22 hasil untuk "Syed Muhammad Anwar"
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Artikel Jurnal
A Modified U-Net Based Framework for Automated Segmentation of Hippocampus Region in Brain MRI
An accurate localization of the brain anatomical structure for correct and reliable diagnostic strategies is of great concern in many bio-medical applications. Towards this end, manual or semi-automated delineation methods used are found to be time consuming. Herein, to address this problem, we…
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Artikel Jurnal Teks lengkap
FL-Swarm MRCM: A Novel Federated Learning Framework for Cross-Site Medical Image Reconstruction
Magnetic Resonance Imaging (MRI) reconstruction is computationally heavy from under sampled data, and centralized data sharing within deep learning models was met with privacy concerns. We therefore propose FL-Swarm MRCM, a novel federated learning framework that integrates FedDyn dynamic…
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Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis…
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Artikel Jurnal Teks lengkap
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise…
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Artikel Jurnal
Multi-Domain Feature Fusion for Emotion Classification Using DEAP Dataset
Emotion recognition in real-time using electroencephalography (EEG) signals play a key role in human-computer interaction and affective computing. The existing emotion recognition models, that use stimuli such as music and pictures in controlled lab settings and limited number of emotion classes…
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Artikel Jurnal Teks lengkap
Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia
Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single…
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Artikel Jurnal
A Multimodal Perceived Stress Classification Framework Using Wearable Physiological Sensors
Mental stress is a common condition that poses serious health risks, but proper management can greatly improve quality of life. We propose a robust multimodal framework for perceived stress classification using data from forty subjects collected via three physiological modalities…
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Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records
Abstract Mental disorders represent a critical global health challenge that affects millions around the world and significantly disrupts daily life. Early and accurate detection is paramount for timely intervention, which can lead to improved treatment outcomes. Electroencephalography (EEG)…
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Artikel Jurnal
A novel image retrieval based on rectangular spatial histograms of visual words
Content-based image retrieval (CBIR) provides a solution to search the images that are similar to a query image. From last few years, the bag-of-visual-words (BoVW) model gained significance and improved the performance of CBIR. In a standard BoVW model, an image is represented as an order-less…
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Artikel Jurnal Teks lengkap
EEG in game user analysis: A framework for expertise classification during gameplay
Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for…
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Artikel Jurnal
A Vision-Based Path Planning and Object Tracking Framework for 6-DOF Robotic Manipulator
Industrial robots are widely used for repetitive, humanly unmanageable, and hazardous tasks. Hence, an improvement in the production efficiency of industrial robot manipulators is of prime concern. This can be achieved through machine vision and path planning techniques with a focus on localization…
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Artikel Jurnal
MR image normalization dilemma and the accuracy of brain tumor classification model
In clinical practice, a detailed medical history and physical exam, which includes a thorough neurological examination, are used to diagnose a brain tumor. The size, form, margin, and texture of the tumor, among other things, can influence the diagnosis of brain tumors. Even tumor types that are…
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Artikel Jurnal
EEG Compression Using Motion Compensated Temporal Filtering and Wavelet Based Subband Coding
Electroencephalography (EEG) signals are commonly used in medical applications for prevention, diagnosis, and detection of neurological diseases. These EEG signals have also been used in designing brain computer interfaces for assistive technologies. For densely placed electrodes and long EEG…
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PedsORAI: An AI-Driven Framework for Predicting Surgical Case and PACU Duration in Pediatric Procedures
Operating rooms (ORs) are among the most resource-intensive units in pediatric hospitals, yet perioperative planning often depends on inaccurate duration estimates derived from manual judgment or historical averages. This challenge is particularly pronounced in pediatric settings, where variability…
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Artikel Jurnal Teks lengkap
EEG Based Classification of Long-Term Stress Using Psychological Labeling
Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this…
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Artikel Jurnal
Chaos Based Enhanced RC5 Algorithm for Security and Integrity of Clinical Images in Remote Health Monitoring
The demand for remote health monitoring will significantly increase in the near future due to a decrease in the doctor/patient ratio as the world population grows. In remote health monitoring, one of the major goals is to send patient's data to clinical experts at geographically distant locations…
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Artikel Jurnal Teks lengkap
A Game Player Expertise Level Classification System Using Electroencephalography (EEG)
The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users’ attention. This leads to the need to analyse the cognitive…
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Artikel Jurnal
Secure EEG Signal Transmission for Remote Health Monitoring Using Optical Chaos
For the very first time, we present the use of optical chaos for the secure transmission of electroencephalogram (EEG) signals through optical fiber medium in remote health monitoring systems. In our proposed scheme, a semiconductor laser source is used to generate optical chaos, which hides EEG…