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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Kumar, Pramod Alghayadh, Faisal Yousef Tamta, Vinay Prasad Anand, Monika Soni, Mukesh Chaudhary, Jitendra Kumar Ganga Institute of Technology and Management Bhadana- Bahadurgarh Road Kablana Haryana Jhajjar India AlMaarefa University Computer Science and Information Systems Department College of Applied Sciences Riyadh Saudi Arabia Department of Information Technology Uttarakhand Srinagar India Chandigarh University Department Cse Punjab Mohali India Lovely Professional University Division of Research and Development Punjab Phagwara India Graphic Era Hill University School of Computing Bhimtal Campus Uttarakhand India

ISBN: (纸本)9798350350067

Automatic medical report production is a key text summarization application. Traditional text summarizing systems cannot completely interpret and use medical texts' complicated vocabulary due to the disparities between medical consultation data and general domain data. The crucial knowledge in medical consultations has not been successfully harnessed. Traditional text summary algorithms cannot automatically identify, and filter key information based on medical reports' structure and generate structured text. An AI-based knowledge-assisted structured medical report creation solution addresses these difficulties. This method uses entity-guided previous domain knowledge and structure-guided task decoupling to fully utilize medical consultation data and medical report structure. The suggested strategy improves ROUGE scores by 2% to 3% compared to similar methods, resulting in more accurate medical reports, according to IMCS21 dataset experiments. © 2024 IEEE.

关键词： Electronic health record

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Influence of Contextual Information on Bengali-English Forward and Backward Transliteration Using Binary Coding

Influence of Contextual Information on Bengali-English Forwa...

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Optimization Techniques for Learning (ICOTL), International Conference on

作者： Md. Fahad Amrita Das Tipu Ashis Kumar Mandal Basabi Chakraborty Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science University of Saskatchewan Saskatoon Canada Research and Regional Cooperation Division Iwate Prefectural University Takizawa Japan School of Computing Madanapalle Institute of Technology and Science Madanapalle India

Transliteration of words across different scripts carries importance for both social media platforms and messaging apps. It preserves the phonetic meaning of words across various writing systems. This study evaluates the influence of contextual information within words on the transliteration of Bengali and English language pair. It utilizes the Dakshina and NEWS 2018 datasets and binary encoding for feature representation. With the random forest classifier, the results demonstrate that using neighboring transliteration units as context yields the best performance on both datasets, obtaining the highest accuracy of 81.04%. These findings highlight the vital role of contextual information within words in enhancing the performance of transliteration.

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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An Efficient Feature Selection in Diabetes Prediction Using Clustering Greedy Stepwise Search Algorithm 15

An Efficient Feature Selection in Diabetes Prediction Using ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajeswarappa, G. Vignesh, R. Ratha, Ashis Kumar Devi, S. Nachiya, R.A.M. Jainaf Selvakumar, M. G Pulla Reddy Engineering College Department of Cse Kurnool518007 India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Karunya Nagar Coimbatore India Iilm University School of Computer Science and Engineering Department of Computing and Security Uttar Pradesh Greater Noida India Nandha Engineering College Department of Mca Tamilnadu Erode India B.S. Abdur Rahman Crescent Institute of Science and Technology Crescent School of Pharmacy Chennai Vandalur600048 India Bannari Amman Institute of Technology Department of Information Technology India

ISBN: (纸本)9798350370249

Diabetes is a metabolic disease that is common across the world and has a high yearly risk of complications that can be life-threatening. These problems have the potential to kill several important organs if left unchecked. Preventing severe consequences can be achieved by guaranteeing prompt treatment, contingent upon early identification. Refining datasets for efficient data mining operations necessitates data pretreatment, which frequently entails eliminating extra functions that may jeopardize predicted precision. In statistical machine learning, feature selection is a well-established topic that uses methods like K-means clustering and the greedy stepwise search algorithm. Even with limited capabilities, empirical testing of the SVM classifier on the Pima Indian Diabetes (PID) dataset repeatedly shows that it is more accurate than other approaches. The PID dataset is a popular benchmark for evaluating data mining techniques, demonstrating the usefulness of this dataset for research and development projects. © 2024 IEEE.

关键词： Diabetes Feature Selection Glucose Greedy Stepwise SVM Classifier

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Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching

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IEEE Transactions on Communications 2025年

作者： Tharakan, Krishnendu S. Dahrouj, Hayssam Kouzayha, Nour Elsawy, Hesham Al-Naffouri, Tareq Y. Queen's University School of Computing Kingston Canada University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955-6900 Saudi Arabia

Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of O(1/√T) is obtained for the proposed algorithm, where T is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms. © 1972-2012 IEEE.

关键词： Federated learning

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Defending Emotional Privacy with Adversarial Machine Learning for Social Good

Defending Emotional Privacy with Adversarial Machine Learnin...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Shawqi Al-Maliki Mohamed Abdallah Junaid Qadir Ala Al-Fuqaha Information and Computing Technology (ICT) Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar

Protecting the privacy of personal information, including emotions, is essential, and organizations must comply with relevant regulations to ensure privacy. Unfortunately, some organizations do not respect these regulations, or they lack transparency, leaving human privacy at risk. These privacy violations often occur when unauthorized organizations misuse machine learning (ML) technology, such as facial expression recognition (FER) systems. Therefore, researchers and practitioners must take action and use ML technology for social good to protect human privacy. One emerging research area that can help address privacy violations is the use of adversarial ML for social good. Evasion attacks, which are used to fool ML systems, can be repurposed to prevent misused ML technology, such as ML-based FER, from recognizing true emotions. By leveraging adversarial ML for social good, we can prevent organizations from violating human privacy by misusing ML technology, particularly FER systems, and protect individuals' personal and emotional privacy. In this work, we propose an approach called Chaining of Adversarial ML Attacks (CAA) to create a robust attack that fools misused technology and prevents it from detecting true emotions. To validate our proposed approach, we conduct extensive experiments using various evaluation metrics and baselines. Our results show that CAA significantly contributes to emotional privacy preservation, with the fool rate of emotions increasing proportionally to the chaining length. In our experiments, the fool rate increases by 48% in each subsequent chaining stage of the chaining targeted attacks (CTA) while keeping the perturbations imperceptible ($\epsilon = 0.0001$).

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Enhancing Trust and Security in the Vehicular Metaverse: A Reputation-Based Mechanism for Participants with Moral Hazard

Enhancing Trust and Security in the Vehicular Metaverse: A R...

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IEEE Conference on Wireless Communications and Networking

作者： Lotfi Ismail Marwa Qaraqe Ali Ghrayeb Dusit Niyato Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Electrical and Computer Engineering (ECE) department Texas A&M University at Qatar Doha Qatar School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we tackle the issue of moral hazard within the realm of the vehicular Metaverse. A pivotal facilitator of the vehicular Metaverse is the effective orchestration of its market elements, primarily comprised of sensing internet of things (SIoT) devices. These SIoT devices play a critical role by furnishing the virtual service provider (VSP) with real-time sensing data, allowing for the faithful replication of the physical environment within the virtual realm. However, SIoT devices with intentional misbehavior can identify a loophole in the system post-payment and proceeds to deliver falsified content, which cause the whole vehicular Metaverse to collapse. To combat this significant problem, we propose an incentive mechanism centered around a reputation-based strategy. Specifically, the concept involves maintaining reputation scores for participants based on their interactions with the VSP. These scores are derived from feedback received by the VSP from Metaverse users regarding the content delivered by the VSP and are managed using a subjective logic model. Nevertheless, to prevent “good” SIoT devices with false positive ratings to leave the Metaverse market, we build a vanishing-like system of previous ratings so that the VSP can make informed decisions based on the most recent and accurate data available. Finally, we validate our proposed model through extensive simulations. Our primary results show that our mechanism can efficiently prevent malicious devices from starting their poisoning attacks. At the same time, trustworthy SIoT devices that had a previous miss-classification are not banned from the market.

关键词： Ethics Accuracy Metaverse Hazards Real-time systems Social Internet of Things Sensors

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A Novel Security Mechanism of 6G for IMD using Authentication and Key Agreement Scheme

A Novel Security Mechanism of 6G for IMD using Authenticatio...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Poongodi, M Hamdi, Mounir Gao, Jiechao Rauf, Hafiz Tayyab College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Division of Information and Computing Technology Doha Qatar University of Virginia Department of Computer Science United States University of Bradford Faculty of Engineering Informatics Department of Computer Science United Kingdom

ISBN: (纸本)9781665423908

In 6G-enabled devices there is lot of convenience which includes the flexibility and speed, which can be well deployed in various industrial applications and significantly useful in medical related areas, so that, it is very primary motivation of all the researchers to work on cons, which the possibility various passive and active attacks due to the insecure channel communication between entities. In IMD(Implantable Medicinal Device) application present three-factor remote client validation convention for implantable therapeutic gadgets cor-respondence condition, the proposed plan, in that the client (for instance, a patient) their body is set in with (IMDs), such as insulin, siphon and pacemaker. All these IMDs screen the patient's wellbeing. IMDs have its very own functionalities and offer administrations to the patient based on their side effects. IMDs likewise have remote correspondence include (for instance, Bluetooth innovation) utilizing through which the they can transfer the patient's verified information to the close-by controller hub. Gathers detected data safely from IMD. Assume there is a client (for instance, a specialist) User needs to get to the ongoing information from a specific hub for observing and analysis of the patient remotely. In this situation, we require verification among User and hubs. After common verification among User and hubs, they set up a session key for the further secure correspondence. Next the fruitful common confirmation no one but, Nodes in the 6G can get to the live information from the embedded hubs in the body of patient's with the assistance of hubs. © 2021 IEEE.

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Tackling the Curse of Dimensionality with Physics-Informed Neural Networks

arXiv

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arXiv 2023年

作者： Hu, Zheyuan Shukla, Khemraj Karniadakis, George Em Kawaguchi, Kenji Department of Computer Science School of Computing National University of Singapore Singapore119077 Singapore Division of Applied Mathematics Brown University ProvidenceRI02912 United States

The curse-of-dimensionality taxes computational resources heavily with exponentially increasing computational cost as the dimension increases. This poses great challenges in solving high-dimensional partial differential equations (PDEs), as Richard E. Bellman first pointed out over 60 years ago. While there has been some recent success in solving numerical PDEs in high dimensions, such computations are prohibitively expensive, and true scaling of general nonlinear PDEs to high dimensions has never been achieved. We develop a new method of scaling up physics-informed neural networks (PINNs) to solve arbitrary high-dimensional PDEs. The new method, called Stochastic Dimension Gradient Descent (SDGD), decomposes a gradient of PDEs’ and PINNs’ residual into pieces corresponding to different dimensions and randomly samples a subset of these dimensional pieces in each iteration of training PINNs. We prove theoretically the convergence and other desired properties of the proposed method. We demonstrate in various diverse tests that the proposed method can solve many notoriously hard high-dimensional PDEs, including the Hamilton-Jacobi-Bellman (HJB) and the Schrödinger equations in tens of thousands of dimensions very fast on a single GPU using the PINNs mesh-free approach. Notably, we solve nonlinear PDEs with nontrivial, anisotropic, and inseparable solutions in less than one hour for 1,000 dimensions and in 12 hours for 100,000 dimensions on a single GPU using SDGD with PINNs. Since SDGD is a general training methodology of PINNs, it can be applied to any current and future variants of PINNs to scale them up for arbitrary high-dimensional *** Codes 14J60 © 2023, CC BY.

关键词： Stochastic systems

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