Browsing by Author "Ohidujjaman"

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A Knowledge Base Data Mining Based on Parkinson's Disease
(IEEE, 2019-11) Hassan, Md. Redone; Kadir, S.K. Obidul; Islam, Md. Aminul; Abujar, Sheikh; Zannat, Raihana; Ohidujjaman
The approaches to detecting Parkinson's disease in the human body from voice data by using Classification techniques apply three different algorithms for finding the growth rate of this disease. Unified Parkinson's disease rating scale deals with motor fluctuations and changes over voice after a certain period and that can measure the people affected by this disease and the difference with healthy people. Hoehn & Yahr scale measures the symptoms which are being working through the improvement of Parkinson's disease in the human body. Classifier algorithms used to detect the factors and symptoms which are involved in the advancement of this disease in the human body using voice data. From the distinctions of all algorithms measures the growth rate and find out which algorithm gives the best result for several approaches to diagnosis Parkinson's disease and chances of had this disease in the human body.
An Approach for Bengali Text Summarization using Word2Vector
(Scopus, 2019-12-30) Abujar, Sheikh; Masum, Abu Kaisar Mohammad; Mohibullah, Md.; Ohidujjaman; Hossain, Syed Akhter
Text Summarization is one of the mentionable research areas of Natural language processing. Several approaches have already been developed in this concern. Such as - Abstractive approach and extractive approach. Most recent recurrent neural network methods are producing much better results. Several mentionable research has already been discussed for English language summarizer, but a few have already done for the Bengali language. There are so many prerequisites for data analysis purpose-word2vector is one of them. Understanding the vector representation of any text leads the way to identify the key main points of that specific text and helps to measure the relationship of that text with other texts in similarity/dissimilarity [11]. Generated matrix using word2vector can easily applicable for identifying top-ranked sentence/words, either domain specific or in general form. In this paper, a word2vector approach has been discussed in the context of text summarization for the Bengali language.
Automatic Machine Translation for Bangla and English Resolving Ambiguities
(Scopus, 2021) Ohidujjaman; Faysal, Fahim; Sumon, Shams; Huda, Mohammad Nurul
The strategic borderless knowledge sharing and development of communication interacts with dialects. Significant factors such as education, medical, business, research and others are vastly diffused over the world based on various lingoes. Bilingual or multilingual expression is the standard of having unknown/new linguistic along with its resources. The initial endeavor of the study is to implement the MT (machine translation) approaches for English to Bangla language processing and vice-versa. The emphasis of the study is the distinct ambiguities are identified along with their best solutions. Certain machine translation approaches such as word-to-word, direct, transfer, interlingua, corpus-based and statistical translation are surviving and, few of them are deployed in this Smart Natural Language Processing (SNLP) for dispatching the source to the target language and vice-versa. Two different dictionaries (bilingual and monolingual) are developed for the execution process. An eminent resource Stanford POS Tagger (as a toolkit) is used for identifying the grammatical structure of the source (English) dialect. This research also focuses on output acquiring through performance analyzing of different translation models.
Automatic Machine Translation for Bangla and English Resolving Ambiguities
(2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), IEEE, 2021-01) Ohidujjaman; Faysal, Fahim; Sumon, Shams; Huda, Mohammad Nurul
The strategic borderless knowledge sharing and development of communication interacts with dialects. Significant factors such as education, medical, business, research and others are vastly diffused over the world based on various lingoes. Bilingual or multilingual expression is the standard of having unknown/new linguistic along with its resources. The initial endeavor of the study is to implement the MT (machine translation) approaches for English to Bangla language processing and vice-versa. The emphasis of the study is the distinct ambiguities are identified along with their best solutions. Certain machine translation approaches such as word-to-word, direct, transfer, interlingua, corpus-based and statistical translation are surviving and, few of them are deployed in this Smart Natural Language Processing (SNLP) for dispatching the source to the target language and vice-versa. Two different dictionaries (bilingual and monolingual) are developed for the execution process. An eminent resource Stanford POS Tagger (as a toolkit) is used for identifying the grammatical structure of the source (English) dialect. This research also focuses on output acquiring through performance analyzing of different translation models.
Ill-condition enhancement for BC speech using RMC method
(2024-10-19) Ohidujjaman; Hasan, Mahmudul; Zhang, Shiming; Nurul Huda, Mohammad; Shorif Uddin, Mohammad
This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical linear prediction (LP) methods. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. Principally, eigenvalue expansion causes ill-condition in numerical analysis. To mitigate this problem, the regularized least squares (RLS) technique is commonly used. Motivated by the RLS concept, we derive the regularized modified covariance (RMC) method for BC speech analysis in this study. The RMC method reduces eigenvalue expansion by compressing the spectral dynamic range of the speech signal. Thus, the RMC method resolves the ill-conditioned problem of LP. In experiments, we show that the RMC method provides compressed eigenvalue expansion than the conventional methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. In this paper, the regularization parameter in practice is iteratively and rule-based derived. The RMC method with such a setting provides the best performance for BC speech analysis.
Machine Learning Applied to Kidney Disease Prediction
(10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE, 2019-12-30) Rabby, A.K.M. Shahariar Azad; Mamata, Rezwana; Laboni, Monira Akter; Ohidujjaman; Abujar, Sheikh
Machine learning has earned a remarkable position in healthcare sector because of its capability to enhance the disease prediction in healthcare sector. Artificial intelligence and Machine learning techniques are being used in healthcare sector. Nowadays, one of the world's crucial health related problem is kidney disease. It is increasing day by day because of not maintaining proper food habits, drinking less amount of water and lack of health consciousness. So we need some technique that will continuously monitor health condition effectively. Here, we have proposed an approach for real time kidney disease prediction, monitoring and application (KDPMA). Our aim is to find an optimized and efficient machine learning (ML) technique that can effectively recognize and predict the condition of chronic kidney disease. In this work, we used ten most popular machine learning technique to predict kidney disease. In this process, the data has been divided into two sections. In one section train dataset got trained and another section got evaluated by test dataset. The analysis results show that Decision Tree Classifier and Gaussian Naive Bayes achieved highest performance than the other classifiers, obtaining the accuracy score of 100% and 1 recall(Sensitivity) score. Now we are developing mobile application based on the best output results classifier technique to predict Kidney Disease from patient report.
Packet Loss Concealment Estimating Residual Errors of Forward-Backward Linear Prediction for Bone-Conducted Speech
(IJACSA, 2024-01-15) Ohidujjaman; Yasui, Nozomiko; Sugiura, Yosuke; Shimamura, Tetsuya; Makinae, Hisanori
"This study proposes a suitable model for packet loss concealment (PLC) by estimating the residual error of the linear prediction (LP) method for bone-conducted (BC) speech. Instead of conventional LP-based PLC techniques where the residual error is ignored, we employ forward-backward linear prediction (FBLP), known as the modified covariance (MC) method, by incorporating the residual error estimates. The MC method provides precise LP estimation for a short data length, reduces the numerical difficulties, and produces a stable model, whereas the conventional autocorrelation (ACR) method of LP suffers from numerical problems. The MC method has the effect of compressing the spectral dynamic range of the BC speech, which improves the numerical difficulties. Simulation results reveal that the proposed method provides excellent outcomes from some objectiv"
Packet Loss Concealment Estimating Residual Errors of Forward-Backward Linear Prediction for Bone-Conducted Speech
(2024-03-29) Ohidujjaman; Yasui, Nozomiko; Sugiura, Yosuke; Shimamura, Tetsuya; Makinae, Hisanori
This study proposes a suitable model for packet loss concealment (PLC) by estimating the residual error of the linear prediction (LP) method for bone-conducted (BC) speech. Instead of conventional LP-based PLC techniques where the residual error is ignored, we employ forward-backward linear prediction (FBLP), known as the modified covariance (MC) method, by incorporating the residual error estimates. The MC method provides precise LP estimation for a short data length, reduces the numerical difficulties, and produces a stable model, whereas the conventional autocorrelation (ACR) method of LP suffers from numerical problems. The MC method has the effect of compressing the spectral dynamic range of the BC speech, which improves the numerical difficulties. Simulation results reveal that the proposed method provides excellent outcomes from some objective evaluation scores in contrast to conventional PLC techniques.
PAPR Reduction of OFDM Signal by Scrutiny of BER Assessment and SPS-SLM Method via AWGN Channel
(2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE, 2021-01-26) Ohidujjaman; Zannat, Raihana; Khatun, Tania; Rahman, Mahfujur; Raza, Salim; Huda, Mohammad Nurul
Orthogonal Frequency Division Multiplexing (OFDM) has been presently underneath powerful exploration for broadband radio transmission owing to its strength in opposition to multi-path diminishing. Nevertheless, implementation of the OFDM method necessitates numerous complications. The foremost downside is high Peak-to-Average Power Ratio (PAPR) that hints to rise Bit Error Rate (BER) on account of nonlinearity of the peak ability amplifier. The Selected Mapping (SLM) technique is prominent method to lessen PAPR of OFDM signal. With SLM technique, lateral evidence bits are required to recuperate actual data which lead to increase the proportion of data damage. In our article, an exact set of sequential phase sequences (SPS) has been developed to perform SLM technique along OFDM transceiver without requiring of side information. SPS based SLM (SPS-SLM) technique has been able to reduce almost same PAPR compared to the conventional SLM technique. Moreover, the BER performance has been studied considering different number of sub-carriers as well as modulation order.
Product Review Analysis Using Social Media Data Based on Sentiment Analysis
(Scopus, 2020) Chowdhury, S. M. Mazharul Hoque; Abujar, Sheikh; Ohidujjaman; Badruzzaman, Khalid Been Md.; Hossain, Syed Akhter
In this current world where everyday people are generating a large amount of data and different business organizations are becoming more and more dependent on it, it has become very important to come out of the traditional methods of data analysis and focusing on the techniques that can prepare much more accurate and valid result to make business decision more easy and simple. This thesis proposes a technique to collect and analyze Twitter posts based on a different keyword-based product searching to generate products market statistical report. Using this program, it can be determined that if any product is getting popularity or losing its market. Few types of results were generated in this project. Each of them has their own type of importance. Overall this type of application can be trusted support for a business analyst or decision makers.
Spectral Analysis of Bone-conducted Speech Using Modified Linear Prediction
(Springer Nature, 2024-10-16) Ohidujjaman; Hasan, Mahmudul; Zhang, Shiming; Huda, Mohammad Nurul; Uddin, Mohammad Shorif
This paper improves the performance of linear prediction (LP) in precise spectral estimation of bone-conducted (BC) speech. Inherently, BC speech contains a wide spectral dynamic range that causes ill conditioning in the autocorrelation (ACR) method and its variants, where the Levinson–Durbin (L–D) algorithm is commonly implemented. Instead of the conventional LP-based spectral estimation methods, we utilize the covariance-based method, specifically the modified covariance (MC) method, where the orthogonal decomposition algorithm is deployed. In this paper, we derive the MC method from the least squares (LS) technique for BC speech analysis. The MC method reduces the eigenvalue expansion that compresses the spectral dynamic range of the BC speech signal. The effect of spectral dynamic range compression declines the ill-conditioned properties of LP. Through the proposed method using synthetic BC speech, the resulting power spectrum provides more accurate peaks than the conventional methods. The validity of the proposed method is also analyzed by inspecting real BC speech. This study reveals the utmost use of BC speech in speech processing systems. The experimental results demonstrate that the proposed method provides more accurate spectral estimation for synthetic and real BC speeches compared with conventional spectral estimation methods.
Text Analysis for Bengali Text Summarization Using Deep Learning
(10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE, 2019-12-30) Munzir, Abdullah Al; Rahman, Md. Lutfor; Abujar, Sheikh; Ohidujjaman; Hossain, Syed Akhter
Text summarization is an approach by which the size of one or more document is shortened and the shorten passage presents the core information of the document. In this modern era of information technology, we are over flooded with online data which raised the necessity of summary of the original text. Many methods have already implemented for English text and the effort for Bengali text are gaining alongside. In this paper, we propose an extractive text summarization technique based on a deep learning model of Recurrent Neural Network (RNN) for single document summary. Our method is to classify the sentences as significant or not for the summary. We have used Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) based RNN. Between them, we found LSTM more promising and we achieved average F1 scores- 0.63, 0.59, 0.56 for Rouge-1, Rouge-2 and Rouge-3 in some respects.