Identification of Candidate Tolerogenic CD8+ T Cell Epitopes for Therapy of Type 1 Diabetes in the NOD Mouse Model by Cailin Yu, Jeremy C. Burns, William H. Robinson, Paul J. Utz, Peggy P. Ho, Lawrence Steinman, Alan B. Frey

“…Proteins that elicit autoantibodies in human type 1 diabetes were analyzed by predictive algorithms for candidate epitopes. Using several different tolerizing regimes using synthetic peptides, two new predicted tolerogenic CD8+ T cell epitopes were identified in the murine homolog of the major human islet autoantigen zinc transporter ZnT8 (aa 158–166 and 282–290) and one in a non-β cell protein, dopamine β-hydroxylase (aa 233–241). …”
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Using artificial intelligence and promoter-level transcriptome analysis to identify a biomarker as a possible prognostic predictor of cardiac complications in male patients with Fa... by Hiroshi Kobayashi, Norio Nakata, Sayoko Izuka, Kenichi Hongo, Masako Nishikawa

“…Cardiac complications, such as cardiomyopathy, cardiac muscle fibrosis, and severe arrhythmia, are the most common mortality causes in patients with Fabry disease. To predict cardiac complications of Fabry disease, we extracted RNA from the venous blood of patients for cap analysis of gene expression (CAGE), performed likelihood ratio tests for each RNA expression dataset obtained from individuals with and without cardiac complications, and analyzed the correlation between cardiac functional factors observed using magnetic resonance imaging data extracted using artificial intelligence algorithms and RNA expression. …”
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Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment by Ebrahimpourboura Zahra, Mosalpuri Manish, Jonas Baltrusaitis, Dubey Pallavi, Mba Wright Mark

“…TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. …”
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Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study by Guangyu Lu, Zhixia Su, Xiaoping Yu, Yuhang He, Taining Sha, Kai Yan, Hong Guo, Yujian Tao, Liting Liao, Yanyan Zhang, Guotao Lu, Weijuan Gong

“…We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. …”
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QoS Routing in Telecommunications Networks by N. I. Listopad, O. A. Lavshuk

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Ethical Aspects of the Influence of Neural Networks on the Lives of Students in the Context of a Sociological Approach (using the Example of Students of the IT Department of ETU) by A. S. Kurapova, S. V. Kurapov, A. D. Derzhavina, N. V. Shatilo

“…Its possibilities in the educational process based on the ethical principles of a human-oriented approach are discussed (A.V. …”
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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. …”
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Nasopharyngeal Carcinoma Subtype Discovery via Immune Cell Scores from Tumor Microenvironment by Yanbo Sun, Yun Liu, Hanqi Chu

“…Machine learning models, including random forest (RF) and support vector machine (SVM) algorithms, were constructed to predict the immune subtype. …”
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Modeling Estuarine Algal Bloom Dynamics with Satellite Data and Spectral Index-Based Classification by Mayya Podsosonnaya, Maria J. Schreider, Sergei Schreider

“…This can be achieved by implementation of processing algorithms and indices on multi-spectral satellite images. …”
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Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review by Carlos M. Ferreira-Vanegas, Jorge I. Vélez, Guisselle A. García-Llinás

“…By introducing a new approach based on computational algorithms and data visualization, this SLR fills a gap in the area of RTA analysis and provides a clear picture of the current scientific production in the field. …”
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The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review by Isabelle J. Museck, Daniel L. Brinton, Jesse C. Dean

“…Overall, this review can help guide the future development of ML algorithms and wearable sensor technologies to estimate postural mechanics.…”
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A human pose estimation network based on YOLOv8 framework with efficient multi-scale receptive field and expanded feature pyramid network by Shaobin Cai, Han Xu, Wanchen Cai, Yuchang Mo, Liansuo Wei

“…Abstract Deep neural networks are used to accurately detect, estimate, and predict human body poses in images or videos through deep learning-based human pose estimation. …”
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Machine Learning-Based Carbon Compliance Forecasting and Energy Performance Assessment in Commercial Buildings by Aditya Ramnarayan, Felipe de Castro, Andres Sarmiento, Michael Ohadi

“…The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. …”
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Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection by Syed Talha Abid Ali, Sebin Kim, Young-Joon Kim

“…To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. …”
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Employing Streaming Machine Learning for Modeling Workload Patterns in Multi-Tiered Data Storage Systems by Edson Ramiro Lucas Filho, George Savva, Lun Yang, Kebo Fu, Jianqiang Shen, Herodotos Herodotou

“…Recently, different Machine-Learning (ML) algorithms have been used to model access patterns from complex workloads. …”
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A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation by Or Inbar, Omri Inbar, Ronen Reuveny, Michael J. Segel, Hayit Greenspan, Mickey Scheinowitz

“…The disease classification results show that the overall predictive power of the proposed interpretive model ranged from 96% to 100%, indicating very high predictive power. …”
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Diagnostic Analysis of Crosswalk Safety Hazards in Pedestrian Environments: A SHAP-Enhanced Machine Learning Approach With Street-View Imagery by Caryl Anne M. Barquilla, Jeongwoo Lee

“…It used data from 36,750 crosswalks in Seoul, South Korea, to rigorously evaluate multiple machine learning algorithms for predicting pedestrian crash risk. Among the models assessed, the Random forest (RF) demonstrated the highest precision, aligning with the objective of enhancing pedestrian safety through accurate risk identification. …”
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Allocation of Oral Cholera Vaccines in Africa by Elisa M. Maffioli, Yutong Lu

“…Using machine learning algorithms, we assess the relative importance of socio-demographic, governance, and weather variables in predicting cholera outbreaks. …”
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Identification of mitophagy and ferroptosis-related hub genes associated with intracerebral haemorrhage through bioinformatics analysis by Yan Wang, Rufeng Wang, Jianzhong Zhu, Ling Chen

“…Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. …”
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Image-Based Laser-Beam Diagnostics Using Statistical Analysis and Machine Learning Regression by Tayyab Imran, Muddasir Naeem

“…All parameters were derived using intensity-weighted algorithms and directional cross-sectional analysis to ensure accurate and consistent quantification of the beam’s dynamic behavior. …”
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