Run‐time neuro‐fuzzy type‐2 controller for power optimisation of GP‐GPU architecture

Najam, Shaheryar; Jameel, Ahmed

doi:10.1049/iet-cds.2020.0233

Cited by 1 publication

(1 citation statement)

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“…Jetson is a low-power system and is designed for accelerating machine learning applications. An operating system used for this platform was Ubuntu, a Debian-based Linux operating system [62]. NVIDIA's embedded Tegra K1 typically comes pre-installed with Linux4 Tegra OS (basically Ubuntu 14.04 with pre-configured drivers).…”

Section: Methodsmentioning

confidence: 99%

Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

et al. 2021

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The liver is a vital human body organ and its functionality can be degraded by several diseases such as hepatitis, fatty liver disease, and liver cancer and so forth. Hence, the early diagnosis of liver diseases is extremely crucial for saving human lives. With the rapid development of multimedia technology, it is now possible to design and implement a non-invasive system that can chronic liver diseases. For this purpose, machine learning and Artificial Intelligence (AI) have been used within the past few years. In this regard, digital image processing supported by AI methods has been implemented in the diagnosis of diseases that also showed high reliability. Therefore, in this paper, an iris feature-based non-invasive technique is proposed by incorporating a novel machine-learning algorithm. The experimental setup involved data set for the models' training included 879 subjects from Pakistan, of which 453 subjects have chronic liver disease and 426 are healthy. The iris images were collected using an infrared camera that consists of a lens, a thermal sensor and digital electronics processing. The lens focuses on the infrared energy on the sensor, using distinctive forms of features twenty-two physiological and thirty-three iris features. The designed classification model for a non-invasive system combined eleven different classifiers and used cross-validation techniques for comparing the results. The overall performance of the model was analyzed using five parameters: accuracy, precision, F-score, specificity, and sensitivity. The results confirmed that the proposed non-invasive model is capable of predicting chronic liver diseases with 98% of accuracy.

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Section: Methodsmentioning

confidence: 99%