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Mr. Mukunthan Tharmakulasingam
University of Surrey, Faculty of Engineering and Physical Sciences

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0 Bioinformatics
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0 Artifical Intelligence
0 Genome data mining

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Short Biography

Mukunthan Tharmakulasingam received a B.Sc. degree (Hons.) in electronics and telecommunications engineering from the University of Moratuwa, Moratuwa, Sri Lanka, in 2014. He is currently pursuing a PhD at Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK. Prior to this, He worked as Lecturer (Probationary) at the University of Jaffna, Sri Lanka, and a Software Engineer in LSEG Technology. His current interest focuses on applying Machine learning techniques to biological data.

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Journal article
Published: 26 August 2021 in Electronics
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An artificial intelligence-assisted low-cost portable device for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is presented here. This standalone temperature-controlled device houses tubes designed for conducting reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays. Moreover, the device utilises tubes illuminated by LEDs, an in-built camera, and a small onboard computer with automated image acquisition and processing algorithms. This intelligent device significantly reduces the normal assay run time and removes the subjectivity associated with operator interpretation of colourimetric RT-LAMP results. To further improve this device’s usability, a mobile app has been integrated into the system to control the LAMP assay environment and to visually display the assay results by connecting the device to a smartphone via Bluetooth. This study was undertaken using ~5000 images produced from the ~200 LAMP amplification assays using the prototype device. Synthetic RNA and a small panel of positive and negative SARS-CoV-2 patient samples were assayed for this study. State-of-the-art image processing and artificial intelligence algorithms were applied to these images to analyse them and to select the most efficient algorithm. The template matching algorithm for image extraction and MobileNet CNN architecture for classification results provided 98.0% accuracy with an average run time of 20 min to confirm the endpoint result. Two working points were chosen based on the best compromise between sensitivity and specificity. The high sensitivity point has a sensitivity value of 99.12% and specificity value of 70.8%, while at the high specificity point, the sensitivity is 96.05% and specificity 93.59%. Furthermore, this device provides an efficient and cost-effective platform for non-health professionals to detect not only SARS-CoV-2 but also other pathogens in resource-limited laboratories, factories, airports, schools, universities, and homes.

ACS Style

Mukunthan Tharmakulasingam; Nouman S. Chaudhry; Anil Fernando; Manoharanehru Branavan; Wamadeva Balachandran; Aurore C. Poirier; Mohammed A. Rohaim; Muhammad Munir; Roberto M. La Ragione. An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2. Electronics 2021, 10, 2065 .

AMA Style

Mukunthan Tharmakulasingam, Nouman S. Chaudhry, Anil Fernando, Manoharanehru Branavan, Wamadeva Balachandran, Aurore C. Poirier, Mohammed A. Rohaim, Muhammad Munir, Roberto M. La Ragione. An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2. Electronics. 2021; 10 (17):2065.

Chicago/Turabian Style

Mukunthan Tharmakulasingam; Nouman S. Chaudhry; Anil Fernando; Manoharanehru Branavan; Wamadeva Balachandran; Aurore C. Poirier; Mohammed A. Rohaim; Muhammad Munir; Roberto M. La Ragione. 2021. "An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2." Electronics 10, no. 17: 2065.

Journal article
Published: 01 September 2020 in Viruses
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Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

ACS Style

Mohammed Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar Khalifa; Mohammad Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Jayde Whittingham-Dowd; Elisabeth Shaw; Matt Hodges; Lisa Butler; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2. Viruses 2020, 12, 972 .

AMA Style

Mohammed Rohaim, Emily Clayton, Irem Sahin, Julianne Vilela, Manar Khalifa, Mohammad Al-Natour, Mahmoud Bayoumi, Aurore Poirier, Manoharanehru Branavan, Mukunthan Tharmakulasingam, Nouman Chaudhry, Ravinder Sodi, Amy Brown, Peter Burkhart, Wendy Hacking, Judy Botham, Joe Boyce, Hayley Wilkinson, Craig Williams, Jayde Whittingham-Dowd, Elisabeth Shaw, Matt Hodges, Lisa Butler, Michelle Bates, Roberto La Ragione, Wamadeva Balachandran, Anil Fernando, Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2. Viruses. 2020; 12 (9):972.

Chicago/Turabian Style

Mohammed Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar Khalifa; Mohammad Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Jayde Whittingham-Dowd; Elisabeth Shaw; Matt Hodges; Lisa Butler; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. 2020. "Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2." Viruses 12, no. 9: 972.

Other
Published: 10 July 2020
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Until vaccines and effective therapeutics become available, the practical way to transit safely out of the current lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of result, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms, and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ∼200 coronavirus disease (CoVID-19)-suspected patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. The system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

ACS Style

Mohammed A Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar E Khalifa; Mohammed Q Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman S Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2. 2020, 1 .

AMA Style

Mohammed A Rohaim, Emily Clayton, Irem Sahin, Julianne Vilela, Manar E Khalifa, Mohammed Q Al-Natour, Mahmoud Bayoumi, Aurore Poirier, Manoharanehru Branavan, Mukunthan Tharmakulasingam, Nouman S Chaudhry, Ravinder Sodi, Amy Brown, Peter Burkhart, Wendy Hacking, Judy Botham, Joe Boyce, Hayley Wilkinson, Craig Williams, Michelle Bates, Roberto La Ragione, Wamadeva Balachandran, Anil Fernando, Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2. . 2020; ():1.

Chicago/Turabian Style

Mohammed A Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar E Khalifa; Mohammed Q Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman S Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. 2020. "Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2." , no. : 1.

Journal article
Published: 18 May 2020 in The Knowledge Engineering Review
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The software and hardware applications are clearly on the way of becoming an integral tool of business, communication and popular culture in many parts of the world. People are interacting with the environment via the Internet to perform physical activities remotely. These applications are hosted in the public or private servers under the control of the server admin. The users’ online usage data can be stored in public or private cloud platforms, used for processing and monitoring users’ online behaviour and emotional factors and shared with third parties to facilitate making their business decisions. When users allow their data to be collected via software applications and mobile devices, users need to have some level of trust and control over their data. But, software applications or mobile devices connected to the cloud server using client–server architecture does not ensure the reliability, security and integrity among their data. To get over these kinds of limitations, we propose a database management system using blockchain technology that can be used by any software applications. The blockchain database connected to the cloud server can be used to increase the trustfulness of the application. Blockchain has the capability to provide decentralization, immutability and owner-controlled digital assets among software applications. Since users can save their data in a shared transaction repository with tamper-resistant records, it enables related parties to access and control users’ data without the need for a central control system.

ACS Style

Jeyakumar Samantha Tharani; Mukunthan Tharmakulasingam; Vallipuram Muthukkumarasamy. A blockchain-based database management system. The Knowledge Engineering Review 2020, 35, 1 .

AMA Style

Jeyakumar Samantha Tharani, Mukunthan Tharmakulasingam, Vallipuram Muthukkumarasamy. A blockchain-based database management system. The Knowledge Engineering Review. 2020; 35 ():1.

Chicago/Turabian Style

Jeyakumar Samantha Tharani; Mukunthan Tharmakulasingam; Vallipuram Muthukkumarasamy. 2020. "A blockchain-based database management system." The Knowledge Engineering Review 35, no. : 1.

Conference paper
Published: 01 January 2020 in 2020 IEEE International Conference on Consumer Electronics (ICCE)
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This paper presents the application of the backward feature elimination technique on an electronic nose (E-nose) to aid the rapid detection of pathogens using Volatile Organic Compounds (VOCs). The timely identification of pathogens is vital to facilitate control of diseases. E-noses are widely used for the identification of VOCs as a non-invasive tool. However, the identification of VOC signatures associated with microbial pathogens using E-nose is currently inefficient for the timely identification of pathogens. Therefore, we proposed an E-nose system integrating the backward feature elimination. Comprehensive experiments of backward feature elimination showed that they improve the classification accuracy.

ACS Style

Mukunthan Tharmakulasingam; Cihan Topal; Anil Fernando; Roberto La Ragione. Backward Feature Elimination for Accurate Pathogen Recognition Using Portable Electronic Nose. 2020 IEEE International Conference on Consumer Electronics (ICCE) 2020, 1 -5.

AMA Style

Mukunthan Tharmakulasingam, Cihan Topal, Anil Fernando, Roberto La Ragione. Backward Feature Elimination for Accurate Pathogen Recognition Using Portable Electronic Nose. 2020 IEEE International Conference on Consumer Electronics (ICCE). 2020; ():1-5.

Chicago/Turabian Style

Mukunthan Tharmakulasingam; Cihan Topal; Anil Fernando; Roberto La Ragione. 2020. "Backward Feature Elimination for Accurate Pathogen Recognition Using Portable Electronic Nose." 2020 IEEE International Conference on Consumer Electronics (ICCE) , no. : 1-5.

Conference paper
Published: 13 November 2019 in Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering
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The timely identification of pathogens is vital in order to effectively control diseases and avoid antimicrobial resistance. Non-invasive point-of-care diagnostic tools are recently trending in identification of the pathogens and becoming a helpful tool especially for rural areas. Machine learning approaches have been widely applied on biological markers for predicting diseases and pathogens. However, there are few studies in the literature that have utilized volatile organic compounds (VOCs) as non-invasive biological markers to identify bacterial pathogens. Furthermore, there is no comprehensive study investigating the effect of different distance and similarity metrics for pathogen classification based on VOC data. In this study, we compared various non-Euclidean distance and similarity metrics with Euclidean metric to identify significantly contributing VOCs to predict pathogens. In addition, we also utilized backward feature elimination (BFE) method to accurately select the best set of features. The dataset we utilized for experiments was composed from the publications published between 1977 and 2016, and consisted of associations in between 703 VOCs and 11 pathogens.We performed extensive set of experiments with five different distance metrics in both uniform and weighted manner. Comprehensive experiments showed that it is possible to correctly predict pathogens by using 68 VOCs among 703 with 78.6% accuracy using k-nearest neighbour classifier and Sorensen distance metric.

ACS Style

Mukunthan Tharmakulasingam; Cihan Topal; Anil Fernando; Roberto La Ragione. Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN. Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering 2019, 1 .

AMA Style

Mukunthan Tharmakulasingam, Cihan Topal, Anil Fernando, Roberto La Ragione. Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN. Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering. 2019; ():1.

Chicago/Turabian Style

Mukunthan Tharmakulasingam; Cihan Topal; Anil Fernando; Roberto La Ragione. 2019. "Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN." Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering , no. : 1.

Journal article
Published: 31 December 2015 in Rajshahi University Journal of Science and Engineering
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Remote medical monitoring and consultation has become indispensable in order to enhance the availability of better health-care services to the patients in remote rural areas in the country. This paper proposes an inexpensive, easy to handle Remote Medical Consultation System (RMCS) which supports the healthcare workers to carry out their services through bi-directional video and voice communication between the remote end and doctor’s end as well as automated measuring of medical parameters that can be controlled from both ends. RMCS is consisted of a wearable sensors kit, a centralized hardware platform which connects to the medical sensors and devices and a software platform with database for operating and managing the system. RMCS is capable of remotely measuring patient’s blood pressure, heart rate, body temperature, electrocardiogram (ECG), heart sounds and the system’s platform supports to add-on more medical sensors or devices. The key aspect of the system is that it reduces most of the complexity in operation and facilitates the doctors to monitor and diagnose the patients in real-time. RMCS was essentially developed to eliminate the issues of low quality healthcare services in rural areas and to assist in monitoring immobilized patients.

ACS Style

Sisil P. Kumarawadu; Nuwan D Nanayakkara; Brinthan Thavaneswaran; Mukunthan Tharmakulsingam; Nirosan Paramanathan; Lalitha Perera; Sajith Mohamed. A Portable Remote Medical Consultation System for the Use of Distant Rural Communities. Rajshahi University Journal of Science and Engineering 2015, 43, 81 -87.

AMA Style

Sisil P. Kumarawadu, Nuwan D Nanayakkara, Brinthan Thavaneswaran, Mukunthan Tharmakulsingam, Nirosan Paramanathan, Lalitha Perera, Sajith Mohamed. A Portable Remote Medical Consultation System for the Use of Distant Rural Communities. Rajshahi University Journal of Science and Engineering. 2015; 43 ():81-87.

Chicago/Turabian Style

Sisil P. Kumarawadu; Nuwan D Nanayakkara; Brinthan Thavaneswaran; Mukunthan Tharmakulsingam; Nirosan Paramanathan; Lalitha Perera; Sajith Mohamed. 2015. "A Portable Remote Medical Consultation System for the Use of Distant Rural Communities." Rajshahi University Journal of Science and Engineering 43, no. : 81-87.