International Journal of Cognitive Computing in Engineering最新文献

{"title":"A decentralised federated learning scheme for heterogeneous devices in cognitive IoT","authors":"Huanhuan Ge ,&nbsp;Xingtao Yang ,&nbsp;Jinlong Wang ,&nbsp;Zhihan Lyu","doi":"10.1016/j.ijcce.2024.08.001","DOIUrl":"10.1016/j.ijcce.2024.08.001","url":null,"abstract":"<div><p>Cognitive Internet of Things (IoT) technologies typically rely on substantial data collected from edge devices for data analysis and decision-making. However this reliance often leads to the inadvertent exposure of private data from smart edge devices. Federated learning (FL) is a distributed machine learning framework that protects user privacy by performing collaborative training without uploading private data. Nevertheless, applying classical FL to cognitive IoT systems to preserve privacy preservation faces significant challenges, such as central server failure and communication burden. Furthermore, when edge devices with heterogeneous data and systems participate in federated training, the learning process becomes slow and the performance of edge devices is compromised. To address these challenges, we propose a decentralised FL framework for cognitive IoT, termed DFL–MKF. In DFL– MKF, the centralised server is eliminated and each edge device is dynamically connected. We initialised models for edge devices based on computational and storage capabilities to accommodate system heterogeneity. Edge devices learned from the knowledge of multiple neighbours via knowledge transfer, and knowledge fusion was employed to aggregate the knowledge of multiple neighbours, thereby improving the performance of local models, and addressing data heterogeneity. Comprehensive experiments were performed on three image classification tasks. The results of these experiments demonstrate that the proposed method achieved superior performance compared to various baselines and improved communication efficiency.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 357-366"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000299/pdfft?md5=e029201b31143d3e9d6e997ad3677fdc&pid=1-s2.0-S2666307424000299-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-end solution for automatic beverage stock detection in supermarkets based on image processing and convolutional neural networks","authors":"Jorge Muñoz,&nbsp;Alonso Sanchez,&nbsp;Guillermo Kemper","doi":"10.1016/j.ijcce.2024.09.001","DOIUrl":"10.1016/j.ijcce.2024.09.001","url":null,"abstract":"<div><div>This study addresses the challenge of detecting and identifying stock shortages in large warehouses through an advanced algorithm that integrates image processing and artificial intelligence techniques. Presently, many companies contend with the limitations of manual inventory management, such as susceptibility to errors, slow inventory actualizations, and consequent adverse economic effects. In contrast to solutions based on robotics, the proposed approach continuously monitors shelves throughout warehouse aisles using several fixed cameras, each connected to a single-board computer that processes the acquired images, identifies stock levels using deep learning, and updates a centralized database with stock analysis results. The algorithmic process begins with an image validation step based on a convolutional neural network to ensure obstacle-free images of the shelves. Subsequently, an application-specific YOLOv2 detector trained via transfer learning identifies product types captured in the images and estimates their stock levels. The proposed solution not only reduces the need for manual intervention and operational costs but also drastically enhances inventory supervision efficiency. The fully implemented system achieves an average accuracy of over 98 %, surpassing the human visual inspection performance. The proposed solution also incorporates the aspect of user-friendliness through a developed mobile application. This application connects to the centralized database, allowing inventory supervisors to receive alerts when the stock of a product falls below a user-configured threshold. This technological integration within a centralized system signifies a substantial advancement in inventory management, offering prompt responses to product scarcity situations and optimizing warehouse operational efficiency.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 453-474"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Handwritten alphabet classification in Tamil language using convolution neural network","authors":"Jayasree Ravi","doi":"10.1016/j.ijcce.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.03.001","url":null,"abstract":"<div><p>Handwritten Alphabet Recognition can be defined as the way of detecting characters from images of Handwritten language alphabets. This is one of the important problems that can be solved by Convolution Neural Networks (CNN). Recent developments in CNN have made it possible to expand this problem area from English character recognition or Numbers recognition to Regional Languages character recognition, there has not been sufficient studies conducted in the domain of regional languages. This study has attempted to give deep learning approach to Tamil Handwritten Alphabets classification. This article aims to develop 3 models of CNN – THAC-CNN1, THAC-CNN2 and THAC-CNN3 to recognize Tamil Handwritten Alphabets and classify them based on its category. Our proposed models use a combination of benchmark dataset and a customized dataset which totals to over 2800 images of different Tamil alphabets after various data augmentation techniques. The proposed models are compared with a popular image classification pre-trained models - VGG-11 and VGG-16. We use the standard classification metric - accuracy to measure the performance of our proposed models. With our dataset and augmentation techniques, one of our models THAC-CNN1 achieves 97% accuracy on the training dataset and 92.5% accuracy on test dataset as opposed to 72% and 73.5% accuracy on training dataset and test dataset by pre-trained models.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 132-139"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000093/pdfft?md5=c89c714d621067e1adee2fabc9b8739d&pid=1-s2.0-S2666307424000093-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system","authors":"Balasubramani S ,&nbsp;John Aravindhar D ,&nbsp;P.N. Renjith ,&nbsp;K. Ramesh","doi":"10.1016/j.ijcce.2023.12.001","DOIUrl":"10.1016/j.ijcce.2023.12.001","url":null,"abstract":"<div><p>Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307423000372/pdfft?md5=12cd691609fc96914d9f5c7d530ff47b&pid=1-s2.0-S2666307423000372-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of continuous-time Hammerstein model using improved Archimedes optimization algorithm","authors":"Muhammad Shafiqul Islam ,&nbsp;Mohd Ashraf Ahmad ,&nbsp;Cho Bo Wen","doi":"10.1016/j.ijcce.2024.09.004","DOIUrl":"10.1016/j.ijcce.2024.09.004","url":null,"abstract":"<div><div>Although various optimization algorithms have been widely employed in multiple applications, the traditional Archimedes optimization algorithm (AOA) has presented imbalanced exploration with exploitation phases and the propensity for local optima entrapment. Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. The proposed algorithm employed two principal modifications to mitigate these issues and enhance identification accuracy: (i) exploration and exploitation phase recalibrations using a revised density decreasing factor and (ii) local optima entrapment alleviation utilizing safe experimentation dynamics. Various advantages were observed with this proposed algorithm, including a lower number of coefficient criteria, improved accuracy in Hammerstein model identification, and diminished processing demands by reducing gain redundancy between nonlinear and linear subsystems. This proposed algorithm also discerned linear and nonlinear subsystem variables within a continuous-time Hammerstein model utilizing input and output data. The process was evaluated using a numerical example and two practical experiments [twin-rotor system (TRS) and electro-mechanical positioning system (EMPS)]. Several parameters were then analyzed, such as the convergence curve of the fitness function, frequency and time domain-related responses, variable deviation index, and Wilcoxon's rank-sum test. Consequently, the proposed algorithm reliably determined the most optimal design variables during numerical trials, demonstrating 54.74 % mean fitness function and 75.34 % variable deviation indices enchantments compared to the traditional AOA. Improved mean fitness function values were also revealed in the TRS (11.63 %) and EMPS (69.63 %) assessments, surpassing the conventional algorithm. This proposed algorithm produced solutions with superior accuracy and consistency compared to various established metaheuristic strategies, including particle swarm optimizer, grey wolf optimizer, multi-verse optimizer, AOA, and a hybrid optimizer (average multi-verse optimizer-sine-cosine algorithm).</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 475-493"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized charging-station placement and UAV trajectory for enhanced uncertain target detection in intelligent UAV tracking systems","authors":"Haythem Bany Salameh ,&nbsp;Ameerah Othman ,&nbsp;Mohannad Alhafnawi","doi":"10.1016/j.ijcce.2024.08.004","DOIUrl":"10.1016/j.ijcce.2024.08.004","url":null,"abstract":"<div><p>Unmanned Aerial Vehicle (UAV) technology is proposed to improve social safety, provide specialized services, and improve overall well-being in crowded indoor spaces. The deployment of drones in indoor environments can improve emergency response time, offer various wireless services, allow efficient tracking, and improve awareness in crowded scenarios. In this paper, we propose a UAV-based tracking framework that relies on energy-limited UAVs that attempts to determine the appropriate placement of UAV charging stations (CHSs) and design a UAV path planning strategy to effectively carry out detection/tracking tasks of uncertain phenomena. The proposed framework comprises a CHS placement method and a UAV path planning algorithm. The CHS placement method attempts to find the optimal placement of a given number of available CHSs so that the energy consumed by a UAV to reach the nearest CHS is reduced. This, consequently, preserves the UAV’s energy, reducing the time required to return to the CHS and the period of none-tracking during the return time to the CHS. This can extend the tracking mission time and enhance detection performance. Based on the obtained optimal CHS placement, we design a reinforcement learning (RL)–based UAV trajectory algorithm to effectively detect and track a target (event of interest) with unknown behavior. The proposed RL-based UAV trajectory algorithm leverages long-term spatio-temporal behavior knowledge of uncertain targets (i.e., observed and learned events) to improve detection accuracy. Improving the detection of uncertain targets leads to better decision-making, faster responses, and improved security, safety, and efficiency in applications such as surveillance, defense, and search and rescue. The simulation results demonstrate the superior detection accuracy achieved by the proposed framework. Compared to a reference RL-based approach, the proposed algorithm achieves up to 65% higher detection accuracy in symmetric monitored areas and 20% increased accuracy in asymmetric monitored areas.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 367-378"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000317/pdfft?md5=fcd305d6b76ee8d6b22da640e2286de5&pid=1-s2.0-S2666307424000317-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel rice plant leaf diseases detection using deep spectral generative adversarial neural network","authors":"K. Mahadevan ,&nbsp;A. Punitha ,&nbsp;J. Suresh","doi":"10.1016/j.ijcce.2024.05.004","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.05.004","url":null,"abstract":"<div><p>The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The proposed Deep Spectral Generative Adversarial Neural Network (DSGAN²) method is used for detecting rice plant leaf disease. Initially, fed into the input of healthy and non-healthy leaves from the collected dataset. Then, apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S²AFS) method is applied to select optimal features and the closest weight from segmented rice plant leaves. Social Spider Optimization will select the feature using the Closest Weight (S²O-FCW) algorithm to analyze the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN²) algorithm detects rice plant disease based on selected features. With an accuracy of 97 %, the model helps farmers identify and identify Rice Plant diseases. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN²) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 237-249"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000172/pdfft?md5=c059ad1c9713fc665786256bfa935f53&pid=1-s2.0-S2666307424000172-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141290120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making","authors":"Chao Zhang ,&nbsp;Jiahui Zhang ,&nbsp;Wentao Li ,&nbsp;Oscar Castillo ,&nbsp;Jiayi Zhang","doi":"10.1016/j.ijcce.2024.01.001","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.01.001","url":null,"abstract":"<div><p>In the continuously evolving context of urbanization, more people flock to cities for job opportunities and an improved quality of life, resulting in undeniable pressure on transportation networks. This leads to severe daily commuting challenges for residents. To mitigate this urban traffic pressure, most cities have adopted urban dockless bicycle sharing systems (UDBSS) as an effective measure. However, making accurate decisions regarding UDBSS demand in different city locations is crucial, as incorrect choices can worsen transportation problems, causing difficulties in finding bicycles or excessive deployments leading to disorderly accumulation. To address this decision-making challenge, it is essential to consider uncertain factors like daily weather, temperature, and workdays. To tackle this effectively, we construct an adjustable multi-granularity (MG) complex intuitionistic fuzzy (CIF) information system using complex intuitionistic fuzzy sets (CIFSs). This system objectively determines classification thresholds using an evaluation-based three-way decision (TWD) method, creating adjustable MG CIF probabilistic rough sets (PRSs). Additionally, to recognize the irrationality of decision-makers (DMs), we propose a method that combines prospect theory (PT) with regret theory (RT), providing a more comprehensive understanding of the influence of DMs' psychological factors on decision outcomes. Building upon these foundations, we present static rebalancing strategies for UDBSS based on MG PRSs and prospect-regret theory (P-RT) within the CIF information system. Finally, using UDBSS data collected from various sensors, we conduct experimental analysis to verify its feasibility and stability. In summary, this approach considers residents’ daily usage preferences, including bicycles utilization and return, with the aim of minimizing unmet resident demands and predicting usage patterns for the next day. It effectively addresses the issue of UDBSS distribution inefficiencies and holds a significant advantage in prediction, making it suitable for broader applications in transportation systems and contributing to the establishment of more advanced modern intelligent transportation systems (MITSs) in the future.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 27-43"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000020/pdfft?md5=5709fc98711fe58c23a05ece2e6cc0f5&pid=1-s2.0-S2666307424000020-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stacked ensemble approach to detect cyber attacks based on feature selection techniques","authors":"Wahida Ferdose Urmi ,&nbsp;Mohammed Nasir Uddin ,&nbsp;Md Ashraf Uddin ,&nbsp;Md. Alamin Talukder ,&nbsp;Md. Rahat Hasan ,&nbsp;Souvik Paul ,&nbsp;Moumita Chanda ,&nbsp;John Ayoade ,&nbsp;Ansam Khraisat ,&nbsp;Rakib Hossen ,&nbsp;Faisal Imran","doi":"10.1016/j.ijcce.2024.07.005","DOIUrl":"10.1016/j.ijcce.2024.07.005","url":null,"abstract":"<div><p>The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual Information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 316-331"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000263/pdfft?md5=828b5cf23a6da444c5619047b16891c1&pid=1-s2.0-S2666307424000263-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement","authors":"Mohamed Abdel Hameed ,&nbsp;M. Hassaballah ,&nbsp;Riem Abdelazim ,&nbsp;Aditya Kumar Sahu","doi":"10.1016/j.ijcce.2024.08.002","DOIUrl":"10.1016/j.ijcce.2024.08.002","url":null,"abstract":"<div><p>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 patient’s privacy and data integrity.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 379-397"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000305/pdfft?md5=c77d0ff8e6f6ef8f125596b01d1f19d8&pid=1-s2.0-S2666307424000305-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}