Article
Computer Science, Artificial Intelligence
Zhiwang Zhang, Jing He, Hui Zheng, Jie Cao, Gang Wang, Yong Shi
Summary: This paper proposes a new classifier method called AMSLC, which addresses complex and redundant data classification problems by alternating minimization and provides high predictive accuracy and interpretability.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Computer Science, Artificial Intelligence
Pei Li, Wenlin Zhang, Chengjun Lu, Rui Zhang, Xuelong Li
Summary: A novel robust kernel principal component analysis method with optimal mean (RKPCA-OM) is proposed to enhance the robustness of KPCA by automatically eliminating the optimal mean. The theoretical proof guarantees the convergence of the algorithm and the obtained optimal subspaces and means. Exhaustive experimental results validate the superiority of the proposed method.
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Da Zhao, Yan-Ru Guo, Xiang-Yu Hua
Summary: Feature selection is crucial for solving high-dimensional regression problems by extracting relevant features containing useful information to improve learning performance. The sparse LSSVR based on L-p-norm offers an effective method for feature selection, avoiding singularity issues and ensuring convergence. Experimental results demonstrate the effectiveness of SLSSVR in both feature selection ability and regression performance.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Biochemical Research Methods
Charles C. David, Chris S. Avery, Donald J. Jacobs
Summary: JEDi software is an upgraded tool that employs multithreading and user-friendly interface for rapid investigation of conformational motions of biopolymers, including multiple chain proteins. It offers options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance, correlation, and partial correlation matrices.
BMC BIOINFORMATICS
(2021)
Article
Chemistry, Analytical
Jamile Mohammad Jafari, Roma Tauler, Hamid Abdollahi
Summary: The paper introduces a new method - Balanced Scaling (BS) method, combined with Multivariate Curve Resolution Alternating Least Squares (BSMCR-ALS) method, for analyzing data sets with heteroscedastic noise, showing good performance especially in environmental data analysis. Comparisons with other methods revealed that BS-MCR-ALS and MLPCA-MCR-ALS solutions were very similar in performance.
MICROCHEMICAL JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
James A. Mckelvy, Irina Novikova, Eugeniy E. Mikhailov, Mario A. Maldonado, Isaac Fan, Yang Li, Ying-Ju Wang, John Kitching, Andrey B. Matsko
Summary: In this study, an unsupervised machine learning algorithm and nonlinear dimensionality reduction technique were used to accurately determine the longitudinal angle of the local magnetic field through spectroscopic observations of EIT spectra. The algorithm represented each EIT spectrum measurement as a coordinate in a new reduced dimensional feature space, and a supervised support vector regression machine modeled the relationship between the KPCA projections and field direction. The results showed that the proposed method could predict the longitudinal angle of the local magnetic field with high accuracy and resolution.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Spectroscopy
Ali S. Abdelhameed, Mohamed W. Attwa, Mohamed I. Attia, Amer M. Alanazi, Obaid S. Alruqi, Haitham AlRabiah
Summary: New precise, responsive, and selective univariate and multivariate chemometric spectrophotometric methods were developed for determination of vandetanib, dasatinib, and sorafenib in various samples, showing promising results for pharmaceutical analysis.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Computer Science, Artificial Intelligence
Yunlong Gao, Tingting Lin, Jinyan Pan, Feiping Nie, Youwei Xie
Summary: This paper proposes a new technique called Fuzzy Sparse Deviation Regularized Robust Principal Component Analysis (FSD-PCA) to improve the robustness of Principal Component Analysis (PCA) to noise samples. By introducing sparse deviation and fuzzy weighting, FSD-PCA is able to process noise and principal components separately, thus enhancing its ability to retain principal component information.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Statistics & Probability
Oh-Ran Kwon, Zhaosong Lu, Hui Zou
Summary: Sparse principal component analysis (PCA) aims to find principal components without sacrificing the fidelity. Most existing methods produce correlated components, while many applications prefer uncorrelated ones. This article proposes an exactly uncorrelated sparse PCA method named EUSPCA, which solves a non-smooth constrained non-convex manifold optimization problem. EUSPCA produces uncorrelated components and maintains a similar or better level of fidelity compared to existing methods.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2023)
Article
Agricultural Engineering
Jinming Liu, Shuo Jin, Changhao Bao, Yong Sun, Wenzhe Li
Summary: This study proposed a rapid detection method based on near-infrared reflectance spectroscopy to measure the contents of cellulose, hemicellulose, and lignin in corn stover. By constructing a BiPLS-PCA-SVM model, the study demonstrated an alternative strategy for detecting lignocellulosic components in pre-treated corn stover in the anaerobic digestion process.
BIORESOURCE TECHNOLOGY
(2021)
Article
Statistics & Probability
H. Robert Frost
Summary: EESPCA is a novel technique for sparse principal component analysis, offering significant improvement in computational speed and accuracy in identifying principal component loadings. Compared to other methods, it achieves faster computation while maintaining low error rates.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2022)
Article
Spectroscopy
Feng Chen, Wanjie Lu, Yanwu Chu, Deng Zhang, Cong Guo, Zhifang Zhao, Qingdong Zeng, Jiaming Li, Lianbo Guo
Summary: Fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) is suitable for remote analysis and complex environments, but limited by fiber loss and attenuation; this study used linear and nonlinear models to quantify trace metal elements in pig iron, with the nonlinear SVM model showing the best performance.
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
(2021)
Article
Biology
Yixuan Qiu, Jing Lei, Kathryn Roeder
Summary: In this research, we combined the geometric structure of sparse principal component analysis with convex optimization to develop gradient-based algorithms that can efficiently handle high-dimensional data with provable numerical and statistical performance guarantees.
Article
Engineering, Environmental
Yongni Shao, Yutian Wang, Di Zhu, Xin Xiong, Zhengan Tian, Alexey V. Balakin, Alexander P. Shkurinov, Duo Xu, Yimei Wu, Yan Peng, Yiming Zhu
Summary: In this study, microalgae and terahertz spectroscopy technology were used to establish a prediction model for heavy metal concentration in water. The results showed that the detection time was significantly reduced and the accuracy was improved compared to traditional methods. This new method provides a fast and accurate way for biological monitoring of heavy metal pollution in water.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Spectroscopy
Syeda Shafaq, Muhammad Irfan Majeed, Haq Nawaz, Nosheen Rashid, Maria Akram, Nimra Yaqoob, Ayesha Tariq, Samra Shakeel, Anwar ul Haq, Mudassar Saleem, Muhammad Zaman Nawaz, Rana Zaki Abdul Bari
Summary: Raman spectroscopy has been investigated for its potential in analyzing solid dosage forms of Losartan potassium in the pharmaceutical field. The spectral data showed a gradual change in Raman spectral features associated with the active pharmaceutical ingredient (API) of Losartan potassium as the concentration changed. Principal Component Analysis (PCA) was used for classification, and Partial Least Square Regression (PLSR) analysis was performed for quantitative analysis. The results demonstrated that Raman spectroscopy can be used for quick and reliable quantitative analysis of pharmaceutical solids.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)