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Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing

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Discover Artificial Intelligence 2025年第1期5卷 1-29页

作者： Islam, Md. Rezaul Chowdhury, Aniruddha Islam Shama, Sharmin Lamyea, Md. Masudul Hasan Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet3114 Bangladesh Department of Educational Technology and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur1750 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1212 Bangladesh

Thyroid disorders are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate prediction of disease progression and understanding the interplay of clinical features are essential for effective diagnosis and treatment. Our study addresses these challenges by employing a standard machine learning model, enhanced with comprehensive clinical feature analysis and an ensemble learning technique. By leveraging machine learning, we can identify key risk factors and improve diagnostic accuracy. To achieve optimal prediction outcomes, we evaluated seventeen machine learning models and implemented an Ensemble ML classifier using a hard voting strategy. Class balancing techniques, particularly random oversampling, significantly improved classification performance. Our experimental results demonstrate that the proposed model outperforms existing methods, achieving 100% sensitivity and 99.72% accuracy using the XGBoost algorithm and SelectKBest feature selection. By addressing feature reduction and high class-imbalance, the ensemble ML classifier with hard voting proves more effective in handling classification challenges. © The Author(s) 2025.

关键词： Adversarial machine learning

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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computer Modeling in engineering & sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

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Deep features and metaheuristics guided optimization-based method for breast cancer diagnosis

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Multimedia Tools and Applications 2025年第16期84卷 16683-16707页

作者： Asad, Emon Mollah, Ayatullah Faruk Basu, Subhadip Chakraborti, Tapabrata Department of Computer Science and Engineering Aliah University Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India The Alan Turing Institute and University College London London United Kingdom

Breast cancer is one of the most prevalent cancer types and the second leading cause of death among women. But fortunately, early diagnosis and treatment of breast cancer reduces mortality rates and improves the quality of life significantly. In this work, a computer-aided three-stage pipeline for the diagnosis of breast cancer from mammogram images has been proposed. Firstly, an attention-aided VGG16 network has been applied for deep feature extraction from mammogram images. Then, the extracted features are passed to the adaptive beta hill climbing-aided whale optimization algorithm for the most informative feature selection. Lastly, the features from stage 2 are again evaluated by the grey wolf optimizer combined with the adaptive beta hill climbing algorithm for the selection of the best subsets of features and subsequent identification of cancers using the k-nearest neighbours classifier. The proposed three-stage method has achieved an average accuracy of 97.63% and has reduced 81.78% of the original features. Considering the fact that well annotated imaging data is a costly resource for modern data intensive deep networks, this work shows that a fairly lightweight off the shelf network like VGG-16 can be coupled with a robust feature selection policy to outperform several recent approaches which employ more data intensive learning strategies with larger networks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mammography

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Hybrid Flow Shop with Setup Times Scheduling Problem

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computer Systems science & engineering 2023年第1期44卷 563-577页

作者： Mahdi Jemmali Lotfi Hidri Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityMajmaah11952Saudi Arabia Mars Laboratory University of SousseSousseTunisia Department of Computer Science Higher Institute of Computer Science and Mathematics of MonastirMonastir UniversityMonastir5000Tunisia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The two-stage hybridflow shop problem under setup times is addressed in this *** problem is *** the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high *** this kind of problem requires the development of adapted *** this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this *** approximate solutions are using the optimal solution of the parallel machines under release and delivery ***,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be *** general solution is then a concatenation of all the solutions in each *** addition,three lower bounds based on the relaxation method are *** lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative *** experimental result is discussed to evaluate the performance of the developed *** total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and *** indicators are given to compare between *** results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

关键词： Hybridﬂow shop genetic algorithm setup times heuristics lower bound

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Research on stock trend prediction method based on optimized random forest

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 274-284页

作者： Lili Yin Benling Li Peng Li Rubo Zhang School of Computer and Science and Technology Harbin University of Science and TechnologyHarbinChina Department of Computer Science and Engineering Dalian Nationalities UniversityDalianChina

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

关键词： ensemble learning finance random forest random search technical indicator

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Towards Accurate Renal Micro-Structure Segmentation: An Assessment of YOLOv8 and Mask R-CNN Models

Towards Accurate Renal Micro-Structure Segmentation: An Asse...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Ahmed, Md. Estiak Pinki, Farhana Tazmim Quader, Mozdaher Abdul Quader, Montaser Abdul Ayrin, Fateha Jannat Anwar, Ahmed Selim Roy, Prosenjit Computer Science and Engineering Discipline Bangladesh Department of Computer Science and Engineering Bangladesh Department of Computer Science and Engineering Bangladesh Department of Eee Bangladesh Prairie View A & M University Department of Computer Science TX United States

ISBN: (纸本)9798331535476

In the field of medical imaging, correct instance segmentation is essential. This work attempts to address the problems related to renal micro-structure segmentation by using the power of YOLOv8 and special MASK R-CNN models to facilitate the development of more rapid and accurate nephrology diagnosis, which will benefit physicians and other healthcare providers. Improved medical imaging analysis is made possible by the model's high precision rate of 'glomerulus' and 'blood vessel' structure detection. The application of the MASKRCNN framework, which successfully integrated pertinent annotations to boost visual understanding, increased the clarity of image interpretation. Additionally, this work uses the HuBMAP dataset to segregate renal microstructures using the YOLOv8 and Mask R-CNN techniques. Strong performance was shown by the YOLOv8 model, which received an IoU score of 84.55%. The Mask R-CNN model received an IoU score of 80.66%, indicating its potential. It is an impressive accomplishment to obtain a high IoU score for YOLOv8 in the context of medical photo segmentation, since this suggests that there is still room for further improvement and optimization of the model. For medical professionals, the IoU scores are crucial since they offer a technique to improve accuracy and reliability when it comes to nephrology diagnosis. In order to improve diagnosis accuracy and enable broader clinical adoption, future research aims to improve these IoU ratings. The observed disparity underscored the inherent complexities associated with differentiating between ambiguous kidney structures and also the pressing need for further advancement. Our preliminary work demonstrates the potential of MASK-RCNN and YOLOv8 in the challenging field of renal micro-structure segmentation. © 2024 IEEE.

关键词： Image segmentation

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Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

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computers, Materials & Continua 2023年第1期74卷 2179-2194页

作者： R.Poonguzhali Sultan Ahmad P.Thiruvannamalai Sivasankar S.Anantha Babu Pranav Joshi Gyanendra Prasad Joshi Sung Won Kim Department of Computer Science and Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science and Engineering Jain Deemed to-be UniversityBangalore560069India Department of Neurology Annapurna Neuro HospitalKathmandu44600Nepal Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival *** location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and *** automatic recognition,place,and classifier process was desired and *** study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor *** presentedADRUSCM model majorly focuses on the segmentation and classification of *** accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in *** addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain ***,VGG-19 model is exploited as a feature ***,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes *** performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

关键词： Brain tumor diagnosis image classification biomedical images image segmentation deep learning

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Hepatitis C Prediction Using Machine Learning and Deep Learning-Based Hybrid Approach with Biomarker and Clinical Data

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Biomedical Materials and Devices 2025年第1期3卷 558-575页

作者： Ripa, Rokiya Uddin, Khandaker Mohammad Mohi Alam, Mir Jafikul Rahman, Md. Mahbubur Department of Computer Science and Engineering Dhaka International University Dhaka 1205 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka 1216 Bangladesh

Chronic liver damage is believed to be mostly caused by the Hepatitis C virus (HCV). About 90% of hepatitis C infections progress to chronic hepatitis. Acute HCV infection is a condition that frequently progresses to liver cirrhosis and eventually liver cancer;therefore, understanding this stage of the virus is essential. Molecular and serological testing approaches are often expensive and difficult to perform for diagnosing HCV infection. Machine learning technology can be effectively employed to identify patterns or associations for diagnosing HCV infection. The study utilized machine learning techniques to develop classification models for hepatitis C illness, aiming to anticipate the virus responsible for the infection. Our research integrates various machine learning algorithms, including Random Forest, Cat Boost, Bagging Classifier, SGD Classifier, Gaussian NB, Bernoulli NB, Multinomial NB, Linear Discriminant Analysis, ANN, and MLP. Prior to training the models, preprocessing methods, including normalization, filtering, and SMOTE, were used to improve the dataset’s attributes. The classification accuracy scores show encouraging results, with ANN scoring 79.63%, MLP scoring 46.29%, Bernoulli NB scoring 79.62%, Cat Boost scoring 98.14%, and Random Forest scoring 99.53%. Among classification methods, random forest demonstrates the highest accuracy in diagnosing HCV infection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification Deep learning Hepatitis C virus Machine learning Predictions

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