Development and validation of a novel risk-predicted model for early sepsis-associated acute kidney injury in critically ill patients: a retrospective cohort study by Bo Li, Kun Zhang, Cong-Cong Zhao, Zi-Han Nan, Yan-Ling Yin, Li-Xia Liu, Zhen-Jie Hu

“…The least absolute shrinkage and selection operator regression method was used to screen the risk factors, and the final screened risk factors were constructed into four machine learning models to determine an optimal model. …”
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Comprehensive approach to predictive analysis and anomaly detection for road crash fatalities by Chopparapu Gowthami, S. Kavitha

“…The research offers policymakers, transportation authorities, and safety advocates practical insights by utilizing sophisticated machine-learning algorithms and integrating multiple datasets. …”
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Comparison of new secondgeneration H1 receptor blockers with some molecules; a study involving DFT, molecular docking, ADMET, biological target and activity by Velid Unsal, Erkan Oner, Reşit Yıldız, Başak Doğru Mert

“…This study demonstrated the potential of machine learning methods in understanding and discovering H1 receptor blockers. …”
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Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications by Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Siddheshwari Bankar, Sartaj Bhuvaji, Sungroul Kim, Choon-Sik Park

“…With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields, including health risk prediction. …”
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Learning and forecasting open quantum dynamics with correlated noise by Xinfang Zhang, Zhihao Wu, Gregory A. L. White, Zhongcheng Xiang, Shun Hu, Zhihui Peng, Yong Liu, Dongning Zheng, Xiang Fu, Anqi Huang, Dario Poletti, Kavan Modi, Junjie Wu, Mingtang Deng, Chu Guo

“…Here we propose a physics-inspired supervised machine learning approach to efficiently and accurately predict the functioning of quantum processors in the presence of correlated noise, which only requires data from randomized benchmarking experiments. …”
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Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks by Miao Zhang, Yiran Wan, Haiyang He, Yuanjia Hu, Changhong Zhang, Jingyuan Nie, Yanlei Wu, Kaiying Deng, Xun Lei, Xianliang Huang

“…Risk indicator screening and data preprocessing were performed first, and the weight of each indicator was calculated by gray correlation analysis to formulate a comprehensive risk value label. Then, three machine learning models, Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were used to predict the comprehensive risk values. …”
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A Comparison of Classification Algorithms for Predicting Dis-tinctive Characteristics in Fine Aroma Cocoa Flowers Using WE-KA Modeler by Daniel Tineo, Yuriko S. Murillo, Mercedes Marín, Darwin Gomez, Victor H. Taboada, Malluri Goñas, Lenin Quiñones Huatangari

“…This research provides a comprehensive overview of the use of machine learning to analyze functional traits of flowers that most influence cocoa genetic diversity. …”
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A Survey of Differential Privacy Techniques for Federated Learning by Wang Xin, Li Jiaqian, Ding Xueshuang, Zhang Haoji, Sun Lianshan

“…As a distributed machine learning technology, federated learning can effectively solve the problem of privacy security and data silos. …”
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Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment by Fatma S. Alrayes, Mohammed Maray, Asma Alshuhail, Khaled Mohamad Almustafa, Abdulbasit A. Darem, Ali M. Al-Sharafi, Shoayee Dlaim Alotaibi

“…Privacy-preserving machine learning (ML) training in the development of aggregation permits a demander to firmly train ML techniques with the delicate data of IoT collected from IoT devices. …”
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Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China by Yi Zhang, Chunxiao Cheng, Zhihui Wang, Hongxin Hai, Lulu Miao

“…Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. …”
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Development of a metabolome-based respiratory infection prognostic during COVID-19 arrival by John I. Robinson, Laura R. Marks, Andrew L. Hinton, Jane A. O'Halloran, Charles W. Goss, Peter J. Mucha, Jeffrey P. Henderson

“…We obtained LC-MS profiles in the initial cohort and used machine learning methods to define a simplified urine metabolomic signature associated with respiratory failure or death by 90 days. …”
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Urine proteomics defines an immune checkpoint-associated nephritis signature by Cassian Yee, Jamie S Lin, James P Long, Shailbala Singh, Yanlan Dong

“…Using statistical and machine learning methods, we constructed a novel urine biomarker signature—IL-5+Fas—that achieved an area under the curve of 0.94 for diagnosing ICI-AIN.By leveraging high-sensitivity proteomics, we developed a non-invasive strategy for diagnosing ICI-AIN. …”
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Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity by Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill, Jonathan T. Kane, Astrid Sanna, James S. Begley, Susan J. Prichard, Maureen C. Kennedy, Craig Bienz, Ryan D. Haugo, Annie C. Smith, Van R. Kane, C. Alina Cansler

“…Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. …”
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Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature by Mehdi Gheisari, Jana Shafi, Saeed Kosari, Samaneh Amanabadi, Saeid Mehdizadeh, Christian Fernandez Campusano, Hemn Barzan Abdalla

“…These models integrate ensemble empirical mode decomposition (EEMD) with machine learning techniques for forecasting WT across multiple time horizons (one, three, and five days). …”
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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data by Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

“…Recent advances have highlighted the effectiveness and ability of Machine Learning (ML) models in analyzing Remote Sensing (RS) data for this purpose. …”
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Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information by Zhenkang Wang, Nan Xia, Song Hua, Jiale Liang, Xiankai Ji, Ziyu Wang, Jiechen Wang

“…The spectral, textural, and structural features were extracted from Google RSI by machine-learning classifiers for each segmented block. …”
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Explainable AI for Healthcare 5.0: Opportunities and Challenges by Deepti Saraswat, Pronaya Bhattacharya, Ashwin Verma, Vivek Kumar Prasad, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro, Ravi Sharma

“…The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. …”
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Decision support systems for waste-to-energy technologies: A systematic literature review of methods and future directions for sustainable implementation in Ghana by Theophilus Frimpong Adu, Lena Dzifa Mensah, Mizpah Ama Dziedzorm Rockson, Francis Kemausuor

“…Future research directions identified by this study include the development of Ghana-specific DSS models, integration of real-time data collection methodologies, creation of user-friendly interfaces for local decision-makers, and exploration of emerging technologies such as blockchain and IoT or Machine learning (ML) for enhancing DSS in WtE management.…”
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A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN by S. Ambareesh, Pundalik Chavan, S. Supreeth, Rajesh Nandalike, P. Dayananda, S. Rohith

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Information Security and Artificial Intelligence–Assisted Diagnosis in an Internet of Medical Thing System (IoMTS) by Pi-Yun Chen, Yu-Cheng Cheng, Zi-Heng Zhong, Feng-Zhou Zhang, Neng-Sheng Pai, Chien-Ming Li, Chia-Hung Lin

“…For a symmetric cryptography scheme, this study proposed a key generator combining a chaotic map and Bell inequality and generating unordered numbers and unrepeated 256 secret keys in the key space. Then, a machine learning - based model was employed to train the encryptor and decryptor for both biosignals and image infosecurity. …”
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