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Blind image authentication using singular value decomposition with enhanced robustness by augmenting hamming code

International Journal of computers and Applications 2024年第11期46卷 1020-1044页

作者： Ghosh, Bibek Ranjan Mandal, Jyotsna Kumar Das, Swarnadeep Laha, Ayantanu Saha, Rajarshi Banerjee, Siddhartha Narendrapur India Department of Computer Science & Engineering Kalyani University Kalyani India Department of Computer Science & Engineering University of Calcutta Kolkata India Department of Computer Science Ramakrishna Mission Vivekananda Educational and Research Institute Howrah India

Singular value decomposition (SVD) based image authentication has widespread applications for digital media protection including forensic and medical domains due to the high stability and robustness of singular values under small perturbations. However, most of these techniques are non-blind and have high redundancies for the extraction of secret data. The proposed scheme employs a blind extraction strategy by utilizing the intrinsic relationship of coefficient pairs of the U matrix of the host image blocks. This relationship is utilized to hide a secret binary bit after computing a threshold value in constant time. The host image underwent block-wise decomposition and hash-based randomization of blocks, followed by block-wise SVD for embedding secret bits in the U matrix. Hamming coded watermark is embedded for high precision extraction under active attacks. High imperceptibility and strong robustness are observed under attacks when tested across general purpose as well as image datasets and the system outperforms many existing works. Experimental observation shows the average PSNR between the host and marked image and average NCC between embedded and extracted watermark to be 36.98 dB and 0.999 respectively for the SIPI dataset whereas 52.7 dB and 1 respectively for the X-Ray dataset. The system exhibited high robustness under Salt-and-pepper noise (SAPN), Gaussian Noise (GAUN), Cropping (CRP), Median Filtering (MF), Histogram Equalization (HEQ), and Jpeg Compression (JPC) with average NCC values of 0.998, 1, 0.932, 0.999 and 0.967 respectively for X-Ray dataset. The time complexity O (M × N) is observed for both embedding and extraction algorithms for a host image of size M × N. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Median filters

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Enhancement of patient's health prediction system in a graphical representation using digital twin technology

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Multimedia Tools and Applications 2025年第17期84卷 18625-18649页

作者： Sobhana, M. Ch, Smitha Chowdary Koneru, Sowmya Mohan, G. Krishna Kumar, K. Kranthi Department of Computer Science and Engineering V.R. Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Dhanekula Institute of Engineering & Technology Andhra Pradesh Vijayawada521139 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of IT Vasireddy Venkatadri Institute of Technology Andhra Pradesh Guntur522508 India

The patient health prediction system is the most critical study in medical research. Several prediction models exist to predict the patient's health condition. However, a relevant result was not attained because of poor quality. The IoT-sensed data contains more noise content, which maximizes the complexity of the health prediction. These demerits resulted in low prediction and performance scores. So the proposed work aims to develop a novel Coati-based Recurrent Digital Twin Framework (CbRDTF) to predict the patients' health conditions. The novelty of this research lies in the combined function of the coati optimization and recurrent network with the digital twin for health prediction. Initially, the IoT-sensed data was imported, the data were preprocessed, and meaningful features were selected. Then predict the health condition of the patients and classify the conditions. Here the incorporated coati function at the classification layer extracted the relevant features from the sensed medical data for the robust prediction and also modified the parameters of the recurrent digital twin to improve the prediction and classification accuracy. Finally, the performance was measured;the presented model attained a high exactness score of 99.81% in the prediction accuracy, recall, f-value, and precision;it also, validated the computation time of 611.81 s. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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A systematic review on deep learning implementation in brain tumor segmentation, classification and prediction

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Multimedia Tools and Applications 2025年 1-40页

作者： Abid, Muhammad Adeel Munir, Kashif Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Institute of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan

The brain is the central part of the body that controls the overall functionality of the human body. The formulation of abnormal cells in the brain may lead to a brain tumor. Manual examination of a brain tumor is challenging and time-consuming. Deep learning is purely based on neural networks, and it's beneficial in identifying and diagnosing brain tumors. Different groups of researchers put efforts into implementing deep learning for the classification, segmentation, and prediction of brain tumor. They proposed different models of deep learning and the accuracy of their models in terms of dice score or evaluation parameters that are quite reasonable and acceptable. The primary purpose of this study is to review the different articles implementing deep learning in the segmentation, classification, and prediction of brain tumors. Appropriate keywords are used to extract articles from January 2018 to September 2023. Evaluation is done based on the implementation of deep learning and the accuracy of the proposed model, and a complete data sheet is maintained against each article. A total of 154 articles were collected, and 80 research articles were selected for this study after complete analysis. Each selected article proposed models based on deep learning with reasonable and acceptable accuracy in identifying the brain tumor. 2-D CNN and 3-D CNN are used on Magnetic Resonance Imaging (MRI) images of the brain for brain tumor detection. Thus, much more attention is required in the future on this topic to improve the accuracy of brain tumor identification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

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Constructing metal-phenolic network(MPN)microspheres as efficient adsorbent for pollutants removal

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Particuology 2025年第3期98卷 241-248页

作者： Qiang Liu Lin Han Tiantian Feng Ying Li Tao Jiang Jian Li Lanzhou Petrochemical Research Center Petrochemical Research InstitutePetroChinaLanzhou730060China Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical TechnologyBeijing100029China College of Chemical Engineering and Materials Science Tianjin University of Science and TechnologyTianjin300457China

Metal-phenolic networks(MPNs)are a versatile class of self-assembled systems that are generating interest for applications in catalysis,bioimaging,and drug *** contrast to traditional MPNs coating,in this research,we fabricated novel MPNs microspheres through a facile process in the aqueous *** MPNs microspheres are characterized by SEM,TEM,and *** as-prepared microspheres were utilized in the removal of tetracycline(TC),methylene blue(MB),and rhodamine B(RhB).It exhibits high capacity(1331 mg/g)for the adsorption of MB,and a rapid reaction rate(186.9 mg/g/min)for the adsorption of TC.

关键词： Metal-phenolic network(MPN) Self-assembly Microspheres Adsorption Pollutants removal

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Elastic Stack: A Comprehensive Overview 6

Elastic Stack: A Comprehensive Overview

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Shaji, Ayda Sara George, Mithu Mary Toc H Institute of Science and Technology Department of Computer Science and Engineering Kerala India Muthoot Institute of Technology and Science Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350381689

This document thoroughly explores the Elastic Stack, comprising Elasticsearch, Logstash, Kibana, and Beats, emphasizing its effectiveness in real-time data analysis, log handling, and visualization. It examines the essential components, architecture, and practical uses in different fields, offering in-depth technical insights into each element, integration methods, and practical examples. Readers will gain a comprehensive grasp of the Elastic Stack's capabilities and its application potential in various scenarios by the conclusion of this paper. © 2024 IEEE.

关键词： Data handling

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A Survey of Stock Market Prediction - Based on Machine Learning Techniques 2

A Survey of Stock Market Prediction - Based on Machine Learn...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kangana, W.M. Allagi, Shridhar Laddi, Mahantesh S G Balekundri Institute of Technology Department of Computer Science and Engineering Belagavi India Kle Institute of Technology Department of Computer Science and Engineering Hubli India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350388916

Stock market Prediction has been a topic of attention for numerous researchers since its beginning. Often traditional statistical methods get conflict to grab the complex, non-linear patterns in stock market data. Due to various factors predicting stock prices is challenging, including a company's financial performance, present assets, politics, general economic conditions, politics, unanticipated occurrence, global situation, etc. The fluctuating behavior of the inventory place market makes it difficult to expect stock prices, as they constantly change. This paper comprehensively reviews machine and deep learning techniques used to predict the stock market and highlights the proper techniques for predicting inventory cost. This paper also covers the performance metrics for measuring the performance of prediction models, research gaps, and issues found in previous work. © 2024 IEEE.

关键词： Marketplaces

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Machine learning approaches to detect, prevent and mitigate malicious insider threats: State-of-the-art review

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Multimedia Tools and Applications 2024年 1-41页

作者： Jaiswal, Ayshwarya Dwivedi, Pragya Dewang, Rupesh Kumar Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India Computer Science and Engineering Education Department National Institute of Technical Teachers’ Training and Research Madhya Pradesh Bhopal462002 India

Insider threats are profoundly damaging and pose serious security challenges. These threats, perpetrated by insiders, may arise from delinquency, retaliation, or motives such as ambition for success, recognition, financial gain, or knowledge acquisition. They manifest in various forms;for example, an insider might disrupt systems by inserting a malicious script or engage in intellectual property theft. Due to their diverse nature, detection is highly complex and challenging, as standard security devices such as intrusion detection systems, firewalls, or antivirus software cannot detect it;hence, it entails careful and diligent work. This survey reviews existing research between 2010 and 2024 on detecting insider threats. It not only expounds on the novel taxonomy based on previous works and diverse motivations for insider threats but also identifies challenges and gaps in detecting malicious insiders. It highlights the state-of-the-art tools, techniques, and methodologies and also discusses the limitations of the same. Finally, the paper provides an overview of identifying optimal solutions and discusses future research directions that could lead to new methods for detecting insider threats. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Knowledge acquisition

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Machine learning approach for data analysis and predicting coronavirus using COVID-19 India dataset

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 47-73页

作者： Singh, Soni Ramkumar, K.R. Kukkar, Ashima Department of Computer Science and Engineering Chitkara Institute of Engineering and Technology Chitkara University Punjab India

According to the World Health Organisation (WHO), the COVID-19 virus would infect 83,558,756 persons worldwide in 2020, resulting in 646,949 deaths. In this research, we aim to find the link between the time series data and current circumstances to predict the future outbreak and try to figure out which technique is best for modelling for accurate predictions. The performance of different machine learning (ML) models such as sigmoid function, Facebook (FB) prophet model, seasonal auto-regressive integrated moving average with eXogenous factors (SARIMAX) model, support vector machine (SVM) learning model, linear regression (LR) model, and polynomial regression (PR) model are analysed along with their error rate. A comparison is also done to evaluate a best-suited model for prediction based on different categorisation approaches on the WHO authenticated dataset of India. The result states that the PR model shows the best performance with time-series data of COVID-19 whereas the sigmoid model has the consistently smallest prediction error rates for tracking the dynamics of incidents. In contrast, the PR model provided the most realistic prediction to identify a plateau point in the incident’s growth curve. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： COVID-19

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Renal Cyst Detection using Deep Learning 2

Renal Cyst Detection using Deep Learning

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Velusamy, Dhanalakshmi Harish, U. Lingesh, P. Kaviyarasan, P. Cyril Poornaraj, J. Prem, D. Sharan, K. Jeppiaar Institute of Technology Department of Computer Science and Engineering Chennai India Jeppiaar Institute of Technology Bachelor of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

Around the world, kidney diseases are a major open prosperity concern. Environmental components, tall salt levels, and additional minutes are all risk factors. Kidney dissatisfaction can be a critical prosperity issue, and it has gotten to be more transcendent in afterward a long time, with horribleness and mortality extending by 100% over the ultimate decade. Several kidney sicknesses, tallying those caused by renal stones and rankles, can be easily recognized through CT imaging. In separate, manual examination of a CT picture is time-consuming and requires a portion of effort. Deep learning methods, checking 3D CNNs, can be utilized to recognize plans of hurt and make assurance easier. By planning these calculations on clarified datasets, they can distinguish kidney stones and rankles with tall accuracy. Despite its potential to assist inside the early recognizing verification and treatment of ailments, modified revelation has to be utilized with caution and not as an elective to restorative information. © 2024 IEEE.

关键词： Deep learning

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