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In Search for Model-Driven eXplainable Artificial Intelligence 1

10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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A positive and unlabeled learning framework based on extreme learning machine for drug-drug interactions discovery

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 1-12页

作者： Bi, Xin Ma, He Li, Jianhua Ma, Yuliang Chen, Deyang Sino-Dutch Biomedical and Information Engineering School Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China

Drug-drug interactions (DDIs) lead to Adverse Drug Reactions (ADRs) in most cases, which increase medical costs tremendously, and may cause medical negligence or even fatal accidents. Many researchers introduce binary classification methods into DDIs discovery task to avoid expensive and inefficient experimental trials. However, we find that drug-drug pairs without DDI labels cannot be simply viewed as negative samples in practice, since unobserved DDIs may be discovered from unlabeled pairs. Therefore, different from traditional positive-negative classification, in this paper, we treat DDIs discovery as a positive-unlabeled learning (PU learning) problem, in which each drug-drug pair is either a positive sample (labeled as DDI) or an unlabeled one (labeled as non-DDI). We propose a PU learning framework based on Extreme Learning Machine (ELM) named PU-ELM, which consists of two components, namely reliable negative extraction and classifier learning. To improve the quality of reliable negative set, we propose a negative extraction method named OC-ELM-RN (One-Class ELM for Reliable Negative extraction). As to the learning module, we first apply the semi-supervised learning strategy in PU-ELM (denoted as PU-ELM-SS) to achieve extremely fast learning speed, and then we propose an entropy based weighted learning method named PU-ELM-EW (PU-ELM with Entropy Weighted learning) to improve the PU learning performance. Extensive experiments are conducted on a synthetic dataset with varied settings. We also apply PU-ELM to a real-world drug dataset. The results indicate that PU-ELM achieves better DDIs discovery ability compared with state-of-the-art methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Extraction

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Detecting Uteropelvic Junction Obstruction at an Early Stage using Ultrasound Images through an Interplanetary File System 5

Detecting Uteropelvic Junction Obstruction at an Early Stage...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kaviya, R. Prasad, Mridula Pavithra, S. Kapali, B. Suresh Chander Vellore Institute of Technology School of Computer Science and Engineering Chennai India Easwari Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350389609

Ureteropelvic junction obstruction (UPJO) is a medical condition characterized by a blockage at the junction between the ureter and the kidney, leading to impaired urine flow and increased fluid pressure. If left untreated, UPJO can progress to hydronephrosis and potentially result in irreversible kidney damage. This paper presents a novel system for the early detection and severity grading of UPJO using Deep Learning computer-Aided Diagnosis (DL-CAD) techniques. The proposed approach leverages ultrasound imaging as a primary diagnostic tool, noted for its cost-effectiveness, absence of radiation, and non-invasive nature. To enhance the security and integrity of medical data transmission, we integrate blockchain technology into our framework. Our solution combines a DL- CAD model featuring an attention-based pyramid semantic segmentation network with a discrete wavelet transformation- processed residual classification network. An IoT device is employed to detect patient abnormalities and transmit this data to the diagnosis model via an InterPlanetary File System (IPFS). This decentralized storage solution facilitates secure data management across a peer-to-peer network. Ultimately, the proposed framework not only identifies the presence of UPJO but also assesses its severity, ensuring that patient data remains protected throughout the diagnostic process. © 2024 IEEE.

关键词： Semantic Segmentation

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Convolutional Neural Network-Based Identity Recognition Using ECG atDifferent Water Temperatures During Bathing

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computers, Materials & Continua 2022年第4期71卷 1807-1819页

作者： Jianbo Xu Wenxi Chen Biomedical Information Engineering Laboratory Graduate School of Computer Science and EngineeringUniversity of AizuAizu-Wakamatsu965-8580Japan

This study proposes a convolutional neural network(CNN)-based identity recognition scheme using electrocardiogram(ECG)at different water temperatures(WTs)during bathing,aiming to explore the impact of ECG length on the recognition *** data was collected using non-contact electrodes at five different WTs during *** young student subjects(seven men and three women)participated in data *** ECG recordings were collected at each preset bathtub WT for each *** recording is 18 min long,with a sampling rate of 200 *** total,150 ECG recordings and 150 WT recordings were *** R peaks were detected based on the processed ECG(baseline wandering eliminated,50-Hz hum removed,ECG smoothing and ECG normalization)and the QRS complex waves were *** segmented waves were then transformed into binary images,which served as the *** each subject,the training,validation,and test data were taken from the first,second,and third ECG recordings,*** number of training and validation images was 84297 and 83734,*** the test stage,the preliminary classification results were obtained using the trained CNN model,and the finer classification results were determined using the majority vote method based on the preliminary *** validation rate was 98.71%.The recognition rates were 95.00%and 98.00%when the number of test heartbeats was 7 and 17,respectively,for each subject.

关键词： Electrocardiogram QRS recognition rate water temperatures convolutional neural network majority vote

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A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms

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Iranian Journal of Medical Physics 2024年第4期21卷 237-248页

作者： Firouzmand, Mohamad Majidzadeh, Keivan Jafari, Maryam Haghighat, Shahpar Esmaeili, Rezvan Moradi, Leila Misaghi, Nima Ensafi, Mahsa Batmanghelich, Fatemeh Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Biomedical engineering Department Iranian Research Organization for Science & Technology Tehran Iran Breast Cancer Research Center Motamed Cancer Institute ACECR Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Introduction: Several types of cancer can be detected early through thermography, which uses thermal profiles to image tissues in recent years, thermography has gained increasing attention due to its non-invasive and radiation-free nature. There is a growing need for thermographic images of breast cancer lesions in different nationalities and ages to develop this technique, however. This study aims to introduce a dataset of breast thermograms. Material and Methods: In this study, thermographic images of breast cancer from Iranian samples were prepared and confirmed due to the limited number of breast thermogram databases. The prepared database was tested using artificial intelligence and another well-known DMR database (Database for Mastology Research) in this study to determine its reliability. Results: A variety of deep learning architectures and transfer learning are used to evaluate these databases for accuracy, sensitivity, speed, training compliance, and validation compliance. According to best-fitted structures for both types of databases, the database obtained from this study has a quality comparable to the DMR reference database, with minimum accuracy, sensitivity, specificity, precision, and F-score of 80%, 86%, 86%, 88%, and 87%, respectively. Conclusion: Using thermography as a method of early breast screening is demonstrated to be effective. In comparison to DMR, the lower statistics of the proposed database (between 2 and 7 percent) indicates that more diverse breast thermograms should be captured in conjunction with improvements to imaging equipment as well as adherence to thermography recording protocols in order to improve the reliability and efficiency of the database. © (2024), (Mashhad University of Medical sciences). All rights reserved.

关键词： Medical imaging

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mmSkin: An Over-Gauze Wound Assessment System Using Radio Frequency Technologies

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IEEE Internet of Things Journal 2025年第13期12卷 23773-23787页

作者： Zhang, Xiaoyu Li, Zhengxiong Cheng, Yanda Xu, Chenhan Huang, Chuqin Zhang, Emma Zhan, Ye Bo, Wei Xia, Jun Xu, Wenyao State University of New York Department of Computer Science and Engineering Amherst BuffaloNY14068 United States University of Colorado Denver Department of Computer Science and Engineering DenverCO80204 United States State University of New York at Buffalo Department of Biomedical Engineering AmherstNY14068 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Linde Inc TonawandaNY14150 United States

Skin wounds are often covered with gauze to protect the injury and support the healing process. Accurate wound assessment is essential for monitoring healing progress and guiding treatment decisions. However, existing assessment methods typically require direct exposure of the wound, necessitating the removal of gauze when present. This process disrupts the healing environment and increases the risk of secondary infections. In this paper, we introduce mmSkin, an innovative over-gauze wound assessment system that utilizes millimeter-wave (mmWave) radar technology to evaluate wound characteristics without the need to remove the gauze. Central to this system is the principle that variations in skin moisture, a critical indicator of wound health, significantly influence mmWave signal strength. By analyzing these variations, mmSkin accurately identifies skin moisture levels, thereby enabling precise assessment of wound conditions. To achieve reliable sensing, mmSkin incorporates a denoised mmWave imaging algorithm designed to reduce motion noise and effectively distinguish between signals reflected from the target skin and those from surrounding environmental interference. Additionally, the system integrates a physics-based model to guide the training of its moisture derivation model. This integration ensures that mmSkin can accurately estimate moisture distribution across the wound area, making it a powerful tool for non-invasive wound assessment. Extensive experiments validate the system's high accuracy in over-gauze wound moisture distribution estimation, achieving a mean moisture error of approximately 0.5% in both wound phantom and invivo tests. Additionally, the system demonstrates a structural similarity index measure (SSIM) of about 0.9 compared to groundtruth moisture distributions in both test scenarios. These results highlight mmSkin's potential to revolutionize non-invasive wound assessment and improve patient outcomes. © 2014 IEEE.

关键词： Noninvasive medical procedures

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 755-769页

作者： Arfat Ahmad Khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiIndia School of Technology and Innovations University of VaasaVaasaFinland

Epilepsy is considered as a serious brain disorder in which patients frequently experience *** seizures are defined as the unexpected electrical changes in brain neural activity,which leads to *** researches made an intense effort for predicting the epileptic seizures using brain signal ***,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's *** addition,the existing prediction models have severe issues,such as overfitting and false positive *** overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain *** neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict *** multi‐network design enhances the trained model's generalisability and stability ***,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal *** earlier warning of epilepsy with the proposed model obtains the auxiliary *** data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing *** be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%***,the numerical results unveil that the proposed work outperforms the existing models.

关键词： artificial intelligence techniques classification learning(artificial intelligence)

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Epidermal self-powered sweat sensors for glucose and lactate monitoring

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Bio-Design and Manufacturing 2022年第1期5卷 201-209页

作者： Xingcan Huang Jiyu Li Yiming Liu Tszhung Wong Jingyou Su Kuanming Yao Jingkun Zhou Ya Huang Hu Li Dengfeng Li Mengge Wu Enming Song Shijiao Han Xinge Yu Department of Biomedical Engineering City University of Hong KongHong Kong 999077China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054China

Sweat could be a carrier of informative biomarkers for health status identification;therefore,wearable sweat sensors have attracted significant attention for *** external power source is an important component of wearable sensors,however,the current power supplies,i.e.,batteries,limit further shrinking down the size of these devices and thus limit their application areas and ***,we report a stretchable self-powered biosensor with epidermal electronic format that enables the in situ detec-tion of lactate and glucose concentration in *** biofuel cells serve as self-powered sensing modules allowing the sweat sensor to exhibit a determination coefficient(R2)of 0.98 with a sensitivity of 2.48 mV/mM for lactate detection,and R2 of 0.96 with a sensitivity of 0.11 mV/μM for glucose *** microfluidic channels developed in an ultra-thin soft flexible polydimethylsiloxane layer not only enable the effective collection of sweat,but also provide excellent mechanical properties with stable performance output even under 30%*** presented soft sweat sensors can be integrated at nearly any location of the body for the continuous monitoring of lactate and glucose changes during normal daily activities such as *** results provide a promising approach to develop next-generation sweat sensors for real-time and in situ sweat analysis.

关键词： Sweat sensor Self-powered Epidermal electronics Enzymatic biofuel cells Microfluidics

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Intelligent Internet of Things in Mammography Screening Using Multicenter Transformation Between Unified Capsules

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IEEE Internet of Things Journal 2023年第2期10卷 1536-1545页

作者： Wang, Boyan Hu, Xuegang Zhang, Jinglin Xu, Chenchu Gao, Zhifan Hefei University of Technology School of Computer Science and Information Engineering Hefei China Shandong University School of Control Science and Engineering Jinan250061 China Anhui University School of Computer Science and Technology Hefei230093 China Sun Yat-sen University School of Biomedical Engineering Guangzhou510275 China

Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast cancer mortality rate by AI-driven healthcare. However, it is a huge challenge to extend the conventional single-center into the multicenter mammography screening, thus improving the effectiveness and robustness of intelligent IoT-based devices. To address this problem, we utilize multicenter mammograms by the modified capsule neural network and propose a novel framework called multicenter transformation between unified capsules (MLT-UniCaps) in this article. The proposed MLT-UniCaps is composed of Attentional Pose Embedding, Dynamic Source Capsule Traversal, and Adaptive Target Capsule Fusion to realize an intelligent remote assistant diagnosis. Attentional Pose Embedding extracts feature vectors via variations in position, orientation, scale, and lighting as the poses through an adversarial convolutional neural network with an attention-based layer. Based on the pose presentation, Dynamic Source Capsule Traversal deploys a dynamic routing mechanism between neurons to build a source cancer classifier for single-center mammography screening. Using the source cancer classifier, Adaptive Target Capsule Fusion integrates various centers of mammograms as the universal cancer detectors and optimizes heterogeneous distribution among them by the transformation-likelihood maximization. Owing to the three components, MLT-UniCaps effectively improves the results of single-center mammography screening and works in the multicenter breast cancer diagnosis. By comprehensive experiments on 58 965 samples, the proposed MLT-UniCaps obtains 90.1% of overall classification accuracy on single-center trials and 73.8% of overall F1 score on multicenter trials. All the experimental results illustrated that our MLT-UniCaps, an intelligent IoT-based clinical tool, inures the be

关键词： Mammography

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