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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting

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Neural Computing and Applications 2025年 1-46页

作者： Purohit, Sourav Kumar Panigrahi, Sibarama Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Burla India Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholders to make well-informed decisions and effectively mitigate risks. The decomposition-based hybrid models have been showing promising COP forecasting accuracy than other time series forecasting methods. Despite this fact, no systematic study has been conducted to evaluate the true potential of different decomposition-based hybrid methods employing different forecasting models to forecast the COP. Therefore, a hybrid modeling framework is developed by combining efficient decomposition techniques, namely empirical mode decomposition (EMD), ensemble EMD (EEMD), complete EEMD with adaptive noise (CEEMDAN), and variational mode decomposition (VMD) with seven statistical models, fourteen machine learning (ML) models, and six deep learning (DL) models. Further, a systematic study is conducted on the resulting decomposition-based hybrid models to find the best hybrid model for COP forecasting. Three distinct train-test data splits are employed to ensure a reliable evaluation of the models using four performance metrics. Extensive statistical analysis is conducted to identify the optimal combination of the decomposition technique and forecasting model for precise COP prediction. The results demonstrate that the proposed decomposition-based hybrid model employing VMD and Huber Regression is statistically the best method among all alternatives to forecast monthly COP. The proposed hybrid method VMD-Huber Regression improves the root mean square error (RMSE) by 21% than CEEMDAN-ARIMA, 58.31% than EEMD-Theta, 13.18% than EMD-Random Walk, and 49.44% than VMD-TBATS hybrid methods in 60–40 Train-Test split ratio. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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DGC-TnT: Enhancing Point Cloud Object Classification by Dynamic Graph Convolutions with Transformer in Transformer

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IEEE Access 2024年 12卷 111924-111931页

作者： Lin, Chien-Chou Chen, Po-Yu National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliou64002 Taiwan

Recently, deep learning neural networks have been widely used in object classification. The process of object classification typically involves extracting features from the point cloud using neural networks and integrating these features into a global feature for recognizing the object. However, most existing methods perform well in global feature extraction but still fall short in capturing local details, or lack an attention mechanism to focus on informative feature signals. This paper presents DGC-TnT which combines dynamic graph convolution (DGC) structure with the Point-TnT module. Employing a two-stage TnT architecture, DGC-TnT utilizes a self-attention mechanism to deeply analyze local features derived from dynamic graph convolution, expanding their scope globally. This mechanism effectively operates between individual points and clusters of points, thereby enhancing overall object classification performance. DGC-TnT facilitates enhanced feature communication between points and point clusters within the point cloud through the Transformer module. On the ModelNet40 and ScanobjectNN datasets, DGC-TnT has a significant improvement in accuracy. © 2013 IEEE.

关键词： Three dimensional displays

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Contactless and Real-time Hand Gesture Recognition using Inductive Proximity Technique for Wrist-worn Wearables

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IEEE Sensors Journal 2025年第11期25卷 20474-20485页

作者： Lin, Pei-Jung Shih, Chi-Huang Weng, Tzu-Hsuan Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung411 Taiwan

This study proposes a contactless and real-time hand gesture recognition system suitable for smartwatches. The proposed system adopts inductive proximity sensing to collect Mechanomyography (MMG) signals induced by finger-based gestures above the wrist. After the signal processing and feature extraction stages, machine-learning models can be built for gesture classification. Compared with the Electromyography (EMG)-based method generally deployed in the forearm, inductive MMG applies an electromagnetic field to the body surface and is suitable for use on the wrist. Compared to the existing contact methods, such as EMG and accelerometer-based MMG, the inductive MMG method does not require contact with the body surface to collect muscle action signals and can avoid interference from environmental factors such as sweat and dust. The main contributions of this study are (1) a watch-type prototype with inductive MMG sensing technique, including hardware and firmware;(2) a lightweight and efficient signal processing mechanism that can capture the inductive signal characteristics of gestures for the above-mentioned embedded system (i.e., watch-type prototype);and (3) the tempo-spatial feature extraction method to improve the accuracy of gesture recognition. The experiment results show that for six common machine learning models, the gesture recognition accuracy of the proposed system is above 95%, with a maximum of 97.47%. In the briefing control application for verification purposes, the average accuracy of the inductive gesture recognition system can achieve 98.26%, and the average processing time is 14.37 ms. According to the experimental results, the inductive MMG system can provide a feasible gesture recognition solution for wrist-worn wearable devices. © 2001-2012 IEEE.

关键词： Firmware

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Hyperspectral Target Detection-Based 2-D-3-D Parallel Convolutional Neural Networks for Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 9451-9469页

作者： Chen, Shih-Yu Hsu, Kai-Hsun Kuo, Tzu-Hsien National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin64002 Taiwan

This article presents a novel hyperspectral target detection (HTD) based two-dimensional (2-D)-three-dimensional (3-D) parallel convolutional neural network (HTD 2D-3D-PCNN) model, which integrates the HTD technique to achieve outstanding performance in hyperspectral image classification. The proposed model effectively leverages both spectral and spatial information through a dual-branch architecture in hyperspectral imaging. In the first branch, HTD is utilized to enhance the spectral features of targets of interest, while suppressing background. The resulting enhanced image is then inputted to a 2D-CNN, augmented with an additional deconvolution layer to highlight spatial characteristics. Concurrently, the second branch employs dimensionality reduction via principal component analysis, and a 3D-CNN is employed to capture both spectral and spatial attributes. Subsequently, the feature maps from both convolutional neural networks (CNNs) are combined and processed through fully connected layers for classification. To validate the effectiveness of the proposed HTD-2D-3D-PCNN, extensive experiments are conducted on five widely used hyperspectral public datasets (Indian Pines, Pavia University, Salinas Scene, Kennedy Space Center, and Botswana) with a consistent training sample ratio of either 10% or 5%. The results show that HTD-2D-3D-PCNN can achieve overall accuracy values of 98.41%, 99.85%, 99.92%, 99.82%, and 98.82% for the respective datasets, surpassing the performance of recent methodologies. © 2008-2012 IEEE.

关键词： Image classification

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A Multi-Agent Proximal Policy Optimized Joint Mechanism in mmWave HetNets with CoMP Toward Energy Efficiency Maximization

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第1期8卷 265-278页

作者： Lotfolahi, Amin Ferng, Huei-Wen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106319 Taiwan

A novel cluster-based traffic offloading and user association (UA) algorithm alongside a multi-agent deep reinforcement learning (DRL) based base station (BS) activation mechanism is proposed in this paper. Our design aims to maximize the energy efficiency (EE) of the heterogeneous network (HetNet) while maintaining high quality of service (QoS). By taking advantage of the dense deployment of BSs in a HetNet, a clustering algorithm is first proposed to facilitate traffic offloading among BSs. Then, a multi-agent proximal policy optimization (MAPPO) based DRL algorithm is employed to trigger the BS activation decision based on the current environmental condition. Finally, a UA algorithm is deployed to improve further the (normalized) data rate of all users, known as the (normalized) sum rate. Via simulation, we show that our proposed mechanism can remarkably enhance the EE and excel over the closely related mechanisms. It satisfies the required data rate, improves the sum rate, and exhibits excellent scalability when many BSs are deployed. © 2017 IEEE.

关键词： Quality of service

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Centralized RANSAC-Based Point Cloud Registration With Fast Convergence and High Accuracy

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 5431-5442页

作者： Chung, Kuo-Liang Chang, Wei-Tai National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

For point cloud registration, the purpose of this article is to propose a novel centralized random sample consensus (RANSAC) (C-RANSAC) registration with fast convergence and high accuracy. In our algorithm, the novel contributions are, first, the proposal of a scale histogram-based outlier removal to delete outliers from the initial line vector set L for constructing a reduced line vector set Lred;second, the handshake cooperation between the host RANSAC (H-RANSAC) only working on L and the local RANSAC (LCL-RANSAC) only working on Lred;third, in each handshake process, after receiving the global registration solution and the global iteration number xH from H-RANSAC, LCLRANSAC uses the received global solution as the initial solution of the modified TEASER++ (M-TEASER++) method to calculate its first local registration solution. If the first local registration solution satisfies the global iteration number inheritance condition, LCL-RANSAC directly sends the accumulated iteration number, xH + 1, and the first local solution back to H-RANSAC;otherwise, LCL-RANSAC iteratively refines its local solution using the M-TEASER++ method, and then sends the resultant local solution and the required local iteration number xLCL to H-RANSAC for updating the global solution, the global iteration number to xH := xH + xLCL, and the global confidence level. Due to |Lred| |L| and employing the global iteration number inheritance condition test into our algorithm, we have conducted extensive experiments on testing point cloud pairs to show the registration accuracy and execution time merits of our algorithm relative to the state-of-theart methods. © 2008-2012 IEEE.

关键词： Iterative methods

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CNN-HOG based hybrid feature mining for classification of coffee bean varieties using image processing

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Multimedia Tools and Applications 2025年第2期84卷 749-764页

作者： Molla, Yirga Kene Mitiku, Emebet Abeje Department of Computer Science Gafat Institute of Technology Debre Tabor University Debre Tabor Ethiopia Department of Computer Science Faculty of Natural and Computational Science Debark University Debark Ethiopia

Ethiopia, known as the birthplace of coffee, relies on coffee exports as a major source of foreign currency. This research paper focuses on developing a hybrid feature mining technique to automatically classify Ethiopian coffee beans based on their provenance: Harrar, Jimma, Limu, Sidama, and Wellega, which correspond to their botanical origins. A dataset of coffee bean images is collected from various regions through the Ethiopian Commodity Exchange (ECX) in Addis Ababa. The proposed system incorporates preprocessing phases including image resizing, filtering, contrast enhancement, noise removal, grayscale conversion, and segmentation using a combined thresholding and K-means approach for grayscale and RGB images, respectively. Classification is performed using a radial basis function (RBF) kernel function of support vector machine (SVM). To address the color-feature similarity challenge, the study explores merging color and texture features using the histogram of oriented gradients (HOG) local feature descriptor. Performance evaluation is conducted for HOG feature extraction, CNN feature extraction, and a hybrid feature vector (HOG-CNN) using a multi-class SVM classifier, achieving accuracies of 74.17%, 85.83%, and 97.5%, respectively. The deep-shallow-based feature (CNN-HOG) combination demonstrates the highest accuracy of 97.5% in this study. The findings highlight the effectiveness of the proposed hybrid feature mining approach in automatically classifying Ethiopian coffee bean varieties, with potential applications in quality control and traceability within the coffee industry. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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