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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Syrotkina, Olena Kobti, Ziad Aleksieiev, Mykhailo Moroz, Borys Udovyk, Iryna Shvachych, Hennadii University of Windsor School of Computer Science Windsor Canada Dnipro University of Technology Department of Software Engineering Dnipro Ukraine

This paper addresses the issue of creating and applying mathematical models and methods for finding generalized solutions when working with structured collections of 'big data'. We reviewed the modern methodologies used to solve problems of this class. The mathematical model presented describes an ordered set of all subsets formed from a finite ordered base set of arbitrary size and data type. We explored a set of functional dependencies of five discrete input variables to work with this mathematical model. Some of these functional dependencies are derived for specific solutions with specified boundary conditions. The paper also presents examples of how the derived functional dependencies are applied in the implementation of mathematical methods using this model. This required us to conduct a comparative assessment of the search time for a solution with and without the use of these mathematical methods. Comparative graphs are demonstrated to show the rate of increase in the number of operations depending on the size of the original finite base set with and without the use of these mathematical methods. As a result of this, logical conclusions are drawn regarding the impact of mathematical methods for working with structured collections on minimizing time and computational resources. © 2023 IEEE.

关键词： Big data

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Enhanced Diagnosis of Diabetic Retinopathy Utilizing Convolutional Neural Networks: A Deep Learning Approach

Enhanced Diagnosis of Diabetic Retinopathy Utilizing Convolu...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Singh, Neelam Srivastava, Rohit Mahdi, Hussain Falih Sati, Subhangi Joshi, Purvika Sar, Ayan Singh, Devansh Choudhury, Tanupriya Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India School of Computer Science University of Petroleum and Energy Studies Bidholi Uttarakhand Dehradun248007 India University of Diyala Department of Computer and Software Engineering Baquba Iraq Thapar University Punjab Patiala147001 India

ISBN: (纸本)9798350379716

Diabetic Retinopathy (DR) is one of the most severe eye complications associated with diabetes, hence the need to diagnose and treat at the initial stages. The current approaches of DR detection that use ophthalmoscopic inspection of the optic fundus images are subjective and take an ample amount of time. This paper focuses on the application of Convolutional Neural Networks (CNNs) in automating DR detection as well as its classification process using large archive annotated datasets for training and testing. The structure of our CNN excels in capturing stipples of the retinal image and reaches a level higher than 90% accuracy while remaining sensitive and specific. By presenting feature maps and class activation maps, there is an improved credibility as the process the model goes through is explained. The analysis shows that using the CNN-based system can bring notable benefits in the form of early detection, a decrease in the amount of work performed by ophthalmologists, and enhancement of patient outcomes. Future work seeks to implement this model in clinical practice and work to overcome problems such as data privacy and real-time processing, thus demonstrating the revolutionary impact that deep learning holds for ophthalmology. © 2024 IEEE.

关键词： Convolutional neural networks

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Sparse is Enough in Fine-tuning Pre-trained Large Language Models 41

Sparse is Enough in Fine-tuning Pre-trained Large Language M...

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41st International Conference on Machine Learning, ICML 2024

作者： Song, Weixi Li, Zuchao Zhang, Lefei Zhao, Hai Du, Bo National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for low-cost adaptation. Although PEFT has demonstrated effectiveness and been widely applied, the underlying principles are still unclear. In this paper, we adopt the PAC-Bayesian generalization error bound, viewing pre-training as a shift of prior distribution which leads to a tighter bound for generalization error. We validate this shift from the perspectives of oscillations in the loss landscape and the quasi-sparsity in gradient distribution. Based on this, we propose a gradient-based sparse finetuning algorithm, named Sparse Increment Fine-Tuning (SIFT), and validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning. The code is accessible at https://***/song-wx/SIFT. Copyright 2024 by the author(s)

关键词： Benchmarking

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A Systematic Survey of Human Ear Biometrics Recognition by using 2D images 3

A Systematic Survey of Human Ear Biometrics Recognition by u...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Elmarzaki, Howayda Abedallah Gargoum, Bothaina F. Abdo, Amina A University of Benghazi Department of Software Engineering Benghazi Libya University of Benghazi Department of Computer Science Benghazi Libya Collage of Computer Technology Department of Artificial Intelligence Benghazi Libya

ISBN: (纸本)9798350319897

Nowadays, one of the most serious issues is secure verification, especially with the advent of artificial intelligence and machine learning and deep learning algorithms. As a result, the research field of recognizing human biometrics has grown significantly, and various types of algorithms have been proposed to help with security. From the available literature, we noticed that researchers were focusing on various systematic surveys for the recognition of human biometrics, including faces, eyes, finger prints, palms, and etc. Despite the popularity of machine and deep learning methods in the field of human hearing prediction, surveys of such methods are rare. In this work, we present an up-to-date survey of the ear recognition field by using 2D images and display the most relevant papers from the most popular databases by applying different techniques based on several criteria. This review presents an overview of the similarities and differences between different works and provides a statistical analysis view of common techniques commonly used in ear recognition literature. The results presented in this report provide insight on the patterns of this field's research. © 2023 IEEE.

关键词： Biometrics

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Enhancing Chest X-ray Diagnostics with Neighbor-assisted Multimodal Integration

Enhancing Chest X-ray Diagnostics with Neighbor-assisted Mul...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xu, Chenjie Pan, Yang Hu, Bing Zhang, Yang Hong, Yi Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Shanghai Baosight Software Co. Ltd. Shanghai China

ISBN: (纸本)9798350386226

In the field of medical imaging analysis, particularly in interpreting chest X-rays, deep learning models have shown remarkable progress. Nonetheless, these models often face challenges such as limited annotation and inadequate utilization of public data resources. This is particularly apparent with databases containing multimodal data, such as images and medical reports, where the effective integration of this multimodal information remains difficult. To address these limitations, we propose the Neighbor-Assisted Multimodal Attention Network (NAMAN), a novel approach designed to leverage retrieval augmentation techniques to enhance disease classification performance. NAMAN combines nearest neighbor search with multimodal fusion, utilizing both visual features from similar X-ray images and textual information from corresponding medical records. The experimental results demonstrate the efficacy of incorporating retrieved neighbor information and multimodal integration mechanisms in NAMAN. Our ablation studies offer insights into the optimal configuration of the model, including the effects of various attention mechanisms and the number of retrieved neighbors. This work contributes to the expanding field of retrieval-augmented approaches in medical imaging, presenting a promising avenue for leveraging large-scale, multimodal medical databases to enhance diagnostic accuracy and reliability. © 2024 IEEE.

关键词： Integration

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Unconditional Generation of Scalable Chinese Characters as Bézier Curve Sequences 3

Unconditional Generation of Scalable Chinese Characters as B...

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3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Lasrado, Rohan Nolan Vijayasherly, V. VIT University School of Computer Science and Engineering Vellore India VIT University Department of Software Systems Vellore India

ISBN: (纸本)9798350320749

Neural generative modelling of sketches has been an active research direction. SketchRNN set a milestone with their sequence-to-sequence variational autoencoder architecture being able to generate hand drawn sketches in various classes by modelling them as sequences of displacements between consecutive stroke points. The diversity and variety in the set of handwritten Chinese characters makes them a good candidate for such generative modelling enabling their unconditional generation. However, modelling them as sequences of points causes coarse looking strokes and much longer sequences, consequently requiring use of polygonal approximation algorithms to cut down on points. Instead, we propose and investigate the modelling of Chinese characters as sequences of Bézier curves using the SketchRNN architecture with a few modifications, to allow the model to directly generate smooth curves. This way the encoded representation is smaller while more of the stroke's characteristics are retained and the generated characters are truly scalable. We also suggest the appropriate preprocessing strategy for the KanjiVG dataset to make it suitable for this purpose. Qualitative evaluation of the results suggests the model demonstrates generation of characters with mostly well-structured and ordered strokes. This was substantiated by quantitative evaluation based on the FrFréchetchet Inception Distance score. © 2023 IEEE.

关键词： Learning systems

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Industrial Automation Information Analogy for Smart Grid Security

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computers, Materials & Continua 2022年第5期71卷 3985-3999页

作者： Muhammad Asif Ishfaq Ali Shahbaz Ahmad Azeem Irshad Akber Abid Gardezi Fawaz Alassery Habib Hamam Muhammad Shafiq Department of Computer Science National Textile UniversityFaisalabad37610PunjabPakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Industrial automation or assembly automation is a strictly monitored environment,in which changes occur at a good *** are many types of entities in the focusing environment,and the data generated by these devices is *** addition,because the robustness is achieved by sensing redundant data,the data becomes *** data generating device,whether it is a sensing device or a physical device,streams the data to a higher-level deception device for calculation,so that it can be driven and configured according to the updated *** the emergence of the Industry 4.0 concept that includes a variety of automation technologies,various data is generated through numerous ***,the data generated for industrial automation requires unique Information Architecture(IA).IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all *** understand its applicability,we used an example smart grid *** smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the *** addition,in a smart grid system,it needs to report changes on either side of the system,i.e.,consumers and suppliers configure and reconfigure the system according to the *** this article,we propose an analogy of a physical phenomenon.A point charge is used as a data generating device,the streamline of electric flux is used as a data flow,and the charge distribution on a closed surface is used as a ***,the intensity changes are used in the physical process,e.g.,the smart *** analogy is explained by metaphors,and the structural mapping framework is used for its theoretical *** proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows,definition changes(deterministic and non-deterministic),events,and i

关键词： Industry 4.0 information architecture industrial automation

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, QiSong Wu, JiaRou Wang, YingZhuo Qu, HuiLi Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet's strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images. © 2023 IEEE.

关键词： COVID-19 Downsample LDE-UNet Semantic segmentation

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Mathematical Model and AI Integration for COVID-19: Improving Forecasting and Policy-Making

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SN computer science 2024年第2期5卷 246页

作者： Panda, Dev Sourav Dixit, Rahul Dixit, Anuja Dwaracherla, Harshitha Shukla, Anupam Bosch Digital Department Bosch Global Software Technologies Private Limited Karnataka Bangalore India Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithm LSTM model Reproduction number SEIR-type compartmental model Social media Transmission dynamics

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Enhancing Anxiety Prediction: Leveraging Machine Learning with Functional Design Metrics 21

Enhancing Anxiety Prediction: Leveraging Machine Learning wi...

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21st International Conference on Electrical engineering, Computing science and Automatic Control, CCE 2024

作者： Javed, Abqa Ajmal, Sahar Tajammal, Taliah Aslam, Muhammad Martinez-Enriquez, A.M. Sheikh, Usman Sadiq University of Engineering and Technology Department of Computer Science Lahore Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Pakistan Virtual University of Pakistan Department of Computer Science and Information Technology Lahore Pakistan CINVESTAV-IPN D.F. Department of Computer Science Mexico Medicine Bronglais General Hospital Clinical Fellow in Respiratory SY23 1ER United Kingdom

ISBN: (纸本)9798350377545

Fear, nervousness, and social phobia all contribute to anxiety episodes reaching their peak in seconds. Using data collected from patient questionnaires, this study seeks to anticipate and diagnose anxiety disorders effectively and precisely using machine learning approaches. However, current research offers standardized metrics for assessing machine learning approaches throughout the design process. This work presents a systematic method for efficiently extracting literature from digital sources and emphasizes the need for data engineers to evaluate the efficiency of machine learning approaches to assure forecast quality. The International Organization for Standardization (ISO) 9126 standard is used to assess machine learning approaches for anxiety prediction to address this gap. This evaluation considers parameters such as functionality, accuracy, resilience, throughput, explainability, privacy, and correctness. These defined characteristics serve as the benchmark to measure the efficacy of machine learning approaches in predicting anxiety disorders. © 2024 IEEE.

关键词： Adversarial machine learning

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