Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Information Fusion, Год журнала: 2024, Номер 110, С. 102472 - 102472

Опубликована: Май 16, 2024

Язык: Английский

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Navigating the metaverse: unraveling the impact of artificial intelligence—a comprehensive review and gap analysis

Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)

Опубликована: Авг. 20, 2024

Abstract In response to the burgeoning interest in Metaverse—a virtual reality-driven immersive digital world—this study delves into pivotal role of AI shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, potential future developments, our research draws from a comprehensive analysis grounded meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, WoS, encompasses 846 retrieved studies. Through rigorous selection process, 54 papers were identified as relevant, forming basis for specific taxonomy Metaverse. Our examination spans diverse dimensions Metaverse, encompassing augmented reality, mixed Blockchain, Agent Systems, Intelligent NPCs, Societal Educational Impact, HCI Systems Design, Technical Aspects. Emphasizing necessity adopting trustworthy findings underscore enhance experience, safeguard privacy, promote responsible technology use. This paper not only sheds light scholarly Metaverse but also explores impact human behavior, education, societal norms, community dynamics. Serving foundation development implementation concept, identifies addresses seven open issues, providing indispensable insights subsequent studies integration

Язык: Английский

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A Review of Urban Digital Twins Integration, Challenges, and Future Directions in Smart City Development

Sustainability, Год журнала: 2024, Номер 16(19), С. 8337 - 8337

Опубликована: Сен. 25, 2024

This review paper explores Urban Digital Twins (UDTs) and their crucial role in developing smarter cities, focusing on making urban areas more sustainable well-planned. The methodology adopted an extensive literature across multiple academic databases related to UDTs smart sustainability, environments, conducted by a bibliometric analysis using VOSviewer identify key research trends qualitative through thematic categorization. shows how can significantly change cities are managed planned examining examples from like Singapore Dubai. study points out the main hurdles gathering data, connecting systems, handling vast amounts of information, different technologies work together. It also sheds light what is missing current research, such as need for solid rules effectively, better cooperation between various city deeper look into affect society. To address gaps, this highlights necessity interdisciplinary collaboration. calls establishing comprehensive models, universal standards, comparative studies among traditional UDT methods. Finally, it encourages industry, policymakers, academics join forces realizing sustainable, cities.

Язык: Английский

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ATD Learning: A secure, smart, and decentralised learning method for big data environments

Information Fusion, Год журнала: 2025, Номер unknown, С. 102953 - 102953

Опубликована: Янв. 1, 2025

Язык: Английский

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Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook

Information Fusion, Год журнала: 2024, Номер 113, С. 102606 - 102606

Опубликована: Авг. 5, 2024

Язык: Английский

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MEFF – A model ensemble feature fusion approach for tackling adversarial attacks in medical imaging

Intelligent Systems with Applications, Год журнала: 2024, Номер 22, С. 200355 - 200355

Опубликована: Март 16, 2024

Adversarial attacks pose a significant threat to deep learning models, specifically medical images, as they can mislead models into making inaccurate predictions by introducing subtle distortions the input data that are often imperceptible humans. Although adversarial training is common technique used mitigate these on it lacks flexibility address new attack methods and effectively improve feature representation. This paper introduces novel Model Ensemble Feature Fusion (MEFF) designed combat in image applications. The proposed model employs fusion combining features extracted from different DL then trains Machine Learning classifiers using fused features. It uses concatenation method merge features, forming more comprehensive representation enhancing model's ability classify classes accurately. Our experimental study has performed evaluation of MEFF, considering several challenging scenarios, including 2D 3D greyscale colour binary classification, multi-label classification. reported results demonstrate robustness MEFF against types across six distinct A key advantage its capability incorporate wide range without need train scratch. Therefore, contributes developing diverse robust defense strategy. More importantly, leveraging ensemble modeling, enhances resilience face attacks, paving way for improved reliability analysis.

Язык: Английский

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Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Journal of Intelligent Systems, Год журнала: 2024, Номер 33(1)

Опубликована: Янв. 1, 2024

Abstract This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies methodologies in adversarial attacks using machine learning (ML) deep methods. A methodology was conducted guarantee literature analysis of the studies sources such as ScienceDirect, Scopus, IEEE Xplore, Web Science. question shaped retrieve articles published from 2019 April 2024, which ultimately produced total 704 papers. rigorous screening, deduplication, matching inclusion exclusion criteria were followed, hence 42 included quantitative synthesis. The considered papers categorized into coherent classification including three categories: security enhancement techniques, attack mechanisms, innovative mechanisms solutions. In this article, we have presented comprehensive earlier opened door potential future by discussing depth four challenges motivations attacks, while recommendations been discussed. science mapping also performed reorganize summarize results address issues trustworthiness. Moreover, covers large variety network cybersecurity applications subjects, intrusion detection systems, anomaly detection, ML-based defenses, cryptographic techniques. relevant conclusions well demonstrate what achieved against attacks. addition, revealed few emerging tendencies deficiencies area be remedied through better more dependable mitigation methods advanced persistent threats. findings crucial implications for community researchers, practitioners, policy makers artificial intelligence applications.

Язык: Английский

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Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Artificial Intelligence in Medicine, Год журнала: 2024, Номер 155, С. 102935 - 102935

Опубликована: Июль 26, 2024

Deep learning (DL) in orthopaedics has gained significant attention recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation osteoarthritis severity. The utilisation is expected increase, owing its ability present accurate diagnoses more efficiently than traditional methods many scenarios. This reduces the time cost diagnosis for patients surgeons. To our knowledge, no exclusive study comprehensively reviewed all aspects currently used practice. review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, Web between 2017 2023. authors begin with motivation orthopaedics, enhance treatment planning. then covers various applications detection supraspinatus tears MRI, osteoarthritis, prediction types arthroplasty implants, age assessment, joint-specific soft tissue disease. We also examine challenges implementing scarcity data train lack interpretability, as well possible solutions these common pitfalls. Our work highlights requirements achieve trustworthiness outcomes generated by DL, need accuracy, explainability, fairness models. pay particular fusion techniques one ways increase trustworthiness, which been address multimodality orthopaedics. Finally, we approval set forth US Food Drug Administration enable use applications. As such, aim function guide researchers develop reliable application tasks scratch market.

Язык: Английский

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SSP: self-supervised pertaining technique for classification of shoulder implants in x-ray medical images: a broad experimental study

Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)

Опубликована: Авг. 18, 2024

Abstract Multiple pathologic conditions can lead to a diseased and symptomatic glenohumeral joint for which total shoulder arthroplasty (TSA) replacement may be indicated. The long-term survival of implants is limited. With the increasing incidence surgery, it anticipated that revision surgery will become more common. It challenging at times retrieve manufacturer in situ implant. Therefore, certain systems facilitated by AI techniques such as deep learning (DL) help correctly identify implanted prosthesis. Correct identification reduce perioperative complications complications. DL was used this study categorise different based on X-ray images into four classes (as first case small dataset): Cofield, Depuy, Tornier, Zimmer. Imbalanced public datasets poor performance model training. Most methods literature have adopted idea transfer (TL) from ImageNet models. This type TL has been proven ineffective due some concerns regarding contrast between features learnt natural (ImageNet: colour images) (greyscale images). To address that, new approach (self-supervised pertaining (SSP)) proposed resolve issue datasets. SSP training models (ImageNet models) large number unlabelled greyscale medical domain update features. are then trained labelled data set implants. shows excellent results five models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, EfficientNet with precision 96.69%, 95.45%, 98.76%, 98.35%, 96.6%, respectively. Furthermore, shown domains (such ImageNet) do not significantly affect images. A lightweight scratch achieves 96.6% accuracy, similar using standard extracted train several ML classifiers show outstanding obtaining an accuracy 99.20% Xception+SVM. Finally, extended experimentation carried out elucidate our approach’s real effectiveness dealing imaging scenarios. Specifically, tested without SSP, 99.47% CT brain stroke 98.60%.

Язык: Английский

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Predictive analysis-based sustainable waste management in smart cities using IoT edge computing and blockchain technology

Computers in Industry, Год журнала: 2025, Номер 166, С. 104234 - 104234

Опубликована: Янв. 5, 2025

Язык: Английский

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