Hyperspectral Imaging for the Dynamic Mapping of Total Phenolic and Flavonoid Contents in Microgreens by Pawita Boonrat, Manish Patel, Panuwat Pengphorm, Preeyabhorn Detarun, Chalongrat Daengngam

“…This study investigates the application of hyperspectral imaging (HSI) combined with machine learning (ML) models for the dynamic mapping of total phenolic content (TPC) and total flavonoid content (TFC) in sunflower microgreens. …”
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Bridging climate science, policy, and communities: collaborative pathways for climate resilience in the Indo-Pacific by Aditi Modi, Mathew Koll Roxy, Shipra Jain, Chi Huyen Truong, Quang-Van Doan, Christopher Jack, Svetlana Jevrejeva, Arvind Singh, Chirag Dhara, Sahana Ghosh

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Recent advances in metal oxide-biochar composites for water and soil remediation: A review by Hermann Tamaguelon Dzoujo, Victor Odhiambo Shikuku, Sylvain Tome, Aurelle Clandy Ntinkam Simo, Emily C. Ng'eno, Zachary M. Getenga, Marie Annie Etoh, David Daniel Joh Dina

“…Remediation mechanisms for various adsorbates in aqueous media and soils generally include electrostatic attraction, oxidation/reduction, complexation and precipitation. Life cycle assessment (LCA), pilot-scale, cost analysis, potential environmental risks, and machine learning modelling studies are found to be lacking for metal-biochar composites and provide areas for future research.…”
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A recurrent multimodal sparse transformer framework for gastrointestinal disease classification by V. Sharmila, S. Geetha

“…Further, the model employs principal component analysis (PCA) for dimensionality reduction and gradient boosting machines (GBMs) for semantic conflict resolution. …”
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Thermal comfort and energy related occupancy behavior in Dutch residential dwellings by Anastasios Ioannou

“…The future in understanding the energy related occupancy behaviour, and therefore using it towards a more sustainable built environment, lies in the advances of sensor technology, big data gathering, and machine learning. Technology will enable us to move from big population models to tailor made solutions designed for each individual occupant.   …”
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Gut microbiome alterations and hepatic encephalopathy post-TIPS in liver cirrhosis patients by Shengpeng Li, Zhengguo Xu, Hua Diao, An Zhou, Dianji Tu, Sumin Wang, Yunxuan Feng, Xiaojie Feng, Yi Lai, Shiming Yang, Bo Tang

“…Conclusion Multiple machine learning models revealed that P.vulgatus may serve as a significant predictive microbe for hepatic encephalopathy after TIPS, which may be closely related to its ability to metabolize ammonia. …”
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Remote-Management of COPD: Evaluating the Implementation of Digital Innovation to Enable Routine Care (RECEIVER): the protocol for a feasibility and service adoption observational... by David J Lowe, Grace McDowell, Anna Taylor, Stephanie Lua, Shane Burns, Paul McGinness, Christopher M Carlin

“…The digital infrastructure will also provide a foundation to explore the feasibility of approaches to predict outcomes and exacerbation in people with COPD through machine learning analysis.Ethics and dissemination Ethical approval for this clinical trial has been obtained from the West of Scotland Research Ethics Service. …”
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Trackformers: in search of transformer-based particle tracking for the high-luminosity LHC era by Sascha Caron, Nadezhda Dobreva, Antonio Ferrer Sánchez, José D. Martín-Guerrero, Uraz Odyurt, Roberto Ruiz de Austri Bazan, Zef Wolffs, Yue Zhao

“…One such step in need of an overhaul is the task of particle track reconstruction, a.k.a., tracking. A Machine Learning-assisted solution is expected to provide significant improvements, since the most time-consuming step in tracking is the assignment of hits to particles or track candidates. …”
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Objective monitoring of motor symptom severity and their progression in Parkinson’s disease using a digital gait device by Tamara Raschka, Jackrite To, Tom Hähnel, Stefano Sapienza, Alzhraa Ibrahim, Enrico Glaab, Heiko Gaßner, Ralph Steidl, Jürgen Winkler, Jean-Christophe Corvol, Jochen Klucken, Holger Fröhlich

“…Furthermore, we employed machine learning to evaluate whether digital gait assessments were prognostic for patient-level motor symptom progression. …”
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ARTIFICIAL INTELLIGENCE AND FUTURE BUSINESS PROSPECTS': A GENERAL ANALYSIS by Anurag Hazarika, Samikshya Madhukullya, Anwesha Hazarika

“…It is anticipated that the integration of AI technologies, such as robotic process automation, natural language processing, and machine learning, would result in considerable cost reductions and productivity increases. …”
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Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

“…We analyze how preprocessing techniques affect some of the most commonly used Machine Learning (ML) algorithms, namely K-means, SVM, and KNN, emphasizing their role in reducing computational load, energy consumption, and carbon emissions in data centers. …”
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Economic Evaluation of a Novel Lung Cancer Diagnostic in a Population of Patients with a Positive Low-Dose Computed Tomography Result by Michael J. Morris, Sheila A. Habib, Maggie L. Do Valle, John E. Schneider

“… # Objectives This study evaluated the potential cost savings for US payers of CyPath® Lung, a novel diagnostic tool utilizing flow cytometry and machine learning for the early detection of lung cancer, in patients with positive LDCT scans with indeterminate pulmonary nodules (IPNs) ranging from 6 to 29 mm…”
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Screening for breast cancer: a systematic review update to inform the Canadian Task Force on Preventive Health Care guideline by Alexandria Bennett, Nicole Shaver, Niyati Vyas, Faris Almoli, Robert Pap, Andrea Douglas, Taddele Kibret, Becky Skidmore, Martin Yaffe, Anna Wilkinson, Jean M. Seely, Julian Little, David Moher

“…To expedite the screening process, machine learning was used to prioritize relevant references. …”
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Therapy response prediction of focal cortex stimulation based on clinical parameters: a multicentre, non-interventional study protocol by Andreas Schulze-Bonhage, Ann Mertens, Felix von Podewils, Martin Hirsch, Yaroslav Winter, Susanne Knake, Ekaterina Pataraia, Thomas Mayer, Yvonne G Weber, Rainer Surges, Jan Wagner, Hajo M Hamer, Tim Wehner, Christoph Baumgartner, Lukas Imbach, Bernhard J Steinhoff, Sotirios Kalousios, Jürgen Hesser, Matthias Dümpelmann, Elisabeth Kaufmann, Josua Kegele, Georg Leonhardt, Carlos M Quesada, Berthold R Voges

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Assessing the Impact of Traffic Emissions on Fine Particulate Matter and Carbon Monoxide Levels in Hanoi through COVID-19 Social Distancing Periods by Nhung H. Le, Bich-Thuy Ly, Phong K. Thai, Gia-Huy Pham, Ich-Hung Ngo, Van-Nguyet Do, Thuy T. Le, Luan V. Nhu, Ha Dang Son, Yen-Lien T. Nguyen, Duong H. Pham, Tuan V. Vu

“…To overcome this challenge, weather normalized concentrations of those pollutants were estimated using the random forest model, a machine learning technique. The normalized weather concentrations showed smaller reductions by 7–10% for PM2.5 and 5–11% for CO, indicating the presence of favorable weather conditions for better air quality during the social distancing period. …”
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The Effect of Predicted Compliance With a Web-Based Intervention for Anxiety and Depression Among Latin American University Students: Randomized Controlled Trial by Corina Benjet, Nur Hani Zainal, Yesica Albor, Libia Alvis-Barranco, Nayib Carrasco Tapia, Carlos C Contreras-Ibáñez, Jacqueline Cortés-Morelos, Lorena Cudris-Torres, Francisco R de la Peña, Noé González, Raúl A Gutierrez-Garcia, Eunice Vargas-Contreras, Maria Elena Medina-Mora, Pamela Patiño, Sarah M Gildea, Chris J Kennedy, Alex Luedtke, Nancy A Sampson, Maria V Petukhova, Jose R Zubizarreta, Pim Cuijpers, Alan E Kazdin, Ronald C Kessler

“…Subgroup analysis showed that this longer-term superiority of self-guided wb-CBT was confined to the 40% (528/1319) of participants with high predicted self-guided wb-CBT compliance beyond 3 months based on a counterfactual nested cross-validated machine learning model. The 12-month outcome differences were nonsignificant across arms among other participants (all P>.05). …”
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Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation by Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Eduardo P. Cavieres, Eduardo P. Cavieres, Eduardo P. Cavieres, Matías Salinas, Matías Salinas, Matías Salinas, Diego Mellado, Diego Mellado, Diego Mellado, Diego Mellado, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Steren Chabert, Steren Chabert, Steren Chabert, Marvin Querales, Marvin Querales, Julio Sotelo, Rodrigo Salas, Rodrigo Salas, Rodrigo Salas

“…Dimensionality reduction is performed using machine learning, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed to produce interpretable decision rules. …”
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Derivation and external validation of a risk score for predicting HIV-associated tuberculosis to support case finding and preventive therapy scale-up: A cohort study. by Andrew F Auld, Andrew D Kerkhoff, Yasmeen Hanifa, Robin Wood, Salome Charalambous, Yuliang Liu, Tefera Agizew, Anikie Mathoma, Rosanna Boyd, Anand Date, Ray W Shiraishi, George Bicego, Unami Mathebula-Modongo, Heather Alexander, Christopher Serumola, Goabaone Rankgoane-Pono, Pontsho Pono, Alyssa Finlay, James C Shepherd, Tedd V Ellerbrock, Alison D Grant, Katherine Fielding

“…<h4>Methods and findings</h4>We used Botswana XPRES trial data for adult HIV clinic enrollees collected during 2012 to 2015 to develop a parsimonious multivariable prognostic model for active prevalent TB using both logistic regression and random forest machine learning approaches. …”
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Radiomic analysis of patient and interorgan heterogeneity in response to immunotherapies and BRAF-targeted therapy in metastatic melanoma by Riyue Bao, Yana G Najjar, Diwakar Davar, John M Kirkwood, Jason John Luke, Sarah Newman, Hassane M Zarour, Afsaneh Amouzegar, Rivka R Colen, Murat Ak, Priyadarshini Mamindla, Serafettin Zenkin, Vishal Peddagangireddy, Sarah Behr, Alexandra G Tompkins, Zane N Gray, Rebekah E Dadey, Nasim Batavani, Nursima Ak, Taha Yasin Pak, Mohammadreza Amjadzadeh, Amy Goodman, Darcy L Ploucha, Curtis Tatsuoka

“…Overall and organ-specific machine-learning models were constructed to predict disease control (DC) versus progressive disease (PD) using XGBoost. 291 patients with MEL were identified, including 242 ICI (91 I+N, 151 PD-1) and 49 BRAF. 667 metastases were analyzed, including 541 ICI (236 I+N, 305 PD-1) and 126 BRAF. …”
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SIDNet: A SQL Injection Detection Network for Enhancing Cybersecurity by Debendra Muduli, Shantanu Shookdeb, Abu Taha Zamani, Surabhi Saxena, Anuradha Shantanu Kanade, Nikhat Parveen, Mohammad Shameem

“…Our comprehensive evaluation includes a comparison of the performance of these customized CNN models against traditional machine learning approaches, highlighting improvements in classification accuracy and reductions in false alarm rates. …”
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