Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…Keyword trends show traditional deep learning techniques like LSTMs and CNNs are widely used in power systems, while newer methods like advanced reinforcement learning, graph neural networks, and physics-informed neural networks are emerging, promising advancements in optimal power flow, V2G integrations, and grid resilience. …”
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Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients by Jianan Zheng, Haoran Lin, Wei Ye, Mingjun Du, Chenjun Huang, Jun Fan

“…Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”
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Design of a controller with an intelligent hybrid structure by Botirov Tulkin, Sodiqov Baxtiyor, Jumabaev Elbek, Salahiddinov Aziz, Jurayeva Ruxsora

“…Various optimization techniques, such as metaheuristic algorithms, are employed to fine-tune the controller parameters for superior performance. …”
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Artificial intelligence applications in pediatric ophthalmology: A comprehensive review by Anupama Janardhanan, Meenakshi Ravindran, Allapitchai Fathima, Neelam Pawar

“…The future of AI in pediatric ophthalmology appears bright, with ongoing research aimed at optimizing algorithms, personalizing treatment, and expanding telemedicine capabilities. …”
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Pertinence of contact duration as edge feature for epidemic spread analysis by Ramya D. Shetty, Shrutilipi Bhattacharjee

“…Existing studies consider the edges mostly equally while designing the algorithms for the unweighted contact networks, where each connection explicitly shows whether the individuals are in contact or not. …”
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HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning. by Ebrahim Parcham, Mansoor Fateh, Vahid Abolghasemi

“…This paper introduces HybridBranchNetV2, an optimized hybrid architecture designed to address these challenges. …”
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Effective temperature in approximate quantum many-body states by Yu-Qin Chen, Shi-Xin Zhang

“…Our findings reveal a consistent exponential pattern in the energy eigenbasis decomposition contributions of approximate states across different ansatzes, optimization objectives, and quantum systems, characterized by remarkably small decay rates, i.e., high effective temperatures. …”
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Lightweight image super-resolution network based on muti-domain information enhancement by KOU Qiqi, LIU Gui, JIANG He, CHEN Liangliang, CHENG Deqiang

“…Extensive experimental comparisons and analyses with the existing advanced algorithms on the recognized benchmark datasets demonstrate that the proposed network model can achieve remarkable reconstruction results while enjoying a high trade-off between performance and efficiency.…”
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Revisiting universal extra-dimension model with gravity mediated decays by Kirtiman Ghosh, Katri Huitu, Rameswar Sahu

“…Recognizing that current LHC search strategies are tailored for supersymmetric scenarios and may not fully capture the distinct signatures, we propose optimized strategies using machine learning algorithms to tag boosted SM bosons and enhance signal discrimination against SM backgrounds. …”
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Optimising 3D-printed carbon fibre composites using machine learning: Balancing strength and efficiency by José Humberto S. Almeida, Jr., Guilherme Ferreira Gomes

“…This study introduces a data-driven framework that integrates machine learning (ML) and genetic algorithms (GA) to optimise interlaminar strength and minimise printing time simultaneously. …”
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Machine learning approaches for predicting the link of the global trade network of liquefied natural gas. by Pei Zhao, Hao Song, Guang Ling

“…This study demonstrates that machine learning algorithms-specifically random forest and decision tree-outperform in predicting links within the global LNG trade network based on local information proximity, while the graph attention network, a deep learning model, exhibits stable optimization and effective feature learning. …”
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Evaluating Material Parameter Influence on Drapability Using VStitcher by Malin Schiller, Soraya Flohr, Jeanne Marisol Delmas, Anja Krüger, Anne-Marie Miene, Patrizia Zimmermann, Lena Wolf, Miriam Seffers, Michelle-Sophie Ruchay-Drammeh, Ulrike Reinhardt, Lilia Sabantina

“…The goal of 3D simulation in the apparel industry is highly relevant in terms of sustainability, as the realistic visualization of textile drape and the drapability of textile surfaces plays a crucial role in reducing textile waste and optimizing resource use. It is a matter not only of generating photo-realistic images in 3D garment simulations, but also of making reliable predictions about the physical behavior of textile materials in order to achieve realistic outcomes. …”
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Advancing Stability in Robot Manipulators: A Review of Recent Progress and Parameters by Shabnom Mustary, Mohammod Abul Kashem, Jannatul Mawya Sony, Nayem Hossain, Mohammad Asaduzzaman Chowdhury

“…ABSTRACT In this review, we present an overview of the current state‐of‐the‐art in robotics while emphasizing stability and control aspects with pragmatic consideration over robotic manipulators. …”
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Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems by Mi Gan, Xinyuan Li, Fadong Zhang, Zhenggang He

“…The models determine not only the facility location, initial distribution planning, roadway construction, and expansion decisions but also offer an optimal solution to the logistics network service level and truck-type selections. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms.…”
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Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…The results reveal that Gradient Boosting performs optimally on smaller datasets, while LightGBM demonstrates superior accuracy with larger datasets, showcasing the pipeline’s flexibility and scalability. …”
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A Comprehensive Survey on Machine and Deep Learning for Optical Communications by Mohammad Ali Amirabadi, S. Alireza Nezamalhosseini, Mohammad Hossein Kahaei, Lawrence R. Chen

“…Specific emphasis is placed on identifying how these algorithms enhance system performance and optimization. …”
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Treatment Strategies and Prognostic Outcomes in Acute Limb Ischemia: A Systematic Review and Meta-Analysis Comparing Thrombolytic Therapy and Open Surgical Interventions by Mircea Cătălin Coșarcă, Nicolae Alexandru Lazăr, Suzana Vasilica Șincaru, Bogdan Corneliu Bandici, Eduard Costin Argatu, Cosmin Carașca, Ráduly Gergő, Dorin Constantin Dorobanțu, Cristian Trâmbițaș, Adrian Vasile Mureșan

“…Future research should focus on refining treatment algorithms, investigating novel thrombolytic agents, and expanding the role of minimally invasive techniques to improve long-term outcomes while mitigating complications such as bleeding and reperfusion injuries.…”
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Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation by Nizam Sohail, Codi Allison, Rogawski McQuade Elizabeth, Benkeser David

“…We also describe new methods for the estimation of optimal treatment policies (OTPs), an area where interpretable, auditable treatment decision rules are often desirable. …”
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