Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms by Wenzhi Wang, Feng Jin, Lina Song, Jinfang Yang, Yingjian Ye, Junjie Liu, Lei Xu, Peng An

“…Abstract Objectives This study aimed to develop a model for predicting peripheral lymph node metastasis (LNM) in thyroid cancer patients by combining enhanced CT radiomic features with machine learning algorithms. …”
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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“…Furthermore, this study will evaluate and compare the performance metrics of the 4 different ML algorithms, and the best model will be used to develop an HIV testing predictive model. …”
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Diagnostic performance of a new algorithm combining simple, non-invasive and inexpensive tests for predicting the presence of advanced liver fibrosis in patients with chronic hepatitis B by Jean Nana, Jean Luc Bosson, Kristina Skaare, Céline Vermorel, Vincent Leroy, Tarik Asselah, Michael Adler, Jean-Pierre Zarski

“…Conclusion A new algorithm combining simple, non-invasive, and inexpensive tests demonstrates a good diagnostic value in predicting advanced liver fibrosis in patients with CHB or excluding significant fibrosis. …”
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Using a machine learning algorithm and clinical data to predict the risk factors of disease recurrence after adjuvant treatment of advanced-stage oral cavity cancer by Sheng-Yao Huang, Ren-Jun Hsu, Dai-Wei Liu, Wen-Lin Hsu

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A predictive model for functional cure in chronic HBV patients treated with pegylated interferon alpha: a comparative study of multiple algorithms based on clinical data by Ya-mei Ye, Yong Lin, Fang Sun, Wen-yan Yang, Lina Zhou, Chun Lin, Chen Pan

“…Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis. Subsequently, predictive models were developed via logistic regression, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms. …”
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A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP by Xiaofeng Shang

“…CNN-LSTM-GA is superior to the other two algorithms in terms of prediction accuracy and overall performance of FJSP. …”
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HAMF: A Novel Hierarchical Attention-Based Multi-Modal Fusion Model for Parkinson’s Disease Classification and Severity Prediction by Anitha Rani Palakayala, P. Kuppusamy, D. Kothandaraman, Gunakala Archana, Jaideep Gera

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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning by Muhammad Zubair Yameen, Zhigang Lu, Fayez F. M. El-Sousy, Waqar Younis, Baqar Ali Zardari, Abdul Khalique Junejo

Subjects: “…Model Predictive Control…”
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Calibrating multiplex serology for Helicobacter pylori by Emmanuelle A. Dankwa, Martyn Plummer, Daniel Chapman, Rima Jeske, Julia Butt, Michael Hill, Tim Waterboer, Iona Y. Millwood, Ling Yang, Christiana Kartsonaki

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Fusion of motion smoothing algorithm and motion segmentation algorithm for human animation generation. by Shinan Ding

“…In order to improve the naturalness and diversity of human animation generation, a method combining motion smoothing algorithm and motion segmentation algorithm is proposed. …”
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Automated diabetes detection prediction system based on patients’ medical data by S.V. Pidopryhora, Yu.V. Bogoyavlenska

“…Given the continuous growth of medical data volumes, there is a clear need for modern information technologies capable of automating disease analysis and prediction processes. This paper examines the potential and benefits of implementing machine learning (ML) and artificial intelligence (AI) algorithms for medical data analysis aimed at diabetes detection. …”
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Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity by Bingsheng Chen, Huijie Chen, Mengshan Li

“…The swarm intelligence algorithm simulates the behavior of animal populations in nature and is a new type of intelligent solution that is different from traditional artificial intelligence. …”
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Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients by Yong Si, Armin Abdollahi, Negin Ashrafi, Greg Placencia, Elham Pishgar, Kamiar Alaei, Maryam Pishgar

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Study on the gas outflow pattern and outflow prediction model of the return mining face under complex geological conditions by Yi Sun, Lulin Zheng, Hong Lan, Zhaoxing Yu, Jin Wang, Bo Li, Feng Yang, Fangbo Wen

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PREDICTING TRAVEL-TIME RELIABILITY IN ROAD NETWORKS: A FITRNET-BASED APPROACH – A CASE STUDY OF ENGLAND by Navid KHORSHIDI, Shahriar AFANDIZADEH ZARGARI, Hamid MIRZAHOSSEIN, Hanif HEIDARI, Travis WALLER

Subjects: “…fitrnet machine learning algorithm…”
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Raising the Quality of Spesialists’ Training: the Procedure Selection by M. N. Oskina

Subjects: “…system of higher vocational education…”
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Voltage and frequency regulation in wind penetrated deregulated power system using an electric vehicle and IPFC assisted model predictive controller by Vineet Kumar, Vineet Kumar, Ark Dev

Subjects: “…Harris hawks optimization algorithm…”
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Accurate Rotor Temperature Prediction of Permanent Magnet Synchronous Motor in Electric Vehicles Using a Hybrid RIME-XGBoost Model by Jianzhao Shan, Zhongyuan Che, Fengbin Liu

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A Multi-Input Multi-Output Considering Correlation and Hysteresis Prediction Method for Gravity Dam Displacement with Interpretative Functions by Bo Xu, Yuan Yao, Xuan Wang, Linsong Sun, Bin Ou, Yanming Zhang

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Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle by Rachappa Jopate, Piyush Kumar Pareek, DivyaJyothi M. G, Ariam Saleh Zuwayid Juma Al Hasani

“…Finally, the parameters of the AEHOA model are fed into a Convolutional Neural Network (CNN) to categorise data and enhance prediction. The accuracy of classification predictions was also increased by tweaking the dataset. …”
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