Real-Time Algal Monitoring Using Novel Machine Learning Approaches by Seyit Uguz, Yavuz Selim Sahin, Pradeep Kumar, Xufei Yang, Gary Anderson

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Improved bio-inspired with machine learning computing approach for thyroid prediction by Divya Kesavulu, Kannadasan R

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Impact of data bias on machine learning for crystal compound synthesizability predictions by Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, Eva Zurek, Sara Kadkhodaei

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Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies by Osasere Oture, Muhammad Zahid Iqbal, Xining (Ning) Wang

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Sensorless Deadbeat Control Strategy With Adaptive Capability for PMSM Drive Machines by Sadeq Ali Qasem Mohammed, Mohammad M. Almuhaini, Kyo-Beum Lee

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A machine learning framework for predicting healthcare utilization and risk factors by Yead Rahman, Prerna Dua

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Defect modeling in semiconductors: the role of first principles simulations and machine learning by Md Habibur Rahman, Arun Mannodi-Kanakkithodi

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Explainable machine learning framework for cataracts recognition using visual features by Xiao Wu, Lingxi Hu, Zunjie Xiao, Xiaoqing Zhang, Risa Higashita, Jiang Liu

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Optimization of Fluid-Type Roasting Machine on Robusta Roasted Coffee Characteristics by Ari Rahayuningtyas, Ida Farikha Azizah, Steven Witman, Dadang Dayat Hidayat, Alifia Fauziah Rohzan, Yusep Ikrawan, Ngatinem Ngatinem

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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…Through feature engineering, sampling strategies and hyperparameter tuning the models performed well on all the datasets. …”
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An ensemble-driven machine learning framework for enhanced water quality classification by Preet Singh, Taniya Hasija, Salil Bharany, Hafiza Nazra Tun Naeem, B. Chinna Rao, Seada Hussen, Ateeq Ur Rehman

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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

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Efficient use and development of wheel-working machines for the locomotive facility needs by Rafik H. Rafikov, Andrey S. Shinkaruk, Evgeniy I. Eliseykin

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Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases by Mohammed Fadhil Mahdi, Arezoo Jahani, Dhafar Hamed Abd

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An ensemble machine learning approach for classification tasks using feature generation by Wenjuan Feng, Jin Gou, Zongwen Fan, Xiang Chen

“…Although machine learning classifiers have been successfully used in the medical and engineering fields, there is still room for improving the predictive accuracy of model classification. …”
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Predicting medication wastage using machine learning based on patient beliefs by Firdaus Aziz, Sorayya Malek, Shubathira Sooriamoorthy, Ilham Asyilah Mahamood, Chong Wei Wen, Sharifah M. Syed Ahmad, Putri Nur Fatin Amir Rudin, Adliah Mhd Ali

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A Critical Review of Machine Learning Application Models in Dam Settlement Monitoring by Behrang Beiranvand, Taher Rajaee

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Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms by Sandra D’Souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, aneesha acharya k

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Approximation Algorithms and an FPTAS for the Single Machine Problem with Biased Tardiness Penalty by G. Moslehi, K. Kianfar

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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…Attackers use various deceptive techniques, including link manipulation, filter evasion, covert redirection, website forgery, and social engineering. This study introduces an advanced phishing detection framework using machine learning (ML) models. …”
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