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Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2001年第3期39卷 693-695页

作者： Chan, JCW Huang, CQ DeFries, R Univ Maryland Dept Geog Lab Global Remote Sensing Studies College Pk MD 20742 USA Raytheon ITSS EROS Data Ctr USGS Sioux Falls SD 57101 USA Univ Maryland Dept Geog Earth Syst Sci Interdisciplinary Ctr College Pk MD 20742 USA

Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation [1] that bagging improves unstable, but not stable, learning algorithms, While boosting enhanced accuracy of a weak learner, its behavior is subject to the characteristics of each learning algorithm.

关键词： algorithm performance bagging boosting voting classifications

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Manifold Coordinate Representations of Hyperspectral Imagery: Improvements in algorithm performance and Computational Efficiency 30

Manifold Coordinate Representations of Hyperspectral Imagery...

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30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action

作者： Bachmann, Charles M. Ainsworth, Thomas L. Fusina, Robert A. Topping, Rusty Gates, Tell USN Res Lab Remote Sensing Div Coastal Sci & Interpretat Sect Code 7232 4555 Overlook Ave SW Washington DC 20375 USA Celestech Inc Phoenix AZ 85048 USA

ISBN: (纸本)9781424495665

Several years ago, NRL first demonstrated a computationally efficient framework for optimizing a set of intrinsic manifold coordinates for high-dimensional data such as hyperspectral imagery (HIS). Since that time, NRL has continued to improve the fidelity of the representation to describe the details of the nonlinear structure in even greater detail. In addition, working closely with CelesTech, several improvements have been made that increase the computational efficiency, accelerating processing speeds from hours to minutes.

关键词： algorithm performance computational efficiency geophysical image processing high-dimensional data hyperspectral imagery manifold coordinate representations nonlinear structure remote sensing

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Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of algorithm performance

Designing Alternative Representations of Confusion Matrices ...

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Proceedings of the ACM on Human-Computer Interaction

作者： Shen, Hong Jin, Haojian Cabrera, Ángel Alexander Perer, Adam Zhu, Haiyi Hong, Jason I. Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States

Ensuring effective public understanding of algorithmic decisions that are powered by machine learning techniques has become an urgent task with the increasing deployment of AI systems into our society. In this work, we present a concrete step toward this goal by redesigning confusion matrices for binary classification to support non-experts in understanding the performance of machine learning models. Through interviews (n=7) and a survey (n=102), we mapped out two major sets of challenges lay people have in understanding standard confusion matrices: the general terminologies and the matrix design. We further identified three sub-challenges regarding the matrix design, namely, confusion about the direction of reading the data, layered relations and quantities involved. We then conducted an online experiment with 483 participants to evaluate how effective a series of alternative representations target each of those challenges in the context of an algorithm for making recidivism predictions. We developed three levels of questions to evaluate users' objective understanding. We assessed the effectiveness of our alternatives for accuracy in answering those questions, completion time, and subjective understanding. Our results suggest that (1) only by contextualizing terminologies can we significantly improve users' understanding and (2) flow charts, which help point out the direction of reading the data, were most useful in improving objective understanding. Our findings set the stage for developing more intuitive and generally understandable representations of the performance of machine learning models. © 2020 ACM.

关键词： Machine learning Behavioral research Image resolution Matrix algebra Terminology algorithm performance Binary classification Completion time Confusion matrices Machine learning models Machine learning techniques On line experiments Public understanding

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Decoding algorithm appreciation: Unveiling the impact of familiarity with algorithms, tasks, and algorithm performance

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DECISION SUPPORT SYSTEMS 2024年 179卷

作者： Mahmud, Hasan Islam, A. K. M. Najmul Luo, Xin (Robert) Mikalef, Patrick Univ New Mexico Anderson Sch Management Albuquerque NM USA LUT Univ Software Engn Dept Lappeenranta Finland Norwegian Univ Sci & Technol Dept Comp Sci Sem Saelandsvei 9 N-7034 Trondheim Norway Univ Ljubljana Sch Econ & Business Kardeljeva Ploscad 17 Ljubljana 1000 Slovenia

algorithm appreciation, defined as an individual's reliance or tendency to rely on algorithms in decision-making, has emerged as a subject of growing scholarly interest. Inquiries into this subject are crucial to understanding human decision-making processes as in the era of artificial intelligence, algorithms are increasingly being integrated into decision-making. To contribute to this evolving field, this study examines three factors that might play significant roles in enhancing trust in algorithms: familiarity with algorithms, familiarity with tasks, and familiarity with algorithm performance. Drawing upon prior studies, a conceptual model was developed and empirically tested using a scenario study. Data on 327 individuals showed a strong positive association between familiarity with algorithms and trust in algorithms. In contrast, task familiarity appeared to have no significant influence on trust. Trust, in turn, was identified as a key driver of algorithm appreciation. The study also revealed the moderating role of familiarity with algorithm performance in the relationship between familiarity with algorithms and trust in algorithms. Post hoc analysis highlighted that trust fully mediates the relationship between algorithm familiarity and algorithm appreciation. The study underscores the significance of algorithm familiarity and performance transparency in shaping trust in algorithms. The study contributes theoretically by offering important insights about the influences of different forms of familiarity on trust and practically by prescribing practical guidelines to enhance algorithm appreciation.

关键词： algorithm appreciation Familiarity Trust algorithm performance algorithmic decision -making Transparency

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Research on Improvement of B&B algorithm performance of Single Machine Total Tardiness Schedule

Research on Improvement of B&B Algorithm Performance of Sing...

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Xiaochuan Luo Key Laboratory of Process Industry Automation(Northeastern University) Shenyang,Liaoning 110004,China Dancheng LI,Hang Song Software College,Northeastern University Shenyang,Liaoning 110004,China

This paper addresses the schemes to improve B&B algorithm performance for the NP-hard problem of scheduling N jobs on a single machine with due dates, sequence-dependent setup times,and no preemption,where the objective is to minimize the total *** dominance rules are proposed to reduce the expanded nodes and explored nodes.A comparison with Ragatz's B&B approaches indicates that the algorithm,which adopts our schemes,is competitive and has a certain advantage for larger problems.

关键词： Dominance Rule algorithm performance B&B algorithm Total Tardiness Single Machine Scheduling

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Classification algorithms trained on simple (symmetric) lifting data perform poorly in predicting hand loads during complex (free-dynamic) lifting tasks

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APPLIED ERGONOMICS 2025年 125卷 104427页

作者： Taori, Sakshi Lim, Sol Virginia Polytech Inst & State Univ Dept Ind & Syst Engn 1145 Perry St Blacksburg VA 24061 USA

The performance of machine learning (ML) algorithms is dependent on which dataset it has been trained on. While ML algorithms are increasingly used for lift risk assessment, many algorithms are often trained and tested on controlled simulation datasets, lacking the diversity of the lifting conditions. Consequently, concerns arise regarding their applicability in real-world scenarios characterized by substantial variations in lifting scenarios and postures. Our study investigates the impact of different lifting scenarios on the performance of ML algorithms trained on surface electromyography (sEMG) armband sensor data to classify hand-load levels (2.3 and 6.8 kg). Twelve healthy participants (6 male and 6 female) performed repetitive lifting tasks employing various lifting scenarios, including symmetric (S), asymmetric (A), and free-dynamic (F) techniques. Separate algorithms were developed using diverse training datasets (S, A, S+A, and F), ML classifiers, and sEMG features, and tested using the F dataset, representing unconstrained and naturalistic lifts. The mean accuracy and sensitivity were significantly lower in models trained on constrained (S) datasets compared to those trained on naturalistic lifts (F). The accuracy, precision, and sensitivity of models trained with frequency-domain sEMG features were greater than those trained with the time-domain features. In conclusion, ML algorithms trained on controlled symmetric lifts showed poor performance in predicting loads for dynamic, unconstrained lifts;thus, particular attention is needed when using such algorithms in real-world scenarios.

关键词： EMG Lifting load classification Wearable armband Lifting scenario algorithm performance

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Development and validation of selection algorithms for a non-ventilator hospital-acquired pneumonia semi-automated surveillance system

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CLINICAL MICROBIOLOGY AND INFECTION 2025年第4期31卷 582-587页

作者： Mueller, Anna Pfister, Marc Hesse, Mirjam Faes Zingg, Walter Wolfensberger, Aline Univ Zurich Univ Hosp Zurich Dept Infect Dis & Hosp Epidemiol Ramistr 100 CH-8091 Zurich Switzerland

Objectives Semi-automated surveillance systems save time compared with traditional manual methods, particularly for non-ventilator hospital-acquired pneumonia (nvHAP), a nosocomial infection which can affect all non-intubated patients. In semi-automated surveillance, a computerized algorithm selects patients with high probability (i.e. "at risk") for subsequent manual confirmation. This study aimed to evaluate the performance of several single indicators and algorithms to preselect patients at risk for nvHAP. Methods Single nvHAP indicators, identified based on literature, expert opinion and data availability, were combined to simple and complex algorithms. Both single indicators and algorithms were applied on a patient cohort of 157 902 patients, including 947 patients with nvHAP according to our reference standard, i.e. validated semi-automated nvHAP surveillance system plus the manual surveillance of patients with hospital-acquired pneumonia discharge diagnostic codes. performance characteristics like sensitivity, workload reduction, and number of patients needed to be screened to detect one case of nvHAP were assessed. Results Compared with the reference standard, single indicators had a sensitivity ranging from 35.1% (332/947) (oxygen desaturation) to 99.7% (944/947) (radiologic procedure). The workload reduction varied from 57.3% (90 505/157 902) (length of hospital stay >5 days) to 98.4% (155 453/157 902) (ICD-10 discharge diagnostic code). The highest workload reduction was found in complex algorithms, e.g. the combination "radiologic procedure including full text AND temporally related abnormal white blood count or fever AND antimicrobials AND C-reactive protein AND decreased oxygenation AND hospital stay >= 5 days AND no intubation" which reduced the number of patients who have to undergo manual review by 96.2% (151 867/157 902), while maintaining a sensitivity of 92% (871/947). The number needed to screen applying this algorithm was 6.4 patients. Discussi

关键词： algorithm performance Electronically assisted surveillance Non-ventilator hospital-acquired pneumonia Semi-automated surveillance Sensitivity and specificity

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Determining Relative Positioning for Aerial Refueling Through DNN Bounding Box Geometry

Determining Relative Positioning for Aerial Refueling Throug...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Maheshwari, Akshat Lowe, Ryan Mwaffo, Violet Clement, Danielle Mottino, Sarah Costello, Donald H. Weapons Robotics and Control Department United States Naval Academy AnnapolisMD21402 United States Aeronautics Division Lockheed Martin Corporation Fort WorthTX United States MATRIX Lab A. Clark School of Engineering University of Maryland College ParkMD United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The Office of Naval Research (ONR) has initiated the Advanced Autonomous Air-to- Air Refueling System (A4RS) project to establish standards and explore the use of deep neural networks (DNN) for autonomous refueling tasks. This research aims to develop a monovision-based camera system integrated with a DNN to achieve accurate bearing and range measurements to a refueling drogue within the final 15-20 feet before contact. As part of this study, a pre-trained DNN was used to identify the drogue by placing a bounding box around it. A similar-triangle-based algorithm was then applied to determine the relative range to the center of the drogue (x, y, and z translations) from the camera. The derived ranges were then compared with the actual ranges to determine if the errors would be acceptable for use in autonomous aerial refueling of an uncrewed system. The research found that as the range decreased, the error decreased to acceptable levels (less than four inches when the camera was five feet from the drogue). The results also showed that the more misalignment with the drogue, the larger the errors were to reality. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Air to Air Refueling Convolutional Neural Network Unmanned Aircraft System algorithm performance Computer Vision Fixed Wing Aircraft Autonomous Systems Robotics Data Acquisition System Obstacle Avoidance

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FOX-TSA: Navigating Complex Search Spaces and Superior performance in Benchmark and Real-World Optimization Problems

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AIN SHAMS ENGINEERING JOURNAL 2025年第1期16卷

作者： Aula, Sirwan A. Rashid, Tarik A. Soran Univ Comp Sci Dept Erbil Iraq Univ Kurdistan Hewler Artificial Intelligence & Innovat Ctr Comp Sci & Engn Dept Erbil Iraq

In the dynamic field of optimisation, hybrid algorithms have garnered significant attention for their ability to combine the strengths of multiple methods. This study presents the Hybrid FOX-TSA algorithm, a novel optimisation technique that merges the exploratory capabilities of the FOX algorithm with the exploitative power of the TSA algorithm. The primary objective is to evaluate the efficiency, robustness, and scalability of this hybrid approach across multiple CEC benchmark suites, including CEC2014, CEC2017, CEC2019, CEC2020, and CEC2022, alongside real-world engineering design problems. The results demonstrate that the Hybrid FOX-TSA algorithm consistently outperforms established optimisation techniques, such as PSO, GWO, and the original FOX and TSA algorithms, in terms of convergence speed, solution quality, and computational efficiency. Notably, the hybrid approach avoids premature convergence and navigating complex search spaces, producing optimal or near-optimal solutions in various test cases. For instance, the algorithm achieved superior performance in minimizing design costs in the Pressure Vessel and Welded Beam Design problems, as well as effectively handling the complex landscapes of the CEC2020 and CEC2022 benchmarks. These results affirm the Hybrid FOX-TSA algorithm as a powerful and adaptable tool for tackling complex optimization problems, particularly in highdimensional and multimodal landscapes. The integration of statistical analyses, such as t-tests and Wilcoxon signed-rank tests, further supports the statistical significance of its performance improvements.

关键词： Multi-objective optimization Hybrid optimization FOX-TSA algorithm CEC benchmark suites Particle swarm optimization Grey wolf optimizer Engineering design problems Convergence analysis algorithm performance

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Exploratory literature review and scientometric analysis of artificial intelligence applied to geopolymeric materials

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 145卷

作者： de Carvalho, Aldo Ribeiro Pita, Romario Parreira de Oliveira, Thais Mayra Silva, Guilherme Jorge Brigolini Mendes, Julia Castro Univ Fed Ouro Preto Postgrad Programme Civil Engn BR-35402163 Ouro Preto MG Brazil Univ Fed Juiz de Fora Postgrad Programme Civil Engn BR-36036900 Juiz De Fora MG Brazil

The goal of this work is to present and analyze studies that link artificial intelligence (AI) and geopolymer composites through an exploratory literature review. The systematic search comprised articles in the Scopus database, from 2000 to 2023. The results from 48 articles show that AI has been applied mainly to predict compressive strength and slump. Comparing the AI techniques to their R2, no clear trend was observed, i.e., there was no overall best algorithm;and their performance was not directly related to database size. Still, Random Forest obtained superior results in articles that compared multiple techniques. Some of the main gaps identified are the lack of studies with geopolymers synthesized in acidic environments or with waste-based or nanomaterials;and that AI has not been largely applied to properties besides compressive strength (e.g., fire resistance, durability, porosity, and thermal properties). Regarding transparency, authors have seldom publicized their codes, impairing proper review of the models. In conclusion, if properly and ethically adopted, AI is a promising tool for the development of geopolymer composites, which can optimize time and employee resources;and many applications remain unexplored.

关键词： Geopolymer Alkali-activated composite Compressive strength prediction Slump prediction Artificial intelligence algorithm performance

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