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Ameksa M, Elamrani Abou Elassad Z, Lamjadli S, Mousannif H. Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics. Comput Methods Biomech Biomed Engin 2024:1-18. [PMID: 38902976 DOI: 10.1080/10255842.2024.2363946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/28/2024] [Indexed: 06/22/2024]
Abstract
Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.
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Affiliation(s)
- Mohammed Ameksa
- LISI Laboratory, Computer Science Department, FSSM, Cadi Ayyad University, Marrakesh, Morocco
| | | | - Saad Lamjadli
- Immunology Laboratory, Arrazi Hospital, CHU Mohamed VI, Marrakech, Morocco
| | - Hajar Mousannif
- LISI Laboratory, Computer Science Department, FSSM, Cadi Ayyad University, Marrakesh, Morocco
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2
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Rahman MA, Brown DJ, Mahmud M, Harris M, Shopland N, Heym N, Sumich A, Turabee ZB, Standen B, Downes D, Xing Y, Thomas C, Haddick S, Premkumar P, Nastase S, Burton A, Lewis J. Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning. Brain Inform 2023; 10:14. [PMID: 37341863 DOI: 10.1186/s40708-023-00193-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 05/15/2023] [Indexed: 06/22/2023] Open
Abstract
Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.
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Affiliation(s)
- Muhammad Arifur Rahman
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - David J Brown
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
- Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
- Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
| | - Matthew Harris
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Nicholas Shopland
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Nadja Heym
- School of Social Sciences, Nottingham Trent University, Shakespeare St, Nottingham, NG1 4FQ, UK
| | - Alexander Sumich
- School of Social Sciences, Nottingham Trent University, Shakespeare St, Nottingham, NG1 4FQ, UK
| | - Zakia Batool Turabee
- School of Social Sciences, Nottingham Trent University, Shakespeare St, Nottingham, NG1 4FQ, UK
| | - Bradley Standen
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - David Downes
- Nottingham School of Art & Design, Nottingham Trent University, Shakespeare St, Nottingham, NG1 4FQ, UK
| | - Yangang Xing
- School of ADBE, Nottingham Trent University, Shakespeare St, Nottingham, NG1 4FQ, UK
| | - Carolyn Thomas
- Nottingham School of Art & Design, Nottingham Trent University, Shakespeare St, Nottingham, NG1 4FQ, UK
| | - Sean Haddick
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Preethi Premkumar
- Division of Psychology, London South Bank University, London, SE1 0AA, UK
| | | | - Andrew Burton
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - James Lewis
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
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3
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Rana P, Patil B. Cyber security threats in IoT: A review. JOURNAL OF HIGH SPEED NETWORKS 2023. [DOI: 10.3233/jhs-222042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
The Internet of Things (IoT) is the most secure platform for making human existence easier and more comfortable. IoT has made a big contribution to a variety of software programs. The rapid proliferation of smart devices, as well as their trust in data transfer and the use of Wi-Fi mechanics, has increased their vulnerability to cyber-attacks. As a result, the cost of cybercrime is rising every day. As a result, investigating IoT security threats and possible countermeasures can assist researchers in creating acceptable ways to deal with a variety of stressful scenarios in cybercrime research. The IoT framework, as well as IoT architecture, protocols, and technology, are all covered in this assessment research. Various protection issues at each tier, as well as correction strategies, are also detailed. In addition, this article discusses the use of IoT forensics in cybercrime investigations in a variety of areas, including cybercrime research, Artificial intelligence, system learning, cloud computing, fog computing, and blockchain technology all play a role in this discussion. Finally, some open research on challenging situations in IoT is detailed to enhance cybercrime investigations, providing a cutting-edge course for future research.
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Othman RA, Darwish SM, Abd El-Moghith IA. A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks. MATHEMATICS 2023; 11:1128. [DOI: 10.3390/math11051128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.
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Affiliation(s)
| | - Saad M. Darwish
- Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria 21544, Egypt
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Bahbouh NM, Compte SS, Valdes JV, Sen AAA. An empirical investigation into the altering health perspectives in the internet of health things. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2023; 15:67-77. [PMID: 35874858 PMCID: PMC9294750 DOI: 10.1007/s41870-022-01035-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 06/28/2022] [Indexed: 02/06/2023]
Abstract
Healthcare is on top of the agenda of all governments in the world as it is related to the well-being of the people. Naturally, this domain has attracted the attention of many researchers globally, who have studied the development of its different phases, including E-Health and the Internet of Health Things (IoHT). In this paper, the difference between the recent concepts of healthcare (E-health, M-Health, S-Health, I-Health, U-Health, and IoHT/IoMT) is analyzed based on the main services, applications, and technologies in each concept. The paper has also studied the latest developments in IoHT, which are linked to existing phases of development. A classification of groups of services and constituents of IoHT, linked to the latest technologies, is also provided. In addition, challenges, and future scope of research in this domain concerning the wellbeing of the people in the face of ongoing COVID-19 and future pandemics are explored.
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Affiliation(s)
- Nour Mahmoud Bahbouh
- Department of Information and Communication Sciences, Granada University, Granada, Spain
| | | | - Juan Valenzuela Valdes
- Department of Signal Theory, Telematics and Communications, Granada University, Granada, Spain
| | - Adnan Ahmed Abi Sen
- Faculty of Computer and Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
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Ebrahimi M, Tadayon MH, Haghighi MS, Jolfaei A. A Quantitative Comparative Study of Data-oriented Trust Management Schemes in Internet of Things. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3476248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
In the Internet of Things (IoT) paradigm, all entities in the IoT network, whether home users or industrial things, receive data from other things to make decisions. However, in the decentralized, heterogeneous, and rapidly changing IoT network with billions of devices, deciding about where to get the services or information from is critical, especially because malicious entities can exist in such an unmanaged network. Security provisioning alone cannot solve the issue of service quality or reliability. One way to elevate security and reliability in the IoT network is to bridge the gap of trust between objects, and also between humans and objects, while taking into account the IoT network characteristics. Therefore, a proper trust management system must be established on top of the IoT network service architecture. Trust is related to the manner expected from objects in providing services and recommendations. Recommendations are the basis of decision making in every trust management system. Since trust management ideas in the IoT are still immature, the purpose of this article is to survey, analyze, and compare the approaches that have been taken in building trust management systems for the IoT. We break down the features of such systems by analysis and also do quantitative comparisons by simulation. This article is organized into two main parts. First, studies and approaches in this field are compared from four perspectives: (1) trust computation method, (2) resistance to attacks (3) adherence to the limitations of IoT networks and devices, and (4) performance of the trust management scheme. The second part is quantitative and simulates four major methods in this field and measures their performance. We also make extensive analytical comparisons to demonstrate the similarities and discrepancies of current IoT trust management schemes and extract the essence of a resilient trust management framework.
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Affiliation(s)
| | | | | | - Alireza Jolfaei
- Department of Computing, Macquarie University, NSW, Australia
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7
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Patnaik A, Mallik B, Krishna MV. Blockchain based holistic trust management protocol for ubiquitous and pervasive IoT network. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2021.1960641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Anup Patnaik
- Research Scholar, Department of Computer Science and Engineering, Centurion University of Technology and Management, Bhubaneswar, Odisha, India
| | - Banitamani Mallik
- School of Applied Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, India
| | - M. Vamsi Krishna
- Department of Computer Science and Engineering, Chaitanya College of Science and Technology, Kakinada, India
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8
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Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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9
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Biswas M, Tania MH, Kaiser MS, Kabir R, Mahmud M, Kemal AA. ACCU3RATE: A mobile health application rating scale based on user reviews. PLoS One 2021; 16:e0258050. [PMID: 34914718 PMCID: PMC8675707 DOI: 10.1371/journal.pone.0258050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/13/2021] [Indexed: 11/23/2022] Open
Abstract
Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.
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Affiliation(s)
- Milon Biswas
- Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur, Dhaka, Bangladesh
| | - Marzia Hoque Tania
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - M. Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
- * E-mail:
| | - Russell Kabir
- School of Allied Health, Faculty of Health, Education, Medicine and Social Care, Chelmsford, United Kingdom
| | - Mufti Mahmud
- Department of Computer Science, Nottingham TrentUniversity, Nottingham, United Kingdom
| | - Atika Ahmad Kemal
- Management and Marketing at Essex Business School (EBS), University of Essex, Colchester, United Kingdom
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10
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Linear Regression Trust Management System for IoT Systems. CYBERNETICS AND INFORMATION TECHNOLOGIES 2021. [DOI: 10.2478/cait-2021-0040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
This paper aims at creating a new Trust Management System (TMS) for a system of nodes. Various systems already exist which only use a simple function to calculate the trust value of a node. In the age of artificial intelligence the need for learning ability in an Internet of Things (IoT) system arises. Malicious nodes are a recurring issue and there still has not been a fully effective way to detect them beforehand. In IoT systems, a malicious node is detected after a transaction has occurred with the node. To this end, this paper explores how Artificial Intelligence (AI), and specifically Linear Regression (LR), could be utilised to predict a malicious node in order to minimise the damage in the IoT ecosystem. Moreover, the paper compares Linear regression over other AI-based TMS, showing the efficiency and efficacy of the method to predict and identify a malicious node.
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Torre-Bastida AI, Díaz-de-Arcaya J, Osaba E, Muhammad K, Camacho D, Del Ser J. Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions. Neural Comput Appl 2021:1-31. [PMID: 34366573 PMCID: PMC8329000 DOI: 10.1007/s00521-021-06332-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.
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Affiliation(s)
| | - Josu Díaz-de-Arcaya
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Eneko Osaba
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul, 143-747 Republic of Korea
| | - David Camacho
- Universidad Politécnica de Madrid, 28031 Madrid, Spain
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
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12
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Industry 4.0 Applications for Medical/Healthcare Services. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2021. [DOI: 10.3390/jsan10030043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.
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Singh AK, Kumar A, Mahmud M, Kaiser MS, Kishore A. COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier. Cognit Comput 2021:1-13. [PMID: 33688379 PMCID: PMC7931982 DOI: 10.1007/s12559-021-09848-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 02/04/2021] [Indexed: 12/24/2022]
Abstract
A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.
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Affiliation(s)
- Asu Kumar Singh
- CSE Department, Maharaja Agrasen Institute of Technology, Delhi, India
| | - Anupam Kumar
- CSE Department, Maharaja Agrasen Institute of Technology, Delhi, India
| | - Mufti Mahmud
- Department of Computer Science and Medical Technology Innovation Facility, Nottingham Trent University, Clifton, NG11 8NS Nottingham, UK
| | - M Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Savar, 1342 Dhaka, Bangladesh
| | - Akshat Kishore
- CSE Department, Maharaja Agrasen Institute of Technology, Delhi, India
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Mahmud M, Kaiser MS, McGinnity TM, Hussain A. Deep Learning in Mining Biological Data. Cognit Comput 2021; 13:1-33. [PMID: 33425045 PMCID: PMC7783296 DOI: 10.1007/s12559-020-09773-x] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/28/2020] [Indexed: 02/06/2023]
Abstract
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures-known as deep learning (DL)-have been successfully applied to solve many complex pattern recognition problems. To investigate how DL-especially its different architectures-has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.
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Affiliation(s)
- Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton, NG11 8NS Nottingham, UK
- Medical Technology Innovation Facility, Nottingham Trent University, NG11 8NS Clifton, Nottingham, UK
| | - M. Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Savar 1342 Dhaka, Bangladesh
| | - T. Martin McGinnity
- Department of Computer Science, Nottingham Trent University, Clifton, NG11 8NS Nottingham, UK
- Intelligent Systems Research Centre, Ulster University, Northern Ireland BT48 7JL Derry, UK
| | - Amir Hussain
- School of Computing , Edinburgh, EH11 4BN Edinburgh, UK
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An Artificial Intelligence Based Approach Towards Inclusive Healthcare Provisioning in Society 5.0: A Perspective on Brain Disorder. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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17
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Rakib AB, Rumky EA, Ashraf AJ, Hillas MM, Rahman MA. Mental Healthcare Chatbot Using Sequence-to-Sequence Learning and BiLSTM. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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19
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Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding. SENSORS 2020; 20:s20174926. [PMID: 32878171 PMCID: PMC7506946 DOI: 10.3390/s20174926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/23/2020] [Accepted: 08/26/2020] [Indexed: 11/17/2022]
Abstract
With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enterprise anti-counterfeiting certification. Based on information hiding technology, the proposed approach can solve these challenging problems and provide a low-cost, difficult to forge, and easy to identify solution, while achieving the function of conventional 2D barcodes. By weighting between the perceptual quality and decoding robustness in sensing recognition, the customized 2D barcode can maintain a better aesthetic appearance for anti-counterfeiting and achieve fast encoding. A new picture-embedding scheme was designed to consider 2D barcode, within a unit image block as a basic encoding unit, where the 2D barcode finder patterns were embedded after encoding. Experimental results demonstrated that the proposed customized barcode could provide better encoding characteristics, while maintaining better decoding robustness than several state-of-the-art methods. Additionally, as a closed source 2D barcode that could be visually anti-counterfeit, the customized 2D barcode could effectively prevent counterfeiting that replicate physical labels. Benefitting from the high-security, high information capacity, and low-cost, the proposed customized 2D barcode with sensing recognition scheme provide a highly practical, valuable in terms of marketing, and anti-counterfeiting traceable solution for future smart IoT applications.
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Kaur J, Khan AI, Abushark YB, Alam MM, Khan SA, Agrawal A, Kumar R, Khan RA. Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective. Risk Manag Healthc Policy 2020; 13:355-371. [PMID: 32425625 PMCID: PMC7196436 DOI: 10.2147/rmhp.s233706] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 03/07/2020] [Indexed: 11/23/2022] Open
Abstract
Introduction The imperative need for ensuring optimal security of healthcare web applications cannot be overstated. Security practitioners are consistently working at improvising on techniques to maximise security along with the longevity of healthcare web applications. In this league, it has been observed that assessment of security risks through soft computing techniques during the development of web application can enhance the security of healthcare web applications to a great extent. Methods This study proposes the identification of security risks and their assessment during the development of the web application through adaptive neuro-fuzzy inference system (ANFIS). In this article, firstly, the security risk factors involved during healthcare web application development have been identified. Thereafter, these security risks have been evaluated by using the ANFIS technique. This research also proposes a fuzzy regression model. Results The results have been compared with those of ANFIS, and the ANFIS model is found to be more acceptable for the estimation of security risks during the healthcare web application development. Conclusion The proposed approach can be applied by the healthcare web application developers and experts to avoid the security risk factors during healthcare web application development for enhancing the healthcare data security.
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Affiliation(s)
- Jasleen Kaur
- Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, India
| | - Asif Irshad Khan
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Yoosef B Abushark
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Md Mottahir Alam
- Department of Electrical & Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Suhel Ahmad Khan
- Department of Computer Science, Indira Gandhi National TribalUniversity, Amarkantak, MP, India
| | - Alka Agrawal
- Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, India
| | - Rajeev Kumar
- Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, India
| | - Raees Ahmad Khan
- Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, India
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A Survey on Secure Transmission in Internet of Things: Taxonomy, Recent Techniques, Research Requirements, and Challenges. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020. [DOI: 10.1007/s13369-020-04461-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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A Monitoring System for Patients of Autism Spectrum Disorder Using Artificial Intelligence. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Al Nahian MJ, Ghosh T, Uddin MN, Islam MM, Mahmud M, Kaiser MS. Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_25] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Ruiz J, Mahmud M, Modasshir M, Shamim Kaiser M, Alzheimer’s Disease Neuroimaging In FT. 3D DenseNet Ensemble in 4-Way Classification of Alzheimer’s Disease. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_8] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Machine Learning Based Early Fall Detection for Elderly People with Neurological Disorder Using Multimodal Data Fusion. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Artificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Yahaya SW, Lotfi A, Mahmud M. A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105613] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things. ELECTRONICS 2019. [DOI: 10.3390/electronics8070744] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Information security is important for the Internet of Things (IoT), the security of front-end information is especially critical. With this consideration, the integrity and authenticity of sensed information directly impacts the results of back-end big data and cloud computing. The front end of the IoT faces many security threats. In these security threats, internal attacks cannot be defended by traditional security schemes, such as encryption/decryption, authentication, and so on. Our contribution in this paper is that a DirichletDistribution-based Trust Management Scheme (DDTMS) in IoT is proposed to defend against the internal attacks. The novelty of our scheme can be summed up in two aspects. The first aspect considers the actual physical channel to extend the node behaviors from success and failure to success, failure, and uncertainty, meanwhile, the corresponding behaviors are weighted by using <ws, wf, wu>, in order to limit the measurement of each behavior by custom. In the second aspect, we introduce a third-party recommendation to calculate the trust value more acurrately. The simulated results demonstrate that DDTMS is better than the other two reputation models (Beta distribution and Gaussian distribution),and can more accurately describe the reputation changes to detect the malicious node quickly.
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González A, Pérez R, Romero-Zaliz R. An Incremental Approach to Address Big Data Classification Problems Using Cognitive Models. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09655-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Trust-based recommendation systems in Internet of Things: a systematic literature review. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2019. [DOI: 10.1186/s13673-019-0183-8] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
Internet of Things (IoT) creates a world where smart objects and services interacting autonomously. Taking into account the dynamic-heterogeneous characteristic of interconnected devices in IoT, demand for a trust model to guarantee security, authentication, authorization, and confidentiality of connected things, regardless of their functionality, is imperative. However, as far as we know, against the centrality of trust-based recommendation mechanisms in the IoT environment, there is no ambient study for investigating its techniques. In this paper, we present a systematic literature review (SLR) of trust based IoT recommendation techniques so far. Detailed classifications based on extracted parameters as well as investigation existing techniques in three different IoT layers put forth. Moreover, the advantages, disadvantages and open issues of each approach are introduced that can expand more frontier in obtaining accurate IoT recommendation in the future.
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Fernandez A, Triguero I, Galar M, Herrera F. Guest Editorial: Computational Intelligence for Big Data Analytics. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09647-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Awan KA, Din IU, Zareei M, Talha M, Guizani M, Jadoon SU. HoliTrust-A Holistic Cross-Domain Trust Management Mechanism for Service-Centric Internet of Things. IEEE ACCESS 2019; 7:52191-52201. [DOI: 10.1109/access.2019.2912469] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Mahmud M, Vassanelli S. Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals. ADVANCES IN NEUROBIOLOGY 2019; 22:233-250. [PMID: 31073939 DOI: 10.1007/978-3-030-11135-9_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The recent years have seen unprecedented growth in the manufacturing of neurotechnological tools. The latest technological advancements presented the neuroscientific community with neuronal probes containing thousands of recording sites. These next-generation probes are capable of simultaneously recording neuronal signals from a large number of channels. Numerically, a simple 128-channel neuronal data acquisition system equipped with a 16 bits A/D converter digitizing the acquired analog waveforms at a sampling frequency of 20 kHz will generate approximately 17 GB uncompressed data per hour. Today's biggest challenge is to mine this staggering amount of data and find useful information which can later be used in decoding brain functions, diagnosing diseases, and devising treatments. To this goal, many automated processing and analysis tools have been developed and reported in the literature. A good amount of them are also available as open source for others to adapt them to individual needs. Focusing on extracellularly recorded neuronal signals in vitro, this chapter provides an overview of the popular open-source tools applicable on these signals for spike trains and local field potentials analysis, and spike sorting. Towards the end, several future research directions have also been outlined.
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Affiliation(s)
- Mufti Mahmud
- Computing and Technology, School of Science and Technology, Nottingham Trent University, Nottingham, UK.
| | - Stefano Vassanelli
- NeuroChip Lab, Department of Biomedical Sciences, University of Padova, Padova, Italy
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