Optimizing Renewable Energy Systems Placement Through Advanced Deep Learning and Evolutionary Algorithms by Konstantinos Stergiou, Theodoros Karakasidis

“…Validation against real-world data demonstrates improved prediction accuracy using metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). …”
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TONE MAPPING METHOD AND IMAGE PROCESSING algorithms under BRIght ambient lightING by A. V. Sycev, A. A. Stepanov, A. G. Smirnov, K. .. Blankenbach

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Comparison of Different Machine Learning Algorithms to Classify Epilepsy Seizure from EEG Signals by Pankaj Kunekar, Chanchal Kumawat, Vaishnavi Lande, Sushant Lokhande, Ram Mandhana, Malhar Kshirsagar

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Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation by Oriel Kiss, Utkarsh Azad, Borja Requena, Alessandro Roggero, David Wakeham, Juan Miguel Arrazola

“…The results show that the predictions from the quantum algorithm align closely with the DMRG-converged energies at larger bond dimensions while requiring several orders of magnitude fewer samples than theoretical estimates suggest. …”
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Selection Algorithms of the Optimal Reinforcement for Strength and Crack Resistance of Reinforced Concrete Elements and Their Verification by T. M. Hlukhava

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Integrating novel stellarator single-stage optimization algorithms to design the Columbia stellarator experiment by A. Baillod, E.J. Paul, G. Rawlinson, M. Haque, S.W. Freiberger, S. Thapa

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Innovative Landslide Susceptibility Mapping Portrayed by CA-AQD and K-Means Clustering Algorithms by Mao Yimin, Li Yican, Deborah Simon Mwakapesa, Wang Genglong, Yaser Ahangari Nanehkaran, Muhammad Asim Khan, Zhang Maosheng

“…It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…First, eigenvalue screening is carried out based on the dredging knowledge and mechanism, then outliers are removed, and finally data processing is performed using Spearman correlation coefficient and PCA dimensionality reduction techniques. Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. …”
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Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms by Aleksander M. Ivanov, Artem V. Gorbarenko, Maria B. Kireeva, Elena S. Povalishnikova

“…Clustering and classification algorithms were given eight parameters as a basis for prediction. …”
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The role of hybrid models in financial decision-making: Forecasting stock prices with advanced algorithms by Xiaoyi Zhu

“…This study introduces a novel hybrid model to tackle the abovementioned issues by integrating various algorithms, including bidirectional long short-term memory and random forest. …”
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Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms by Hasan Alkahtani, Theyazn H. H. Aldhyani

“…The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. …”
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A Comparative Study of Sentiment-Aware Collaborative Filtering Algorithms for Arabic Recommendation Systems by Sumaia Mohammed Al-Ghuribi, Shahrul Azman Mohd Noah, Mawal A. Mohammed, Neeraj Tiwary, Nur Izyan Yasmin Saat

“…Secondly, using this lexicon we then propose three distinct sentiment-aware ratings, leveraging sentiment analysis of Arabic reviews to enrich traditional rating predictions. Thirdly, these sentiment-aware ratings are integrated into ten diverse CF algorithms from the Surprise library and a deep autoencoder neural network, covering a spectrum of traditional and modern approaches. …”
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Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms by Shikha Sharda, Sumit Kumar, Raj Setia, Prince Dhiman, N. R. Patel, Brijendra Pateriya, Ali Salem, Ahmed Elbeltagi

“…Recently few studies related to machine learning based wheat lodging have been reported; however, the literature still lacks comprehensive assessments of machine learning algorithms for wheat lodging over Indian agricultural fields. …”
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Innovative multi objective optimization based automatic fake news detection by Cebrail Barut, Suna Yildirim, Bilal Alatas, Gungor Yildirim

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PREDICTION OF THE USE OF REFRIGERANTS IN LOW-TEMPERATURE EQUIPMENT by J. V. Tatarenko, V. M. Mizin, N. O. Rachkovskiy

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Guided-Aloha for Secondary Access With Spectrum Prediction by Sithamparanathan Kandeepan, Madhulika Tripathi, Ke Wang, Don Gossink, Tharaka Samarasinghe, Chamath Divarathne

“…Despite the benefits of intelligent DSA protocols, many algorithms are complex, and thus prohibitively difficult to implement in a real-time system. …”
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Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach by Ishwer Shivakoti, Sunny Diyaley, Partha Protim Das, Abhijit Bhowmik, A. Johnson Santhosh

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Machine learning to predict bacteriuria in the emergency department by Johnathan M. Sheele, Ronna L. Campbell, Derick D. Jones

“…These findings suggest that machine learning algorithms could be valuable tools in clinical settings by helping predict culture results and guiding decisions on whether to initiate empiric antibiotic treatment.…”
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Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems by Tarana Khan, Urfi Khan, Adnan Khan, Calahan Mollan, Inga Morkvenaite-Vilkonciene, Vijitashwa Pandey

“…Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. …”
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Multimodal deep learning for allergenic proteins prediction by Lezheng Yu, Yuxin Luo, Shiqi Wu, Siyi Chen, Li Xue, Runyu Jing, Jiesi Luo

“…Results Here, we present Multimodal-AlgPro, a unified framework based on a multimodal deep learning algorithm designed to predict allergens by integrating multiple dimensions, including physicochemical properties, amino acid sequences, and evolutionary information. …”
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