ORCID Profile
0000-0001-6019-7245
Current Organisations
University of South Australia
,
Taif University
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Publisher: Elsevier BV
Date: 05-2009
Abstract: This was a phase I trial to determine the maximum tolerated dose (MTD) of a marine lipid extract from the New Zealand green-lipped mussel (Perna canaliculus), as an inhibitor of 5- and 12-lipo-oxygenase enzymes, in patients with advanced breast and prostate cancers. This was an open-labelled, phase I, dose-escalation study. Proprietary form of green-lipped mussel lipid extract (GLMLE), 260-mg capsule, was administered on a twice-daily schedule, orally. Patients remained on study until disease progression or unacceptable toxicity. From December 1999 to May 2003, 17 patients were enrolled. Fifteen of them were male with advanced prostate cancer and two were female with advanced breast cancer. The median age of the patients was 74 years (range 56-85 years). Sixteen patients were assessable for adverse events and dose-limiting toxicity (DLT). Reason for withdrawal from the study included progressive disease (n = 12), death (n = 1) and DLT (n = 3). Two patients had evidence of grade 4 hepatic dysfunction. The MTD was not reached. There were no objective tumour responses noted. GLMLE appears to be a well-tolerated compound in this setting. There appears to be no objective benefit. However, grade 3/4 hepatic toxicity noted in two patients is of concern and should be considered while evaluating patients taking GLMLE or while designing studies with this agent.
Publisher: Informa UK Limited
Date: 24-02-2020
Publisher: Informa UK Limited
Date: 03-11-2019
Publisher: SAGE Publications
Date: 26-02-2019
Publisher: Informa UK Limited
Date: 24-06-2021
Publisher: Elsevier BV
Date: 07-2021
Publisher: Informa UK Limited
Date: 16-09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 11-01-2022
DOI: 10.3390/S22020528
Abstract: Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models.
Publisher: Association for Computing Machinery (ACM)
Date: 18-05-2023
DOI: 10.1145/3533430
Abstract: Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Informa UK Limited
Date: 08-08-2023
Publisher: Cold Spring Harbor Laboratory
Date: 05-07-2020
DOI: 10.1101/2020.07.02.20136721
Abstract: At present, the whole world is witnessing a horrifying outbreak caused by the Coronavirus Disease 2019 (COVID-19). The virus responsible for this disease is called SARS-CoV-2. It affects its victims’ respiratory system and causes severe lung inflammation, making it harder for them to breathe. The virus is airborne, and so has a high infection rate. Originated in China last December, the virus has spread across seven continents, affecting the population of over 210 countries, making it one of the fiercest pandemics ever recorded. Despite multiple independent and collaborative attempts to develop a vaccine or a cure, an effective solution is yet to come out. While the disease has put the world in a standstill, detecting the positive subjects and isolating them from the others as soon as possible is the only way to minimize its spread. However, many countries are currently experiencing a massive shortage of diagnostic equipment and medical personals. This insufficiency inspired us to work on a computer-based automatic method for the diagnosis of COVID-19. In this paper, we proposed a sequential Convolutional Neural Network (CNN)-based model to detect COVID-19 through analyzing Computed Tomography (CT) scan images. The model is capable of identifying the disease with almost 92.5% accuracy. We believe the implementation of this model will help the physicians and pathologists all over the world to single out the victims quickly and thus reduce the prevalence of COVID-19.
Publisher: Informa UK Limited
Date: 20-02-2023
Publisher: Informa UK Limited
Date: 26-06-2019
Publisher: Informa UK Limited
Date: 23-02-2023
Publisher: Informa UK Limited
Date: 29-07-2022
Publisher: Springer Science and Business Media LLC
Date: 18-03-2023
DOI: 10.1007/S10671-023-09338-3
Abstract: Education plays an important role in the successful settlement and life outcomes of young people from refugee backgrounds. Because of this, research into young people from refugee backgrounds in education systems tends to focus on ex les of “good practice” in terms of how these young people experience education. Yet, ex les of good practice commonly fail to take into account that schools are engaging in particular practices from very different contexts. This article contributes to the study of refugee education by drawing attention to the role that school contexts play in how different schools enact “good practice”. It presents data from a large multi-stage study which explored how the schooling experiences of students from refugee backgrounds are shaped by educational policies and school practices. By outlining case studies of seven schools, it highlights the impact of differing school contexts on how schools respond to the needs of students from refugee backgrounds. In this way, this article highlights that the notion of “good practice” within refugee-background education is always nuanced and contextual.
Publisher: Routledge
Date: 17-03-2021
Publisher: Informa UK Limited
Date: 12-01-2021
Publisher: Elsevier BV
Date: 2021
Publisher: MDPI AG
Date: 19-06-2020
DOI: 10.3390/S20123482
Abstract: Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained in iduals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.
Publisher: MDPI AG
Date: 28-09-2022
Abstract: Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.
Publisher: MDPI AG
Date: 26-10-2021
DOI: 10.3390/APP11219999
Abstract: Blockchain is a promising technology in the context of digital healthcare systems, but there are issues related to the control of accessing the electronic health records. In this paper, we propose a novel framework based on blockchain and multiple certificate authority that implement smart contracts and access health records securely. Our proposed solution provides the facilities of flexible policies to update a record or invoke the policy such that a patient has complete authority. A novel approach towards multiple certificate’s authority (CA) is introduced in the design through our proposed framework. Our proposed policies and methods overcome the shortcoming and security breaches faced by single certificate authority. Our proposed scheme provides a flexible access control mechanism for securing electronic health records as compared to the existing benchmark models. Moreover, our proposed method provides a re-enrolment facility in the case of a user lost enrolment.
Publisher: Informa UK Limited
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Wiley
Date: 12-10-2021
DOI: 10.1002/BERJ.3767
Abstract: This article reports on a survey of 332 Year 3 students from 14 Australian schools. We are interested in exploring Year 3 primary school student aspirations and what this data shows us about any societal changes, or not. This study is timely as it reports on contemporary data within an Australian educational context marked by significant investment in improving equitable gendered participation, particularly for girls entering STEM. Drawing on conceptions of masculinities and femininities as social constructions, we report on the participants’ desired occupations and explore their justifications for such choices. The top three occupations for boys included careers in professional sports, STEM‐related jobs and policing/defence. Girls reported wanting to be teachers, veterinarians or to work in the arts as their top choices. As part of our exploration, we found issues of money and power—traditionally coded masculine—and conceptions of love and care—traditionally coded feminine—ingrained in boys’ and girls’ justifications for their desired trajectories. Findings are significant for illustrating how traditional constructions of gender are ingrained in career choices in the early years of primary school and how policy agendas to widen participation need to start early in life.
Publisher: SAGE Publications
Date: 10-06-2020
Abstract: The relationship between working-class masculinities and industrial (and post-industrial) employment has been of sustained interest to sociologists for the last 40 years. This article draws on recent research examining the experiences of upwardly mobile working-class young men navigating casual employment within an urban part of Australia adapting to post-industrialisation. In presenting three longitudinal case studies, the theoretical frameworks of selfhood, possible selves and imagined futures are used to understand how service sector employment contributes to the development of aspirations during the transition beyond compulsory schooling. The focus is on how service employment informed the young men’s lives, aspirations and their sense of self. An argument is presented which articulates how, to varying extents, this service work is where the participants both accrue value and become valued.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
No related grants have been discovered for Mehedi Masud.