Three-Dimensional Shape Reconstruction from Digital Freehand Design Sketching Based on Deep Learning Techniques by Ding Zhou, Guohua Wei, Xiaojun Yuan

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Supervised autoencoder MLP for financial time series forecasting by Bartosz Bieganowski, Robert Ślepaczuk

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Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study by Yunsheng Ma, Yanan Cheng, Dapeng Zhang

“…This study compares a multi-scale fusion-enhanced physical model and deep learning algorithms to optimize intelligent processing. The physical model, based on the Jaffe–McGlamery model, integrates multi-scale histogram equalization, wavelength compensation, and Laplacian sharpening, using cluster analysis to target enhancements. …”
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A Consensus-Driven Distributed Moving Horizon Estimation Approach for Target Detection Within Unmanned Aerial Vehicle Formations in Rescue Operations by Salvatore Rosario Bassolillo, Egidio D’Amato, Immacolata Notaro

“…It provides a fully distributed architecture that enables UAVs to estimate the position and velocity of other fleet members while simultaneously detecting static and dynamic targets. …”
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The optimum sizing of zero-emission water-cooled VCR cycle based on exergo-economic-environmental assessment criteria by triple-objective MPSO by Seyedeh Mohadeseh Miri, Mahmood Farzaneh-Gord, Alireza Hosseinpour, Mohit Bajaj, Ievgen Zaitsev

“…By the help of databank of parametric analysis, the optimum design variables are determined by employing the GA optimization algorithm. In the following, an intelligence neural network is developed to learn the reliable correlation between the inputs and outputs data. …”
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A Practical Performance Benchmark of Post-Quantum Cryptography Across Heterogeneous Computing Environments by Maryam Abbasi, Filipe Cardoso, Paulo Váz, José Silva, Pedro Martins

“…National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. …”
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Black-box and white-box machine learning tools to estimate the frost formation condition during cryogenic CO2 capture from natural gas blends by Farag M.A. Altalbawy, Fadhel F. Sead, Dharmesh Sur, Anupam Yadav, José Gerardo León Chimbolema, Suhas Ballal, Abhayveer Singh, Anita Devi, Kamal Kant Joshi, Nizomiddin Juraev, Hossein Mahabadi Asl

“…Precise estimation of the CO2 frost formation temperature (FFT) is vital for optimizing CCC processes and maximizing efficiency. …”
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Machine Learning in the National Economy by Azamjon A. Usmonov

“…The practical part of the study included the development of machine learning algorithms for predicting economic indexes. A linear regression model was used to forecast GDP growth, while more complex models, such as random forests and gradient boosting, were applied to analyze more intricate economic relationships. …”
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SHAPING THE STOCK PORTFOLIO BY THE INVESTMENT RATING METHOD by Svyatoslav N. Digo, Aleksandra M. Sokolova

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Joint user plane function instance and base station scheduling in mobile networks by Seokwon Jang, Namseok Ko, Jaewook Lee, Yeunwoong Kyung, Haneul Ko

“…Evaluation results demonstrate that J-UBSA can substantially reduce the OPEX while guaranteeing a suitable average data rate for mobile devices.…”
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LEO satellite constellation design with multi-QoS guarantee for non-terrestrial network by Ruyan WANG, Xianyi YE, Peng HE, Yaping CUI, Dapeng WU, Fedotov Alexander

“…LEO satellite constellation (LSC) offers seamless and fast connectivity for non-terrestrial network (NTN).However, the lack of QoS guarantees for users significantly impacts the performance of the NTN system.Considering multi-QoS metrics, a capacity and downlink budget model for LSC was established, and subsequently, the establishment of inter-satellite links was framed as a link budget issue for the QoS guarantee.The non-dominated sorting genetic algorithm (INSGA-II) with improved crossover and mutation operators was proposed to optimize LSC, aiming to maximize coverage and system capacity while minimizing constellation costs.The numerical results demonstrate that the designed LSC exhibits comparable or superior performance to Telesat and Kepler, and its scale is only 64% of the Kepler system.…”
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A Multi-Objective Gray Consistency Correction Method for Mosaicking Regional SAR Intensity Images with Brightness Anomalies by Jiaying Wang, Xin Shen, Deren Li, Litao Li, Yonghua Jiang, Jun Pan, Zezhong Lu, Wei Yao

“…To synchronously solve the truncation values of brightness anomaly images and linear stretch parameters of all images, a hybrid framework that combines the non-dominated sorting genetic algorithm II (NSGA-II) with the quadratic programming (QP) algorithm was proposed. …”
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Objective Functions for the Blade Shape Optimisation of a Cross-Flow Tidal Turbine under Constraints by Karla Ruiz-Hussmann, Pierre-Luc Delafin, Cyrille Bonamy, Yves Delannoy, Dominique Thévenin, Stefan Hoerner

“…The optimization procedure couples Computational Fluid Dynamics (CFD) with a Genetic Algorithm. …”
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Pre-disaster evacuation transport network design under uncertain demand and connectivity reliability: a novel bi-level programming model by Junxiang Xu, Divya Jayakumar Nair, S. Travis Waller

“…For the solution of this model, an Improved Genetic Algorithm combined with Non-dominated Sorting Genetic Algorithm II(IGA-NSGA-II) is designed. …”
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A State-of-the-Art Survey on Advanced Electromagnetic Design: A Machine-Learning Perspective by Masoud Salmani Arani, Reza Shahidi, Lihong Zhang

“…Limitations of conventional optimization methods are discussed, while the need for novel machine learning techniques capable of handling multiple objectives and large design spaces is highlighted. …”
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Mixed Time/Event-Triggered Model Predictive Tracking Control for Networked Mobile Robots by Huixin Liu, Yonghua Lai, Hongsong Lian, Guobin Wang, Dongsheng Zheng

“…The method integrates time-triggered and event-triggered mechanisms, where the time-triggered module improves the performance of the MPC, and the event-triggered module reduces the resource consumption without sacrificing the control performance. The MPC algorithm is developed based on the auxiliary optimization problem (OP), which is constrained by linear matrix inequalities. …”
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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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Study on intelligent height adjustment control method of shearer based on coal-rock interface recognition by Songyong LIU, Cheng CHENG, Hongzhuang WU, Yuming CUI, Deyuan MENG, Lei SI

“…Considering the specific requirements of coal mining process and equipment constraints, the cutting trajectory is optimized using a constrained optimization algorithm with the maximum recovery rate as the objective. …”
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Learning Rate Tuner with Relative Adaptation (LRT-RA): Road to Sustainable Computing by Saptarshi Biswas, Sumagna Dey, Subhrapratim Nath

“…The proposed method achieves 84.96% accuracy on the CIFAR-10 dataset while reducing the power usage to 0.07 kWh, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula> emissions to 0.05, and both <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><msub><mi>O</mi><mi>x</mi></msub></mrow></semantics></math></inline-formula> emissions to 0.00003 pounds, during the whole training and testing process.…”
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Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models by Nikos Themelis, George Nikolaidis, Vasilios Zagkas

“…The power prediction models are data-driven based on machine learning algorithms. The process includes feature engineering, filtering, and data smoothing, while an evaluation of regression algorithms of the decision tree family is performed. …”
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