virus entry mediator (sHVEM), T-cell immunoglobulin and mucin-domain containing-3 (sTIM-3), and Toll-like receptor 2 (sTLR-2) and CKs, including chemokine CC motif ligand 2 (CCL2), interleukin-6 (IL-6), IL-8, IL-10, IL-13, granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor-α (TNF- α), were statistically significantly increased in the non-survivors compared to those of in the survivors. …”
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CRYPTO-RESISTANT METHODS AND RANDOM NUMBER GENERATORS IN INTERNET OF THINGS (IOT) DEVICES by Petro Klimushyn, Tetiana Solianyk, Oleksandr Mozhaiev, Yurii Gnusov, Oleksandr Manzhai, Vitaliy Svitlychny

“…The analysis of technologies and circuit solutions allowed to draw the following conclusions: protection of IoT solutions includes: security of IoT network nodes and their connection to the cloud using secure protocols, ensuring confidentiality, authenticity and integrity of IoT data by cryptographic methods, attack analysis and network cryptographic stability; the initial basis for the protection of IoT solutions is the true randomness of the formed RNG sequences and used in algorithms for cryptographic transformation of information to protect it; feature of IoT devices is their heterogeneity and geographical distribution, limited computing resources and power supply, small size; The most effective (reduce power consumption and increase the generation rate) for use in IoT devices are RNG exclusively on a digital basis, which implements a three-stage process: the initial digital circuit, normalizer and random number flow generator; Autonomous Boolean networks (ABN) allow to create RNG with unique characteristics: the received numbers are really random, high speed – the number can be received in one measure, the minimum power consumption, miniature, high (up to 3 GHz) throughput of Boolean chaos; a promising area of ABN development is the use of optical logic valves for the construction of optical ABN with a bandwidth of up to 14 GHz; the classification of known classes of RNG attacks includes: direct cryptanalytic attacks, attacks based on input data, attacks based on the disclosure of the internal state of RNG, correlation attacks and special attacks; statistical test packages to evaluate RNG sequences have some limitations or shortcomings and do not replace cryptanalysis; Comparison of cryptoaccelerators with cryptographic transformation software shows their significant advantages: for AES block encryption algorithm, speeds increase by 10-20 times in 8/16-bit cryptoaccelerators and 150 times in 32-bit, growth hashing of SHA-256 in 32-bit cryptoaccelerators more than 100 times, and for the NMAS algorithm - up to 500 times. …”
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Determinants and risk prediction models for frailty among community-living older adults in eastern China by Lin Qi, Lin Qi, Jianyu Liu, Xuhui Song, Xinle Wang, Mengmeng Yang, Xinyi Cao, Yan He

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Multi video stream collaborative adaptive offloading scheme based on equilibrium game theory by Shijun Yuan

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A risk prediction model for gastric cancer based on endoscopic atrophy classification by Yadi Lan, Weijia Sun, Shen Zhong, Qianqian Xu, Yining Xue, Zhaoyu Liu, Lei Shi, Bing Han, Tianyu Zhai, Mingyue Liu, Yujing Sun, Hongwei Xu

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The association between cystatin C and hypertension risk in diabetes patients: A multi-cohort cross-sectional study by Ye Kuang, Jia Wang, Yang Wang, Chuanmei Peng, Pei He, Yong Ji, Jinrong Tian, Yong Yuan, Lei Feng

“…Analyzing 5210 DM patients from three cohorts, this study identified serum cystatin C (CysC) as an independent risk factor for DM + HTN through univariate and multivariate logistic regression. …”
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CLASSIFICATION OF IRRIGATION MANAGEMENT PRACTICES IN MAIZE HYBRIDS USING MULTISPECTRAL SENSORS AND MACHINE LEARNING TECHNIQUES by João L. G de Oliveira, Dthenifer C. Santana, Izabela C de Oliveira, Ricardo Gava, Fábio H. R. Baio, Carlos A da Silva Junior, Larissa P. R. Teodoro, Paulo E. Teodoro, Job T de Oliveira

“…Three accuracy metrics were utilized to evaluate the algorithms in the classification of irrigation management: correct classifications (CC), Kappa coefficient and F-Score. …”
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Prediction of Clinical Bronchiectasis from Asymptomatic Radiological Bronchiectasis by Fan Y, Li Z, Jiang N, Zhou Y, Song J, Yu F, Zhang J, Wang X

“…Whether the ARB could progress to CB and the risk factors to speed up the process are poorly understood.Methods: This was an observational cohort study. 370 patients with radiological bronchiectasis were included in Wuhan Union Hospital in 2018. 296 ARB patients were followed up in 2022 to verify if they progressed to CB and divided the development and validation of clinical prediction models into a training set (n=207) and a validation set (n=89) by the ratio of 7:3. LASSO algorithm and multivariable logistic regression analysis were performed to construct a new nomogram model. …”
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Exploring the Landscape of Operating Room Scheduling: A Bibliometric Analysis of Recent Advancements and Future Prospects by Md Al Amin, Majed Hadid, Adel Elomri, Rabah Ismaen, Ismail Dergaa, Hind Alashi, Amal Jobran Al-Hajaji, Moustafa Alkhalil, Omar M Aboumarzouk, Abdelfatteh EL Omri

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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

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Peri- and postoperative complications of laparoscopic tubal ligation versus salpingectomy for permanent contraception: An ACS-NSQIP analysis by Vaishnavi J. Patel, Devki Patel, Kimberly Toumazos, Young Son, Komal Sharma, Virgil Kevin DeMario, Shelby Boock, Grace Lara, Alexandra McQuillen, Brianna Clark

“…Statistical analysis involved t test, chi-square test, and logistic regression analysis with the use of the random forest algorithm. The primary outcomes were perioperative and postoperative complications. …”
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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

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Modeling and Simulation for Effectiveness Evaluation of Dynamic Discrete Military Supply Chain Networks by Biao Xiong, Bixin Li, Rong Fan, Qingzhong Zhou, Wu Li

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Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning by Ye Wang, Zhen Pan, Huajun Cai, Shoufeng Li, Ying Huang, Jinfu Zhuang, Xing Liu, Guoxian Guan

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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. …”
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Using machine learning for mortality prediction and risk stratification in atezolizumab‐treated cancer patients: Integrative analysis of eight clinical trials by Yougen Wu, Wenyu Zhu, Jing Wang, Lvwen Liu, Wei Zhang, Yang Wang, Jindong Shi, Ju Xia, Yuting Gu, Qingqing Qian, Yang Hong

“…The whole cohort was randomly split into development and validation cohorts in a 7:3 ratio. Machine‐learning algorithms (extreme gradient boosting, random forest, logistic regression with lasso regularization, support vector machine, and K‐nearest neighbor) were applied to develop prediction models. …”
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