Showing items 61 - 80 of 15500

Adult inpatients’ perceptions of their fall risk : a scoping review

- Dabkowski, Elissa, Cooper, Simon, Duncan, Jhodie, Missen, Karen

  • Authors: Dabkowski, Elissa , Cooper, Simon , Duncan, Jhodie , Missen, Karen
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: Healthcare (Switzerland) Vol. 10, no. 6 (2022), p.
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  • Description: Patient falls in hospitals continue to be a global concern due to the poor health outcomes and costs that can occur. A large number of falls in hospitals are unwitnessed and mostly occur due to patient behaviours and not seeking assistance. Understanding these patient behaviours may help to direct fall prevention strategies, with evidence suggesting the need to integrate patients’ perspectives into fall management. The aim of this scoping review was to explore the extent of the literature about patients’ perceptions and experiences of their fall risk in hospital and/or of falling in hospital. This review was conducted using a five-stage methodological framework recommended by Arksey and O’Malley. A total of nine databases were searched using key search terms such as “fall*”, “perception” and “hospital.” International peer-reviewed and grey literature were searched between the years 2011 and 2021. A total of 41 articles, ranging in study design, met the inclusion criteria. After reporting on the article demographics and fall perception constructs and measures, the qualitative and quantitative findings were organised into five domains: Fall Risk Perception Measures, Patients’ Perceptions of Fall Risk, Patients’ Perceptions of Falling in Hospital, Patients’ Fear of Falling and Barriers to Fall Prevention in Hospital. Approximately two-thirds of study participants did not accurately identify their fall risk compared to that defined by a health professional. This demonstrates the importance of partnering with patients and obtaining their insights on their perceived fall risk, as this may help to inform fall management and care. This review identified further areas for research that may help to inform fall prevention in a hospital setting, including the need for further research into fall risk perception measures. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications

- Rashid, Md Mamunur, Kamruzzaman, Joarder, Mehedi Hassan, Mohammad, Imam, Tasadduq, Wibowo, Santoso, Gordon, Steven, Fortino, Giancarlo

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Mehedi Hassan, Mohammad , Imam, Tasadduq , Wibowo, Santoso , Gordon, Steven , Fortino, Giancarlo
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Computers and Security Vol. 120, no. (2022), p.
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  • Description: Intrusion Detection Systems (IDS) based on deep learning models can identify and mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models, which support the IDS's decision, could be vulnerable to a cyberattack known as adversarial attack. In this type of attack, attackers create adversarial samples by introducing small perturbations to attack samples to trick a trained model into misclassifying them as benign applications. These attacks can cause substantial damage to IoT-based smart city models in terms of device malfunction, data leakage, operational outage and financial loss. To our knowledge, the impact of and defence against adversarial attacks on IDS models in relation to smart city applications have not been investigated yet. To address this research gap, in this work, we explore the effect of adversarial attacks on the deep learning and shallow machine learning models by using a recent IoT dataset and propose a method using adversarial retraining that can significantly improve IDS performance when confronting adversarial attacks. Simulation results demonstrate that the presence of adversarial samples deteriorates the detection accuracy significantly by above 70% while our proposed model can deliver detection accuracy above 99% against all types of attacks including adversarial attacks. This makes an IDS robust in protecting IoT-based smart city services. © 2022 Elsevier Ltd

Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan, Choudhury, Tanveer

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Robotica Vol. 40, no. 2 (2022), p. 329-347
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  • Description: This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests. © 2022 Cambridge University Press. All rights reserved.

Agoraphilic navigation algorithm under dynamic environment

- Hewawasam, Hasitha, Ibrahim, Yousef, Appuhamillage, Gayan, Choudhury, Tanveer

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Appuhamillage, Gayan , Choudhury, Tanveer
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE/ASME Transactions on Mechatronics Vol. 27, no. 3 (2022), p. 1727-1737
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  • Description: This article presents a summary of the work done on the development of a new algorithm for mobile robot navigation in unknown dynamic environment. The developed humanlike algorithm uses a free-space attraction (Agoraphilic) concept for robot navigation. The algorithm presented in this article is an advanced development of the Agoraphilic navigation algorithm. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for free spaces toward the goal (solutions) to follow. The original Agoraphilic while it was able to overcome the limitations of the traditional algorithms had its own limitations in navigating robots in environments cluttered with moving obstacles. The new Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE) was developed to overcome those limitations. ANADE consists of seven main modules reported in this article. The objects tracking and objects prediction methodologies developed for the algorithm estimate future growing free-space passages toward the goal. The algorithm generates a time-varying single attractive force to pull the robot through the free space toward the predicted (future) growing free-space passages leading to the goal. The new algorithm was tested, not only through simulation, but also through experimental work. Summary of the experimental results is presented and discussed in this article. © 1996-2012 IEEE.

Air pollution perception in ten countries during the COVID-19 pandemic

- Lou, Baowen, Barbieri, Diego, Passavanti, Marco, Hui, Cang, Gupta, Akshay, Hoff, Inge, Lessa, Daniela, Sikka, Gaurav, Chang, Kevin, Fang, Kevin, Lam, Louisa, Maharaj, Brij, Ghasemi, Navid, Qiao, Yaning, Adomako, Solomon, Foroutan Mirhosseini, Ali, Naik, Bhaven, Banerjee, Arunabha, Wang, Fusong, Tucker, Andrew, Liu, Zhuangzhuanga, Wijayaratna, Kasunt, Naseri, Sahra, Yu, Lei, Chen, Hao, Shu, Benan, Goswami, Shubham, Peprah, Prince, Hessami, Amir, Abbas, Montasir, Agarwal, Nithin

American imaginaries : nations, societies, and capitalism in the many Americas

- Smith, Jeremy

  • Authors: Smith, Jeremy
  • Date: 2022
  • Type: Text , Book
  • Relation: Social Imaginaries
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  • Description: American Imaginaries examines the diverse societies and nations of the Western hemisphere as they have emerged across the course of the nineteenth and twentieth centuries. Exploring cities, capitalism, nations, nationalism, and politics from both comparative and transnational perspectives, the book develops a unique approach based on the paradigms of civilizational analysis and social imaginaries. In addition to providing a fresh perspective on the Americas, American Imaginaries gives proper analysis of multinational and intra-national regions and, crucially, the civilizational force of resurgent indigenous nations. The book also covers regions often underemphasized in histories of the hemisphere, such as Central America and the Caribbean. The book will appeal to scholars and students of history, Atlantic studies, comparative and historical sociology, and social theory. In addition, it will gain audiences amongst academics and graduate students who follow debates about modernity, civilizations, historical constellations, and social imaginaries.

An adaptive fault ride-through scheme for grid-forming inverters under asymmetrical grid faults

- Li, Zilin, Chan, Ka, Hu, Jiefeng, Or, Siu

  • Authors: Li, Zilin , Chan, Ka , Hu, Jiefeng , Or, Siu
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Electronics Vol. 69, no. 12 (2022), p. 12912-12923
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  • Description: Three-phase four-wire grid-forming (GFM) inverters are promising to interface distributed energy resources into low-voltage networks. However, these inverters are prone to overcurrent under grid faults. Physically increasing the inverter current capacity is not cost-effective to cope with complicated fault conditions. In this article, an adaptive fault ride-through (FRT) scheme based on instantaneous saturators and virtual negative- and zero-sequence resistances is proposed. It features not only overcurrent limitation by modifying voltage references, but also seamless transition between normal and grid fault conditions. The proposed FRT scheme is first analyzed from different aspects, including the virtual sequence resistances, grid short-circuit ratio, fault types, and fault levels. The virtual sequence resistances are then designed to be adaptive to ensure high voltage quality at the healthy phase. The proposed FRT scheme is verified by MATLAB/Simulink simulations under asymmetrical faults. A laboratory platform with a grid-connected 3kW GFM inverter is further constructed to demonstrate its effectiveness (a video of the experimental results under three asymmetrical faults is attached). © 1982-2012 IEEE.

An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers

- Naseem, Usman, Rashid, Junaid, Ali, Liaqat, Kim, Jungeun, Haq, Qazi, Awan, Mazhar, Imran, Muhammad

  • Authors: Naseem, Usman , Rashid, Junaid , Ali, Liaqat , Kim, Jungeun , Haq, Qazi , Awan, Mazhar , Imran, Muhammad
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 10, no. (2022), p. 78242-78252
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  • Description: Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC's early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods. © 2013 IEEE.

An effective data-collection scheme with AUV path planning in underwater wireless sensor networks

- Khan, Wahab, Hua, Wang, Anwar, Muhammad, Alharbi, Abdullah, Imran, Muhammad, Khan, Javed

  • Authors: Khan, Wahab , Hua, Wang , Anwar, Muhammad , Alharbi, Abdullah , Imran, Muhammad , Khan, Javed
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Wireless Communications and Mobile Computing Vol. 2022, no. (2022), p.
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  • Description: Data collection in underwater wireless sensor networks (UWSNs) using autonomous underwater vehicles (AUVs) is a more robust solution than traditional approaches, instead of transmitting data from each node to a destination node. However, the design of delay-aware and energy-efficient path planning for AUVs is one of the most crucial problems in collecting data for UWSNs. To reduce network delay and increase network lifetime, we proposed a novel reliable AUV-based data-collection routing protocol for UWSNs. The proposed protocol employs a route planning mechanism to collect data using AUVs. The sink node directs AUVs for data collection from sensor nodes to reduce energy consumption. First, sensor nodes are organized into clusters for better scalability, and then, these clusters are arranged into groups to assign an AUV to each group. Second, the traveling path for each AUV is crafted based on the Markov decision process (MDP) for the reliable collection of data. The simulation results affirm the effectiveness and efficiency of the proposed technique in terms of throughput, energy efficiency, delay, and reliability. © 2022 Wahab Khan et al.

An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane

- Pham, Hung, Hoang, Quoc-Dong, Pham, Minh, Do, Dung, Phi, Nha-Hoang, Hoang, Duy, Le, Hai, Kim, Thai, Nguyen, Linh

  • Authors: Pham, Hung , Hoang, Quoc-Dong , Pham, Minh , Do, Dung , Phi, Nha-Hoang , Hoang, Duy , Le, Hai , Kim, Thai , Nguyen, Linh
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 11, no. 5 (2022), p.
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  • Description: The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

An efficient network intrusion detection and classification system

- Ahmad, Iftikhar, Haq, Qazi, Imran, Muhammad, Alassafi, Madini, Alghamdi, Rayed

  • Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 10, no. 3 (2022), p.
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  • Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

An electromyographic assessment pilot study on the reliability of the forearm muscles during multi-planar maximum voluntary contraction grip and wrist articulation in young males

- Hunter, Henry, Sorbie, Graeme, Grace, Fergal, Gu, Yaodong, Lam, Wing-Kai, Baker, Julien, Dutheil, Frederic, Dias, Tilak, Ugbolue, Ukadike

  • Authors: Hunter, Henry , Sorbie, Graeme , Grace, Fergal , Gu, Yaodong , Lam, Wing-Kai , Baker, Julien , Dutheil, Frederic , Dias, Tilak , Ugbolue, Ukadike
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Technology and Health Care Vol. 30, no. 3 (2022), p. 713-724
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  • Description: BACKGROUND: Electromyographic systems are widely used in scientific and clinical practice. The reproducibility and reliability of these measures are crucial when conducting scientific research and collecting experimental data. OBJECTIVE: To test the reliability of surface electromyography signals from both the Flexor Digitorum Superficialis (FDS) and Extensor Carpi Radialis Brevis (ECRB) muscles of both the left and right arms during an individual, static multi-planar maximum voluntary contraction handgrip task using the Myon 320 system (Myon AG, Switzerland). METHODS: Eight right-handed male participants performed two maximal handgrip tests in five separate wrist positions using both hands. Muscle activity was recorded from both forearms. Reliability was measured using the Standard Error of Measurement (SEM), Coefficient of Variation (CV) and Intra-class correlation coefficients. Wrist joint position correlations within and between the FDS and ECRB muscle activities were also analysed. RESULTS: Absolute reliability was shown across all positions for both hands with CV and SEM recorded at below 10%. The output measures indicate that the Myon 320 system (Myon AG, Switzerland) produces good to fair reliability when assessing forearm muscle activity. Correlations in the left FDS muscles were negative. Correlations between the left ECRB and left FDS muscles were variable but positive between the right ECRB and right FDS muscles. CONCLUSIONS: The data sets retrieved from all participants were reliably evaluated. Wrist position correlations within and between the FDS and ECRB muscles may have been influenced by hand dominance. The findings demonstrate that the methods and systems outlined in this study can be used reliably in future research. © 2022 - IOS Press. All rights reserved.

An ensemble of machine learning and clinician set thresholds for vital signs alarms

- Mai, Shenhan, Balasubramanian, Venki, Arora, Teena

An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms

- Linardon, Jake, Fuller-Tyszkiewicz, Matthew, Shatte, Adrian, Greenwood, Christopher

  • Authors: Linardon, Jake , Fuller-Tyszkiewicz, Matthew , Shatte, Adrian , Greenwood, Christopher
  • Date: 2022
  • Type: Text , Journal article
  • Relation: International Journal of Eating Disorders Vol. 55, no. 6 (2022), p. 845-850
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  • Description: Objective: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms. Method: Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM). Results: The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48–0.52), but adequate for symptom-level change (R2 =.15–.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75–0.93) and adherence (AUC = 0.92–0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors. Conclusion: A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made. © 2022 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC.

An extreme learning machine based adaptive VISMA for stability enhancement of renewable rich power systems

- Setiadi, Herlambang, Shah, , Rakibuzzaman, Islam, Md Rabiul, Asfani, Dimas, Nasution, Tigor, Abdillah, Muhammad, Megantoro, Prisma, Krismanto, Awan

  • Authors: Setiadi, Herlambang , Shah, , Rakibuzzaman , Islam, Md Rabiul , Asfani, Dimas , Nasution, Tigor , Abdillah, Muhammad , Megantoro, Prisma , Krismanto, Awan
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
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  • Description: Maintaining power system stability in renewable-rich power systems can be a challenging task. Generally, the renewable-rich power systems suffer from low and no inertia due to the integration of power electronics devices in renewable-based power plants. Power system oscillatory stability can also be affected due to the low and no inertia. To overcome this problem, additional devices that can emulate inertia without adding synchronous machines can be used. These devices are referred to as virtual synchronous machines (VISMA). In this paper, the enhancement of oscillatory stability of a realistic representative power system using VISMA is proposed. A battery energy storage system (BESS) is used as the VISMA by adding an additional controller to emulate the inertia. The VISMA is designed by using Fruit Fly Optimization. Moreover, to handle the uncertainty of renewable-based power plants, the VISMA parameters are designed to be adaptive using the extreme learning machine method. Java Indonesian Power Grid has been used as the test system to investigate the efficacy of the proposed method against the conventional POD method and VISMA tuning using other methods. The simulation results show that the proposed method can enhance the oscillatory stability of the power system under various operating conditions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

An intelligent and efficient network intrusion detection system using deep learning

- Qazi, Emad-ul-Haq, Imran, Muhammad, Haider, Noman, Shoaib, Muhammad, Razzak, Imran

  • Authors: Qazi, Emad-ul-Haq , Imran, Muhammad , Haider, Noman , Shoaib, Muhammad , Razzak, Imran
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Computers and Electrical Engineering Vol. 99, no. (2022), p.
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  • Description: With continuously escalating threats and attacks, accurate and timely intrusion detection in communication networks is challenging. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges due to the continuous increase of new threats that current systems do not understand. Motivated by the outstanding performance of deep learning (DL) in many detection and recognition tasks, we introduce an intelligent and efficient network intrusion detection system (NIDS) based on DL. This study proposes a non-symmetric deep auto-encoder for network intrusion detection problems and presents its detailed functionality and performance. We validate the robustness and effectiveness of the proposed NIDS using a benchmark dataset, i.e., KDD CUP'99. Our DL-based method is implemented in the TensorFlow library and GPU framework, and it achieves an accuracy of 99.65%. The proposed system can be used in network security research domains and DL-based detection and classification systems. © 2022

An interpretive study of stakeholders privacy issues in blockchain : a healthcare context

- Singh, Supreet, Firmin, Selena, Chadhar, Mehmood

  • Authors: Singh, Supreet , Firmin, Selena , Chadhar, Mehmood
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 235-238
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  • Description: Ever-growing and rapidly changing healthcare information systems (HIS) encourage many new technologies to integrate with it to enrich the treatment and facilitate patients. Blockchain, a decentralised digital ledger, is legitimately disrupting traditional HIS due to its characteristics such as immutability, interoperability, decentralise, and security. Blockchain' applications in healthcare industries are attracting investors and organisations to develop platforms for future. However, security and privacy concerns hinder blockchain adoption in the health sector. Therefore, there is a need to develop deeper understandings about these issues and require strategies to address these issues so that the desired values can be obtained. Besides, privacy could mean different to different people such as patients, doctors, and admin staff. Therefore, there is a need to explore it from various stakeholder perspectives too. Using interpretive qualitative research approach, this research-in-progress will extend the body of knowledge by scrutinising the stakeholders' perception of privacy concerns and its relationship in blockchain based HIS. The findings of this study will contribute to address privacy issues emerged from the research and help to eliminate them before implementing blockchain. This paper supplies an appropriate research approach for multidimensional research in healthcare. In addition, this research-in progress will formulate a framework which provide awareness to stakeholders about privacy issues when they use blockchain based HIS in future. © 2022 ACM.

An IoT-based smart healthcare system to detect dysphonia

- Ali, Zulfiqar, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Ali, Zulfiqar , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 34, no. 14 (2022), p. 11255-11265
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  • Description: Smart healthcare systems for the internet of things (IoT) platform are cost-efficient and facilitate continuous remote monitoring of patients to avoid unnecessary hospital visits and long waiting times to see practitioners. Presenting a smart healthcare system for the detection of dysphonia can reduce the suffering and pain of patients by providing an initial evaluation of voice. This preliminary feedback of voice could minimize the burden on ENT specialists by referring only genuine cases to them as well as giving an early alarm of potential voice complications to patients. Any possible delay in the treatment and/or inaccurate diagnosis using the subjective nature of tools may lead to severe circumstances for an individual because some types of dysphonia are life-threatening. Therefore, an accurate and reliable smart healthcare system for IoT platform to detect dysphonia is proposed and implemented in this study. Higher-order directional derivatives are used to analyze the time–frequency spectrum of signals in the proposed system. The computed derivatives provide essential and vital information by analyzing the spectrum along different directions to capture the changes that appeared due to malfunctioning the vocal folds. The proposed system provides 99.1% accuracy, while the sensitivity and specificity are 99.4 and 98.1%, respectively. The experimental results showed that the proposed system could provide better classification accuracy than the traditional non-directional first-order derivatives. Hence, the system can be used as a reliable tool for detecting dysphonia and implemented in edge devices to avoid latency issues and protect privacy, unlike cloud processing. © 2021, Springer-Verlag London Ltd., part of Springer Nature.

An online critical thinking course reduces misconceptions in the knowledge of personal trainers

- Jolley, Daniel, Davis, Melissa, Lavender, Andrew, Roberts, Lynne

  • Authors: Jolley, Daniel , Davis, Melissa , Lavender, Andrew , Roberts, Lynne
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Studies in Continuing Education Vol. 44, no. 1 (2022), p. 39-54
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  • Description: Personal trainers are a popular source of exercise guidance for the public but have been shown to have fundamental errors in their knowledge, and hold misconceptions about some exercise and nutritional concepts. Critical thinking skills have been found to relate inversely to misconceptions in other populations but this has not been examined in personal trainers. This study assessed the impact of an online, domain-specific, critical thinking course on the misconceptions and critical thinking ability (CTA) of personal trainers. One hundred and twenty-five participants were recruited into a randomised control trial. Participants completed measures of knowledge, misconceptions, CTA, and their use and perceived trustworthiness of sources of information about exercise and nutrition, then were allocated to either an intervention or control group. The intervention group completed the course over six weeks, while the control group waited six weeks before being surveyed again. Participants showed reduced endorsement of misconceptions and increased trust in reliable sources after completing the intervention. CTA improved in both the intervention and control groups. These results provide evidence that targeting critical thinking skills tailored to relevant content may contribute positively to the professional development of personal trainers. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

An online scalarization multi-objective reinforcement learning algorithm : TOPSIS Q-learning

- Mirzanejad, Mohammad, Ebrahimi, Morteza, Vamplew, Peter, Veisi, Hadi

  • Authors: Mirzanejad, Mohammad , Ebrahimi, Morteza , Vamplew, Peter , Veisi, Hadi
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Knowledge Engineering Review Vol. 37, no. 4 (2022), p.
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  • Description: Conventional reinforcement learning focuses on problems with single objective. However, many problems have multiple objectives or criteria that may be independent, related, or contradictory. In such cases, multi-objective reinforcement learning is used to propose a compromise among the solutions to balance the objectives. TOPSIS is a multi-criteria decision method that selects the alternative with minimum distance from the positive ideal solution and the maximum distance from the negative ideal solution, so it can be used effectively in the decision-making process to select the next action. In this research a single-policy algorithm called TOPSIS Q-Learning is provided with focus on its performance in online mode. Unlike all single-policy methods, in the first version of the algorithm, there is no need for the user to specify the weights of the objectives. The user's preferences may not be completely definite, so all weight preferences are combined together as decision criteria and a solution is generated by considering all these preferences at once and user can model the uncertainty and weight changes of objectives around their specified preferences of objectives. If the user only wants to apply the algorithm for a specific set of weights the second version of the algorithm efficiently accomplishes that. ©