Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models by Duo Xu, Jenna Karcheski, Chi-Yan Law, Ye Zhu, Chia-Jung Hsu, Jonathan C. Tan

“…Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds, remains a significant challenge. We present a machine learning approach using denoising diffusion probabilistic models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, orientation angles of the dust continuum polarization vector, and line-of-sight (LOS) nonthermal velocity dispersion. …”
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Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors by Laetitia Lebrun, Nathalie Gilis, Manon Dausort, Chloé Gillard, Stefan Rusu, Karim Slimani, Olivier De Witte, Fabienne Escande, Florence Lefranc, Nicky D’Haene, Claude Alain Maurage, Isabelle Salmon

“…DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. …”
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Intelligent Electrochemical Sensing: A New Frontier in On-the-Fly Coffee Quality Assessment by Simone Grasso, Maria Vittoria Di Loreto, Alessandro Zompanti, Davide Ciarrocchi, Laura De Gara, Giorgio Pennazza, Luca Vollero, Marco Santonico

“…A specific experimental setup has been designed, and the data has been analyzed using machine learning techniques. The results obtained from Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) show the sensor’s capability to distinguish between samples of different quality, with a percentage of correct classification of 86.6%. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. …”
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Maximum carbon uptake potential through progressive management of plantation forests in Guangdong Province, China by Xueyan Li, Chongyuan Bi, Jianping Wu, Chaoqun Zhang, Wenting Yan, Zhenzhen Xiao, Ying-Ping Wang, Jiashun Ren, Yongxian Su

“…Here we employed fine-resolution satellite data, forest growth models, and machine learning to identify key drivers of carbon accumulation. …”
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Discrimination between the facial gestures of vocalising and non-vocalising lemurs and small apes using deep learning by Filippo Carugati, Olivier Friard, Elisa Protopapa, Camilla Mancassola, Emanuela Rabajoli, Chiara De Gregorio, Daria Valente, Valeria Ferrario, Walter Cristiano, Teresa Raimondi, Valeria Torti, Brice Lefaux, Longondraza Miaretsoa, Cristina Giacoma, Marco Gamba

“…We extracted and labelled frames of different primate species, trained deep-learning models to identify key points on their faces, and computed distances between them to identify facial gestures. We used machine learning algorithms to classify vocalised and non-vocalised gestures across different species. …”
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Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study by Fucun Liu, Juelan Ye, Shouli Wang, Yang Li, Yuhang Yang, Jianru Xiao, Aimin Jiang, Xuhua Lu, Yunli Zhu

“…Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. …”
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A QUALITATIVE ANALYSIS OF THE INFLUENCE OF MANAGEMENT INFORMATION SYSTEMS ON ORGANIZATIONAL DECISION-MAKING PROCESSES by Mubashshir Bin Mahbub, Mushfiq Nabil, Taskin Ahmed

“…Looking toward the future, participants expressed optimism about the potential of advanced technologies, such as artificial intelligence and machine learning, to enhance the capabilities of MIS and further refine decision-making processes. …”
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A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China by Yangda Chen, Aiqun Bao, Yapeng Li, Yingfeng Xiang, Wanlong Cai, Zhaoqiang Xia, Jialei Li, Mingyang Ning, Jing Sun, Haixi Zhang, Xianpeng Sun, Xiaoming Wei

“…To overcome the challenges concerning heterogeneity, redundancy, and interdependence among different data sources, this paper proposed a novel energy consumption method that integrates multi-source data through feature engineering and machine learning techniques, which significantly enhances the efficiency of data utilization and improves prediction accuracy. …”
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Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. …”
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GANCE: Generative Adversarial Network Assisted Channel Estimation for Unmanned Aerial Vehicles Empowered 5G and Beyond Wireless Networks by Chirag Gupta, Ramani Kumar Das, Rabindra K. Barik, Shahazad Niwazi Qurashi, Diptendu Sinha Roy, Satyendra Singh Yadav

“…The proposed model has given an improvement of 70%, 55%, and 45% in BER performance and 40%, 30%, and 20% in OP compared to the conventional LS estimator, machine learning-based estimator, and deep learning-based estimator, respectively. …”
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Evaluation of an Interdisciplinary Educational Program to Foster Learning Health Systems: Education Evaluation by Sathana Dushyanthen, Nadia Izzati Zamri, Wendy Chapman, Daniel Capurro, Kayley Lyons

“…The course covered a number of topics including background on LHS, establishing learning communities, the design thinking process, data preparation and machine learning analysis, process modeling, clinical decision support, remote patient monitoring, evaluation, implementation, and digital transformation. …”
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Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model by Mahmoud M. Selim, Mohammed S. Assiri

“…Natural Language Processing (NLP) and machine learning (ML) techniques provide powerful tools for analysing social media text data, helping detect emotional distress and providing timely support. …”
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The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review by Ricardo Smits Serena, Florian Hinterwimmer, Rainer Burgkart, Rudiger von Eisenhart-Rothe, Daniel Rueckert

“…Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. …”
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PPARG/SPP1/CD44 signaling pathway in alveolar macrophages: Mechanisms of lipid dysregulation and therapeutic targets in idiopathic pulmonary fibrosis by Ganggang Li, Yuwei Zhang, Huanyu Jiang, Xuanyu Wu, Yanwei Hao, Yuchen Su, Yutong Zou, Wenjia Xian, Fei Wang, Quanyu Du

“…To address this, we analyzed 12 bulk RNA-seq and 2 single-cell RNA-seq datasets from the GEO database using machine learning approaches. As a result, we identified four key lipid-related genes—PPARG, SPP1, CASP3, and PECAM1—that are expressed across various cell types. …”
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Proteomic, metabolomic and lipidomic profiles in community acquired pneumonia for differentiating viral and bacterial infections by Samuel Rischke, Robert Gurke, Ann-Sophie Zielbauer, Nicole Ziegler, Lisa Hahnefeld, Michaela Köhm, Aimo Kannt, Maria JGT Vehreschild, Gerd Geisslinger, Gernot Rohde, Carla Bellinghausen, Frank Behrens, CAPNETZ Study group

“…Statistical methods, machine learning and pathway analyses were conducted investigating differences between the infection types. …”
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Alzheimer’s disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study by Juan Wang, Juan Wang, Jiamei Zhao, Xiaoling Chen, Xiaoling Chen, Bowen Yin, Xiaoli Li, Xiaoli Li, Ping Xie, Ping Xie

“…These findings highlight the complementary nature of EC and EO conditions in assessing and differentiating AD cohorts.ConclusionOur results underscore the potential of utilizing low-density EEG data from resting-state paradigms, combined with machine learning techniques, to improve the identification and classification of AD.…”
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Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers by Shujie Xu, Zhongnan Ye, Shu-Chien Hsu, Xiaoyi Liu, Chunmiao Zheng

“…This study presents a data-driven algal bloom alert model employing machine learning and data discretization techniques to solve the data issue, utilizing monthly water quality data of 12 rivers and daily weather data in Hong Kong. …”
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NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion by Xudi Pang, Xuejun He, Ying Yang, Ling Wang, Yuzhen Sun, Huiming Cao, Yong Liang

“…Evaluations of anti-noise ability indicated that NeuTox 2.0 has excellent noise resistance relative to traditional machine learning. We applied the NeuTox 2.0 model to predict the neurotoxicity of 315,790 compounds in the REACH database. …”
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Radiomorphometric Parameters in Mandibular Panoramic Radiographs of Hypothyroid Patients: A Cross-Sectional Study by Nair A, Patil V, Bhardwaj I, Singhal DK, Smriti K, Chhaparwal Y, Chhaparwal S, Prabhu D

“…Applying newer technologies like machine learning can also help us determine whether mandibular morphometric parameters can predict changes in bone mineral density in hypothyroid cases.Keywords: hypothyroidism, bone mineral density, radiomorphometric indices, panoramic radiographs, osteoporosis…”
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