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A Framework for Predictive - Diagnosis of Prevalent Illness among University Students
Get PDFThe issue of identifying the prevalence of sickness that is linked to the population of a nation, state, neighborhood, organization, or school has not been taken into consideration by the majority of prior studies on the prediction of illness among populations. They frequently merely choose any sickness based on assumption, while those that determined the prevalence of the condition before developing their framework utilized survey data or data from web repositories, which removes idiosyncrasies from those data. In order to increase performance, this research suggests an enhanced data analytics framework for the predictive diagnosis of common illnesses affecting university students. In order to do this, exploratory data analysis (EDA) using a multivariate analytic technique was conducted using a high-level model methodology using CRISP-DM stages. When the suggested strategy was evaluated on support vector machines, ensemble gradient boosting, random forest, decision tree, K-neighbors, and linear regression machine learning models, experimental findings revealed that it outperformed current methods.
In comparison to other reviewed frameworks that used survey datasets, standardized or online repositories' dataset, the framework with emphasis on the ensemble Gradient Boosting classifier and regression had accuracy of 100% and mean absolute error of 0.18, respectively. It is also steady due to its ability to manage both small and big data sets without impacting the model's performance. The enhanced results through localized dataset demonstrate the benefit of including local data sources in the process of developing models for the diagnosis and prognosis of prevalent illnesses of any area with people
NUMERICAL SOLUTION FOR HIV INFECTION OF CD4+T CELLS USING TAYLOR SERIES METHOD, RUNGE KUTTA AND ADAMS BASHFORTH METHOD
Get PDFThe HIV infection of CD4+ T cells has been modeled by a system of first-order nonlinear differential equations.We applied the TSM, RK4, and ABM in this study. The model’s numerical solution has been found inthis work. The results show that, in comparison to the TSM approach and RK4, the relative error achieved bythe ABM is smaller. When solving systems of nonlinear differential equations, the ABM is highly accurat
The solution of fifth and sixth order linear and non linear boundary value problems by the Improved Residual Power Series Method
Get PDFIn this paper we solve some fifth and sixth order boundary value problems (BVPs) by the improved residual power series method (IRPSM). IRPSM is a method that extends the residual power series method (RPSM) to (BVPs) without requiring exact solution. The presented method is capable to handle both linear and nonlinear boundary value problems (BVPs) effectively. The solutions provided by IRPSM are compared with the actual solution and with the existing solutions. The results demonstrate that the approach is extremely accurate and dependable
English in Bilingual Promotional Material of Chinese Maritime-related Exhibition Publicity: A discourse Analysis Study
Get PDF In recent years, the numbers of bilingual advertising texts have increased in China’s international maritime exhibitions. Nevertheless, the shortcomings in wording and phrasing, usage of translation strategies and intercultural communication in these China English texts are not only contrary to the Plain English Movement which advocated a more concise and economical use of words and sentences in the late 20th century, but also adversely affect the Belt and Road Initiative as well as intercultural communication. In this paper, various promotional material collected from major maritime-related English exhibitions in China over the past three years will be taken as examples. This paper will adopt the discourse analysis theory to summarize the problems of redundancy, Chinglish, misuse of translation strategies and mismanagement of intercultural conflict in relevant bilingual advertisements. The paper then suggests a series of ways for improvement in order to improve the quality of China English texts as well as promote intercultural communication
E2IDS: An Enhanced Intelligent Intrusion Detection System Based On Decision Tree Algorithm
Get PDFDue to the increased usage of the Internet of Things and heterogeneous distributed devices, the development of effective and reliable intrusion detection systems (IDS) has become more critical. The massive volume of data with various dimensions and security features, on the other hand, can influence detection accuracy and raise the computation complexity of these systems. Fortunately, Artificial Intelligence (AI) has recently attracted a lot of attention, and it is now a principal component of these systems. This work presents an enhanced intelligent intrusion detection model (E2IDS) to detect state of the art known cyberattacks. The model design is Decision Tree (DT) algorithm-based, with an approach to data balancing since the data set used is highly unbalanced and one more approach for feature selection. Furthermore, accuracy, recall and F-score are selected as the performance evaluation metrics. The experimental results show that our E2IDS not only overcomes the benchmark work but also reduces the complexity of the computing process
Development of a Predictive Model of Student Attrition Rate
Get PDFEnrollment in courses is a key performance indicator in educational systems for maintaining academic and financial viability. Today, a lot of factors, comprising demographic and individual features like age, gender, academic background, financial capabilities, and academic degree of choice, contribute to the attrition rates of students at various higher education institutions. In this study, we developed prediction models for students' attrition rate in pursuing a computer science degree as well as those who have a high chance of dropping out before graduation using machine learning methodologies. This approach can assist higher education institutions in creating effective interventions to lower attrition rates and raise the likelihood that students will succeed academically. Student data from 2015 to 2022 were collected from the Federal University Lokoja (FUL), Nigeria. The data was preprocessed using existing WEKA machine learning libraries where our data was converted into attribute-related file form (ARFF). Further, the resampling techniques were used to partition the data into the training set and testing set, and correlation-based feature selection was extracted and used to develop the students' attrition model to identify the students' risk of attrition. Random Forest and decision tree machine learning algorithms were used to predict students' attrition. The results showed that Random Forest has 79.45% accuracy while the accuracy of Random tree stood at 78.09%. This is an improvement over previous results, where an accuracy of 66.14%. and 57.48% were recorded for random forest and Random tree respectively. This improvement was because of the techniques demonstrated in this study. It is recommended that applying techniques to the classification model will improve the performance of the model
Taylor Series Expansion Method To Compute Approximate Solution for Nonlinear Dynamical System
Get PDFIn this manuscript we have studied a five compartmental mathematical model of Ebola epidemic. Thesuggested mathematical model is classified into susceptible, incubation, infected, isolated infected and recoveredclasses. The Taylor series method (TSM) is used to achieve the approximate results for each compartment.The graphical presentation that corresponds to some real facts is given
Opial-Jensen and functional inequalities for convex functions
Get PDFThe main of this article are presenting generalized Opial type inequalities which will be defined as theOpial-Jensen inequality for convex function. Further, new Opial type inequalities will be given for functionalsdefined with the help of the Opial inequalities
Multinational Corporations as Cultural Translators: Interpreting Difference in Diversity and Inclusion
Get PDFThis article brings Translation Studies and Language-Sensitive International Business Studies into sustained and in-depth critical conversation through an investigation into how multinational corporations (MNCs) represent and culturally translate discourses of diversity and inclusion (D&I). Grounded in a discrete textual analytical investigation of MNC approaches to D&I, the authors use a skeptical interpretive perspective to examine D&I video content published on MNC group websites. The study points towards the imposition of a common corporate language as an act of mediation on the part of parent companies. D&I discourse remains ambiguous and attempts to reach multiple audiences simultaneously. While the primary audience appears to be job applicants, the use of language also demonstrates a sensitivity to the needs and expectations of business-facing audiences, including customers, investors, and shareholders. Interdisciplinary in character, this article employs the concept of cultural translation as a critical lens through which to demonstrate how positional power shapes the representation of D&I discourse published on MNC websites. By extending understandings of the role and value of cultural translation in an MNC setting, the authors demonstrate the value of continued expansion of the field through enhanced comparative analysis of MNC self-perception with regard to D&I, versus published representations of the same
In silico evaluation of natural bioactive compounds as Mycobacterium leprae enoyl acyl carrier protein reductase inhibitors
Get PDFBackground and Objectives:
The present study was designed to assess the antileprotic effectiveness of some bioactive natural compounds towards enoyl acyl carrier protein reductase inhibition. Leprosy still constitutes a global pandemic in spite of long years of discovery. The current therapy option is multi-drug treatment using a combination of Dapsone, Rifampicin and Clofazimine. However, mycobacterium leprae counteracted by mutating the drug targets which necessitates the search for novel targets. One such target is enoyl acyl carrier protein reductase that mediates the fatty acid biosynthesis.
Materials and Methods:
Multiple (14) ligands of natural origin were drawn from PubChem database and their ADMET parameters were preicted using ADMETLab 2.0 webserver. After, the ligands were docked against the enzyme (PDB ID: 2NTV) at its active site using iGEMDOCK software.
Results:
ADMET parameters of the tested ligands proven to be accepted by Lipinski's rule of five except for two ligands. Furthermore, molecular docking results revealed that all of the tested compounds showed better binding energy than the reference drug Dapsone. The best of which was silymarin.
Conclusion:
The tested natural ligands have the capability to control M.leprae