Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…Sensitive analysis experiments show that binary classification algorithms based on SDP approaches using the DQN-based feature extraction outperform those without using it. …”
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A Review of Vehicle Dynamics and Control Approaches for Mitigating Motion Sickness in Autonomous Vehicles by Ilhan Yunus, Georgios Papaioannou, Jenny Jerrelind, Lars Drugge

“…Furthermore, the critical role of trajectory planning and tracking algorithms in mitigating MS is reviewed, emphasising their potential through optimal control and the incorporation of MS metrics into cost functions. …”
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Heat Loss Determination of District Heating Pipelines. A Comparison of Numerical and Analytical Methods by Wieland Aaron, Dollhopf Stefan, Weidlich Ingo, Langroudi Pakdad Pourbozorgi

“…With the finite element method, a high degree of detail can be achieved, but the number of input parameters for solving the algorithms increases. An emerging trend in district heating involves reducing operational temperatures in both new and existing networks. …”
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A Novel Long Short-Term Memory-Based Approach for Microgrid Fault Detection and Classification Using the Wavelet Scattering Transform by Naema M. Mansour, Abdelazeem A. Abdelsalam, Ibrahim A. Awaad

“…These limitations underscore the urgent need for intelligent, adaptive, and fast-responding fault detection and classification algorithms tailored specifically to the nature of microgrids. …”
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Efficiently Learning an Encoder That Classifies Token Replacements and Masked Permuted Network-Based BIGRU Attention Classifier for Enhancing Sentiment Classification of Scientific... by Muhammad Inaam Ul Haq, Khalid Mahmood, Qianmu Li, Ashok Kumar Das, Sachin Shetty, Majid Hussain

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Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea by Sooyeon Yi, G. Mathias Kondolf, Samuel Sandoval Solis, Larry Dale

“…Multiple machine learning algorithms—Random Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. …”
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A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems by Achin Jain, Arun Kumar Dubey, Shakir Khan, Arvind Panwar, Mohammad Alkhatib, Abdulaziz M Alshahrani

“…Compared to Ant Colony Optimization (ACO) and Firefly algorithms, which achieve accuracies of 83% and 85% respectively, our approach demonstrates up to a 3% improvement in key performance metrics. …”
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The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions by Stavros Pantelakos, Martha Nifora, Georgios Agrogiannis

“…However, no study has attempted to link a wider spectrum of potential diagnostic tasks performed by artificial intelligence algorithms to financial figures. Methods Herein, we employ a workload measurement tool for the purpose of monetizing particular outcomes associated with the implementation of a pathology artificial intelligence solution. …”
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A Review of Quantitative Characterization of Phase Interface Dynamics and Optimization of Heat Transfer Modeling in Direct Contact Heat Transfer by Mingjian Wang, Jianxin Xu, Shibo Wang, Hua Wang

“…Based on the findings above, three key directions are proposed to guide future research to inform the exploration of direct contact heat transfer mechanisms in future work: 1 dynamic analysis of multi-scale spatio-temporal coupling mechanisms, 2 accurate quantification of unsteady interfacial heat transfer processes, and 3 synergistic integration of intelligent optimization algorithms with experimental datasets.…”
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Wireless Body Area Network (WBAN) based Health Care Monitoring: A Comprehensive Review by Qaisar Ayyub

“…It is an upcoming research area in which we implement new advancement techniques, such as various algorithms. In this research, we explore how to secure IoT devices from cyber-attacks and also define how to handle the communication packets between IoT devices. …”
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Development and validation of machine learning models for predicting acute kidney injury in acute-on-chronic liver failure: a multimodel comparative study by Jing Zhang, Shuxuan Tang, Jingyuan Liu, Ang Li

“…Therefore, this study aimed to develop prediction models for AKI in ACLF patients based on machine learning (ML) algorithms.Methods This retrospective study enrolled 1,076 adult patients diagnosed with ACLF, with AKI defined according to the International Club of Ascites criteria. …”
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Real-Time Anomaly Detection in IoMT Networks Using Stacking Model and a Healthcare- Specific Dataset by Hadjer Goumidi, Samuel Pierre

“…Seven machine learning algorithms, including Random Forest, XGBoost, and Artificial Neural Networks (ANN), were rigorously tested, leading to the development of a novel stacking ensemble model. …”
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A comprehensive review of energy harvesting and routing strategies for IoT sensors sustainability and communication technology by Hesam Nejati Sharif Aldin, Mostafa Razavi Ghods, Farnoush Nayebipour, Masoud Niazi Torshiz

“…Furthermore, the study explores routing algorithms within the IoT network context, recognizing their crucial role in addressing challenges related to sensor battery lifespan and energy conservation. …”
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How does the level of functional impairment vary in individuals with non-communicable disease and comorbidity? Cross-sectional analysis of linked census and administrative data in... by Barry Milne, Nicholas Bowden, Lisa Underwood, Andrea M Teng, Hamish Jamieson

“…Nine NCDs among these individuals were identified from national health data sets using existing and adapted algorithms.Primary and secondary outcome measures Functional impairment was assessed via hospitalisations, comorbidities (Elixhauser index), activity limitations (census) and income support.Results Three-tenths (29%) of the population had at least one NCD. …”
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Peculiarities of acoustic diagnostic methods in pediatric practice (literature review, own research) by Yu.V. Marushko, O.V. Khomych

“…The conducted experimental studies demonstrated the ability of the "Trembita-Corona" device to accurately diagnose and localize lung lesions in community-acquired pneumonia, confirming the effectiveness of the developed methods and algorithms. No conflict of interests was declared by the authors.…”
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An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution by Minghong Duan, Linhao Qu, Zhiwei Yang, Manning Wang, Chenxi Zhang, Zhijian Song

“…Experiments on publicly available datasets, including TMA, HistoSR, and the TCGA lung cancer datasets, demonstrate that ISTE outperforms existing fixed-scale and arbitrary-scale SR algorithms across various scaling factors. Additionally, extensive experiments have shown that the histopathology images reconstructed by the proposed ISTE are applicable to downstream pathology image analysis tasks.…”
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Defect Detection and 3D Reconstruction of Complex Urban Underground Pipeline Scenes for Sewer Robots by Ruihao Liu, Zhongxi Shao, Qiang Sun, Zhenzhong Yu

“…Comprehensive comparisons and analyses are conducted with 12 mainstream detection algorithms to validate the superiority of the proposed model. …”
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Benchmarking miRNA reference genes in B-cell precursor acute lymphoblastic leukemia by Teresa Mack, Tommaso Gianferri, Alexandra Niedermayer, Klaus-Michael Debatin, Lüder H. Meyer, Vera Muench

“…We used four different algorithms (Normfinder, ∆CT, geNorm, and BestKeeper) to assess the most stably expressed reference across all samples. …”
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Fostemsavir resistance in clinical context: a narrative review by Jonathan M. Schapiro, Rolf Kaiser, Mark Krystal, Chris M. Parry, Allan R. Tenorio, Eugene Stewart, Bruce Gilliam, Margaret Gartland, Andrew Clark, Jose R. Castillo-Mancilla

“…Due to these factors and limited phenotypic clinical data, thus far, no relevant phenotypic cutoff or genotypic algorithms have been derived that reliably predict response to fostemsavir-based therapy in individuals who are HTE; therefore, pre-treatment temsavir resistance testing may be of limited benefit. …”
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AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation by Abdullah Ahmed Al-Dulaimi, Muhammet Tahir Guneser, Raghad Al-Shabandar, Yeonghyeon Gu, Muhammad Syafrudin, Norma Latif Fitriyani

“…An autoencoder backpropagation neural network (AE-BPNN) was developed for unsupervised processing, dimensionality reduction, feature extraction, and SOH estimation. Two optimization algorithms—Scaled Conjugate Gradient (SCG) and Resilient Backpropagation (RBP)—were utilized to tune network weights and enhance performance. …”
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