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33 results on '"Akhtar, Md. Nasim"'

3. Diagnosis and Classification of Fetal Health Based on CTG Data Using Machine Learning Techniques

Author

Islam, Md. Monirul, Rokunojjaman, Md., Amin, Al, Akhtar, Md. Nasim, Sarker, Iqbal H., Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Satu, Md. Shahriare, editor, Moni, Mohammad Ali, editor, Kaiser, M. Shamim, editor, and Arefin, Mohammad Shamsul, editor

Published
2023
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4. Water Monitoring Using Internet of Things

Author

Islam, Md. Monirul, Rony, Jahid Hasan, Akhtar, Md. Nasim, Shakil, Shalah Uddin Perbhez, Uddin, Jia, Chlamtac, Imrich, Series Editor, Marques, Gonçalo, editor, and González-Briones, Alfonso, editor

Published
2023
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5. Designing a Virtual Machine Placement Algorithm to Enhance Energy Efficiency in a Cloud Environment

Author

Islam, Litan, Akhtar, Md. Nasim, Rahman, Mostafijur, Shakil, MD., Baromon, Rathindranath, Ahmed, R. B., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hossain, Sazzad, editor, Hossain, Md. Shahadat, editor, Kaiser, M. Shamim, editor, Majumder, Satya Prasad, editor, and Ray, Kanad, editor

Published
2022
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11. Graph Directed Coalescence Hidden Variable Fractal Interpolation Functions

Author

Akhtar, Md. Nasim and Prasad, M. Guru Prem

Subjects
Mathematics - Dynamical Systems
Abstract

Fractal interpolation function (FIF) is a special type of continuous function which interpolates certain data set and the attractor of the Iterated function system (IFS) corresponding to the data set is the graph of the FIF. Coalescence Hidden-variable Fractal Interpolation Function (CHFIF) is both self-affine and non self-affine in nature depending on the free variables and constrained free variables for a generalized IFS. In this article graph directed iterated function system for a finite number of generalized data sets is considered and it is shown that the projections of the attractors on $\mathbb{R}^{2}$ is the graph of the CHFIFs interpolating the corresponding data sets.

Published
2015

13. Diabetes and heart disease prediction using machine learning classifiers based on Weka, python.

Author

Islam, Md. Monirul, Rahman, Md Shohanur, Siddique, Md. Nahid, Amin, Al, and Akhtar, Md. Nasim

Subjects
PYTHON programming language, SUPERVISED learning, HEART diseases, MACHINE learning, CARDIAC patients, DATA analytics
Abstract

Diabetes and heart disease are some of the most critical diseases for human beings. Lots of people are suffering from these two diseases. Early-stage diagnosing of these diseases is very essential for doctors and patients. Machine learning (ML)can play a vital role in this section. To this, ML algorithms can analyze the health data using various Data analytics tools. In this paper, we have found out the prediction of heart disease and diabetes patients. To validate the experimental analysis, we analyzed two datasets named diabetes dataset and heart disease prediction dataset in two popular analytics tools including WEKA and Python. Also, we used 6 supervised machine learning (SML) classifiers named Random forest (RF), Naive Bayes (NB), Decision Tree Classifier (DTC), Logistic regression (LR), K-NN, and support vector machine (SVM) for predicting heart and diabetes diseases. As a performance scale, we used accuracy, precision, recall, and F1 measure. In the case of diabetes disease, Random Forest outperforms the performance metrics by achieving 81% accuracy in python and DTC outperforms by placing 65% in Weka. On the other hand, in case of heart disease, LR achieves the highest score of 75% accuracy in Python and DTC gets the highest value of 79% accuracy in Weka. At last, the comparison result is shown between WEKA and Python tools in this paper. We got better results in Python than in the WEKA tool for the diabetes data set. [ABSTRACT FROM AUTHOR]

Published
2023
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17. Image compression using fractal multiwavelet transform

Author

Akhtar, Md. Nasim, Guru Prem Prasad, M., and Kapoor, G. P.

Abstract

Multiwavelets occur in wavelet theory for more general multiresolution analysis (MRA) using several scaling functions. The muliwavelets feature orthogonality, short support, symmetry, approximation order and regularity at a time which is not possible by using only a single wavelet system. In the present work, theory of MRA and wavelet using several scaling functions is reviewed to define Discrete Multiwavelet Transform and Fractal Multiwavelet Transform in matrix form, using a multifilter. As an application the defined transforms are applied to an image for its compression and the resulting entropy and PSNR values are compared with those occurring with earlier used transforms like Haar and D4 transforms.

Published
2024
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32. BOX DIMENSIONS OF -FRACTAL FUNCTIONS.

Author

AKHTAR, MD. NASIM, PRASAD, M. GURU PREM, and NAVASCUÉS, M. A.

Subjects
*GRAPH theory, *SET theory, *ITERATIVE methods (Mathematics), *MATHEMATICAL functions, *FRACTAL analysis
Abstract

The box dimension of the graph of non-affine, continuous, nowhere differentiable function which is a fractal analogue of a continuous function corresponding to a certain iterated function system (IFS), is investigated in the present paper. The estimates for box dimension of the graph of -fractal function for equally spaced as well as arbitrary data sets are found. [ABSTRACT FROM AUTHOR]

Published
2016
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