Advancing deep learning-based segmentation for multiple lung cancer lesions in real-world multicenter CT scans by Xavier Rafael-Palou, Ana Jimenez-Pastor, Luis Martí-Bonmatí, Carlos F. Muñoz-Nuñez, Mario Laudazi, Ángel Alberich-Bayarri

“…We developed an automated three-step pipeline, including thoracic bounding box extraction, multi-instance lesion segmentation, and false positive reduction via a novel multiscale cascade classifier to filter spurious and non-lesion candidates. …”
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Conceptual Framework of Inquiry-Creative-Process Learning Model to Promote Critical Thinking Skills of Physics Prospective Teachers by W. Wahyudi, N. N. S. P. Verawati, S. Ayub, S. Prayogi

“…This research is the first step of development research which produce learning model with valid criteria on content validity and construct validity aspects. …”
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Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images by Masoud Tafavvoghi, Anders Sildnes, Mehrdad Rakaee, Nikita Shvetsov, Lars Ailo Bongo, Lill-Tove Rasmussen Busund, Kajsa Møllersen

“…Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. …”
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“Narrative images” as a learning approach: (transformative) adaptation scenarios for dealing with urban water risks in Hamburg, Germany by Franziska S. Hanf, Linda Meier, Tom Hawxwell, Jürgen Oßenbrügge, Jörg Knieling, Jana Sillmann, Jana Sillmann

“…This study focuses on the potential of visual communication of scenarios to stimulate both learning among scientists (during the process of creating the scenarios) and social learning (as a next step using the developed “narrative images”) to motivate diverse societal actors to engage with the complexity of sustainable urban water management. …”
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Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy by Eyachew Misganew Tegaw, Betelhem Bizuneh Asfaw

“…., chemotherapy, hormone therapy, surgery, and radiation therapy is an essential step towards personalization in treatment planning. …”
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An Efficient Computational Risk Prediction Model of Heart Diseases Based on Dual-Stage Stacked Machine Learning Approaches by Subhash Mondal, Ranjan Maity, Yachang Omo, Soumadip Ghosh, Amitava Nag

“…This work presents a novel dual-stage stacked machine learning (ML) based computational risk prediction model for cardiac disorders. …”
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The power of learning from the bottom up: working towards a blueprint for community-led biodiversity protection and restoration by Emma Verling, Maria Power, Melanie Biausque, Lee Wah Pay, Aoife Deane, Rory Scarrott, Darragh Ó Súilleabháin

“…This research serves as a step towards preparing blueprints to inform research, policy and practice in this space to enable stakeholders to respond collectively…”
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Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD: a framework for automatic diagnosis by Ying Mao, Ying Mao, Xuchen Qi, Xuchen Qi, Xuchen Qi, Lingyan He, Shan Wang, Zhaowei Wang, Fang Wang, Fang Wang

“…These findings provide strong evidence for revealing the electrophysiological mechanisms through multidimensional EEG characteristics and move a step forward towards future automatic diagnosis of ADHD.…”
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Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis by Somayeh Farahani, Marjaneh Hejazi, Sahar Moradizeyveh, Antonio Di Ieva, Emad Fatemizadeh, Sidong Liu

“…<b>Background/Objectives:</b> Integrating deep learning (DL) into radiomics offers a noninvasive approach to predicting molecular markers in gliomas, a crucial step toward personalized medicine. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…Methods The data was preprocessed in the first step, and was randomly allocated at an 8:2 ratio. …”
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Uncertainty-Aware Deep Learning for Robust and Interpretable MI EEG Using Channel Dropout and LayerCAM Integration by Óscar Wladimir Gómez-Morales, Sofia Escalante-Escobar, Diego Fabian Collazos-Huertas, Andrés Marino Álvarez-Meza, German Castellanos-Dominguez

“…Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, <i>TCNet Fusion</i> achieved the highest peak accuracy of 74.4% using 32 EEG channels. …”
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Manner of death prediction: A machine learning approach to classify suicide and non-suicide using blood metabolomics by Witchayawat Sunthon, Thitiwat Sopananurakkul, Giatgong Konguthaithip, Yutti Amornlertwatana, Somlada Watcharakhom, Kanicnan Intui, Churdsak Jaikang

“…The classification of the manner of death (MOD) is a critical step in forensic investigations. The process is based on scene investigation, autopsy, histological and toxicological findings. …”
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El Aprendizaje Cooperativo: Modelo Pedagógico para Educación Física (Cooperative learning: Pedagogical Model for Physical Education) by Javier Manuel Fernández-Rio, Antonio Méndez-Giménez

“…It is the time to move from cooperative games to Cooperative learning as the benchmark pedagogical model. …”
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Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference by Fateme Fotouhi, Fateme Fotouhi, Kevin Menke, Aaron Prestholt, Ashish Gupta, Matthew E. Carroll, Hsin-Jung Yang, Edwin J. Skidmore, Matthew O’Neal, Nirav Merchant, Sajal K. Das, Peter Kyveryga, Baskar Ganapathysubramanian, Baskar Ganapathysubramanian, Asheesh K. Singh, Arti Singh, Soumik Sarkar, Soumik Sarkar

“…HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. …”
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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability by Abdennabi Morchid, Abdennacer Elbasri, Zahra Oughannou, Hassan Qjidaa, Rachid El Alami, Badre Bossoufi, Saleh Mobayen, Pawel Skruch

“…This paper represents an important step towards improving food security and sustainable water management, particularly in regions facing increasing climatic challenges.…”
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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…Finally, using the cellular automata model, the growth simulation of Tabriz city based on land use and change potential maps obtained from machine learning algorithms for the mentioned periods was performed. …”
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Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy by Lindsay A. Edwards, Christina Yang, Surbhi Sharma, Zih-Hua Chen, Lahari Gorantla, Sanika A. Joshi, Nicolas J. Longhi, Nahom Worku, Jamie S. Yang, Brandy Martinez Di Pietro, Saro Armenian, Aarti Bhat, William Border, Sujatha Buddhe, Nancy Blythe, Kayla Stratton, Kasey J. Leger, Wendy M. Leisenring, Lillian R. Meacham, Paul C. Nathan, Shanti Narasimhan, Ritu Sachdeva, Karim Sadak, Eric J. Chow, Patrick M. Boyle

“…We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors using serial echocardiograms from five centers. …”
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An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data by F. Quispe-Huaynasi, F. Roig, N. Holanda, V. Loaiza-Tacuri, Romualdo Eleutério, C. B. Pereira, S. Daflon, V. M. Placco, R. Lopes de Oliveira, F. Sestito, P. K. Humire, M. Borges Fernandes, A. Kanaan, C. Mendes de Oliveira, T. Ribeiro, W. Schoenell

“…Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…However, detecting small-sized individual insects is challenging using image-based machine learning techniques, especially in natural field settings. …”
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Network-based analyses of multiomics data in biomedicine by Rachit Kumar, Joseph D. Romano, Marylyn D. Ritchie

“…This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.…”
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