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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Akhtar, Samia Aftab, Shabib Ahmad, Munir Ihnaini, Baha Virtual University Of Pakistan Department Of Computer Science Lahore Pakistan School Of Computer Science Ncba&e Lahore Pakistan Ianlp College Of Science Mathematics And Technology Wenzhou-Kean University Department Of Computer Science China

ISBN: (纸本)9798350394962

Diabetic retinopathy is a condition associated with diabetes that damages the blood vessels within the retina resulting in vision impairment or even blindness. Early detection and classification of DR enables timely intervention, which is crucial for preventing vision loss and blindness in diabetic patients. This research presents a framework for binary classification, employing transfer learning to identify diabetic retinopathy in individuals with diabetes. APTOS19 and IDRiD, two datasets containing fundus images, are merged together for training the transfer learning models to predict the presence or absence of the disease. Many preprocessing techniques have been applied to these images like resizing, Gaussian filtering, and dataset splitting. After the split, training set is augmented using zooming, rotation, flipping etc. to increase diversity. The transfer learning models used are: ResNet50 and DenseNet121. These models are fine tuned for classification. The results highlight that the DenseNet121 model achieved a superior test accuracy of 97.22% as compared to ResNet50. © 2024 IEEE.

关键词： Blood vessels

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Power Density Limits of Propulsion Motor for Electric Aircraft: A Study on Insulation Thickness

Power Density Limits of Propulsion Motor for Electric Aircra...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Wang, Jundong Dong, Jianning Niasar, Mohamad Ghaffarian Zhu, Gangwei Bauer, Pavol Delft University of Technology Department of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9798350370607

This paper studies the power density limits of propulsion motor for electric aircraft considering thermal aspects and breakdown voltage reduction of insulation. The study em-ploys multi-objective optimization (MOO) to explore various mo-tor cooling options and filter configurations. The results show that motors with direct winding heat exchanger (DWHE) can reach higher specific power, while those equipped with water jacket cooling (WJC) offer a moderate design with simpler structure. Furthermore, the impact of sine wave and dv/dt filters on electric motors design is studied. The findings demonstrate that dv/dt filters enable designs with higher overall specific power compared to sine wave filters. Through simulations, this study identify the challenge faced by aviation motor design in significantly increased insulation thickness, necessitating advanced insulation materials with a minimum thermal conductivity of 5 W/(m.K) to facilitate a high specific power design. Based on this assumption, a preliminary design of 9.6 kW/kg with an efficiency of 98% is presented. © 2024 IEEE.

关键词： Thermal insulation

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Data Fusion Based Ensemble Transfer Learning Approach to Detect Diabetic Retinopathy 2

Data Fusion Based Ensemble Transfer Learning Approach to Det...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ihnaini, Baha Akhtar, Samia Ahmad, Munir Aftab, Shabib College Of Science Mathematics And Technology Wenzhou-Kean University Ianlp Department Of Computer Science China Virtual University Of Pakistan Department Of Computer Science Lahore Pakistan School Of Computer Science Ncba&e Lahore Pakistan

ISBN: (纸本)9798350394962

Diabetic retinopathy is an eye disease damaging the blood vessels of retina as a result of long term diabetes. In this research, an ensemble classification system is proposed by combining two transfer learning models to identify diabetic retinopathy among diabetic patients. A fusion of two datasets containing fundus images is used in this study: 1) APTOS, which contains 3662 images and 2) IDRiD, which contains 516 images. For binary classification, the dataset is divided into two classes: DR and No DR. Preprocessing steps are applied on the dataset such as resizing the images to 150x150x3, applying Gaussian filtering, balancing minority classes using SMOTE and splitting the dataset to 80:20 ratios. Data augmentation techniques like zooming, rotation etc. are also used to augment the images. Two transfer learning models, Xception and EfficientNetB3, are used for classification after fine-tuning. An ensemble of these models is built which achieved the highest test accuracy of 97.47% outperforming individual models. © 2024 IEEE.

关键词： Classification (of information)

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A Classification Framework for Diabetic Retinopathy Detection Using Transfer Learning 2

A Classification Framework for Diabetic Retinopathy Detectio...

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2nd International Conference on Cyber Resilience, ICCR 2024

ISBN: (纸本)9798350394962

Diabetic Retinopathy is a serious eye condition resulting from long-term diabetes mellitus that can lead to permanent blindness if not treated on time. Early detection can reduce the likelihood of serious disability. This research introduces a binary classification framework using Transfer Learning to identify diabetic retinopathy in diabetic patients. The research makes use of an image-based dataset, APTOS 2019, sourced from Kaggle. These images are used to train transfer learning models for predicting the presence or absence of this disease among patients. Preprocessing steps including resizing, Gaussian filtering and dataset split are employed prior to classification. Afterwards, data augmentation techniques like rotation, zooming, flipping, shifting and rescaling are applied to increase the training set. For classification, two Transfer Learning models, EfficientNetB3 and VGG16, are used after fine-tuning. The results indicate that EfficientNetB3 model outperformed VGG16 model due to its computationally efficient architecture, achieving a test accuracy of 97.82%. © 2024 IEEE.

关键词： Classification (of information)

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Trident: Generating Noisy Synthetic Processes with Ground Truth

Trident: Generating Noisy Synthetic Processes with Ground Tr...

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Doctoral Consortium and Demo Track 2023 at the International Conference on Process Mining, ICPM-DCDT 2023

作者： Sommers, Dominique Varadarajan, Durairaja V. Sidorova, Natalia Eindhoven University of Technology Mathematics and Computer Science Eindhoven Netherlands

We present Trident: a tool allowing to modify a process model in order to impute realistic behavioral noise in the modeled behavior and generate an event log that can be used to evaluate the performance of process mining techniques on realistically noisy data with knowledge of the "ground truth", i.e., the true behavior of the system. Traditional approaches for generating noisy process data take a process model that represents the true process, generate a simulated event log and impute noise in the log, while in reality, both the recorded and the modeled behavior are imprecise representations of a process, and noise in the log often follows certain patterns. In our approach, noise is introduced through a series of model transformations applying user-defined deviation patterns to a designed base process model, which creates a basis for more advanced evaluation of process mining methods and tools. © 2023 Copyright for this paper by its authors.

关键词： Data mining

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Floyd-Warshall Algorithm Based on Picture Fuzzy Information

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computer Modeling in Engineering & sciences 2023年第9期136卷 2873-2894页

作者： Shaista Habib Aqsa Majeed Muhammad Akram Mohammed M.Ali Al-Shamiri School of Systems and Technology University of Management and TechnologyLahorePakistan Department of Mathematics University of the PunjabNew CampusLahorePakistan Department of Mathematics Faculty of Science and ArtsMahayl AssirKing Khalid UniversityAbhaSaudi Arabia Department of Mathematics and Computer Faculty of ScienceIbb UniversityIbbYemen

The Floyd-Warshall algorithm is frequently used to determine the shortest path between any pair of *** works well for crisp weights,but the problem arises when weights are vague and *** us take an example of computer networks,where the chosen path might no longer be appropriate due to rapid changes in network *** optimal path from among all possible courses is chosen in computer networks based on a variety of *** this paper,we design a new variant of the Floyd-Warshall algorithm that identifies an All-Pair Shortest Path(APSP)in an uncertain situation of a *** the proposed methodology,multiple criteria and theirmutual associationmay involve the selection of any suitable path between any two node points,and the values of these criteria may change due to an uncertain *** use trapezoidal picture fuzzy addition,score,and accuracy functions to find *** compute the time complexity of this algorithm and contrast it with the traditional Floyd-Warshall algorithm and fuzzy Floyd-Warshall algorithm.

关键词： Trapezoidal picture fuzzy number score function accuracy function shortest path problem Floyd-Warshall algorithm

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Exception Analysis of Running Complex and Computation-intensive Deep Learning Models 10

Exception Analysis of Running Complex and Computation-intens...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Verhoeven, Richard Van Berlo, Bram Meratnia, Nirvana Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands

ISBN: (纸本)9798350329674

Advances in GPU have facilitated design and execution of complex and computation-intensive deep learning models. As the model complexity increases, the risk of encountering problems due to very large model size, individual tensor size, Not a Number (NaN) value, and memory leak increases as well. When untreated, these problems lead to substantial increase of execution time, generating unpredictable results, and memory leak exceptions. In this paper, we address these problems and particularly large tensor support, C++ kernel changes, and recompilation of the TensorFlow framework. In addition, issues related to NaN value debugging with existing debugging toolkits and solutions to alleviate memory leaks will be explored. Based on experience gained from our analysis, we propose solutions related to better tensor dimension sanity checks, alternative tensor loop procedures, different ways of applying kernels to tensors, a debug trace file filter method, and ways how memory leak exceptions can be resolved. While these problems and solutions may be applicable to running any complex and computation-intensive deep learning model, we described how we encountered them in a use case, in which we designed a deep learning model for activity and gesture recognition using radio data aiming to mitigate domain shift problem. © 2023 IEEE.

关键词： Tensors

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Finite Difference Method for Neutrosophic Fuzzy Second Order Differential Equation under Generalized Hukuhara Differentiability

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Neutrosophic Sets and Systems 2023年 58卷 311-329页

作者： Kamal, Bassem Khalid, Huda E. Salama, A.A. El-Baghdady, Galal I. Department of Mathematics and Engineering Physics Faculty of Engineering Delta University for Science and Technology Egypt Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Science Port Saied University Egypt Department of Mathematics and Engineering Physics Faculty of Engineering Mansoura University Egypt

This paper solves the boundary value problem of the second-order differential equation under the neutrosophic fuzzy boundary condition. The proposed solution is approximated using the finite difference method but deneutrosophication utilizing strongly generalized Hukuhara differentiability. The error calculation is then processed by comparing the proposed numerical solution with the analytical solution presented in our earlier work [20]. © (2023). All Rights Reserved.

关键词： Boundary conditions

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Design and evaluation of Swift routing for payment channel network

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Blockchain(Research and Applications) 2024年第2期5卷 62-72页

作者： Neeraj Sharma Kalpesh Kapoor V.Anirudh Department of Computer Science and Engineering Indian Institute of Technology GuwahatiGuwahati 781039AssamIndia Department of Mathematics Indian Institute of Technology GuwahatiGuwahati 781039AssamIndia

Payment Channel Networks(PCNs)are a promising alternative to improve the scalability of a blockchain network.A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure,thereby sacrificing the ability to confirm transactions *** uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a *** performance of most of the existing centralized path-finding algorithms does not scale with network *** rapid growth of Bitcoin PCN necessitates considering distributed ***,the existing decentralized algorithms suffer from resource *** present a decentralized routing algorithm,Swift,focusing on fee *** concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the ***,we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud *** secret path also helps in edge load sharing,which results in an improvement of *** routing achieves up to 21%and 63%in fee and throughput optimization,*** results from the simulations follow the trends identified in the theoretical analysis.

关键词： Blockchain Payment channel networks Bitcoin Lightning network Routing protocols Layer 2 protocols

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