A Type‐3 Fuzzy‐Based Model Predictive Control Approach for Management of Constant Energy by Man‐Wen Tian, Lixing Zhu, Mohammad Hosein Sabzalian, Jafar Tavoosi, Amirhosein Khosravi Sarvenoee, Pierpaolo D'Urso, Ebrahim Ghaderpour, Ardashir Mohammadzadeh

“…To tackle this challenge, a disturbance observer and a continuous‐time model predictive control based on type‐3 fuzzy logic systems (T3‐FLSs) are applied to the bidirectional DC‐DC converter. …”
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Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques by Kalyan Chatterjee, Muntha Raju, Bhoomeshwar Bala, U. Nikitha, Gampala Prabhas, Naim Ahmad, Ayman Qahmash, Wade Ghribi, Bernardo Lemos, Saurav Mallik

“…The model excels in dynamic environments by refining the accuracy of the proposed Kal-ANN algorithm achieving a predictive accuracy of up to 96. 55%, a root mean square error, a Mean Absolute Error, and a Mean Squared Prediction Error as low as 9.85, 6.12, and <inline-formula> <tex-math notation="LaTeX">$3.15~g/m$ </tex-math></inline-formula>. …”
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Predictive model for sarcopenia in patients with non-small cell lung cancer and malignant pleural effusion by Hengxing Gao, Xuexue Zou, Meng Fan, Mingwei Chen

“…Nevertheless, there have been limited studies that specifically examined clinical parameters to predict sarcopenia in individuals with malignant pleural effusion (MPE). …”
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Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development by Tahir Rashid, Inam Illahi, Qasim Umer, Muhammad Arfan Jaffar, Waheed Yousuf Ramay, Hanadi Hakami

“…A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. …”
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Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population by Alexander Bremner, Li Yen Chan, Courtney Jones, Shaheen P. Shah

“…Four weight-gain-based algorithms are compared for the prediction of type 1 ROP in an Australian cohort: the weight, insulin-like growth factor, neonatal retinopathy of prematurity (WINROP) algorithm, the Children’s Hospital of Philadelphia Retinopathy of Prematurity (CHOPROP), the Colorado Retinopathy of Prematurity (CO-ROP) algorithm, and the postnatal growth, retinopathy of prematurity (G-ROP) algorithm. …”
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Prediction of electricity production by small wind power using artificial neural networks by Justyna Zalewska-Lesiak, Mateusz Oszczypała, Jerzy Małachowski

“…Additionally, a sensitivity analysis of the predictive model was performed. The neural network model presented in the article provides accurate predictions of the power generated by a wind turbine. …”
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Prediction of Metastasis in Paragangliomas and Pheochromocytomas Using Machine Learning Models: Explainability Challenges by Carmen García-Barceló, David Gil, David Tomás, David Bernabeu

“…A wide variety of algorithms have been applied for the development of predictive models, with a focus on interpreting their outputs to support clinical insights. …”
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Prediction of the remaining useful life of a milling machine using machine learning by Abbas Al-Refaie, Majd Al-atrash, Natalija Lepkova

“…A web application for maintenance was finally developed with several ML algorithms at the evaluation stage. Maintenance engineers can utilize the developed smart web application to monitor the machine's health state and predict failure occurrence. …”
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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Hamza Imran, Miguel Angel Duque Vaca, Greys Carolina Herrera Morales, Nestor Ulloa, Krishna Prakash Arunachalam

“…Abstract This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). …”
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Multi-feature stock price prediction by LSTM networks based on VMD and TMFG by Zhixin Zhang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…(sh600009), the VMD–TMFG–LSTM model achieves a 69.76% reduction in Root Mean Squared Error (RMSE), a 71.41% reduction in Mean Absolute Error (MAE), a 46.28% reduction in runtime, and an improvement of 0.2184 in R-squared (R2), indicating significantly higher prediction accuracy. …”
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Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

“…Notably, RF and GLM algorithms outperformed the other algorithms, underscoring their efficacy in predicting the distribution of endemics in the Mediterranean region. …”
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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…Machine learning methods, particularly classification algorithms, have demonstrated their potential to accurately predict the risk of cardiovascular disease (CVD) by analyzing patient data. …”
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Machine Learning for Predicting Bank Stability: The Role of Income Diversification in European Banking by Karim Farag, Loubna Ali, Noah Cheruiyot Mutai, Rabia Luqman, Ahmed Mahmoud, Nol Krasniqi

“…Conversely, the results also show that the machine-learning algorithms used play a crucial role in enhancing the predictive performance of bank stability.…”
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Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning by Dthenifer Cordeiro Santana, Rafael Felipe Ratke, Fabio Luiz Zanatta, Cid Naudi Silva Campos, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Natielly Pereira da Silva, Gabriela Souza Oliveira, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Matildes Blanco, Paulo Eduardo Teodoro

“…This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of these algorithms. …”
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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

“…Incorporating PRS as an additional feature in the model resulted in a 6% improvement in model sensitivity and a 9% reduction in specificity. A positive MDD diagnosis and high stress contributed the most to predicting suicidality risk.ConclusionA cost-sensitive AdaBoost model developed using the sociodemographic, psychosocial, and clinical data of PLWH in Uganda can predict suicidality risk, albeit with modest PPV. …”
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Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO by Sachikanta Dash, Sasmita Padhy, Preetam Suman, Sandip Mal, Lokesh Malviya, Amrit Suman, Jaydeep Kishore

“…FLWCO demonstrates superiority over existing federated learning algorithms in real-world heart illness prediction. Furthermore, the proposed model can be employed to estimate the likelihood of heart disease in individuals with diabetes. …”
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Direction-of-arrival estimation performance analysis of the rank reduction estimator in the presence of finite samples by Ding WANG, Hui YAO, Ying WU

Subjects: “…rank reduction estimator…”
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Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by Qianchang Wang, Zhe Wang, Fangfeng Liu, Zhengjian Wang, Qingqiang Ni, Hong Chang

“…Based on these findings, a predictive model for CR-POPF was developed using machine learning algorithms. …”
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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

“…However, we chose all the variables to construct the models for other machine learning algorithms. All models were evaluated using the receiver operating characteristic curve (ROC), predictive histograms, and decision curve analysis (DCA). …”
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Construction of a machine learning-based prediction model for mitral annular calcification by LI Runqian, TAN Yanyi, GE Tiantian, QI Lei, BAI Song, TONG Jiayi

“…Objective To develop a risk prediction model for mitral annular calcification (MAC) using various machine learning algorithms to enable early identification and risk assessment of MAC. …”
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