Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function

Brain Informatics, Journal Year: 2023, Volume and Issue: 10(1)

Published: Feb. 17, 2023

Alzheimer's disease (AD) is a neurodegenerative that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of will reduce suffering patients their family members. Towards this aim, paper, we propose Siamese Convolutional Neural Network (SCNN) architecture employs triplet-loss function for representation input MRI images as k-dimensional embeddings. We used both pre-trained non-pretrained CNNs transform into embedding space. These embeddings are subsequently 4-way classification disease. The model efficacy was tested using ADNI OASIS datasets which produced an accuracy 91.83% 93.85%, respectively. Furthermore, obtained results compared with similar methods proposed literature.

Language: Английский

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Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

Cognitive Computation, Journal Year: 2023, Volume and Issue: 15(6), P. 1767 - 1812

Published: June 24, 2023

Abstract The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways automate the process make it more objective facilitate needs healthcare industry. Artificial Intelligence (AI) machine learning (ML) emerged as most promising approaches CHA process. In this paper, we background delve into extensive research recently undertaken in domain provide a comprehensive survey state-of-the-art. particular, careful selection significant works published literature is reviewed elaborate range enabling technologies AI/ML techniques used for CHA, including conventional supervised unsupervised learning, deep reinforcement natural language processing, image processing techniques. Furthermore, an overview various means data acquisition benchmark datasets. Finally, discuss open issues challenges using AI ML along with some possible solutions. summary, paper presents tools, lists methods provides technological advancements, usage issues, domain. We hope first-of-its-kind will significantly contribute identifying gaps complex rapidly evolving interdisciplinary mental health field.

Language: Английский

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State-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence

Cognitive Computation, Journal Year: 2023, Volume and Issue: 16(2), P. 455 - 481

Published: Oct. 12, 2023

Abstract Recent advancements in the manufacturing and commercialisation of miniaturised sensors low-cost wearables have enabled an effortless monitoring lifestyle by detecting analysing physiological signals. Heart rate variability (HRV) denotes time interval between consecutive heartbeats.The HRV signal, as detected devices, has been popularly used indicative measure to estimate level stress, depression, anxiety. For years, artificial intelligence (AI)-based learning systems known for their predictive capabilities, recent AI models with deep (DL) architectures successfully applied achieve unprecedented accuracy. In order determine effective methodologies collection, processing, prediction stress from data, this work presents depth analysis 43 studies reporting application various algorithms. The methods are summarised tables thoroughly evaluated ensure completeness findings reported results. To make comprehensive, a detailed review conducted on sensing technologies, pre-processing multi-modal employed models. This is followed critical examination how Machine Learning (ML) models, utilised predicting data. addition, reseults selected carefully analysed identify features that enable perform better. Finally, challenges using predict listed, along some possible mitigation strategies. aims highlight impact AI-based expected aid development more meticulous techniques.

Language: Английский

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Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Brain Informatics, Journal Year: 2023, Volume and Issue: 10(1)

Published: June 21, 2023

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in safe environment, recognise specific triggers and gradually increase their perceived threats. Public-speaking anxiety (PSA) prevalent form of social anxiety, characterised by stressful arousal generated when presenting an audience. In self-guided VRET, participants can tolerance reduce anxiety-induced PSA over time. However, creating such VR environment determining physiological indices or distress open challenge. Environment modelling, character creation animation, psychological state determination the use machine learning (ML) models for stress detection are equally important, multi-disciplinary expertise required. this work, we have explored series ML with publicly available data sets (using electroencephalogram heart rate variability) predict states. If detect arousal, trigger calming activities allow cope overcome distress. Here, discuss means effective selection parameters detection. We propose pipeline model problem different parameter settings context virtual therapy. This be extended other domains interest where crucial. Finally, implemented biofeedback framework VRET successfully provided feedback as brain laterality index from our acquired multimodal anxiety.

Language: Английский

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Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 12378 - 12393

Published: Jan. 1, 2023

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of disease plays a vital role in better management patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate pipeline accounting for accurate diagnosis, overcoming limitations manual methods. This work proposes CAD that detects and classifies abnormalities lung CT images using Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting suspicious regions from non-COVID COVID patients an ABC region growing process extracting texture intensity features those regions. Further, model whose input features, initial weights hidden nodes optimisation abnormal into classes. is evaluated collected public datasets. In comparison to other techniques, achieved high classification accuracy 92.37% when set 470 images.

Language: Английский

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A Hybrid Approach for Stress Prediction from Heart Rate Variability

Lecture notes in networks and systems, Journal Year: 2023, Volume and Issue: unknown, P. 111 - 121

Published: Jan. 1, 2023

Language: Английский

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Towards Machine Learning-Based Emotion Recognition from Multimodal Data

Lecture notes in networks and systems, Journal Year: 2023, Volume and Issue: unknown, P. 99 - 109

Published: Jan. 1, 2023

Language: Английский

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Book recommendation system: An AI companion for curated book suggestions

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3279, P. 020199 - 020199

Published: Jan. 1, 2025

Language: Английский

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Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis

IEEE Access, Journal Year: 2022, Volume and Issue: 11, P. 1629 - 1653

Published: Dec. 26, 2022

Parkinson's disease (PD) is a prominent neurodegenerative that damages the neurons of substantia nigra, causing irreversible impairments leading to involuntary movements. As this disrupts patients' daily activities in mature stage, early detection crucial. Several methods based on nature-inspired (NI) algorithms have been proposed for PD and patient management. there are several NI feature selection, mapping with an individual machine learning (ML) classifier necessary obtain optimal performance pipeline. To fill gap, work, 13 11 ML classifiers were selected, critical comparisons performed regarding their combined detecting PD. Each algorithm was employed select set which then classified by keeping same parameters. This generated 143 NI-ML pairs, carefully compared find best-performing pairs considering assessment criteria such as accuracy, cross-validation mean score, precision, recall F1-score. The results extensive comparative analysis allowed ranking 50th, 75th 95th percentile identify pairs. analyses revealed 12 models obtained testing accuracy over 91%, above value. Flower Pollination Algorithm Extreme Gradient Boost pair highest 93%. study remarkable boosting promoting explainable detection.

Language: Английский

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A Deep Concatenated Convolutional Neural Network-Based Method to Classify Autism

Communications in computer and information science, Journal Year: 2023, Volume and Issue: unknown, P. 446 - 458

Published: Jan. 1, 2023

Language: Английский

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