Fahima Tabassum

Work place: Institute of Information Technology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

E-mail: fahima@juniv.edu

Website: https://orcid.org/0000-0002-0183-3907

Research Interests: Image Processing, Machine Learning


Fahima Tabassum is currently serving as a Professor at Institute of Information Technology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh. She has completed her B.Sc. (Hons.) from the department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, Bangladesh in 2003 and M.Sc from the same department in 2010. She has achieved the degree of Doctor of Philosophy in Image Processing from the same department in 2023. She is currently doing his research in image processing, machine learning and software system analysis and development.

Author Articles
Detection of Diabetes using Combined ML Algorithm

By Shifat Jahan Setu Fahima Tabassum Sarwar Jahan Md. Imdadul Islam

DOI: https://doi.org/10.5815/ijisa.2024.01.02, Pub. Date: 8 Feb. 2024

Recently data clustering algorithm under machine learning are used in ‘real-life data’ to segregate them based on the outcome of a phenomenon. In this paper, diabetes is detected from pathological data of 768 patients using four clustering algorithms: Fuzzy C-Means (FCM), K-means clustering, Fuzzy Inference system (FIS) and Support Vector Machine (SVM). Our main objective is to make binary classification on the data table in a sense that presence or absence of diabetes of a patient. We combined the four machine learning algorithms based on entropy-based probability to enhance accuracy of detection. Before applying combining scheme, we reduce the size of variables applying multiple linear regression (MLR) on the table then logistic regression is again applied on the resultant data to keep the outlier within a narrow range. Finally, entropy based combining scheme with some modification is applied on the four ML algorithms and we got the accuracy of detection about 94% from the combining technique.

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The Performance Analysis of Digital Filters and ANN in De-noising of Speech and Biomedical Signal

By Humayra Ferdous Sarwar Jahan Fahima Tabassum Md. Imdadul Islam

DOI: https://doi.org/10.5815/ijigsp.2023.01.06, Pub. Date: 8 Feb. 2023

A huge number of algorithms are found in recent literature to de-noise a signal or enhancement of signal. In this paper we use: static filters, digital adaptive filters, discrete wavelet transform (DWT), backpropagation, Hopfield neural network (NN) and convolutional neural network (CNN) to de-noise both speech and biomedical signals. The relative performance of ten de-noising methods of the paper is measured using signal to noise ratio (SNR) in dB shown in tabular form. The objective of this paper is to select the best algorithm in de-noising of speech and biomedical signals separately. In this paper we experimentally found that, the backpropagation NN is the best for de-noising of biomedical signal and CNN is found as the best for de-noising of speech signal, where the processing time of CNN is found three times higher than that of backpropagation.

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