Resource-Efficient Context-Aware Dynamical Decoupling Embedding for Arbitrary Large-Scale Quantum Algorithms by Paul Coote, Roman Dimov, Smarak Maity, Gavin S. Hartnett, Michael J. Biercuk, Yuval Baum

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Post-Quantum Cryptographic Frameworks for Internet of Things (IoT) and Internet of Medical Things (IoMT) Authentication Systems by Thomas L Barna, Samson Isaac, Christopher Habu, Saratu Habu, Abimbola A Joseph

“…This work contributes three key advancements: (1) an optimized NTRU implementation for medical IoT devices, (2) a novel integration of metaheuristic optimization with post-quantum cryptography, and (3) comprehensive validation across IoMT device classes. …”
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An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement by Teng Li, Amin Rezaeipanah, ElSayed M. Tag El Din

“…MCEMS uses the bi-weighting policy to solve the model selection associated problem to improve ensemble clustering. Specifically, multiple AHC individual methods cluster the data from different aspects to form the primary clusters. …”
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Optimizing concrete strength: How nanomaterials and AI redefine mix design by Dan Huang, Guangshuai Han, Ziyang Tang

“…XGB was identified as the most effective ML algorithm for predicting compressive strength among others in this study (R2=0.974). …”
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A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion by Chao Yan, Tao Li, Yandong Gao, Shijin Li, Xiang Zhang, Xuefei Zhang, Di Zhang, Huiqin Liu

“…To solve this problem, this paper combines a deep neural network model with the traditional PhU model and proposes a novel two-stage learning-based phase unwrapping (TLPU) algorithm via multimodel fusion. The major advantages of TLPU are as follows: 1) A high-resolution U-Net (HRU-Net) model trained on a dataset constructed according to InSAR interferometric geometry is utilized for the PhU for the first time, which effectively improves the performance of the DLPU. 2) TLPU utilizes the traditional PhU method to optimize the results of DLPU, addressing the issue of weak generalization ability of a single DLPU, while improving accuracy in areas with large-gradient changes. …”
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Frequency Optimization Objective during System Prototyping on Multi-FPGA Platform by Mariem Turki, Zied Marrakchi, Habib Mehrez, Mohamed Abid

“…Using this technique, the system frequency is improved by an average of 12.8% compared to constructive routing algorithm.…”
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Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus by Xiyao Wan, Yuan Wang, Ziyi Liu, Ziyan Liu, Shuting Zhong, Xiaohua Huang

“…The radiomics model was developed based on the optimal features retained after dimensionality reduction, utilizing the extreme gradient boosting (XGBoost) algorithm. …”
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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

“…In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8 % reduction in root mean square error (RMSE). …”
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Detection of capsaicin content by near-infrared spectroscopy combined with optimal wavelengths by LÜ Xiaohan, JIANG Jinlin, YANG Jing, CHEN Jianying, CEN Haiyan, FU Hongfei, ZHOU Yifei

“…In addition, compared with the full spectra of 200 wavelengths, the number of the optimal wavelengths selected by CARS was reduced by 96%, which indicated that optimal wavelengths can be used to simplify the models and improve the operation efficiency. …”
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Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection by Yijie Zhang, Yuhang Cai

“…Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. …”
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Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis by XinPei Liu, SanXi Ai, RuiMing Yu, ChaoJi Zhang, Qi Miao

“…Machine learning models enable early prediction of post-surgical AKI, facilitating targeted perioperative optimization and risk stratification in this distinct patient group.…”
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Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm by Dawei Qiu, Chang Liu, Yuangfeng Shang, Zixu Zhao, Jinlin Shi

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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. …”
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Optimizing Feature Selection for IOT Intrusion Detection Using RFE and PSO by zahraa mehssen agheeb Alhamdawee

“…Two feature selection mechanisms, which are Particle Swarm Optimization Algorithm (PSO) and Correlation-based Feature Selection Recursive Feature Elimination (RFE) have been used to compare their performances. …”
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Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning by Lejla Banjanovic-Mehmedovic, Senad Karic, Fahrudin Mehmedovic

“…Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration) and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. …”
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Adaptive Q-Learning Grey Wolf Optimizer for UAV Path Planning by Golam Moktader Nayeem, Mingyu Fan, Golam Moktader Daiyan

“…Grey Wolf Optimization (GWO) is one of the most popular algorithms for solving such problems. …”
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Application of the joint clustering algorithm based on Gaussian kernels and differential privacy in lung cancer identification by Hang Yanping, Zheng Haixia, Yang Minmin, Wang Nan, Kong Miaomiao, Zhao Mingming

“…For the LLCS dataset, For the LLCS dataset, the DPFCM_GK demonstrates significant improvement as the privacy budget increases, especially in low-budget scenarios, where the performance gap is most pronounced (T=4.20, 8.44, 10.92, 3.95, 7.16, 8.51, P < 0.05). …”
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Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery by Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn

“…Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. …”
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Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms by Gökhan Ekinci, Harun Kemal Ozturk

“…These findings provide practical insights for optimizing wind energy forecasting models, which can improve energy trading strategies, enhance grid stability, and support informed decision making in renewable energy investments. …”
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Wireless Energy Transmission Link Optimization considering Microwave Energy Relay by Yuanming Song, Yajie Liu, Xu Yang

“…Based on the idea of microwave energy relay transmission, the paper constructs a microwave wireless transmission link planning model which considers power relay and uses the evolutionary algorithm to solve the wireless transmission link planning model with two-layer optimization. …”
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