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11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Nikolić, Mina Janković, Dragan Stanimirović, Aleksandar Stoimenov, Leonid Faculty of Electronic Engineering Computer Science Department Niš Serbia

ISBN: (纸本)9798350386998

Deep convolutional neural network architectures have in recent years been widely used for enhancing various computer vision tasks, such as Image classification, Semantic Segmentation and Object detection. With great advancements in terms of quality of the obtained results, the path was paved for using these kinds of neural networks in the medical domain. But, when working with sensitive matters involving human lives, there is a need to consider the interpretability and explainability of these models and not just the typical evaluation metrics for the given task. To do such a thing, tools such as LIME and PyTorch Grad-CAM can be used, among many others. The integration of Explainable AI (XAI) methods proposed in this paper aims to enable the paradigm of XAI to be used in medical image classification tasks with the standardized MedMNIST dataset. By doing such an integration, a deeper analysis regarding the quality of the model can be enabled. In that way, instances that were misclassified can be visually examined and used to paint a clearer picture of the complete model's decision-making process. © 2024 IEEE.

关键词： Convolutional neural networks

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A Study on Existing EHR Models Used for Validating the Clinical Records 6th

A Study on Existing EHR Models Used for Validating the Clini...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Sharma, Priyanka Kumar, Tapas Tyagi, S.S. Department of Computer Science Engineering Faculty of Engineering MRIIRS Faridabad India Department of Computer Science Engineering Faculty of Engineering IIMT Greater Noida India

ISBN: (纸本)9789819933143

EHR was a statistical database of electronic health records for individuals or demographics that can be accessed through diverse healthcare environments. Planning for long-term retention and handling of electronic health information is an important factor in the planning process. The majority of commercial EHR models were designed to integrate data from vast ancillary facilities like radiology, pharmacy, and laboratory with different clinical care components. Electronic health records (EHRs) were digital representations of hospital records that were renovated in real time. An (electronic health record (EHR) was a comprehensive statement on a person's health. EHRs relief in the provision of evidence-based care, the tracing of a patient's treatment development, and the enabling of improved healthcare judgments. This paper aims to review the comparison of different EHR models that are being used throughout the world. The usage of various machine learning approaches in existing EHR has also been explored in this study. A thorough introduction of EHR is provided initially, followed by a focus on some related work that explains the adoption principles employed by various authors in EHR. In addition, discuss the different EHR models and the methodologies they have used and challenges in existing EHR models, their advantages, need, and then finally the challenges of EHR. This paper can be concluded in a way that to create an effective framework of potential data exchange agreements between health services and patients more public dialogue around electronic health records is needed. Quantitative research, such as polls of a wide variety of customers and doctors, may also be performed in order to obtain better outcomes. In next paper, we intend to propose framework of such EHR model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Patient treatment

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Emotion identification in human faces through ensemble of deep learning models

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Journal of Intelligent and Fuzzy systems 2023年第6期45卷 9729-9752页

作者： Naveen Kmuar, M. Godfrey Winster, S. Department of Computer Science and Engineering Saveetha Engineering College Thandalam India Department of Computing Technologies Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India

Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset. © 2023 - IOS Press. All rights reserved.

关键词： Emotion Recognition

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Fingerprint Fraud Explainability Using Grad-Cam for Forensic Procedures 23

Fingerprint Fraud Explainability Using Grad-Cam for Forensic...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Manai, Elyes Mejri, Mohamed Fattahi, Jaouhar Department of Computer & Software Engineering Faculty of Science and Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

This paper investigates the application of GradCAM, an explainable AI (XAI) technique, to enhance the transparency and precision of fingerprint authentication systems in forensics, particularly in detecting fingerprint mutilation-a common method used to evade biometric security measures. Employing the SOCOfing dataset, which contains both unaltered and synthetically altered fingerprint images, we apply GradCAM to visualize and understand the decision-making process of a convolutional neural network (CNN) model trained to recognize and classify these alterations. Our study not only demonstrates the model's effectiveness in identifying different types of fingerprint modifications but also identifies areas where the model's performance can be enhanced. Through detailed visual analysis, we uncover the model's focus points and assess its reliability across various alteration types and difficulty levels. The insights gained underline the potential of XAI in improving the robustness and reliability of biometric verification systems, paving the way for more secure and equitable AI applications in high-stakes environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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ROUGE-SciQFS: A ROUGE-based Method to Automatically Create Datasets for Scientific Query-Focused Summarization 31

ROUGE-SciQFS: A ROUGE-based Method to Automatically Create D...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ramirez-Orta, Juan Milios, Evangelos Maguitman, Ana Soto, Axel J. Faculty of Computer Science Dalhousie University Canada Institute for Computer Science and Engineering UNS - CONICET Department of Computer Science and Engineering Universidad Nacional del Sur Argentina

ISBN: (纸本)9798891761964

So far, the task of Scientific Query-Focused Summarization (Sci-QFS) has lagged in development when compared to other areas of Scientific Natural Language Processing because of the lack of data. In this work, we propose a methodology to take advantage of existing collections of academic papers to obtain large-scale datasets for this task automatically. After applying it to the papers from our reading group, we introduce a novel dataset for Sci-QFS composed of 8,695 examples, each one with a query, the sentences of the full text from a paper and the relevance labels for each. After testing several classical and state-of-the-art embedding models on this data, we found that the task of Sci-QFS is far from being solved, although it is relatively straightforward for humans. Surprisingly, we found that classical methods outperformed modern pre-trained Deep Language Models (sometimes by a large margin), showing the need for large datasets to better fine-tune the latter. We share our experiments, data and models at https://***/jarobyte91/rouge_sciqfs. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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Document Summarizer: A Machine Learning approach to PDF summarization 4

Document Summarizer: A Machine Learning approach to PDF summ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Peddarapu, Rama Krishna Anogna, Sudagoni Harshini, M.N.S. Sri Sireesha, J. Rathna, K. Guna Department of Computer Science and Engineering Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today's professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts. © 2025 The Authors. Published by Elsevier B.V.

关键词： Data handling

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh

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Data Intelligence 2024年第3期6卷 834-868页

作者： M.Raihan Sami Azam Laboni Akter Md.Mehedi Hassan Ryana Quadir Asif Karim Saikat Mondal Arun More Department of Computer Science and Engineering North Western UniversityKhulna 9100Bangladesh Faculty of Science and Technology Charles Darwin UniversityNT 0909Australia Departmentof Biomedical Engineering Khulna University of Engineering&TechnologyKhulnaBangladeh Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Computer Science and Engineering Daffodil International University(DIU)Bangladesh Department of Cardiology Ter Institute of Rural Health and ResearchMurud-413510India

Ischemic heart disease(IHD)is one of the leading causes of death ***,different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of *** study examines the current IHD condition in southern Bangladesh,a Southeast Asian middle-income *** main approach to this research is an Al-based proposal of a reduced set of the greatest impact clinical traits that may cause *** approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical ***,diagnostic,and symptomatic features were considered for analysing this clinical *** pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations.A proposed voting mechanism ranked the selected 138 features by their impact *** this regard,diverse patterns in correlations with variables,including age,sex,career,family history,obesity,etc.,were calculated and explained in terms of voting *** the 138 risk factors,three labels were categorized:high-risk,medium-risk,and low-risk features;19 features were regarded as high,25 were medium,and 94 were considered low impactful *** research's technological methodology and practical goals provide an innovative and resilient framework for addressing IHD,especially in less developed cities and townships of Bangladesh,where the general population's socioeconomic conditions are often *** data collection,pre-processing,and use of this study's complete and comprehensive IHD patient dataset is another innovative *** believe that other relevant research initiatives will benefit from this work.

关键词： Ischemic heart disease Machine learning CVD Data categorization Medical data

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Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

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Intelligent Automation & Soft Computing 2024年第1期39卷 1-13页

作者： M.L.Maruthi Prasad R.Santhosh Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatoreTamil NaduIndia

This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women *** major research problem is the lack of automated model to attain earlier *** existing model fails to give better prediction ***,a novel clinical decision support system is framed to make the proper decision during a time of *** stages are followed in the proposed framework,which plays a substantial role in thyroid *** steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier *** classifiers aremerged to handle the issues identified in others and intend to enhance the prediction *** proposed model provides superior outcomes and gives good quality prediction *** simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing *** model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.

关键词： Thyroid machine learning pre-processing classification prediction rate

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Handling Imbalanced Datasets in Researcher Scientific Classification Using Oversampling and Undersampling Approaches 11

Handling Imbalanced Datasets in Researcher Scientific Classi...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Zulius, Antoni Defit, Sarjon Stiawan, Deris Abdiansah, Abdiansah Samsuryadi, Samsuryadi Rahmi, Nadya Alinda Universitas Bina Insan Lubuklinggau Universitas Sriwijaya Faculty of Engineering Sciences Faculty of Engineering Palembang Indonesia Universitas Putra Indonesia YPTK Department of Information Technology Faculty of Computer Science Padang Indonesia Universitas Sriwijaya Department of Computer Engineering Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Department of Informatic Engineering Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350355314

The challenge of class imbalance in datasets is a prevalent issue in machine learning, often resulting in biased and inaccurate predictions. This study investigates the effectiveness of different data balancing techniques in enhancing classifier accuracy when applied to an imbalanced Indonesian National Scientific Manuscript dataset. Specifically, we examine how various classifiers respond to three widely used data balancing methods: Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling, and Random Over-sampling. We assess the performance of six classifiers-Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), and Gradient Boosting (GB)-when these techniques are applied. The analysis reveals diverse outcomes, with some classifiers showing substantial accuracy gains, while others experience moderate or even adverse effects. The findings underscore the importance of selecting an appropriate balancing method based on the dataset's characteristics and the specific classification algorithm employed in the knowledge classification process. © 2024 IEEE.

关键词： Adversarial machine learning

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