Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

Deleted Journal, Год журнала: 2024, Номер 4(2), С. 20 - 62

Опубликована: Май 23, 2024

Cutting-edge technologies have been widely employed in healthcare delivery, resulting transformative advances and promising enhanced patient care, operational efficiency, resource usage. However, the proliferation of networked devices data-driven systems has created new cybersecurity threats that jeopardize integrity, confidentiality, availability critical data. This review paper offers a comprehensive evaluation current state context smart healthcare, presenting structured taxonomy its existing cyber threats, mechanisms essential roles. study explored (SHSs). It identified discussed most pressing attacks SHSs face, including fake base stations, medjacking, Sybil attacks. examined security measures deployed to combat SHSs. These include cryptographic-based techniques, digital watermarking, steganography, many others. Patient data protection, prevention breaches, maintenance SHS integrity are some roles ensuring sustainable healthcare. The long-term viability depends on constant assessment risks harm providers, patients, professionals. aims inform policymakers, practitioners, technology stakeholders about imperatives best practices for fostering secure resilient ecosystem by synthesizing insights from multidisciplinary perspectives, such as cybersecurity, management, sustainability research. Understanding recent is controlling escalating networks encouraging intelligent delivery.

Язык: Английский

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Towards unbiased skin cancer classification using deep feature fusion

BMC Medical Informatics and Decision Making, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 31, 2025

Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing width augmentation to enhance efficiency. The proposed model addresses potential biases associated with conditions, particularly in individuals darker tones or excessive hair, by incorporating fusion assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, Melanoma Skin Cancer-comprising images categorized into benign classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, employed interpretability model’s decisions. Comparative analysis was performed three pre-existing learning networks-EfficientNet, MobileNet, Darknet. results demonstrate superiority, achieving an accuracy 99.86% F1 score 99.95%, underscoring its efficacy gradient propagation capture across various levels. research highlights significant advancing classification, providing robust tool accurate early diagnosis. integration enhances mitigates hair tones. outcomes this contribute improved patient healthcare practices, showcasing exceptional capabilities classification.

Язык: Английский

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Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches

Applied Data Science and Analysis, Год журнала: 2024, Номер 2024, С. 121 - 147

Опубликована: Авг. 7, 2024

There is a considerable threat present in genres such as machine learning due to adversarial attacks which include purposely feeding the system with data that will alter decision region. These are committed presenting different models way model would be wrong its classification or prediction. The field of study still relatively young and has develop strong bodies scientific research eliminate gaps current knowledge. This paper provides literature review defenses based on highly cited articles conference published Scopus database. Through assessment 128 systematic articles: 80 original papers 48 till May 15, 2024, this categorizes reviews from domains, Graph Neural Networks, Deep Learning Models for IoT Systems, others. posits findings identified metrics, citation analysis, contributions these studies while suggesting area’s further development robustness’ protection mechanisms. objective work basic background defenses, need maintaining adaptability platforms. In context, contribute building efficient sustainable mechanisms AI applications various industries

Язык: Английский

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Attack-data independent defence mechanism against adversarial attacks on ECG signal

Computer Networks, Год журнала: 2025, Номер unknown, С. 111027 - 111027

Опубликована: Янв. 1, 2025

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Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

International Journal of Computational Intelligence Systems, Год журнала: 2024, Номер 17(1)

Опубликована: Июнь 17, 2024

Abstract In the context of autism spectrum disorder (ASD) triage, robustness machine learning (ML) models is a paramount concern. Ensuring ML faces issues such as model selection, criterion importance, trade-offs, and conflicts in evaluation benchmarking models. Furthermore, development must contend with two real-time scenarios: normal tests adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge domains fuzzy multicriteria decision-making (MCDM). First, utilized dataset comprises authentic information, encompassing 19 medical sociodemographic features from 1296 autistic patients who received diagnoses via intelligent triage method. These were categorized into one labels: urgent, moderate, or minor. We employ principal component analysis (PCA) algorithms to fuse large number features. Second, fused forms basis for rigorously testing eight models, considering scenarios, evaluating classifier performance using nine metrics. The third phase developed robust framework encompasses creation decision matrix (DM) 2-tuple linguistic Fermatean opinion score method (2TLFFDOSM) multiple-ML perspectives, accomplished through individual external group aggregation ranks. Our findings highlight effectiveness PCA algorithms, yielding 12 components acceptable variance. ranking, logistic regression (LR) emerged top-performing terms 2TLFFDOSM (1.3370). A comparative five benchmark studies demonstrated superior our across all six checklist comparison points.

Язык: Английский

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Fuzzy Decision‐Making Framework for Evaluating Hybrid Detection Models of Trauma Patients

Expert Systems, Год журнала: 2025, Номер 42(3)

Опубликована: Фев. 13, 2025

ABSTRACT This study introduces a new multi‐criteria decision‐making (MCDM) framework to evaluate trauma injury detection models in intensive care units (ICUs). research addresses the challenges associated with diverse machine learning (ML) models, inconsistencies, conflicting priorities, and importance of metrics. The developed methodology consists three phases: dataset identification pre‐processing, hybrid model development, an evaluation/benchmarking framework. Through meticulous is tailored focus on adult patients. Forty were by combining eight ML algorithms four filter‐based feature‐selection methods principal component analysis (PCA) as dimensionality reduction method, these evaluated using seven weight coefficients for metrics are determined 2‐tuple Linguistic Fermatean Fuzzy‐Weighted Zero‐Inconsistency (2TLF‐FWZIC) method. Vlsekriterijumska Optimizcija I Kompromisno Resenje (VIKOR) approach applied rank models. According 2TLF‐FWZIC, classification accuracy (CA) precision obtained highest weights 0.2439 0.1805, respectively, while F1, training time, test time lowest 0.1055, 0.0886, 0.1111, respectively. benchmarking results revealed following top‐performing models: Gini index logistic regression (GI‐LR), decision tree (GI_DT), information gain (IG_DT), VIKOR Q score values 0.016435, 0.023804, 0.042077, proposed MCDM assessed examined systematic ranking, sensitivity analysis, validation best‐selected two unseen datasets, mode explainability SHapley Additive exPlanations (SHAP) We benchmarked against other benchmark studies achieved 100% across six key areas. provides several insights into empirical synthesis this study. It contributes advancing medical informatics enhancing understanding selection ICUs.

Язык: Английский

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A Scalable and Generalised Deep Learning Framework for Anomaly Detection in Surveillance Videos

International Journal of Intelligent Systems, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, vandalism. While deep learning (DL) has shown excellent performance this area, existing approaches have struggled apply DL models across different anomaly tasks without extensive retraining. This repeated retraining time‐consuming, computationally intensive, unfair. To address limitation, a new framework introduced study, consisting three key components: transfer enhance feature generalization, model fusion improve representation, multitask classification generalize classifier multiple training from scratch when task introduced. The framework’s main advantage its ability requiring for each task. Empirical evaluations demonstrate effectiveness, achieving an accuracy 97.99% on RLVS (violence detection), 83.59% UCF dataset (shoplifting 88.37% both datasets using single Additionally, tested unseen dataset, achieved 87.25% 79.39% violence shoplifting datasets, respectively. study also utilises two explainability tools identify potential biases, ensuring robustness fairness. research represents first successful resolution generalization issue detection, marking significant advancement field.

Язык: Английский

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Generalisable deep Learning framework to overcome catastrophic forgetting

Intelligent Systems with Applications, Год журнала: 2024, Номер 23, С. 200415 - 200415

Опубликована: Июль 10, 2024

Generalisation across multiple tasks is a major challenge in deep learning for medical imaging applications, as it can cause catastrophic forgetting problem. One commonly adopted approach to address these challenges train the model from scratch, incorporating old and new data, classes, tasks. However, this solution comes with its downsides, time-consuming, requires high computational resources, susceptible bias, lacks flexibility. To effectively issues, paper introduces generalisable DL framework that consists of three key components: self-supervised learning, feature fusion single task, classes or Using proposed framework, models SVM classifier accurately detect abnormalities X-ray tasks, including humerus wrist, achieving an accuracy 92.71% 90.74%, respectively. These results were achieved using minimal training requirements when introduced. Another experiment was performed on chest X-rays, where added pre-existing ones. Without requiring retraining both our combined class 98.18%. This demonstrates has not forgotten data. The enhances performance brings flexibility efficiency process, saving time resources.

Язык: Английский

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A Hybrid-Transformer-Based Cyber-Attack Detection in IoT Networks

International Journal of Interactive Mobile Technologies (iJIM), Год журнала: 2024, Номер 18(14), С. 90 - 102

Опубликована: Авг. 2, 2024

The concept of the Internet Things (IoT) is significant in today’s world and opens up new opportunities for several organizations. IoT solutions are proliferating fields such as self-driving cars, smart homes, transportation, healthcare, services constantly being created. Over previous decade, society has seen a expansion connectivity. In reality, connectivity will expand variety domains over next few years. Various problems must be overcome to permit effective secure operations. However, growing connections increase potential cyber-attacks since attackers can exploit broad network linked devices. Artificial intelligence (AI) detects prevents cyber assaults by developing adjusting threats weaknesses. this study, we offer novel cyber-detection model networks based on convolutional neural (CNN) transformers. study aims enhance system’s ability identify detect cyberattacks, sophisticated assaults, its performance. experimental findings, using cybersecurity CICIoT2023 dataset, show that CNN-Transformer hazards with an overall accuracy 99.49%. identifying hazardous activity, MLP 99.39%, while XGBoost-pipeline 99.40%.

Язык: Английский

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Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review

Computers in Biology and Medicine, Год журнала: 2024, Номер 184, С. 109391 - 109391

Опубликована: Ноя. 22, 2024

Язык: Английский

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