Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions by Norizal Abdullah, Masri Ayob, Meng Chun Lam, Nasser R. Sabar, Graham Kendall, Mohamad Khairulamirin Md Razali

“…We examine a wide range of optimization methodologies, including mathematical programming, heuristics, matheuristics, and machine learning, highlighting their strengths and limitations in addressing the multifaceted nature of PSP. …”
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Dynamic ultrasound-based modeling predictive of response to neoadjuvant chemotherapy in patients with early breast cancer by Xinyi Wang, Yuting Zhang, Mengting Yang, Nan Wu, Shan Wang, Hong Chen, Tianyang Zhou, Ying Zhang, Xiaolan Wang, Zining Jin, Ang Zheng, Fan Yao, Dianlong Zhang, Feng Jin, Pan Qin, Jia Wang

“…We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model. This retrospective study included 304 EBC patients recruited from multiple centers. …”
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Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks by Ying Yu, Min Li, Liangliang Liu, Yaohang Li, Jianxin Wang

“…The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. …”
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Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning by D. Conde, F. L. Castillo, C. Escobar, C. García, J. E. García, V. Sanz, B. Zaldívar, J. J. Curto, S. Marsal, J. M. Torta

“…We implement a type of machine‐learning model called long short‐term memory (LSTM) network. …”
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Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms by Norhan Khallaf, Osama Abd-El Rouf, Abeer D. Algarni, Mohy Hadhoud, Ahmed Kafafy

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Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study by Devender Kumar, David Haag, Jens Blechert, Josef Niebauer, Jan David Smeddinck

“…ObjectiveThe aim of the study was to use machine learning to balance the feature set size of EMA questions with the prediction accuracy regarding of enacting PA. …”
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Human–computer interaction in healthcare: Comprehensive review by Meher Langote, Saniya Saratkar, Praveen Kumar, Prateek Verma, Chetan Puri, Swapnil Gundewar, Palash Gourshettiwar

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Analysis and validation of serum biomarkers in brucellosis patients through proteomics and bioinformatics by Xiao Li, Bo Wang, Xiaocong Li, Juan He, Yue Shi, Rui Wang, Dongwei Li, Ding Haitao, Ding Haitao

“…IntroductionThis study aims to utilize proteomics, bioinformatics, and machine learning algorithms to identify diagnostic biomarkers in the serum of patients with acute and chronic brucellosisMethodsProteomic analysis was conducted on serum samples from patients with acute and chronic brucellosis, as well as from healthy controls. …”
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Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis by Termeh Sarrafan Sadeghi, Seyed AmirHossein Ourang, Fatemeh Sohrabniya, Soroush Sadr, Parnian Shobeiri, Saeed Reza Motamedian

“…Abstract Background Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. …”
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Digital modeling of soil-borne fusarium’s nutritional: Investigating microbiological and microecological dynamics in Moroccan agroecosystems by El Hilali Alaoui Youssef, Bouda Said, Chabaa Samira, Elouali Alami Mohammed, Khoudi Zakaria, Essarioui Adil

“…These results highlight the potential of machine learning approaches in understanding the nutritional behavior of Fusarium communities. …”
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Kombinasi Seleksi Fitur Berbasis Filter dan Wrapper Menggunakan Naive Bayes pada Klasifikasi Penyakit Jantung by Siti Roziana Azizah, Rudy Herteno, Andi Farmadi, Dwi Kartini, Irwan Budiman

“…Data yang digunakan dalam penelitian ini adalah data penyakit jantung yang didapatkan dari UCI Machine Learning Repository. Dari implementasi pemodelan yang akan dilakukan menghasilkan nilai akurasi tertinggi sebesar 91.80% pada algoritma Naive Bayes dengan kombinasi union hasil seleksi fitur Information Gain dan Gain Ratio menggunakan perbandingan data latih dan data uji 80:20. …”
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Elucidating the emotional persona in the Romanian university students’ academic discourse: a corpus-based exploration by Diana Paula Dudău, Diana Paula Dudău, Madalina Chitez, Florin Alin Sava

“…Advanced data analysis techniques included supervised machine learning for language classification, network analysis to explore interactions among linguistic features, and cluster analysis to detect discipline- and genre-specific linguistic patterns.ResultsThe findings reveal distinct emotional patterns between Romanian and English academic writing. …”
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Clinical utility of receptor status prediction in breast cancer and misdiagnosis identification using deep learning on hematoxylin and eosin-stained slides by Gil Shamai, Ran Schley, Alexandra Cretu, Tal Neoran, Edmond Sabo, Yoav Binenbaum, Shachar Cohen, Tal Goldman, António Polónia, Keren Drumea, Karin Stoliar, Ron Kimmel

“…Here we explore the clinical utility of predicting receptor status from digitized hematoxylin and eosin-stained (H&E) slides using machine learning trained and evaluated on a multi-institutional dataset. …”
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A Survey on Adversarial Attacks for Malware Analysis by Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar, Bhavani Thuraisingham

“…Machine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting and classifying evasive and growing malware threats. …”
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Learning from Demonstrations and Human Evaluative Feedbacks: Handling Sparsity and Imperfection Using Inverse Reinforcement Learning Approach by Nafee Mourad, Ali Ezzeddine, Babak Nadjar Araabi, Majid Nili Ahmadabadi

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DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts by Chaonan Ji, Tonio Fincke, Vitus Benson, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Fabian Gans, Guido Kraemer, Francesco Martinuzzi, David Montero, Karin Mora, Oscar J. Pellicer-Valero, Claire Robin, Maximilian Söchting, Mélanie Weynants, Miguel D. Mahecha

“…Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. …”
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New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret by T. Netzel, A. Miebach, T. Litt, A. Hense

“…This includes consideration of various machine learning (ML) algorithms for solving the classification problem of biome presence and absence, taking into account uncertainties in the proxy–climate relationship. …”
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Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study by Olivia L. Finnegan, R. Glenn Weaver, Hongpeng Yang, James W. White, Srihari Nelakuditi, Zifei Zhong, Rahul Ghosal, Yan Tong, Aliye B. Cepni, Elizabeth L. Adams, Sarah Burkart, Michael W. Beets, Bridget Armstrong

“…This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. …”
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Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 by Gamal AbdElNasser Allam Abouzied, Guoqiang Tang, Simon Michael Papalexiou, Martyn P. Clark, Eleonora Aruffo, Piero Di Carlo

“…Multi‐strategy merging strategy based on the Modified Kling‐Gupta efficiency (MS1) shows the highest performance as an individual precipitation gap‐filling strategy. However, the machine learning strategy using random forest (ML3) has the most outstanding share in the final estimates among all other strategies. …”
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Peningkatan Akurasi Klasifikasi Algoritma C 4.5 Menggunakan Teknik Bagging pada Diagnosis Penyakit Jantung by Erwin Prasetyo, Budi Prasetiyo

“…Data penyakit jantung diambil dari dataset UCI Machine Learning Repository. Tujuan dari penulis melakukan penelitian ini yaitu untuk mengetahui penerapan teknik bagging pada algoritma C4.5, mengetahui hasil akurasi dalam algoritma C4.5, dan membandingkan tingkat akurasi dari penerapan teknik bagging pada algoritma C4.5. …”
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