Review
Medical Laboratory Technology
Maryam Saberi-Karimian, Zahra Khorasanchi, Hamideh Ghazizadeh, Maryam Tayefi, Sara Saffar, Gordon A. Ferns, Majid Ghayour-Mobarhan
Summary: Data mining utilizes mathematical sciences, statistics, artificial intelligence, and machine learning to determine relationships between variables in large datasets, and has been increasingly applied to disease prediction, diagnosis, and survival outcome analysis. Through analyzing biochemical biomarker data, machine learning provides significant assistance in evaluating disease risks, demonstrating substantial potential for clinical applications.
CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES
(2021)
Article
Medical Laboratory Technology
Maryam Saberi-Karimian, Amin Mansoori, Maryam Mohammadi Bajgiran, Zeinab Sadat Hosseini, Amir Kiyoumarsioskouei, Elias Sadooghi Rad, Mostafa Mahmoudi Zo, Negar Yeganeh Khorasani, Mohadeseh Poudineh, Sara Ghazizadeh, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan
Summary: This study used machine learning approaches to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM). The results showed that waist circumference (WC) was the most important predictor for T2DM.
JOURNAL OF CLINICAL LABORATORY ANALYSIS
(2023)
Article
Automation & Control Systems
Behnam Tavakkol, Myong K. Jeong, Susan L. Albin
Summary: This paper discusses the measurement of scatter and classification methods for uncertain data objects, introducing covariance matrix, within scatter matrix, and between scatter matrix as measures of scatter, and extending the concept of Fisher discriminant analysis. Experimental results show that the developed uncertain Fisher discriminants outperform other methods in classifying uncertain data objects.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Burcu Caglar Gencosman, Tulin Inkaya
Summary: The study focuses on the formal employment of Syrian refugees in Turkey and proposes a data mining methodology to analyze their profiles using clustering, classification, and association rule mining. The results can inform policies to facilitate immigrant labor market integration and potentially be applied to analyze immigration data in other countries as well.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Environmental Studies
Reza ShakorShahabi, Ali Nouri Qarahasanlou, Seyed Reza Azimi, Adel Mottahedi
Summary: Most mines in Iran are categorized as Artisanal and Small-scale mines (ASM), contributing significantly to the country's employment and mining sector production. Despite facing challenges, ASM requires fewer resources compared to large mines for development and restoration. Utilizing data mining methods to evaluate and classify these mines can provide valuable insights for making strategic decisions.
Article
Green & Sustainable Science & Technology
Qian He, Hong He
Summary: Cloud computing is gaining more importance due to the growth of technologies and user needs in the field of information technology, but its open structure also makes it more vulnerable to security threats.
Article
Environmental Sciences
David A. Olson, John H. Offenberg, Michael Lewandowski, Tadeusz E. Kleindienst, Kenneth S. Docherty, Mohammed Jaoui, Jonathan Krug, Theran P. Riedel
Summary: This research utilized data mining methods to analyze laboratory data and identified key factors influencing the formation of secondary organic aerosol (SOA).
ATMOSPHERIC ENVIRONMENT
(2021)
Article
Construction & Building Technology
Hao Zhou, Juan Yu, Yang Zhao, Chenchen Chang, Jiajun Li, Borong Lin
Summary: The study focused on extracting occupant presence information from indoor environment data, and found that incorporating information of light and AC operations significantly improved recognition accuracy.
ENERGY AND BUILDINGS
(2021)
Article
Health Care Sciences & Services
Jihye Lim
Summary: This study aimed to identify ischemic heart disease-related factors and vulnerable groups in Korean middle-aged and older women. The prevalence of ischemic heart disease was 2.77% and factors associated with the disease included age, family history, hypertension, dyslipidemia, stroke, arthritis, and depression. The most vulnerable group were women who had hypertension, a family history of ischemic heart disease, and were menopausal. This study provides valuable data for national policy decision making in the management of chronic diseases.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Chemistry, Multidisciplinary
Nidia Rodriguez-Mazahua, Lisbeth Rodriguez-Mazahua, Asdrubal Lopez-Chau, Giner Alor-Hernandez, Isaac Machorro-Cano
Summary: Data warehousing provides a systematic approach for enterprise administrators to utilize data effectively for strategic decision-making. One of the main challenges faced by data warehouse designers is fragmentation, with FTree being a horizontal fragmentation method that uses decision trees to improve efficiency in data warehouse design. Experimental results confirm the efficacy of this method in improving OLAP query response time and data loading maintenance tasks.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Areej Fatemah Meghji, Naeem Ahmed Mahoto, Yousef Asiri, Hani Alshahrani, Adel Sulaiman, Asadullah Shaikh
Summary: Higher educational institutes generate massive amounts of student data, and the educational data mining approach can extract useful knowledge from it. This research uses classification to predict student performance and proposes a student segmentation framework to identify different levels of academic performance. Experimental results show the effectiveness of the proposed framework in classifying students into multiple performance levels using a small subset of courses.
PEERJ COMPUTER SCIENCE
(2023)
Article
Green & Sustainable Science & Technology
Kuo-Chih Cheng, Mu-Jung Huang, Cheng-Kai Fu, Kuo-Hua Wang, Huo-Ming Wang, Lan-Hui Lin
Summary: This study integrates decision tree algorithm with Apriori algorithm to establish a stock investment decision model for sports and leisure related industries, aiming to provide investors with important reference for minimizing investment risks and maximizing profits.
Article
Chemistry, Physical
Afshin Tatar, Zohre Esmaeili-Jaghdan, Amin Shokrollahi, Abbas Zeinijahromi
Summary: This study used machine learning techniques to predict hydrogen solubility in hydrocarbons, finding that the GB model in ensemble methods has the highest accuracy. The results of the study contribute to a better understanding of hydrogen solubility in hydrocarbons.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Engineering, Electrical & Electronic
Jinhui Duan, Rui Gao
Summary: By analyzing the feasibility of data mining technology in college English teaching, a new teaching program was proposed which led to an increase in the qualified rate of students' English performance.
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
(2021)
Article
Education & Educational Research
Seda Goktepe Korpeoglu, Sevda Goktepe Yildiz
Summary: This study estimated middle school students' attitudes towards STEM using data mining algorithms, and found that grade level, mother's occupation, and academic achievement level are the most influential factors in shaping students' attitudes towards STEM.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Engineering, Electrical & Electronic
Tian Tian, Ishwar Sethi, Delie Ming, Nilesh Patel
IEEE SIGNAL PROCESSING LETTERS
(2015)
Article
Engineering, Electrical & Electronic
Mahdi Khosravy, Neeraj Gupta, Ninoslav Marina, Ishwar K. Sethi, Mohammad Reza Asharif
IEEE SIGNAL PROCESSING LETTERS
(2017)
Editorial Material
Health Care Sciences & Services
Ashish Khare, Moongu Jeon, Ishwar K. Sethi, Benlian Xu
JOURNAL OF HEALTHCARE ENGINEERING
(2017)
Article
Engineering, Electrical & Electronic
Neeraj Gupta, Mahdi Khosravy, Kumar Saurav, Ishwar K. Sethi, Ninoslav Marina
ELECTRICAL ENGINEERING
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Gaurav Tyagi, Nilesh Patel, Ishwar K. Sethi
2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Gaurav Tyagi, Nilesh Patel, Ishwar Sethi
2015 IEEE 16TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION
(2015)
Proceedings Paper
Computer Science, Information Systems
Eralda Caushaj, Huirong Fu, Haissam Badih, Ishwar Sethi, Ye Zhu, Supeng Leng
PROCEEDINGS OF THE 2012 INFORMATION SECURITY CURRICULUM DEVELOPMENT CONFERENCE (INFOSEC CD '12)
(2012)
Article
Computer Science, Artificial Intelligence
Mingkun Li, Ishwar K. Sethi
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2006)
Article
Computer Science, Artificial Intelligence
Mingkun Li, Ishwar K. Sethi
PATTERN RECOGNITION
(2006)
Article
Engineering, Biomedical
Dingguo Chen, Jun Tan, Vipin Chaudhary, Ishwar K. Sethi
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
(2006)
Article
Engineering, Biomedical
Jun Tan, Dingguo Chen, Vipin Chaudhary, Ishwar Sethi
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
(2006)
Article
Computer Science, Artificial Intelligence
Jie Ouyang, Nilesh Patel, Ishwar K. Sethi
INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS
(2011)
Article
Engineering, Electrical & Electronic
Daniela Stan Raicu, Ishwar K. Sethi
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
(2006)
Article
Computer Science, Information Systems
Aiyesha Ma, Nilesh Patel, Mingkun Li, Ishwar K. Sethi
MULTIMEDIA CONTENT REPRESENTATION, CLASSIFICATION AND SECURITY
(2006)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)