Papers by Md. Anwar Hussen Wadud
Applied sciences, Dec 22, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Applied Sciences
Fake news detection techniques are a topic of interest due to the vast abundance of fake news dat... more Fake news detection techniques are a topic of interest due to the vast abundance of fake news data accessible via social media. The present fake news detection system performs satisfactorily on well-balanced data. However, when the dataset is biased, these models perform poorly. Additionally, manual labeling of fake news data is time-consuming, though we have enough fake news traversing the internet. Thus, we introduce a text augmentation technique with a Bidirectional Encoder Representation of Transformers (BERT) language model to generate an augmented dataset composed of synthetic fake data. The proposed approach overcomes the issue of minority class and performs the classification with the AugFake-BERT model (trained with an augmented dataset). The proposed strategy is evaluated with twelve different state-of-the-art models. The proposed model outperforms the existing models with an accuracy of 92.45%. Moreover, accuracy, precision, recall, and f1-score performance metrics are ut...
International Journal of Information Management Data Insights
Computer Systems Science and Engineering
Offensive messages on social media, have recently been frequently used to harass and criticize pe... more Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers (Deep-BERT) to identify offensive posts on social media that are used to harass others. This paper explores a variety of ways to deal with multilingualism, including collaborative multilingual and translation-based approaches. Then, the Deep-BERT is tested on the Bengali and English datasets, including the different bidirectional encoder representations from transformers (BERT) pre-trained word-embedding techniques, and found that the proposed Deep-BERT's efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%. The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.
2021 International Conference on Science & Contemporary Technologies (ICSCT), 2021
2021 5th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2021
In conventional medical data monitoring systems are suffering key challenges in phases of informa... more In conventional medical data monitoring systems are suffering key challenges in phases of information immutability, traceability, transparency, observation, data validation, access permission, reliability, privacy, and safety. Personal Health Records(PHR) have various advantages globally, but at PHRs data is ruled to essential safety and privacy concerns. This paper suggests a method to implement a reliable clarification to these points. Traditionally sophisticated methods trading with the security of health records ordinarily makes information inaccessible system to patients. Certain methods struggle to adjust information reliability, patient desire, and regular communication with supplier information. Blockchain(BC) resolves the preceding difficulties from it shares data in a decentralized and transactional way. The utilize of BC could support the healthcare division to adjust the accessibility, privacy, and security of PHRs. This document suggests a BC framework to efficiently and securely collect and keep health records. It represents a reliable and skilled means of achieving healthcare data for patients, physicians, and security insurance agencies while defending the patients of information. The goal of this activity is to show how the suggested system fits the safety requirements of participants (patients), physicians, and third performances and discusses privacy and safety attention in the medical division.
International Journal of Computing and Digital Systems, 2022
Wireless Medical Sensor Network (WMSN) consists of biosensors connected with each other implanted... more Wireless Medical Sensor Network (WMSN) consists of biosensors connected with each other implanted within the human body. It transmits data to remote medical centers. Medical professionals can access the sensors of the human body to inquire about his health condition remotely. Transmitting patient data over insecure wireless channels is a major challenge because health data are very sensitive and must not be disclosed to unauthorized users, so ensuring secure authentication and preserving anonymity is very important. To address this issue, many researchers have provided many protocols for WMSNs. An anonymous patient monitoring system using WMSN presented by Amin et al. and demanded that their system preserves mutual authentication, user anonymity and security against stolen smart device attacks. By studying thorough and in-depth analyses, we found that this system is attackable to privileged insider attacks and stolen smart device attacks. In addition, it does not protect user anonymity. Additionally, it fails to protect denial of service attack. Furthermore, it has an error in the password modification stage. To overcome the above limitations of the existing systems we have proposed an advanced and mask identity-based secure mutual authentication protocol using WMSN. An informal security analysis is performed, which shows that our protocol is secure against different types of attacks. Furthermore, in our proposed protocol we have used the BAN logic model to prove the correctness of the mutual authentication feature. In addition, it offers ease login, secure authentication and strong password change phases.
Iraqi Journal of Science
   Natural Language Processing (NLP) deals with analysing, understanding and generating language... more    Natural Language Processing (NLP) deals with analysing, understanding and generating languages likes human. One of the challenges of NLP is training computers to understand the way of learning and using a language as human. Every training session consists of several types of sentences with different context and linguistic structures. Meaning of a sentence depends on actual meaning of main words with their correct positions. Same word can be used as a noun or adjective or others based on their position. In NLP, Word Embedding is a powerful method which is trained on large collection of texts and encoded general semantic and syntactic information of words. Choosing a right word embedding generates more efficient result than others. Most of the papers used pretrained word embedding vector in deep learning for NLP processing. But, the major issue of pretrained word embedding vector is that it can’t use for all types of NLP processing. In this paper, a local word embedding vector fo...
2021 3rd International Conference on Sustainable Technologies for Industry 4.0 (STI), 2021
Recently, during the COVID-19 situation, the requirement and importance of tracking patients from... more Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor’s care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. There is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals. The most current remote patient monitoring system is organization-centric and patient’s privacy and security rely on healthcare providers’ mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have appl...
Coronavirus Disease 2019 (COVID-19) was identified in late 2019 and world health Organization (WH... more Coronavirus Disease 2019 (COVID-19) was identified in late 2019 and world health Organization (WHO) declared as a pandemic on March 11, 2019. World top researchers, physician and pharmacists are trying to find out remedy but it is still in research phase. COVID-19 spread through the air by coughing or sneezing also depends on environment. In this paper, our main goal is to COVID-19 threat analysis in South Asian people based on their habits, culture, consciousness etc. compare to Europe and North American culture. The research work is formulated in three steps. First, we formulate a dynamic infection transmission model by considering the fertility rate, mortality rate, transmission rate, and cure rate of the COVD-19 caused death rate as variables. Second, we define the variables of the model based on the census of south Asia. Finally, we analyze the threat that COVID-19 can cause to the population of crowded country likes Bangladesh, India etc. in south Asia.
Informatics in Medicine Unlocked
IEEE Access
Due to the significant increase in Internet activity since the COVID-19 epidemic, many informal, ... more Due to the significant increase in Internet activity since the COVID-19 epidemic, many informal, unstructured, offensive, and even misspelled textual content has been used for online communication through various social media. The Bengali and Banglish(Bengali words written in English format) offensive texts have recently been widely used to harass and criticize people on various social media. Our deep excavation reveals that limited work has been done to identify offensive Bengali texts. In this study, we have engineered a detection mechanism using natural language processing to identify Bengali and Banglish offensive messages in social media that could abuse other people. First, different classifiers have been employed to classify the offensive text as baseline classifiers from real-life datasets. Then, we applied boosting algorithms based on baseline classifiers. AdaBoost is the most effective ensemble method called adaptive boosting, which enhances the outcomes of the classifiers. The long short-term memory (LSTM) model is used to eliminate long-term dependency problems when classifying text, but overfitting problems occur. AdaBoost has strong forecasting ability and overfitting problem does not occur easily. By considering these two powerful and diverse models, we propose L-Boost, the modified AdaBoost algorithm using bidirectional encoder representations from transformers (BERT) with LSTM models. We tested the L-Boost model on three separate datasets, including the BERT pretrained word-embedding vector model. We find our proposed L-Boost's efficacy better than all the baseline classification algorithms reaching an accuracy of 95.11%. INDEX TERMS Offensive text, social media harassment, natural language processing, ensemble learning, BERT model.
International Journal of Advanced Computer Science and Applications
Text coherence analysis is the most challenging task in Natural Language Processing (NLP) than ot... more Text coherence analysis is the most challenging task in Natural Language Processing (NLP) than other subfields of NLP, such as text generation, translation, or text summarization. There are many text coherence methods in NLP, most of them are graph-based or entity-based text coherence methods for short text documents. However, for long text documents, the existing methods perform low accuracy results which is the biggest challenge in text coherence analysis in both English and Bengali. This is because existing methods do not consider misspelled words in a sentence and cannot accurately assess text coherence. In this paper, a text coherence analysis method has been proposed based on the Misspelling Oblivious Word Embedding Model (MOEM) and deep neural network. The MOEM model replaces all misspelled words with the correct words and captures the interaction between different sentences by calculating their matches using word embedding. Then, the deep neural network architecture is used to train and test the model. This study examines two different types of datasets, one in Bengali and the other in English, to analyze text consistency based on sentence sequence activities and to evaluate the effectiveness of this model. In the Bengali language dataset, 7121 Bengali text documents have been used where 5696 (80%) documents have been used for training and 1425 (20%) documents for testing. And in the English language dataset, 6000 (80%) documents have been used for training and 1500 (20%) documents for model evaluation out of 7500 text documents. The efficiency of the proposed model is compared with existing text coherence analysis techniques. Experimental results show that the proposed model significantly improves automatic text coherence detection with 98.1% accuracy in English and 89.67% accuracy in Bengali. Finally, comparisons with other existing text coherence models of the proposed model are shown for both English and Bengali datasets.
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Papers by Md. Anwar Hussen Wadud