Predict Network, Application Performance Using Machine Learning and Predictive Analytics

In this thesis, a study is performed to find the effect of applications on resource consumption in computer networks and how to make use of available technologies such as predictive analytics, machine learning and business intelligence to predict if an application can degrade the network performance or consume computer system resources. In recent years, having a healthy computer system and the network is essential for continuity of business. The study focusses on analyzing the performance metrics collected from real networks using scripts and available programs created specifically for monitoring applications and network in real-time. This work has significant importance because monitoring real-time performance doesn’t give accurate or concise information about the reasons behind any degradation in network or application performance. On the other hand, analyzing those performance metrics over a certain period and find a correlation between metrics and applications gives much more relevant information about the root cause of problems. The findings proved that there is a correlation between certain performance metrics, besides correlation found between metrics and applications which conclude the study objectives. The benefits of this study could be seen in analyzing complex networks where there is uncertainty in determining the root cause of a problem in applications or networks

Machine learning approach for credit score analysis : a case study of predicting mortgage loan defaults

Dissertation submitted in partial fulfilment of the requirements for the degree of Statistics and Information Management specialized in Risk Analysis and ManagementTo effectively manage credit score analysis, financial institutions instigated techniques and models that are mainly designed for the purpose of improving the process assessing creditworthiness during the credit evaluation process. The foremost objective is to discriminate their clients – borrowers – to fall either in the non-defaulter group, that is more likely to pay their financial obligations, or the defaulter one which has a higher probability of failing to pay their debts. In this paper, we devote to use machine learning models in the prediction of mortgage defaults. This study employs various single classification machine learning methodologies including Logistic Regression, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. To further improve the predictive power, a meta-algorithm ensemble approach – stacking – will be introduced to combine the outputs – probabilities – of the afore mentioned methods. The sample for this study is solely based on the publicly provided dataset by Freddie Mac. By modelling this approach, we achieve an improvement in the model predictability performance. We then compare the performance of each model, and the meta-learner, by plotting the ROC Curve and computing the AUC rate. This study is an extension of various preceding studies that used different techniques to further enhance the model predictivity. Finally, our results are compared with work from different authors.Para gerir com eficácia a análise de risco de crédito, as instituições financeiras desenvolveram técnicas e modelos que foram projetados principalmente para melhorar o processo de avaliação da qualidade de crédito durante o processo de avaliação de crédito. O objetivo final é classifica os seus clientes - tomadores de empréstimos - entre aqueles que tem maior probabilidade de pagar suas obrigações financeiras, e os potenciais incumpridores que têm maior probabilidade de entrar em default. Neste artigo, nos dedicamos a usar modelos de aprendizado de máquina na previsão de defaults de hipoteca. Este estudo emprega várias metodologias de aprendizado de máquina de classificação única, incluindo Regressão Logística, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Para melhorar ainda mais o poder preditivo, a abordagem do conjunto de meta-algoritmos - stacking - será introduzida para combinar as saídas - probabilidades - dos métodos acima mencionados. A amostra deste estudo é baseada exclusivamente no conjunto de dados fornecido publicamente pela Freddie Mac. Ao modelar essa abordagem, alcançamos uma melhoria no desempenho do modelo de previsibilidade. Em seguida, comparamos o desempenho de cada modelo e o meta-aprendiz, plotando a Curva ROC e calculando a taxa de AUC. Este estudo é uma extensão de vários estudos anteriores que usaram diferentes técnicas para melhorar ainda mais o modelo preditivo. Finalmente, nossos resultados são comparados com trabalhos de diferentes autores

Planning Inspection of Sewer Pipelines Using Defect Based Risk Approach

Due to the poor conditions of wastewater networks, there is an increasing need in the capital investments allocated for enhancing their condition. As per the Canadian Infrastructures Report Card, one third of the total lengths of sewer pipes in Canada is in fair to very poor condition (Canadian Infrastructures Report Card, 2016). As such, there is an urgent need for inspection planning tools, with which decision makers could assess the condition of pipelines and identify pipes with higher risk of failure. These tools are potentially of service in prioritizing and optimizing inspection activities that lead to decisions regarding appropriate courses of action, especially in cases of limited resources and funding. The goal of this research is to develop an optimization model for scheduling the inspection of sewer pipelines by performing defect-based risk assessment. The risk of failure is determined to identify critical pipe sections; by combining likelihood and consequence of failure values using the Sugeno Fuzzy Inference System. The developed optimization model determines the inspection sequence of pipeline sections in addition to optimizing the utilization of inspection crews by minimizing both time and cost of inspections. The risk assessment model is divided into two sub models: likelihood and consequences of failure. Structural and operational defects and pipeline characteristics in an existing sewage network are used to develop the likelihood model that determines the structural, operational and overall condition ratings of pipelines. Method-wise, Bayesian Belief Network (BBN) is used to develop a static condition assessment model using probabilities of occurrences and conditional probabilities. Moreover, time dimension is introduced to the developed BBN model using logistic regression as temporal links which are required to convert BBN into Dynamic Bayesian Network (DBN). The accuracy of the model’s prediction is examined through referencing of actual data, where the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for the BBN model are 0.67, 1.06, 0.56 and 1.05, 1.60, 0.95 for structural, operational and overall conditions, respectively. The second sub-model representing the consequences of failure is developed to determine the impact of sewer pipelines’ failure using Cost Benefit Analysis (CBA). Developing this sub model involves identifying and analyzing costs of failure and benefits resulting from avoiding such failures. In order to validate the CBA model, actual costs from a real failure incident are compared with the proposed model's outputs. During the implementation of the CBA model, it is found that the indirect costs resulting from sewer pipelines’ failure represent a significant portion of the total failure costs. The proposed risk assessment model is validated using actual data derived from inspected sewer pipelines. Cost savings of around 67% could be achieved if the risk assessment model is applied and deployed over ongoing inspection practices followed by municipalities. A Mixed Integer Linear Programming (MILP) model is developed to optimize scheduling of inspection activities by including sewer sections, time and cost of inspections. This model is developed using GAMS and solved using CPLEX to maximize the number of sections and minimize time and cost. The output from the MILP model is compared to the results of another model solved using the Genetic Algorithm (GA) approach. It is found that the MILP model could perform better than the GA model in terms of optimal solutions. Additionally, a resulting inspection cost reduction of approximately 38% could be achieved when utilizing the MILP model. It is expected that the proposed inspection scheduling model could help decision makers better assess the condition of sewer pipelines and improve their decision-making on proactive or reactive measures. The proposed model could help allocate budgets more efficiently in addition, to being an enabler for better inspection programs, particularly in cases of limited funds and task forces

Da'wa in Islamic thought : The work of 'Abd Allah ibn 'Alawi al-Haddad.

Imam 'Abd Allah ibn 'Alawi al-Haddad was born in 1044/1634, he was a scholar of the Ba 'Alawi sayyids, a long line of Hadrami scholars and gnostics. The Imam led a quiet life of teaching and, although blind, travelled most of Hadramawt to do da'wa, and authored ten books, a diwan of poetry, and several prayers. He was considered the sage of his time until his death in Hadramawt in 1132/1721. Many chains of transmission of Islamic knowledge of East Africa and South East Asia include his name. Al-Haddad's main work on da'wa, which is also the core of this study, is al-Da'wa al-Tamma wal-Tadhkira al-'Amma (The Complete Call and the General Reminder). Six main points can be derived from it. They are: the definition of da'wa, the knowledges of da'wa, the legal rulings on da'wa, the reasons people might avoid da'wa, the eight categories of its recipients, and the probable results of da'wa. His other works reflect his own da'wa and as such confirm and elaborate upon his opinions on da'wa found in al-Da'wa al-Tamma. The focal points in these works are steadily and consistently upon the most essential aspects of Islam: the heart, the intention, submission, and obedience. While Imam al-Haddad was known among the Ba 'Alawi circles during his life, his teachings spread to the international Islamic community only after his death. In the Fourteenth/Twentieth Century Mufti of Egypt, Hasanayn Muhammad Hasanayn Makhluf oversaw their first modem prints, while Ba 'Alawi scholar Habib Ahmad Mashhur al-Haddad was the first to have a sizeable following of Westemers. Today, Imam al-Haddad's teaching on da'wa is manifest in the institutional form of Dar al-Mustafa in Yemen and his treatises are finding currency in the West for their simple and non-technical style

Cost benefit analysis for failure of sewer pipelines

Sewer pipelines failure in sewage networks can have adverse potential impacts on socio-economic aspects in any community. Due to the fact that it's difficult to capture the relationship between the physical and economical aspects as a result of critical sewer pipelines failure, economic concepts are used to evaluate the economic loss as a result of these failures. In this paper an analysis for the costs resulting from sewer pipelines failure and the benefits achieved from avoiding failures are presented. The costs included in the cost benefit analysis are the direct costs used to reinstate failed pipelines and the indirect costs, borne by the society and economy. In the benefits analysis, only the tangible and measurable benefits limited to the health sector and preventing diseases are addressed in this paper. It is expected that the proposed approach could help in estimating the economic losses due to sewer pipelines failure especially for the intangible factors that are difficult to measure. In addition it could help decision makers in taking necessary measures to preserve critical assets that could have adverse potential impacts on valuable natural resources such as surface and groundwater and soil surrounding failed pipelines.This publication was made possible by NPRP grant # (NPRP6-357-2-150) from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Also the authors would like to thank the public works authority of Qatar (ASHGAL) for their support in the data collection.Scopu

Evaluation Of Platelet-Rich Plasma Effect On Treatment Of Temporomandibular Joint Anterior Disc Displacement

The use of Platelet-Rich-Plasma (PRP) may provide a new and improved treatment option for early and late  Temporomandibular Joint (TMJ) disc displacement. However, there are no long-term studies on its use in TMJ arthritis in the literature. The present study evaluate 28 patients with different degrees of disc displacement over a period of time. These patients had experienced no pain reduction following conservative approaches (including splint therapy) and  minimally invasive arthrocentesis treatment. All patients had evidence of disc displacement associated with pain and discomfort, and sometimes clicking. The patients were without systemic joint disease, septicarthritis, or autoimmune arthritis. Only patients who had not responded to conservative therapy were included in the present study. Pain intensity was recorded for each patient using a 0-10 VAS scale. Maximum Inter-incisal Opening (MIO) was also recorded. This assessment was performed at the pretreatment stage and then examinations 3,6,9,12 months respectively after administration of two intra-articular injections of autologous PRP.The results after 12 months revealed that intra-articular injection of autologous PRP appeared to be an effective treatment method for patients with disc displacement in this study. At the 12-months follow-up, all patients  improved their mouth opening significantly. The majority of the PRP patients  showed decreased pain. The average pain score before PRP administration was 7.5, while 3 months after PRP administration the pain score was 4.2. The pain score continued to decrease, reaching nearly 2 after 6 months and  0.5 by the end of 12 months. In conclusion, the use of PRP was found to be an effective and predictable treatment option for disc displacement

Effects of types and doses of yeast on gas production and in vitro digestibility of diets containing maize (Zea mays) and lucerne (Medicago sativa) or oat hay

Two yeast products formulated with Saccharomyces cerevisiae were evaluated at the same colonyforming units (CFUs) per gram of substrate. Samples of maize, lucerne and oat hays were mixed (0.5 kg) to a proportion of 80% forage (lucerne or oat) with 20% maize (DM basis) and combined with each yeast to obtain 1.5 x 107 or 3.0 x 107 CFU/g DM. There was also a control without yeast. In vitro gas production was measured at 0, 2, 4, 6, 8, 10, 14, 18, 24, 30, 36, 42, 48, 60, and 72 h incubation. There was no forage/yeast interaction. Both yeast products tended to reduce the maximum volume produced quadratically and lag time linearly, while in vitro dry matter digestibility (IVDMD) increased linearly. Ruminal ammonia N and lactic acid were not affected, whereas methane and carbon dioxide tended to be reduced with the intermediate dose of yeast. When the mixture included oat hay, the total volume of gas increased, the lag time decreased, and there was higher IVDMD than in the lucerne-based mixtures, which were associated with lower methane production. Ammonia and lactic acid remained unchanged. The two yeast products showed the same effects on the dynamics of gas production and in vitro digestibility when dosed at the same number of viable cells or CFUs, and there was no interaction with forage quality

Basic Life Support (BLS) Knowledge Among General Population; a Multinational Study in Nine Arab Countries

Introduction: Basic Life Support (BLS) is a medical treatment used in life-threatening emergencies until the sufferer can be properly cared for by a team of paramedics or in a hospital. This study aimed to assess the level of knowledge regarding BLS and the contributing factors among the Arab non-medical population. Methods: An online survey-based cross-sectional study was conducted among non-medical populations in nine Arab countries between April 13, 2022, and June 30, 2022. The utilized questionnaire consisted of two parts: part one included socio-demographic characteristics and part two measured knowledge of BLS through an online survey. Results: The research included a total of 4465 participants. 2540 (56.89%) of the participants were knowledgeable about BLS. The mean basic life support knowledge scores of participants who received training were higher than those who had not (20.11 ± 4.20 vs. 16.96 ± 5.27; p< 0.01). According to the nations, Yemen scored the highest, while Morocco had the lowest levels of BLS knowledge (19.86 ± 4.71 vs. 14.15 ± 5.10, respectively; p< 0.01). Additionally, individuals who resided in urban areas scored on average higher than those who did in rural areas (17.86 ± 5.19 vs. 17.13 ± 5.24, p= 0.032) in understanding basic life support. Age, information sources, and previous training with theoretical and practical classes were significant predictors of BLS knowledge. Conclusion: The level of BLS knowledge among non-medical people in Arab nations is moderate but insufficient to handle the urgent crises that we face everywhere. In addition to physicians being required to learn the BLS principles, non-medical people should also be knowledgeable of the necessary actions to take in emergency events