Article
Biochemical Research Methods
Alex Foote, Amina Asif, Nasir Rajpoot, Fayyaz Minhas
Summary: This paper presents the first domain-specific Robustness Evaluation and Enhancement Toolbox (REET) for computational pathology applications. It offers a set of algorithmic strategies for assessing the robustness of predictive models with specialized image transformations and also enables efficient and robust training of deep learning pipelines in computational pathology. The Python implementation of REET can be found at https://github.com/alexjfoote/reetoolbox.
Article
Engineering, Electrical & Electronic
Yankun Xia, Wenzhang Tang
Summary: This study proposes a method for calculating harmonic impedance in the presence of different background harmonics using Bayesian optimized Gaussian process regression. The accuracy and robustness of the method under different background harmonics are analyzed, and its effectiveness is verified through case analysis.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Hoai Phuong Le, Juergen Branke
Summary: This article discusses the use of Bayesian optimization to find robust solutions, which are expected to perform well even with disturbances and noise. The author proposes a variant of the knowledge gradient acquisition function and provides analytical expressions for uniformly and normally distributed disturbances. Empirical evaluation shows that the new acquisition function performs better than alternative approaches.
ENGINEERING OPTIMIZATION
(2022)
Article
Computer Science, Artificial Intelligence
Jun Guo, Wei Bao, Jiakai Wang, Yuqing Ma, Xinghai Gao, Gang Xiao, Aishan Liu, Jian Dong, Xianglong Liu, Wenjun Wu
Summary: Deep neural networks have achieved remarkable performance but are vulnerable to adversarial examples, leading to the need for evaluating and benchmarking model robustness. Current evaluations lack an understanding of defense limitations and weaknesses, resulting in an arms race between attack and defense. To address this, the authors propose a comprehensive evaluation framework with 23 metrics that consider both data and model perspectives. Through large-scale experiments, they demonstrate the effectiveness of their framework in inspiring deeper understanding and improving model robustness.
PATTERN RECOGNITION
(2023)
Article
Materials Science, Multidisciplinary
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Summary: The study introduces a new nonlocal gradient method DGS for optimizing highly multi-modal loss functions. However, the method is currently limited to unconstrained optimization problems, while this research extends the method to constrained inverse design frameworks to achieve better design outcomes.
MATERIALS & DESIGN
(2021)
Article
Chemistry, Applied
Leonardo Valderrama, Patricia Valderrama, Eduardo Carasek
Summary: The study developed a classification model to detect the content of Carbendazim in grape juices using portable UV-Visible spectroscopy and partial least squares discriminant analysis, with sensitivity and specificity ranging from 83 to 100%. The model showed robustness in evaluating grape juices from different grape varieties, and VIP scores were used to identify key variables. This approach is fast, requires minimal sample preparation, cost-effective, solvent-free, and contributes to quality control in the juice industry.
Article
Automation & Control Systems
Haiquan Zhao, Boyu Tian, Badong Chen
Summary: In this paper, a robust stable iterative maximum correntropy criterion UKF (RS-IMCC-UKF) is proposed by using nonlinear measurement function directly and numerical stability methods, achieving more accurate results.
Review
Computer Science, Theory & Methods
Nuno Laranjeiro, Joao Agnelo, Jorge Bernardino
Summary: With the increasing complexity and widespread use of computer systems, obtaining assurances regarding their robustness has become of vital importance. This survey discusses the state of the art on software robustness assessment, with emphasis on key aspects like types of systems being evaluated, assessment techniques used, the types of faults used, and how system behavior is classified. The survey concludes with the identification of gaps and open challenges related with robustness assessment.
ACM COMPUTING SURVEYS
(2021)
Article
History & Philosophy Of Science
Ryan O'Loughlin, Dan Li
Summary: The paper discusses the challenges of applying robustness analysis to economic models, points out the author's misconception of the goal of robustness analysis, and proposes solutions: error analysis and independent empirical support.
Article
Automation & Control Systems
Xiangrui Zhang, Chunyue Song, Jun Zhao, Xiaogang Deng
Summary: This article proposes a robust domain adaptation method to mitigate the negative effect of sensor degradation on soft sensor modeling. By decomposing industrial data into Gaussian domains, a domain discrepancy indicator is designed for domain adaptation and process mode recognition. Furthermore, a domain mapping is applied to correct the drifted online input data, enhancing the robustness of the soft sensor.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Biology
Stefan Landmann, Caroline M. Holmes, Mikhail Tikhonov
Summary: Bacteria can learn the structure of environmental fluctuations through evolution, while also inferring and acting upon environmental statistics through physiological mechanisms. Research shows that a common regulatory motif is sufficient for bacteria to learn the statistical structure of the environment and translate this information into predictive behavior, accomplishing these tasks near-optimally.
Article
Mathematics
Angel Felipe, Maria Jaenada, Pedro Miranda, Leandro Pardo
Summary: In this paper, we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. The robustness of MDPDGE is examined through influence function analysis. We provide constrained estimators to accommodate restrictions of the underlying distribution in practical situations, and derive robust test statistics for testing null hypotheses.
Article
Geochemistry & Geophysics
Weiwei Xu, Yanhui Zhou, Xiaokai Wang, Wenchao Chen
Summary: This paper proposes a robust sparse representation model based on a mixture of Gaussian distribution for suppressing seismic erratic noise. Experimental results demonstrate that the method can effectively suppress noise and preserve useful signal.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Automation & Control Systems
Qing Cai, Sameer Alam, Mahardhika Pratama, Jiming Liu
Summary: This article explores the robustness of multipartite networks under node failures and proposes a generic percolation theory for evaluating their robustness. The experiments demonstrate a good agreement between simulation results and the proposed theory.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Andrea Patane, Arno Blaas, Luca Laurenti, Luca Cardelli, Stephen Roberts, Marta Kwiatkowska
Summary: This paper presents a framework for analyzing the adversarial robustness of GPs by computing bounds on the prediction range. The algorithm is capable of handling regression and classification tasks and is applicable to most commonly used kernels.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Health Care Sciences & Services
Kari Blindheim, Mads Solberg, Ibrahim A. Hameed, Rigmor Einang Alnes
Summary: This pilot study aims to gain insight about the experiences of residents and healthcare professionals when interacting with the social robot Pepper in long-term care facilities. The qualitative content analysis identified three major themes: activity, joy and ambivalence; challenges when introducing social robots in contexts of care; and thoughts about the future. The study found that while there were opportunities for novel activities and actions, there were also concerns and challenges.
INFORMATICS FOR HEALTH & SOCIAL CARE
(2023)
Article
Computer Science, Hardware & Architecture
Mingxuan Zhang, Muhammad Umair Hassan, Dongmei Niu, Xiuyang Zhao, Raheel Nawaz, Ibrahim A. Hameed, Saeed-Ul Hassan
Summary: This paper presents an automatic dense correspondence method for matching the mesh vertices of two 3D shapes under near-isometric and non-rigid deformations. The method combines three types of graphic structure information and includes three major steps: describing the vertices based on three types of graphical information, formulating the match as an optimization problem, and resolving the optimal solution using the projected descent optimization procedure. The method achieves superior performance to existing methods in quantitative and qualitative evaluations on challenging 3D shape matching datasets.
Article
Physics, Multidisciplinary
Ibrahim A. Hameed, Luay Hashem Abbud, Jaafar Ahmed Abdulsaheb, Ahmad Taher Azar, Mohanad Mezher, Anwar Ja'afar Mohamad Jawad, Wameedh Riyadh Abdul-Adheem, Ibraheem Kasim Ibraheem, Nashwa Ahmad Kamal
Summary: A disturbance estimation and rejection technique based on the improved active disturbance rejection control (IADRC) approach is proposed and verified on a ground two-wheel differential drive mobile robot. The IADRC is adopted to eliminate the effect of system uncertainties and external torque disturbance on both wheels. A novel nonlinear sliding mode extended state observer (NSMESO) is used to observe and cancel the generalized disturbance in real-time. Numerical simulations show a significant reduction in the ITAE index for both wheels, validating the efficacy of the proposed dynamic speed controller in damping the chattering phenomena and providing high insusceptibility to torque disturbance.
Article
Chemistry, Multidisciplinary
Ahmad Taher Azar, Drai Ahmed Smait, Sami Muhsen, Moayad Abdullah Jassim, Asaad Abdul Malik Madhloom AL-Salih, Ibrahim A. Hameed, Anwar Ja'afar Mohamad Jawad, Wameedh Riyadh Abdul-Adheem, Vincent Cocquempot, Mouayad A. Sahib, Nashwa Ahmad Kamal, Ibraheem Kasim Ibraheem
Summary: In this paper, a Nonlinear Higher Order Extended State Observer (NHOESO) is proposed to replace the Linear Extended State Observer (LESO) in Conventional Active Disturbance Rejection Control (C-ADRC) solutions. The NHOESO extends the standard LESO by incorporating a two-term smooth nonlinear function with saturation-like characteristics. It allows for precise observation of generalized disturbances with higher-order derivatives. The stability of the NHOESO is analyzed using the Lyapunov method. Simulation results on an uncertain nonlinear Single-Input-Single-Output (SISO) system with time-varying external disturbances demonstrate the effectiveness of the proposed NHOESO in handling generalized disturbances compared to other ESOs.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Syed Adil Hussain, Muhammad Umair Hassan, Wajeeha Nasar, Sara Ghorashi, Mona M. Jamjoom, Abdel-Haleem Abdel-Aty, Amna Parveen, Ibrahim A. Hameed
Summary: The analysis of individuals' movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2023)
Article
Computer Science, Information Systems
Arezki Fekik, Ahamad Taher Azar, Ibrahim A. Hameed, Mohamed Lamine Hamida, Karima Amara, Hakim Denoun, Nashwa Ahmad Kamal
Summary: Many methods have been developed to improve the photovoltaic (PV) production by achieving the maximum power point (MPP) generated by PV fields. The optimized steepest gradient technique (OSGM) is a promising methodology for extracting the maximum power produced by a PV field with a multicell series converter. OSGM uses the derivatives of the power function to find the optimal voltage and achieve the MPP, demonstrating faster response time, fewer oscillations, and minimal energy loss.
Article
Mathematics
Sundarapandian Vaidyanathan, Ahmad Taher Azar, Ibrahim A. Hameed, Khaled Benkouider, Esteban Tlelo-Cuautle, Brisbane Ovilla-Martinez, Chang-Hua Lien, Aceng Sambas
Summary: This research paper models a new 3-D chaotic jerk system with a stable equilibrium and shows that it exhibits hidden attractors. Bifurcation analysis is performed on the system, revealing its multistability with coexisting attractors. Backstepping control is applied to synchronize a pair of new jerk systems, with Lyapunov stability theory used to establish synchronization results. The FPGA design of the jerk system is implemented using the FPGA Zybo Z7-20 development board, and experimental attractors are found to align with simulation results.
Article
Multidisciplinary Sciences
Muhammad Umair Hassan, Saleh Alaliyat, Raheem Sarwar, Raheel Nawaz, Ibrahim A. Hameed
Summary: Computer science graduates often lack industry-relevant skills when transitioning from academics to industry. This study proposes a framework that uses deep learning and big data to map the required skills with those acquired by computing graduates, and recommends enhancing the computing curriculum to align with industry needs. The framework consists of four layers: data, embedding, mapping, and curriculum enhancement.
Article
Computer Science, Interdisciplinary Applications
Di Wu, Jincheng Liu, Manuel Cordova, Christina Carrozzo Hellevik, Jakob Bonnevie Cyvin, Allan Pinto, Ibrahim A. Hameed, Helio Pedrini, Ricardo da Silva Torres, Annik Magerholm Fet
Summary: Marine plastic pollution is widespread and poses threats to organisms and human health. Monitoring and registering litter is crucial, and the PlastOPol system integrates external data sources and citizen science initiatives to achieve this. The system employs machine learning for litter detection and registration, while also involving citizen scientists for ongoing improvement. It also includes a geographic visualization tool for decision-makers to analyze litter distribution. The PlastOPol system has the potential to connect citizens, researchers, and decision-makers in addressing marine litter.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Computer Science, Artificial Intelligence
Faisal Jamil, Ibrahim A. Hameed
Summary: This paper develops an intelligent student evaluation model based on a predictive optimization approach that considers various factors to evaluate students' answers. A deep neural network is used to learn from training data, and particle swarm optimization and gradient descent are used to optimize weighting parameters. The proposed model is validated using real exam data. The end goal is to provide grades for students' answers given as input in the developed platform.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Mohamed Abdel-Basset, Reda Mohamed, Ibrahim M. Hezam, Karam M. Sallam, Ahmad M. Alshamrani, Ibrahim A. Hameed
Summary: This paper introduces an improved Kepler optimization algorithm for solving the 0-1 Knapsack problem and compares it with several other optimization algorithms. The results show that the algorithm outperforms other algorithms in most cases.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Mathematics
Mohamed Abdel-Basset, Ibrahim Alrashdi, Hossam Hawash, Karam Sallam, Ibrahim A. Hameed
Summary: The need for efficient and reliable disease diagnosis in smart cities has become increasingly serious in the aftermath of the COVID-19 pandemic. This study introduces BFLPD, a blockchain-based federated learning framework tailored for the diagnosis of pandemic diseases in smart cities, with a focus on COVID-19. BFLPD utilizes decentralized blockchain technology to design collaborative intelligence for automated diagnosis while ensuring trustworthiness metrics such as privacy, security, and data sharing.
Article
Computer Science, Information Systems
Mohamed Abdel-Basset, Reda Mohamed, Ibrahim M. Hezam, Karam M. Sallam, Ahmad M. Alshamrani, Ibrahim A. Hameed
Summary: This paper adapts several recently published metaheuristic algorithms to optimize the NP-hard problem of decision and resource allocation in mobile edge computing enabled blockchain networks. Different encoding schemes are used to represent the mining decisions, transmission power, and computing resources of the miners, and three algorithm variants are proposed for optimization.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Mathematics, Interdisciplinary Applications
Mohamed Jasim Mohamed, Bashra Kadhim Oleiwi, Layla H. Abood, Ahmad Taher Azar, Ibrahim A. Hameed
Summary: This paper proposes six control structures that combine the advantages of PID controller, integer and fractional order, and neural networks to design hybrid controllers for solving the trajectory tracking problem of a 2-Link Rigid Robot Manipulator (2-LRRM).
FRACTAL AND FRACTIONAL
(2023)
Article
Computer Science, Artificial Intelligence
Syed Hammad Hussain Shah, Anniken Susanne T. Karlsen, Mads Solberg, Ibrahim A. Hameed
Summary: Aging poses challenges to elderly individuals' social lives due to declining physical abilities, but group exercise in long-term care facilities is crucial for maintaining their physical and social well-being. However, accommodating these needs can be difficult due to staff shortages and lacking resources. To address this, a robotic exercise coach could be helpful. However, accurate and efficient human activity recognition is necessary for intelligent human-robot interaction in this context.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
