Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…Abstract Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures. …”
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Decoding Motor Excitability in TMS Using EEG-Features: An Exploratory Machine Learning Approach by Lisa Haxel, Oskari Ahola, Paolo Belardinelli, Maria Ermolova, Dania Humaidan, Jakob H. Macke, Ulf Ziemann

“…This one-size-fits-all approach overlooks individual neurophysiological differences and the dynamic nature of brain states, potentially compromising therapeutic efficacy. We present a supervised machine learning framework that predicts individual motor excitability states from pre-stimulus EEG features. …”
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A Hybrid Deep Learning Framework for Deepfake Detection Using Temporal and Spatial Features by Fazeel Zafar, Talha Ahmed Khan, Salas Akbar, Muhammad Talha Ubaid, Sameena Javaid, Kushsairy Abdul Kadir

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Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning by José Guilherme de Almeida, Ana Sofia Castro Verde, Carlos Bilreiro, Inês Santiago, Joana Ip, Manolis Tsiknakis, Kostas Marias, Daniele Regge, Celso Matos, Nickolas Papanikolaou, ProCAncer-I

“…Abstract Objectives To present an accurate machine-learning (ML) method and knowledge-based heuristics for automatic sequence-type identification in multi-centric multiparametric MRI (mpMRI) datasets for prostate cancer (PCa) ML. …”
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Pore Structure Quantitative Characterization of Tight Sandstones Based on Deep Learning and Fractal Analysis by Xinglei Song, Congjun Feng, Teng Li, Qin Zhang, Jiaqi Zhou, Mengsi Sun

“…Sandstone reservoirs exhibit strong heterogeneity and complex microscopic pore structures, presenting challenges for quantitative characterization. …”
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Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms by Juchao Chen, Zicheng Lei, Zongcai Duan, Zhili Wen

“…Abstract This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction model for spontaneous rupture bleeding via a machine learning algorithm, and validate and evaluate the predictive efficacy of the model. …”
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Effect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV by Zhang W, Ren L, Yang K, Yan J, Yu Q, Qi S, Ruan H, Zhao D, Ruan L

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Bangladeshi Vehicle Classification and Detection Using Deep Convolutional Neural Networks With Transfer Learning by Farid, Proshanta Kumer Das, Monirul Islam, Ebna Sina

“…Vehicle classification and detection has been a field of application for deep learning and image processing which play a very important role in intelligent transport management and AI-assisted driving systems. …”
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Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions by Mohamed Heshmat, Lyes Saad Saoud, Muayad Abujabal, Atif Sultan, Mahmoud Elmezain, Lakmal Seneviratne, Irfan Hussain

“…The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. …”
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Development of E-Modules based on Contextual Teaching and Learning (CTL) on the actualization of Pancasila values by R. Riyana, Didik Sukriono, Sri Untari

“…The results of the development of the CTL-based e-module show that the e-module is capable of presenting learning materials engagingly and interactively, as well as facilitating students in applying the values of Pancasila in their daily lives. …”
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Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness by Ali Omidkar, Razieh Es’haghian, Hua Song

“…However, the substantial cost disparity between biofuels and conventional fossil fuels presents a formidable obstacle to their widespread adoption. …”
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Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis by Xin Li, Wen-yu Yang, Fan Zhang, Rui Shan, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Jing Chen, Run-ze Hu, Yang Yang, Yi-hang Yang, Jing-yao Liu, Chun-Hui Yuan, Zheng Liu

“…In the large-sized group, tumors presenting with a nodule-in-nodule appearance (OR 3.30, 95% CI 1.30-7.90) exhibited a higher malignancy risk. …”
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Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging by Paul J. Weiser, Georg Langs, Wolfgang Bogner, Stanislav Motyka, Bernhard Strasser, Polina Golland, Nalini Singh, Jorg Dietrich, Erik Uhlmann, Tracy Batchelor, Daniel Cahill, Malte Hoffmann, Antoine Klauser, Ovidiu C. Andronesi

“…Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps. …”
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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

“…This study employs remote sensing and machine learning techniques to evaluate the effectiveness of such interventions in a village forest area in central Malawi. …”
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Estimation of soil free Iron content using spectral reflectance and machine learning algorithms by Wanzhu Ma, Hongkui Zhou, Hao Hu, Zhiqing Zhuo, Kangying Zhu, Guangzhi Zhang

“…Given these valuable findings, the present study benefits soil properties mapping, crop nutrient management and improving environmental issues.…”
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Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration by Houda Lamane, Latifa Mouhir, Rachid Moussadek, Bouamar Baghdad, Ozgur Kisi, Ali El Bilali

“…Machine learning (ML) has become a powerful tool for predicting suspended sediment concentration (SSC). …”
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Lessons learned from 5 years of pegvaliase in US clinics: A case series by Erin Cooney, Zineb Ammous, Tricia Bender, Gillian E. Clague, Marilyn Clifford, Angela Crutcher, Laura Davis-Keppen, Kirsten Havens, Melissa Lah, Stephanie Sacharow, Amarilis Sanchez-Valle, Erika Vucko, Bridget Wardley, Leah Wessenberg, Hans C. Andersson

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Deep learning for human locomotion analysis in lower-limb exoskeletons: a comparative study by Omar Coser, Omar Coser, Christian Tamantini, Christian Tamantini, Matteo Tortora, Matteo Tortora, Leonardo Furia, Rosa Sicilia, Loredana Zollo, Paolo Soda, Paolo Soda

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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

“…Detecting acute lymphoblastic leukemia (ALL) at an early stage is critical to reducing its associated mortality rate. This study presents an empirical analysis of various pre-trained deep learning models, including VGG16, VGG19, ResNet50, Xception, ResNet152, EfficientNet- B0, NASNetMobile, DenseNet169, DenseNet121, and EfficientNetV2B0, for the detection and classification of ALL. …”
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An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images by Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Qi Chen, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He

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