Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor–Recipient Matching by Nahed Alowidi, Razan Ali, Munera Sadaqah, Fatmah M. A. Naemi

“…Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. …”
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A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD by Fengfeng Bie, Xueping Ding, Qianqian Li, Yuting Zhang, Xinyue Huang

“…Initially, the critical parameters (modal number n and filter length L) of FMD are optimized using an improved genetic algorithm (IGA), and the refined FMD is employed to decompose the vibration signals from the planetary gearbox. …”
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GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model by Jie Zhao, Xu Lin, Zhengdao Yuan, Nage Du, Xiaolong Cai, Cong Yang, Jun Zhao, Yashi Xu, Lunwei Zhao

“…Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. …”
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Modern aspects of diagnosis and treatment of patients with spontaneous coronary artery dissection by Sh. Sh. Zainobidinov, D. A. Khelimsky, A. A. Baranov, A. G. Badoyan, O. V. Krestyaninov

“…The angiographic classification of SCAD, the diagnostic algorithm and the choice of optimal treatment depending on clinical manifestations are also described.…”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…Existing work in this context utilizes XAI mostly in pre-processing the data or post-analysis of the results, however, IHML incorporates XAI into the learning process in an iterative manner and improves the prediction performance of the meta-learner. …”
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A New Routing Protocol for Heterogeneous Mobile Ad Hoc Networks by Bahareh Shafaie, Marjan Kuchaki Rafsanjani

“…Homogeneous Mobile Ad hoc Networks are networks in which all nodes have the same sources and capabilities, and this is in contrast with nature of MANETs because nodes are independent and have different sources, capabilities (such as battery lifetime, bandwidth, transmission range,...) and mobility. In this paper, we improve one of proactive routing protocols named OLSR (Optimized Link State Routing Protocol) so that this protocol becomes appropriate for HMANET and do not lose its capability and scalability. …”
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AHA: Design and Evaluation of Compute-Intensive Hardware Accelerators for AMD-Xilinx Zynq SoCs Using HLS IP Flow by David Berrazueta-Mena, Byron Navas

“…We outline criteria for selecting algorithms to improve speed and resource efficiency in HLS design. …”
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MAB-Based Online Client Scheduling for Decentralized Federated Learning in the IoT by Zhenning Chen, Xinyu Zhang, Siyang Wang, Youren Wang

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Method of Diagnostics of Operation Modes of Individual Heat Supply Units, Allowing to Detect Pre-Emergency Situations at an Early Stage by Dvortsevoy A.I., Borush O.V., Khoreva V.A., Yakovina I.N.

“…This was confirmed by the "Elbow Method", which determined the optimal number, which made it possible to significantly improve the forecasting of emergency modes. …”
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Joint Nuclear Norm and ℓ_1–2-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems by Kaiwen Yu, Min Shen, Rui Wang, Yun He

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A Machine Learning Approach to Analyze Manpower Sleep Disorder by Reza Amiri

“…Moreover, a combination of machine learning and metaheuristic algorithms such as eXtreme Gradient Boosting and particle swarm optimization are used to make an accurate predictive model. …”
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Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection by Jia Xu, Han Pu, Dong Wang

“…However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…The results underscore the potential of ML models to significantly enhance prediction accuracy over a wide data range, reduce computational costs, and improve the efficiency of CCUS operations. This work demonstrates the robustness and adaptability of ML approaches for modeling complex subsurface conditions, paving the way for optimized carbon sequestration strategies.…”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. …”
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A Data-Driven Method for Determining DRASTIC Weights to Assess Groundwater Vulnerability to Nitrate: Application in the Lake Baiyangdian Watershed, North China Plain by Xianglong Hou, Liqin Peng, Yuan Zhang, Yan Zhang, Yunxia Wang, Wenzhao Feng, Hui Yang

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Errors in the diagnosis of types of diabetes mellitus: causes and prevention strategies (literature review and own research results) by K.I. Gerush, N.V. Pashkovska, O.Z. Ukrainets

“…This is due to the increasing heterogeneity of DM, blurring of the boundaries between its types, atypical disease course, the decreased diagnostic value of the essential criteria for DM types (age, presence of metabolic syndrome signs, ketosis, dependency on insulin therapy), presence of comorbid conditions, and limited availability of diagnostic tests to specify the type of diabetes. To optimize diagnosis and prevent diagnostic errors, we have developed a Telegram bot DiaType based on a multilevel algorithm for the differential diagnosis of various types of DM. …”
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A comprehensive techno-economic analysis for a PHEV-integrated microgrid system involving wind uncertainty and diverse demand side management policies by Bishwajit Dey, Laishram Khumanleima Chanu, Gulshan Sharma, Pitshou N. Bokoro

“…The research investigation employed the Differential Evolution (DE) algorithm as an optimization technique. Numerical results show that the total operating cost (TOC) of the MG system reduced from $25,575 during the base load model to $24,521 when the proposed hybrid DSM was implemented. …”
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A Feedback-Assisted Inverse Neural Network Controller for Cart-Mounted Inverted Pendulum by ManMahendra Singh Daksh, Puneet Mishra

“…Further, we have used a bio-inspired optimization algorithm, that is, particle swarm optimization (PSO), to optimize the initial weights of the INN along with the PID controller’s parameters to get an optimal control performance. …”
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Lymph Node Assessment in Endometrial Cancer: Towards Personalized Medicine by Fabien Vidal, Arash Rafii

“…Finally, the use of peroperative algorithm for risk determination could improve patient's staging with a reduction of lymphadenectomy-related morbidity.…”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. …”
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