Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, ...
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Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information...
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Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.
Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected ...
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Multi-person pose estimation based on monocular cameras is one of the hot research topics in computer vision. Current monocular multi-person 3D pose estimation methods often treat individuals as independent entities f...
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The development of Decision Support Systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architect...
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In the realm of medical imaging, a scarcity of reliable, sizable datasets for training supervised deep learning models persists. One solution involves leveraging Generative Adversarial Networks (GANs) to fabricate syn...
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Lesion detection in Computed Tomography(CT) images is a challenging task in the field of computer-aided *** important issue is to locate the area of lesion *** a branch of Convolutional Neural Networks(CNNs),3D Contex...
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Lesion detection in Computed Tomography(CT) images is a challenging task in the field of computer-aided *** important issue is to locate the area of lesion *** a branch of Convolutional Neural Networks(CNNs),3D Context-Enhanced(3DCE) frameworks are designed to detect lesions on CT *** False Positives(FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals,which slow down the inference *** solve the above problems,a new method is proposed,a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion *** the restriction of "anchors" on ratios and scales,anchors are decomposed to independent "anchor strings".Anchor segments are dynamically combined in accordance with probability,and anchor strings with different lengths dynamically compose bounding *** show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.
This paper provides a new robot positioning and navigation technique that combines GNSS-RTK and visual SLAM to address the shortcomings of existing single robot positioning and navigation techniques, such as limited a...
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Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and control...
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A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconc...
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A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity *** use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional *** suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.
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