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A lightweight blockchain based framework for underwater ioT

- Uddin, Md Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
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  • Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

A patient agent controlled customized blockchain based framework for internet of things

- Uddin, Md Ashraf

  • Authors: Uddin, Md Ashraf
  • Date: 2021
  • Type: Text , Thesis , PhD
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  • Description: Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.
  • Description: Doctor of Philosophy

A patient centric agent assisted private blockchain on hyperledger fabric for managing remote patient monitoring

- Wadud, Md Anwar, Amir-Ul-Haque Bhuiyan, T., Uddin, Md Ashraf, Rahman, Md Motiur

  • Authors: Wadud, Md Anwar , Amir-Ul-Haque Bhuiyan, T. , Uddin, Md Ashraf , Rahman, Md Motiur
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 11th International Conference on Electrical and Computer Engineering, ICECE 2020 p. 194-197
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  • Description: 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 applied public blockchains where health data is available to all participants with the property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. The private blockchain will be implemented on Hyperledger Fabric where a Patient-centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database. © 2020 IEEE.

A survey of blockchain-based IoT eHealthcare : applications, research issues, and challenges

- Rahman, Md Shafiur, Islam, Md Amirul, Uddin, Md Ashraf, Stea, Giovanni

  • Authors: Rahman, Md Shafiur , Islam, Md Amirul , Uddin, Md Ashraf , Stea, Giovanni
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: Internet of Things (Netherlands) Vol. 19, no. (2022), p.
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  • Description: Blockchain (BC) technology has recently emerged as an essential component for different applications, including healthcare and IoT, because of its decentralized ledger, source provenance, and tamper-proof nature. The Internet of Things (IoT) and BC have enabled health systems to expand their scalability and maintain consistency on a decentralized platform. As a result, many researchers have developed BC-enabled IoT eHealth systems and explored the application of BC technology in diverse fields of eHealthcare. This paper conducts a comprehensive survey on the emerging applications of BC technology in healthcare. We summarize applications, research issues, security threats, research challenges, opportunities, and the future scope of BC technologies in the IoT-enabled healthcare system when BC is adopted to handle the privacy and storage of current and future medical records. Furthermore, we analyze the state-of-the-art BC works in the medical area, assessing their benefits-drawbacks, and guiding future researchers to overcome the limitations of the existing articles. © 2022 Elsevier B.V.

Automatic driver distraction detection using deep convolutional neural networks

- Hossain, Md Uzzol, Rahman, Md Ataur, Islam, Md Manowarul, Akhter, Arnisha, Uddin, Md Ashraf, Paul, Bikash

  • Authors: Hossain, Md Uzzol , Rahman, Md Ataur , Islam, Md Manowarul , Akhter, Arnisha , Uddin, Md Ashraf , Paul, Bikash
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Intelligent Systems with Applications Vol. 14, no. (2022), p.
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  • Description: Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the driver's behavior during the driving time to detect the distraction and mitigate the number of road accident. To detect various kinds of behavior like- using cell phone, talking to others, eating, sleeping or lack of concentration during driving; machine learning/deep learning can play significant role. However, this process may need high computational capacity to train the model by huge number of training dataset. In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization. Four architectures namely CNN, VGG-16, ResNet50 and MobileNetV2 have been adopted for transfer learning. To verify the effectiveness, the proposed model is trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained MobileNetV2 model has the best classification efficiency. © 2022 The Author(s)

Cancer classification utilizing voting classifier with ensemble feature selection method and transcriptomic data

- Khatun, Rabea, Akter, Maksuda, Islam, Md Manowarul, Uddin, Md Ashraf, Talukder, Md Alamin, Kamruzzaman, Joarder, Azad, Akm, Paul, Bikash, Almoyad, Muhammad, Aryal, Sunil, Moni, Mohammad

  • Authors: Khatun, Rabea , Akter, Maksuda , Islam, Md Manowarul , Uddin, Md Ashraf , Talukder, Md Alamin , Kamruzzaman, Joarder , Azad, Akm , Paul, Bikash , Almoyad, Muhammad , Aryal, Sunil , Moni, Mohammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Genes Vol. 14, no. 9 (2023), p.
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  • Description: Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods. © 2023 by the authors.

CRICRATE : A cricket match conduction and player evaluation framework

- Uddin, Md Ashraf, Hasan, Mahmudul, Halder, Sajal, Ahamed, Sajeeb, Acharjee, Uzzal

Cyberbullying detection on social networks using machine learning approaches

- Islam, Md Manowarul, Uddin, Md Ashraf, Islam, Linta, Akter, Arnisha, Sharmin, Selina, Acharjee, Uzzal

  • Authors: Islam, Md Manowarul , Uddin, Md Ashraf , Islam, Linta , Akter, Arnisha , Sharmin, Selina , Acharjee, Uzzal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
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  • Description: The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms. © 2020 IEEE.

Device agent assisted blockchain leveraged framework for Internet of Things

- Nasrullah, Tarique, Islam, Md Manowarul, Uddin, Md Ashraf, Khan, Md Anisauzzaman, Layek, Md Abu, Stranieri, Andrew, Huh, Eui-Nam

  • Authors: Nasrullah, Tarique , Islam, Md Manowarul , Uddin, Md Ashraf , Khan, Md Anisauzzaman , Layek, Md Abu , Stranieri, Andrew , Huh, Eui-Nam
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 11, no. (2023), p. 1254-1268
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  • Description: Blockchain (BC) is a burgeoning technology that has emerged as a promising solution to peer-to-peer communication security and privacy challenges. As a revolutionary technology, blockchain has drawn the attention of academics and researchers. Cryptocurrencies have already effectively utilized BC technology. Many researchers have sought to implement this technique in different sectors, including the Internet of Things. To store and manage IoT data, we present in this paper a lightweight BC-based architecture with a modified raft algorithm-based consensus protocol. We designed a Device Agent that executes a novel registration procedure to connect IoT devices to the blockchain. We implemented the framework on Docker using the Go programming language. We have simulated the framework on a Linux environment hosted in the cloud. We have conducted a detailed performance analysis using a variety of measures. The results demonstrate that our suggested solution is suitable for facilitating the management of IoT data with increased security and privacy. In terms of throughput and block generation time, the results indicate that our solution might be 40% to 45% faster than the existing blockchain. © 2013 IEEE.

Dynamically recommending repositories for health data : a machine learning model

- Uddin, Md Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2020
  • Type: Text , Conference proceedings
  • Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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  • Description: Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility. © 2020 ACM.
  • Description: E1

Link prediction by correlation on social network

- Rahman, Md Shafiur, Dey, Leema Rani, Haider, Sajal, Uddin, Md Ashraf, Islam, Manowarul

  • Authors: Rahman, Md Shafiur , Dey, Leema Rani , Haider, Sajal , Uddin, Md Ashraf , Islam, Manowarul
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 2017 20th International Conference of Computer and Information Technology (ICCIT); Dhaka, Bangladesh; 22-24 December 2017 p. 1-6
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  • Description: In a social network, the topology of the network grows through the formation of the link. the connection between two nodes in a social network indicates a confidence in terms of the similarity of some activities. Generally, a new link in the social network is created from different perspectives such as familiarity, cohesiveness, geographical locations etc. The concept of the link in the social network has been utilized to discover the hidden meaning of different fields such as e-commerce, bioinformatics and information retrieval. The prediction of a new link between two nodes in the social network is normally accomplished based on the nature of the topology and the similarity function among the nodes is defined with the help of the number of common friends. In this paper, we propose two link prediction algorithms: Local Link Prediction Algorithm and Global Link prediction by taking into consideration of user's activities as well as the common friends. We apply two formulas called correlation based cScore and influential score based iScore to measure the similarity between the two predicted nodes. Finally, we analyze the performance of the proposed algorithms by using DBLP, PPI, PB, and USAir data sets and the experimental result attests that our link predicted algorithm outperforms over the existing algorithms.

Sequence-to-sequence learning-based conversion of pseudo-code to source code using neural translation approach

- Acharjee, Uzzal, Arefin, Minhazul, Hossen, Kazi, Uddin, Mohammed, Uddin, Md Ashraf, Islam, Linta

  • Authors: Acharjee, Uzzal , Arefin, Minhazul , Hossen, Kazi , Uddin, Mohammed , Uddin, Md Ashraf , Islam, Linta
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 10, no. (2022), p. 26730-26742
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  • Description: Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy. © 2013 IEEE.
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