Emergent Self‐Adaptation in an Integrated Photonic Neural Network for Backpropagation‐Free Learning by Alessio Lugnan, Samarth Aggarwal, Frank Brückerhoff‐Plückelmann, C. David Wright, Wolfram H. P. Pernice, Harish Bhaskaran, Peter Bienstman

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An algorithm for heterogeneous wireless network connections for user preferences and services by S. Dinesh Krishnan, A. Daniel, S. Ayyasamy, Balamurugan Balusamy, Shitharth Selvarajan, Taher Al-Shehari, Nasser A. Alsadhan

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Optimized Machine Learning Models for Enhanced Stock Market Predictions: A Case Study on the SSE Index by Vahid Babazadeh, Ahmad Faramarzi, Ali Rahnamaei

“…In this research, three optimizers—the Genetic algorithm, the Artificial Bee Colony, and the Aquila optimizer—were chosen to modify the parameters of the chosen model to assess how well Adaptive Boosting performed in stock price prediction. …”
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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

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A Fast and Effective MIMO Algorithm Using CLR-RNN for Hybrid MDM and WDM Optical Communication System by Danni Zhang, Zhongwei Tan, Xinyuan Ma, Shun Lu, Wenhua Ren, Fengping Yan

“…The results show that the introduction of an adaptive machine learning model in MIMO detection for WDM-MDM optical transmission systems can significantly improve the quality of the transmitted signals and achieve better performance than other MIMO detection algorithms while maintaining a faster computational speed and a lower number of parameters.…”
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Multi-dimensional constraint-based coal mining machine cutting path planning technology by Shuyang SONG, Shibo WANG, Yun WANG, Lijie WANG, Dongshuai SONG

“…The proposed method enables the cutting path planning for coal mining machines based on a three-dimensional geological model, effectively improving the coal recovery rate, reducing rock spoilage, and enhancing the adaptability of fully mechanized mining equipment in the cutting space. …”
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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

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Regularizing building outlines extracted from remote sensing images by integrating multiple algorithms by Min Yang, Renwei Zou, Tinghua Ai, Xiongfeng Yan

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Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm by Dike Fitriansyah Putra, Mohd Zaidi Jaafar, Ku Muhd Na’im Khalif, Apri Siswanto, Ichsan Lukman, Ahmad Kurniawan

“…This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. …”
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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

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Application of Machine Learning for Methanolysis of Waste Cooking Oil Using Kaolinite Geopolymer Heterogeneous Catalyst by Pascal Mwenge, Hilary Rutto, Tumisang Seodigeng

“…This work uses three machine learning techniques, response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) to optimise and model biodiesel production from waste cooking oil using process parameters such as methanol-to-oil ratio, catalyst loading, reaction temperature, and reaction time. …”
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Reinventing the Trochoidal Toolpath Pattern by Adaptive Rounding Radius Loop Adjustments for Precision and Performance in End Milling Operations by Santhakumar Jayakumar, Sathish Kannan, Poongavanam Ganeshkumar, U. Mohammed Iqbal

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Adaptive Refinement of Segmented Object Contour Based on the Brightness of Neighboring Pixels Using the Ensemble Method by Vladyslav D. Koniukhov

“…To solve such tasks, it is suggested to use segmentation with the help of machine learning, and to increase the accuracy of determining the boundaries of objects, it is necessary to use adaptive approaches. …”

Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.) by Fazilat Fakhrzad, Warqaa Muhammed ShariffAl-Sheikh, Mohammed M. Mohammed, Heidar Meftahizadeh

“…Abstract Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. …”
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An Improved Marriage in Honey-Bee Optimization Algorithm for Minimizing Earliness/Tardiness Penalties in Single-Machine Scheduling with a Restrictive Common Due Date by Pedro Palominos, Mauricio Mazo, Guillermo Fuertes, Miguel Alfaro

“…This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. …”
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Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique by Lijie Jiang, Qi Li, Huiqing Liao, Hourong Liu, Bowen Tan

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MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics by Hudhaifa Mohammed Abdulwahab, S. Ajitha, Mufeed Ahmed Naji Saif, Belal Abdullah Hezam Murshed, Fahd A. Ghanem

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The role of hybrid models in financial decision-making: Forecasting stock prices with advanced algorithms by Xiaoyi Zhu

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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. …”
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An Adaptive Framework for Collective Anomaly Detection in Key Performance Indicators From Mobile Networks by Madalena Cilinio, Thaina Saraiva, Marco Sousa, Pedro Vieira, Antonio Rodrigues

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