Estimating Shape Parameters of Piecewise Linear-Quadratic Problems by Zheng, Peng, Ramamurthy, Karthikeyan Natesan, Aravkin, Aleksandr Y.

“…Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. Common examples of PLQ penalties include least squares, Huber, Vapnik, 1-norm, and their asymmetric generalizations. …”
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GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach by Diego Bravo, Juan Frias, Felipe Vera, Juan Trejos, Carlos Martínez, Martín Gómez, Fabio González, Eduardo Romero

“…Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures. …”
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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Texture feature column scheme for single‐ and multi‐script writer identification by Faycel Abbas, Abdeljalil Gattal, Chawki Djeddi, Imran Siddiqi, Ameur Bensefia, Kamel Saoudi

“…Application of image analysis and machine learning techniques to this problem allows development of computerised solutions which can facilitate forensic experts in reducing the search space against a questioned document. …”
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Low‐dimensional neural ordinary differential equations accounting for inter‐individual variability implemented in Monolix and NONMEM by Dominic Stefan Bräm, Bernhard Steiert, Marc Pfister, Britta Steffens, Gilbert Koch

“…Abstract Neural ordinary differential equations (NODEs) are an emerging machine learning (ML) method to model pharmacometric (PMX) data. …”
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Loss Architecture Search for Few-Shot Object Recognition by Jun Yue, Zelang Miao, Yueguang He, Nianchun Du

“…Few-shot object recognition, which exploits a set of well-labeled data to build a classifier for new classes that have only several samples per class, has received extensive attention from the machine learning community. In this paper, we investigate the problem of designing an optimal loss function for few-shot object recognition and propose a novel few-shot object recognition system that includes the following three steps: (1) generate a loss function architecture using a recurrent neural network (generator); (2) train a base embedding network with the generated loss function on a training set; (3) fine-tune the base embedding network using the few-shot instances from a validation set to obtain the accuracy and use it as a reward signal to update the generator. …”
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Cloud Node Auto-Scaling System Automation Based on Computing Workload Prediction by Tri Fidrian Arya, Reza Fuad Rachmad, Achmad Affandi

“…This research uses machine learning to predict resource requirements based on workload work patterns and design an automatic scaling system. …”
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Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network by Hao Han, Kejie Li, Yi Li

“…The recognition rate of the established fatigue degree recognition model for driver’s awake state, mild fatigue, moderate fatigue, and severe fatigue is 100%, 93.1%, 98.4%, and 100% respectively, and the total recognition rate can reach 97.8%, which is higher than the recognition accuracy of the traditional machine learning approach.…”
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TXtreme: transformer-based extreme value prediction framework for time series forecasting by Hemant Yadav, Amit Thakkar

“…The latest advancements have significantly enhanced TSF using machine learning and other methods. However, forecasting extreme events remains challenging. …”
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Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network by Chengtao Wang, Wei Li, Gaifang Xin, Yuqiao Wang, Shaoyi Xu

“…A method combining electrochemical experiment with the machine learning algorithm was utilized in this research to study the corrosion current density under the coupling action of stray current and chloride ion. …”
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Automatic Speech Recognition: A survey of deep learning techniques and approaches by Harsh Ahlawat, Naveen Aggarwal, Deepti Gupta

“…Significant research has been conducted during the last decade on the application of machine learning for speech processing, particularly speech recognition. …”
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Comparing self reported and physiological sleep quality from consumer devices to depression and neurocognitive performance by Samir Akre, Zachary D. Cohen, Amelia Welborn, Tomislav D. Zbozinek, Brunilda Balliu, Michelle G. Craske, Alex A. T. Bui

“…Correlations between self-reported and physiological sleep measures were generally weak. Machine learning models revealed that self-reported sleep quality could detect all depression symptoms measured using the Patient Health Questionnaire-14, whereas physiological sleep measures detected “sleeping too much” and low libido. …”
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Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models by Richard J. Licata, Piyush M. Mehta

“…In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM), a physics‐based thermosphere model. …”
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An intelligent humidity sensing system for human behavior recognition by Huabin Yang, Qiming Guo, Guidong Chen, Yuefang Zhao, Meng Shi, Na Zhou, Chengjun Huang, Haiyang Mao

“…With the assistance of a machine learning algorithm, a behavior recognition system based on the humidity sensor has been constructed, enabling behavior states to be classified and identified with an accuracy of up to 96.2%. …”
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From Vision to Reality: The Use of Artificial Intelligence in Different Urban Planning Phases by Frank Othengrafen, Lars Sievers, Eva Reinecke

“…The key to this is “machine learning” that has the ability to recognise patterns, capture models, and learn on the basis of big data via the application of automated statistical methods. …”
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Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers by Arpit Pandey, Sridharan Naveen Venkatesh, Prabhakaranpillai Sreelatha Anoop, B. R. Manju, Vaithiyanathan Sugumaran

“…This study focuses on nitrogen‐filled pneumatic tires due to their uniform pressure management and thermal stability advantages over air‐filled tires. Using machine learning, the research analyzes TPMS data to enhance understanding of tire behavior and vehicle safety. …”
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Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm by Lu Gao, Karsten Schulz, Matthias Bernhardt

“…To this end, a new machine learning method, LASSO algorithm (least absolute shrinkage and selection operator), is used to address the disparity between ERA-Interim forecast precipitation data (0.25° grid) and point-scale meteorological observations. …”
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Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods by Haixiong Li, Fei Wang

“…This study explores and validates an integrated evaluation system that enhances the accuracy of predicting coal seam mining mode by comparing traditional evaluation methods with machine-learning techniques. The weights of the evaluation indicators for coal seam mining were allocated using the coefficient of variation method, followed by a comprehensive evaluation using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). …”
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Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer by Zhai Yue, Tan Dianhuan, Lin Xiaona, Lv Heng, Chen Yan, Li Yongbin, Luo Haiyu, Dan Qing, Zhao Chenyang, Xiang Hongjin, Zheng Tingting, Sun Desheng

“…By integrating clinical data with ultrasonic features, predictive models are developed using machine learning techniques, aiming to refine the capability to diagnose and personalize treatment plans for breast cancer patients. …”
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Treatment of gouty lumbar spinal stenosis: a case report and bioinformatics analysis by Xiao Zhang, Wenbo Gu, Di Luo, Xi Zhu, Xusheng Li, Haifeng Yuan

“…In addition, in order to further investigate the deep mechanism of LSS associated with gout, we obtained the intersecting genes of the two diseases based on a machine learning approach by obtaining the dataset GSE113212 related to LSS from the Gene Expression Omnibus (GEO) database, and the genes related to gout from the human gene database. …”
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