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Explainable Business Process Remaining Time Prediction Using Reachability Graph

Chinese Journal of Electronics 2023年第3期32卷 625-639页

作者： CAO Rui ZENG Qingtian NI Weijian LU Faming LIU Cong DUAN Hua College of Computer Science and Engineering Shandong University of Science and Technology School of Computer Science and Technology Shandong University of Technology

With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph,which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next,the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.

关键词： Deep learning Training Visualization Recurrent neural networks Petri nets Transfer learning Process control

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CM-GCN: Crossbred Method based-Graph Convolution Networks for identifying influential spreaders from directed networks 16

CM-GCN: Crossbred Method based-Graph Convolution Networks fo...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Saha, Nilanjana Namtirtha, Amrita Dutta, Animesh National Institute of Technology Computer Science and Engineering Durgapur India Jis College of Engineering Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350383119

Influential spreaders play a critical role, either maximizing information dissemination or controlling epidemic spreads. Much of the existing research concentrates on identifying optimal spreaders in undirected networks. However, recognizing the significance of edge direction in spreading processes is essential when pinpointing influential spreaders. Graph Convolutional Neural Networks (GCNs) have shown remarkable efficacy in various graph-based tasks. Nevertheless, the challenge of over-smoothing in GCNs leads to nodes converging to similar representations with increasing depth, restricting the practical usage of shallow models. In response, this paper introduces a novel approach, the Crossbred Method-based Graph Convolutional Network (CM-GCN), to identify superior spreaders, considering the intricacies of directed networks. The proposed CM-GCN method considers the spreading dynamics specific to directed networks. Through a series of experiments on real-world datasets, our results demonstrate that the CM-GCN model outperforms prevailing baseline approaches. This advancement is particularly unable compared to crossbred method and other centrality methods in directed networks. Moreover, the CM-GCN method effectively addresses over-smoothing in deep GCNs while mitigating information loss attributable to DropEdge. The spreading performance of the proposed method, evaluated using the Directed Susceptible-Infected-Recovered (DBIR) epidemic model on six real networks, showcases significant enhancements in spreading dynamics compared to the established crossbred method. © 2024 IEEE.

关键词： Crossbred method Directed Network DSIR epidemic model Graph Convolution Network Influential Spreaders Kendall τ rank correlation

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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Energy Storage System Using Digital Twins with AI and IoT for Efficient Energy Management and Prolonged Battery Life in Electric Vehicles

Energy Storage System Using Digital Twins with AI and IoT fo...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Lakshmi, M. Sri Sarma, Vanam Ajay G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798331509828

This research proposes an integrated framework of a digital twin, incorporating artificial intelligence and the Internet of Things to optimize energy management and prolong the lifespan of the battery in electric vehicles. Under typical operating conditions, the system exhibits predictive accuracy with mean absolute error relative to actual data equal to 0.042 and root mean square error equal to 0.055. These values reflect improvement in responsiveness of the system since latency has been reduced to 0.12 seconds with feedback response times established at 0.45 seconds. Moreover, it has increased by 15% in energy efficiency while promoting its longevity with a capacity fade rate measured at 0.0025% per cycle. Besides, the model shows strong anomaly detection capability - maintenance alert precision is found to be 95% while detection rate reaches76%. All these results point out the possibility that combining Digital Twin technology with AI and IoT may end up significantly boosting energy management systems for electric vehicles compared to more traditional approaches. © 2025 IEEE.

关键词： Artificial Intelligence (AI) Digital Twin Technology Electric Vehicle (EV) Battery Management Energy Efficiency Optimization Internet of Things (IoT) Predictive Maintenance

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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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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Web service discovery with incorporation of web services clustering

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International Journal of computers and Applications 2023年第1期45卷 51-62页

作者： Jalal, Sunita Yadav, Dharmendra Kumar Negi, Chetan Singh Department of Computer Engineering College of Technology GBPUAT Pantnagar India Department of Computer Science and Engineering MNNIT Allahabad Prayagraj India

With the rapid growth and adoption of web-based technologies, more and more service providers offer various services on the web. This can facilitate the organizations to develop business applications using web services composition over the Internet according to their business requirements. The primary requirement for developing composite web service is to discover the most relevant web service for realizing each function of the required composite web service. Finding the most relevant web service is still a challenging task. In this paper, we propose web service discovery approach that incorporates web services clustering. Clustering technique combines latent Dirichlet allocation(LDA) and k-Medoids and reduces search space for web service discovery. Web service discovery uses a match-making algorithm that utilizes WordNet database and Hungarian method to compute the semantic similarity score for a web service based on the requested query. Experimental results show that the proposed approach performs better than LDA-based approach. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Web services

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Prompt Learning for Few-Shot Question Answering via Self-Context Data Augmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 589-603页

作者： Qiu, Jian-Qiang Zhang, Chun-Yang Chen, C. L. Philip Fuzhou University College of Computer and Data Science Fuzhou350108 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

Pretrained language models (PLMs) have shown remarkable performance on question answering (QA) tasks, but they usually require fine-tuning (FT) that depends on a substantial quantity of QA pairs. Therefore, improving the performance of PLMs in scenarios with only a small number of training examples, also known as a few-shot setting, is of great practical significance. Current mitigation strategies for the few-shot QA task largely rely on pretraining a QA task-specific language model from scratch, overlooking the potential of foundational PLMs to generate QA pairs, particularly in the few-shot setting. To address this issue, we propose a prompt-based QA data augmentation method aimed at automating the creation of high-quality QA pairs. It employs the PFT method, adapting the question generation process of PLMs to the few-shot setting. Additionally, we introduce a dynamic text filling training strategy. This strategy simulates the progressive human learning process, thereby alleviating overfitting of PLMs in the few-shot setting and enhancing their reasoning capability to tackle complex questions. Extensive experiments demonstrate that the proposed method outperforms existing approaches across various few-shot configurations. 2691-4581 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://***/publications/rights/*** for more information.

关键词： Zero-shot learning

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Towards A Wearable Passive Lower-Limb Exoskeleton Based on the Compact Gear-Spring Mechanism

Towards A Wearable Passive Lower-Limb Exoskeleton Based on t...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Minh Pham, Quang Phuoc Nguyen, Hung Quang Vo, Quan Minh Dac Lai, Tien Vu Thu Nguyen, Hien Nguyen, Vu Linh College of Engineering and Computer Science Vin University Viet Nam College of Engineering and Computer Science VinUniversity Hanoi Viet Nam

ISBN: (纸本)9781665481090

Exoskeletons have been proven to have remarkable effects in rehabilitation as they effectively provide mechanical support and motorized assistance to people with muscle weakness. Current design methods for such devices often face challenges regarding the weight to be worn by humans and high cost. In this paper, we propose a new passive exoskeleton design that utilizes a compact gear-spring mechanism (CGSM) for lower-limb rehabilitation. The use of the CGSM ensures the exoskeleton is lightweight and also does not rely on batteries and actuators as active exoskeletons do. The effectiveness of the proposed exoskeleton was evaluated using electromyography (EMG) signal analysis by comparing the muscle activity of humans with and without the device. The results indicate that the proposed passive exoskeleton can significantly reduce muscular efforts during rehabilitation tasks, suggesting its potential to enhance the rehabilitation process and accelerate recovery times while maintaining a light weight attached to users. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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