Prognostic and Diagnostic Value of Spontaneous Tumor-Related Antibodies by Sebastian Kobold, Tim Luetkens, Yanran Cao, Carsten Bokemeyer, Djordje Atanackovic

“…Larger prospective trials should be initiated to identify sets of tumor-associated autoantibodies suited for the use in diagnostic algorithms for cancer detection and followup.…”
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Initial production prediction for horizontal wells in tight sandstone gas reservoirs based on data-driven methods by Jian Sun, Jianwen Gao, Kang Tang, Long Ren, Yanjun Zhang, Zhipeng Miao, Zhe Zhang

“…Six machine learning algorithms were utilized to establish models, and the relevant calculation indicators of different models are compared. …”
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Genomic copy number variants: evidence for association with antibody response to anthrax vaccine adsorbed. by Michael I Falola, Howard W Wiener, Nathan E Wineinger, Gary R Cutter, Robert P Kimberly, Jeffrey C Edberg, Donna K Arnett, Richard A Kaslow, Jianming Tang, Sadeep Shrestha

“…Multivariable regression analyses were used to identify CNV-specific associations after adjusting for relevant non-genetic covariates.<h4>Results</h4>Within the 22 autosomal chromosomes, 2,943 non-overlapping CNV regions were detected by both algorithms. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. …”
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Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis by Xiao Chun Ling, Henry Shen-Lih Chen, Po-Han Yeh, Yu-Chun Cheng, Chu-Yen Huang, Su-Chin Shen, Yung-Sung Lee

“…<b>Conclusions:</b> DL algorithms achieve excellent performance in diagnosing glaucoma using fundus photography and OCT imaging. …”
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Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review by Wilson W S Tam, Kang Sim, Jing Ling Tay, Yun Ling Ang

“…Logistic regression and random forest were the best performing algorithms. Factors influencing outcomes included demographic (age, ethnicity), illness (duration of untreated illness, types of symptoms), functioning (baseline functioning, interpersonal relationships and activity engagement), treatment variables (use of higher doses of antipsychotics, electroconvulsive therapy), data from passive sensor (call log, distance travelled, time spent in certain locations) and online activities (time of use, use of certain words, changes in search frequencies and length of queries).Conclusion Machine learning methods show promise in the prediction of prognosis (specifically functioning, relapse and remission) of mental disorders based on relevant collected variables. …”
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Application of state of health estimation and remaining useful life prediction for lithium-ion batteries based on AT-CNN-BiLSTM by Feng-Ming Zhao, De-Xin Gao, Yuan-Ming Cheng, Qing Yang

“…Comparative analysis with hybrid neural network algorithms such as LSTM, BiLSTM, and CNN-LSTM confirms the proposed model’s high accuracy and stability in SOH estimation and RUL prediction.…”
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Robust and Unified Semi-Supervised Unmixing of Hyperspectral Imaging for Linear and Multilinear Models by Daniel Ulises Campos-Delgado, Juan Nicolas Mendoza-Chavarria, Omar Gutierrez-Navarro, Laura Quintana-Quintana, Raquel Leon, Samuel Ortega, Himar Fabelo, Carlos Lopez, Marylene Lejeune, Gustavo M. Callico

“…All scripts for the proposed algorithms are freely available in <uri>https://github.com/Nicothe4th/ESSEAE-NESSEAE</uri>.…”
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Integrating Handcrafted Features with Machine Learning for Hate Speech Detection in Albanian Social Media by Fetahi Endrit, Hamiti Mentor, Susuri Arsim, Zenuni Xhemal, Ajdari Jaumin

“…We utilized several machine-learning algorithms, including Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR), and extracted a considerable number of handcrafted features. …”
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Network intrusion detection model using wrapper based feature selection and multi head attention transformers by Muhammad Umer, Muhammad Tahir, Muhammad Sardaraz, Muhammad Sharif, Hela Elmannai, Abeer D. Algarni

“…The proposed model improves the accuracy of intrusion detection by selecting the most relevant features while reducing the feature space. …”
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Forecast Padding Enhances Accuracy and Robustness of EEG-Phase-Synchronized TMS by Yu-Cheng Chang, Pin-Hsuan Chao, Yan-Ming Kuan, Chiu-Jung Huang, Li-Fen Chen, Wei-Chung Mao, Tung-Ping Su, Sin-Horng Chen, Chun-Shu Wei

“…These challenges arise from the vulnerability of existing algorithms to filter edge effects. Inspired by predictive coding theory in neuroscience, we propose a novel signal padding method (forecast padding) to mitigate the filter edge effect. …”
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Development of a Hyperspectral Imaging Protocol for Painting Applications at the University of Seville by Giovanna Vasco, Hélène Aureli, Isabel Fernández-Lizaranzu, Javier Moreno-Soto, Anabelle Križnar, Rubén Parrilla-Giraldez, Emilio Gómez-González, Miguel Angel Respaldiza Galisteo

“…However, a considerable limitation was given by redundant and time-consuming features, with the necessary application of statistical algorithms and image-processing tools to extract relevant information. …”
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MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification by Lei Cheng, Qian Huang, Zhengqun Zhu, Yanan Li, Shuguang Ge, Longzhen Zhang, Ping Gong

“…Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. …”
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A Review on Trending Machine Learning Techniques for Type 2 Diabetes Mellitus Management by Panagiotis D. Petridis, Aleksandra S. Kristo, Angelos K. Sikalidis, Ilias K. Kitsas

“…Our work indicates that Gradient Boosting and tree-based models are the most successful ones, the SHAPley and Wrapper algorithms being quite popular feature interpretation and evaluation methods, highlighting urinary markers and dietary intake as emerging diabetes predictors besides the typical invasive ones. …”
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Least to Most Range (LMR): A Novel Consistent Weighting Multicriteria Method Applied to Green Supplier Selection by Vinicius Moretti, Everton L. Melo, Joao Carlos Colmenero

“…Multicriteria decision-making models (MCDM) aim to reduce the complexity of decision-making by using mathematical and algorithmic procedures to guide the process. Given the various issues and complexities that current methods for weighting criteria present; this study aims to develop a new subjective multicriteria method for criteria weighting applied in Green Supplier Selection (GSS). …”
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Hybrid Cat-Transmon Architecture for Scalable, Hardware-Efficient Quantum Error Correction by Connor T. Hann, Kyungjoo Noh, Harald Putterman, Matthew H. Matheny, Joseph K. Iverson, Michael T. Fang, Christopher Chamberland, Oskar Painter, Fernando G.S.L. Brandão

“…With this level of performance, the qubit overhead required to reach algorithmically relevant logical error rates with the cat-transmon architecture matches that of an unbiased-noise architecture with physical error rates in the range 10^{−5}–10^{−4}.…”
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η-ensemble path integral Monte Carlo approach to the free energy of the warm dense electron gas and the uniform electron liquid by Tobias Dornheim, Panagiotis Tolias, Zhandos A. Moldabekov, Jan Vorberger

“…Specifically, we present an in-depth study of the relevant algorithmic details such as the choice of the free weighting parameter and the choice of the optimum number of intermediate η-steps to connect the real, nonideal system (η=1) with the ideal limit (η=0). …”
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Development of the structure of a computerized information and measurement system based on the analysis of the problems of using an asynchronous electric drive in pumping equipment by Yu.O. Podchashynskyi, Ya.V. Mahaletskyi

“…In addition, the article considers such modern requirements as energy efficiency, prompting a discussion on the optimization of engine operation algorithms and the introduction of modern control systems. …”
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Artificial Intelligence for Unstructured Data Processing by Yohanes Bowo Widodo, Febrianti Widyahastuti, Mohammad Narji, Sondang Sibuea

“…By using deep learning models and advanced algorithms, AI can identify patterns and relationships in complex data, thereby providing deeper insights for better decision making. …”
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Enhancing DDoS Attack Classification through SDN and Machine Learning: A Feature Ranking Analysis by Aymen AlAwadi, Kawthar Rasoul ALesawi

“…Due to their high accuracy and lower complexity, KNN and NB outperform ML algorithms in this study…”
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