Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Research investigations demonstrate that the ELNET-BDE model attains significantly lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than contesting Machine Learning (ML) algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). …”
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A human visual cognitive mechanism based network for surface defect detection(基于人类视觉认知机制的表面缺陷检测) by 崔丽莎(CUI Lisha), 代润鹏(DAI Runpeng), 姜晓恒(JIANG Xiaoheng), 李飞蝶(LI Feidie), 陈恩庆(CHEN Enqing), 徐明亮(XU Mingliang)

“…HVCM-Net achieves superior detection performance on defect datasets GB-DET, NEU-DET, and DAGM2007 compared to other algorithms, demonstrating the effectiveness of the proposed method.…”
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A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce regionZENODO by Rodrigo de Q. Miranda, Rodolfo L.B. Nóbrega, Anne Verhoef, Estevão L.R. da Silva, Jadson F. da Silva, José C. de Araújo Filho, Magna S.B. de Moura, Alexandre H.C. Barros, Alzira G.S.S. Souza, Wanhong Yang, Hui Shao, Raghavan Srinivasan, Feras Ziadat, Suzana M.G.L. Montenegro, Maria do S.B. Araújo, Josiclêda D. Galvíncio

“…Our approach addresses multicollinearity through a recursive feature selection algorithm. We applied this framework to a tropical region characterized by a ∼700-km longitudinal gradient of contrasting topography, climate, and vegetation (∼98,000 km²; NE Brazil), where scarce soil physicochemical data limit environmental modeling. …”
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Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions by Krishnashree Achuthan, Sasangan Ramanathan, Sethuraman Srinivas, Raghu Raman

“…Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats. …”
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Detection of Malicious Office Open Documents (OOXML) Using Large Language Models: A Static Analysis Approach by Jonas Heß , Kalman Graffi

“…Given the accelerated pace of change in the threat landscape, these methods are unable to adapt effectively to the evolving environment. Existing machine learning approaches are capable of identifying sophisticated features that enable the prediction of a file’s nature, achieving sufficient results on existing samples. …”
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Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review by Yasmin Abdelaal, Michaël Aupetit, Abdelkader Baggag, Dena Al-Thani

“…Post hoc methods such as Shapley Additive Explanations have gained traction for their adaptability in explaining complex algorithms visually. …”
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Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks by Aya M. A. Heikal, Shady H. E. Abdel Aleem, Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz

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Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review by Yuanhui Xu, Xingcheng Gan, Ji Pei, Wenjie Wang, Jia Chen, Shouqi Yuan

“…This paper provides a comprehensive review of the application of AI and CI technologies in hydraulic optimisation of centrifugal pumps, with a focus on emerging techniques such as machine learning frameworks and intelligent algorithms. …”
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Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives by M. Hamidou Harouna Omar, Kouraogo Justin P., Kabre Windmi Jonathan M., Tapsoba Stanislas David Wendkouni, Sie Oumarou Pr

“…To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. …”
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A Survey on Adversarial Attacks for Malware Analysis by Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar, Bhavani Thuraisingham

“…We systematically analyze adversarial generation algorithms from broader domains adapted to malware evasion attacks, proposing a taxonomy of adversarial evasion attacks within malware detection based on target domains(Windows, Android and PDF). …”
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A Dataset of Real and Synthetic Speech in Ukrainian by Khrystyna Lipianina-Honcharenko, Hennadii Bohuta, Adam Ivaniush, Mariana Soia

“…The dataset contains a unique collection of audio recordings that include both real and synthesized Ukrainian speech, providing unprecedented opportunities for improving machine learning algorithms aimed at speech recognition and analysis. …”
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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. …”
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Opportunities and challenges of quantum computing for climate modeling by Mierk Schwabe, Lorenzo Pastori, Inés de Vega, Pierre Gentine, Luigi Iapichino, Valtteri Lahtinen, Martin Leib, Jeanette Miriam Lorenz, Veronika Eyring

“…We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimization problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Convolutional Neural Networks (CNNs) are a class of deep learning algorithms most commonly applied. They are particularly powerful for tasks involving data recognition, classification, and analysis due to their ability to automatically and adaptively learn spatial hierarchies of features. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…To leverage the strengths of both models, this study aimed to integrate human domain knowledge into a machine learning framework. IIASA's wildfire cLimate impacts and Adaptation Model (FLAM)—a process-based model incorporating biophysical and human impacts—was developed as a neural network called FLAM-Net. …”
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Review on the Innovation of Investment Banks’ Credit Risk Assessment System in a Highly Volatile Market by Wei Yakun

“…This study proposes a machine learning-driven framework to address these gaps, leveraging real-time multi-source data (e.g., macroeconomic indicators, market sentiment, transactional behavior) and nonlinear algorithms to enhance predictive accuracy. …”
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Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing by Sefati Seyed Salar, Vulpe Alexandru, Popovici Eduard, Fratu Octavian

“…Traditional heuristic-based scheduling approaches struggle to adapt to dynamic workloads and heterogeneous virtual machines (VMs), leading to suboptimal performance. …”
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Interpreting Predictive Models through Causality: A Query-Driven Methodology by Mahdi Hadj Ali, Yann Le Biannic, Pierre-Henri Wuillemin

“…Machine learning algorithms have been widely adopted in recent years due to their efficiency and versatility across many fields. …”
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Reconfigurable Manufacturing Systems: Enhancing Efficiency via Product Family Optimization by Bahtat Chaymae, El Barkany Abdellah

“…Unlike traditional approaches, our method emphasizes the practical implementation of the Analytic Hierarchy Process (AHP) and the Average Linkage Clustering (ALC) algorithm to optimize RMS configurations. By evaluating specific comparison criteria—such as assembly sequence, machining sequence, components, production tools, and production demand—we aim to enhance resource utilization and adaptability to market changes. …”
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Evidence on the performance of nature-based solutions interventions for coastal protection in biogenic, shallow ecosystems: a systematic map by Avery B. Paxton, Trevor N. Riley, Camille L. Steenrod, Brandon J. Puckett, Jahson B. Alemu I., Savannah T. Paliotti, Alyssa M. Adler, Laura Exar, Josette E. T. McLean, James Kelley, Y. Stacy Zhang, Carter S. Smith, Rachel K. Gittman, Brian R. Silliman

“…Potentially relevant articles were deduplicated and then screened by title and abstract with assistance from a machine learning algorithm. Following title and abstract screening, we conducted full text screening, extracted relevant metadata into a predefined codebook, and analyzed the evidence base to determine the distribution and abundance of evidence and answer our research questions on NBS performance. …”
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