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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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Improving the learning performance by exploiting multimedia in eXtreme apprenticeship

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Multimedia Tools and Applications 2024年 1-25页

作者： Persia, Fabio D’Auria, Daniela Ge, Mouzhi Pilato, Giovanni Department of Information Engineering Computer Science and Mathematics University of L’Aquila Via Vetoio L’Aquila67100 Italy Faculty of Computer Science Free University of Bozen-Bolzano Piazza Domenicani 3 Bozen-Bolzano39100 Italy Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany Institute for High Performance Computing and Networking National Research Council of Italy Via Ugo La Malfa 153 Palermo90146 Italy

From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computer science evolves rapidly and XA tackles the gap between university education and industry development. Additionally, distance learning by multimedia is more and more important, sometimes even becoming necessary. As a result, this paper intends to study how to integrate XA and multimedia to improve the learning performance in computer science, with particular reference to the programming courses. Since computer science is mostly driven by the new applications, XA and multimedia are considered to be effective and efficient to reduce the cognitive efforts by practical demonstration. The experimental results, based on the case study of the operating systems course at the Bachelor’s level, show that exploiting multimedia in XA can significantly improve the learning performance in terms of grade distributions, reliability, knowledge transfer and student satisfaction with respect to more traditional approaches based on frontal lectures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distance education

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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I/O complexity of attention, or how optimal is flashattention? 24

I/O complexity of attention, or how optimal is flashattentio...

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Proceedings of the 41st International Conference on Machine Learning

作者： Barna Saha Christopher Ye Department of Computer Science and Engineering University of California San Diego La Jolla CA

Attention is at the heart of the popular Transformer architecture, yet suffers from quadratic time and memory complexity. In a recent significant development, FlashAttention shows that the I/O complexity of attention is the true bottleneck in scaling Transformers. Given two levels of memory hierarchy, a fast cache (e.g. GPU on-chip SRAM) where computation happens and a slow memory (e.g. GPU high-bandwidth memory) where the data resides, the I/O complexity measures the number of accesses to the slow memory. FlashAttention is an I/O-aware algorithm for self-attention that requires N2d2/M I/O operations where N is the dimension of the attention matrix, d is the head-dimension and M is the size of cache. Naturally, to further reduce the computational costs of Attention, the authors ask the question: is FlashAttention's I/O complexity optimal for every value of M? We resolve the above question in its full generality by showing an I/O complexity lower bound that matches the upper bound provided by FlashAttention for any values of M ≥ d2 within any constant factors. Moreover, our lower bounds do not rely on using combinatorial matrix multiplication for computing the attention matrix: even if one uses fast matrix multiplication, the above I/O complexity bounds cannot be improved. Further, we give a better algorithm with lower I/O complexity for M < d2, and show that it is optimal for combinatorial algorithms. We do so by introducing a new communication complexity protocol for matrix compression, and connecting communication complexity to I/O complexity. We believe this connection could be of independent interest and will find more applications in proving I/O complexity lower bounds in future.

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"Parent seeking Roblox Safety Help": Comparing Parental Roblox Concerns to Roblox Offerings

"Parent seeking Roblox Safety Help": Comparing Parental Robl...

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International Symposium on Technology and Society (ISTAS)

作者： Andrew Smithwick Chuong Nguyen Emily Gorial Natasha Tran Alexis Morales Flores Imani N. S. Munyaka Computer Science and Engineering Department University of California San Diego La Jolla CA

ISBN: (数字)9798331540708

ISBN: (纸本)9798331540715

Due to greater accessibility, diverse game options, and the social experiences provided by gaming platforms, the number of children engaging with the Roblox platform has increased over time. This increase in gaming from children of all ages has led to the parental challenge of balancing child safety with fun. Although parents typically know when their children are online, it can be challenging to trust that Roblox will protect data and minimize risky experiences due to the publicized criticisms of inadequate protections and the requirement of parent engagement. In this study, we examine parental Roblox concerns by (1) reviewing the game’s privacy policy and features, (2) characterizing parental concerns expressed on Reddit, and (3) surveying adults about their Roblox opinions. Our findings indicate gaps exist between what safety features Roblox provides and what parents need. Additionally, Roblox could improve how they convey their privacy and security practices to players and parents.

关键词： Surveys Privacy Social networking (online) Blogs Games Security Protection Videos

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Efficient Privacy-Preserving and Secure Authentication Framework for the Internet of Vehicles 4

Efficient Privacy-Preserving and Secure Authentication Frame...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Goel, Om Gajbhiye, Bipin Bhimanapati, Viharika Cheruku, Saketh Reddy Shekhar, Sumit Alzubaidi, laith H. Abes Engineering College A.K.T.U. Department of CSE-DS U.P. India Johns Hopkins University Department of Cse BaltimoreMD United States Southern University and A&m College Department of Computer Science LA United States Wichita State University Department of Computer Science Wichita United States Columbia University Department of Cse New York United States The Islamic University Najaf Iraq Najaf Iraq

ISBN: (纸本)9798331528324

Vehicles connected to the Internet of Things (IoT) have significantly improved the safety and quality of ITS services. In addition, a semantic security analysis has shown that it is resilient to multiple IoV attack vectors, as well as anonymous, untraceable, and key-secretive. As part of its V2V communication security strategy, this paper introduces a robust protocol to authenticate all components of the V2V communication system. Vehicles, Registration Authorities (RAs), Trusted Authorities (TAs), and Vehicle Servers (VSs) are the four key participants in the model. Initiating communication involves sending queries to the VS, which keeps track of real-time vehicle data and ensures that it is accessible only to authenticated vehicles. RA registration is mandatory, while TA regulation and authentication ensure vehicle- VS communication, ensuring network security and data traffic control. As a result of the blockchain infrastructure, vehicles can only communicate within the network if they are registered and authenticated. With this model, we aim to ensure that V2V communication networks are safe and reliable by providing essential security features, such as authentication, integrity, and confidentiality. © 2024 IEEE.

关键词： Authentication Protocol

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Super-resolution and Focus Recovery in Whole Slide Images using CycleGAN

Super-resolution and Focus Recovery in Whole Slide Images us...

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Microscopy Histopathology and Analytics, Microscopy 2022

作者： Zhuge, Huimin Ashman, Kimberly Summa, Brian Brown, J. Quincy Department of Biomedical Engineering Tulane University New OrleansLA United States Department of Computer Science Tulane University New OrleansLA United States

The unsupervised Cycle conditional generative adversarial networks (Cycle GAN) was modified to achieve high-resolution outputs from low-resolution inputs, and dynamic focus detection and recovery, in large- scale whol... 详细信息

ISBN: (纸本)9781557528209

关键词： Generative adversarial networks

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SCS-Voxel2Mesh: a Self-Calibrated Separable 3D Mesh Generation Network for Lung Nodule Spikes Classification and Malignancy Prediction

SCS-Voxel2Mesh: a Self-Calibrated Separable 3D Mesh Generati...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ronald Bbosa Jia Ni Zou Kafui Efio-Akolly Yi-Ping Phoebe Chen Wen Cai Huang Feng Liu School of Computer Science Wuhan University Wuhan China Department of Radiology General Hospital of Central Theater Command of the PLA Wuhan China Department of Computer Science and Information Technology La Trobe University Melbourne Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Radiologists use the standardized Lung-RADS clinical scoring criteria to assess and report spiculations/lobulations and sharp/curved spikes on the surface of lung nodules, because they are good predictors of lung cancer. Manual spiculation/lobulation annotation and classification is a tedious task for radiologists due to the nodule’s 3D geometry and 2D slice-by-slice assessment. This work presents SCS-Voxel2Mesh, a multi-class end-to-end deep learning model that segments pulmonary nodules (while preserving spikes), classifies spikes (sharp/spiculation and curved/lobulation) and performs malignancy prediction. Comprehensive experiments were conducted to evaluate and compare the performance of SCS-Voxel2Mesh with other segmentation and 3D mesh generation methods using the CIR dataset. The results demonstrate competitive performance of SCS-Voxel2Mesh on both the nodule spike classification and malignancy prediction tasks.

关键词： Three-dimensional displays Convolution Lungs Lung cancer Manuals Predictive models Feature extraction Mesh generation Standards Tumors

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Self-Supervised Multimodal Fusion Network for Knee Osteoarthritis Severity Grading

Self-Supervised Multimodal Fusion Network for Knee Osteoarth...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Wu, Wenhua Hu, Kun Yue, Wenxi Li, Wei Simic, Milena Li, Changyang Xiang, Wei Wang, Zhiyong The University of Sydney School of Computer Science CamperdownNSW Australia The University of Sydney Faculty of Medicine and Health CamperdownNSW Australia Sydney Polytechnic Institute Pty Ltd SydneyNSW Australia La Trobe University School of Computing Engineering & Mathematical Sciences Australia

ISBN: (纸本)9798350382204

Knee osteoarthritis (OA) is a highly prevalent form of arthritis and a leading cause of physical disability, given the growing aging population. To assist the knee OA assessment, there is a demanding interest in computer-aided grading algorithms. Existing grading methods generally require resource-intensive annotated datasets for supervised training. Moreover, they only consider unimodal data, whilst multimodal medical images are rarely utilised to formulate better knee OA patterns. Therefore, in this study, a novel Self-supervised Multimodal Fusion Network (S-MFN) is proposed for multimodal unsupervised knee OA grading with X-ray and magnetic resonance imaging (MRI) modalities. Specifically, S-MFN involves two modality-specific streams to obtain knee OA representations from the two corresponding modalities. A modality-aware information exchange mechanism is devised to interactively formulate cross-modal patterns in a multi-scale manner regarding the scales of feature maps. To this end, a multimodal contrastive learning is introduced in a self-supervised manner through modality-specific and cross-modal modelling. Comprehensive experimental results on the widely used dataset, Osteoarthritis Initiative (OAI), demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Grading

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