Smart Prosthetic Arm Using Cognitive Application

Kanna, R. Kishore; Prasath Alias Surendhar, S.; AL-Hameed, Mazin Riyadh; Lafta, Alaa M.; Khalid, Raed; Hussain, Ahmed

doi:10.1109/icacite57410.2023.10182794

Cited by 23 publications

(3 citation statements)

References 11 publications

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“…To this end, a sample of 367 stroke patients was selected for the purpose of conducting a regression analysis. 500 individuals who had not previously had a stroke and had been monitored for 2 years made up the control group [17]. Based on data pertaining to a number of well-known modifiable risk variables, it created a logistic regression model [18].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Stroke Predictor Application

Kishore Kanna,

Rami Reddy,

Panigrahi

et al. 2024

EAI Endorsed Trans IoT

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When blood flow to the brain stops or slows down, brain cells die because they don't get enough oxygen and nutrients. This condition is known as an ischemic stroke. It is now the biggest cause of death in the whole planet. Examining the afflicted people has shown a number of risk variables that are thought to be connected to the stroke's origin. Numerous studies have been conducted to predict the illnesses associated with stroke using these risk variables. The prompt identification of various warning symptoms associated with stroke has the potential to mitigate the severity of the stroke. The utilization of machine learning techniques yields prompt and precise predictive outcomes. Although its uses in healthcare are expanding, certain research domains have a stronger need for more study. We think that machine learning algorithms may aid in a deeper comprehension of illnesses and make an excellent healthcare partner. The textual dataset of numerous patients, which includes many medical variables, is gathered for this study. The missing values in the dataset are located and dealt with during processing. The dataset is used to train machine learning algorithms including Random Forest, Decision Tree classifier, and SVM. The method that delivers the greatest accuracy for our dataset is then selected once the accuracy of the algorithms has been determined. This aids patients in determining the likelihood of a brain stroke and ensuring they get the right medical attention.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Based Stroke Predictor Application

Kishore Kanna,

Rami Reddy,

Panigrahi

et al. 2024

EAI Endorsed Trans IoT

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“…This study examines various manifestations of anxiety, including negative thoughts, excessive worry, restlessness, fear, and hypersensitivity Individuals who exhibit high levels of anxiety tend to exhibit high levels of trait anxiety revealed [8]. Furthermore, these individuals were also found to have high levels of national anxiety in the evaluative condition.…”

Section: Introductionmentioning

confidence: 99%

Anxiety Controlling Application using EEG Neurofeedback System

Kishore Kanna,

Athawale,

Naniwadekar

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: This study aims to investigate the correlation between the oscillations of electroencephalography (EEG) bands and the level of anxiety in a sample of sixteen youth athletes aged 17–21. The research utilizes a mobile EEG system to collect data on EEG band oscillations. OBJECTIVES: The aim of this research study is to investigate the brain wave oscillations during relaxation, specifically comparing the contrast between eyes open and eyes closed state Electroencephalography (EEG) using a state-of-the-art wireless EEG headset system. METHODS: The system incorporates dry, non-interacting EEG sensor electrodes, developed exclusively by NeuroSky. In addition, the addition of the ThinkGear module and MindCap XL skull facilitated EEG recording. The aim of the present study was to investigate the effect of eyes open and eyes closed conditions on alpha-band activity in the prefrontal cortex The results showed a statistically significant difference (p≤0.006); appeared between these two states. The present study examined the relationship between the alpha band of the prefrontal cortex and anxiety levels. Specifically, we examined the relationship between these variables in the eyes-closed condition. RESULTS: Our analysis revealed a statistically significant correlation, with the alpha band showing a negative slope (p≤0.029). The present study examines the comparison of data obtained from single-channel wireless devices with data obtained from conventional laboratories The findings of this study show a striking similarity between the results obtained with both types of devices. The aim of the present study was to investigate the specific characteristics of the correlation between electroencephalographic (EEG) alphaband oscillations in the prefrontal cortex in relation to eye position and anxiety levels in young athletes. CONCLUSION: This study seeks to shed light on the possible relationship between this vibration and individuals' internal cognitive and affective states.

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“…CNN-based facial recognition systems have applications in various industries such as social networking, advertising, security, and surveillance [5 However, facial recognition technology has become an issue of ethical privacy, especially due to potential bias and manipulation of technology [6,7]. important implications for potential applications in psychology, human-computer interaction, marketing, security, and in research, such as improving human-robot interaction, driver fatigue in vehicle insights, advertising and retail and improving the customer experience [8].…”

Section: Introductionmentioning

confidence: 99%

CNN Based Face Emotion Recognition System for Healthcare Application

Kishore Kanna,

Panigrahi,

Sahoo

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Because it has various benefits in areas such psychology, human-computer interaction, and marketing, the recognition of facial expressions has gained a lot of attention lately. OBJECTIVES: Convolutional neural networks (CNNs) have shown enormous potential for enhancing the accuracy of facial emotion identification systems. In this study, a CNN-based approach for recognizing facial expressions is provided. METHODS: To boost the model's generalizability, transfer learning and data augmentation procedures are applied. The recommended strategy defeated the existing state- of-the-art models when examined on multiple benchmark datasets, including the FER-2013, CK+, and JAFFE databases. RESULTS: The results suggest that the CNN-based approach is fairly excellent at properly recognizing face emotions and has a lot of potential for usage in detecting facial emotions in practical scenarios. CONCLUSION: Several diverse forms of information, including oral, textual, and visual, maybe applied to comprehend emotions. In order to increase prediction accuracy and decrease loss, this research recommended a deep CNN model for emotion prediction from facial expression.

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Smart Prosthetic Arm Using Cognitive Application

Cited by 23 publications

References 11 publications

Machine Learning Based Stroke Predictor Application

Machine Learning Based Stroke Predictor Application

Anxiety Controlling Application using EEG Neurofeedback System

CNN Based Face Emotion Recognition System for Healthcare Application

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