Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and...
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Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,...
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Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.
Delineation of retinal vessels in fundus images is essential for detecting a range of eye disorders. An automated technique for vessel segmentation can assist clinicians and enhance the efficiency of the diagnostic pr...
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For computers to understand human activity or behavior in a variety of scenarios, reliable 3D human posture estimation is a prerequisite. Several difficulties have made such work more complex as it is influenced by va...
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This research article introduces a novel approach to text-independent speaker recognition by integrating Mel-Frequency Cepstral Coefficients (MFCC) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, with noi...
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Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin dis...
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Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image ...
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Intrusion Detection and Prevention Systems (IDPS) play a key role in protecting networks by keeping an eye out for suspicious activity, spotting threats, and taking action to stop them. These systems were originally d...
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Intrusion Detection and Prevention Systems (IDPS) play a key role in protecting networks by keeping an eye out for suspicious activity, spotting threats, and taking action to stop them. These systems were originally designed for traditional, fixed networks, but they struggle to keep up with the fast-paced and constantly changing nature of cloud computing environments. Cloud computing has revolutionized technology, bringing many innovations in how organizations operate. Organizations rely heavily on the use of cloud storage to store and retrieve their sensitive data. Security issues in the cloud computing environment are a big challenge as, despite various protection measures, the cloud environment is vulnerable to security threats. Intrusion Detection and Prevention System (IDPS) is a significant component in securing the cloud environment against emerging threats in cyber-attacks. This paper takes a close look at intrusion detection systems (IDS) that are specifically built for cloud computing. The cloud brings its own set of challenges like constantly changing resources, sharing space between many users, and limited visibility into all the network traffic. Unlike traditional IDS that work in fixed, local networks, cloud-based IDS have to handle traffic that moves between virtual machines and scale up or down quickly. Cloud computing has transformed over time, improving access to scalability while offering vulnerabilities that increase the probability of intrusion or attacks. This review addresses these research gaps by comprehensively surveying state-of-the-art IDPS techniques tailored for cloud computing environments. IDPS is further classified into different categories, such as signature-based, anomaly-based, and hybrid-based. Recently, combining Machine Learning (ML) and Deep Learning (DL) with Intrusion Detection Systems (IDS) has shown to be very effective, as it allows for more precise detection and large-scale use. However, notable challenges include small
To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are parti...
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Technological advancements have brought a new era of growth for the healthcare industry. Nowadays, the security of healthcare data and the preservation of user privacy inside smart healthcare systems are being severel...
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