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Reordering-enhanced Grad-CAM for unveiling hidden patterns in multi-source financial data

MEMETIC COMPUTING 2025年第2期17卷 1-16页

作者： Zhang, Zhigang Liu, Kehui Lei, Junli Hubei Univ Econ Sch Stat & Math Wuhan 430205 Peoples R China Hubei Univ Econ Hubei Data & Anal Ctr Wuhan 430205 Peoples R China Hubei Univ Econ Collaborat Innovat Ctr Emiss Trading Syst Coconstr Wuhan 430205 Peoples R China Beijing Acad Sci & Technol Inst Urban Syst Engn Beijing 100190 Peoples R China

Convolutional Neural Networks (CNNs) excel in feature extraction and pattern recognition in areas like image classification and speech processing. However, their application in the financial sector has been limited due to the complexity, high dimensionality, and temporal nature of financial data, as well as the need for model interpretability. This study, based on CNN technology, proposes a Reordering-Enhanced Grad-CAM algorithm to improve model interpretability and reliability, offering transparent and dependable tools for financial decision-making. The innovation of this study lies in two key aspects: firstly, it replaces the traditional manual variable selection approach with automatic feature extraction and fusion using CNNs, demonstrating the effectiveness of deep learning in handling large-scale financial data. Secondly, we propose a novel reordering-based iterative algorithm that adapts Grad-CAM, originally designed for image classification, to multi-source financial time series data, treating sliding window data segments as pseudo-images to improve interpretability and identify critical features. Using data from Shanghai and Shenzhen A-shares (1990-2020), the Reordering-Enhanced Grad-CAM technique generated heatmaps that identified key predictive indicators, leading to improved model performance. robustness analysis demonstrated that over 70% of important variables were consistently identified, with some models reaching up to 100%, confirming the reliability and stability of our method in financial distress prediction.

关键词： Convolutional neural networks Interpretability Financial distress early warning Grad-CAM Reordering technique algorithm robustness

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Study of beam density reconstruction algorithms for FEEP thrusters by simulated experiment

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VACUUM 2025年 233卷

作者： Sun, Yiming Cheng, Long Deng, Hanwen Kang, Xiaoming Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

The precise reconstruction of the beam density distribution in FEEP thrusters is crucial for studying key performance parameters such as beam divergence angle, thrust vector. Tomography can simplify measuring device complexity, accelerate the scanning process, and provide better resolution, leading to its gradual application in beam characterization. However, it is challenging to quantitatively study and evaluate the accuracy of reconstruction algorithms, the influence of scanning parameters, and the effects of noise through experimental methods. This paper employs simulated experiments approach to investigate the reconstruction processes and robustness of both single-scan and tomography-scan reconstruction algorithms. The results indicate that the accuracy of beam profile and position in tomographic reconstruction are primarily affected by probe diameter and angle intervals, with tomography-scan demonstrating greater robustness than the single-scan algorithm. Additionally, the study simulates and analyzes the distribution patterns of probe currents for beam arrays and slit beams in tomography-scan.

关键词： FEEP Beam density reconstruction Tomography-scan Single-scan algorithm robustness

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INCREASING robustness IN GLOBAL ADAPTIVE QUADRATURE THROUGH INTERVAL SELECTION HEURISTICS

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 1984年第2期10卷 117-139页

作者： SHAPIRO, HD University of New Mexico

Adaptive quadrature methods are usually characterized as either local or global. The methods are distinguished by the scope and nature of the information used to determine when algorithm termination should occur and which intervals should be selected for refinement. By treating quadras the generation and search of a tree whose nodes represent intervals, a unified nonalgorithmic framework is developed which encompasses both approaches. This framework provides a method for improving the numerical properties of local adaptive quadrature with respect to termination of intervals by relative error criteria, and total algorithm termination due to excessive function evaluations. The framework also suggests heuristics for interval selection in global adaptive quadrature that bring the actual error closer to the requested tolerance. Extensive computational experience with several heuristics for global adaptive quadrature is described. © 1984, ACM. All rights reserved.

关键词： algorithm robustness Interval selection strategy

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FUZZY CONTROL OF BAKERS-YEAST FED-BATCH BIOPROCESS - A robustness STUDY

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FOOD RESEARCH INTERNATIONAL 1994年第2期27卷 145-153页

作者： MAHJOUB, M MOSRATI, R LAMOTTE, M FONTEIX, C MARC, I INPL SCI GENIE CHIM LABCNRSUPR 68111 RUE GRANDVILLEBP 451F-54001 NANCYFRANCE FAC SCI NANCY CTR RECH AUTOMAT NANCYCNRSURA D0821F-54506 VANDOEUVRE NANCYFRANCE

The purpose of the investigation was to determine if, based on the monitoring of the residual substrate concentration, fuzzy control is an effective way of controlling a fermentation process. Various simulations were made using a knowledge model, first to evaluate the performance of the control for the biomass concentration, and then to analyze how it reacted in the presence of disturbances. Results showed high final productivity and biomass concentration. Despite different disturbances, as the feeding glucose concentration changed or the temporary substrate feed-rate increased, the fuzzy controller provided process stability and robustness near the set-point. Moreover, these properties were maintained in the case of noisy measurements.

关键词： FUZZY LOGIC algorithm robustness YEAST PRODUCTION FED-BATCH PROCESS

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Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2023年第3期70卷 941-953页

作者： De Giovanni, Elisabetta Teijeiro, Tomas Millet, Gregoire P. P. Atienza, David Ecole Polytech Fed Lausanne Embedded Syst Lab Lausanne Switzerland Univ Basque Country UPV EHU Dept Math Leioa Spain Univ Lausanne Inst Sport Sci Lausanne Switzerland

Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sudden physiological changes occur to the signals, compromising the robustness of standard algorithms. Methods: Our method, called BayeSlope, is based on unsupervised learning, Bayesian filtering, and non-linear normalization to enhance and correctly detect the R peaks according to their expected positions in the ECG. Furthermore, as BayeSlope is computationally heavy and can drain the device battery quickly, we propose an online design that adapts its robustness to sudden physiological changes, and its complexity to the heterogeneous resources of modern embedded platforms. This method combines BayeSlope with a lightweight algorithm, executed in cores with different capabilities, to reduce the energy consumption while preserving the accuracy. Results: BayeSlope achieves an F1 score of 99.3% in experiments during intense cycling exercise with 20 subjects. Additionally, the online adaptive process achieves an F1 score of 99% across five different exercise intensities, with a total energy consumption of 1.55 +/- 0.54 mJ. Conclusion: We propose a highly accurate and robust method, and a complete energy-efficient implementation in a modern ultra-low-power embedded platform to improve R peak detection in challenging conditions, such as during high-intensity exercise. Significance: The experiments show that BayeSlope outperforms state-of-the-art QRS detectors up to 8.4% in F1 score, while our online adaptive method can reach energy savings up to 38.7% on modern heterogeneous wearable platforms.

关键词： Adaptive R peak detection algorithm robustness biosignal processing edge computing energy-accuracy trade-off heterogeneous nodes intense exercise machine learning ultra-low power computing wellness

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Digital media zero watermark copyright protection algorithm based on embedded intelligent edge computing detection

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第5期18卷 6771-6789页

作者： Xu, Hongyan Harbin Normal Univ Dept Acad Res Harbin 150025 Heilongjiang Peoples R China

With the rapid development of computer technology and network communication technology, copyright protection caused by widely spread digital media has become the focus of attention in various fields. For digital media watermarking technology research emerge in endlessly, but the results are not ideal. In order to better realize the copyright identification and protection, based on the embedded intelligent edge computing detection technology, this paper studies the zero watermark copyright protection algorithm of digital media. Firstly, this paper designs an embedded intelligent edge detection module based on Sobel operator, including image line buffer module, convolution calculation module and threshold processing module. Then, based on the embedded intelligent edge detection module, the Arnold transform of image scrambling technology is used to preprocess the watermark, and finally a zero watermark copyright protection algorithm is constructed. At the same time, the robustness of the proposed algorithm is tested. The image is subjected to different proportion of clipping and scaling attacks, different types of noise, sharpening and blur attacks, and the detection rate and signal-to-noise ratio of each algorithm are calculated respectively. The performance of the watermark image processed by this algorithm is evaluated subjectively and objectively. Experimental data show that the detection rate of our algorithm is the highest, which is 0.89. In scaling attack, the performance of our algorithm is slightly lower than that of Fourier transform domain algorithm, but it is better than the other two algorithms. The Signal to Noise Ratio of the algorithm is 36.854% in P6 multiplicative noise attack, 39.638% in P8 sharpening edge attack and 41.285% in fuzzy attack. This shows that the algorithm is robust to conventional attacks. The subjective evaluation of 33% and 39% of the images is 5 and 4. The mean values of signal to noise ratio, peak signal to noise ratio, mean squar

关键词： embedded intelligent edge detection zero watermarking algorithm copyright protection digital media algorithm robustness

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Robust and accurate performance anomaly detection and prediction for cloud applications: a novel ensemble learning-based framework

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 1-16页

作者： Xin, Ruyue Liu, Hongyun Chen, Peng Zhao, Zhiming Univ Amsterdam Multiscale Networked Syst MNS Res Grp Amsterdam Netherlands Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China

Effectively detecting run-time performance anomalies is crucial for clouds to identify abnormal performance behavior and forestall future incidents. To be used for real-world applications, an effective anomaly detection framework should meet three main challenging requirements: high accuracy for identifying anomalies, good robustness when application patterns change, and prediction ability for upcoming anomalies. Unfortunately, existing research about performance anomaly detection usually focuses on improving detection accuracy, while little research tackles the three challenges simultaneously. We conduct experiments for existing detection methods on multiple application monitoring data, and results show that existing detection methods usually focus on different features in data, which will lead to their diverse performance on different data patterns. Therefore, existing anomaly detection methods have difficulty improving detection accuracy and robustness and predicting anomalies. To address the three requirements, we propose an Ensemble Learning-Based Detection (ELBD) framework which integrates existing well-selected detection methods. The framework includes three classic linear ensemble methods (maximum, average, and weighted average) and a novel deep ensemble method. Our experiments show that the ELBD framework realizes better detection accuracy and robustness, where the deep ensemble method can achieve the most accurate and robust detection for cloud applications. In addition, it can predict anomalies in the next four minutes with an F1 score higher than 0.8. The paper also proposes a new indicator ARP_score to measure detection accuracy, robustness, and multi-step prediction ability. The ARP_score of the deep ensemble method is 5.1821, which is much higher than other detection methods.

关键词： Performance anomaly detection algorithm robustness Anomaly prediction Ensemble learning Deep ensemble

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Current challenges of implementing artificial intelligence in medical imaging

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2022年 100卷 12-17页

作者： Saw, Shier Nee Ng, Kwan Hoong Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Univ Malaya Dept Biomed Imaging Kuala Lumpur 50603 Malaysia Kaohsiung Med Univ Coll Hlth Sci Dept Med Imaging & Radiol Sci Kaohsiung Taiwan

The idea of using artificial intelligence (AI) in medical practice has gained vast interest due to its potential to revolutionise healthcare systems. However, only some AI algorithms are utilised due to systems' uncertainties, besides the never-ending list of ethical and legal concerns. This paper intends to provide an overview of current AI challenges in medical imaging with an ultimate aim to foster better and effective communication among various stakeholders to encourage AI technology development. We identify four main challenges in implementing AI in medical imaging, supported with consequences and past events when these problems fail to mitigate. Among them is the creation of a robust AI algorithm that is fair, trustable and transparent. Another issue is on data governance, in which best practices in data sharing must be established to promote trust and protect the patients' privacy. Next, stakeholders, such as the government, technology companies and hospital management, should come to a consensus in creating trustworthy AI policies and regulatory frameworks, which is the fourth challenge, to support, encourage and spur innovation in digital AI healthcare technology. Lastly, we discussed the efforts of various organizations such as the World Health Organisation (WHO), American College of Radiology (ACR), European Society of Radiology (ESR) and Radiological Society of North America (RSNA), who are already actively pursuing ethical developments in AI. The efforts by various stakeholders will eventually overcome hurdles and the deployment of AI-driven healthcare applications in clinical practice will become a reality and hence lead to better healthcare services and outcomes.

关键词： Artificial intelligence Medical imaging Challenges Ethics Data governance algorithm robustness

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EXPERIMENTAL STUDY ON NAVIGATION FOR WHEAT SEEDLING ROOT CUTTING BASED ON DEEP LEARNING

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INMATEH-AGRICULTURAL ENGINEERING 2023年第3期71卷 522-534页

作者： Lin, Haibo Xu, Chenhe Lu, Yuandong Qingdao Univ Technol Sch Mech & Automot Engn Qingdao 266520 Peoples R China Qingdao Univ Technol Key Lab Ind Fluid Energy Conservat & Pollut Contro Minist Educ Qingdao 266520 Peoples R China Shandong Lingong Construct Machinery Co Ltd Linyi 016000 Shandong Peoples R China

In response to the automatic extraction of navigation lines for wheat root cutting, this paper conducted field experiments and analyses on the navigation line extraction algorithm, based on the improved YOLOv5 algorithm. Firstly, based on the characteristics of wheat seedling rows during the wheat rejuvenation period, the YOLOv5 algorithm was improved by using rotation detection box labels, and navigation lines were extracted by fitting the detection boxes using clustering methods. Then, an experimental system was established to conduct field experiments on the algorithm: (1) Tests were conducted at three speeds of 0.5 m/s, 1.0 m/s and 1.5 m/s respectively, and the position error of the root cutter was measured and analyzed, indicating that the actual navigation path position error increased with the speed. The best navigation performance was observed at 1 m/s, with an average positional error of 18.56 mm, meeting the requirements for wheat root cutting. (2) robustness analysis of the algorithm was conducted using data collected from 2019 to 2022. Comparative tests were conducted from four aspects: different years, different time periods, different environments, and different yaw angles. The results showed that the algorithm proposed in this paper has stronger robustness and higher accuracy.

关键词： Deep learning Agricultural machinery navigation line Field experiment Complex conditions algorithm robustness

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Neural Network robustness Analysis Using Sensor Simulations for a Graphene-Based Semiconductor Gas Sensor

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CHEMOSENSORS 2022年第5期10卷 152页

作者： Schober, Sebastian A. Bahri, Yosra Carbonelli, Cecilia Wille, Robert Infineon Technol AG D-85579 Neubiberg Germany Johannes Kepler Univ Linz Inst Integrated Circuits A-4040 Linz Austria Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany Software Competence Ctr Hagenberg GmbH SCCH A-4232 Hagenberg Austria

Despite their advantages regarding production costs and flexibility, chemiresistive gas sensors often show drawbacks in reproducibility, signal drift and ageing. As pattern recognition algorithms, such as neural networks, are operating on top of raw sensor signals, assessing the impact of these technological drawbacks on the prediction performance is essential for ensuring a suitable measuring accuracy. In this work, we propose a characterization scheme to analyze the robustness of different machine learning models for a chemiresistive gas sensor based on a sensor simulation model. Our investigations are structured into four separate studies: in three studies, the impact of different sensor instabilities on the concentration prediction performance of the algorithms is investigated, including sensor-to-sensor variations, sensor drift and sensor ageing. In a further study, the explainability of the machine learning models is analyzed by applying a state-of-the-art feature ranking method called SHAP. Our results show the feasibility of model-based algorithm testing and substantiate the need for the thorough characterization of chemiresistive sensor algorithms before sensor deployment in order to ensure robust measurement performance.

关键词： gas sensors graphene neural networks modeling algorithm robustness simulation model explainability sensor ageing sensor variations

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