Current Advancements in Serum Protein Biomarkers for Hepatitis B Virus‐Associated Hepatocyte Remodeling and Hepatocellular Carcinoma by Adane Adugna, Gashaw Azanaw Amare, Mohammed Jemal

“…One of the main concerns in the perspective of HBV‐induced hepatocyte remodeling and liver cancer is accurately identifying cancer stages to maximize early screening and detection. Biological signatures have a significant impact on solving this problem. …”
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Pemanfaatan Data PDDIKTI sebagai Pendukung Keputusan Manajemen Perguruan Tinggi by Ngatmari Ngatmari, Muhammad Bisri Musthafa, Cahya Rahmad, Rosa Andrie Asmara, Faisal Rahutomo

“…Techniques in providing solutions to these problems are classification techniques to assist decision making, for example Decission Tree (C4.5, ID3, CHAID, rule induction) and forecasting techniques using simple moving average (SMA) methods. …”
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Sensing technology for greenhouse tomato production: A systematic review by Jingxin Yu, Jiang Liu, Congcong Sun, Jiaqi Wang, Jianchao Ci, Jing Jin, Ni Ren, Wengang Zheng, Xiaoming Wei

“…The review covers four key areas: (1) a comprehensive analysis of critical environmental factors influencing tomato growth, such as temperature, humidity, light intensity, and CO2 concentration; (2) an exploration of high-throughput, non-destructive sensing technologies, including chlorophyll fluorescence imaging, infrared CO2 sensing, and multispectral imaging; (3) an investigation of the algorithms based on multi-sensor data fusion and data-driven diagnostic systems for disease detection and growth forecasting; and (4) a discussion on potential research topics in the future to address the limitations of the existing methods. …”
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Artificial intelligence model predicts M2 macrophage levels and HCC prognosis with only globally labeled pathological images by Huiyuan Tian, Yongshao Tian, Dujuan Li, Minfan Zhao, Qiankun Luo, Lingfei Kong, Tao Qin

“…Background and aimsThe levels of M2 macrophages are significantly associated with the prognosis of hepatocellular carcinoma (HCC), however, current detection methods in clinical settings remain challenging. …”
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Comparison of the quality of logistic regression models and a classification tree in predicting hospital mortality in elderly patients with non-ST-elevation myocardial infarction by K. G. Pereverzeva, S. S. Yakushin, N. N. Peregudova, M. V. Mishutina

“…Using the CHAID (Chi Squared Automatic Interaction Detection) method to develop a classification tree for predicting hospital mortality in patients with non-ST-elevation myocardial infarction (non-STEMI) aged 75 years and older and compare the quality of the constructed model with the logistic regression model.Material and methods. …”
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A new gridded offshore wind profile product for US coasts using machine learning and satellite observations by J. Frech, J. Frech, K. Saha, K. Saha, P. D. Lavin, P. D. Lavin, H.-M. Zhang, J. Reagan, B. Fung

“…<p>Offshore wind speed data around wind turbine hub heights are fairly limited, available through in situ observations from wind masts, sonic detection and ranging (sodar) instruments, or floating light detection and ranging (lidar) buoys at selected locations or as forecasting-model-based output from reanalysis products. …”
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Development of a neural network for diagnosing the risk of depression according to the experimental data of the stop signal paradigm by M. O. Zelenskih, A. E. Saprygin, S. S. Tamozhnikov, P. D. Rudych, D. A. Lebedkin, A.  N. Savostyanov

“…Improving the quality and volume of information, complicating its presentation, the need to detect hidden connections makes it ineffective, and most often impossible, to use classical statistical forecasting methods. …”
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Landsat Time Series Reconstruction Using a Closed-Form Continuous Neural Network in the Canadian Prairies Region by Masoud Babadi Ataabadi, Darren Pouliot, Dongmei Chen, Temitope Seun Oluwadare

“…To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. …”
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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…Our analysis reveals a 13-fold increase in AI adoption since 2011, with innovations such as adaptive neuro-fuzzy inference systems (ANFIS) and deep neural networks (DNNs) facilitating real-time anomaly detection and contamination forecasting. The novelty of this review lies in its dual focus—quantifying AI's scalability for global water security while critically addressing unresolved challenges in data standardization, model interpretability, and ethical governance. …”
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Using Deep Convolutional Neural Networks for Earthquake and Explosion Classification by Mingquan Hong, Hongcai Zhang, Lihua Wu, Jialiang Chen, Lijin Dai, Lujun Wang, Tengchao Dong, Jinling Yang, Lihua Fang

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Prognostic Factors in Postoperative Brain Metastases Derive From Non-small Cell Lung Cancer: A Retrospective Analysis by Haibin Chen, Liang Sun, Zhi Yang, Yuanyuan Qu, Nanyang Tong, Caixing Sun, Liang Xia

“…Background: Brain metastases are crucial in cancer progression, requiring focused efforts on the screening, early detection, and treatment. However, accurate forecasting the postoperative prognosis of patients with non-small cell lung cancer brain metastasis remains a challenge. …”
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FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach by Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey

“…Currently, identification of these active regions faces challenges due to limited signal-to-noise, resulting in reliable detection of only large and strong active regions. …”
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Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification by Seyed Ali Hosseini, Francesco Grimaccia, Alessandro Niccolai, Silvia Trimarchi

“…In this paper, the authors introduce a novel feature extraction method based on pattern detection in financial data to enhance the performance of deep learning models for financial time series classification. …”
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Predicting Rolling Element Bearings’ Deterioration Vibration Trend based on Limited Historical Data for a Desired Confidence Level using Machine Learning Algorithms by Mohammad Reza Seif, Somaye Mohammadi, Parham Rahimi, Mehdi Behzad

“…This study proceeds in three distinct phases: selecting the key characteristic for predicting its deterioration trend, identifying the feature to pinpoint the failure onset, and determining the most suitable model for forecasting future bearing vibration states. RMS has emerged as the optimal characteristic for trend prediction, while Peak and Kurtosis have been identified as effective indicators for failure onset detection. …”
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Desensitized Financial Data Generation Based on Generative Adversarial Network and Differential Privacy by Fan Zhang, Luyao Wang, Xinhong Zhang

“…Artificial intelligence has been widely used in the financial field, such as credit risk assessment, fraud detection, and stock prediction. Training deep learning models requires a significant amount of data, but financial data often contains sensitive information, some of which cannot be disclosed. …”
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Trustworthiness of Deep Learning Under Adversarial Attacks in Power Systems by Dowens Nicolas, Kevin Orozco, Steve Mathew, Yi Wang, Wafa Elmannai, George C. Giakos

“…In power grids, DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are commonly utilized for tasks like state estimation, load forecasting, and fault detection, depending on their ability to learn complex, non-linear patterns in high-dimensional data such as voltage, current, and frequency measurements. …”
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Genomic investigation of a non-travel Plasmodium falciparum case linked to imported malaria in China’s post-elimination era by Fei Luo, Dan Jiang, Jing-Ru Xu, Yan Tan, Meng-Ping Yang, Jun Xie, Sen-Ping Yang, Hai-Mo Shen, Shuang Zhou, Jun-Hu Chen

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Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data by Zhiwei Xun, Farag M. A. Altalbawy, Prakash Kanjariya, R. Manjunatha, Debasish Shit, M. Nirmala, Ajay Sharma, Sarbeswara Hota, Shirin Shomurotova, Fadhil Faez Sead, Hojjat Abbasi, Mohammad Mahtab Alam

“…Each plays a vital role in forecasting the oil production rate. To ensure reliability, robust data preprocessing was conducted using the Monte Carlo outlier detection (MCOD) method to recognize and manage data outliers. …”
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APPLIED ASPECTS OF MODERN ANTI-CRISIS FINANCIAL MANAGEMENT by Zoia S. Pestovska, Кateryna S. Romanova, Olesya S. Rosenberg

“…For early recognition of the signs and causes of the crisis in the enterprise, its prevention and elimination, it is necessary to use special methods of comprehensive diagnosis of the state of the enterprise. …”
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