Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement in Cloud Data Centers Using Deep Q-Networks and Agglomerative Clustering by Maraga Alex, Sunday O. Ojo, Fred Mzee Awuor

“…Driven by real-time input, the adaptive decision-making capacity of the algorithm allows it to dynamically react to changing data center circumstances and workloads. …”
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Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review by Tianyi Liu, Andrew J. Krentz, Zhiqiang Huo, Vasa Ćurčin

“…Recent advancements in machine learning (ML) have demonstrated substantial potential in augmenting risk stratification for primary prevention, surpassing conventional statistical models in predictive performance. …”
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Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N₂O Emissions in China by Yuan Zeng, Sujuan Chen, Yunpeng Li, Li Xiong, Cheng Liu, Muhammad Azeem, Xiaoting Jie, Mei Chen, Longjiang Zhang, Jianfei Sun

“…This study uses a global dataset of BBF field experiments to build predictive models with three machine learning algorithms for crop yields and N₂O emissions, and to assess BBFs’ potential to increase yields and mitigate emissions in China’s major crops. …”
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Building a Radionuclide Metrology Algorithm Comparison Platform (NuCodeComP): Insights from Rapid Integration with Microsoft PowerApps by Macedo Eric, Haoran Liu, Fan Zihao, Navarro Marcus, Peixoto José Guilherme, Coulon Romain

“…The platform was designed to be reliable, adaptable, and future-proof, aligning with the D-SI framework to ensure machine-readable, structured, and interoperable data. …”
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Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye... by Yasemin Ayaz Atalan, Hasan Şahin, Abdulkadir Keskin, Abdulkadir Atalan

“…For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …”
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Random forest algorithm integrated with an initial basic feasible solution in buckling analysis of a two-dimensional functionally graded porous taper beam by Ravikiran Chinthalapudi, Jagadesh Kumar Jatavallabhula, Geetha Narayanan Kannaiyan, Bridjesh Pappula, Seshibe Makgato

“…This work provides practical insights for the design and optimization of advanced graded structures where conventional models fall short, establishing a novel pathway for the integration of machine learning in structural mechanics.…”
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Research on Active–Passive Training Control Strategies for Upper Limb Rehabilitation Robot by Yongming Yang

“…However, the existing single mode has limitations in adapting to the training needs of different rehabilitation stages. …”
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Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

“…To model the association of the bone’s current health status with prevalent FXs, three prediction algorithms—extreme gradient boosting (XGB), support vector machine, and multilayer perceptron—were trained using two-dimensional dual-energy X-ray absorptiometry (2D-DXA) analysis results and subsequently benchmarked. …”
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Application of Artificial Intelligence in Tinnitus Diagnosis and Treatment: A Pilot Study by Yu Wang, Kaixiang Pan, Richard Tyler, Zhaoyi Lu, Shan Xiong, Yufei Xie, Tao Pan

“…The complexity of tinnitus features and lack of well-adapted prognostic treatments present an excellent opportunity for Artificial Intelligence (AI) and Machine Learning (ML). …”
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A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories by Hannelore Sebestyen, Daniela Elena Popescu, Rodica Doina Zmaranda

“…These percentages highlight the research community’s focus and indicate areas requiring further investigation. From leveraging machine learning and blockchain for anomaly detection and real-time threat response to optimising lightweight algorithms for resource-limited devices, researchers propose innovative and adaptive solutions to address emerging threats. …”
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A method to manage the energy consumption of cloud centers for predictability in neuro-fuzzy networks by Ying Zhang

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Mast Cells and Basophils in Major Viral Diseases: What Are the Correlations with SARS-CoV-2, Influenza A Viruses, HIV, and Dengue? by Luca Gammeri, Serena Sanfilippo, Clara Alessandrello, Sebastiano Gangemi, Paola Lucia Minciullo

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Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
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A Method for Predicting Trajectories of Concealed Targets via a Hybrid Decomposition and State Prediction Framework by Zhengpeng Yang, Jiyan Yu, Miao Liu, Tongxing Peng, Huaiyan Wang

“…To address this, we propose a state-of-the-art autonomous prediction framework that integrates an Improved Sequential Variational Mode Decomposition (ISVMD) algorithm with an Extreme Learning Machine (ELM), synergistically optimized by the novel Red-billed Blue Magpie Optimizer (RBMO). …”
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Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect by Jintong Ren, Wunian Yang, Xin Yang, Xiaoyu Deng, He Zhao, Fang Wang, Lei Wang

“…In order to evaluate the applicability of different classification algorithms to the classification, the object-oriented classification algorithm which is based on machine learning algorithm, such as KNN, SVM and Random Trees were used to classify the different GF-2 fusion images. …”
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Real defect partial discharge identification method for power cables joints based on integrated PJS-M and GA-SVM algorithm with multi-source fusion by Ling-Xuan Zhang, Yi-Yang Zhou, Shen-Jiong Yao, Jia-Luo Chai, Ying-Jing Chen, Zhou-Sheng Zhang

“…These features were used to train a novel Genetic Algorithm Weighted Support Vector Machine (GAW-SVM) model, which incorporates an adaptive PJS-M weighting coefficient and a correlation-analysis–based dynamic correction mechanism into the conventional GA-SVM framework. …”
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Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks by C. Christy, A. Nirmala, A. Mary Odilya Teena, A. Isabella Amali

“…A new method for improving VANET security, a multi-stage Lightweight IntrusionDetection System Using Random Forest Algorithms (MLIDS-RFA), focuses on feature selection and ensemble models based on machine learning (ML). …”
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Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort studyRes... by Renée A.M. Tuinte, Luuk P.J. Smolenaers, Bram T. Knoop, Konstantin Föhse, Tamar J. van der Aart, Hjalmar R. Bouma, Mihai G. Netea, Katrijn Van Deun, Jaap ten Oever, Jacobien J. Hoogerwerf

“…Methods: This multicentre cohort and machine learning study was conducted in two Dutch tertiary care hospitals. …”
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Research on dynamic allocation of network slicing resources based on OS-MBRL by YAN Jiahui, ZHONG Weixuan, DONG Ligang, JIANG Xian, WANG Guangchang, LU Lingrong

“…The online support vector machines algorithm was utilized to construct a system model that could handle dynamically changing data streams and continuously update the model to adapt to new data, ensuring a lower number of SLA violations when allocating fewer resources. …”
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Impact of massive open online courses in higher education using machine learning and decision based fuzzy frank power aggregation operators models by Feng Ye

“…Abstract Massive open online courses (MOOCs) have transformed higher education by providing widespread access to quality educational content, and the integration of machine learning (ML) has significantly enhanced their effectiveness and adaptability. …”
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