An Ensemble Self-Structuring Neural Network Approach to Solving Classification Problems with Virtual Concept Drift and its Application to Phishing Websites

Classification in data mining is one of the well-known tasks that aim to construct a classification model from a labelled input data set. Most classification models are devoted to a static environment where the complete training data set is presented to the classification algorithm. This data set is assumed to cover all information needed to learn the pertinent concepts (rules and patterns) related to how to classify unseen examples to predefined classes. However, in dynamic (non-stationary) domains, the set of features (input data attributes) may change over time. For instance, some features that are considered significant at time Ti might become useless or irrelevant at time Ti+j. This situation results in a phenomena called Virtual Concept Drift. Yet, the set of features that are dropped at time Ti+j might return to become significant again in the future. Such a situation results in the so-called Cyclical Concept Drift, which is a direct result of the frequently called catastrophic forgetting dilemma. Catastrophic forgetting happens when the learning of new knowledge completely removes the previously learned knowledge. Phishing is a dynamic classification problem where a virtual concept drift might occur. Yet, the virtual concept drift that occurs in phishing might be guided by some malevolent intelligent agent rather than occurring naturally. One reason why phishers keep changing the features combination when creating phishing websites might be that they have the ability to interpret the anti-phishing tool and thus they pick a new set of features that can circumvent it. However, besides the generalisation capability, fault tolerance, and strong ability to learn, a Neural Network (NN) classification model is considered as a black box. Hence, if someone has the skills to hack into the NN based classification model, he might face difficulties to interpret and understand how the NN processes the input data in order to produce the final decision (assign class value). In this thesis, we investigate the problem of virtual concept drift by proposing a framework that can keep pace with the continuous changes in the input features. The proposed framework has been applied to phishing websites classification problem and it shows competitive results with respect to various evaluation measures (Harmonic Mean (F1-score), precision, accuracy, etc.) when compared to several other data mining techniques. The framework creates an ensemble of classifiers (group of classifiers) and it offers a balance between stability (maintaining previously learned knowledge) and plasticity (learning knowledge from the newly offered training data set). Hence, the framework can also handle the cyclical concept drift. The classifiers that constitute the ensemble are created using an improved Self-Structuring Neural Networks algorithm (SSNN). Traditionally, NN modelling techniques rely on trial and error, which is a tedious and time-consuming process. The SSNN simplifies structuring NN classifiers with minimum intervention from the user. The framework evaluates the ensemble whenever a new data set chunk is collected. If the overall accuracy of the combined results from the ensemble drops significantly, a new classifier is created using the SSNN and added to the ensemble. Overall, the experimental results show that the proposed framework affords a balance between stability and plasticity and can effectively handle the virtual concept drift when applied to phishing websites classification problem. Most of the chapters of this thesis have been subject to publicatio

Improved Multi-Verse Optimizer Feature Selection Technique With Application To Phishing, Spam, and Denial Of Service Attacks

Intelligent classification systems proved their merits in different fields including cybersecurity. However, most cybercrime issues are characterized of being dynamic and not static classification problems where the set of discriminative features keep changing with time. This indeed requires revising the cybercrime classification system and pick a group of features that preserve or enhance its performance. Not only this but also the system compactness is regarded as an important factor to judge on the capability of any classification system where cybercrime classification systems are not an exception. The current research proposes an improved feature selection algorithm that is inspired from the well-known multi-verse optimizer (MVO) algorithm. Such an algorithm is then applied to 3 different cybercrime classification problems namely phishing websites, spam, and denial of service attacks. MVO is a population-based approach which stimulates a well-known theory in physics namely multi-verse theory. MVO uses the black and white holes principles for exploration, and wormholes principle for exploitation. A roulette selection schema is used for scientifically modeling the principles of white hole and black hole in exploration phase, which bias to the good solutions, in this case the solutions will be moved toward the best solution and probably to lose the diversity, other solutions may contain important information but didn’t get chance to be improved. Thus, this research will improve the exploration of the MVO by introducing the adaptive neighborhood search operations in updating the MVO solutions. The classification phase has been done using a classifier to evaluate the results and to validate the selected features. Empirical outcomes confirmed that the improved MVO (IMVO) algorithm is capable to enhance the search capability of MVO, and outperform other algorithm involved in comparison

The adoption of bitcoins technology: The difference between perceived future expectation and intention to use bitcoins: Does social influence matter?

Bitcoin is a decentralized system that tries to become a solution to the shortcomings of fiat and gold-based currencies. Considering its newness, the adoption level of bitcoin is yet understood. Hence, several variables are proposed in this work in examining user perceptions regarding performance expectancy, effort expectancy, trust, adoption risk, decentralization and social influence interplay, with the context of user’s future expectation and behavioral intentions to use bitcoins. Data were gathered from 293 completed questionnaire and analised using AMOS 18. The outcomes prove the sound predictability of the proposed model regarding user’s future expectations and intentions toward bitcoins. All hypotheses were supported, they were significantly affecting the dependent variables. Social influence was found as the highest predictor of behavioral intention to negatively utilize bitcoins. The significant impact of social influence, adoption risk and effort expectancy which affect behavioral intention to use bitcoins the most, are demonstrated in this study. Bitcoins should thus, present an effective, feasible and personalized program which will assist efficient usage among users. Additionally, the impacts of social influence, adoption risk and perceived trust on behavioral intention to utilize new technology were compared, and their direct path was tested together, for the first time in this context

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance

Intravesical Therapy in Patients with Intermediate-risk Non–muscle-invasive Bladder Cancer: A Systematic Review and Network Meta-analysis of Disease Recurrence

Context Patients with intermediate-risk non–muscle-invasive bladder cancer (NMIBC) may pose a clinical dilemma without an agreed evidence-based decision tree for personalized treatment. Objective To perform a systematic review and network meta-analysis (NMA) to summarize available evidence on the oncologic outcomes of intravesical therapy in patients with intermediate-risk NMIBC. Evidence acquisition The MEDLINE, EMBASE, and ClinicalTrials.gov databases were searched in October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Studies were deemed eligible if they reported on oncologic outcomes in patients with intermediate-risk NMIBC treated with transurethral resection of bladder tumor with and without intravesical chemotherapy or bacillus Calmette-Guérin (BCG) immunotherapy. Evidence synthesis Twelve studies were included in a qualitative synthesis (systematic review); three were deemed eligible for a quantitative synthesis (NMA). An NMA of five different regimens was conducted for the association of treatment with the 5-yr recurrence risk. Chemotherapy with maintenance was associated with a lower likelihood of 5-yr recurrence than chemotherapy without maintenance (odds ratio [OR] 0.51, 95% credible interval [CI] 0.26–1.03). Immunotherapy, regardless of whether a full- or reduced-dose regimen, was not associated with a significantly lower likelihood of 5-yr recurrence when compared with chemotherapy without maintenance (OR 0.90, 95% CI 0.39–2.11 vs OR 0.93, 95% CI 0.40–2.19). Analysis of the treatment ranking revealed that chemotherapy with maintenance had the lowest 5-yr recurrence risk (P score 0.9666). Conclusions Our analysis indicates that chemotherapy with a maintenance regimen confers a superior oncologic benefit in terms of 5-yr recurrence risk compared to chemotherapy without maintenance in patients with intermediate-risk NMIBC. Regardless of the dose regimen, immunotherapy with BCG does not appear to be superior to chemotherapy in patients with intermediate-risk NMIBC in term of disease recurrence. However, owing to the lack of comparative studies, there is an unmet need for well-designed, large-scale trials to validate our findings and generate robust evidence on disease recurrence and progression. Patient summary A maintenance schedule of chemotherapy reduces the rate of long-term recurrence of bladder cancer that has not invaded the bladder muscle. Chemotherapy inserted directly into the bladder and immunotherapy without maintenance schedules seem to have limited benefit in preventing cancer recurrence

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

An embedding technique to determine ττ backgrounds in proton-proton collision data

An embedding technique is presented to estimate standard model tau tau backgrounds from data with minimal simulation input. In the data, the muons are removed from reconstructed mu mu events and replaced with simulated tau leptons with the same kinematic properties. In this way, a set of hybrid events is obtained that does not rely on simulation except for the decay of the tau leptons. The challenges in describing the underlying event or the production of associated jets in the simulation are avoided. The technique described in this paper was developed for CMS. Its validation and the inherent uncertainties are also discussed. The demonstration of the performance of the technique is based on a sample of proton-proton collisions collected by CMS in 2017 at root s = 13 TeV corresponding to an integrated luminosity of 41.5 fb(-1).Peer reviewe

Calibration of the CMS hadron calorimeters using proton-proton collision data at root s=13 TeV

Methods are presented for calibrating the hadron calorimeter system of theCMSetector at the LHC. The hadron calorimeters of the CMS experiment are sampling calorimeters of brass and scintillator, and are in the form of one central detector and two endcaps. These calorimeters cover pseudorapidities vertical bar eta vertical bar ee data. The energy scale of the outer calorimeters has been determined with test beam data and is confirmed through data with high transverse momentum jets. In this paper, we present the details of the calibration methods and accuracy.Peer reviewe