Predictive Modelling of Student Academic Performance – the Case of Higher Education in Middle East

One of the main issues in higher education is student retention. Predicting students' performance is an important task for higher education institutions in reducing students' dropout rate and increasing students' success. Educational Data mining is an emerging field that focuses on dealing with data related to educational settings. It includes reading the data, extracting the information and acquiring hidden knowledge. This research used data from one of the Gulf Cooperation Council (GCC) universities, as a case study of Higher Education in the Middle East. The concerned University has an enrolment of about 20,000 students of many different nationalities. The primary goal of this research is to investigate the ability of building predictive models to predict students' academic performance and identify the main factors that influence their performance and grade point average. The development of a generalized model (a model that could be applied on any institution that adopt the same grading system either on the Foundation level (that use binary response variable (Pass/ Fail) or count response variable which is the Grade Average Point for students enrol in the undergraduate academic programs) to identify students in jeopardy of dismissal will help to reduce the dropout rate by early identification of needed academic advising, and ultimately improve students' success. This research showed that data science algorithms could play a significant role in predicting students' Grade Point Average by adopting different regression algorithms. Different algorithms were carried out to investigate the ability of building predictive models to predict students' Grade Point Average after either 2, 4 or 6 terms. These methods are Linear/ Logistic Regression, Regression Trees and Random Forest. These predictive models are used to predict specific students' Grade Point Average based on other values in the dataset. In this type of model, explicit instruction is given about what the model needs to learn. An optimization function (the model) is formed to find the target output based on specific input values. This research opens the door for future comprehensive studies that apply a data science approach to higher-education systems and identifying the main factors that influence student performance

Monitoring and modelling of non methane hydrocarbons (NMHCs) in various areas in Pulau Pinang, Malaysia.

Hidrokarbon bukan metana (NMHC) memainkan peranan penting dalam proses pembentukan ozon dalam persekitaran bandar, di mana pembebasan dari asap kenderaan adalah dominan. Non Methane Hydrocarbons (NMHC) plays a vital role in the formation process of ozone in urban environment, where vehicle emissions are dominant

TORT3D: A MATLAB code to compute geometric tortuosity from 3D images of unconsolidated porous media

Tortuosity is a parameter that plays a significant role in the characterization of complex porous media systems and it has a significant impact on many engineering and environmental processes and applications. Flow in porous media, diffusion of gases in complex pore structures and membrane flux in water desalination are examples of the application of this important micro-scale parameter. In this paper, an algorithm was developed and implemented as a MATLAB code to compute tortuosity from three-dimensional images. The code reads a segmented image and finds all possible tortuous paths required to compute tortuosity. The code is user-friendly, easy to use and computationally efficient, as it requires a relatively short time to identify all possible connected paths between two boundaries of large images. The main idea of the developed algorithm is that it conducts a guided search for connected paths in the void space of the image utilizing the medial surface of the void space. Once all connected paths are identified in a specific direction, the average of all connected paths in that direction is used to compute tortuosity. Three-dimensional images of sand systems acquired using X-ray computed tomography were used to validate the algorithm. Tortuosity values were computed from three-dimensional images of nine different natural sand systems using the developed algorithm and compared with predicted values by models available in the literature. Findings indicate that the code can successfully compute tortuosity for any unconsolidated porous system irrespective of the shape (i.e., geometry) of particles. 1 2017 Elsevier B.V.Scopu

Effect of Exposure to Cement Dust on Pulmonary Function among Cement Plants Workers in the Middle Governorate, Gaza- Palestine

This study was conducted to investigate the level of PM air pollution in cement plants, and their impacts on respiratory system health and pulmonary function for cement plants workers. Case-control study was conducted on all cement plants at Middle Gaza Governorates. 100 individuals participated, case (exposed) and control (non exposed) groups contain 50, 50 respectively. All participants were subjected to questionnaire, lung function measuring by electronic spirometery. The findings of this study showed that an average particulate matter from 106.3 to 143.3, which is about more than 4 times higher than the particulate matter (PM2.5) existing standard of 35 μg/m3 also an average particulate matter from 615 to 656, which is about more than 4 times higher than the particulate matter (PM10) existing standard of 150 μg/m3. As well as, it showed clear links between PM exposure and respiratory health and pulmonary function. Cough, dyspnea and sputum buildup were more common among the exposed group, Furthermore, the mean of FEV1/ FVC (%) for control group is significantly greater than that for FEV1/ FVC (%) for case group. Among other recommendations, this paper infers that Environmental and engineering control of cement dust emissions, protective techniques, procedures, measures and equipment and periodic medical examinations.This study was conducted to investigate the level of PM air pollution in cement plants, and their impacts on respiratory system health and pulmonary function for cement plants workers. Case-control study was conducted on all cement plants at Middle Gaza Governorates. 100 individuals participated, case (exposed) and control (non exposed) groups contain 50, 50 respectively. All participants were subjected to questionnaire, lung function measuring by electronic spirometery. The findings of this study showed that an average particulate matter from 106.3 to 143.3, which is about more than 4 times higher than the particulate matter (PM2.5) existing standard of 35 μg/m3 also an average particulate matter from 615 to 656, which is about more than 4 times higher than the particulate matter (PM10) existing standard of 150 μg/m3. As well as, it showed clear links between PM exposure and respiratory health and pulmonary function. Cough, dyspnea and sputum buildup were more common among the exposed group, Furthermore, the mean of FEV1/ FVC (%) for control group is significantly greater than that for FEV1/ FVC (%) for case group. Among other recommendations, this paper infers that Environmental and engineering control of cement dust emissions, protective techniques, procedures, measures and equipment and periodic medical examinations

A Survey on Malware Detection with Graph Representation Learning

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.Comment: Preprint, submitted to ACM Computing Surveys on March 2023. For any suggestions or improvements, please contact me directly by e-mai

Artificial Neural Network for Predicting Car Performance Using JNN

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas

A Sustainable Approach for Removing Organic Pollutants from Food Processing Effluents Using Unmodified Cocopeat as an Adsorbent

Food production (chips) uses raw materials such as tapioca, cassava, banana, and many more, which contribute to excessive pollutants in the water. Thus, there is a need to treat effluent sufficiently to prevent undesired pollutants from being released into the receiving water bodies, such as rivers and agricultural drainage systems. This study aims to investigate the effectiveness of cocopeat in removing targeted parameters such as suspended solids (SS), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and total phosphorus (TP) from the chips processing effluent. Batch experiments were conducted to determine optimum operating parameters, including the adsorbent dosage, contact time, and shaking speed. This was done to identify the best removal rates of SS, COD, NH3-N and TP from effluent food processing samples taken from two different discharge points based on their usages. The experimental results show that at the optimum conditions of pH 7, cocopeat dosages of 800 mg/L, contact time of 30 minutes, and shaking speeds of 200 rpm, the unmodified cocopeat achieved 17.3% and 19.8% of SS removal, 35.5% and 28.9% of COD removal, 40.7% and 30.5% of NH3-N removal, and 53.5% and 59.2% of TP removal, from Point A and Point B effluent, respectively. Besides, the maximum adsorption capacity achieved by unmodified cocopeat towards SS (1.5-14.0 mg/g), COD (16.88-17.75 mg/g), NH3-N (0.31-0.32 mg/g) and TP (1.46-1.50 mg/g) are comparable to the adsorption capacities reported by previous researchers. This finding suggests that cocopeat could potentially replace the commercially developed adsorbents for the treatment. Furthermore, this study gave insights into the feasibility of sustainable treatment using cocopeat as an adsorbent for medium-strength effluent. However, it is suggested that further alteration of the cocopeat characteristics, either by chemical or physical modifications, and its sludge disposal method could be explored further to enhance the treatment performance

Wharton’s jelly mesenchymal stem cells: a concise review of their secretome and prospective clinical applications

Accumulating evidence indicates that most primary Wharton’s jelly mesenchymal stem cells (WJ-MSCs) therapeutic potential is due to their paracrine activity, i.e., their ability to modulate their microenvironment by releasing bioactive molecules and factors collectively known as secretome. These bioactive molecules and factors can either be released directly into the surrounding microenvironment or can be embedded within the membrane-bound extracellular bioactive nano-sized (usually 30–150 nm) messenger particles or vesicles of endosomal origin with specific route of biogenesis, known as exosomes or carried by relatively larger particles (100 nm–1 μm) formed by outward blebbing of plasma membrane called microvesicles (MVs); exosomes and MVs are collectively known as extracellular vesicles (EVs). The bioactive molecules and factors found in secretome are of various types, including cytokines, chemokines, cytoskeletal proteins, integrins, growth factors, angiogenic mediators, hormones, metabolites, and regulatory nucleic acid molecules. As expected, the secretome performs different biological functions, such as immunomodulation, tissue replenishment, cellular homeostasis, besides possessing anti-inflammatory and anti-fibrotic effects. This review highlights the current advances in research on the WJ-MSCs’ secretome and its prospective clinical applications

Evaluation of the Correlation between Particulate Matter (PM2.5) and Meteorological Parameters

Particulate matters are emitted from a wide range of man-made and natural sources. Particulate matters (ð‘ƒð‘€2.5) pose the greatest problems and consequences to human health. Batu Pahat is considered as main urban area with high industrial activity and other anthropogenic activities. Due to awareness of the people health in Batu Pahat this study was performed. The focus of this research is to examine the levels of ð‘ƒð‘€2.5 in several areas in Batu Pahat and to examine the correlations of ð‘ƒð‘€2.5 with the weather parameters using the Pearson correlation coefficient. Two stations of selected areas were chosen, namely Batu Pahat, and Parit Sulong represented as an urban area, and residential area. Three parameters observed for 3 consecutive months starting from December 2020 to February 2021 in two phases which were phase 1 (7a.m.) and phase 2 (7p.m.). The data was obtained from the website of Department of Environment (DOE). The statistical analysis of the results obtained for the air particulate matters ð‘ƒð‘€2.5 at the study areas will be compared with the Malaysian Ambient Air Quality Guidelines (MAAQG). It was found out that the concentration of ð‘ƒð‘€2.5 at station B (Parit Sulong) the residential area, was higher in phase 1 and phase 2 with value of 15.04 μg/ð‘š3 and 14.12 μg/ð‘š3 respectively. It can be seen that station B have higher value of ð‘ƒð‘€2.5 than station A (Batu Pahat). Air quality index (AQI) values for both stations was less than the permitted value by Malaysian Ambient Air Quality Guidelines

Assessment of Indoor Air Quality in Neonatal Intensive Care Units in Government Hospitals in Gaza Strip- Palestine

This study was conducted to assess indoor air quality (IAQ) in three neonatal intensive care units (NICUs), which were chosen to geographically represent the Gaza strip. The study collected both: objective temperature and air quality measures ofcarbon dioxide (CO2), carbon monoxide (CO), temperature, relative humidity (RH%) and suspended particles (PM10, PM2.5); and clinical staff perceptions of indoor air quality and its impact. The study conducted daily air quality measurements between 4 March until 22 March 2013, and gathered 108 questionnaires. The study showed that the average concentrations of carbon dioxide were often close to the maximum standard of the United States Environmental Protection Agency, and sometimes exceed the limit, especially in the NICU of Shifa Hospital. Temperature fell within normal ranges, but approached recommended limits at Shifa Hospital. Carbon monoxide and suspended particle concentrations and relative humidity were within the standards recommended by the Environmental Protection Agency in all three NICUs. More than half of the clinical staff (60%) suffered from sick building syndrome, 83% suffer from tiredness and fatigue, and 76% suffer from headache. Nearly 85% believe that these symptoms are related to their workplace, and 71% report disappearance of the symptoms after they leave work. We conclude that indoor air quality merits more attention from the Ministry of Health, and that NICU staff be engaged around issues of environmental health.This study was conducted to assess indoor air quality (IAQ) in three neonatal intensive care units (NICUs), which were chosen to geographically represent the Gaza strip. The study collected both: objective temperature and air quality measures ofcarbon dioxide (CO2), carbon monoxide (CO), temperature, relative humidity (RH%) and suspended particles (PM10, PM2.5); and clinical staff perceptions of indoor air quality and its impact. The study conducted daily air quality measurements between 4 March until 22 March 2013, and gathered 108 questionnaires. The study showed that the average concentrations of carbon dioxide were often close to the maximum standard of the United States Environmental Protection Agency, and sometimes exceed the limit, especially in the NICU of Shifa Hospital. Temperature fell within normal ranges, but approached recommended limits at Shifa Hospital. Carbon monoxide and suspended particle concentrations and relative humidity were within the standards recommended by the Environmental Protection Agency in all three NICUs. More than half of the clinical staff (60%) suffered from sick building syndrome, 83% suffer from tiredness and fatigue, and 76% suffer from headache. Nearly 85% believe that these symptoms are related to their workplace, and 71% report disappearance of the symptoms after they leave work. We conclude that indoor air quality merits more attention from the Ministry of Health, and that NICU staff be engaged around issues of environmental health