Recovering Pulsar Periodicity from Time-of-arrival Data by Finding the Shortest Vector in a Lattice by Dotan Gazith, Aaron B. Pearlman, Barak Zackay

“…Identifying such a periodicity from a discrete set of arrival times is a difficult algorithmic problem, In particular when the pulsar is in a binary system. …”
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Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches by Md. Atiqur Rahman, Israt Jahan, Maheen Islam, Taskeed Jabid, Md Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Md. Hasanul Ferdaus, Md Mostofa Kamal Rasel, Mahmuda Rawnak Jahan, Shayla Sharmin, Tanzina Afroz Rimi, Atia Sanjida Talukder, Md. Mafiul Hasan Matin, M. Ameer Ali

“…This fact highlights the need for accurate machine learning algorithms (MLAs) for analyzing, monitoring, and diagnosing sleep disorders. …”
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Extended homogeneous field correction method based on oblique projection in OPM-MEG by Fulong Wang, Fuzhi Cao, Yujie Ma, Ruochen Zhao, Ruonan Wang, Nan An, Min Xiang, Dawei Wang, Xiaolin Ning

“…Subspace projection algorithms are widely used in MEG to suppress noise. …”
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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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Diagnóstico y tratamiento de los trastornos de la presión intracraneal: Documento de consenso del Grupo de Estudio de Cefaleas de la Sociedad Española de Neurología by J. García-Ull, N. González-García, M. Torres-Ferrús, D. García-Azorín, I.F.J. Molina-Martínez, I. Beltrán-Blasco, S. Santos-Lasaosa, G. Latorre, A.B. Gago-Veiga, J.M. Láinez, J. Porta-Etessam, C. Nieves-Castellanos, A. Mínguez-Olaondo, A. López-Bravo, S. Quintas, N. Morollón, S. Díaz-Insa, R. Belvís, P. Irimia

“…Therefore, the Spanish Society of Neurology's Headache Study Group considered it necessary to prepare this consensus document with the inclusion of diagnostic and therapeutic algorithms to facilitate and improve their management in clinical practice.This document was created by a committee of experts of the Spanish Society of Neurology's Headache Study Group based on a systematic review of the literature, incorporating the experience of the participants, and establishing practical recommendations with levels of evidence and grades of recommendation.…”
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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

“…This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.MethodsMASLD risk prediction models were constructed based on seven machine learning algorithms using all variables, insulin-related variables, demographic characteristics variables, and other indicators, respectively. …”
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COOL-LAMPS. VII. Quantifying Strong-lens Scaling Relations with 177 Cluster-scale Strong Gravitational Lenses in DECaLS by Simon D. Mork, Michael D. Gladders, Gourav Khullar, Keren Sharon, Nathalie Chicoine, Aidan P. Cloonan, Håkon Dahle, Diego Garza, Rowen Glusman, Katya Gozman, Gabriela Horwath, Benjamin C. Levine, Olina Liang, Daniel Mahronic, Viraj Manwadkar, Michael N. Martinez, Alexandra Masegian, Owen S. Matthews Acuña, Kaiya Merz, Yue Pan, Jorge A. Sanchez, Isaac Sierra, Daniel J. Kavin Stein, Ezra Sukay, Marcos Tamargo-Arizmendi, Kiyan Tavangar, Ruoyang Tu, Grace Wagner, Erik A. Zaborowski, Yunchong Zhang, (COOL-LAMPS Collaboration)

“…Additionally, the correlations described here should have utility in ranking strong-lensing candidates in upcoming imaging surveys—such as Rubin/Legacy Survey of Space and Time—in which an algorithmic treatment of strong lenses will be needed due to the sheer volume of data these surveys will produce.…”
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Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning... by Feiyang Xia, Tingting Fan, Mengjie Wang, Lu Yang, Da Ding, Jing Wei, Yan Zhou, Dengdeng Jiang, Shaopo Deng

“…Advanced machine learning (ML) algorithms help identify key microbial indicators for different pollution types (CAHs, BTEX plumes, and mixed plumes). …”
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Event-Based Slip Estimation Framework for Space Rovers Traversing Soft Terrains by Ruqayya Alhammadi, Yahya Zweiri, Ahmad Abubakar, Mubarak Yakubu, Laith Abuassi, Lakmal Seneviratne

“…Precise estimation of this slip is important for rover navigation algorithms, as it helps to prevent rovers from traversing areas with excessive slippage, thereby avoiding entrapment —an outcome that could lead to mission failure. …”
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Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects by Soheil Mohseni, Alan C. Brent

“…In this intricate landscape, the interdependence of optimisation variables that underlie sustainable energy system planning and optimisation processes underscores the critical role of advanced computational models, notably optimisation algorithms and forecasting techniques, as well as innovative business models tailored to renewable energy in the context of diverse distributed energy options. …”
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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. …”
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Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context by Xueting Jin, Fangwu Wei, Srinivasa Srivatsav Kandala, Tejas Umesh, Kayleigh Steele, John N. Galgiani, Manfred D. Laubichler

“…Interpretation: LSTM algorithms, combined with traditional statistical methods, could help with the forecasting of CM cases. …”
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G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images by Seungyoo Lee, Kyujin Han, Hangyeul Shin, Harin Park, Seunghyon Kim, Jeonghun Kim, Xiaopeng Yang, Jae Do Yang, Hee Chul Yu, Heecheon You

“…The proposed method was evaluated on three public datasets, the Liver Tumor Segmentation (LiTS) dataset, the 3D Image Reconstruction for Comparison of Algorithms Database (3D-IRCADb), and the Segmentation of the Liver Competition 2007 (Sliver07) dataset, and achieved 97.38%, 97.50%, and 97.32% in terms of the dice similarity coefficient for liver segmentation on the three datasets, respectively. …”
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Recent Developments of Directional Overcurrent Relay Coordination in Ring Distribution Network Based on Hybrid Optimization Techniques by Annas Alamshah, Suzana Pil Ramli, Nurulafiqah Nadzirah Mansor, Muhamad Usama, Hazlie Mokhlis, Hadi Suyono, Rini Nur Hasanah

“…The paper delves into Nature-Inspired Algorithms (NIAs), metaheuristic approaches, mathematical formulations, and artificial intelligence methods, critically assessing their capabilities in navigating the complex landscape of relay coordination. …”
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FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection by Vinay Gautam, Gaganpreet Kaur, Meena Malik, Ankush Pawar, Akansha Singh, Krishna Kant Singh, S. S. Askar, Mohamed Abouhawwash

“…This limitation arises because these algorithms are trained on publicly available centralized datasets and do not prioritize privacy and security considerations. …”
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Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning by Meiyue Shen, Yine Zhang, Runqing Zhan, Tingwei Du, Peixuan Shen, Xiaochuan Lu, Shengnan Liu, Rongrong Guo, Xiaoli Shen

“…Subsequently, six machine learning models were constructed, including random forest, decision tree, gradient boosting decision tree, k-nearest neighbor, support vector machine, and AdaBoost algorithms. Feature importance analysis, partial dependence plot, and shapley additive explanations were integrated to enhance the interpretability of the CVD prediction model. …”
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Modern advancements of energy storage systems integrated with hybrid renewable energy sources for water pumping application by Marwa M. Ahmed, Haneen M. Bawayan, Mohamed A. Enany, Mahmoud M. Elymany, Ahmed A. Shaier

“…The study concludes by identifying gaps in existing research and proposing future directions, such as integrating hydrogen generation, advanced AI algorithms, and innovative energy storage techniques to further enhance the feasibility and impact of HREWPS.…”
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Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning by Nanding Li, Naoshi Kondo, Yuichi Ogawa, Keiichiro Shiraga, Mizuki Shibasaki, Daniele Pinna, Moriyuki Fukushima, Shinichi Nagaoka, Tateshi Fujiura, Xuehong De, Tetsuhito Suzuki

“…This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. …”
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Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges by Ruhul Amin Khalil, Ziad Safelnasr, Naod Yemane, Mebruk Kedir, Atawulrahman Shafiqurrahman, NASIR SAEED

“…In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. …”
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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
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