Browsing by Author "Molla, M. M. Imran"

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    Design and Performance Improvement of Microstrip Patch Antenna Using Graphene Material for Communication Applications
    (2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE, 2021-05-26) Mollah, Mohammad Sarwar Hossain; Faruk, Omar; Hossain, Md. Selim; Islam, Md. Tarequl; Shafi, A.S.M.; Molla, M. M. Imran
    In this paper, we discuss the process to fabricate a microstrip patch antenna by using graphene as a substrate material. Ours is the age of modern technology. Wireless communication is one of them. In wireless communication, graphene is widely used in the twenty-first century. Microstrip patch antenna provides better performance and better anticipation compared to other antennas. Microstrip patch antennas are more preferable compared to others because of their low cost, small size, and high performance. The graphene has a reconfigurable surface conductivity that can be tuned to operate at the desired frequencies. The main concept we focus on here to locate the best feed point of a microstrip patch antenna. This paper represents a graphene-based microstrip patch antenna. It has a 2.45 GHz resonant frequency for wireless communication. Here we use CST STUDIO SUITE 2017 software for designing and simulation our antenna. It shows the voltage standing wave ratio (VSWR), return loss, H-plane radiation pattern, and input impedance. The simulation output result shows a gain 6.801 dB and directivity of 7.302 dBi. It has return loss -23.673dB. Finally, we build up a design process of a graphene-based microstrip patch antenna.
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    Feature Selection and Prediction of Heart Disease Using Machine Learning Approaches
    (Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering. Lecture Notes in Electrical Engineering, vol 842. Springer, Singapore., 2022-01-09) Molla, M. M. Imran; Islam, Md. Sakirul; Shafi, A. S. M.; Alam, Mohammad Khurshed; Islam, Md. Tarequl; Jui, Julakha Jahan
    Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Ran-dom Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the ROC/AUC curve is calculated for each classification al-gorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification ac-curacy is about 91%.