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Results for “"Khademul Islam Molla"”

16+ results

Feature Selection for Intrusion Detection Using Random Forest

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Md. Al Mehedi Hasan, Mohammed Nasser, Shamim Ahmad, Khademul Islam Molla

Journal: Journal of Information SecurityYear: 2016Citations: 208

An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-releva...

Physical SciencesComputer ScienceComputer Networks and CommunicationsOpen Access
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Single-Mixture Audio Source Separation by Subspace Decomposition of Hilbert Spectrum

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Md. Khademul Islam Molla, Keikichi Hirose

Journal: IEEE Transactions on Audio Speech and Language ProcessingYear: 2007Citations: 88

A novel technique is developed to separate the audio sources from a single mixture. The method is based on decomposing the Hilbert spectrum (HS) of the mixed signal into independent source subspaces. Hilbert transform combined with empirical mode decomposition (EMD) constitutes HS, which is a fine-r...

Physical SciencesComputer ScienceSignal Processing
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Discriminative Feature Selection-Based Motor Imagery Classification Using EEG Signal

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Md. Khademul Islam Molla, Abdullah Al Shiam, Md. Rabiul Islam, Toshihisa Tanaka

Journal: IEEE AccessYear: 2020Citations: 77

Achieving a reliable classification of motor imagery (MI) tasks is a major challenge in brain-computer interface (BCI) implementation. The set of relevant and discriminative features plays an important role in the classification scheme. This paper presents a supervised approach to select discriminat...

Life SciencesNeuroscienceCognitive NeuroscienceOpen Access
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Artifact suppression from EEG signals using data adaptive time domain filtering

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Md. Khademul Islam Molla, Md. Rabiul Islam, Toshihisa Tanaka, Tomasz M. Rutkowski

Journal: NeurocomputingYear: 2012Citations: 61
Life SciencesNeuroscienceCognitive Neuroscience
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Epileptic seizure detection in EEG using mutual information-based best individual feature selection

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Kazi Mahmudul Hassan, Md. Rabiul Islam, Thanh Thi Nguyen, Md. Khademul Islam Molla

Journal: Expert Systems with ApplicationsYear: 2022Citations: 60
Life SciencesNeuroscienceCognitive NeuroscienceOpen Access
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Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

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Md. Rabiul Islam, Toshihisa Tanaka, Md. Khademul Islam Molla

Journal: Journal of Neural EngineeringYear: 2018Citations: 48

OBJECTIVE: When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operatio...

Life SciencesNeuroscienceCognitive Neuroscience
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Protein subcellular localization prediction using multiple kernel learning based support vector machine

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Md. Al Mehedi Hasan, Shamim Ahmad, Md. Khademul Islam Molla

Journal: Molecular BioSystemsYear: 2017Citations: 47

Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered proteins has been growing exponentially, which in...

Life SciencesBiochemistry, Genetics and Molecular BiologyMolecular Biology
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Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

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Md. Humaun Kabir, Shabbir Mahmood, Abdullah Al Shiam, Abu Saleh Musa Miah et al.

Journal: MathematicsYear: 2023Citations: 44

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successfu...

Life SciencesNeuroscienceCognitive NeuroscienceOpen Access
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predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue

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Md. Al Mehedi Hasan, Jinyan Li, Shamim Ahmad, Md. Khademul Islam Molla

Journal: Analytical BiochemistryYear: 2017Citations: 36

The carbonylation is found as an irreversible post-translational modification and considered a biomarker of oxidative stress. It plays major role not only in orchestrating various biological processes but also associated with some diseases such as Alzheimer's disease, diabetes, and Parkinson's disea...

Life SciencesBiochemistry, Genetics and Molecular BiologyMolecular Biology
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Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA

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Md. Rabiul Islam, Md. Khademul Islam Molla, Masaki Nakanishi, Toshihisa Tanaka

Journal: Journal of Neural EngineeringYear: 2017Citations: 35

OBJECTIVE: Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper de...

Life SciencesNeuroscienceCognitive Neuroscience
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Climatic data analysis for groundwater level simulation in drought prone Barind Tract, Bangladesh: Modelling approach using artificial neural network

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Ripon Hasda, Md. Ferozur Rahaman, Chowdhury Sarwar Jahan, Khademul Islam Molla et al.

Journal: Groundwater for Sustainable DevelopmentYear: 2020Citations: 33

This study presents implementation of non-linear autoregressive model with exogenous inputs (NARX) of Artificial neural network (ANN), used for groundwater level (GWL) simulation to predict its weekly level up to 52 weeks ahead in selected 14 Permanent Hydrograph Stations (PHSs) in the drought prone...

Physical SciencesEnvironmental ScienceEnvironmental Engineering
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Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography

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Md. Khademul Islam Molla, Kazi Mahmudul Hassan, Md. Rabiul Islam, Toshihisa Tanaka

Journal: SensorsYear: 2020Citations: 28

Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into...

Life SciencesNeuroscienceCognitive NeuroscienceOpen Access
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Automatic Life-Logging: A novel approach to sense real-world activities by environmental sound cues and common sense

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Mostafa Al Masum Shaikh, Md. Khademul Islam Molla, Keikichi Hirose

Year: 2008Citations: 27

There are many studies that collect and store life log for personal memory. The paper explains how a system can create someone's life log in an inexpensive way to share daily life events with family or friends through socialnetwork or messaging. In the modern world where people are usually busier th...

Physical SciencesComputer ScienceComputer Vision and Pattern Recognition
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Natural Human Emotion Recognition Based on Various Mixed Reality(MR) Games and Electroencephalography (EEG) Signals

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Abu Saleh Musa Miah, Jungpil Shin, Md. Minhajul Islam, Abdullah Abdullah et al.

Year: 2022Citations: 23

We estimated willing and natural emotions while playing Mixed reality (MR) games. We have shown the performance accuracy of the labeling with game type and self-assessment EEG data. This study is conducted to improve the Virtual reality (VR) and MR world to be more realistic and suitable to the need...

Life SciencesNeuroscienceCognitive Neuroscience
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Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy

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Most. Sheuli Akter, Md. Rabiul Islam, Toshihisa Tanaka, Yasushi Iimura et al.

Journal: EntropyYear: 2020Citations: 23

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencepha...

Life SciencesNeuroscienceCognitive NeuroscienceOpen Access
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