Context-dependent security enforcement of statistical databases
- Authors: Ryan, Joe , Mishra, Vivek , Stranieri, Andrew , Miller, Mirka
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the 4th WSEAS International Conference on Information Security, Communications and Computers, Tenerife, Spain, 16-18 December 2005, Tenerife, Spain : 16th December, 2005
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- Description: E1
- Description: 2003001390
Continuous patient monitoring with a patient centric agent : A block architecture
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
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- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
Cost-analysis of teledentistry in residential aged care facilities
- Authors: Mariño, Rodrigo , Tonmukayakul, Utsana , Manton, David , Stranieri, Andrew , Clarke, Ken
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Telemedicine and Telecare Vol. 22, no. 6 (2016), p.326-332
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- Description: Introduction: The purpose of this research was to conduct a cost-analysis, from a public healthcare perspective, comparing the cost and benefits of face-to-face patient examination assessments conducted by a dentist at a residential aged care facility (RACF) situated in rural areas of the Australian state of Victoria, with two teledentistry approaches utilizing virtual oral examination. Methods: The costs associated with implementing and operating the teledentistry approach were identified and measured using 2014 prices in Australian dollars. Costs were measured as direct intervention costs and programme costs. A population of 100 RACF residents was used as a basis to estimate the cost of oral examination and treatment plan development for the traditional face-to-face model vs. two teledentistry models: an asynchronous review and treatment plan preparation; and realtime communication with a remotely located oral health professional. Results: It was estimated that if 100 residents received an asynchronous oral health assessment and treatment plan, the net cost from a healthcare perspective would be AU$32.35 (AU$27.19–AU$38.49) per resident. The total cost of the conventional face-to-face examinations by a dentist would be AU$36.59 ($30.67–AU$42.98) per resident using realistic assumptions. Meanwhile, the total cost of real-time remote oral examination would be AU$41.28 (AU$34.30–AU$48.87) per resident. Discussion: Teledental asynchronous patient assessments were the lowest cost service model. Access to oral health professionals is generally low in RACFs; however, the real-time consultation could potentially achieve better outcomes due to twoway communication between the nurse and a remote oral health professional via health promotion/disease prevention delivered in conjunction with the oral examination
Criteria to measure social media value in health care settings : narrative literature review
- Authors: Ukoha, Chukwuma , Stranieri, Andrew
- Date: 2019
- Type: Text , Journal article , Review
- Relation: Journal of Medical Internet Research Vol. 21, no. 12 (2019), p.
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- Description: Background: With the growing use of social media in health care settings, there is a need to measure outcomes resulting from its use to ensure continuous performance improvement. Despite the need for measurement, a unified approach for measuring the value of social media used in health care remains elusive. Objective: This study aimed to elucidate how the value of social media in health care settings can be ascertained and to taxonomically identify steps and techniques in social media measurement from a review of relevant literature. Methods: A total of 65 relevant articles drawn from 341 articles on the subject of measuring social media in health care settings were qualitatively analyzed and synthesized. The articles were selected from the literature from diverse disciplines including business, information systems, medical informatics, and medicine. Results: The review of the literature showed different levels and focus of analysis when measuring the value of social media in health care settings. It equally showed that there are various metrics for measurement, levels of measurement, approaches to measurement, and scales of measurement. Each may be relevant, depending on the use case of social media in health care. Conclusions: A comprehensive yardstick is required to simplify the measurement of outcomes resulting from the use of social media in health care. At the moment, there is neither a consensus on what indicators to measure nor on how to measure them. We hope that this review is used as a starting point to create a comprehensive measurement criterion for social media used in health care. © 2019 Chukwuma Ukoha, Andrew Stranieri.
CWDM: A case-based diabetes management web system
- Authors: Nguyen, Linh Hoang , Sun, Zhaohao , Stranieri, Andrew , Firmin, Sally
- Date: 2013
- Type: Text , Conference paper
- Relation: 24th Australasian Conference on Information Systems, 4-6th December, 2013 p. 1-10
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- Description: Treatment refers to the therapy to treat a disease or a health issue. Treatment in this situation is similar to medical treatment which mainly uses medicines in an attempt to relieve the pain or even stop the disease. However, medicines themselves could not entirely cure the disease (in this case, diabetes), the patients will need more intervention which will be introduced in the next section. In most of documents for diabetic treatment, insulin therapy may be the main factor, however it would seem that diabetic patient needs more than just insulin. Therefore, TCM – traditional Chinese medicine – is recommended in the diabetic treatment as a lot of its remedies not only adjust insulin but also maintain good health for the patients. This section presents some of the TCM remedies to treat diabetes. As mentioned, diabetic patients are treated by lifestyle intervention and insulin therapy according to their diabetic status. The prevalence of diabetes and its complications leads to the requirement of treatment and care plan. Guidelines for T2D treatment indicated the following primary areas: lifestyle improvement which involves at least two and half hours of physical operations every week, dietary plan which decreases the fat intake, and weight management which requires weight loss approximately 7% of the baseline weight; cardiovascular risk factor reduction by managing blood pressure, cholesterol level, control smoking status, hypertension; and blood glucose management such as mono-therapy methods using oral medications to reduce A1c levels (Ripsin, Kang, & Urban, 2009). Self-monitoring of blood glucose levels for T2D treatment is also suggested. The self-monitoring of blood glucose method is recommended because it could enhance the patients’ self-consciousness of managing their diabetic status and require greater behaviours, responsibilities and efforts. Besides, this method is cost-effective in long term for diabetic complications treatment (Szymborska-Kajaneka, Psureka, Heseb, & Strojek, 2009). Another related study recommended that for T2D patients who are using insulin, self-monitoring of blood glucose should be carried out daily at least three times; and for patients without insulin usage the frequency of blood glucose self-monitoring should be adjusted individually (Varanauskiene, 2008). Both studies indicate that there have been controversies whether self-monitoring of blood glucose is useful for T2D patients without insulin treatment. We recommend traditional Chinese medicine (TCM) as the major medicine for treating diabetes according to a report of natural Chinese medicines (Li, Zheng, Bukuru, & Kimpe, 2004) which indicates the results from many cases in various research and medical activities.
Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis
- Authors: Jelinek, Herbert , Stranieri, Andrew , Yatsko, Andrew , Venkatraman, Sitalakshmi
- Date: 2016
- Type: Text , Journal article
- Relation: Computers in Biology and Medicine Vol. 75, no. (2016), p. 90-97
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- Description: Glycated haemoglobin (HbA1c) is being more commonly used as an alternative test for the identification of type 2 diabetes mellitus (T2DM) or to add to fasting blood glucose level and oral glucose tolerance test results, because it is easily obtained using point-of-care technology and represents long-term blood sugar levels. HbA1c cut-off values of 6.5% or above have been recommended for clinical use based on the presence of diabetic comorbidities from population studies. However, outcomes of large trials with a HbA1c of 6.5% as a cut-off have been inconsistent for a diagnosis of T2DM. This suggests that a HbA1c cut-off of 6.5% as a single marker may not be sensitive enough or be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied on a large clinical dataset to identify an optimal cut-off value for HbA1c and to identify whether additional biomarkers can be used together with HbA1c to enhance diagnostic accuracy of T2DM. T2DM classification accuracy increased if 8-hydroxy-2-deoxyguanosine (8-OhdG), an oxidative stress marker, was included in the algorithm from 78.71% for HbA1c at 6.5% to 86.64%. A similar result was obtained when interleukin-6 (IL-6) was included (accuracy=85.63%) but with a lower optimal HbA1c range between 5.73 and 6.22%. The application of data analytics to medical records from the Diabetes Screening programme demonstrates that data analytics, combined with large clinical datasets can be used to identify clinically appropriate cut-off values and identify novel biomarkers that when included improve the accuracy of T2DM diagnosis even when HbA1c levels are below or equal to the current cut-off of 6.5%. © 2016 Elsevier Ltd.
Data analytics to select markers and cut-off values for clinical scoring
- Authors: Stranieri, Andrew , Yatsko, Andrew , Venkatraman, Sitalakshmi , Jelinek, Herbert
- Date: 2018
- Type: Text , Conference proceedings
- Relation: ACSW '18: Proceedings of the Australasian Computer Science Week Multiconference; Brisbane; 29th January -2nd February 2018 p. 1-6
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- Description: Scoring systems such as the Glasgow-Coma scale used to assess consciousness AusDrisk to assess the risk of diabetes, are prevalent in clinical practice. Scoring systems typically include relevant variables with ordinal values where each value is assigned a weight. Weights for selected values are summed and compared to thresholds for health care professionals to rapidly generate a score. Scoring systems are prevalent in clinical practice because they are easy and quick to use. However, most scoring systems comprise many variables and require some time to calculate an final score. Further, expensive population-wide studies are required to validate a scoring system. In this article, we present a new approach for the generation of a scoring system. The approach uses a search procedure invoking iterative decision tree induction to identify a suite of scoring rules, each of which requires values on only two variables. Twelve scoring rules were discovered using the approach, from an Australian screening program for the assessment of Type 2 Diabetes risk. However, classifications from the 12 rules can conflict. In this paper we argue that a simple rule preference relation is sufficient for the resolution of rule conflicts.
Data Mining and Analytics 2011: Proceedings of the Ninth Australasian Data Mining Conference
- Authors: Vamplew, Peter , Stranieri, Andrew , Ong, Kok-Leong , Christen, Peter , Kennedy, Paul
- Date: 2011
- Type: Text , Edited book
- Full Text: false
Data mining Traditional Chinese Medicine (TCM) : Lessons learnt from mining in law and allopathic medicine
- Authors: Stranieri, Andrew , Sahama, Tony
- Date: 2012
- Type: Text , Conference proceedings
- Full Text: false
- Description: Key decisions at the collection, pre-processing, transformation, mining and interpretation phase of any knowledge discovery from database (KDD) process depend heavily on assumptions and theoretical perspectives relating to the type of task to be performed and characteristics of data sourced. In this article, we compare and contrast theoretical perspectives and assumptions taken in data mining exercises in the legal domain with those adopted in data mining in TCM and allopathic medicine. The juxtaposition results in insights for the application of KDD for Traditional Chinese Medicine. © 2012 IEEE.
- Description: 2003009797
Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic
- Authors: Stranieri, Andrew , Yatsko, Andrew , Jelinek, Herbert , Venkatraman, Sitalakshmi
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 5, no. 1 (2015), p. 111-134
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- Description: Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc.
Decision support based needs assessment for cancer patients
- Authors: Stranieri, Andrew , Kulkarni, Siddhivinayak , Macfadyen, Alyx , Love, Anthony , Vaughan, Stephen
- Date: 2011
- Type: Text , Conference paper
- Relation: Australasian workshop on health informatics and knowledge management (HIKM)
- Full Text: false
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- Description: Regular assessment of wellness or quality of life for patients throughout a cancer journey is important so as to identify aspects of life that could lead to distress and impede recovery or acceptance. The emerging trends in assessment are to deploy validated, quality of life instruments on touchscreen computers in medical waiting rooms. However, these add to workload of health care professionals and can be impersonal for patients to use. In this article, an alternate approach is presented that involves a decision support system with natural dialogue that elicits the patient's specific context in a far finer grained manner than is possible with questionnaire based instruments. The system includes a model of heuristics that health care professionals in a locality use to make inferences regarding a patient's quality of life and avenues for referral.
- Description: E1
Decoding employee ambidexterity : understanding drivers, constraints, and performance implications for thriving in the evolving work landscapes - a scoping review
- Authors: Joseph, Jane , Firmin, Sally , Oseni, Taiwo , Stranieri, Andrew
- Date: 2023
- Type: Text , Journal article
- Relation: Heliyon Vol. 9, no. 12 (2023), p.
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- Description: Employee ambidexterity (EA) is becoming increasingly recognised as a significant factor in enhancing individual and organisational performance across diverse industries. Ambidexterity refers to the capacity to exploit and explore organisational resources simultaneously. Scholars from diverse industry sectors have been motivated to delve deeper into the topic of EA due to its growing popularity. The objective of conducting a scoping review was to scrutinise the existing literature and identify the key drivers and constraints that impact EA to thrive in the changing work landscape. The insights gained from this review can assist decision-makers in formulating effective strategies to cultivate the ambidexterity skills of their workforce and achieve desirable outcomes. This review adheres to the PRISMA-ScR protocol. Articles were obtained from databases including Scopus, Web of Science, and EBSCOhost (Academic Search Complete, Business Source Complete). The body of literature concerning EA is in its nascent stage. 23 articles assessing EA's performance outcomes were identified using targeted search terms and thorough screening. After conducting a thorough thematic analysis using the iterative categorisation (IC) technique, tailored for scoping a review, we successfully identified twenty-nine factors contributing to the enhancement of EA, meticulously organised into five distinct categories: organisational factors, social connectedness, employee behaviour, employee personality, and work environment related factors. Similarly, we discovered four factors that impede EA: functional tenure, team identification, bounded discretion, and conscientiousness. Our findings underscore the profound impact of employee ambidexterity on distinct types of performance. Among the sixteen types of performance reported to be enhanced by EA, ten are linked to individual performance, while six are tied to organisational performance. Notably, our analysis revealed that nearly all studies have relied on cross-sectional research methods except for one. However, we advocate for the exploration of longitudinal studies as they hold the promise of offering a more comprehensive understanding of EA. The paper presents valuable insights into how to cultivate ambidextrous capabilities in the workforce for unparalleled success in today's rapidly evolving work environment. Additionally, it identifies several intriguing avenues for future research that could further elucidate and bridge existing knowledge gaps. © 2023
Deep learning model to empower student engagement in online synchronous learning environment
- Authors: Godly, Cinthia , Balasubramanian, Venki , Stranieri, Andrew , Saikrishna, Vidya , Mohammed, Rehena , Chackappan, Godly
- Date: 2022
- Type: Text , Conference paper
- Relation: 19th IEEE India Council International Conference, INDICON 2022, Kochi India, 24-26 November 2022, INDICON 2022 - 2022 IEEE 19th India Council International Conference
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- Description: Following the start of the pandemic, online synchronous learning has grown significantly. The higher education sector is searching for new creative ways to provide the information online because of the switch from face-to-face to online synchronous course delivery. Students are also becoming accustomed to studying online, and research has shown that synchronous online learning has a variety of effects on student engagement. For instance, according to statistics from the National Survey of Student Engagement, students are less likely to participate in collaborative learning, studentfaculty interactions, and conversations when learning online if they use quantitative reasoning during face-to-face instruction. Additionally, studies suggest that because they depend on their devices to take online classes, students feel more alienated from their lecturers. This has been linked to a drop in contacts with peers and teachers as a result. By using a cutting-edge deep learning model to predict learner engagement behaviour in a synchronous teaching environment, our research intends to improve online engagement. The model with a clever trigger will encourage the disengaged pupils to communicate with the teachers online. Smart triggers will be built around factors that have been found, focusing on disengaged students to engage them in real-time with automatic, personalized feedback. © 2022 IEEE.
Deep reinforcement-based conversational ai agent in healthcare system
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Mahableshwarkar, Ameya , Kulkarni, Mrunalini
- Date: 2022
- Type: Text , Book chapter
- Relation: Studies in Computational Intelligence p. 233-249
- Full Text: false
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- Description: Conversational AI is a sub-domain of artificial intelligence that deals with speech-based or text-based AI agents that have the capability to simulate and automate conversations and verbal interactions. A Goal Oriented Conversational Agent (GOCA) is a conversational AI agent that attempts to solve a specific problem for the users as per their inputs. The development of Reinforcement Learning algorithms has opened up new opportunities in research related to conversational AI, due to the striking similarity the algorithm bears to the way a conversation takes place. This chapter aims to describe a novel, hybrid conversational AI architecture using Deep Reinforcement Learning that can give state-of-the-art results on the tasks of Intent Classification, Entity Recognition, Dialog Management, State Tracking, Information Retrieval and Natural Language Response Generation. The architecture also consists of external AI modules, focused on carrying out intelligent tasks pertaining to the healthcare sector. The AI tasks that the conversational agent is capable of performing are—Text-based Question Answering, Text Summarization and Visual Question Answering. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Deliberation using three dimensions
- Authors: Macfadyen, Alyx , Stranieri, Andrew
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Second Australasian Conference on Interactive Entertainment, University of Technology, Sydney : 23rd - 25th November, 2005
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- Description: Three dimensional games are compelling and provide a forum for interactivity and engagement. A dramatically different environment from typical settings for the discussion of issues in addition the interactivity and all-engaging nature of the 3D environment is expected to facilitate deliberative attitudes. Complex reasoning if represented in a 3D environment is likely to be more compelling and interesting than the same issue represented using other means.
- Description: E1
- Description: 2003001380
Deliberative discourse and reasoning from generic argument structures
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2009
- Type: Text , Journal article
- Relation: AI and Society Vol. 23, no. 3 (2009), p. 353-377
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- Description: In this article a dialectical model for practical reasoning within a community, based on the Generic/Actual Argument Model (GAAM) is advanced and its application to deliberative dialogue discussed. The GAAM, offers a dynamic template for structuring knowledge within a domain of discourse that is connected to and regulated by a community. The paper demonstrates how the community accepted generic argument structure acts to normatively influence both admissible reasoning and the progression of dialectical reasoning between participants. It is further demonstrated that these types of deliberation dialogues supported by the GAAM comply with criteria for normative principles for deliberation, specifically, Alexy's rules for discourse ethics and Hitchcock's Principles of Rational Mutual Inquiry. The connection of reasoning to the community in a documented and transparent structure assists in providing best justified reasons, principles of deliberation and ethical discourse which are important advantages for reasoning communities. © Springer-Verlag London Limited 2006.
Deriving value from health 2.0 : a study of social media use in australian healthcare organizations
- Authors: Ukoha, Chukwuma , Stranieri, Andrew , Chadhar, Mehmood
- Date: 2017
- Type: Text , Conference paper
- Relation: 21st Pacific Asia Conference on Information Systems: Societal Transformation Through IS/IT, PACIS 2017, Langkawi Island, Malaysia
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- Description: Health 2.0 is becoming increasingly ubiquitous. The features and functionalities of social media make it suitable for health-related communication. Many healthcare organizations use social media however, the value that they derive from it is unclear. At the moment, there is no consensus on how best the value derived from Health 2.0 can be measured. In order to address this problem, this study explores how Australian healthcare organizations derive value from Health 2.0, and how the derived value can be measured. It is expected that this study will make significant contributions to both theory and practice. The study will put forward a Health 2.0 value-evaluation framework, based on both the research findings, and IS literature. The outcome of this study would help healthcare organizations to understand how value is derived from Health 2.0 and how to measure it. The result of this study will also provide digital health leaders with relevant information that would enable them to make better investment decisions. Overall, the findings of this study will help healthcare organizations to design social media strategies that can yield tangible value. © PACIS 2017.
Detection of CAN by ensemble classifiers based on Ripple Down rules
- Authors: Kelarev, Andrei , Dazeley, Richard , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Book chapter
- Relation: Knowledge Management and Acquisition for Intelligent Systems p. 147-159
- Full Text: false
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- Description: It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.
Device agent assisted blockchain leveraged framework for Internet of Things
- Authors: Nasrullah, Tarique , Islam, Md Manowarul , Uddin, Md Ashraf , Khan, Md Anisauzzaman , Layek, Md Abu , Stranieri, Andrew , Huh, Eui-Nam
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Access Vol. 11, no. (2023), p. 1254-1268
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- Description: Blockchain (BC) is a burgeoning technology that has emerged as a promising solution to peer-to-peer communication security and privacy challenges. As a revolutionary technology, blockchain has drawn the attention of academics and researchers. Cryptocurrencies have already effectively utilized BC technology. Many researchers have sought to implement this technique in different sectors, including the Internet of Things. To store and manage IoT data, we present in this paper a lightweight BC-based architecture with a modified raft algorithm-based consensus protocol. We designed a Device Agent that executes a novel registration procedure to connect IoT devices to the blockchain. We implemented the framework on Docker using the Go programming language. We have simulated the framework on a Linux environment hosted in the cloud. We have conducted a detailed performance analysis using a variety of measures. The results demonstrate that our suggested solution is suitable for facilitating the management of IoT data with increased security and privacy. In terms of throughput and block generation time, the results indicate that our solution might be 40% to 45% faster than the existing blockchain. © 2013 IEEE.
Diagnostic with incomplete nominal/discrete data
- Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi , Bagirov, Adil
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 4, no. 1 (2015), p. 22-35
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- Description: Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise application of readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown. Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, no special handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation. Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour, and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the entered missing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification. The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents a number of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing down of the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. The proposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating a significant improvement.