Chest X-ray (CXR) is a low-cost medical imaging technique. It common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents use generative adversarial networks (GAN) perform task lung segmentation on given CXR. GANs are popular generate realistic data by learning mapping from one domain another. In our work, generator GAN trained segmented mask input The discriminator distinguishes between ground truth generated mask, updates...

With the recent advancements in field of wearable technologies, opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection can help improve healthcare and minimizes negative impact long-term stress. This paper reports outcomes a pilot study associated stress-monitoring dataset, named "Stress-Predict Dataset", created by collecting signals from healthy subjects wrist-worn watches with photoplethysmogram (PPG) sensor....

Everyday responsibilities and lifestyle issues are the main cause of physical psychological stress, which deteriorates individual's health. Prolonged exposure to stress triggers adrenocorticotrophic hormonal (ACTH) system causes release cortisol hormones from adrenal cortex. Many other biomarkers affected by but is considered most vital potentially clinically useful biomarker for estimation monitoring. Accurate timely detection increased levels might improve diagnosis, treatment, prevention...

Respiratory rate can provide auxiliary information on the physiological changes within human body, such as physical and emotional stress. In a clinical setup, abnormal respiratory be indicative of deterioration patient's condition. Most existing algorithms for estimation using photoplethysmography (PPG) are sensitive to external noise may require selection certain algorithm-specific parameters, through trial-and-error method. This paper proposes new algorithm estimate sensor signal health...

Time-series features are the characteristics of data periodically collected over time. The calculation time-series helps in understanding underlying patterns and structure data, as well visualizing data. manual selection feature from a large temporal dataset time-consuming. It requires researchers to consider several signal-processing algorithms analysis methods identify extract meaningful given These core machine learning-based predictive model designed describe informative signal. For...

In the era of big data, artificial intelligence (AI) algorithms have potential to revolutionize healthcare by improving patient outcomes and reducing costs. AI frequently been used in health care for predictive modelling, image analysis drug discovery. Moreover, as a recommender system, these shown promising impacts on personalized provision. A system learns behaviour user predicts their current preferences (recommends) based previous preferences. Implementing improves this prediction...

Stress is known as a silent killer that contributes to several life-threatening health conditions such high blood pressure, heart disease, and diabetes. The current standard for stress evaluation based on self-reported questionnaires standardized scores. There no gold independently evaluate levels despite the availability of numerous biophysiological indicators. With an increasing interest in wearable monitoring recent years, studies have explored potential various indicators this purpose....

Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of monitoring systems are built on supervised learning classification algorithm. These rely collection sensor reference data during phase. One most challenging tasks physiological or pathological is labeling signals collected an experiment. Commonly, different types self-reporting questionnaires used label perceived...

An EEG-based noninvasive neuro-feedback SoC for emotion classification of Autistic children is presented. The AFE comprises two entirely shared EEG-channels using sampling capacitors to reduce the area by 30% and achieve an overall integrated input-referred noise 0.55μ VRMS with cross-talk - 79dB. 4-layers Deep Neural Network (DNN) classifier on-sensor classify (4 emotions) >85% accuracy. 16mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Physiological pressure measurement is one of the most common applications sensors in healthcare. Particularly, continuous monitoring provides key information for early diagnosis, patient-specific treatment, and preventive This paper presents a thin-film flexible wireless sensor wide range medical but mainly focused on interface during compression therapy to treat venous insufficiency. The based pressure-dependent capacitor (C) printed inductive coil (L) that form an inductor-capacitor (LC)...

Abstract In certain healthcare settings, such as emergency or critical care units, where quick and accurate real-time analysis decision-making are required, the system can leverage power of artificial intelligence (AI) models to support prevent complications. This paper investigates optimization AI based on time complexity, hyper-parameter tuning, XAI for a classification task. The highlights significance lightweight convolutional neural network (CNN) analysing classifying Magnetic Resonance...

Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in field of cardiovascular disease. Major efforts have focused on biosensor component contrast with those employed creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations this context. objective is propose a new algorithm, compliant current clinical guidelines, which can...

Abstract Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under scenarios, speedy assessment food from agricultural land is critical it supports aid activity in the disaster‐hit areas. In this article, a deep learning approach was presented for detection segmentation coconut trees aerial imagery provided through AI competition organised by World Bank collaboration with OpenAerialMap WeRobotics . Masked Region‐based...

Retinal blood vessels generally appear as wire mesh structures that have different widths. Their morphology plays a crucial role in indication and timely handling of fatal diseases, for instance diabetic retinopathy or hypertension. To address the aforementioned issues, this paper presents robust vessel segmentation technique. First, input retinal image is splitted into Red, Green, Blue Channels. On Green channel pre-processing done to eradicate noise other disease. This step also extracts...

A capsule is formed when a group of additional neurons added to existing convolutional layer in typical neural network (CNN). Capsules have activity vector that represents instantiation parameters an object or part object. Capsule has recently been introduced by Hinton overcome the shortcomings CNN model trained with back-propagation. In this work, we investigate use networks for recognition handwritten digits Urdu. Our results show multi-layer achieves better (98.5% accuracy) than deep...

Artificial Intelligence (AI) techniques provide many intelligent methods for security solutions in various domains such as finance, networking, cloud computing, health records and individual's identity. AI achieves mechanisms like antivirus, firewalls, intrusion detection system (IDS) cryptography by using machine learning data analysis techniques. As the modern help improving systems, criminal activities are also becoming updated simultaneously. Machine along with tools have become popular...

Abstract Aims . Ambulatory video‐EEG monitoring has been utilized as a cost‐effective alternative to inpatient for non‐surgical diagnostic evaluation of symptoms suggestive epileptic seizures. We aimed assess incidence epileptiform discharges in ambulatory recordings according seizure symptom history obtained during clinical evaluation. Methods This was retrospective cohort study. queried from 9,221 consecutive studies 35 states over one calendar year. assessed each symptom, including that...