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High hiding capacity, secure and robust ECG steganography method based on multi-chaotic and sample modulation in time domain

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Research on Biomedical Engineering 2025年第1期41卷 1-18页

作者： Ali, Ahmed Hussain AlSabhany, Ahmed A. Qasim, Asaad F. George, Loay Edwar Studies Planning and Follow-Up Directorate Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Computer Science Al-Maarif University College Al Anbar Iraq IT Unit Al-Maarif University College Al Anbar Iraq University of Information Technology and Communications Baghdad Iraq

Purpose: ECG steganography technique used ECG signals as a carrier to provide a secure transmission channel. Achieving trade-offs among the steganography performance aspects, namely capacity, quality, robustness, and security, is still a challenging task. In this paper, an ECG steganography method is proposed to enhance the robustness and security of ECG steganography while maintaining the capacity and the transparency. Moreover, the method uncovers the embedding factors to tune the performance towards any aspect. Method: This paper proposes an ECG steganography method based on multi-chaotic functions for randomly selecting the secret bits and cover samples and sample modulation for embedding in the time domain, which will be called ESMS. The dataset MIT-BIH arrhythmia and measurements, PSNR, NC, and PRD are used for the assessment of the performance of the proposed ESMS method. Results: The results show enhancing the hiding capacity by about 40% that can be reached 100% with 44 dB, 0.9998, and 1.69 of PSNR, NC, and PRD, respectively. The stego ECG signals of the ESMS method withstand an AWGN attack at 5 dB with a hiding capacity of 100%, and PSNR, NC, and BER of 39.99, 0.9996, and 0.03, respectively, resist against the brute force attack and are sensitive to any slight changes in the secret key. Conclusion: The performance results reveal that the ESMS method achieves a trade-off in terms of hiding capacity, robustness, and security while still managing to achieve acceptable ECG quality. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： Time domain analysis

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Thai Conversational Chatbot Classification Using BiLSTM and Data Augmentation 1st

Thai Conversational Chatbot Classification Using BiLSTM and ...

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1st International Conference on Data science and Artificial Intelligence, DSAI 2023

作者： Lhasiw, Nunthawat Tanantong, Tanatorn Sanglerdsinlapachai, Nuttapong Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks with Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9789819979684

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. Data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep Learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Classification (of information)

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NR-Isomap: An Incremental Approach with Gaussian Process Kernels for Denoising 6

NR-Isomap: An Incremental Approach with Gaussian Process Ker...

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6th International Conference on Big Data and Artificial Intelligence, BDAI 2023

作者： Yousaf, Mahwish Khan, Muhammad Saaadat Shakoor Ullah, Shamsher Wang, Shou Jing, Li University of Science and Technology of China School of Computer Science and Technology Hefei China Chinese Academy of Sciences Institute of Solid-State Physics Key Laboratory of Materials Physics Hefei China Unit of System and Technology University of Management and Technology Department Knowledge Sialkot Pakistan

ISBN: (纸本)9798350303667

The Isomap is a popular nonlinear dimensionality reduction method often used for low-dimensional embedding space. When the big data is noisy, Isomap often fails because of the graph structure' Short-Circuit Edges (SCE) problem. In this paper, an incremental Noise-Reduction Isomap (NR-Isomap) approach is to address the SCE problem in a graph structure. The GP kernels are combined with the manifold learning approach and provide significant results. NR-Isomap is based on the Hessian Locally Linear Embedding (HLLE) algorithm with Gaussian Process (GP) kernels. The HLLE algorithm provides the optimal solution to the SCE problem caused by an overly noisy dataset. NR-Isomap provides an answer to the Isomap SCE problem in noisy big data problems that cannot be handled using existing methods. Therefore, the GP kernel significantly improves and measures the performance of the NRI-somap accuracy. Our NR-Isomap method outperforms experiments on SK-Learn datasets and shows efficient results compared to Isomap. Our NR Isomap is much more efficient and faster compared to Isomap. The efficiency and accuracy of the NR-Isomap method are demonstrated theoretically and compared with other manifold learning methods. © 2023 IEEE.

关键词： Noise abatement

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of science and technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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A New Contrastive Learning Based Model for Estimating Degree of Multiple Personality Traits Using Social Media Posts 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Biswas, Kunal Shivakumara, Palaiahnakote Pal, Umapada Sarkar, Ram Jadavpur University Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Estimating the degree of multiple personality traits in a single image is challenging due to the presence of multiple people, occlusion, poor quality etc. Unlike existing methods which focus on the classification of a single personality using images, this work focuses on estimating different personality traits using a single image. We believe that when the image contains multiple persons and modalities, one can expect multiple emotions and expressions. This work separates given input images into different faces of people, recognized text, meta-text and background information using face segmentation, text recognition and scene detection techniques. Contrastive learning is explored to extract features from each segmented region based on clustering. The proposed work fuses textual and visual features extracted from the image for estimating the degree of multiple personality traits. Experimental results on our benchmark datasets show that the proposed model is effective and outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image segmentation

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Anomaly Detection in Streaming Environment by Evolving Neural Network with Interim Decision

Anomaly Detection in Streaming Environment by Evolving Neura...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Boral, Subhadip Poddar, Sayan Ghosh, Ashish Univerity of Calcutta Technology Innovation Hub Indian Statistical Institute Department of Computer Science & Engineering Kolkata India Pondicherry University Department of Computer Science Pondicherry India Indian Statistical Institute Machine Intelligence Unit Kolkata India

ISBN: (纸本)9781665482585

Algorithms in a streaming environment recognise a concept in real time since patterns are presented continuously;yet, in the presence of anomalies, algorithms fail to recognise the underlying concept, making anomaly identification a critical task. Neural networks detect anomalies efficiently;however, to use neural networks in a streaming environment, the architecture must be adaptive to learn ideas that vary over time. The proposed architecture places each node of the neural network in individual sites, and each node is made up of a perceptron. These perceptrons have two activation functions: one is used for architecture training, while the other is utilised for classification. Each layer has a decision-making node that takes decisions from the last layer nodes and decides the anomalous nature by majority voting. These layer-wise decisions help to select the training phase and appropriate architecture based on the desired performance. To demonstrate its usefulness, the proposed structure is tested on real-world data sets and compared to conventional and alternative neural networks. © 2023 IEEE.

关键词： Decision making

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A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Halder, Arnab Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Nanjing University Nanjing China University of Technology Sydney Sydney Australia

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical activation

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Test Optimization in Memristor Crossbars Based on Path Selection

Test Optimization in Memristor Crossbars Based on Path Selec...

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作者： Mondal, Manobendra Nath Sur-Kolay, Susmita Bhattacharya, Bhargab B. Indian Statistical Institute ACM Unit Kolkata700108 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Memristors have recently shown significant promise in designing memory and logic subsystems. A 2D-crossbar architecture built with memristor arrays provides a convenient platform for storing multivalued memory states by utilizing the analog variation of current-induced resistance through these cells. The integration of CMOS components with non-CMOS memristor cells further enhances the scope of their applications to various complex system designs. However, present-day memristors by virtue of their inherent structure are prone to various manufacturing defects and sensitive to operational modalities. Existing techniques for testing memristor arrays are either ad hoc in nature or suited for application-specific designs with little concern for optimizing test time. In this work, we envisage a 2-D memristor crossbar as a network and identify certain paths that are suitable for fault sensitization. For full-size square and rectangular memristive crossbars, the proposed method optimizes test time using a path-based technique guided by maximum matching in bipartite graphs. An integer linear programming (ILP) formulation is then used to solve the problem for a general crossbar, either full or incomplete. Simulation results with LTspice demonstrate the effectiveness and superiority of the method to the prior art in terms of test time and fault coverage. © 1982-2012 IEEE.

关键词： Timing circuits

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Human posture estimation at low resolution 23

Human posture estimation at low resolution

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6th International Conference on Signal Processing and Machine Learning, SPML 2023

作者： Xu, Tingting Chen, Yi Zhu, Min Chen, Wei Liu, Yingchun Liu, Jueting School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Department of Intensive Care Unit Xuzhou First People's Hospital Xuzhou221002 China

ISBN: (纸本)9798400707575

Human pose estimation is an important research direction in the field of computer vision, which is widely used in human-computer interaction, behavior analysis, and intelligent surveillance. Although existing human pose estimation algorithms possess high accuracy for high-resolution images, they perform poorly for low-resolution images that are prevalent in practical applications, and thus are difficult to be widely used in people's daily lives. In this paper, we propose a new end-to-end network framework for accurate human pose estimation of low-resolution images by combining super-resolution assistance and quantization error optimization. In addition, a composite loss function is designed to jointly train the super-resolution network to generate high-resolution images that contribute to human pose estimation instead of simple pre-processing. The experimental results show that the mAP of our method reaches 68.1% and 61.4% on the COCO datasets downsampled to 128×96 and 64×48. © 2023 ACM.

关键词： Human computer interaction

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