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Advancing Autonomous Vehicle Safety: machine learning to Predict Sensor-Related Accident Severity

IEEE ACCESS 2024年 12卷 25933-25948页

作者： Shafique, Rahman Rustam, Furqan Murtala, Sheriff Jurcut, Anca Delia Choi, Gyu Sang Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Univ Coll Dublin Sch Comp Sci Dublin 4 Ireland

Autonomous vehicles (AVs) represent an exciting frontier in transportation, promising increased safety and efficiency on the roads. However, like any technological advancement, they are not immune to accidents. Understanding the severity of accidents involving AVs is crucial for enhancing their reliability and ensuring public trust in this transformative technology. To address this challenge, our study has employed cutting-edge natural language processing techniques combined with machine learning to predict the severity of accidents involving AVs. Our study has contributed significantly by creating a novel dataset derived from post-disengagement accident reports, covering the years 2019-2022. This dataset comprises detailed descriptions of accidents, sensor information, and other critical parameters. Moreover, we have introduced a novel approach called Multi-Distance Synthetic Technique (MDST) to balance the imbalanced nature of our dataset, which included only 334 samples due to the rarity of such accident data. Utilizing MDST for data balancing, we aimed to enhance the robustness of our analysis. Additionally, we employed Recursive Feature Selection (RFS) to extract a valuable feature set that was crucial in predicting accident severity. Leveraging this selected feature set, we trained an ensemble model, which remarkably outperformed expectations, achieving an impressive accuracy score of 0.92.

关键词： machine learning algorithms machine learning Safety Road accidents Prediction algorithms Radio frequency Predictive models Autonomous vehicles Feature detection Accidents Vehicle safety feature selection machine learning accident severity prediction data balancing

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Experimental investigation of machine-learning-based soft-failure management using the optical spectrum

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JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING 2024年第2期16卷 94-103页

作者： Kruse, Lars E. Kuehl, Sebastian Dochhan, Annika Pachnicke, Stephan Univ Kiel Chair Commun Kaiserstr 2 D-24143 Kiel Germany

The demand for high-speed data is exponentially growing. To conquer this, optical networks have undergone significant changes, getting more complex and versatile. The increasing complexity necessitates that the fault management be more adaptive to enhance network assurance. In this paper, we experimentally compare the performance of soft-failure management of different machine learning algorithms. We further introduce a machine-learning-based soft-failure management framework. It utilizes a variational autoencoder-based generative adversarial network (VAE-GAN) running on optical spectral data obtained by optical spectrum analyzers. The framework is able to reliably run on a fraction of available training data as well as identify unknown failure types. The investigations show that the VAE-GAN outperforms the other machine learning algorithms when up to 10% of the total training data are available in identification tasks. Furthermore, the advanced training mechanism for the GAN shows a high F1-score for unknown spectrum identification. The failure localization comparison shows the advantage of a low complexity neural network in combination with a VAE over established machine learning algorithms.

关键词： Optical fiber networks Generative adversarial networks Optical receivers Location awareness Adaptive optics Training machine learning algorithms

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Prediction of Hydrogen Adsorption and Moduli of Metal-Organic Frameworks (MOFs) Using machine learning Strategies

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ENERGIES 2024年第4期17卷

作者： Borja, Nicole Kate Fabros, Christine Joy E. Doma Jr, Bonifacio T. Mapua Univ Sch Chem Biol & Mat Sci & Engn Manila 1002 Philippines

For hydrogen-powered vehicles, the efficiency cost brought about by the current industry choices of hydrogen storage methods greatly reduces the system's overall efficiency. The physisorption of hydrogen fuel onto metal-organic frameworks (MOFs) is a promising alternative storage method due to their large surface areas and exceptional tunability. However, the massive selection of MOFs poses a challenge for the efficient screening of top-performing MOF structures that are capable of meeting target hydrogen uptakes. This study examined the performance of 13 machine learning (ML) models in the prediction of the gravimetric and volumetric hydrogen uptakes of real MOF structures for comparison with simulated and experimental results. Among the 13 models studied, 12 models gave an R2 greater than 0.95 in the prediction of both the gravimetric and the volumetric uptakes in MOFs. In addition, this study introduces a 4-20-1 ANN model that predicts the bulk, shear, and Young's moduli for the MOFs. The machine learning models with high R2 can be used in choosing MOFs for hydrogen storage.

关键词： hydrogen uptakes in metal-organic frameworks (MOFs) machine learning algorithms modulus prediction of MOFs

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On the Generalization for Transfer learning: An Information-Theoretic Analysis

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第10期70卷 7089-7124页

作者： Wu, Xuetong Manton, Jonathan H. Aickelin, Uwe Zhu, Jingge Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Univ Melbourne Sch Comp & Informat Syst Melbourne Vic 3010 Australia

Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In this work, we give an information-theoretic analysis of the generalization error and excess risk of transfer learning algorithms. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence D mu||mu') plays an important role in the characterizations where mu and mu ' denote the distribution of the training data and the testing data, respectively. Specifically, we provide generalization error and excess risk upper bounds for learning algorithms where data from both distributions are available in the training phase. Recognizing that the bounds could be sub-optimal in general, we provide improved excess risk upper bounds for a certain class of algorithms, including the empirical risk minimization (ERM) algorithm, by making stronger assumptions through the central condition. To demonstrate the usefulness of the bounds, we further extend the analysis to the Gibbs algorithm and the noisy stochastic gradient descent method. We then generalize the mutual information bound with other divergences such as Phi-divergence and Wasserstein distance, which may lead to tighter bounds and can handle the case when mu is not absolutely continuous with respect to mu '. Several numerical results are provided to demonstrate our theoretical findings. Lastly, to address the problem that the bounds are often not directly applicable in practice due to the absence of the distributional knowledge of the data, we develop an algorithm (called InfoBoost) that dynamically adjusts the importance weights for both source and target data based on certain information measures. The empirical results show the effectiveness of the proposed algorithm.

关键词： Transfer learning machine learning algorithms Mutual information Upper bound Training Testing Training data generalization error KL divergence mutual information phi-divergence

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Instrumentation Amplifier Input Impedance Calibration With machine learning-Based Optimizations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第2期72卷 394-398页

作者： Abdelfattah, Safaa Hussein, Hussein M. E. Shrivastava, Aatmesh Onabajo, Marvin Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

This brief introduces a digital calibration technique to boost the input impedance of instrumentation amplifiers (IAs) with digitally tunable input impedance. The technique employs two machine learning-driven optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, to efficiently control integrated capacitor banks within the IA for the determination of the optimal input impedance. These algorithms offer a significant time reduction compared to a calibration with an exhaustive search, reducing calibration time by a factor of over 10(6) (with four 9-bit digital control words) while conserving computational resources. A prototype platform was developed to automatically optimize a fabricated IA test chip designed with 65-nm CMOS technology, which allows to test the machine learning algorithms using a microcontroller to control the digitally tunable input impedance. With an extra input capacitance of 100 pF, the GA algorithm achieved an input impedance of 1.75 G Omega after four generations (iterations), while the PSO algorithm achieved 1.27 G Omega with five iterations.

关键词： Impedance Optimization Genetic algorithms Capacitors Calibration Capacitance machine learning algorithms Cables Particle swarm optimization Impedance measurement Input impedance calibration instrumentation amplifier genetic algorithm particle swarm optimization algorithm negative capacitance generation feedback (NCGFB)

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Rumor Containment in Hypergraph Representation of Social Networks: A Deep Reinforcement learning-Based Solution

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年

作者： Kundu, Gouri Ghosh, Smita Choudhury, Sankhayan Univ Calcutta Acharya Prafulla Chandra Roy Shiksha Prangan Dept Comp Sci & Engn Kolkata 700106 India Santa Clara Univ Dept Math & Comp Sci Santa Clara CA 95053 USA

Existing solutions for rumor containment typically model social networks as regular graphs, focusing solely on dyadic relationships between pairs of individuals. However, these solutions overlook the crowd influence, which arises from higher order relationships involving multiple individuals. This crowd influence is a distinct concept that cannot be substituted by the cumulative effect of individual influences through dyadic relationships. Therefore, it is important to consider the impact of crowd influence when modeling influence diffusion in the network. Moreover, traditional rumor containment methods lack generalization capacity. These methods require complete reexecution of algorithms whenever the target network changes, rendering them less efficient. In this work, we model the network as a hypergraph to effectively capture the crowd influence through higher order social relationships. We propose RCDRL-H, a deep reinforcement learning framework capable of constructing a trained model for containing rumors in previously unseen networks. Additionally, we introduce hyper-structure2vec, a node embedding technique for hypergraphs, and an efficient rumor containment estimation function that eliminates the need for computationally expensive Monte Carlo simulations during training. Experiments carried out on real-world social networks demonstrate that improved rumor containment can be achieved by treating the network as a hypergraph. It has also been observed that the achieved rumor containment is close to that of the greedy approach. Moreover, using the new estimation function significantly reduces training time.

关键词： Social networking (online) Training Reinforcement learning Q-learning Estimation Computational modeling Marine vehicles Optimization Monte Carlo methods machine learning algorithms Deep reinforcement learning hypergraphs rumor containment social network

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A Novel Hybrid machine learning Model for Analyzing E-learning Users' Satisfaction

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2024年第16期40卷 4193-4214页

作者： Sandiwarno, Sulis Niu, Zhendong Nyamawe, Ally S. Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Univ Mercu Buana Sch Comp Sci Jakarta Indonesia Univ Dodoma Dept Comp Sci & Engn Dodoma Tanzania

Analyzing lecturers' and students' satisfaction with using e-learning is important to improve the teaching-learning processes. The existing approaches have been widely employing machine learning algorithms, usage-based, and System Usability Scale (SUS) metrics based on users' opinions, activities, and usability testing, respectively. However, the usage-based and SUS metrics fail to cover users' opinions about e-learning systems and they involve manual features engineering. Whereas, the machine learning classifiers do not analyze satisfaction based on activities and usability. Toward this end, we propose a machine learning model that employs CNN and BiLSTM algorithms to concatenate the features extracted from users' activities, usability testing, and users' opinions. The proposed model is coined as E-learning Users' Satisfaction Detection (El-USD). Experimental results suggest that there is a significant correlation between satisfaction analysis by achieving an average r = 0.778. The evaluation results further suggest that our proposed approach can analyze users' satisfaction accurately.

关键词： E-learning users' satisfaction usage-based metrics SUS machine learning algorithms

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machine learning for Detecting Fertilizer Solution Information Based on Dielectric Frequency Response

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IEEE SENSORS JOURNAL 2024年第24期24卷 41717-41730页

作者： Li, Jianian Cheng, Yingying Ma, Yongzheng Li, Yuecong Wu, Jun Wang, Dejin Kunming Univ Sci & Technol Fac Modern Agr Engn Int Joint Lab Intelligent Agr Engn Technol & Equip Kunming 650032 Peoples R China Yunnan Field Sci Observ Water Soil Crop Syst Seaso Kunming 650500 Peoples R China

Rapid detection of fertilizer solutions is essential for improving water-fertilizer integration systems. However, existing detection methods are hindered by high costs, complexity, and slow response times, making real-time monitoring challenging. To solve these issues, we developed a qualitative-quantitative model that leverages machine learning (ML) algorithms based on the dielectric frequency response to detect the type and concentration of seven fertilizer solutions (N, P, K, PN, PK, NK, and NPK). Each fertilizer had unique dielectric frequency features, with different fluctuation, peak numbers, and maximum peaks. A linear relationship was also observed between the dielectric features (gain or phase shift) and fertilizer concentration within a specific frequency band. Following a "qualitative first, then quantitative" strategy, a qualitative-quantitative model was constructed using these results, including one classification model and seven concentration prediction models. For identifying solution types, the random forest (RF) algorithm was selected as the optimal qualitative model, with an accuracy of 98.57%. For detecting solution concentrations, seven optimal models were identified with $R<^>{algorithms}$ values exceeding 0.98 and root-mean-square error (RMSE) values below 0.4%, each incorporating the observed linear relationships mentioned above and different ML algorithms. Notably, the least-squares support vector machine (LSSVM) and backpropagation (BP) regression models outperformed the partial least-squares regression (PLSR) model. These results demonstrated that the developed qualitative-quantitative model effectively integrates dielectric features with ML algorithms to accurately detect fertilizer solution components. This model can provide feedback on fertilizer information, supporting rapid online identification of fertilizer components and significantly enhancing their water-fertilizer utilization efficiency.

关键词： Fertilizers Ions Sensors machine learning algorithms Frequency response Predictive models Electric fields Capacitive sensors Accuracy machine learning Detective model dielectric frequency response feature extraction fertilizer solution machine learning (ML)

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machine-learning-Based Colorimetric Sensor on Smartphone for Salivary Uric Acid Detection

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IEEE SENSORS JOURNAL 2024年第20期24卷 32991-33000页

作者： Liu, Weiran Liu, Shixian Fan, Kexin Li, Zijian Guo, Zijun Cheng, Davy Liu, Guozhen Chinese Univ Hong Kong Sch Med Integrated Devices & Intelligent Diag ID2 Lab CUHK SZ Boyalife Joint Lab Regenerat Med Engn Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Sch Med Artificial Intelligent Med Educ Shenzhen 518172 Peoples R China

Point-of-care sensors with colorimetric signal readout are attractive in providing efficient detection outcomes, but normally with qualitative results. Quantitative information is vital for deriving accurate and sensitive diagnoses toward precise medicine. Herein, we developed a generic methodology for quantifying optical intensity on a microfluidic paper test strip to detect salivary uric acid (UA) using a machine learning-based colorimetric sensor on a smartphone. A colorimetric sensor adaptable to the smartphone was designed to image the paper test trip of UA. The complete algorithm associated with the sensor consists of four modules: the region of interest (ROI) detection module for locating key areas in the image, the color calibration module for excluding interference from different lights, the feature extraction module that extracts multidimensional features from the reaction area on the test strip, and the feature analysis module built with machine learning models to provide prediction base on extracted features. The performance of our algorithm was evaluated for quantifying UA in both artificial saliva and clinically collected saliva by the machine learning-based colorimetric sensor. The best-performing machine learning model, the decision tree model achieves a mean absolute error (MAE) of 4.2 ppm on artificial saliva samples. For the clinical samples, we perform correlation analysis between predicted salivary UA concentration and actual blood UA level. Our method for the detection of salivary UA achieves an r-score of 0.6140 and a p-value <0.0001, comparing the commercially available test trips for the detection of UA in finger-prick blood.

关键词： Image color analysis Feature extraction Sensors Medical diagnostic imaging machine learning algorithms Optical sensors Strips Colorimetric sensors image processing machine learning multivariate regression salivary uric acid (UA)

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Review and Empirical Analysis of machine learning-Based Software Effort Estimation

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IEEE ACCESS 2024年 12卷 85661-85680页

作者： Rahman, Mizanur Sarwar, Hasan Kader, MD. Abdul Goncalves, Teresa Tin, Ting Tin Western Illinois Univ Sch Comp Sci Macomb IL 61455 USA United Int Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh Univ Malaysia Pahang Al Sultan Abdullah Fac Comp Pekan 26600 Malaysia Univ Evora Dept Informat P-7004516 Evora Portugal INTI Int Univ Fac Data Sci & Informat Technol Nilai 71800 Malaysia

The average software company spends a huge amount of its revenue on Research and Development (R&D) for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation can pose significant challenges, highlighting the need for continuous improvement in estimation techniques. This study reviews recent machine learning approaches employed to enhance the accuracy of software effort estimation (SEE), focusing on research published between 2020 and 2023. The literature review employed a systematic approach to identify relevant research on machine learning techniques for SEE. Additionally, comparative experiments were conducted using five commonly employed machine learning (ML) methods: K-Nearest Neighbor, Support Vector machine, Random Forest, Logistic Regression, and LASSO Regression. The performance of these techniques was evaluated using five widely adopted accuracy metrics: Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation was carried out on seven benchmark datasets: Albrecht, Desharnais, China, Kemerer, Mayazaki94, Maxwell, and COCOMO, which are publicly available and extensively used in SEE research. By carefully reviewing study quality, analyzing results across the literature, and rigorously evaluating experimental outcomes, clear conclusions were drawn about the most promising techniques for achieving state-of-the-art accuracy in estimating software effort. This study makes three key contributions to the field: firstly, it furnishes a thorough overview of recent machine learning research in software effort estimation (SEE);secondly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation;and

关键词： Estimation machine learning algorithms Software reliability Software algorithms Research and development Software development management Linear regression Support vector machines Random forests Software effort estimation software development efforts estimation linear regression support vector machine random forest LASSO KNN R&D investment

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