Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning by Payam Fatehi Karjou, Fabian Stupperich, Phillip Stoffel, Drk Müller

“…They use sensors and algorithms to learn user behavior and optimize heating schedules accordingly. …”
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Raising awareness of potential biases in medical machine learning: Experience from a Datathon. by Harry Hochheiser, Jesse Klug, Thomas Mathie, Tom J Pollard, Jesse D Raffa, Stephanie L Ballard, Evamarie A Conrad, Smitha Edakalavan, Allan Joseph, Nader Alnomasy, Sarah Nutman, Veronika Hill, Sumit Kapoor, Eddie Pérez Claudio, Olga V Kravchenko, Ruoting Li, Mehdi Nourelahi, Jenny Diaz, W Michael Taylor, Sydney R Rooney, Maeve Woeltje, Leo Anthony Celi, Christopher M Horvat

“…<h4>Objective</h4>To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.…”
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Structural Magnetic Resonance Imaging Brain Age Investigation in Athletes with Persistent Postconcussion Syndrome by Samuel Guay, Camille Charlebois-Plante, Sophie-Andrée Vinet, Marie-Eve Bourassa, Louis De Beaumont

“…Brain age prediction algorithms using structural magnetic resonance imaging (MRI) estimate the biological age of the brain by comparing it to a normal aging trajectory, allowing for the identification of deviations that may indicate slower or accelerated biological aging. …”
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The Role of Intelligent Transport Systems and Smart Technologies in Urban Traffic Management in Polish Smart Cities by Ewa Puzio, Wojciech Drożdż, Maciej Kolon

“…The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. …”
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Workload Forecasting Methods in Cloud Environments: An Overview by Samah Aziz, Manar Kashmoola

“…In this study, we discuss some methods of predicting workload in cloud environments. This study presents an overview of workload prediction techniques in cloud systems, with a special emphasis on long short-term memory (LSTM) networks. …”
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determination of dust using remote sensing techniques and numerical simulation by Razieh Pilehvaran, Zahra Rastgu, Sara Karami, Behrooz Moradpour

“…In the second step, dust source detection has been carried out by the WRF/Chem simulation of the dust concentration and tracking the dust path using the HYSPLIT Lagrangian model.Results and discussion: The results of the simultaneous study of dust detection using satellite imagery, the simulation of dust concentration using the WRF-Chem coupled model, and the particle movement pattern with the HYSPLIT model showed that East of Syria and northern Iraq in the case studies of the warm season and the west and center of Iraq in the case studies of the cold season are the main sources of dust episodes in the west and southwest of Iran.Conclusion: Studying the source and transport of dust with the aid of WRF-Chem simulations and the HYSPLIT Lagrangian model provided a complementary decision making to predict the direction of dust motions and using the results in the air quality prediction and management.…”
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Explainable few-shot learning workflow for detecting invasive and exotic tree species by Caroline M. Gevaert, Alexandra Aguiar Pedro, Ou Ku, Hao Cheng, Pranav Chandramouli, Farzaneh Dadrass Javan, Francesco Nattino, Sonja Georgievska

“…While few-shot learning algorithms can address the first problem, they still lack sufficient explanations for the results. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What&#x2019;s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…As a result, the efficient allocation of public security resources based on spatio-temporal crime prediction models has become a critical concern for urban management. …”
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On the strength and failure mechanism of confined pre-holed jointed rock mass via DIC by Wei Wang, Wei Wang, Yundong Bi, Shuo Yang, Shuo Yang

“…Currently, it is difficult to reasonably predict localized deformation of jointed rock mass with existing theory. …”
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On Complexities of Impact Simulation of Fiber Reinforced Polymer Composites: A Simplified Modeling Framework by M. Alemi-Ardakani, A. S. Milani, S. Yannacopoulos

“…Different numerical algorithms have been developed over the past decades for impact modeling of composites, yet a considerable gap often exists between predicted and experimental observations. …”
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Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review by P. Ayesha Barvin, T. Sampradeepraj

“…We utilize supervised learning to structure input information as nodes in a graph with edges reflecting plausible feature relationships to predict the optimal crop based on environmental conditions. …”
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Daily runoff forecasting using novel optimized machine learning methods by Peiman Parisouj, Changhyun Jun, Sayed M. Bateni, Essam Heggy, Shahab S. Band

“…This study advances hydrological forecasting by validating the efficacy of ARO-ML models, offering a robust framework for enhanced predictive accuracy and providing deeper insights into the key drivers of streamflow in diverse hydrological contexts.…”
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PREACT-digital: study protocol for a longitudinal, observational multicentre study on digital phenotypes of non-response to cognitive behavioural therapy for internalising disorder... by Christine Knaevelsrud, Sebastian Burchert, Till Langhammer, Leona Hammelrath, Annette Brose, Manuel Heinrich, Pavle Zagorscak

“…Primary outcomes on (non)-response are assessed after 20 therapy sessions and therapy end. We employ predictive and exploratory analyses. Predictive analyses focus on classification of non-response using basic algorithms (ie, logistic regression and gradient boosting) for straightforward interpretability and advanced methods (LSTM, DSEM) to capture complex temporal and hierarchical patterns. …”
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Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs by Yu Li, Huiqing Liu, Peng Jiao, Qing Wang, Dong Liu, Liangyu Ma, Zhipeng Wang, Hao Peng

“…In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. …”
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Variant Gradient Projection Methods for the Minimization Problems by Yonghong Yao, Yeong-Cheng Liou, Ching-Feng Wen

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Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model by Changjiang Liang, Changjiang Liang, Dandan Liu, Dandan Liu, Weiyi Ge, Weiyi Ge, Wenzhong Huang, Wenzhong Huang, Yubin Lan, Yubin Lan, Yubin Lan, Yongbing Long, Yongbing Long, Yongbing Long, Yongbing Long

“…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…”
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A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof... by José Pablo Martínez Barbero, Francisco Javier Pérez García, David López Cornejo, Marta García Cerezo, Paula María Jiménez Gutiérrez, Luis Balderas, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez, Antonio Jesús Láinez Ramos-Bossini

“…The precision values were 72%, 74%, and 78% for the three models, respectively, while the accuracy was 63%, 70%, and 71%. Key predictive variables included radiomics features like wavelet-HHH_firstorder_Mean and mean normalized TIC values. …”
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Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils by Mao Zhenxuan, Chen Kun, Liu Qiang, Xu Mengjiao

“…In this study, the BP neural network algorithm was applied to build a prediction model of plant productivity in salt-affected soils improved by biochar, and the internal mechanism of biochar application affecting plant growth in salt-affected soils was deeply revealed. …”
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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks by Shayla Islam, S.M. Topazal, Mohammad Kamrul Hasan, Raenu AL Kolandaisamy, Huda Saleh Abbas, Nguyen Vo, Rahul Thakkar, Muhammad Attique Khan, Deepak Gupta, Dina Abdulaziz AlHammadi

“…The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. …”
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Augmenting brain-computer interfaces with ART: An artifact removal transformer for reconstructing multichannel EEG signals by Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas

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