Article
Engineering, Geological
Tao Yan, Shui-Long Shen, Annan Zhou, Xiangsheng Chen
Summary: This study presents a framework for predicting geological characteristics by integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross validation (K-CV). The accuracy of the SCA can be improved with the use of GS and K-CV. The proposed torque penetration index (TPI) and field penetration index (FPI) express the geological characteristics, while the elbow method (EM) and silhouette coefficient (Si) determine the types of geological characteristics.
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
(2022)
Article
Medicine, Research & Experimental
Evan L. Reynolds, Gulcin Akinci, Mousumi Banerjee, Helen C. Looker, Adam Patterson, Robert G. Nelson, Eva L. Feldman, Brian C. Callaghan
Summary: In participants with longstanding diabetes, neuropathy and kidney disease worsened during follow-up, despite stable to improving MetS components. Early metabolic intervention is necessary to prevent complications in such patients. The number of MetS components was associated with an increased rate of neuropathy progression, and SBP was associated with each complication.
Article
Urology & Nephrology
Helen C. Looker, Chunru Lin, Viji Nair, Matthias Kretzler, Michael Mauer, Behzad Najafian, Robert G. Nelson
Summary: This study suggests that higher serum PTENK27polyUb is associated with an increased risk for GFR decline and kidney failure in American Indians with type 2 diabetes. These findings contribute to our understanding of the mechanisms underlying kidney fibrosis.
AMERICAN JOURNAL OF KIDNEY DISEASES
(2022)
Article
Physics, Multidisciplinary
Zeyang Lin, Jun Lai, Xiliang Chen, Lei Cao, Jun Wang
Summary: This paper proposes a curriculum reinforcement learning method based on K-Fold Cross Validation, which improves the training performance and efficiency of algorithms by estimating the relativity score of task curriculum difficulty and sorting the curriculum tasks.
Article
Computer Science, Artificial Intelligence
Himanshu Gupta, Hirdesh Varshney, Tarun Kumar Sharma, Nikhil Pachauri, Om Prakash Verma
Summary: The study aimed to develop prediction models using deep learning and quantum machine learning techniques to reduce the lethality associated with diabetes. The results showed that the deep learning model outperformed the quantum machine learning model in terms of diabetes prediction accuracy.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Hanaa Salem, Mahmoud Y. Shams, Omar M. Elzeki, Mohamed Abd Elfattah, Jehad F. Al-Amri, Shaima Elnazer
Summary: This paper proposes an algorithm for diabetes classification in pregnant women using the Pima Indians Diabetes Dataset. The algorithm includes a preprocessing step to enhance the dataset's quality, a fuzzy KNN classifier with modified membership functions, and a grid search method to tune the classifier. The proposed TFKNN classifier outperforms other classifiers and has high performance in terms of accuracy, specificity, precision, and average AUC.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Analytical
Khalid Mahmood Aamir, Laiba Sarfraz, Muhammad Ramzan, Muhammad Bilal, Jana Shafi, Muhammad Attique
Summary: A study developed an interpretable early diagnosis model for diabetes using fuzzy logic, combining with cosine amplitude method to construct two fuzzy classifiers, designing fuzzy rules and evaluating the model's performance on a public dataset with an accuracy of 96.47%. The proposed model showed high prediction accuracy, suggesting its potential applications in accurate diabetes diagnosis in the healthcare sector.
Article
Materials Science, Multidisciplinary
Mohsin Ali Khan, Adeel Zafar, Furqan Farooq, Muhammad Faisal Javed, Rayed Alyousef, Hisham Alabduljabbar, M. Ijaz Khan
Summary: In this study, three artificial intelligence techniques were used to establish a reliable and accurate model to estimate the compressive strength of fly ash-based geopolymer concrete. The statistical error checks and criteria suggested in the literature were considered for the verification of the predictive strength of the models. The results showed that the ANFIS predictive model performed the best among the three models.
FRONTIERS IN MATERIALS
(2021)
Article
Chemistry, Physical
Claudia Barile, Caterina Casavola, Giovanni Pappalettera, Vimalathithan Paramsamy Kannan
Summary: In this study, the stages of damage evolution in AlSi10Mg specimens were successfully identified using Acoustic Emission (AE) technique and Convolutional Neural Network (CNN). Continuous Wavelet Transform (CWT) spectrograms were used for processing the AE signals, and a modified SqueezeNet-based CNN was trained with k-fold cross validation to improve classification efficiency. The trained network showed promising results in classifying AE signals from different damage evolution stages.
Article
Engineering, Multidisciplinary
Junxiang Wang, Changshu Zhan, Di Yu, Qiancheng Zhao, Zhijie Xie
Summary: This paper proposes a method based on a stacked sparse autoencoder combined with a softmax classifier for fault diagnosis of rolling bearings. The method extracts frequency-domain features of vibration signals using a stacked sparse autoencoder and utilizes an improved K-fold cross-validation to obtain pre-train set, train set, and test set. The performance of the model is evaluated based on accuracy, macro-precision, macro-recall, and macro-F1 score. The proposed model is validated with high accuracy using data from Case Western Reserve University and XJTU-SY.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Multidisciplinary Sciences
Onur Inan, Mustafa Serter Uzer
Summary: The article introduces a data reduction method named MKMA-RAC, aimed at eliminating noisy data in classification systems to improve performance. Through experiments on datasets related to Hepatitis, Liver Disorders, SPECT images and Statlog (Heart), it is demonstrated that the proposed method achieves higher classification success rates compared to traditional methods.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Chemistry, Physical
Zhaoqiu Lyu, Yang Yu, Bijan Samali, Maria Rashidi, Masoud Mohammadi, Thuc N. Nguyen, Andy Nguyen
Summary: This paper discusses the feasibility of using a novel machine learning approach with K-fold cross-validation to predict the torsional strength of Reinforced Concrete (RC) beams. The study discovers that by optimizing neural network parameters and utilizing K-fold cross-validation and genetic algorithms, the accuracy of the prediction model can be improved.
Article
Public, Environmental & Occupational Health
Anders Aasted Isaksen, Annelli Sandbaek, Lasse Bjerg
Summary: This study validated two register-based algorithms for classifying type 1 and type 2 diabetes in a general population using Danish register data. The classifiers were found to be accurate for most self-reported diabetes cases, but caution should be taken when interpreting the type of diabetes in cases with atypical age at onset.
CLINICAL EPIDEMIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Hong Zhu, Xizhao Wang, Ran Wang
Summary: Monotonic classification is a widespread task in real-life applications. Existing algorithms are sensitive to noise data, while the proposed FMKNN method constructs monotonic classifiers by taking advantage of fuzzy dominance relation, reducing the disturbance caused by noise.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Engineering, Aerospace
Zhixiang Wang, Yongjun Lei, Huiru Cui, Heyang Miao, Dapeng Zhang, Zeping Wu, Guanri Liu
Summary: This paper introduces a novel sliced splitting-based K-fold cross-validation (SSKCV) method to construct an improved radial basis function neural network (RBFNN) metamodel with enhanced generalization capabilities. The SSKCV method overcomes the high variance and loss of information in observed sample points, and the introduction of average expected prediction error (AEPE) as the loss function further evaluates the generalization error of the RBFNN metamodel. The SSKCV method has been demonstrated to achieve excellent generalization performance in high dimensional numerical benchmarks and stiffened cylindrical shells, outperforming other SSKCV variants and blind Kriging.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Thermodynamics
Serafettin Ekinci, Kazim Carman, Humar Kahramanli
Article
Computer Science, Interdisciplinary Applications
Gokhan Altan, Yakup Kutlu, Novruz Allahverdi
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2016)
Article
Computer Science, Artificial Intelligence
Semiye Demircan, Humar Kahramanli
NEURAL COMPUTING & APPLICATIONS
(2018)
Article
Mathematical & Computational Biology
Kursat Zuhtuogullari, Novruz Allahverdi, Nihat Arikan
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
(2013)
Article
Chemistry, Physical
Serafettin Ekinci, Ahmet Akdemir, Humar Kahramanli
SURFACE REVIEW AND LETTERS
(2013)
Article
Computer Science, Artificial Intelligence
Semiye Demircan, Humar Kahramanli Ornek
TRAITEMENT DU SIGNAL
(2020)
Article
Computer Science, Information Systems
Gokhan Altan, Yakup Kutlu, Novruz Allahverdi
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2020)
Article
Engineering, Electrical & Electronic
Emre Avuclu, Abdullah Elen, Humar Kahramanli Ornek
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2020)
Article
Food Science & Technology
Mustafa Nevzat Ornek, Humar Kahramanli Ornek
Summary: In this study, a deep learning approach was developed to predict the volume of carrots based on their physical properties, with both DFN and LSTM networks achieving high predicting accuracy. The statistical measures used confirmed the success and applicability of the developed systems.
JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION
(2021)
Article
Computer Science, Artificial Intelligence
Tahir Sag, Humar Kahramanli Ornek
Summary: This paper introduces a novel classification rule mining model called CRM-PM, based on Pareto-based Multiobjective Optimization. The proposed approach tackles the challenging task of rule extraction in data mining by treating it as a multi-objective optimization problem. Experimental results show that the presented method has promising capability for classification, achieving comparable or superior results.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Multidisciplinary
Semiye Demircan, Humar Kahramanli
MEHRAN UNIVERSITY RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Tahir Sag, Humar Kahramanli
2017 INTERNATIONAL ARTIFICIAL INTELLIGENCE AND DATA PROCESSING SYMPOSIUM (IDAP)
(2017)
Proceedings Paper
Acoustics
Semiye Demircan, Humar Kahramanli
2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
(2017)
Article
Engineering, Multidisciplinary
Murat Koklu, Humar Kahramanli, Novruz Allahverdi
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
(2014)
Proceedings Paper
Engineering, Electrical & Electronic
Metin Allahverdi, Humar Kahramanli
2013 7TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT)
(2013)
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)
