Syllable-, Bigram-, and Morphology-Driven Pseudoword Generation in Greek by Kosmas Kosmidis, Vassiliki Apostolouda, Anthi Revithiadou

“…Pseudowords are essential in (psycho)linguistic research, offering a way to study language without meaning interference. …”
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Solar FaultNet: Advanced Fault Detection and Classification in Solar PV Systems Using SwinProba‐GeNet and BaBa Optimizer Models by Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri, Faisal Alsaif

“…It also proposes the Solar FaultNet‐a novel deep learning‐based approach that significantly improves fault detection performance in solar PV systems and integrates the model with state‐of‐the‐art ML techniques like CNN and LSTM to capture inherent complex patterns and interdependencies of fault data. Besides, the proposed model outperforms conventional machine‐learning algorithms and state‐of‐the‐art deep‐ learning models for better performance by yielding higher accuracy, precision, recall, F1‐score, and low error rate on various fault types such as PV array faults, inverter faults, grid synchronization faults, and environmental faults. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…Integrating spatial and contextual factors with BER establishes a new standard for predictive accuracy in urban energy research.…”
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The effective landscape design parameters with high reflective hardscapes: guidelines for optimizing human thermal comfort in outdoor spaces by design -a case on hot arid climate w... by Reham A. Abdelwahab, Ahmed A. Fekry, Reham El-Dessuky Hamed

“…Through generative design algorithms, an optimized framework was developed to identify effective strategies for urban cooling. …”
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Identification and validation of hub genes related to neutrophil extracellular traps-mediated cell damage and immune recruitment during abdominal aortic aneurysm by Chuanlong Lu, Heng Wang, Maolin Qiao, Runze Chang, Jinshan Chen, Lizheng Li, Keyi Fan, Sheng Yan, Ruijing Zhang, Honglin Dong

“…We performed an analysis on the infiltration of immune cells within the AAA transcriptome dataset, employing CIBERSORT and ssGSEA algorithms (GSE183464). Next, the gene expression patterns associated with NETs formation (GSE178883) were utilized to examine the physiological processes in peripheral blood neutrophils post-PMA (Phorbol 12-myristate 13-acetate) induction. …”
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Biosynthesis, applications, and mathematical modeling dynamics of lactic acid bacteria exopolysaccharides: A review by Ankit Barot, Kalyan Das, Yogesh Patel, et al.

“…White-box models, utilize comprehensive system knowledge, forecast performance, and optimize processes such as EPS biosynthesis while employing algorithms like fuzzy patterns, rule-based systems, and decision trees to increase productivity and identify bottlenecks. …”
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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…However, the variability and non-stationary nature of wind patterns make reliable forecasting a difficult task. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

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Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis by Liang Xu, Jing Li, Wei Gong

“…In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. …”
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Public Perceptions of Ethical Challenges and Opportunities for Enterprises in the Digital Age by Ahmad Mansour, Khaled Alshaketheep, Hind Al-Ahmed, Ahmad Shajrawi, Arafat Deeb

“…The quantitative analysis methods used in this study encompassed descriptive statistical analysis, inferential statistical analysis, correlation analysis, hypothesis testing, and regression analysis to establish patterns of relationships.  Results: The leading ethical issues for participants were: personal data protection (mean score = 4.2), ethical usage of AI algorithms (mean score = 3.9), job displacement due to AI technology (mean score = 3.1), and the digital divide. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Traditional bioinformatics methods often struggle to capture the intricate sequence patterns and high-dimensional signals characteristic of epitope data. …”
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PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

“…This study implements multivariate time series forecasting using gradient boosting algorithms (XGBoost, CatBoost, and LightGBM) to predict cash flow patterns in banking transactions, focusing on constructing reliable prediction intervals. …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Abstract Background In the hyper-arid environment of the United Arab Emirates (UAE), understanding rainfall patterns is essential for effective water resource management, agricultural planning, and ecological conservation. …”
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RICE AlgebraBot: Lessons learned from designing and developing responsible conversational AI using induction, concretization, and exemplification to support algebra learning by Chenglu Li, Wanli Xing, Yukyeong Song, Bailing Lyu

“…Specifically, we first examined computational strategies to mitigate the ethical concerns of ConvAI in educational setting with educational big data (npretraining = 2,097,139) and found that researchers could effectively enhance ConvAI responsibility through the investigated algorithmic strategies. …”
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IRGL-RRI: interpretable graph representation learning for plant RNA–RNA interaction discovery by Qingquan Liao, Xuchong Liu, Wei Zhao, Yu Tong, Fangzheng Xu, Xinxin Liu, Yifan Chen

“…The model enriches sample information by extracting features of different bases from plant RNA data and reconstructs these features using an algorithmic hierarchy approach to capture more complex patterns. …”
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A Comparative study on the impact of urbanisation on microclimate in Cairo (Egypt) and London (UK) using remote sensing and Machine Learning by L. Sabobeh, T. Ali, M. Md. Mortula

“…Several machine learning (ML) algorithms were compared, with Support Vector Machine (SVM) ultimately selected for its superior performance. …”
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Molecular biomarkers in salivary diagnostic materials: Point-of-Care solutions — PoC-Diagnostics and -Testing by Ziyad S. Haidar

“…Recent advancements in nanomaterials and fabrication techniques, coupled with emerging computational approaches such as artificial intelligence (AI), machine learning, and deep learning, have revolutionized high-throughput screening and laboratory automation. AI-driven algorithms now process and analyze salivary proteomic data with remarkable accuracy, identifying patterns and biomarkers associated with diseases such as oral cancer at an early stage. …”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

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Binarized Neural Networks for Resource-Efficient Spike Sorting by Luca M. Meyer, Majid Zamani, Andreas Demosthenous

“…This enables the generation of precise modulatory patterns of neuronal circuits. Deep learning-based spike sorting algorithms are based on power-intensive dot products, which poses challenges for on-chip processing with resource-constrained devices. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the key indicators for assessing the prognosis after coma. …”
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