Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…This study represents the first attempt to demonstrate the applicability and performance of high-spatial resolution ResourceSat-2 remote sensing satellite's LISS-4 sensor, which operates in three spectral bands in the Visible and Near Infrared Region (VNIR), to predict water quality. Spectral bands of each satellite were used as independent parameter to generate the algorithms for pH, Dissolved Oxygen (DO), Total Suspended Solids (TSS) and Total Dissolved Solids (TDS). …”
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Prediction models used in the progression of chronic kidney disease: A scoping review. by David K E Lim, James H Boyd, Elizabeth Thomas, Aron Chakera, Sawitchaya Tippaya, Ashley Irish, Justin Manuel, Kim Betts, Suzanne Robinson

“…<h4>Objective</h4>To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).…”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…Shapley additive explanation (SHAP)-based feature selection is typically applied to enhance interpretability, and its effect is compared with principal component analysis-based dimensionality reduction and nonselection methods. The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. …”
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Research on Sleep Staging Based on Support Vector Machine and Extreme Gradient Boosting Algorithm by Wang Y, Ye S, Xu Z, Chu Y, Zhang J, Yu W

“…Yiwen Wang,1 Shuming Ye,2 Zhi Xu,3 Yonghua Chu,1 Jiarong Zhang,4 Wenke Yu5 1Clinical Medical Engineering Department, The Second Affiliated Hospital, Zhejiang University School of Medicine, HangZhou, ZheJiang, People’s Republic of China; 2Department of Biomedical Engineering, Zhejiang University, HangZhou, ZheJiang, People’s Republic of China; 3China Astronaut Research and Training Center, BeiJing, People’s Republic of China; 4Baidu Inc, BeiJing, People’s Republic of China; 5Radiology Department, ZheJiang Province Qing Chun Hospital, HangZhou, ZheJiang, People’s Republic of ChinaCorrespondence: Yiwen Wang; Shuming Ye, Email karenkaren2010@zju.edu.cn; ysmln@vip.sina.comPurpose: To develop a sleep-staging algorithm based on support vector machine (SVM) and extreme gradient boosting model (XB Boost) and evaluate its performance.Methods: In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. …”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

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A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction by Micheal A Ogundero, Theophilus A Fashanu, Foluso O Agunbiade, Kehinde Orolu, Ahmed A Yinusa, Usman A Daudu, Muhammed O H Amuda

“…Results show that machine learning algorithms including the Logistic Regression, Decision Trees, Random Forest, XGBoost, and Neural Networks schemes reliably predicted water potability in the absence of two missing instrumentation parameters namely: pH and DO. …”
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Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data by Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang, Ting Liu

“…Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, with at least a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>160</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in the fault recall rate.…”
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Validating laboratory predictions of soil rewetting respiration pulses using field data by X. Li, X. Li, M. Pallandt, M. Pallandt, D. Naidu, D. Naidu, J. Rousk, G. Hugelius, G. Hugelius, S. Manzoni, S. Manzoni

“…Caution should be taken when extending laboratory insights for predicting fluxes in ecosystems.</p>…”
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An explainable machine learning model in predicting vaginal birth after cesarean section by Ming Yang, Dajian Long, Yunxiu Li, Xiaozhu Liu, Zhi Bai, Zhongjun Li

“…Cervical Bishop score and interpregnancy interval showed the greatest impact on successful vaginal birth, according to SHAP results.Conclusions Models based on ML algorithms can be used to predict VBAC. The CatBoost model showed best performance in this study. …”
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A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Türkiye by Ulubey Çeken, Fadime Sertçelik, Abdullah İçen

“…In this study, we present new ground-motion prediction models (GMPMs) for shallow crustal earthquakes using strong-motion data recorded in Türkiye. …”
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A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids by Tomoyoshi Asano, Hidetaka Suga, Hirohiko Niioka, Hiroshi Yukawa, Mayu Sakakibara, Shiori Taga, Mika Soen, Tsutomu Miwata, Hiroo Sasaki, Tomomi Seki, Saki Hasegawa, Sou Murakami, Masatoshi Abe, Yoshinori Yasuda, Takashi Miyata, Tomoko Kobayashi, Mariko Sugiyama, Takeshi Onoue, Daisuke Hagiwara, Shintaro Iwama, Yoshinobu Baba, Hiroshi Arima

“…Furthermore, the model obtained by ensemble learning with the two algorithms can predict RAX expression in cells without RAX::VENUS, suggesting that our model can be deployed in clinical applications such as transplantation.…”
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<p><strong>Evaluation of geostatistical method and hybrid artificial neural network with imperialist competitive algorithm for predicting distribution pattern of <em>Tetranychus</em> <em>urticae</em> (Acari: Tetranychidae) in cucumber field of Behbahan, Iran</strong></p> by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi-Sohani

“…In Geostatistics methods ordinary kriging, and ANN with imperialist competitive algorithm were evaluated. Comparison of ANN and geostatistical showed that ANN capability is more than ordinary kriging method so that the ANN predicts distribution of this pest dispersion with 0.98 coefficient of determination and 0.0038 mean squares errors lower than the Geostatistical methods. …”
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Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection by Margaryta Prazdnikova

“…By analyzing the frequency of specific words in medical records, the algorithm successfully predicted a high risk of heart attack for 80 % of patients with an expected event. …”
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Prediction of moisture content of hummus peach based on multi-burr hyperspectral data by GAO Aidi, QIAO Fengzhang, ZHU Wenxuan, ZHONG Xiaopin, DENG Yuanlong

“…For hyperspectral image data with spikes and noise, compared the effects of several data preprocessing methods, including polynomial smoothing algorithm (SG), multivariate scatter correction algorithm (MSC), standard normal variate algorithm (SNV), first-order derivative operator (D1), and second-order derivative operator (D2) on model prediction accuracy. …”
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Mechanism of baricitinib supports artificial intelligence‐predicted testing in COVID‐19 patients by Justin Stebbing, Venkatesh Krishnan, Stephanie de Bono, Silvia Ottaviani, Giacomo Casalini, Peter J Richardson, Vanessa Monteil, Volker M Lauschke, Ali Mirazimi, Sonia Youhanna, Yee‐Joo Tan, Fausto Baldanti, Antonella Sarasini, Jorge A Ross Terres, Brian J Nickoloff, Richard E Higgs, Guilherme Rocha, Nicole L Byers, Douglas E Schlichting, Ajay Nirula, Anabela Cardoso, Mario Corbellino, the Sacco Baricitinib Study Group

“…Abstract Baricitinib is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI) algorithms, to be useful for COVID‐19 infection via proposed anti‐cytokine effects and as an inhibitor of host cell viral propagation. …”
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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…In order to reveal the intrinsic mechanism of prediction by such architectures, we adopted a coupled CNN-LSTM model based on the explainability technique SHapley Additive exPlanations (SHAP) to predict the rainfall-runoff process and identify key input feature factors, and took the Beijiang River Basin in China as an example, so as to improve the explainability and credibility of this black-box model. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The worldwide health epidemic of anaemia which is a condition with low levels of red blood cells or haemoglobin requires accurate prediction models to act promptly and improve patient outcomes because it is widespread and has different causes. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…Finally, from the experimental results of the CAWOA-ELM algorithm, it has excellent prediction effect and practical application value.…”
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Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis by Ana Maria Cristina Jura, Daniela Eugenia Popescu, Cosmin Cîtu, Marius Biriș, Corina Pienar, Corina Paul, Oana Maria Petrescu, Andreea Teodora Constantin, Alexandru Dinulescu, Ioana Roșca

“…While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random Forest (RF) and XGBoost (XGB). …”
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Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis by Zhijian Ren, Minqiao Zhang, Pingping Wang, Kanan Chen, Jing Wang, Lingping Wu, Yue Hong, Yihui Qu, Qun Luo, Kedan Cai

“…Utilizing machine learning to predict blood pressure fluctuations during dialysis has become a viable predictive method. …”
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