Enhancing Security of Error Correction in Quantum Key Distribution Using Tree Parity Machine Update Rule Randomization by Bartłomiej Gdowski, Miralem Mehic, Marcin Niemiec

“…This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. …”
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Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation by Siqi Liao, Wanting Sun, Haoran Zheng, Qiyang Xu

“…In this study, we explore the application of advanced machine learning models in optimizing the dual-column distillation process for wood vinegar production, such as Random Forest algorithms. …”
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Autonomous Drone-Based Pollination Systems for Enhancing Crop Yield in Orchards Using IoT and Machine Learning Optimization by Khan Roohee, Lalnunthari Banti

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Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT. by Yasser Filahi, Omer Melih Gul, Ali Elghirani, Erkut Arican, Ismail Burak Parlak, Seifedine Kadry, Kostas Karpouzis

“…In addition, companies can make simple recommendations using machine learning on the collected data. Our creative implementation of ML algorithms extends beyond simple recommendations. …”
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Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions by Yiqun Pang, Kaiqi Zhang, Fengmei Li

“…Second, to further improve the objectivity and accuracy, an adaptive weighted multi-model decision-making strategy is proposed. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). …”
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Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study by Hussam J. Khasawneh, Waseem M. Al-Khatib, Zaid A. Ghazal, Ahmad M. Al-Hadi, Zaid M. Arabiyat, Osama Habahbeh

“…This study introduces an approach to improving the utilization of solar energy in facilities by integrating advanced machine learning (ML) techniques into solar power scheduling. …”
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Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices by Vipin Sharma, Rajeev Kumar Arya, Sandeep Kumar

“…In this paper, we have proposed a channel prediction scheme based on a deep learning (DL) algorithm possessed by parametric analysis. In deep learning, the pipeline methodology is used along with the image processing technique to predict the channel condition for optimal selection of the adaptive modulation and coding (AMC) profile. …”
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Machine learning-based construction of Immunogenic cell death-related score for improving prognosis and personalized treatment in glioma by Guoyin Li, Yukui Zhao, Yubo He, Zhaoqiang Qian, Yiwen Liu, Xiaoyan Li, Lili Li, Zhiqiang Liu

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Production monitoring and machine tracking in underground mines based on a collision avoidance system: A case study by Artur Skoczylas, Natalia Duda-Mróz, Wioletta Koperska, Paweł Stefaniak, Paweł Śliwiński

“…Consequently, the development of validation algorithms, including error correction and adaptive filtering, was imperative. …”
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A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine by Zhongliang Lv, Baoping Tang, Yi Zhou, Chuande Zhou

“…A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. …”
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A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care by Kunal Chandnani, Suryakant Tripathy, Ashutosh Krishna Parbhakar, Kshitij Takiar, Urvi Singhal, P. Sasikumar, S. Maheswari

“…Microcontrollers like Raspberry Pi and NodeMCU use intelligent machine-learning algorithms to process the collected data and trigger adaptive responses. …”
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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. …”
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A Digital Twin Framework With Meta- and Transfer Learning for Scalable Multi-Machine Modeling and Optimization in Semiconductor Manufacturing by Chin-Yi Lin, Tzu-Liang Tseng, Tsung-Han Tsai

“…This absence of a comprehensive approach impedes widespread industry adoption, given the pressing need for flexible, universal solutions that rapidly adapt to diverse machines and processes. This study introduces MOODFG-MLTL, an innovative algorithm that integrates Meta-Learning and Transfer Learning within a Multi-Objective Optimization using Deep-Feature Gaussian Processes (MOODFG) architecture. …”
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Flood-tech frontiers: smart but just? A systematic review of AI-driven urban flood adaptation and  associated governance challenges by Johannes Bhanye

“…Key limitations include the poor transferability of AI models across geographies, a lack of participatory design, and risks of algorithmic exclusion in already marginalized urban areas. …”
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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability by Abdennabi Morchid, Abdennacer Elbasri, Zahra Oughannou, Hassan Qjidaa, Rachid El Alami, Badre Bossoufi, Saleh Mobayen, Pawel Skruch

“…Discussions reveal that the integration of embedded systems with machine learning algorithms can not only improve irrigation efficiency but also contribute to the sustainability of agriculture by enabling more accurate and adaptive management of water resources. …”
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Multi-Omics Identification of <i>Fos</i> as a Central Regulator in Skeletal Muscle Adaptation to Long-Term Aerobic Exercise by Chaoyang Li, Xinyuan Zhu, Yi Yan

“…Key feature genes were screened using Lasso regression, SVM-RFE, and Random Forest machine learning algorithms, validated by RT-qPCR, and refined through PPI network analysis. …”
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Development and Prospects of National Intelligent System for Testing General Language Competencies Deployed Through Neural Network Solutions by E. M. Bazanova, A. V. Gorizontova, N. N. Gribova, T. M. Chikake, A. V. Samosyuk

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Improved RRT-Based Obstacle-Avoidance Path Planning for Dual-Arm Robots in Complex Environments by Jing Wang, Genliang Xiong, Bowen Dang, Jianli Chen, Jixian Zhang, Hui Xie

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Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage by Natalie Bordag, Katharina Jandl, Ayu Hutami Syarif, Jürgen Gindlhuber, Diana Schnoegl, Ayse Ceren Mutgan, Vasile Foris, Konrad Hoetzenecker, Panja Maria Boehm, Robab Breyer-Kohansal, Katarina Zeder, Gregor Gorkiewicz, Francesca Polverino, Slaven Crnkovic, Grazyna Kwapiszewska, Leigh Matthew Marsh

“…We performed a multilevel immunoinflammatory characterization of patients with COPD using flow cytometry, cytokine profiling, single-cell, or spatial transcriptomics in combination with machine learning algorithms. Our cross-cohort analysis demonstrated shared skewing of immune profiles in COPD lungs toward adaptive immune cells. …”
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