Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms by Mingjun Hu, Jie Hao, Jie Wei

“…Genotyping for 59 SNPs in 35 genes was conducted using Agena MassARRAY platform. Key SNPs associated with glioma susceptibility were identified through LASSO, SVM-RFE algorithm, and likelihood ratio. …”
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A novel multi-label classification algorithm based on -nearest neighbor and random walk by Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan, William Wei Song

“…One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. …”
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Unmanned Aerial Vehicles (UAV) Networking Algorithms: Communication, Control, and AI-Based Approaches by Mien L. Trinh, Dung T. Nguyen, Long Q. Dinh, Mui D. Nguyen, De Rosal Ignatius Moses Setiadi, Minh T. Nguyen

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Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms by Juchao Chen, Zicheng Lei, Zongcai Duan, Zhili Wen

“…Abstract This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction model for spontaneous rupture bleeding via a machine learning algorithm, and validate and evaluate the predictive efficacy of the model. …”
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia by Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Hiwot Altaye Asebe, Beminate Lemma Seifu, Bezawit Melak Fente, Meklit Melaku Bezie, Mamaru Melkam, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Zufan Alamrie Asmare

“…This study used 7 machine learning algorithms for the classification of abortion. The dataset was randomly split into training and testing sets, with 80% allocated for training and 20% for testing. …”
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Pregnancy probability prediction models based on 5 machine learning algorithms and comparison of their performance by REN Chao, REN Chao, YANG Huan, ZHOU Niya, ZHOU Niya

“…In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions, feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set. Five algorithms, that is, Logistic Regression, Naive Bayes, Random Forest, Gradient Boosting Machine, and Support Vector Machine, were used to construct preconception outcome prediction models, and the parameters of each model were optimized using random search combined with grid search. …”
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Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma by Xie J, Zhang M, Qi M

“…The TCGA-SKCM cohort served as the training dataset, and GEO datasets were used for validation.Results: A robust prognostic model based on triaptosis-associated signature (TAS) was established using the SurvivalSVM algorithm. …”
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Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction by Tesfahun Berhane, Tamiru Melese, Abdu Mohammed Seid

“…Peer-to-Peer systems are still in the early stages of development when it comes to the processing of credit and the appraisal of the risk associated with it. In this study, we used a hybrid convolutional neural network with logistic regression, a gradient-boosting decision tree, and a k-nearest neighbor to predict the credit risk in a P2P lending club. …”
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Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State by Fazliddin Makhmudov, Dilmurod Turimov, Munis Xamidov, Fayzullo Nazarov, Young-Im Cho

“…The system is built on a strong architecture and was trained using a diversified dataset under varying lighting circumstances and facial angles. …”
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A machine learning based algorithm accurately stages liver disease by quantification of arteries by Zhengxin Li, Xin Sun, Zhimin Zhao, Qiang Yang, Yayun Ren, Xiao Teng, Dean C. S. Tai, Ian R. Wanless, Jörn M. Schattenberg, Chenghai Liu

“…AD was counted using qVessel (previously trained on manually labeled vessels by stained slides (CD34/a-SMA/CK19) and developed by a decision tree algorithm). …”
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Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data by Yao Liang, Xuejin Sun, Steven D. Miller, Haoran Li, Yongbo Zhou, Riwei Zhang, Shaohui Li

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Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm by Jian Zeng, Bao Jia

“…The algorithm generates association rules based on mining the set of frequent items among basketball technical actions. …”
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Improving speaker-independent visual language identification using deep neural networks with training batch augmentation by Jacob L. Newman

“…We use the YOLO object detection algorithm to track the mouth through time, and we employ an ensemble of 3D Convolutional and Recurrent Neural Networks for this classification task. …”
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Action-Curiosity-Based Deep Reinforcement Learning Algorithm for Path Planning in a Nondeterministic Environment by Junxiao Xue, Jinpu Chen, Shiwen Zhang

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Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury by Chen Lin, Meng Zheng, Wensi Wu, Zhishan Wang, Guofeng Lu, Shaodan Feng, Xinlan Zhang

“…The diagnostic performance outperformed previous models in both the training and validation sets. In addition, cyclosporin A and nine other drugs were identified as potential agents for treating sepsis-associated AKI.ConclusionThis study highlights the potential of integrating bioinformatics and machine-learning approaches to generate a new diagnostic model for sepsis-associated AKI using molecular crossovers with sepsis. …”
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Protein structural domain-disease association prediction based on heterogeneous networks by Jingpu Zhang, Lianping Deng, Lei Deng

“…Then the topological features of the network are extracted according to the meta-paths between domain and disease nodes. Finally, we train a binary classifier based on the XGBOOST (eXtreme Gradient Boosting) algorithm to predict the potential associations between domains and diseases. …”
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Prediction of microbe-drug associations using a CNN-Bernoulli random forest model by Zihao Song, Qingnuo Li, Jincheng Zhao, Qinggang Bu, Zekang Bian, Jia Qu

“…The reduced training set is subsequently used to train a Bernoulli random forest (BRF) to predict potential microbe-drug associations. …”
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OPTIMIZING HEART ATTACK DIAGNOSIS USING RANDOM FOREST WITH BAT ALGORITHM AND GREEDY CROSSOVER TECHNIQUE by Safrizal Ardana Ardiyansa, Natasha Clarissa Maharani, Syaiful Anam, Eric Julianto

“…However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. …”
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Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models by Vasundhara Acharya, Bülent Yener, Gillian Beamer

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Research Progress of Intelligent Evaluation and Virtual Reality Based Training in Upper Limb Rehabilitation afrer Stroke by XIE Qiurong, LIN Wanqi, ZHANG Qi, SHENG Bo, ZHANG Yanxin, HUANG Jia

“…Accurate assessment and training of motor function in stroke patients is essential. …”
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