Predicting and analyzing the performance of students through data mining techniques to improve academic performance

Background and Objectives: Nowadays, significant advancements in information technology and communication field in different societies are seen. Given that these advancements, universities as a leading institution in the field of science, have moved towards electronic processes in the management of education and educational environments, there are databases with a large amount of information. By analyzing this massive data of educational systems, methods can be provided to improve the educational status of students. Educational data mining has sought to discover the knowledge contained in the data of the educational system. One of the applications of educational data mining is to predict students' academic performance. Predicting students' academic performance and providing useful solutions is of particular importance in the success of educational systems and can help managers make the right decisions to increase the efficiency of the educational system and better student performance. The purpose of this paper is to identify the effective indicators on academic performance, predict students' academic status using data mining techniques, and finally present a new trend for modifying unit selection and educational strategies to increase the efficiency of the education system. Methods: steps of this research are determined according to CRISP model. In current research, Databases containing 9 datasets of specialized courses in industrial engineering were used. The students' grade was bachelor's degree. Indicators affecting student performance have been identified based on previous researches and expert opinions. Demographic data and academic records of undergraduate students are entered in database. After data preprocessing, 13 attributes are selected, different models were proposed to predict student's academic status in the next semester. Then, a comparison between the results of 4 different algorithms has been done. Findings: All 13 attributes are identified to be effective according to information gain and gain ratio. This 13 attributes as follow: GPA, Total passed units, Number of conditional terms, Type of admission, Marital status, Gender, University admission year, Living place , Age, Current semester, Prerequisite course score, instructor of the course, Repeat the course. Between of 4 considered models, the Logit Boost algorithm is known as the best model in categorizing in two class and multi-class according to the accuracy rate and ROC. Conclusion: Because of acceptable performance of data mining algorithms, the use of these algorithms in predicting student performance is appropriate and the proposed model can be used as a support tool for decision making in educational systems. Finally, according to the obtained results and the opinion of academic experts, the unit selection process was redesigned. The proposed model can be used as a decision support tool in educational systems. Finally, due to the results obtained and the opinions of the academic experts, the process of unit selection was redesigned. The presented process uses the available data in educational systems and data mining science, provides useful knowledge to decision-makers to make the right and appropriate decision. Decision makers can make appropriate decisions by examining the predictions made by the data mining algorithm and obtaining useful information, in order to make the educational system more efficient.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Comprehensive analysis for air supply fan faults based on HVAC mathematical model

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems. © (2012) Trans Tech Publications

In Vitro Cytotoxicity of Two Categories of Dental Cements

Background and Aim: Resin cements are used widely in restorative dentistry regardless of their biocompatibility. The aim of this study was to compare the cytotoxicity of two categories of dental cements consisting of three chemically set cements (Fuji I, Fuji PLUS and Harvard) and two dual curing cements (BisCem and Duo-Link) by use of MTT assay. Materials and Methods: In this experimental study, four round-shaped samples of each specimen were placed in DMEM culture medium for 24, 48 and 72 hours. The extracts from each sample were applied on L929 mouse fibroblasts. At the end of each period, MTT assay was carried out to estimate the mitochondrial respiration. Data were analyzed by one-way analysis of variance (ANOVA) followed by Tukey's post-hoc test. The degree of cytotoxicity for each sample was determined according to the reference value of the control group. Results: Fuji I cement showed the least cytotoxicity while Harvard and BisCem cements showed the highest cytotoxic effect. The differences were not significant compared to the positive control (distilled water). Conclusion: This study showed that dental cements are capable of eliciting biological response in gingival and pulpal cells. They present a potential risk of tissue damage which depends on the cement's brand and curing modes

Organ at risk dose calculation for left sided breast cancer treatments using intraoperative electron radiotherapy: A Monte Carlo-based feasibility study

The present study aims to calculate the received dose by lungs and heart, as organs at risk (OAR), during intraoperative electron radiotherapy (IOERT) of left breast cancer at the presence and absence of shielding disk using Monte Carlo (MC) simulation. LIAC 12, a dedicated IOERT Linac, and an anthropomorphic phantom were considered in this study to simulate particle tracks of 6, 8, 10, and 12 MeV nominal electron energies using EGSnrc MC particle transport simulation code. The results showed that for increasing electron beam energies in the absence of shielding disk, left lung and heart dose would also be increasing so that, maximum left lung and heart dose respectively increases from 0.512 to 9.920 Gy and from 0 to 0.506 Gy with increment of electron energy from 6 to 12 MeV. Employing the shielding disk in 6 and 8 MeV energy can reduce the heart and left lung maximum dose to zero. On the other hand, this dose reduction at 10 and 12 MeV energy was respectively about 99 and 93.5 for heart and 99.9 and 92.9 for left lung. Right lung did not receive a remarkable dose both in presence and absence of shielding disk. From the results, it can be concluded that employing the shielding disk can effectively reduce the received dose to OARs. © 2019 Elsevier Lt

The release of model macromolecules may be controlled by the hydrophobicity of palmitoyl glycol chitosan hydrogels

A non-covalently cross-linked palmitoyl glycol chitosan (GCP) hydrogel has been evaluated as an erodible controlled release system for the delivery of hydrophilic macromolecules. Samples of GCP with hydrophobicity decreasing in the order GCP12>GCP11>GCP21 were synthesised and characterised by 1H NMR. Hydrogels were prepared by freeze-drying an aqueous dispersion of the polymer in the presence or absence of either a model macromolecule fluorescein isothiocyanate-dextran (FITC-dextran, MW 4400), and/or amphiphilic derivatives Gelucire 50/13 or vitamin E d-α-tocopherol polyethylene glycol succinate. Gels were analysed for aqueous hydration, FITC-dextran release, and bioadhesion, and imaged by scanning electron microscopy. The gels were highly porous and could be hydrated to up to 95× their original weight without an appreciable volume change and most gels eventually eroded. Hydration and erosion were governed by the hydrophobicity of the gel and the presence of the amphiphilic additives. GCP gels could be loaded with up to 27.5% (w/w) of FITC-dextran by freeze-drying a dispersion of GCP in a solution of FITC-dextran. The controlled release of FITC-dextran was governed by the hydrophobicity of the gel following the trend GCP21>GCP11>GCP12. GCP gels were bioadhesive but less so than hydroxypropylmethylcellulose, Carbopol 974NF (7:3) tablets

Utility of Filter Paper for Preserving Insects, Bacteria, and Host Reservoir DNA for Molecular Testing

Background: Appropriate methodology for storage biological materials, extraction of DNA, and proper DNA preservation is vital for studies involving genetic analysis of insects, bacteria, and reservoir hosts as well as for molecular diagnostics of pathogens carried by vectors and reservoirs. Here we tried to evaluate the utility of a simple filter paper-based for storage of insects, bacteria, rodent, and human DNAs using PCR assays. Methods: Total body or haemolymph of individual mosquitoes, sand flies or cockroaches squashed or placed on the paper respectively. Extracted DNA of five different bacteria species as well as blood specimens of human and great gerbil Rhombomys opimus was pipetted directly onto filter paper. The papers were stored in room temperature up to 12 months during 2009 until 2011. At monthly intervals, PCR was conducted using a 1-mm disk from the DNA impregnated filter paper as target DNA. PCR amplification was performed against different target genes of the organisms including the ITS2-rDNA of mosquitoes, mtDNA-COI of the sand flies and cockroaches, 16SrRNA gene of the bacteria, and the mtDNA-CytB of the vertebrates. Results: Successful PCR amplification was observed for all of the specimens regardless of the loci, taxon, or time of storage. The PCR amplification were ranged from 462 to 1500 bp and worked well for the specified target gene/s. Time of storage did not affect the amplification up to one year. Conclusion: The filter paper method is a simple and economical way to store, to preserve, and to distribute DNA samples for PCR analysis

CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm

Purpose: Radiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancer patients based on computed tomography (CT) radiomics, clinical and dosimetric parameters. Methods: In this prospective study, prostate cancer patients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. Stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical�radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, � 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic. Results: Sixty-four prostate cancer patients were studied, and 33 and 52 patients developed � grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical�radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical�radiomics models was 0.71, 0.67 and 0.77, respectively. Conclusions: We have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling. © 2019, Italian Society of Medical Radiology