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  1. Article ; Online: Dismantling the anti-vaxx industry.

    Ahmed, Imran

    Nature medicine

    2021  Volume 27, Issue 3, Page(s) 366

    MeSH term(s) Attitude to Health ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/virology ; COVID-19 Vaccines/administration & dosage ; Humans ; SARS-CoV-2/isolation & purification
    Chemical Substances COVID-19 Vaccines
    Language English
    Publishing date 2021-03-15
    Publishing country United States
    Document type News
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-021-01260-6
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  2. Article ; Online: A Case of False Positive Macro-prolactin.

    Siddiqi, Ahmed Imran

    Journal of the College of Physicians and Surgeons--Pakistan : JCPSP

    2021  Volume 31, Issue 6, Page(s) 749

    MeSH term(s) Humans ; Prolactin ; Protein Binding
    Chemical Substances Prolactin (9002-62-4)
    Language English
    Publishing date 2021-06-02
    Publishing country Pakistan
    Document type Case Reports ; Journal Article
    ZDB-ID 2276646-7
    ISSN 1681-7168 ; 1022-386X
    ISSN (online) 1681-7168
    ISSN 1022-386X
    DOI 10.29271/jcpsp.2021.06.749
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    Zs.A 6532: Show issues Location:
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  3. Article ; Online: Parental Perception of Children’s Online Behaviour

    Ahmed Imran / Nilufa Khanom / Azizur Rahman

    International Journal of Environmental Research and Public Health, Vol 20, Iss 5342, p

    A Study on Ethnic Communities in Australia

    2023  Volume 5342

    Abstract: The overwhelming growth of the Internet in all spheres of life poses new challenges for young children growing up in the digital age, with potential short- and long-term ramifications. Parents have an essential role in the development of the attitudes ... ...

    Abstract The overwhelming growth of the Internet in all spheres of life poses new challenges for young children growing up in the digital age, with potential short- and long-term ramifications. Parents have an essential role in the development of the attitudes and behaviour of their children. However, studies indicate that adults are not adequately mitigating the range of cyber risks that children face and that parent-oriented solutions are simply inadequate. This study attempts to fill research gaps in the status and nature of parents’ perceptions of the online use of their children in Australia based on their ethnic background. This study adopted a mixed-method approach, surveying 204 parents from different ethnic communities in Australia followed by 16 in-depth interviews and three focus-group discussions. The results indicate that parents’ perceptions of online risk for children differ based on their ethnicity, cultural adaptation, gender, and age. Parents from multicultural societies are less equipped to deal with cyber threats that their children face and are ill-equipped to monitor and mitigate the risks posed. The results of this study have important policy implications, from deepening our understanding of the nature of the problems to facilitating the development of short- and long-term strategies, appropriate information systems, policy guidelines, and interventions.
    Keywords parental perception ; children cyber safety ; cyber risk ; ethnic communities ; children online ; multi-cultural ; Medicine ; R
    Subject code 360
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: A Smart-Anomaly-Detection System for Industrial Machines Based on Feature Autoencoder and Deep Learning.

    Ahmed, Imran / Ahmad, Misbah / Chehri, Abdellah / Jeon, Gwanggil

    Micromachines

    2023  Volume 14, Issue 1

    Abstract: Machine-health-surveillance systems are gaining popularity in industrial manufacturing systems due to the widespread availability of low-cost devices, sensors, and internet connectivity. In this regard, artificial intelligence provides valuable ... ...

    Abstract Machine-health-surveillance systems are gaining popularity in industrial manufacturing systems due to the widespread availability of low-cost devices, sensors, and internet connectivity. In this regard, artificial intelligence provides valuable assistance in the form of deep learning methods to analyze and process big machine data. In diverse industrial applications, gears are considered a condemning element; many contributing failures occur due to an unexpected breakdown of the gears. In recent research, anomaly-detection and fault-diagnosis systems have been the gears' most contributing content. Thus, in work, we presented a smart deep learning-based system to detect anomalies in an industrial machine. Our system used vibrational analysis methods as a deciding tool for different machinery-maintenance decisions. We will first perform a data analysis of the gearbox data set to analyze the data's insights. By calculating and examining the machine's vibration, we aim to determine the nature and severity of the defect in the machine and hence detect the anomaly. A gearbox's vibration signal holds the fault's signature in the gears, and earlier fault detection of the gearbox is achievable by examining the vibration signal using a deep learning technique. Therefore, we aim to propose a 6-layer autoencoder-based deep learning framework for anomaly detection and fault analysis using a publically available data set of wind-turbine components. The gearbox fault-diagnosis data set is utilized for experimentation, including collecting vibration attributes recorded using SpectraQuest's gearbox fault-diagnostics simulator. Through comprehensive experiments, we have seen that the framework gains good results compared to others, with an overall accuracy of 91%.
    Language English
    Publishing date 2023-01-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2620864-7
    ISSN 2072-666X
    ISSN 2072-666X
    DOI 10.3390/mi14010154
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  5. Article ; Online: Clinically Meaningful Achievement in Outcomes After Subacromial Balloon Spacer Implantation: Letter to the Editor.

    Khatri, Chetan / Ridha, Ali / Ahmed, Imran

    The American journal of sports medicine

    2023  Volume 51, Issue 12, Page(s) NP43–NP44

    MeSH term(s) Humans ; Rotator Cuff Injuries ; Arthroscopy
    Language English
    Publishing date 2023-09-30
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 197482-8
    ISSN 1552-3365 ; 0363-5465
    ISSN (online) 1552-3365
    ISSN 0363-5465
    DOI 10.1177/03635465231184386
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  6. Article ; Online: Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images.

    Ahmed, Imran / Chehri, Abdellah / Jeon, Gwanggil

    IEEE/ACM transactions on computational biology and bioinformatics

    2023  Volume PP

    Abstract: Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered ... ...

    Abstract Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilised in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualisation approach to all of the tests. As a result, the architecture achieves a classification accuracy of 96%, thus producing excellent results.
    Language English
    Publishing date 2023-07-12
    Publishing country United States
    Document type Journal Article
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2023.3294333
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  7. Article ; Online: Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures.

    Ahmed, Imran / Chehri, Abdellah / Jeon, Gwanggil / Piccialli, Francesco

    IEEE/ACM transactions on computational biology and bioinformatics

    2023  Volume 20, Issue 4, Page(s) 2445–2456

    Abstract: Recent advancement in biomedical imaging technologies has contributed to tremendous opportunities for the health care sector and the biomedical community. However, collecting, measuring, and analyzing large volumes of health-related data like images is a ...

    Abstract Recent advancement in biomedical imaging technologies has contributed to tremendous opportunities for the health care sector and the biomedical community. However, collecting, measuring, and analyzing large volumes of health-related data like images is a laborious and time-consuming job for medical experts. Thus, in this regard, artificial intelligence applications (including machine and deep learning systems) help in the early diagnosis of various contagious/ cancerous diseases such as lung cancer. As lung or pulmonary cancer may have no apparent or clear initial symptoms, it is essential to develop and promote a Computer Aided Detection (CAD) system that can support medical experts in classifying and detecting lung nodules at early stages. Therefore, in this article, we analyze the problem of lung cancer diagnosis by classification and detecting pulmonary nodules, i.e., benign and malignant, in CT images. To achieve this objective, an automated deep learning based system is introduced for classifying and detecting lung nodules. In addition, we use novel state-of-the-art detection architectures, including, Faster-RCNN, YOLOv3, and SSD, for detection purposes. All deep learning models are evaluated using a publicly available benchmark LIDC-IDRI data set. The experimental outcomes reveal that the False Positive Rate (FPR) is reduced, and the accuracy is enhanced.
    Language English
    Publishing date 2023-08-09
    Publishing country United States
    Document type Journal Article
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2022.3192139
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  8. Article ; Online: Analyzing the Anterior Knee Laxity During Passive Flexion

    Ahmed Imran

    Lecture Notes in Engineering and Computer Science, Vol 2218, Iss 1, Pp 1034-

    2015  Volume 1037

    Keywords Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Language English
    Publishing date 2015-07-01T00:00:00Z
    Publisher Newswood and International Association of Engineers
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Analyzing Anterior Knee Laxity with Isolated Fiber Bundles of Anterior Cruciate Ligament

    Ahmed Imran

    Lecture Notes in Engineering and Computer Science, Vol 2224, Iss 1, Pp 869-

    2015  Volume 872

    Keywords Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Language English
    Publishing date 2015-06-01T00:00:00Z
    Publisher Newswood and International Association of Engineers
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses.

    Ahmed, Imran / Jeon, Gwanggil

    Interdisciplinary sciences, computational life sciences

    2021  Volume 14, Issue 2, Page(s) 504–519

    Abstract: Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global ... ...

    Abstract Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population's health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus's origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses' genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively.
    MeSH term(s) Artificial Intelligence ; COVID-19 ; Hemorrhagic Fever, Ebola ; Humans ; SARS-CoV-2/genetics ; Sequence Analysis
    Language English
    Publishing date 2021-08-06
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2493085-4
    ISSN 1867-1462 ; 1913-2751
    ISSN (online) 1867-1462
    ISSN 1913-2751
    DOI 10.1007/s12539-021-00465-0
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