Detecting hierarchical relationships and roles from online interaction networks

In social networks, analysing the explicit interactions among users can help in inferring hierarchical relationships and roles that may be implicit. In this thesis, we focus on two objectives: detecting hierarchical relationships between users and inferring the hierarchical roles of users interacting via the same online communication medium. In both cases, we show that considering the temporal dimension of interaction substantially improves the detection of relationships and roles. The first focus of this thesis is on the problem of inferring implicit relationships from interactions between users. Based on promising results obtained by standard link-analysis methods such as PageRank and Rooted-PageRank (RPR), we introduce three novel time-based approaches, \Time-F" based on a defined time function, Filter and Refine (FiRe) which is a hybrid approach based on RPR and Time-F, and Time-sensitive Rooted-PageRank (T-RPR) which applies RPR in a way that takes into account the time-dimension of interactions in the process of detecting hierarchical ties. We experiment on two datasets, the Enron email dataset to infer managersubordinate relationships from email exchanges, and a scientific publication coauthorship dataset to detect PhD advisor-advisee relationships from paper co-authorships. Our experiments demonstrate that time-based methods perform better in terms of recall. In particular T-RPR turns out to be superior over most recent competitor methods as well as all other approaches we propose. The second focus of this thesis is examining the online communication behaviour of users working on the same activity in order to identify the different hierarchical roles played by the users. We propose two approaches. In the first approach, supervised learning is used to train different classification algorithms. In the second approach, we address the problem as a sequence classification problem. A novel sequence classification framework is defined that generates time-dependent features based on frequent patterns at multiple levels of time granularity. Our framework is a exible technique for sequence classification to be applied in different domains. We experiment on an educational dataset collected from an asynchronous communication tool used by students to accomplish an underlying group project. Our experimental findings show that the first supervised approach achieves the best mapping of students to their roles when the individual attributes of the students, information about the reply relationships among them as well as quantitative time-based features are considered. Similarly, our multi-granularity pattern-based framework shows competitive performance in detecting the students' roles. Both approaches are significantly better than the baselines considered

Application of Machine Learning Techniques for the Prediction of Heart Disease

As important as the heart is to humans, unfortunately, 43% of death is from heart disease [2] declared by Global Burden of Disease research. By 2030, deaths from cardiovascular disease will reach 23.6 million where heart disease takes the lead [3]. Annually, 10 million people die globally according to World Health Organization (WHO). There have been (pre)established conventional ways of detecting this disease in humans like angiography, electrocardiograms among others, which are not only expensive for the common man, but have been proven, but over 17 million individuals have lost their lives to lack of expertise, incapacitation with several side effects [4]. According to a WHO survey, only 67% of the time, doctors can accurately predict heart disease. Hence the need for noninvasive and a more efficient technique thereby leveraging on Data Science (Machine Learning - ML). This research makes use of ML techniques to classifying Heart Disease through the comparative way of their metrics to predict heart disease in individuals, ii. Investigate the most relevant features and the risk factors contributing to predicting heart disease, iii. Evaluate the performance of the developed models using appropriate metrics, iv. Provide insights and recommendations for healthcare professionals to improve early diagnosis and intervention strategies. These involve four classifiers: XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine, to classify and predict heart disease using the Framingham heart disease dataset. Different models were built after handling missing values and outliers in the dataset. Before balancing the dataset, the models built, LR and RF gave the best performance with an accuracy of 85% each. The dataset was later balanced/resampled, and important features selection was done using the XGBoost classifier, Sequential Feature Selection (SFS) and KBest methods respectively, and these improved the performance of the model. Ensemble techniques (AdaBoost and Bagging) were adopted and the AdaBoost model (RF classifier) performed as high as giving an accuracy of 93%. Hyperparameter tuning was done involving Randomized SearchCV and Grid SearchCV, but none outperformed the AdaBoost model’s performance. Lastly, the balanced dataset was split into train and test datasets (ratio of 80:20), and a model was built/trained with the train dataset and then tested with the test dataset, this gave an accuracy of 93% as that of the AdaBoost model, but a better CV_score: 0.9110, R2_score: 0.7078, AUC curve: 0.98, RSME: 0.2701, MAE: 0.0730 with Random Forest classifier

Application of Artificial Intelligence and Data Science in Detecting the Impact of Usability from Evaluation of Mobile Health Applications

Mobile health (mHealth) applications have demonstrated immense potential for facilitating preventative care and disease management through intuitive platforms. However, realizing transformational health objectives relies on creating accessible tools optimized for different users. This research analyses mHealth app usability data sourced from online repositories to reveal the impact of usability (ease of use) from evaluating mobile health applications. Thoroughly examining interfaces with a utilization of statistical tests of significance, platform, integra-tions, and various application features shows complex relationships between usability and users experience. This work shows that applying random forest models can accurately classify the ease-of-use of mHealth applications. This work sheds light on the connections between design choices and their effects, guiding intentional improvements to expand the reach of mHealth. It does so by providing insights into the subtle ways that people interact with mHealth applications. The methodologies and findings provide actionable insights for developers and practitioners passionate about advancing digital healthcare

Creating a Classification Module to Analysis the Usage of Mobile Health Apps

With an ageing society becoming a major issue for many countries, health-related concerns are growing and mobile health applications (MHAs) are rapidly gaining users. The applications available range from those that promote exercise to maintain health, those that help to manage physical condition by recording weight and activity, and those that allow users to consult doctors and pharmacists. On the other hand, there are still many mobile users who do not use MHAs. In this case study from Japan, the range of diverse MHAs were classified into five categories by K-means clustering analysis and the results of a questionnaire on the use of MHAs were analyzed using a scientific approach to find out which types of users mainly use these applications. Based on the results of this analysis, a classifier was created using a Random Forest algorithm to extract MHAs that meet the needs of users based on their attributes and thoughts. With this Random Forest classification model, this paper recommends appropriate models for potential users who are not yet using MHAs

Assessing the Impact of Usability from Evaluating Mobile Health Applications

Software applications that are used to monitor, track, and improve health are called Mobile Health Applications or mHAs. They are developed with or without the help of medical professionals to potentially aid health, achieve health goals and improve lifestyle or behavior. Although mobile Health Applications have been on the market since the advent of mobile applications, the pandemic saw to a 25% increase in the number of mobile health applications available on the app stores. This indicates the growing demand for mHAs. This research was conducted to evaluate the impact of usability of mobile health applications. The dataset used to carry out this research is a review data set of health-condition management focused apps. These apps managed conditions like Diabetes, Depression, Hypertension, etc. System Usability Score, Net Promoter Score, App Ratings, Patient engagement was some of the features that were used to conduct the research. There were low correlations between App’s reaction to dangerous information and usability score (0.17), Existence of privacy policy and usability score (-0.032), IOS App Rating and Usability Score (0.053), Android App Rating and Usability Score (-0.029). Patient, Caregiver/Clinicians engagement-based variables like ‘does the app makes reference to specific disease guidelines’, ‘in what way does the app engage patients’, ‘does the app provide support through social media’ showed higher correlations with usability scores and clinical utility. It is recommended that to evaluate the usability of mobile health applications, a combination of usability measuring methods be used

Detecting hierarchical relationships and roles from online interaction networks

In social networks, analysing the explicit interactions among users can help in inferring hierarchical relationships and roles that may be implicit. In this thesis, we focus on two objectives: detecting hierarchical relationships between users and inferring the hierarchical roles of users interacting via the same online communication medium. In both cases, we show that considering the temporal dimension of interaction substantially improves the detection of relationships and roles. The first focus of this thesis is on the problem of inferring implicit relationships from interactions between users. Based on promising results obtained by standard link-analysis methods such as PageRank and Rooted-PageRank (RPR), we introduce three novel time-based approaches, \Time-F" based on a defined time function, Filter and Refine (FiRe) which is a hybrid approach based on RPR and Time-F, and Time-sensitive Rooted-PageRank (T-RPR) which applies RPR in a way that takes into account the time-dimension of interactions in the process of detecting hierarchical ties. We experiment on two datasets, the Enron email dataset to infer managersubordinate relationships from email exchanges, and a scientific publication coauthorship dataset to detect PhD advisor-advisee relationships from paper co-authorships. Our experiments demonstrate that time-based methods perform better in terms of recall. In particular T-RPR turns out to be superior over most recent competitor methods as well as all other approaches we propose. The second focus of this thesis is examining the online communication behaviour of users working on the same activity in order to identify the different hierarchical roles played by the users. We propose two approaches. In the first approach, supervised learning is used to train different classification algorithms. In the second approach, we address the problem as a sequence classification problem. A novel sequence classification framework is defined that generates time-dependent features based on frequent patterns at multiple levels of time granularity. Our framework is a exible technique for sequence classification to be applied in different domains. We experiment on an educational dataset collected from an asynchronous communication tool used by students to accomplish an underlying group project. Our experimental findings show that the first supervised approach achieves the best mapping of students to their roles when the individual attributes of the students, information about the reply relationships among them as well as quantitative time-based features are considered. Similarly, our multi-granularity pattern-based framework shows competitive performance in detecting the students' roles. Both approaches are significantly better than the baselines considered

User association for energy harvesting relay stations in cellular networks

We consider a cellular wireless network enhanced by relay stations that are powered by renewable energy sources. Such a network consists of the macro base stations (BS), relay stations (RSs), and many mobile stations (MSs). In addition to the traditional data/voice transmission between the BS and the MSs, a higher service tier may be provided by using the energy harvesting RSs for some MSs. We propose a network scenario utilizing the energy harvesting relay stations to improve the service quality without taking the additional licensed frequency band and transmission power, and design a user association algorithm for the energy harvesting RSs in such a network. The goal is to assign each MS an RS for relaying its signal to minimize the probability of the relay service outage, i.e, the probability that an MS’s relay service request is rejected. First, we propose a network scenario and develop a mathematical model to estimate the rejection probability for a given user association. We then propose a low-complexity local search algorithm, which balances the computational complexity and the performance, to obtain a locally optimal user association. Simulation results are provided to demonstrate the superior performance of the proposed techniques over the traditional methods

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Human Ubc9 Contributes to Production of Fully Infectious Human Immunodeficiency Virus Type 1 Virions

Ubc9 was identified as a cellular protein that interacts with the Gag protein of Mason-Pfizer monkey virus. We show here that Ubc9 also interacts with the human immunodeficiency virus type 1 (HIV-1) Gag protein and that their interaction is important for virus replication. Gag was found to colocalize with Ubc9 predominantly at perinuclear puncta. While cells in which Ubc9 expression was suppressed with RNA interference produced normal numbers of virions, these particles were 8- to 10-fold less infectious than those produced in the presence of Ubc9. The nature of this defect was assayed for dependence on Ubc9 during viral assembly, trafficking, and Env incorporation. The Gag-mediated assembly of virus particles and protease-mediated processing of Gag and Gag-Pol were unchanged in the absence of Ubc9. However, the stability of the cell-associated Env glycoprotein was decreased and Env incorporation into released virions was altered. Interestingly, overexpression of the Ubc9 trans-dominant-negative mutant C93A, which is a defective E2-SUMO-1 conjugase, suggests that this activity may not be required for interaction with Gag, virion assembly, or infectivity. This finding demonstrates that Ubc9 plays an important role in the production of infectious HIV-1 virions

GC/MS and LC-MS/MS phytochemical evaluation of the essential oil and selected secondary metabolites of Ajuga orientalis from Jordan and its antioxidant activity

The current investigation aimed to shed light in the volatile and non-volatile secondary metabolites of Ajuga orientalis L. from Jordan. GC/MS and GC/FID analysis of the hydrodistilled essential oil obtained from aerial parts of the plant revealed tiglic acid (18.90 %) as main constituent. Each of the methanol and butanol fractions of A. orientalis were screened for their total phenol content (TPC), total flavonoid content (TFC), and antioxidant activity determined by DDPH and ABTS methods. The extracts were then analyzed by LC-ESI-MS/MS to unveil their chemical constituents, especially phenols and flavonoids. Results showed that the AO-B extract had the highest TPC (217.63 ± 2.65 mg gallic acid/g dry extract), TFC (944.41 ± 4.77 mg quercetin /g dry extract), highest DPPH and ABTS antioxidant activity ((4.00 ± 0.20) × 10-2; (3.00 ± 0.20) × 10-2 mg/mL, respectively) as compared to the AO-M extract. LC-ESI-MS/MS analysis of both extracts revealed the presence of several phenolics, flavonoids and nonphenolic acids