Mohammed Abdul Khaleel

Bio: Mohammed Abdul Khaleel is an academic researcher from Iowa State University. The author has contributed to research in topics: Shear wall & Spar. The author has an hindex of 7, co-authored 18 publications receiving 133 citations. Previous affiliations of Mohammed Abdul Khaleel include University of Malaya & Sambalpur University.

E-learning services to achieve sustainable learning and academic performance: An empirical study

Abstract: E-Learning has proven to be the only resort as a replacement of traditional face-to-face learning methods in the current global lockdown due to COVID-19 pandemic. Academic institutions across the globe have invested heavily into E-Learning and the majority of the courses offered in traditional classroom mode have been converted into E-Learning mode. The success of E-Learning initiatives needs to be ensured to make it a sustainable mode of learning. The objective of the current study is to propose a holistic E-Learning service framework to ensure effective delivery and use of E-Learning Services that contributes to sustainable learning and academic performance. Based on an extensive literature review, a proposed theoretical model has been developed and tested empirically. The model identifies a broad range of success determinants and relates them to different success measures, including learning and academic performance. The proposed model was validated with the response from 397 respondents involved with an E-Learning system in the top five public universities in the southern region of Saudi Arabia through the Partial Least Squares regression technique using SmartPLS software. Five main factors (Learner’s Quality, Instructor’s Quality, Information’s Quality, System’s Quality and Institutional Quality) were identified as a determinant of E-Learning service performance which together explains 48.7% of the variance of perceived usefulness of ELS, 71.2% of the variance of use of the E-Learning system. Perceived usefulness of ELS and use of ELS together explain 70.6% of learning and academic performance of students. Hence the framework will help achieve the sustainable and successful adoption of E-Learning services.

A Survey of Data Mining Techniques on Medical Data for Finding Locally Frequent Diseases

Abstract: In the last decade there has been increasing usage of data mining techniques on medical data for discovering useful trends or patterns that are used in diagnosis and decision making. Data mining techniques such as clustering, classification, regression, association rule mining, CART (Classification and Regression Tree) are widely used in healthcare domain. Data mining algorithms, when appropriately used, are capable of improving the quality of prediction, diagnosis and disease classification. The main focus of this paper is to analyze data mining techniques required for medical data mining especially to discover locally frequent diseases such as heart ailments, lung cancer, breast cancer and so on. We evaluate the data mining techniques for finding locally frequent patterns in terms of cost, performance, speed and accuracy. We also compare data mining techniques with conventional methods.

Non-linear FEM analysis of seismic induced pounding between neighbouring multi-storey structures

Abstract: Pounding of neighbouring construction of structures due to seismic excitation increases the damage of structural components or even causes collapse of structures Among the possible building damages, earthquake induced pounding has been commonly observed in several earthquakes Therefore it is imperative to consider pounding effect for structures This study aims to understand the response behaviour of adjacent buildings with dissimilar heights under earthquake induced pounding Effects of different separation distances between structures are also investigated Nonlinear finite element analysis in time domain has been carried out for pounding of neighbouring structures having varying heights To show the importance of avoiding pounding in structures the results obtained were compared with model having no pounding phenomena The results were obtained in the form of storey shear, pounding force, storey drift, point displacement and acceleration The acceleration at pounding level significantly increases during collision of building The generated extra pounding force may cause severe damage to structural members of structures Pounding produces shear at various story levels, which are greater than those obtained from no pounding case Building with more height suffers greater damage than shorter building when pounding occurs Increasing gap distance tends to reduce story shear in consistent manner The results also show that the conventional modelling of building considering only beams and columns underestimates pounding effects More realistic modelling such as beams, columns and slabs shall be adopted to accurately understand the pounding phenomenon

Optimum structural modelling for tall buildings

Abstract: It is a common practice to model multi-storey tall buildings as frame structures where the loads for structural design are supported by beams and columns. Intrinsically, the structural strength provided by the walls and slabs are neglected. As the building height increases, the effect of lateral loads on multi-storey structures increases considerably. The consideration of walls and slabs in addition to the frame structure modelling shall theoretically lead to improved lateral stiffness. Thus, a more economic structural design of multi-storey buildings can be achieved. In this research, modelling and structural analysis of a 61-storey building have been performed to investigate the effect of considering the walls, slabs and wall openings in addition to frame structure modelling. Sophisticated finite element approach has been adopted to configure the models, and various analyses have been performed. Parameters, such as maximum roof displacement and natural frequencies, are chosen to evaluate the structural performance. It has been observed that the consideration of slabs alone with the frame modelling may have negligible improvement on structural performance. However, when the slabs are combined with walls in addition to frame modelling, significant improvement in structural performance can be achieved. Copyright © 2012 John Wiley & Sons, Lt

Finding Locally Frequent Diseases Using Modified Apriori Algorithm

Abstract: 3 Abstract:Data mining is a phenomenon which analyzes large volumes of data and extracts patterns that can be converted to useful knowledge. The data mining techniques can be applied on medical data which has abundant scope to improve Quality of Service in Healthcare industry. Electronic health records and other historical medical data available in textual and graphical formats are a gold mine to researchers in the field. Medical data mining techniques analyze latent medical attributes and the relationships among them to bring about expert decisions in curing diseases. Data mining techniques can also be used toknow the frequently occurring diseases in the local databases. In this paper we present a methodology to discover locally frequent diseases with the help of Apriori data mining technique. We also used visualization techniques to present the trends graphically. We built a prototype application that demonstrates the efficiency of the method. The empirical results revealed that the prototype is useful and can be used in real world Healthcare tools.

The structural design of tall and special buildings

Abstract: This paper presents some of the oral discussion by the author and others at the 2005 Annual Meeting of the Los Angeles Tall Buildings Structural Design Council. It also includes additional opinions added by the author after the annual meeting. These opinions address the development of a new building code for tall buildings and where the non-structural engineering decision makers can and must make contributions. It also addresses the very important topic of quality control. Copyright © 2005 John Wiley & Sons, Ltd.

Analysis of data mining techniques for heart disease prediction

Abstract: Heart disease is considered as one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. This paper addresses the issue of prediction of heart disease according to input attributes on the basis of data mining techniques. We have investigated the heart disease prediction using KStar, J48, SMO, Bayes Net and Multilayer Perceptron through Weka software. The performance of these data mining techniques is measured by combining the results of predictive accuracy, ROC curve and AUC value using a standard data set as well as a collected data set. Based on performance factor SMO and Bayes Net techniques show optimum performances than the performances of KStar, Multilayer Perceptron and J48 techniques.

Heart disease diagnosis using data mining technique

Abstract: Data mining is an advanced technology, which is the process of discovering actionable information from large set of data, which is used to analyze large volumes of data and extracts patterns that can be converted to useful knowledge. Medical data mining has a great potential for exploring the hidden patterns in the data sets of medical domain. These patterns can be utilized to do clinical diagnosis. These data need to be collected in a standardized form. From the medical profiles fourteen attributes are extracted such as age, sex, blood pressure and blood sugar etc. can predict the likelihood of patient getting heart disease. These attributes are fed in to K-means algorithms, MAFIA algorithm and Decision tree classification in heart disease prediction, applying the data mining technique to heart disease treatment; it can provide as reliable performance as that achieved in diagnosing heart disease. By this medical industries could offer better diagnosis and treatment of the patient to attain a good quality of services. The main advantages of this paper are: early detection of heart disease and its diagnosis correctly on time and providing treatment with affordable cost.