Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting by Abdul Wadood, Hani Albalawi, Aadel Mohammed Alatwi, Hafeez Anwar, Tariq Ali

“…The FWOA-SVR model is comprehensively evaluated against traditional SVR, Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models using training, validation, and testing datasets. …”
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Playing and Listening to Tailor-Made Notched Music: Cortical Plasticity Induced by Unimodal and Multimodal Training in Tinnitus Patients by Janna Pape, Evangelos Paraskevopoulos, Maximilian Bruchmann, Andreas Wollbrink, Claudia Rudack, Christo Pantev

“…Taking into account the fact that uni- and multisensory trainings induce different patterns of cortical plasticity we hypothesized that these two protocols will have different affects. …”
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Optimizing Bearing Fault Diagnosis in Rotating Electrical Machines Using Deep Learning and Frequency Domain Features by Eduardo Quiles-Cucarella, Alejandro García-Bádenas, Ignacio Agustí-Mercader, Guillermo Escrivá-Escrivá

“…Scalograms proved particularly effective in identifying distinct vibration patterns for faults in bearings’ inner and outer races. …”
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PathCare: Integrating Clinical Pathway Information to Enable Healthcare Prediction at the Neuron Level by Dehao Sui, Lei Gu, Chaohe Zhang, Kaiwei Yang, Xiaocui Li, Liantao Ma, Ling Wang, Wen Tang

“…PathCare employs an auxiliary sub-network that models future visit patterns to capture temporal health progression, coupled with a neuron-level filtering gate that adaptively selects relevant features while filtering out redundant information. …”
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Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) by Haytham Elmousalami, Felix Kin Peng Hui, Aljawharah A. Alnaser

“…This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. …”
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Study on the Influence of Strip-Shaped Urban Rail Transit Stations on Urban Vitality Distribution Based on Point of Interest Data by Yuchen Wu, Min’an Yang, Xin Li, Xu Wei, Yongsheng Qian

“…Against the backdrop of the newly constructed urban rail transit network and the ongoing urbanization of strip-shaped cities, this study investigates the distribution and evolution of commercial points of interest (POIs) in the central urban area of Lanzhou. …”
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Multi-Threshold Remote Sensing Image Segmentation Based on Improved Black-Winged Kite Algorithm by Yi Zhang, Xinyu Liu, Wei Sun, Tianshu You, Xin Qi

“…The proposed algorithm significantly enhances the capability of complex remote sensing scenarios by systematic improvements to core algorithm components, including population initialization strategy, attack behavior patterns, migration mechanisms, and opposition-based learning strategy. …”
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Sara Detection on Social Media Using Deep Learning Algorithm Development by M. Khairul Anam, Lucky Lhaura Van FC, Hamdani Hamdani, Rahmaddeni Rahmaddeni, Junadhi Junadhi, Muhammad Bambang Firdaus, Irwanda Syahputra, Yuda Irawan

“…CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. With SMOTE and early stopping, the model successfully manages class imbalance and reduces overfitting. …”
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Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City by D. Pavlov, D. Pavlov, L. L. Vitanova, D. Petrova-Antonova

“…This study investigates the morphological changes in Sofia’s LULC from 1990 to 2018, predicts future LULC patterns, and evaluates the effectiveness of Cellular Automata-Artificial Neural Network (CA-ANN) models for LULC forecasting. …”
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Federated Learning for Real-Time Demand Response by Data Centers Toward Energy Efficiency and Privacy Preservation by Mohannad Alhazmi, Alexis Pengfei Zhao, Wanzi Li, Chenlu Yang

“…Our novel method utilizes real-time data to generate effective and efficient actions in response to operational demands, in compliance with energy conservation and stringent privacy standards. We evaluate various demand response programs by the proposed method in different scenarios and case studies to demonstrate their efficacy in adapting to energy consumption patterns for optimal energy usage and cost reduction. …”
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Bioinformatics-based identification of key genes for Olaparib resistance in breast cancer: prognostic implications and therapeutic relevance by Kezhen Shen, Xiaozhe Li, Sihan Zhao, Xinyu Zhang, Le Yu, Jun Li, Lei Cai

“…Differential expression and drug sensitivity prediction were performed to identify resistance-associated genes, followed by pathway enrichment and protein–protein interaction (PPI) network construction. Kaplan–Meier survival analysis and Cox regression were conducted to evaluate the prognostic value of candidate genes. …”
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Examining the landscape of proctoring in upholding academic integrity: a bibliometric review of online examination practices by Manit Malhotra, Indu Chhabra, Salil Bharany, Ateeq Ur Rehman, Seada Hussen

“…It explores global, institutional, and keyword connections, as well as co-citation patterns among authors, journals, and references, serving as an eye-opener to the scope and direction of ongoing work. …”
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Groundwaterscapes: A Global Classification and Mapping of Groundwater's Large‐Scale Socioeconomic, Ecological, and Earth System Functions by Xander Huggins, Tom Gleeson, Karen G. Villholth, Juan C. Rocha, James S. Famiglietti

“…All large aquifer systems of the world are characterized by multiple groundwaterscapes, highlighting the pitfalls of treating these groundwater bodies as lumped systems in global assessments. We evaluate the distribution of Global Groundwater Monitoring Network wells across groundwaterscapes and find that industrial agricultural regions are disproportionately monitored, while several groundwaterscapes have next to no monitoring wells. …”
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Devising a Breast Cancer Diagnosis Protocol through Machine Learning by Tooba Mujtaba, Saif Ullah Hashmi, Usama Bin Imtiaz, Sheikh Jameel Fathima Nusra

“…Upon following a predefined computational pipeline, we identified 369 genes that had distinct patterns of gene expression profiles in cases of ER-positive (ER + ) and HER2-negative (HER2-) breast cancer. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The proposed study is founded on two principal pillars: the transformation of process tabular data (generated using the Conditional Tabular Generative Adversarial Network (CTGAN)), involving the mapping of features onto a fixed grid in a heatmap structure, and the configuration of the CNN algorithm to extract complex patterns in the data that are not readily apparent in the original tabular format. …”
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Exploring the Association Between the Doppler Findings of Endometriomal Wall Vascularization and Volume of the Endometrioma by Berna Seçkin, Ayla Sargın Oruç, Yunus Yıldız, Mustafa Uğur

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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. …”
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Exploring New Paradigms in Time Series Prediction by Integrating Computer Simulations and Machine Learning by Pengcheng Wu, Yan Shi, Pengfei Zhao, Zhengzhao Gu

“…By merging data-driven learning with simulation-informed constraints, our approach enhances both predictive accuracy and interpretability. Empirical evaluations across diverse benchmark datasets validate the effectiveness of our framework, demonstrating its robustness, scalability, and ability to uncover meaningful temporal patterns in complex time series data.…”
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Land Use Characteristics of Commuter Rail Station Areas and Their Impact on Station Ridership: A Case Study of Japan Railways in the Tokyo Metropolitan Area by Yanan Gao, Xu Cui, Xiaozheng Sun

“…The key findings were as follows: (1) the land use characteristics around commuter rail stations exhibit distinct zonal patterns; within 250 m, public transport stops and public service facilities are the most densely concentrated; the highest residential population density is found between 250 and 750 m; and commercial facilities are mostly clustered in the 500 to 750 m range; (2) the impact of land use factors on ridership varies in intensity across different spatial zones; the density of public transport stops and street network density is most significant within 250 m, whereas commercial facility density is greatest within the 500–750 m PCA; (3) The land use characteristics within 500 m of stations have greater explanatory power for passenger flow, and the goodness of fit of the MGWR model surpasses that of the linear regression model.…”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…In this model, multiple enhanced TCN networks with diverse architectures are organised parallelly to capture hierarchical spatial-temporal patterns from raw inertial signals. …”
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