Tunable and Growing Network Generation Model with Community Structures

Recent years have seen a growing interest in the modeling and simulation of social networks to understand several social phenomena. Two important classes of networks, small world and scale free networks have gained a lot of research interest. Another important characteristic of social networks is the presence of community structures. Many social processes such as information diffusion and disease epidemics depend on the presence of community structures making it an important property for network generation models to be incorporated. In this paper, we present a tunable and growing network generation model with small world and scale free properties as well as the presence of community structures. The major contribution of this model is that the communities thus created satisfy three important structural properties: connectivity within each community follows power-law, communities have high clustering coefficient and hierarchical community structures are present in the networks generated using the proposed model. Furthermore, the model is highly robust and capable of producing networks with a number of different topological characteristics varying clustering coefficient and inter-cluster edges. Our simulation results show that the model produces small world and scale free networks along with the presence of communities depicting real world societies and social networks.Comment: Social Computing and Its Applications, SCA 13, Karlsruhe : Germany (2013

An intelligent multimodal biometric authentication model for personalised healthcare services

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16

Bariatric Surgery Leads to a Reduction in Antibodies to Apolipoprotein A-1: a Prospective Cohort Study

Purpose: Autoantibodies against apolipoprotein A-1 have been associated with cardiovascular disease, poorer CV outcomes and all-cause mortality in obese individuals. The impact of bariatric surgery (BS) on the presence of circulating anti-apoA-1 IgG antibodies is unknown. This study aimed to determine the effect of bariatric surgery on auto-antibodies titres against Apolipoprotein A-1 (anti-apoA-1 IgG), looking for changes associated with lipid parameters, insulin resistance, inflammatory profile and percentage of excess body mass index loss (%EBMIL).Materials and methods: We assessed 55 patients (40 women) before, 6 and 12 months post-operatively. Baseline and post-operative clinical history and measurements of body mass index (BMI), serum cholesterol, triglycerides, high- and low-density lipoprotein cholesterol (HDL-C and LDL-C), apoA-1, highly sensitive C-reactive protein (hsCRP), fasting glucose (FG), glycated haemoglobin (HbA1c) and HOMA-IR were taken at each point. Human anti-apoA-1 IgG were measured by ELISA.Results: The mean age of participants was 50 years. BS significantly improved BMI, %EBMIL triglycerides, HDL-C, apoA-1, hsCRP, HBA1c, FG and HOMA-IR. Baseline anti-apoA-1 IgG seropositivity was 25% and was associated with lower apoA-1 and higher hsCRP levels. One year after BS, anti-apoA-1 IgG seropositivity decreased to 15% (p = 0.007) and median anti-apoA-1 IgG values decreased from 0.70 (0.56-0.84) to 0.47 (0.37-0.61) AU (p Conclusion: Bariatric surgery results in significant reduction in anti-apoA-1 IgG levels, which may adversely influence weight loss. The exact mechanisms underpinning these results are elusive and require further study before defining any clinical recommendations.</p

Managing hyperlipidaemia in patients with COVID-19 and during its pandemic: An expert panel position statement from HEART UK

The emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes Coronavirus Disease 2019 (COVID-19) has resulted in a pandemic. SARS-CoV-2 is highly contagious and its severity highly variable. The fatality rate is unpredictable but is amplified by several factors including advancing age, atherosclerotic cardiovascular disease, diabetes mellitus, hypertension and obesity. A large proportion of patients with these conditions are treated with lipid lowering medication and questions regarding the safety of continuing lipid-lowering medication in patients infected with COVID-19 have arisen. Some have suggested they may exacerbate their condition. It is important to consider known interactions with lipid-lowering agents and with specific therapies for COVID-19. This statement aims to collate current evidence surrounding the safety of lipid-lowering medications in patients who have COVID-19. We offer a consensus view based on current knowledge and we rated the strength and level of evidence for these recommendations. Pubmed, Google scholar and Web of Science were searched extensively for articles using search terms: SARS-CoV-2, COVID-19, coronavirus, Lipids, Statin, Fibrates, Ezetimibe, PCSK9 monoclonal antibodies, nicotinic acid, bile acid sequestrants, nutraceuticals, red yeast rice, Omega-3-Fatty acids, Lomitapide, hypercholesterolaemia, dyslipidaemia and Volanesorsen. There is no evidence currently that lipid lowering therapy is unsafe in patients with COVID-19 infection. Lipid-lowering therapy should not be interrupted because of the pandemic or in patients at increased risk of COVID-19 infection. In patients with confirmed COVID-19, care should be taken to avoid drug interactions, between lipid-lowering medications and drugs that may be used to treat COVID-19, especially in patients with abnormalities in liver function tests

Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study

: The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135-15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359-5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138-5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184-5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598-9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090-6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286-5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p &lt; 0.001; 95% CI 1.912-7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138-0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143-0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990)

A novel framework for optimised ensemble classifiers

Jan, M ORCiD: 0000-0002-5066-4118Ensemble classifiers are created by combining multiple single classifiers to achieve higher classification accuracy. Ensemble classifiers benefit from the ‘perturb and combine’ strategy, where an input data is perturbed to generate sub-samples and base classifiers are trained on generated sub-samples. All trained base classifiers are then suitably combined, and an ensemble decision is formed. One common strategy of perturbing input data is through clustering. Data clusters are generated from the input, and base classifiers are trained on generated data clusters. Such ensemble classifiers are also called clustering-based ensemble classifiers as they utilise clustering algorithms to generate a perturbed input training space. Clustering has been very applicable when it comes to generating ensemble classifiers, however it has certain limitations. One key limitation is that clustering algorithms require the number of data clusters in advance. Most of the existing ensemble approaches use a fixed number of data clusters, that are generated for various datasets, and normally searched through a process of trial and error. Additionally, since clustering works independently of data classes, class imbalances may occur in the data clusters, and data clusters may miss data samples from certain classes. Therefore, not all data clusters are suitable for the training of base classifiers, and redundant or imbalanced data clusters, should be dealt with appropriately. Besides the number of data clusters problem, the choice and type of base classifiers utilised to train on generated data clusters also have significant impact on the ensemble classifier’s performance. The use of all base classifiers to generate an ensemble classifier is not an ideal strategy, so an appropriate classifier selection methodology must be adopted to select the subset of base classifiers that can maximise the ensemble classifier’s accuracy. In this thesis several novel ensemble classifier methods have been proposed to mitigate the limitations and improve accuracy of ensemble classifiers. The first ensemble method is based on a novel strategy of incorporating an evolutionary algorithm to dynamically search for the upper bound of clustering. The second ensemble classifier method incorporates an evolutionary algorithm in two phases by optimising the pool of data clusters rather than a single upper bound and optimising the pool of base classifiers. The third ensemble classifier method is based on a hybrid approach that solves the problem of dimensionality and uses reduced dimensions data to generate an optimised ensemble classifier. The fourth ensemble classifier method is based on a novel cluster balancing strategy that solves the problem of class imbalances by balancing data clusters. The fifth ensemble classifier method contains a novel strategy to find the optimal value of clusters for each data class through the incorporation of cluster validation strategies. The sixth ensemble classifier method is based on a novel classifier selection strategy that selects classifiers from the pool based on accuracy and diversity comparisons. The seventh, and final ensemble classifier method, uses a novel pairwise diversity measure to select classifiers from the pool based on increasing accuracy and diversity. The proposed ensemble methods were evaluated on several benchmark datasets. These datasets are used by other researchers and allow a comparative analysis. In most cases an ensemble classifier’s accuracy was used as a metric to measure the performance, and in other cases different diversity measures were used. Statistical significance testing was also conducted to further validate the efficacy of the results and p-values were reported. The results and analysis presented in this thesis show that the proposed ensemble methods not only achieved classification accuracy better than existing state-of-the-art ensemble methods, but also provide a platform for future research. It was found through experimentation that upper bounds of clustering follow a logarithmic relation with the number of data samples each dataset has. Moreover, through extensive experimentation, it was proved that not all base classifiers should be selected to generate the ensemble, and only a subset of base classifiers is required to generate an ensemble classifier that can achieve the highest classification accuracy. Through the incorporation of optimisation, it was also proved that no preference is given to a specific base classifier and the type of base classifier is dependent on the characteristics of the dataset. Silhouette analysis proved to be an effective cluster validation metric to determine the optimal number of data clusters. Finally, balancing data clusters proved to be effective not only in terms of classification accuracy, but also confirmed that each dataset has different spatial characteristics which, when exploited appropriately, can contribute to overall ensemble classifier accuracy

A novel framework for optimised ensemble classifiers

Ensemble classifiers are created by combining multiple single classifiers to achieve higher classification accuracy. Ensemble classifiers benefit from the ‘perturb and combine’ strategy, where an input data is perturbed to generate sub-samples and base classifiers are trained on generated sub-samples. All trained base classifiers are then suitably combined, and an ensemble decision is formed. One common strategy of perturbing input data is through clustering. Data clusters are generated from the input, and base classifiers are trained on generated data clusters. Such ensemble classifiers are also called clustering-based ensemble classifiers as they utilise clustering algorithms to generate a perturbed input training space. Clustering has been very applicable when it comes to generating ensemble classifiers, however it has certain limitations. One key limitation is that clustering algorithms require the number of data clusters in advance. Most of the existing ensemble approaches use a fixed number of data clusters, that are generated for various datasets, and normally searched through a process of trial and error. Additionally, since clustering works independently of data classes, class imbalances may occur in the data clusters, and data clusters may miss data samples from certain classes. Therefore, not all data clusters are suitable for the training of base classifiers, and redundant or imbalanced data clusters, should be dealt with appropriately. Besides the number of data clusters problem, the choice and type of base classifiers utilised to train on generated data clusters also have significant impact on the ensemble classifier’s performance. The use of all base classifiers to generate an ensemble classifier is not an ideal strategy, so an appropriate classifier selection methodology must be adopted to select the subset of base classifiers that can maximise the ensemble classifier’s accuracy. In this thesis several novel ensemble classifier methods have been proposed to mitigate the limitations and improve accuracy of ensemble classifiers. The first ensemble method is based on a novel strategy of incorporating an evolutionary algorithm to dynamically search for the upper bound of clustering. The second ensemble classifier method incorporates an evolutionary algorithm in two phases by optimising the pool of data clusters rather than a single upper bound and optimising the pool of base classifiers. The third ensemble classifier method is based on a hybrid approach that solves the problem of dimensionality and uses reduced dimensions data to generate an optimised ensemble classifier. The fourth ensemble classifier method is based on a novel cluster balancing strategy that solves the problem of class imbalances by balancing data clusters. The fifth ensemble classifier method contains a novel strategy to find the optimal value of clusters for each data class through the incorporation of cluster validation strategies. The sixth ensemble classifier method is based on a novel classifier selection strategy that selects classifiers from the pool based on accuracy and diversity comparisons. The seventh, and final ensemble classifier method, uses a novel pairwise diversity measure to select classifiers from the pool based on increasing accuracy and diversity. The proposed ensemble methods were evaluated on several benchmark datasets. These datasets are used by other researchers and allow a comparative analysis. In most cases an ensemble classifier’s accuracy was used as a metric to measure the performance, and in other cases different diversity measures were used. Statistical significance testing was also conducted to further validate the efficacy of the results and p-values were reported. The results and analysis presented in this thesis show that the proposed ensemble methods not only achieved classification accuracy better than existing state-of-the-art ensemble methods, but also provide a platform for future research. It was found through experimentation that upper bounds of clustering follow a logarithmic relation with the number of data samples each dataset has. Moreover, through extensive experimentation, it was proved that not all base classifiers should be selected to generate the ensemble, and only a subset of base classifiers is required to generate an ensemble classifier that can achieve the highest classification accuracy. Through the incorporation of optimisation, it was also proved that no preference is given to a specific base classifier and the type of base classifier is dependent on the characteristics of the dataset. Silhouette analysis proved to be an effective cluster validation metric to determine the optimal number of data clusters. Finally, balancing data clusters proved to be effective not only in terms of classification accuracy, but also confirmed that each dataset has different spatial characteristics which, when exploited appropriately, can contribute to overall ensemble classifier accuracy