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Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals

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Sensors 2023年第22期23卷 9199-9199页

作者： Chen, Shuo-Tsung Ye, Ren-Jie Wu, Tsung-Hsien Cheng, Chun-Wen Zhan, Po-You Chen, Kuan-Ming Zhong, Wan-Yu Department of Medical Informatics Chung Shan Medical University Taichung40201 Taiwan Department of Information Center Chung Shan Medical University Hospital Taichung40201 Taiwan Graduate School of Applied Chinese Studies National Yunlin University of Science and Technology Yunlin 64002 Taiwan Bachelor’s Program in Business Management Fu Jen Catholic University New Taipei City242062 Taiwan

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30. © 2023 by the authors.

关键词： Electrocardiograms

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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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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

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

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

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.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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

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

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Model-Based Analysis of Microservice Resiliency Patterns

Model-Based Analysis of Microservice Resiliency Patterns

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Working IEEE/IFIP Conference on Software Architecture

作者： Nabor C. Mendonca Carlos M. Aderaldo Javier Camara David Garlan Post Graduate Program in Applied Informatics University of Fortaleza Brazil Post Graduate Program in Applied Informatics University of Fortaleza CE Brazil University of York York UK School of Computer Science Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9781728146591

ISBN: (纸本)9781728146607

Microservice application developers try to mitigate the impact of partial outages typically by implementing service-to-service interactions that use well-known resiliency patterns, such as Retry, Fail Fast, and Circuit Breaker. However, those resiliency patterns-as well as their available open-source implementations-are often documented informally, leaving it up to application developers to figure out when and how to use those patterns in the context of a particular microservice application. In this paper, we take a first step towards improving on this situation by introducing a model checking-based approach in which we use the PRISM probabilistic model checker to analyze the behavior of the Retry and Circuit Breaker resiliency patterns as continuous-time Markov chains (CTMC). This approach has enabled us to quantify the impact of applying each resiliency pattern on multiple quality attributes, as well as to determine how to best tune their parameters to deal with varying service availability conditions, in the context of a simple client-service interaction scenario.

关键词： Circuit breakers Resilience Integrated circuit modeling Probabilistic logic Transient analysis Circuit faults Analytical models

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A multicriteria approach to support task allocation in projects of distributed software development

A multicriteria approach to support task allocation in proje...

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作者： Simão Filho, Marum Pinheiro, Plácido R. Albuquerque, Adriano B. Simão, Régis P. S. Azevedo, Raimundo S. N. Nunes, Luciano C. Graduate Program in Applied Informatics University of Fortaleza Brazil Information Systems 7 de Setembro University Center Brazil

Organizations are increasingly investing in Distributed Software Development (DSD) over the years. A typical decision-making problem in the distributed scenario consists of deciding which team should be allocated each task. That decision takes into account a relative degree of subjectivity. That setting is suitable for applying Verbal Decision Analysis (VDA). This paper introduces an approach to support the allocation of tasks to distributed units in DSD projects, structured on the hybridisation of methods of Verbal Decision Analysis for classification and rank ordering applied to influencing factors and executing units. Firstly, a review of the literature was conducted aiming to identify the approaches to support the allocation of tasks in DSD contexts. Then, an approach was developed by applying VDA-based methods for classification and ordering. Bibliographic research and the application of surveys with professionals allowed identifying and characterising the main elements that influence task assignment in DSD projects. Afterwards, experiences were carried out in five real-world companies. In the end, the proposed approach has been submitted to the evaluation by the professionals of the participating companies and by some project management experts. The proposed approach comprises a workflow containing responsible actors and descriptions of the activities. Automated tools are also employed in automating the implementation of the approach. After applying the approach in five companies, task assignment recommendations are presented in groups for each company, according to the task type, i.e., requirements, architecture, coding, and testing, ranging from the most to the least preferable office. Results of the experiences and evaluations held during this work present evidence that the proposed approach is flexible, adaptable, and easy to understand and to use. Moreover, it helps to reduce decision subjectivity and to think of new aspects, supporting the task allocatio

关键词： Project management

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm

Psychological Stress Classification Using Extreme Gradient B...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Adaptive optimal multi key based encryption for digital image security

Adaptive optimal multi key based encryption for digital imag...

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作者： Shankar, K. Lakshmanaprabu, S.K. Gupta, Deepak Khanna, Ashish de Albuquerque, Victor Hugo C. School of Computing Kalasalingam Academy of Research and Education Krishnankoil India B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Maharaja Agrasen Institute of Technology Delhi India Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

The security of digital images is an essential and challenging task on shared communication Model. Generally, high secure working environment and data are also secured with an encryption and decryption method by using secret and public keys. In this paper, the innovative encryption technique for image security, ie, Multiple key-based Homomorphic Encryption (MHE) technique is proposed. For increasing the security level of encryption and decryption processes, the optimal key is selected using Adaptive Whale Optimization (AWO) algorithm. Fitness function was considered for optimization as PSNR of plain and cipher images. The original image was transformed into blocks and then rearranged utilizing encryption process, this work achieved maximum security, much better than other encryption techniques. From the outcomes, one can achieve incredible quality of the proposed model, the maximum PSNR, and the minimum MSE contrasted with other encryption schemes. © 2018 John Wiley & Sons, Ltd.

关键词： Cryptography

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Tuning the Size and Stiffness of Inflatable Particles

arXiv

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arXiv 2025年

作者： Pashine, Nidhi Wang, Dong Baines, Robert Goyal, Medha Shattuck, Mark D. O’Hern, Corey S. Kramer-Bottiglio, Rebecca Department of Mechanical Engineering Yale University New HavenCT06520 United States Department of Physics BioInspired Institute Syracuse University SyracuseNY13244 United States Department of Physics Benjamin Levich Institute City College of New York New York CityNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t → 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications. © 2025, CC BY.

关键词： Stiffness

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