Identification and classification of neuro-degenerative diseases using statistical features and support vector machine classifier

Amin, Sobia; Singhal, Abhishek; K., J.

doi:10.1109/icccnt.2017.8204068

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…They provide high precision, making them applicable to datasets with a large number of features. Accordingly, they are highly used in studying the air quality in urban areas of cities, image interpolation, as well as medical classification [30].…”

Section: Support Vector Machinementioning

confidence: 99%

Edge Machine Learning: Enabling Smart Internet of Things Applications

Yazici

Basurra

Gaber

2018

BDCC

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Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.

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Section: Support Vector Machinementioning

confidence: 99%