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Deep learning Based on TensorFlow and Keras for Predictive Monitoring of Business Process Execution Delays 12th

Deep Learning Based on TensorFlow and Keras for Predictive M...

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12th international conference on Model and data engineering, MEDI 2023

作者： Fradj, Walid Ben Turki, Mohamed Gargouri, Faiez MIRACL Laboratory ISIMS University of Sfax P.O. Box 242 Sfax3021 Tunisia

ISBN: (纸本)9783031493324

In order to enhance their performance and responsiveness, organizations must identify, manage, and monitor all business processes that involve crucial knowledge. this can be achieved through a multidisciplinary approach that combines Knowledge Management, Business Process Management, and Process Mining. To achieve these objectives, the implementation of an automated computer system for managing business processes has become paramount. In this context, we adopt the CRISP-DM approach to propose a new method called BPEDPM (Business Process Event data Predictive Monitoring) for predictive process monitoring. this method leverages process mining techniques to exploit the execution data from a Business Process Management System (BPMS) workflow engine. Particularly, in the modeling phase, we apply deep learning based on the TensorFlow and Keras tools. To demonstrate the applicability of the BPEDPM method, we have developed an intelligent system named iBPMS4PED to predict the execution times of business processes. the research focuses on the incoming mail management process within a group health insurance context. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

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Unlocking the Black Box: Towards Interactive Explainable automated Machine learning 24th

Unlocking the Black Box: Towards Interactive Explainable Au...

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24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Garouani, Moncef Bouneffa, Mourad Univ. Littoral Côte d’Opale UR 4491 LISIC Laboratoire d’Informatique Signal et Image de la Côte d’Opale Calais62100 France

ISBN: (纸本)9783031482311

automated machine learning (AutoML) has transformed the process of selecting optimal machine learning (ML) models by autonomously searching for the most appropriate ones and fine-tuning associated hyperparameters. this eliminates the burdensome task of trial-and-error selection and parametrization of ML algorithms. Nonetheless, the lack of transparency and explainability poses a significant challenge when using AutoML, as it hampers user trust in the system’s recommendations. Consequently, users often allocate more resources to the search process, resulting in reduced efficiency of the AutoML systems. To address this challenge, we propose an interactive and explainable AutoML framework that enables users to understand the reasoning behind the recommendations and diagnose any limitations of the suggested models using various explainable AI methods. Additionally, our framework provides the possibility of automated performance refinement. To operationalize the framework, we introduce AMLExplainer, an XAI system for interactive and interpretable AutoML that visualizes and performs all stages of the proposed pipeline(s) within the widely used Bootstrap Dash environment. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Transparency

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A Two-Stage Extended Kalman Filter-Based Approach Against FDI Cyber-Attack in intelligent and Connected Vehicles 11

A Two-Stage Extended Kalman Filter-Based Approach Against FD...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Sun, Bin Yang, Shichun Wang, Yu Lu, Jiayi Cao, Yaoguang School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beihang University Beijing China

ISBN: (纸本)9789897587450

With the widespread integration of artificial intelligence and telecommunication technologies in vehicles, the challenge of cybersecurity in intelligent and Connected Vehicles (ICVs) has gained significant attention. A typical and high-risk cyber-attack technique involves False data Injection (FDI) into sensors through the network, resulting in deviations in subsequent planning and control algorithm outcomes. Existing approaches suffer from limited robustness, being suitable only for simple models or requiring extensive data for the training model, which limits their practicality. therefore, this paper proposes a method based on a Two-stage Extended Kalman Filter (TSEKF), which not only detects cyber-attacks but also restores the vehicle’s true motion state, thereby enhancing the robustness of vehicle ego state perception. the experimental results demonstrate that the proposed method exhibits strong performance across various motion scenarios, offering an effective solution for the safe operation of ICVs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cyber attacks

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Antimicrobial Resistance Prediction using Neural Networks for Gonorrhea 11

Antimicrobial Resistance Prediction using Neural Networks fo...

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11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Vasavi, V.C. Raj, Ritesh Karthikeyan, B. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Bengaluru India Department of Artificial Intelligence India Vellore India

ISBN: (纸本)9798350379136

Antimicrobial resistance (AMR) is an urgent and pressing global health challenge, and requires innovative methods for its prediction and management for better treatment plans for gonorrhea. Machine learning models are developed for predicting AMR in bacteria based on whole-genome sequence and clinical sample data. the ML methods extrapolate learned pattern occurrences in clinical samples and genomic data to infer potential resistance in a pathogen to a given antibiotic. the models used for the AMR prediction include Support Vector Machine (SVM), and neural networks like Convolution NN, Feedforward NN, and Recurrent NN. the review touches on the challenges of microbial genome diversity and the interpretability of machine learning models by visualizing data like average resistance level by country, the most resistant anitbiotic by country, and Spearman and Pearson correlation. the results have variable data quality, feature selection, and model optimization strategy parameters, such as hyperparameter tuning, which affect the accuracy in predicting AMR. Out of the ML models used SVM has the highest accuracy in terms of AMR prediction. It is the promise and use of ML-based AMR prediction systems in clinical practices to provide knowledge-guided personalized options to aid AR and improve the health outcomes of patients. © 2024 IEEE.

关键词： Prediction models

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OVER-PARAMETERIZED MODEL OPTIMIZATION WIth POLYAK-LOJASIEWICZ CONDITION

OVER-PARAMETERIZED MODEL OPTIMIZATION WITH POLYAK-LOJASIEWIC...

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11th international conference on learning Representations, ICLR 2023

作者： Chen, Yixuan Shi, Yubin Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Wang, Yujiang Dick, Robert P. Lv, Qin Zhao, Yingying Yang, Fan Gu, Ning Shang, Li China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Mathematics Statistics The University of Glasgow Glasgow United Kingdom Microsoft Research Asia Shanghai China Department of Engineering Science University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Michigan United States Department of Computer Science University of Colorado Boulder BoulderCO United States School of Microelectronics Fudan University Shanghai China

this work pursues the optimization of over-parameterized deep models for superior training efficiency and test performance. We first theoretically emphasize the importance of two properties of over-parameterized models, i.e., the convergence gap and the generalization gap. Subsequent analyses unveil that these two gaps can be upper-bounded by the ratio of the Lipschitz constant and the Polyak-Lojasiewicz (PL) constant, a crucial term abbreviated as the condition number. Such discoveries have led to a structured pruning method with a novel pruning criterion. that is, we devise a gating network that dynamically detects and masks out those poorly-behaved nodes of a deep model during the training session. To this end, this gating network is learned via minimizing the condition number of the target model, and this process can be implemented as an extra regularization loss term. Experimental studies demonstrate that the proposed method outperforms the baselines in terms of both training efficiency and test performance, exhibiting the potential of generalizing to a variety of deep network architectures and tasks. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Network architecture

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Hybrid CNN-LSTM Model for automated Violence Detection and Classification in Surveillance Systems 12

Hybrid CNN-LSTM Model for Automated Violence Detection and C...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Agarwal, Ambuj Kumar Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350369861

the proliferation of digital images in recent years has prompted the development of automated algorithms that can identify violent and non-violent material in a variety of settings. this study introduces a unique method for categorising violent and nonviolent pictures using a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. Careful data collection from a publicly accessible source yielded a dataset of 11,043 original 416x416 photos. the photos were downsampled to 256x256 as part of the preprocessing phase of the suggested technique for more effective model training. the suggested model was compared against both more conventional Machine learning methods and state-of-the-art Deep learning systems to determine how well each performed. the findings reported here are for a 30-epoch value and a learning rate of 0.01. the experiment explored several hyperparameters. An outstanding 98.63 % accuracy was achieved by the suggested hybrid model, demonstrating its better performance. this proves the usefulness and promise of using the hybrid CNN-LSTM model for automated image classification, which in turn improves detection and classification skills for violent and nonviolent images. © 2023 IEEE.

关键词： Image classification

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Transformer Models in Education: Summarizing Science Textbooks with AraBART, MT5, AraT5, and mBART 1st

Transformer Models in Education: Summarizing Science Textboo...

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1st international conference on intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Masri, Sari Raddad, Yaqeen Khandaqji, Fidaa Ashqar, Huthaifa I. Elhenawy, Mohammed Department of Natural Engineering and Technology Sciences Arab American University P.O. Box 240 Ramallah Palestine Civil Engineering Department Arab American University P.O. Box 240 Jenin Palestine Columbia University New YorkNY United States CARRS-Q Queensland University of Technology Brisbane Australia Centre for Data Science Queensland University of Technology Brisbane Australia

ISBN: (纸本)9783031823763

In an era of rapidly expanding digital archives, effective text summarization tools are crucial for educators and learners. this study presents an advanced text summarization system specifically designed for Arabic textbooks, leveraging state-of-the-art natural language processing (NLP) models. We trained and evaluated four models - AraBART, mBART50, AraT5, and MT5 - on biology textbook content for 11th and 12th grades in the Palestinian curriculum using the ROUGE metric to compare their performance. Our results show that AraBART and mBART50 outperformed the other models in terms of ROUGE-1 (score: 0.24), ROUGE-2 (score: 0.118–0.139) and ROUGE-L (score: 0.24), indicating their effectiveness in summarizing complex biological information while preserving essential details. To further validate the performance of these models, we conducted a subjective evaluation by an educational expert who assessed the summaries for their accuracy, relevance, and overall quality, with AraBART ranking highest due to its ability to capture key concepts and reduce noise in the original text. the study’s findings provide valuable insights into the performance of these models and contribute to the development of effective tools for understanding and generating Arabic text. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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RetinAI: Beyond Retinal Challenges 15

RetinAI: Beyond Retinal Challenges

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shilaskar, Swati Shah, Riya Sawarkar, Sanchit Samarth, Amit Department Of Electronics and Telecommunication Engineering Pune411037 India

ISBN: (纸本)9798350370249

Diabetic retinopathy (DR) is a diabetes complication that can result in blindness. It is expensive and time-consuming to manually examine color fundus images in the traditional manner in order to detect DR in its early stages. Deep learning (DL) techniques have thus been used to fundus images for automated DR screening, taking advantage of their remarkable performance in a variety of domains. An intelligent computer-aided approach for automatic fundus image DR screening is developed and presented, tackling the issues of costly and labor-intensive human screening as well as the scarcity of data for deep learning model training, which frequently results in overfitting. To improve the accuracy of DR detection, the system uses a pre-trained convolutional neural network (CNN) in a two-stage transfer learning strategy. this strategy is a potential option for DR screening since it greatly reduces the issue of category imbalance and boosts diagnostic effectiveness. To diagnose diabetic retinopathy (DR), a convolutional neural network (CNN) model is used. From fundus images, the CNN model learns a hierarchical structure of low- to high-level characteristics. the low-level layers of the model are adjusted to enhance its ability to identify local structures of lesions and normal regions. A new completely connected (FC) layer based on principle component analysis (PCA) for unsupervised feature extraction takes the place of the fully connected (FC) layers, which encode high-level characteristics that are global and domain-specific. By using this method, discriminative characteristics are extracted from the fundus images, which can improve the precision of DR classification. Lastly, we include a classification layer that uses gradient boosting. the assessment of the suggested system using two difficult datasets (Messidor and EyePACS). © 2024 IEEE.

关键词： Convolutional neural network Deep learning Diabetic retinopathy Fundus images Grad-CAM

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PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data 11

PheME: A deep ensemble framework for improving phenotype pre...

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11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Zhang, Shenghan Li, Haoxuan Tang, Ruixiang Ding, Sirui Rasmy, Laila Zhi, Degui Zou, Na Hu, Xia Xi'an Jiaotong-Liverpool University Department of Computer Science Xi'An China Wuhan University Department of Computer Science Wuhan China Rice University Department of Computer Science Houston United States Texas A&m University Department of Computer Science and Engineering College Station United States UTHealth Houston School of Biomedical Informatics Houston United States Texas A&m Univerisity Department of Engineering Technology & Industrial Distribution College Station United States

ISBN: (纸本)9798350302639

Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from the heterogeneous EHR data remains a challenge. In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction. Firstly, we employ multiple deep neural networks to learn reliable representations from the sparse structured EHR data and redundant clinical notes. A multi-modal model then aligns multi-modal features onto the same latent space to predict phenotypes. Secondly, we leverage ensemble learning to combine outputs from single-modal models and multi-modal models to improve phenotype predictions. We chose seven diseases to evaluate the phenotyping performance of the proposed framework. Experimental results show that using multi-modal data significantly improves phenotype prediction in all diseases, the proposed ensemble learning framework can further boost the performance. © 2023 IEEE.

关键词： Forecasting

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Quantized Iterative learning Control for Nonlinear Switched Discrete- Time Systems with Actuator Saturation 11

Quantized Iterative Learning Control for Nonlinear Switched ...

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11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Sun, Shu-Ting Li, Xiao-Dong School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510006 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

A quantized iterative learning control (ILC) scheme is investigated for nonlinear switched discrete-time systems with actuator saturation in repetitive operations. the logarithmic quantizers are utilized to quantize the tracking error signals, and the sector bound method is applied for analysis and mitigation of quantization influence. By using mathematical induction method, it is strictly proved that the tracking error of each subsystem will converge to zero with the increase of iteration. A numerical example is given to illustrate the validity of the quantized ILC algorithm. © 2022 IEEE.

关键词： Digital control systems

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