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Real-Time Classification of Vehicles Using machine learning Algorithm on the Extensive Dataset

IEEE ACCESS 2024年 12卷 98338-98351页

作者： Pemila, M. Pongiannan, R. K. Narayanamoorthi, R. Sweelem, Emad A. Hendawi, Essam Abu El-Sebah, Mohamed I. SRM Inst Sci & Technol Dept Elect & Elect Engn Kattankulathur 603203 India SRM Inst Sci & Technol Sch Comp Dept Comp Technol Kattankulathur 603203 India Elect Res Inst Dept PV Cells Cairo 12622 Egypt Taif Univ Coll Engn Dept Elect Engn Taif 21944 Saudi Arabia Elect Res Inst Dept Power Elect & Energy Convers Cairo 12622 Egypt

Vehicle classification (VC) is a prominent research domain within image processing and machine learning (ML) for identifying vehicle volumes and traffic rule violations. In developed countries, nearly 40% of daily accidents are fatal, while in developing countries, the figure rises to 70%. Traditionally, vehicle detection and classification have been performed manually by experts, which is difficult, time-consuming, and prone to errors. Furthermore, incorrect detection and classification can result in hazardous situations. This highlights the need for more reliable techniques to identify and classify vehicles accurately and practically. In existing applications, numerous automated methods have been proposed. However, employing deep and machine learning algorithms on complex datasets of vehicle images has failed to achieve accuracy in various climate conditions and has been time-consuming. This paper presents an accurate, robust, real-time system to classify vehicles from onsite roads. The proposed system utilizes a random wavelet transform for pre-processing, edge and region-based segmentation for feature extraction, an embedded method for feature selection, and the XGBoost algorithm for VC. The proposed work classifies vehicles under complex weather, illumination, color, and occlusion conditions over 10 datasets, including a novel dataset named SRM2KTR, containing 75,436 vehicle images on an FPGA platform. The results show 98.81% accuracy, outperforming the state-of-the-art (98%). The system was demonstrated with four different classifiers, classifying images in 0.16 ns with an average accuracy of 97.79%. The system exhibits high accuracy, rapid identification time, and robustness in practical use.

关键词： Feature extraction Accuracy Task analysis machine learning algorithms Reluctance motors Image edge detection Classification algorithms Gradient methods Boosting Vehicle classification machine learning eXtreme gradient boost algorithm

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machine learning-Assisted Multiobjective Evolutionary Algorithm for Routing and Packing

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第2期29卷 317-330页

作者： Liu, Fei Zhang, Qingfu Zhu, Qingling Tong, Xialiang Yuan, Mingxuan City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Huawei Noahs Ark Lab Shenzhen 518129 Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

Many combinatorial multiobjective optimization problems involve very costly-to-evaluate objectives and constraints. It is very difficult, if not impossible, for traditional heuristics to solve these problems with an acceptable amount of computational time. In this article, we show that offline machine learning can be very useful to assist multiobjective evolutionary algorithms to tackle this kind of problem. We take a complicated real-life multiobjective routing-packing problem as the test bed. We propose to use offline machine learning methods to replace time-consuming packing heuristics for packing feasibility prediction. Experiments show that the machine learning models can be 1000 times faster than some commonly used packing heuristics and their accuracy can be as high as 98%. We adopt decomposition-based multiobjective evolutionary algorithmic to decompose the problem into a number of single objective subproblems and solve them in a collaborative manner. We propose an encoding strategy to represent each routing scheme and use genetic operators to generate new routes. Experimental studies have been conducted on 100 instances from HUAWEI's real-world logistics application and two test suites from the literature. Our proposed method can solve each HUAWEI instance in around 1 min. Our solutions on the two test suites are comparable to other existing algorithms, and the overall computational cost of our method is significantly lower than others.

关键词： Routing machine learning Evolutionary computation Optimization machine learning algorithms Encoding Vehicle routing Bin packing evolutionary algorithm machine learning multiobjective optimization vehicle routing

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A Decision Theoretic Framework for Message Prioritization via machine learning Algorithm in Vehicular Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 7630-7643页

作者： Gu, Chen Weng, Xiunian Hu, Donghui Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

Connected and Autonomous Vehicles (CAVs) in the network require to process messages received from neighboring vehicles depending on various applications. Due to limits of hardware, the On-board Unit (OBU) on a vehicle cannot deal with such massive received messages in a certain time frame, especially in high-density road networks. That means only certain messages can be timely computed, making it a challenging work in concern of urgent messages response in the vehicular network. Current work focuses on prioritizing messages by a single attribute or manually setup, omitting an efficient framework for deciding how to prioritize messages for multiple attributes in the dynamic vehicular environment. In this paper, we propose a decision-theoretic framework enabling deterministic real-time support for prioritizing messages based on their contents. Specifically, we utilize the computation and storage resources of Roadside Units (RSU) to generate utility functions. Firstly the RSUs profile received messages to decide attributes, and generate utility functions which represent the current traffic status. These functions are then broadcast to the nearby vehicles, helping them to prioritize received messages using decision theory. We show the high efficiency and effectiveness of our framework through extensive simulations in different scenarios.

关键词： Safety machine learning algorithms Decision theory Real-time systems Digital signatures Cams Vehicle dynamics Message Prioritization Decision Theory machine learning Connected Vehicles Intelligent Transportation System

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Online learning Under a Separable Stochastic Approximation Framework

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2025年第2期47卷 1317-1330页

作者： Gan, Min Su, Xiang-xiang Chen, Guang-yong Chen, Jing Chen, C. L. Philip Qingdao Univ Inst Future Qingdao 266071 Peoples R China Coll Comp Sci & Technol Qingdao 266071 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Univ Fujian Fujian Key Lab Network Comp & Intelligent Informat Key Lab Intelligent Metro Fuzhou 350007 Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Beijing 100101 Peoples R China Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

We propose an online learning algorithm tailored for a class of machine learning models within a separable stochastic approximation framework. The central idea of our approach is to exploit the inherent separability in many models, recognizing that certain parameters are easier to optimize than others. This paper focuses on models where some parameters exhibit linear characteristics, which are common in machine learning applications. In our proposed algorithm, the linear parameters are updated using the recursive least squares (RLS) algorithm, akin to a stochastic Newton method. Subsequently, based on these updated linear parameters, the nonlinear parameters are adjusted using the stochastic gradient method (SGD). This dual-update mechanism can be viewed as a stochastic approximation variant of block coordinate gradient descent, where one subset of parameters is optimized using a second-order method while the other is handled with a first-order approach. We establish the global convergence of our online algorithm for non-convex cases in terms of the expected violation of first-order optimality conditions. Numerical experiments demonstrate that our method achieves significantly faster initial convergence and produces more robust performance compared to other popular learning algorithms. Additionally, our algorithm exhibits reduced sensitivity to learning rates and outperforms the recently proposed slimTrain algorithm (Newman et al. 2022). For validation, the code has been made available on GitHub.

关键词： Stochastic processes Approximation algorithms Optimization machine learning algorithms machine learning Convergence Artificial neural networks Training Minimization Convex functions Online learning recursive least squares stochastic approximation variable projection

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Design optimization of hybrid bistable symmetric laminate using machine learning

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2025年

作者： Mukherjee, Paulomi Sahu, Banamali Arockiarajan, A. Ali, Shaikh Faruque Indian Inst Technol Madras Dept Appl Mech Chennai 600036 India

In this study, the snap-through behavior of hybrid bistable symmetric laminates (HBSL) is investigated using a machine learning-based approach. A dataset of 6000 samples is generated via Python-integrated ABAQUS simulations, and various regression-based machine learning (ML) models are trained to predict snap-through loads. The novelty lies in integrating ML with hybrid bistable laminate analysis, enabling data-driven optimization of geometric parameters using Particle Swarm Optimization. This approach significantly reduces computational costs compared to conventional methods, making it highly beneficial for energy harvesting and morphing applications. Experimental validation confirms the model's accuracy within a 10% error margin.

关键词： Hybrid bistable symmetric laminate finite element modeling XGBoost snap-through load machine learning algorithms particle swarm optimization

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Predicting and investigating water quality index by robust machine learning methods

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 381卷 125156页

作者： Han, Zhoulin Zhang, Shijing He, Liangqing Zhu, Bin Mianyang Teachers Coll Sch Urban & Rural Planning & Construct Mianyang 621000 Sichuan Peoples R China Sichuan Polytech Univ Dept Basic Educ Deyang 618000 Sichuan Peoples R China Leshan Normal Univ Dept Fine Arts & Design Leshan 614000 Sichuan Peoples R China

This study addresses the critical challenges of waste management and water quality in urban environments, where accelerated urbanization has exacerbated environmental degradation and public health risks. Employing advanced machine learning algorithms-Long Short-Term Memory (LSTM), Random Forest (RF), Decision Tree (DT), and Support Vector machine (SVM)-this research predicts the Water Quality Index (WQI) to improve urban environmental management. The novelty lies in the integration of multiple algorithms within a single framework, focusing on classifying WQI labels (1-9) for "good" to "poor" water quality, a departure from traditional continuous value predictions. Among the algorithms, LSTM demonstrated the most significant advantages, achieving superior predictive accuracy and precision across training, testing, and validation datasets, with RMSE values of 0.0611, 0.0810, and 0.0754 and R2 values consistently above 0.9964. Comparative analysis revealed LSTM's capacity to capture complex temporal dependencies in data, surpassing RF, DT, and SVM in predictive performance. This approach provides actionable insights into WQI dynamics, enabling the identification of key pollution factors, optimizing waste management practices, and supporting real-time decisionmaking. The integration of climate indicators into the models further enhances their applicability in addressing long-term trends associated with climate change. Statistical evaluations, including AARE and SD, corroborate LSTM's robustness and reliability, making it a transformative tool for urban water quality prediction. This research pioneers a scalable, efficient, and practical solution to urban environmental challenges, contributing to sustainable resource management and improved public health outcomes.

关键词： Waste management Water quality index (WQI) machine learning algorithms Urban environmental management Climate change indicators

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Predicting Diabetic Retinopathy and Nephropathy Complications Using machine learning Techniques

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IEEE ACCESS 2025年 13卷 70228-70253页

作者： Manjunath, D. R. Lohith, J. J. Selva Kumar, S. Das, Abhijit BMS Coll Engn Dept CSE IoT&CS Bengaluru 560019 India Nagarjuna Coll Engn & Technol Dept CSE AI&ML Bengaluru 562110 India BMS Coll Engn Dept CSE Bengaluru 560019 India Manipal Acad Higher Educ Manipal Inst Technol Dept Informat Technol Manipal 560064 India

Diabetes and its complications, especially Diabetic Retinopathy (DR) and Diabetic Nephropathy (DN) is a big challenge to the global healthcare system and needs accurate predictive models to help in early diagnosis and intervention. In this study we used a dataset from a reputed medical center in India with 767 patient records and 22 attributes including demographic details, clinical markers and treatment plans. We used a suite of advanced machine learning algorithms-Random Forest, XGBoost, LightGBM, CatBoost, Neural Networks and ensemble approaches like Voting and Stacking Classifiers to see their performance on original, oversampled and undersampled datasets. Through feature engineering, sampling strategies and hyperparameter tuning the models performed well on all the datasets. Surprisingly the models performed well even on the original imbalanced dataset which can be attributed to the power of the models and hyperparameter tuning. Ensemble methods like Voting and Stacking Classifiers performed better and achieved near perfect metrics (AUC = 1.0) in oversampled datasets. Hyperparameter tuning further improved the performance, reduced RMSE and log loss and increased accuracy and recall in all the configurations. This shows the importance of model optimization in real world clinical datasets which are imbalanced and noisy. This paper shows the possibility of machine learning based frameworks in diabetic complication management by predicting accurately and in time. These models can be integrated into clinical decision support systems (CDSS) to give insights to clinicians, improve patient outcomes through personalized interventions and optimize resource allocation. Future work will be to validate this on different populations, include longitudinal patient data and integrate real time electronic health records (EHR) for deployment in hospitals.

关键词： Diabetes Accuracy machine learning machine learning algorithms Data models Support vector machines Random forests Predictive models Medical diagnostic imaging Classification algorithms Diabetic retinopathy diabetic nephropathy machine learning XGBoost clinical prediction feature importance

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Enhancing E-Commerce Warehouse Order Fulfillment Through Predictive Order Reservation Using machine learning

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5700-5713页

作者： Kang, Yuexin Qu, Zhiqiang Yang, Peng Tsinghua Univ Shenzhen Int Grad Sch Div Logist & Transportat Shenzhen 518055 Peoples R China Tsinghua Univ Inst Data & Informat Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Order batching plays a pivotal role in enhancing order fulfillment efficiency within both manual and robotic warehousing systems. Rare attention has been devoted to the impact of future incoming orders on online order batching. This study addresses this gap by exploring the potential benefits of reserving suitable orders when upcoming orders share similarities with existing orders in the order pool. Specifically, we investigate the online order batching problem with predictive order reservation, employing the Ensemble learning method, to predict similarities between current and future orders. Our proposed approach involves deliberate reservation of certain orders upon arrival, deferring their batching to a subsequent period for additional efficiency gains. To operationalize this predictive order reservation, we develop an algorithmic framework that comprehensively addresses online order batching, encompassing batching, sequencing, and assignment. Experimental results, conducted on real data from an e-commerce warehouse, demonstrate the superiority of our proposed approach over fixed and variable time-window online batching algorithms in terms of order turnover time, with improvements of up to 6.1%. Notably, the benefits are more pronounced when the order arrival rate aligns with the available picking resources.

关键词： Heuristic algorithms Electronic commerce machine learning algorithms Robots Sequential analysis Predictive models Prediction algorithms machine learning online order batching order picking logistics

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machine learning in GNSS Multipath/NLOS Mitigation: Review and Benchmark

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2024年第9期39卷 35-44页

作者： Xu, Penghui Zhang, Guohao Yang, Bo Hsu, Li-Ta Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Global navigation satellite system (GNSS) positioning accuracy is degraded in urban canyons due to signal blockages and reflections, which is still a major challenge. Recently, using machine learning to improve the accuracy of GNSS positioning in urban areas has become a new trend. This article summarizes the works focused on GNSS multipath/non-light-of-sight (NLOS) mitigation using machine learning. The review of the studies is categorized based on the input features, algorithms, and outputs. The categorization shows that the received signal strength, elevation angle, and receiver correlator outputs from a single channel of satellite signal are the most popular input features. For the algorithm selection, the support vector machine and fully connected neural network are the algorithms most widely used. In terms of the outputs, most of the works made improvements in measurement status prediction, namely, LOS, multipath, and NLOS. Besides, this article also provides an open-source dataset with four scenarios for machine learning algorithms for the GNSS multipath/NLOS mitigation. Finally, the benchmarks are established based on the proposed dataset and the FCNN and least-squares estimation to enable performance evaluation in Kaggle.

关键词： Global navigation satellite system machine learning Satellites Receivers machine learning algorithms Measurement uncertainty Urban areas Prediction algorithms Support vector machines Accuracy Prevention and mitigation GNSS multipath NLOS machine learning deep learning

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A neural network model for the evolution of learning in changing environments

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PLOS COMPUTATIONAL BIOLOGY 2024年第1期20卷 e1011840页

作者： Kozielska, Magdalena Weissing, Franz J. Univ Groningen Groningen Inst Evolutionary Life Sci Groningen Netherlands

learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a strong effect on the evolved learning strategy: a shorter lifespan and a narrow resource distribution reduce the likelihood of learning to evolve. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environmen

关键词： learning machine learning Neural networks Foraging Behavior Evolutionary adaptation Organismal evolution machine learning algorithms

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