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iYOLOV7-TPE-SS: Leveraging Improved YOLO Model With Multilevel hyperparameter optimization for Road Damage Detection on Edge Devices

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IEEE ACCESS 2025年 13卷 79247-79263页

作者： Mahmudah, Haniah Aisjah, Aulia Siti Arifin, Syamsul Prastyantyo, Catur Arif Elect Engn Polytech Inst Surabaya Politekn Elekt Negeri Surabaya Dept Elect Engn Surabaya 60111 Indonesia Inst Teknol Sepuluh Nopember Engn Phys Dept Surabaya 60111 Indonesia Inst Teknol Sepuluh Nopember Civil Engn Dept Surabaya 60111 Indonesia

Digital twin cities integrated road damage inspection to support smart road infrastructure. The road's condition was assessed using road damage survey metrics, and the issue was resolved manually. Real-time, automatically, and precisely employing artificial intelligence (AI) is needed to improve road safety and reduce maintenance costs. This research on road defect detection utilizes improved YOLOV7 (iYOLOV7) while multilevel hyperparameter optimization using combination of the Tree-Structured Parzen Estimator (TPE) and Search Space (SS). This model we call TPE-SS. The iYOLOV7 provides real-time processing on edge devices that have limited storage, low-speed processors, and memory. The research investigates how leveraging multilevel hyperparameter optimization using the TPE-SS model improves system performance where road damage is detected. The results of iYOLOV7 model demonstrate the smallest total loss of 0.1, indicating a significant performance improvement where a precision value of 0.986, an average recall (AR) of 0.970, a mean average precision mAP@0.5 of 0.988, a mean average precision mAP@0.5-0.9 of 0.806, and an F1-score of 0.978. The measurement results and analysis findings reveal that our proposed method is accurate enough. The embedded system employs NVIDIA Jetson Nano for inference, which takes only 0.135 seconds and has a scalability performance of 7.470 FPS to recognize things associated with road damage. Although the employed NVIDIA AGX Orin edge device may produce a higher FPS of 67.034 and a faster inference time of 0.014 seconds. This means that the iYOLOV7 model is extremely feasible and practical for usage on edge devices to identify road damage.

关键词： Roads Convolutional neural networks Accuracy Real-time systems Computational modeling hyperparameter optimization System performance Performance evaluation Object recognition Image edge detection iYOLOV7 model multilevel hyperparameter optimization tree-structured parzen estimator search space scalability performance road damage

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Improving the robustness of tree-based prediction model of oil/water relative permeability through hyperparameter optimization and re-ensemble algorithm

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FUEL 2025年 385卷

作者： Wu, Dongwei Xue, Xinhua Zhou, Lei Sichuan Univ Coll Architecture & Environm State Key Lab Intelligent Construct & Hlth Operat Chengdu 610065 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China

In petroleum engineering, accurate prediction of the oil and water relative permeability is vital for increasing production rates, reducing extraction costs, etc. This study utilizes tree-based models to build prediction models of oil/water permeability at different temperatures. To further improve the performance of the prediction model, a two-step combining hyperparameter optimization and re-ensemble algorithms is adopted. Five metrics (i.e. R-square (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Root Mean Squared Logarithmic Error ( RMSLE ) and Logarithmic Discrepancy Ratio (LDR)) are adopted for model performance evaluation. Results indicate the two-step model significantly improves the performance of the model;the mean values of MAE decrease 38.67% and 72.48% for oil and water prediction, respectively. The mean values of RMSE decrease 41.05% and 77.77% for oil and water prediction respectively. This study provides a convenient and economical method to predict oil/water relative permeability rapidly.

关键词： Permeability prediction Oil-water two phases Tree-based model hyperparameter optimization Re-ensemble algorithm SHapley additive exPlanations (SHAP)

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A Parkinson's Auxiliary Diagnosis Algorithm Based on a hyperparameter optimization Method of Deep Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第4期21卷 912-923页

作者： Wang, Xingbo Li, Shujuan Pun, Chi-Man Guo, Yijing Xu, Feng Gao, Hao Lu, Huimin Nanjing Univ Posts & Commun Coll Automat Nanjing 210049 Peoples R China Nanjing Univ Posts & Commun Coll Artificial Intelligence Nanjing 210049 Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou 311100 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Southeast Univ Dept Neurol Zhongda Hosp Nanjing 210009 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu 8048550 Japan

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

关键词： Feature extraction Deep learning Residual neural networks Neural networks Signal processing algorithms Diseases Data models Artificial bee colony algorithm deep learning hyperparameter optimization parkinson's auxiliary speech diagnosis

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Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through hyperparameter optimization

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Computers, Materials & Continua 2024年第9期80卷 3485-3506页

作者： Tajim Md.Niamat Ullah Akhund Waleed M.Al-Nuwaiser Department of Computer Science and Engineering(CSE) Daffodil International UniversityDhaka1216Bangladesh Graduate School of Science and Engineering Saga UniversitySaga8408502Japan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

关键词： Internet of sensing things(IoST) machine learning hyperparameter optimization cardiovascular disease prediction execution time analysis performance analysis wilcoxon signed-rank test

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A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for hyperparameter optimization

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IEEE ACCESS 2024年 12卷 192184-192194页

作者： Kim, Chul Joe, Inwhee Hanyang Univ Seoul 04763 South Korea

This paper introduces a novel approach to hyperparameter optimization (HPO), proposing a methodology that balances exploration and exploitation to enhance optimization performance. While evolutionary algorithms (EAs) have shown potential in HPO, they often struggle with effective exploitation. To address this limitation, we propose an improved hyperparameter optimization (HPO) framework that integrates a linear surrogate model into the genetic algorithm (GA). The GA'ss flexible structure allows for seamless integration of multiple optimization strategies, and the surrogate model significantly boosts its exploitation capabilities. Specifically, we achieved an average performance improvement of 1.89% (max 6.55%, min -3.45%) over the existing state-of-the-art HPO strategy.

关键词： optimization Genetic algorithms Computational modeling Bayes methods Probabilistic logic Linear programming hyperparameter optimization Genetics Evolutionary computation Computational efficiency Exploitation exploration genetic algorithm HPOBench hyperparameter optimization HPO surrogate model

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Distributed hybrid quantum-classical performance prediction for hyperparameter optimization

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QUANTUM MACHINE INTELLIGENCE 2024年第2期6卷 1-14页

作者： Wulff, Eric Amboage, Juan Pablo Garcia Aach, Marcel Gislason, Thorsteinn Eli Ingolfsson, Thorsteinn Kristinn Ingolfsson, Tomas Kristinn Pasetto, Edoardo Delilbasic, Amer Riedel, Morris Sarma, Rakesh Girone, Maria Lintermann, Andreas CERN Espl Particules 1 CH-1211 Geneva Switzerland Forschungszentrum Julich Julich Supercomp Ctr Wilhelm Johnen Str D-52428 Julich Germany Univ Iceland Sch Engn & Nat Sci IS-107 Reykjavik Iceland Rhein Westfal TH Aachen D-52056 Aachen Germany

hyperparameter optimization (HPO) of neural networks is a computationally expensive procedure, which requires a large number of different model configurations to be trained. To reduce such costs, this work presents a distributed, hybrid workflow, that runs the training of the neural networks on multiple graphics processing units (GPUs) on a classical supercomputer, while predicting the configurations' performance with quantum-trained support vector regression (QT-SVR) on a quantum annealer (QA). The workflow is shown to run on up to 50 GPUs and a QA at the same time, completely automating the communication between the classical and the quantum systems. The approach is evaluated extensively on several benchmarking datasets from the computer vision (CV), high-energy physics (HEP), and natural language processing (NLP) domains. Empirical results show that resource costs for performing HPO can be reduced by up to 9% when using the hybrid workflow with performance prediction, compared to using a plain HPO algorithm without performance prediction. Additionally, the workflow obtains similar and in some cases even better accuracy of the final hyperparameter configuration, when combining multiple heuristically obtained predictions from the QA, compared to using just a single classically obtained prediction. The results highlight the potential of hybrid quantum-classical machine learning algorithms. The workflow code is made available open-source to foster adoption in the community.

关键词： hyperparameter optimization Quantum annealing Hyperband Distributed computing

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Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 3221-3232页

作者： Deliang Sun Jing Wang Haijia Wen YueKai Ding Changlin Mi Key Laboratory of GIS Application Research Chongqing Normal UniversityChongqing 401331China Key Laboratory of New Technology for Construction of Cities in Mountain Area Ministry of EducationChongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China School of Civil Engineering Chongqing UniversityChongqing 400045China Natural Resources Development Service Center of Linyi Linyi 276000China

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)***,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM *** investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average *** XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM ***,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM *** boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer ***,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer *** HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test *** model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study *** study offers a scientific reference for LSM and disaster prevention *** study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou *** proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM ***,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.

关键词： Landslide susceptibility hyperparameter optimization Boosting algorithms SHapley additive exPlanations(SHAP)

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Bilevel hyperparameter optimization and Neural Architecture Search for Enhanced Breast Cancer Detection in Smart Hospitals Interconnected With Decentralized Federated Learning Environment

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IEEE ACCESS 2024年 12卷 63618-63628页

作者： Khan, Salabat Nosheen, Fariha Naqvi, Syed Shehryar Ali Jamil, Harun Faseeh, Muhammad Ali Khan, Murad Kim, Do-Hyeun COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Jeju Natl Univ Big Data Res Ctr Jeju 63243 South Korea Jeju Natl Univ Dept Elect Engn Jeju 63243 South Korea Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea

Breast cancer, a widespread malignancy predominantly affecting women aged 40 and above, presents a significant global health challenge with high mortality rates. The scarcity of medical data underscores the need for collaborative efforts among hospitals to enhance automated breast cancer detection. This research employs decentralized Federated Learning (FL) to facilitate cooperative learning across an interconnected smart hospital network, addressing data privacy, regulatory compliance, voluminous medical image data, and the necessity for distributed machine learning. Our innovative approach integrates Ant Colony optimization (ACO) for hyperparameter fine-tuning and Neural Architecture Search (NAS) in a collaborative framework for smart hospitals linked with decentralized edge intelligent networks. This optimization strategy significantly improves the performance of our breast cancer detection system. Through a comprehensive experimental study (including diverse datasets), we classify Normal vs. Mass and Benign vs. Malignant regions in mammograms within a decentralized, federated collaborative learning environment. Empirical results consistently highlight the superiority of models trained using our method over individual hospital client-level training. Our method yielded significant improvements across evaluation measures: for Normal vs. Mass, achieving 92.6% sensitivity, 93.0% specificity, and 93.0% accuracy;for Benign vs. Malignant, achieving 89.6% sensitivity, 91.6% specificity, and 89.7% accuracy. Moreover, it has obtained a 6% and 5% increase in accuracy for Normal vs. Mass and Benign vs. Malignant cases, respectively, compared to the PSO-based HPO method. This evidence underscores the potential of collaborative approaches, emphasizing decentralized FL as a robust paradigm in medical research. The incorporation of ACO optimization reinforces the effectiveness of the proposed computer-aided diagnosis (CAD) system, marking a noteworthy advancement in the ongoing

关键词： Solid modeling Data models Cancer Breast cancer Hospitals Collaboration Training hyperparameter optimization Metaheuristics Detection algorithms neural architecture search metaheuristic ACO decentralized federated learning breast cancer detection

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Fair Federated Learning with Multi-Objective hyperparameter optimization

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2024年第8期18卷 1-13页

作者： Wang, Chunnan Shi, Xiangyu Wang, Hongzhi Harbin Inst Technol Harbin Peoples R China

Federated learning (FL) is an attractive paradigm for privacy-aware distributed machine learning, which enables clients to collaboratively learn a global model without sharing clients' data. Recently, many strategies have been proposed to improve the generality of the global model and thus improve FL effect. However, existing strategies either ignore the fairness among clients or sacrifice performance for fairness. They cannot ensure that the gap among clients is as small as possible without sacrificing federated performance. To address this issue, we propose ParetoFed, , a new local information aggregation method dedicated to obtaining better federated performance with smaller gap among clients. Specifically, we propose to use multi-objective hyperparameter optimization (HPO) algorithm to gain global models that are both fair and effective. Then, we send Pareto Optimal global models to each client, allowing them to choose the most suitable one as the base to optimize their local model. ParetoFed not only make the global models more fair but also make the selection of local models more personalized, which can further improve the federated performance. Extensive experiments show that ParetoFed outperforms existing FL methods in terms of fairness, and even achieves better federated performance, which demonstrates the significance of our method.

关键词： Fair federated learning hyperparameter optimization multi-objective optimization

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Model comparison and hyperparameter optimization for visible and near-infrared (Vis-NIR) spectral classification of dehydrated banana slices

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 283卷

作者： Buzpinar, Mehmet Akif Gunaydin, Seda Kavuncuoglu, Erhan Cetin, Necati Sacilik, Kamil Cheein, Fernando Auat Cumhuriyet Univ Gemerek Vocat Sch Sivas Turkiye Necmettin Erbakan Univ Eregli Fac Agr Dept Biosyst Engn Konya Turkiye Ankara Univ Fac Agr Dept Agr Machinery & Technol Engn Ankara Turkiye Harper Adams Univ Dept Engn Newport England

This study investigated the classification of banana samples from 33 distinct environments using hyperspectral reflectance data and various machine-learning algorithms. Feature selection was performed using Recursive Feature Elimination, reducing the dataset from 750 to 50 wavelengths. The data was partitioned into training, testing, and validation sets using stratified sampling. Traditional machine learning algorithms, including KNN, CART, SVM, Naive Bayes, LDA, QDA, MLP, Ridge, Bagging, Random Forest, AdaBoost, LightGBM, and XGBoost, were evaluated and optimized using Optuna. Deep learning architectures, such as CNN, CNN-LSTM, ConvLSTM, GRU, LSTM, and BiLSTM, were also assessed and tuned using Bayesian optimization and early stopping. Performance metrics included accuracy, training and testing times, and confusion matrices. Among the traditional models, SVM (C = 1000, polynomial kernel) and MLP (hidden_layer_sizes=(100, 50), identity activation, lbfgs solver) achieved the highest test accuracies of 96.07 % and 92.13 %, respectively. XGBoost and Bagging also performed well, with test accuracies of around 82 %. For the deep learning models, GRU (Adamax optimizer, learning rate = 0.01, 100 epochs) outperformed the others with 85.32 % test accuracy, followed by CNN at 81.39 %. LSTM and BiLSTM achieved test accuracies of 76.70 % and 67.78 %, respectively, whereas CNN-LSTM and ConvLSTM struggled at 56.28 % and 43.87 %. This study highlights the trade-off between model complexity and computational efficiency, with GRU and CNN offering a good balance for real-time applications. These findings provide valuable insights for selecting suitable models for hyperspectral data classification in agricultural and environmental monitoring.

关键词： Spectral reflectance Pretreatment Dehydration hyperparameter optimization Artificial intelligence

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