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IEEE International Symposium on Biomedical Imaging

作者： Nathasha Naranpanawa H. Peter Soyer Adam Mothershaw Gayan K. Kulatilleke Zongyuan Ge Brigid Betz-Stablein Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Australia Dermatology Research Centre Frazer Institute The University of Queensland Australia Monash Medical AI Monash University Australia Department of Data Science and AI Faculty of IT Monash University Australia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The concept of the ‘ugly duckling sign’ is a criterion used in diagnosing cutaneous melanoma by identifying visually distinct skin lesions. However, variations in pigmented lesions among patients make it challenging to visually distinguish ugly ducklings. Hence, we propose DMT-Quadruplet -a deep metric learning network to learn lesion features at two tiers - patient-level and lesion-level. We introduce a patient-specific quadruplet mining approach within a tiered quadruplet network, to capture contextual information both globally and locally. We further incorporate a dynamic margin within the patient-specific mining to allow more useful quadruplets to be mined within individuals. Comprehensive experiments show that our proposed method outperforms traditional classifiers, achieving significantly higher sensitivity than baseline models, successfully classifying ugly duckling lesions both in patient-specific and patient-agnostic contexts.

关键词： Manifolds Learning systems Sensitivity Pipelines Melanoma Feature extraction Extraterrestrial measurements

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Examining the Impact of data Mining on Decision-Making Processes 3

Examining the Impact of Data Mining on Decision-Making Proce...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Pandey, Pratik Aggarwal, Prateek Reena, R. Bhagwat, Suvarna R. Sunil, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device. © 2023 IEEE.

关键词： discuss extract information overcome relationships

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Leveraging Deep Learning for Cyberbullying Detection in Arabic Tweets

Leveraging Deep Learning for Cyberbullying Detection in Arab...

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Women in data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Fatima AlShannaq Sawsan Alhalawani Heba Al-Jarrah Mohammad Shehab Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia College of Computer Science and Information Systems Prince Sultan University Riyadh Saudi Arabia Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan College of Computer Sciences and Informatics Amman Arab University Amman Jordan

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Cyberbullying is a growing concern in the digital age, particularly on social media platforms like X (formerly Twitter). This study presents a deep learning-based approach for detecting cyberbullying in Arabic tweets using the Arabic Cyberbullying Corpus (ArCybC). Leveraging the Arabic-BERT model, we conducted binary and multi-class classification tasks to identify cyberbullying content and its associated categories or domains. Our methodology incorporated advanced data-balancing techniques, including oversampling and undersam-pling, to address class imbalance challenges. The study aims to answer the research questions: (1) How effectively can deep learning models detect cyberbullying in Arabic tweets? (2) How do data balancing techniques impact classification performance? (3) Can Arabic-BERT outperform traditional classifiers in cy-berbullying detection? These questions are addressed through our methodology, where we employ Arabic-BERT, implement rebalancing techniques, and compare our model's performance with baseline classifiers. Experimental results demonstrate significant improvements in F1-scores, with 89.1% achieved in binary classification and 91.6% in category-level classification after oversampling. These findings highlight the effectiveness of fine-tuned deep learning models in tackling the linguistic complexity of Arabic and the nuanced nature of cyberbullying detection. This study provides a foundation for future research in addressing online harassment in Arabic-speaking digital communities.

关键词： Deep learning Blogs Cyberbullying Linguistics data science Information age data models Complexity theory

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A C++ Library for Memory Layout and Performance Portability of Scientific Applications 28th

A C++ Library for Memory Layout and Performance Portabilit...

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28th International European Conference on Parallel and Distributed Computing , Euro-Par 2022

作者： Incardona, Pietro Gupta, Aryaman Yaskovets, Serhii Sbalzarini, Ivo F. Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany Center for Systems Biology Dresden Dresden01307 Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden/Leipzig Germany

ISBN: (纸本)9783031312083

We present a C++14 library for performance portability of scientific computing codes across CPU and GPU architectures. Our library combines generic data structures like vectors, multi-dimensional arrays, maps, graphs, and sparse grids with basic, reusable algorithms like convolutions, sorting, prefix sum, reductions, and scan. The memory layout of the data structures is adapted at compile-time using tuples with optional memory mirroring between CPU and GPU. We combine this transparent memory mapping with generic algorithms under two alternative programming interfaces: a CUDA-like kernel interface for multi-core CPUs, Nvidia GPUs, and AMD GPUs, as well as a lambda interface. We validate and benchmark the presented library using micro-benchmarks, showing that the abstractions introduce negligible performance overhead, and we compare performance against the current state of the art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Epidemic Surveillance of Influenza Infections:A Network-Free Strategy—Hong Kong Special Administrative Region,China,2008–2011

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China CDC weekly 2022年第46期4卷 1025-1031,I0002-I0005页

作者： Zhanwei Du Qi Tan Yuan Bai Lin Wang Benjamin J.Cowling Petter Holme WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health Limited(D24H) Hong Kong Science and Technology ParkHong Kong SARChina Department of Genetics University of CambridgeCambridgeUK Department of Computer Science Aalto UniversityEspooFinland

Introduction:The ease of coronavirus disease 2019(COVID-19)non-pharmacological interventions and the increased susceptibility during the past COVID-19 pandemic could be a precursor for the resurgence of influenza,potentially leading to a severe outbreak in the winter of 2022 and future *** recent increased availability of data on Electronic Health Records(EHR)in public health systems,offers new opportunities to monitor individuals to mitigate ***:We introduced a new methodology to rank individuals for surveillance in temporal networks,which was more practical than the static *** targeting previously infected nodes,this method used readily available EHR data instead of the contactnetwork ***:We validated this method qualitatively in a real-world cohort study and evaluated our approach quantitatively by comparing it to other surveillance methods on three temporal and empirical *** found that,despite not explicitly exploiting the contacts’network structure,it remained the best or close to the best *** related the performance of the method to the public health goals,the reproduction number of the disease,and the underlying temporal-network structure(e.g.,burstiness).Discussion:The proposed strategy of using historical records for sentinel surveillance selection can be taken as a practical and robust alternative without the knowledge of individual contact behaviors for public health policymakers.

关键词： network policy networks

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Delay Tolerant and Energy Reduced Task Allocation in Internet of Things with Cloud Systems 1

Delay Tolerant and Energy Reduced Task Allocation in Interne...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Raghunath Patil, Dipak Borkar, Bharat Markad, Ashok Kadlag, Sunil Kumbhkar, Makhan Jamal, Ahmad Amrutvahini College of Engineering Dept. of Computer Engineering Pune Sangamner India Amrutvahini College of Engineering Dept. of Information Technology Pune Sangamner India Amrutvahini College of Engineering Department of Electrical Engineering Pune Sangamner India Christian Eminent College Indore & Visiting Faculty School of Data Science and Forecasting Devi Ahilya University Department of Computer Science & Electronics Indore India Tulas Institute Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665456876

Through the utilization of the Internet, cloud computing supplies storage and computation resources to deliver services for many sectors. The speed of such systems suffers, though, since delay-sensitive systems, such as smart city and health applications, increasingly need for processing over massive volumes of data transmitted to centralised datacenters. When compared to cloud services, the paradigms of fog as well as edge computing provide innovative solutions by affecting the financial closer to the customer and by having low energy consumption and latency effectiveness. To optimise expense and resource efficiency, enhance QoS, and increase security and privacy, it is crucial to discover the best locations for services and assets inside the 3 IoT. The bipartite graph task scheduling method we provide in this research enhances cost effectiveness in real world applications with strict deadlines. The proposed method's performance in terms of delay, network congestion, as well as cost is deployed and tested to use the iFogSim simulator, an expanded version of CloudSim. The evaluation results demonstrate that the suggested algorithm outperforms Round-Robin and Minimum Response Time methods in terms of both cost as well as throughput. © 2022 IEEE.

关键词： Internet of things

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Enhancement of Security in IoT using Secure Hash Cryptography Algorithm with Double Encryption (SHCA-DE) 9

Enhancement of Security in IoT using Secure Hash Cryptograph...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Uganya, G. Rajalakshmi, D. Vangapandu, Ramarao Dinesh, M. Giri, Jayant Odeh, Mahmoud Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vignan's Institute of Engineering for Women Department of Computer Science and Engineering Vishakhapatnam India Saveetha University Department of Vlsi Microelectronics Saveetha School of Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350377972

The 'Internet of things' is the developing technology to transfer the world into smart innovation. 'IoT' security is the most problematic issue in recent applications due to its properties including 'centralized architecture', 'limited capacity devices', and 'ambiguity'. So, blockchain with 'IoT' applications provides secure data communication compared to traditional 'IoT' applications. Blockchain is a recent growing technology that has features including 'decentralized structure', 'transparency', 'immutable records', 'increased capacity', and 'quick backtracks'. The 'cryptography hash algorithm' is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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UNeLF: Unconstrained Neural Light Field for Self-Supervised Angular Super-Resolution

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhao, Mingyuan Sheng, Hao Chen, Rongshan Cong, Ruixuan Cui, Zhenglong Yang, Da Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Data Science and Intelligent Computing Laboratory Hangzhou International Innovation Institute Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

Compared to supervised learning methods, self-supervised learning methods address the domain gap problem between light field (LF) datasets collected under varying acquisition conditions, which typically leads to decreased performance when differences exist in the distribution between the training and test sets. However, current self-supervised light field angular super-resolution (LFASR) techniques primarily focus on exploiting discrete spatial-angular features while neglecting continuous LF information. In contrast to previous work, we propose a self-supervised unconstrained neural light field (UNeLF) to continuously represent LF for LFASR. Specifically, any LF can be described as the camera pose for each sub-aperture image (SAI) and the two-plane that captures these SAIs. To describe the former, we introduce a SAIs-dependent pose optimization method to solve the issue that arises from the narrow baseline of most LF data, which hinders robust camera pose estimation. This mechanism reduces the number of trainable camera parameters from a quadratic to a constant scale, thereby alleviating the complexity of joint optimization. For the latter, we propose a novel adaptive two-plane parameterization strategy to determine the two-plane that captures these SAIs, facilitating refocusing. Finally, we jointly optimize the camera parameters, near-far planes and neural light field, efficiently mapping each adaptive two-plane parameterized ray to its correspondence color in a continuous manner. Comprehensive experiments demonstrate that UNeLF achieves faster training and inference with fewer computational resources while exhibiting superior performance on both synthetic and real-world datasets. © 1991-2012 IEEE.

关键词： Self-supervised learning

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GENERATIVE ADVERSARIAL NETWORKS FOR SPATIO-SPECTRAL COMPRESSION OF HYPERSPECTRAL IMAGES

arXiv

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arXiv 2023年

作者： Fuchs, Martin Hermann Paul Byju, Akshara Preethy Walda, Alisa Rasti, Behnood Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCSE);and ii) HiFiC with 3D convolutions (denoted as HiFiC3D) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCSE and HiFiC3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC. © 2023, CC BY.

关键词： Generative adversarial networks

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