Artificial intelligence in the diagnosis of cerebrovascular diseases using magnetic resonance imaging: A scoping review by Yituo Wang, Zeru Zhang, Ying Peng, Silu Chen, Shuai Zhou, Jiqiang Liu, Song Gao, Guangming Zhu, Cong Han, Bing Wu

“…Although most attention is devoted to improving the performance of AI models, this scoping review provides information on the availability of algorithms, reliability of external validations, and consistency of evaluation metrics and may facilitate improved clinical applicability and acceptance.…”
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Enhancing Daylight and Energy Efficiency in Hot Climate Regions with a Perforated Shading System Using a Hybrid Approach Considering Different Case Studies by Basma Gaber, Changhong Zhan, Xueying Han, Mohamed Omar, Guanghao Li

“…A hybrid approach integrating parametric modeling, machine learning, multi-criteria decision-making (MCDM), and genetic algorithm (GA) is used to optimize the design incorporating architects’ preferences. …”
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Classification of Non-Frozen and Frozen–Thawed Pork with Adaptive Support Vector Machine and Electronic Nose by Paul Christian E. Artista, Abraham M. Mendoza, Dionis A. Padilla

“…To further enhance the model’s performance, future studies are mandated to integrate additional gas sensors, increase sample sizes, advance data preprocessing techniques, and explore different machine learning algorithms or ensemble methods.…”
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Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura by Boris Rainiero Pérez-Gutiérrez, Darío Ernesto Correal, Fredy Humberto Vera-Rivera

“…Conclusions: The data used to train the model, while appropriate, is susceptible to further improvement. …”
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Spectral analysis of rotor current of induction motor as indicator of its effectiveness by V. L. Kodkin, A. S. Anikin, A. A. Baldenkov

“…These experiments demonstrating the operation of the drive at low loads corresponding to a slip of 3%, with a load close to the nominal corresponding slip of 8-10%, convincingly demonstrated that the torque generation algorithm implemented in standard frequency inverters (for example, ATV, Schneider Electric) is not the most effective, at the same time, a constructive solution is proposed that improves the dynamics of the drive by almost half, making it close to the dynamics of permanent magnet motors. …”
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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China by Tong Zhou, Shoutian Ma, Tianyu Liu, Shuihong Yao, Shenglin Li, Yang Gao

“…Among these models, the LSTM model exhibited the most significant performance improvement and the best transferability. …”
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Machine learning-driven condition monitoring for predictive maintenance by Mahliyo Aliyeva

“…ML algorithms, including Artificial Neural Networks and Random Forest Regression, enable the proactive forecasting of impending failures by constructing data-centric thermal models tailored for power electronics modules, thus averting catastrophic malfunctions such as air outlet blockages. …”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. …”
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Most vulnerable diet and health profiles: identifying countries at risk through a three-theme clustering by Ahmet Murat Günal, Salim Yılmaz, Sedat Arslan

“…A k-means clustering algorithm was applied to classify countries into three risk-based clusters for each theme. …”
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Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning by Qingli Liu, Yongjie Ma

“…Finally, the global model parameters are fed back to the vehicles to further optimize the local resource allocation strategy, thus improving the system spectrum efficiency. …”
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A Bioinert Hydrogel Framework for Precision 3D Cell Cultures: Advancing Automated High‐Content and High‐Throughput Drug Screening by Hyunsu Jeon, Tiago Thomaz Migliati Zanon, James Carpenter, Aliciana Ilias, Yamil Colón, Yichun Wang

“…The unique hexagonal‐close‐packed geometry of iCC structure enables HCHTS through conventional plate reader analysis and fluorescent microscopy assisted by house‐developed automated data processing algorithm. This advancement offers promising applications in tissue engineering, disease modeling, and drug development in biomedical research.…”
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Self-Organizing TDMA: A Distributed Contention-Resolution MAC Protocol by Mahsa Derakhshani, Yahya Khan, Duc Tuong Nguyen, Saeedeh Parsaeefard, Atoosa Dalili Shoaei, Tho Le-Ngoc

“…The process of adjusting the transmission duration is modeled as a congestion control problem to develop an additive-increase-multiplicative-decrease (AIMD) algorithm, which monotonically converges to fairness. …”
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An in-depth exploration of supervised and semi-supervised learning on face recognition by Purnawansyah, Wibawa Aji Prasetya, Widiyaningtyas Triyanna, Haviluddin, Darwis Herdianti, Azis Huzain

“…Experimental outcomes reveal that the performance of models depends on the dataset partitioning strategies and the type of algorithms used. …”
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Revealing High‐Temporal‐Resolution Flood Evolution With Low Latency Using GRACE Follow‐On Ranging Data by Hao‐si Li, Shuang Yi, Zi‐ren Luo, Peng Xu

“…To address this problem, this study develops an improved algorithm accounting for peripheral signal sources and temporal correlations in mass variation. …”
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Optimization of MXene-based aqueous ionic liquids for solar systems using conventional and AI-based techniques by Mohamed Bechir Ben Hamida, Ali B. M. Ali, Narinderjit Singh Sawaran Singh, Loghman Mostafa

“…Response surface methodology (RSM) is used for predictive modeling, while enhanced hill climbing (EHC), non-dominated sorting genetic algorithm II (NSGA-II), and the multi-objective generalized normal distribution optimizer (MOGNDO) are applied for multi-objective optimization. …”
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Degree-Constrained k-Minimum Spanning Tree Problem by Pablo Adasme, Ali Dehghan Firoozabadi

“…Our numerical results indicate that the proposed models and algorithms allow obtaining optimal and near-optimal solutions, respectively. …”
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Utility of Domain Adaptation for Biomass Yield Forecasting by Jonathan M. Vance, Bryan Smith, Abhishek Cherukuru, Khaled Rasheed, Ali Missaoui, John A. Miller, Frederick Maier, Hamid Arabnia

“…The novel scale-invariant tabular synthesizer (SITS) is proposed, and it is competitive with or superior to other established synthesizers in producing data that trains strong models. This synthesis algorithm leads to R scores over 100% higher than an established synthesizer in this domain, while ML-based forecasters beat the ARIMA family with symmetric mean absolute percent error (sMAPE) scores as low as 12.81%. …”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…Objective Detection of early osteoarthritis (EOA) of the knee is crucial for effective management and improved outcomes. This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict EOA. (1) To identify distinct groups based on SLS kinematic patterns using unsupervised learning algorithms, (2) to develop supervised learning models to predict EOA status, and (3) to identify the most influential kinematic variables associated with EOA. …”
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Development of PYMUS+ Code for Quantitative Evaluation of Nuclear Material Accounting (NMA) System by Byung Hee Won, Hee-Sung Shin, Se Hwan Park, Seong-Kyu Ahn

“…It is also important to quantitatively evaluate the performance of NMA system including NRTA from the standpoints of Safeguards and Security by Design (SSBD) prior to construction of nuclear-material-handling facilities. Such evaluation improves safeguards effectiveness and efficiency. Modeling and Simulation (M&S) work is a good way to evaluate performance for various NMA systems and to determine the optimal one among different options. …”
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Hybrid active-passive thermal management system for deep wells: Long-term stability and multi-objective optimization by Zongwei Yao, Haomin Li, Hongxu Xu, Hongfei Yang, Qiushi Bi, Tianshuang Xu

“…A multi-objective optimization framework combining Kriging surrogate modeling and NSGA-II algorithm is developed to maximize heat transfer coefficient (h) while minimizing pressure drop (P). …”
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