Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…With the help of machine learning algorithms, the VNF resource capacity demand prediction method VNFPre proposed for polymorphic network scenarios,it can judge the future VNF resource capacity demand of network slices, and provide a priori information for the placement and mapping of VNFs carried by network slices.…”
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Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study by Robert E. Shapiro, Eva Jolanda Muenzel, Robert A. Nicholson, Anthony J. Zagar, Michael L. Reed, Dawn C. Buse, Susan Hutchinson, Sait Ashina, Eric M. Pearlman, Richard B. Lipton

“…Methods The web-based OVERCOME (US) survey study identified adults with active migraine in a demographically representative US sample who answered questions about hesitating to seek care from a healthcare provider for migraine and reasons for hesitating. Supervised machine learning (random forest, least absolute shrinkage and selection operator) identified factors associated with hesitation; logistic regression models assessed association of factors on hesitation. …”
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Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds by Mengdie Fan, Chenhui Sang, Hua Li, Yue Wei, Bin Zhang, Yang Xing, Jing Zhang, Jie Yin, Wei An, Bing Shao

“…Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products, and is time-consuming and labor-intensive. Recently, machine learning and artificial intelligence algorithms have been applied to retention time prediction, which show unparalleled advantages over traditional experimental methods. …”
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Question–Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning by Pablo Corona-Fraga, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Hector Perez-Meana, Jose Portillo-Portillo, Jesus Olivares-Mercado, Luis Javier García Villalba

“…Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevated costs, and limited interpretability. Machine Learning (ML)-based approaches aim to overcome these limitations but encounter challenges related to scalability and adaptability due to their reliance on large labeled datasets and their limited alignment with the requirements of secure development teams. …”
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A novel multi-model estimation of phosphorus in coal and its ash using FTIR spectroscopy by Arya Vinod, Anup Krishna Prasad, Sameeksha Mishra, Bitan Purkait, Shailayee Mukherjee, Anubhav Shukla, Nirasindhu Desinayak, Bhabesh Chandra Sarkar, Atul Kumar Varma

“…In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. …”
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Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population by Daniel Gordon, Jason Hoffman, Keren Gamrasni, Yotam Barlev, Alex Levine, Tamar Landau, Ronen Shpiegel, Avishai Lahad, Ariel Koren, Carina Levin, Osnat Naor, Hannah Lee, Xin Liu, Shwetak Patel, Gilad Chayen, Michael Brandwein

“…Results 823 samples, 531 from a 12.2 megapixel camera and 256 from a 12.2 megapixel camera, were collected. 26 samples were excluded by the study coordinator for irregularities. 97% of fingernails and 68% of skin samples were successfully identified by a post-trained machine learning model. Separate models built to detect anemia using images taken from the Pixel 3 had an average precision of 0.64 and an average recall of 0.4, whereas models built using the Pixel 6 had an average precision of 0.8 and an average recall of 0.84. …”
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End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

“…This transparent analytical framework not only enhances clinical interpretability but also instills confidence in decision-making processes, crucial for translating research findings into clinical practice. Our proposed machine learning pipeline showcases the potential of reservoir computing to define causality and therefore directed graph networks, which can in turn be used in a directed graph classifier and explainable analysis of neuroimaging data. …”
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Dynamics and associations of selected agrometeorological variables in Robusta growing regions of Uganda by Ronald Ssembajwe, Catherine Mulinde, Saul D. Ddumba, Godfrey H. Kagezi, Ronald Opio, Judith Kobusinge, Frank Mugagga, Yazidi Bamutaze, Anthony Gidudu, Geoffrey Arinaitwe, Mihai Voda

“…We employed novel trend test and signal decomposition methods along with machine learning and correlation methods. Results show significantly increasing trends in monthly Vapor Pressure Deficit (VPD) in Amolatar, Kabale and Mbale while, Arua, Kituza and Masindi had decreasing trends. …”
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Application of artificial intelligence in the health management of chronic disease: bibliometric analysis by Mingxia Pan, Mingxia Pan, Rong Li, Junfan Wei, Huan Peng, Ziping Hu, Yuanfang Xiong, Na Li, Yuqin Guo, Weisheng Gu, Hanjiao Liu, Hanjiao Liu

“…Recent trends indicate that mobile health technologies and machine learning have emerged as key focal points in the application of artificial intelligence in the field of chronic disease management.ConclusionDespite significant advancements in the application of AI in chronic disease management, several critical challenges persist. …”
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Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging by Jessica Reyes-Rojas, Julien Guigue, Daniel Žížala, Vít Penížek, Tomáš Hrdlička, Petra Vokurková, Aleš Vaněk, Tereza Zádorová

“…A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO3 content in each profile. …”
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An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study by Julia Thomas, Antonia Lucht, Jacob Segler, Richard Wundrack, Marcel Miché, Roselind Lieb, Lars Kuchinke, Gunther Meinlschmidt

“… BackgroundSuicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. …”
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Development and validation of an integrated model for the diagnosis of liver cirrhosis with portal vein thrombosis combined with endoscopic characters and blood biochemistry data:... by Jie Yang, Xu Zhang, Jia Chen, Xianghong Hou, Minghong Shi, Longlong Yin, Longchun Hua, Cheng Wang, Xiaolong Han, Shuyan Zhao, Guolan Kang, Ping Mai, Rui Jiang, Hongwei Tian

“…Variables were collected from blood test and endoscopic signs using machine learning method (ML). Logistic regression determined risk factors. …”
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AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol by Fergus Gleeson, Alex Novak, Indrajeet Das, Ruchir Shah, Edwin van Beek, Sarim Ather, Howell Fu, Nabeeha Salik, Alan Campbell, Farhaan Khan, Abdala Trinidad Espinosa Morgado, Marusa Kotnik, Louise Wing, John Murchison, Jong Seok Ahn, Sang Hyup Lee, Ambika Seth

“…Introduction A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormalities on CXRs, which may expand diagnostic support to a wider field of health professionals. …”
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Estimating forest aboveground carbon sink based on landsat time series and its response to climate change by Kun Yang, Kai Luo, Jialong Zhang, Bo Qiu, Feiping Wang, Qinglin Xiao, Jun Cao, Yunrun He, Jian Yang

“…Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. …”
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Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness by Hui Li, Hui Li, Hui Li, Linghui Dong, Linghui Dong, Linghui Dong, Wenlong Su, Wenlong Su, Ying Liu, Ying Liu, Zhiqing Tang, Zhiqing Tang, Xingxing Liao, Xingxing Liao, Junzi Long, Junzi Long, Xiaonian Zhang, Xinting Sun, Hao Zhang, Hao Zhang, Hao Zhang, Hao Zhang

“…A combination of machine learning and SHapley Additive exPlanations (SHAP) were used to develop predictive model and interpret the results.ResultsThe results indicated significant abnormalities in low-frequency spectral power, microstate parameters, and amplitudes of MMN and P3a in MCS and UWS. …”
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A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System by Jiahui Cheng, Zhengkang Wang, Yaojun Qiao, Hao Gao, Chenxia Liu, Zhuoze Zhao, Jie Zhang, Baodong Zhao, Bin Luo, Song Yu

“…This is the first-time machine learning (ML) has been used to mitigate the effects of optical path asymmetry caused by temperature variations on radio frequency (RF) transmission systems. …”
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Integrating Two-Tier Optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles by Moneerah Alotaibi, Manal Abdullah Alohali, Khalid Mahmood, Asma A. Alhashmi, Jehad Saad Alqurni, Sultan Refa Alotaibi, Ahmad A. Alzahrani, Imene Issaoui

“…However, AVs might be exposed to cyber-attacks, causing dangers to human life. Machine learning (ML) and deep learning (DL) based anomaly recognition has progressed as a new study track in autonomous driving. …”
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Synthesizing Local Capacities, Multi-Source Remote Sensing and Meta-Learning to Optimize Forest Carbon Assessment in Data-Poor Regions by Kamaldeen Mohammed, Daniel Kpienbaareh, Jinfei Wang, David Goldblum, Isaac Luginaah, Esther Lupafya, Laifolo Dakishoni

“…To improve forest carbon assessment, we employed stacked generalization, combining multiple machine learning algorithms to leverage their complementary strengths and address individual limitations. …”
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Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis by Sharad Patel, Gurkeerat Singh, Samson Zarbiv, Kia Ghiassi, Jean-Sebastien Rachoin

“…We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F. …”
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Evaluating the level of digitalization of the innovation process with artificial intelligence approach in the digital transformation of knowledge-based companies by ali Bagheri, reza radfar, sepehr ghazinoory

“…As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or "training data" in order to predict or make decisions without overt planning (Du et al., 2019). …”
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