Article
Computer Science, Interdisciplinary Applications
Loet Leydesdorff, Lutz Bornmann
Summary: This study discusses the disruption indicator and critical transition indicator in the development of science and technology, quantifying these changes using citations and references. The concept is illustrated with three examples and the calculation of indicators using Web of Science data is demonstrated. The process is automated and can be upscaled for future research.
JOURNAL OF INFORMETRICS
(2021)
Article
Engineering, Civil
Baidurya Bhattacharya
Summary: Progressive collapse is an important mode of system failure where a relatively minor local damage can lead to the collapse of a significant portion of a structure. Structural robustness is crucial in confining initial damages and ranking various structures in terms of their susceptibility to progressive collapse. A new robustness index for binary, coherent structural systems has been proposed to measure a structure's indifference to initial damage and has been demonstrated on an indeterminate truss structure.
Article
Engineering, Civil
Baixue Ge, Sunyong Kim
Summary: This paper introduces an approach to determine the most appropriate probabilistic parameters to update the damage propagation prediction model, which includes a comparison-based method and parametric global sensitivity analysis (GSA).
STRUCTURE AND INFRASTRUCTURE ENGINEERING
(2021)
Article
Chemistry, Analytical
Mehmet Ali Balci, Omer Akguller, Larissa M. Batrancea, Lucian Gaban
Summary: This study proposes a unique kernel function for similarity determination and categorization of point cloud data. The kernel function is determined by the proximity of geodesic route distributions in graphs representing the underlying discrete geometry. The research demonstrates the efficiency of this kernel for similarity measures and point cloud categorization.
Article
Multidisciplinary Sciences
Takuma Ishihara, Kouji Yamamoto, Kouji Tahata, Sadao Tomizawa
Summary: This paper investigates the asymmetry in square contingency table and proposes a new measure of symmetry. It is shown that the estimator of the proposed measure is asymptotically independent for a large sample size.
Article
Physics, Multidisciplinary
Salman Izadkhah, Ebrahim Amini-Seresht, Ali Moradian
Summary: This paper provides sufficiently convenient conditions for the majorization order, shedding new light on the comparison of quantum systems. Stochastic comparisons of density matrices under mixing operations and average magnitudes of nondecreasing energy functions are performed. The order property of quantum densities is determined using Kullback-Leibler relative entropy.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Divyanshu Awasthi, Priyank Khare, Vinay Kumar Srivastavaa
Summary: A transform domain digital image watermarking technique using bacterial foraging optimization (BFO) for telemedicine applications is proposed. The technique shows significant improvement in robustness and imperceptibility compared to other existing techniques, with low computational complexity.
JOURNAL OF ELECTRONIC IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaolong Wang, Hong Zhang, Guohua Peng
Summary: This paper proposes a general method to quantitatively measure the efficacy of individual image features and feature combinations in loop closure detection (LCD). Various statistical distances are used to evaluate feature combinations, and an unsupervised algorithm is proposed to optimize feature combinations. Experimental results show that the proposed indices can measure the efficacies of image features and improve the precision of LCD when maximizing the indices.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2022)
Article
Engineering, Civil
Mohammad Hassan Daneshvar, Alireza Gharighoran, Seyed Alireza Zareei, Abbas Karamodin
Summary: This study proposes hybrid distance methods to address the challenge of applying high-dimensional damage-sensitive features in data-driven methods, achieving accurate localization and quantification of structural damage. The two hybrid distance methods effectively reduce feature sample size, improve damage detection performance, and accurately locate and quantify damage under varying environmental and operational conditions.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2021)
Article
Computer Science, Information Systems
Nisha S. Chandran, Durgaprasad Gangodkar
Summary: This paper proposes an approach to address the semantic gap problem in CBIR by performing clustering-based retrieval, where similar images are grouped using probabilistic semi-supervised clustering to form macro clusters for semantic matching. Experimental evaluations on different databases demonstrate the effectiveness of the proposed method.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Cardiac & Cardiovascular Systems
M. Pille, A. Gapelyuk, K. Berg, S. Bannasch, J. Mockler, L. -S Park, J. -W Park, N. Wessel
Summary: This study explored the use of cardiac magnetic field mapping to detect patients with clinically suspected myocarditis, providing a rapid, non-invasive, and cost-effective screening method. By analyzing historical and recent data, using Kullback-Leibler entropy for dimensionality reduction and topological quantification, and applying linear discriminant analysis, the study successfully distinguished between myocarditis patients and healthy controls.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2023)
Article
Environmental Studies
Yuan Gao, Anyu Zhang, Yaojie Yue, Jing'ai Wang, Peng Su
Summary: This study proposes a method to assess the suitability of land for maize cultivation based on big data, with predicted results matching historical distribution. However, it also indicates a decrease in suitable areas for maize cultivation in the future.
Article
Mathematics
Teresa Aparicio, Inmaculada Villanua
Summary: This paper discusses the problem of selecting the best model from a set of overlapping binary models, and focuses on the case where neither of the competing models is correctly specified. The study concludes that, in general, all criteria perform well.
Article
Statistics & Probability
Mohsen Motavaze, Hooshang Talebi
Summary: In this study, we developed a new design criterion for model discrimination in factorial experiments, considering the potential for missing runs. The proposed Bayesian criterion allows for counting the missing probabilities of runs and takes into account the intercorrelation between the factorial effects. Numerical examples showed that the obtained criterion outperformed existing ones in differentiating rival designs for model discrimination. We also addressed the problem of design robustness to missing observations and suggested a ratio criterion for selecting robust designs.
Article
Computer Science, Artificial Intelligence
Rui She, Qiyu Kang, Sijie Wang, Wee Peng Tay, Yong Liang Guan, Diego Navarro Navarro, Andreas Hartmannsgruber
Summary: This paper investigates the matching of real-time images captured by on-vehicle cameras with landmark patches in an image database, which plays a crucial role in various computer perception tasks for autonomous driving. Current methods focus on local matching for regions of interest without considering the spatial relationships among image patches, which correspond to objects in the environment. In this study, a spatial graph is constructed to capture the spatial neighborhood information, and a joint feature and metric learning model with graph-based learning is proposed. The evaluation using several street-scene datasets demonstrates that the approach achieves state-of-the-art matching results.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Rinki Arya, R. K. Agrawal, Navjot Singh
KNOWLEDGE AND INFORMATION SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Jyoti Singh Kirar, R. K. Agrawal
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Akshansh Gupta, R. K. Agrawal, Jyoti Singh Kirar, Baljeet Kaur, Weiping Ding, Chin-Teng Lin, Javier Andreu-Perez, Mukesh Prasad
Article
Computer Science, Artificial Intelligence
Dhirendra Kumar, R. K. Agrawal, Hanuman Verma
Article
Medical Informatics
Harsh Bhasin, Ramesh Kumar Agrawal
BMC MEDICAL INFORMATICS AND DECISION MAKING
(2020)
Article
Computer Science, Artificial Intelligence
R. K. Agrawal, Baljeet Kaur, Surbhi Sharma
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
R. K. Agrawal, Baljeet Kaur, Parul Agarwal
Summary: The translated passage discusses the challenges of complex function optimization problems and proposes an enhanced Quantum behaved Particle Swarm Optimization (e-QPSO) algorithm to improve performance by using adaptive balance and re-initialization techniques. Results show that e-QPSO outperforms existing variants of QPSO, adaptive variants of PSO, and other evolutionary algorithms statistically.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Akshansh Gupta, Ramesh Kumar Agrawal, Jyoti Singh Kirar, Javier Andreu-Perez, Wei-Ping Ding, Chin-Teng Lin, Mukesh Prasad
Summary: This paper investigates mental task classification in brain-computer interfaces (BCIs) and suggests a method to enhance learning algorithm performance through feature selection. The findings show substantial improvements in the performance of the learning model after applying the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Clinical Neurology
Harsh Bhasin, R. K. Agrawal
Summary: This article proposes a deep learning approach based on a variant of genetic algorithms for classifying MCI converts and non-converts using structural magnetic resonance imaging data. The study explores the impact of activation functions and hyper-parameter selection on the model's performance. The proposed method demonstrates superior accuracy compared to existing methods and holds potential for effective diagnosis of MCI in clinical settings.
ALZHEIMERS & DEMENTIA
(2022)
Article
Computer Science, Artificial Intelligence
Parul Agarwal, R. K. Agrawal, Baljeet Kaur
Summary: In this paper, an enhanced multi-objective particle swarm optimization (EMOPSO) method is proposed to address multimodal and multi-objective optimization problems. The method combines Levy flight for exploration and a parameter for balancing exploration and exploitation to achieve diversity in both the decision and objective space. Experimental results demonstrate the superior performance of the EMOPSO method compared to other state-of-the-art algorithms on both synthetic and real-world problems.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Dhirendra Kumar, R. K. Agrawal, Puneet Kumar
Summary: In this article, a bias-corrected intuitionistic fuzzy c-means with spatial neighborhood information (SNI) method is proposed for MRI image segmentation, which handles artifacts such as noise and bias field effect. The method represents the image using intuitionistic fuzzy sets (IFSs) and takes advantage of IFS theory to address uncertainty and vagueness in the data. The SNI term helps retain fine image details in the segmentation process. Evaluation on publicly available Brain MRI datasets shows significant improvements in segmentation performance compared to other state-of-the-art methods.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Swati Rathi, Baljeet Kaur, R. K. Agrawal
Summary: This research introduces a unimodal framework for depression detection based on facial expressions and motion analysis, employing a hybrid dimensionality reduction approach. Experimental results on the DAIC-WOZ dataset show that ILDA outperforms CKSVM in depression classification and incurs lower computational cost.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Biology
Baljeet Kaur, Swati Rathi, R. K. Agrawal
Summary: This paper proposes a non-intrusive depression detection approach based on speech, which is reliable and computationally inexpensive. The proposed method outperforms traditional methods and has low computational complexity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Ashish Kumar, R. K. Agrawal, Leve Joseph
Summary: This paper proposes a new lightweight segmentation model, IterMiUnet, which significantly reduces the number of model parameters while maintaining network depth. It speeds up training and inference time and has potential applications in the medical field.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Utkarsh Niranjan, Anurag Singh, Ramesh Kumar Agrawal
JOURNAL OF COMPLEX NETWORKS
(2019)