Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems by Francisco J. Baldán, Diego García-Gil, Carlos Fernandez-Basso

“…These advanced systems offer significant advantages, including enhanced objectivity, improved consistency over manual methods, and the ability to detect subtle predictive patterns not discernible by human observation. The emergence of extensive open datasets and big data analytics has enabled the development of more robust models. …”
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GreenNav: Spatiotemporal Prediction of CO₂ Emissions in Paris Road Traffic Using a Hybrid CNN-LSTM Model by Youssef Mekouar, Imad Saleh, Mohammed Karim

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Examining the Relationship Between Leadership and Teachers' Moral Competence: The Mediating Role of Spiritual Climate by Nouman Maqbool Rao, Chaudhary Abdul Rehman, Sami Ullah Bajwa, Nadia Nasir

“…This shows that the use of machine learning in analysing educational data retrieves deep patterns within the data sets. …”
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A Platform for Early Class Dropout Prediction of University Students by Guilherme Antonio Borges, Carolina Filipa Dos Santos Pedro, Julio Cesar Santos Dos Anjos, Andre Rodrigues, Fernando Boavida, Jorge Sa Silva

“…The analysis revealed that student performance, re-enrollment behavior, and attendance patterns are strongly correlated with the risk of early dropout. …”
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Spatial–Temporal Approach for Gridded Wind Forecasting Across Southwest Western Australia by Fuling Chen, Kevin Vinsen, Arthur Filoche

“…This paper shows the potential of our machine learning model for wind forecasts across various prediction horizons and spatial coverage. …”
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Advancing smart tourism destinations: A case study using bidirectional encoder representations from transformers‐based occupancy predictions in torrevieja (Spain) by José Ginés Giménez Manuel, José Giner Pérez de Lucia, Marco Antonio Celdrán Bernabeu, José Norberto Mazón López, Juan Carlos Cano Escribá, José María Cecilia Canales

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Cycle-configuration descriptors: a novel graph-theoretic approach to enhancing molecular inference by Bowen Song, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Tatsuya Akutsu

“…This framework first constructs a prediction function for a fixed property using machine learning models, which is then simulated by mixed-integer linear programming to infer desired molecules. …”
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Melanoma Skin Lesion Classification Using Neural Networks: A systematic review by ahmed hammo, Mohammed Younis

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Exploring the Molecular Mechanism and Role of Glutathione S-Transferase P in Prostate Cancer by Shan Huang, Hang Yin

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Radiomics, Pathomics and Connectomics by Debora GIL RESINA GIL RESINA

“…We propose to use autoencoders (AE) to learn the latent patterns of healthy patches and formulate a specific measure of the reconstruction error of the image to detect H. pylori as an anomaly in the staining of tissue in immunohistochemically images…”
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An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis by Hao Yan, Liangliang Shang, Wan Chen, Mengyao Jiang, Tianqi lu, Fei Li

“…The hybrid kernel functions address the limitations of single kernel functions by effectively capturing both linear and nonlinear patterns in the data. Subsequently, the hierarchical hybrid kernel extreme learning machine (HHKELM) is refined through an enhanced Aquila Optimizer (AO) algorithm, which iteratively optimizes the kernel hyperparameter combination. …”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. …”
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Integrated single-cell and transcriptome sequencing data reveal the value of IL1RAP in gastric cancer microenvironment and prognosis by Weifeng Yang, Xiaohua Wu, Jian Wang, Wenquan Ou, Xing Huang

“…The CIBERSORT and ssGSEA algorithms elucidated immune infiltration patterns, while TIDE and TCGA predicted immune-related outcomes. …”
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Spatiotemporal trends in hernia disease burden and health workforce correlations in aging populations: a global analysis with projections to 2050 by Fangyi Dai, Yuzhou Cai, Huayou Luo, Ruo Shu, Tong Zhang, Yong Dai

“…Spatial and temporal patterns were examined using age-period-cohort and frontier analyses. …”
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A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data by Jun-gyo Jang, Soon-sup Lee, Se-Yun Hwang, Jae-chul Lee

“…While conventional studies typically train DAEs using Gaussian random noise, such noise does not fully reflect the complex noise patterns observed in real-world industrial environments. …”
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Improved Distributed Backdoor Attacks in Federated Learning by Density-Adaptive Data Poisoning and Projection-Based Gradient Updating by Jian Wang, Hong Shen, Wei Ke, Xue Hua Liu

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The impact of Alzheimer's disease on cortical complexity and its underlying biological mechanisms by Leian Chen, Xiao Zhou, Yanan Qiao, Yu Wang, Zhi Zhou, Shuhong Jia, Yu Sun, Dantao Peng

“…The present findings may contribute to a more comprehensive understanding of brain morphological patterns in AD patients.…”
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Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives by Simarjeet Kaur, Jimmy Singla, Lewis Nkenyereye, Sudan Jha, Deepak Prashar, Gyanendra Prasad Joshi, Shaker El-Sappagh, Md. Saiful Islam, S. M. Riazul Islam

“…There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. …”
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A variational quantum circuits architecture with multi-head attention for chaotic time series prediction by Liyun Su, Man Wu, Fenglan Li

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Forecasting Eruptions at Steamboat Geyser: Time Scales, Differentiability, and Detectability of Seismic Precursors Through Data‐Driven Methods by Alberto Ardid, Anna Barth, David Dempsey, Michael Manga, Shane J. Cronin

“…We analyzed seismic data from March to December 2018 to identify patterns that occurred before 31 eruptions. Four seismic amplitude measures and 700 time‐series features were computed from the seismic data. …”
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