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A Blockchain-Based Multi-Factor Authentication Model for a Cloud-Enabled Internet of Vehicles

Sensors (Basel, Switzerland), 2021, Vol.21 (18), p.6018

2021

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FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning

Applied sciences, 2022-03, Vol.12 (6), p.3130

2022

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A cognitive deep learning approach for medical image processing

Scientific reports, 2024-02, Vol.14 (1), p.4539-4539, Article 4539

2024

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ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems

Sensors (Basel, Switzerland), 2022, Vol.22 (18), p.6842

2022

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Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

Forensic Science International. Reports, 2020-12, Vol.2, p.100122, Article 100122

2020

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FedCSD: A Federated Learning Based Approach for Code-Smell Detection

IEEE access, 2024-01, Vol.12, p.1-1

2024

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Active Machine Learning Adversarial Attack Detection in the User Feedback Process

IEEE access, 2021-01, Vol.9, p.1-1

2021

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Digital forensic readiness in operational cloud leveraging ISO/IEC 27043 guidelines on security monitoring

Security and privacy, 2021-05, Vol.4 (3), p.n/a

2021

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Prioritization of god class design smell: A multi-criteria based approach

Journal of King Saud University. Computer and information sciences, 2022-11, Vol.34 (10), p.9332-9342

2022

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Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection

IEEE access, 2021, Vol.9, p.145191-145211

2021

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Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study

Information and software technology, 2023-03, Vol.155, p.107120, Article 107120

2023

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Novel hybrid success history intelligent optimizer with Gaussian transformation: application in CNN hyperparameter tuning

Cluster computing, 2024-06, Vol.27 (3), p.3717-3739

2024

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AI and the decision-making process: a literature review in healthcare, financial, and technology sectors

Journal of decision systems, 2024, p.1-11

2024

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A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

Energy systems (Berlin. Periodical), 2022-08, Vol.13 (3), p.689-705

2022

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TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud

IEEE transactions on industrial informatics, 2020-09, Vol.16 (9), p.6203-6213

2020

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Security by Design for Big Data Frameworks Over Cloud Computing

IEEE transactions on engineering management, 2022-12, Vol.69 (6), p.3676-3693

2022

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Toward efficient resource utilization at edge nodes in federated learning

Progress in artificial intelligence, 2024, Vol.13 (2), p.101-117

2024

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Four vector intelligent metaheuristic for data optimization

Computing, 2024-07, Vol.106 (7), p.2321-2359

2024

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A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Electronics (Basel), 2023-11, Vol.12 (22), p.4604

2023

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Polynomial Kernel Discriminant Analysis for 2D visualization of classification problems

Neural computing & applications, 2019-08, Vol.31 (8), p.3515-3531

2019

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A Federated Interactive Learning IoT-Based Health Monitoring Platform

New Trends in Database and Information Systems, 2021, p.235-246

2021

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Digital forensic readiness intelligence crime repository

Security and privacy, 2021-05, Vol.4 (3), p.n/a

2021

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Quantifying the need for supervised machine learning in conducting liveforensic analysis of emergent configurations (ECO) in IoT environments

Forensic Science International. Reports, 2020, Vol.2

2020

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Erratum to “Quantifying the need for supervised machine learning in conducting live forensics analysis of emergent configurations (ECO) in IoT environments” [Forensic Sci. Int.: Rep. 2 (2020) 100122]

Forensic Science International. Reports, 2020-12, Vol.2, p.100135, Article 100135

2020

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Comparative Study of Artificial Neural Network Models for Forecasting the Indoor Temperature in Smart Buildings

Smart Cities, 2017, Vol.10268, p.29-38

2017

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A Blockchain-Based Multi-Factor Authentication Model for a Cloud-Enabled Internet of Vehicles

FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning

A cognitive deep learning approach for medical image processing

ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems

Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

FedCSD: A Federated Learning Based Approach for Code-Smell Detection

Active Machine Learning Adversarial Attack Detection in the User Feedback Process

Digital forensic readiness in operational cloud leveraging ISO/IEC 27043 guidelines on security monitoring

Prioritization of god class design smell: A multi-criteria based approach

Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection

Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study

Novel hybrid success history intelligent optimizer with Gaussian transformation: application in CNN hyperparameter tuning

AI and the decision-making process: a literature review in healthcare, financial, and technology sectors

A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud

Security by Design for Big Data Frameworks Over Cloud Computing

Toward efficient resource utilization at edge nodes in federated learning

Four vector intelligent metaheuristic for data optimization

A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Polynomial Kernel Discriminant Analysis for 2D visualization of classification problems

A Federated Interactive Learning IoT-Based Health Monitoring Platform

Digital forensic readiness intelligence crime repository

Quantifying the need for supervised machine learning in conducting liveforensic analysis of emergent configurations (ECO) in IoT environments

Erratum to “Quantifying the need for supervised machine learning in conducting live forensics analysis of emergent configurations (ECO) in IoT environments” [Forensic Sci. Int.: Rep. 2 (2020) 100122]

Comparative Study of Artificial Neural Network Models for Forecasting the Indoor Temperature in Smart Buildings

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