scholarly journals Embedded Machine Learning and Embedded Systems in the Industry.

2021 ◽  
Vol 9 (11) ◽  
pp. 1872-1875
Author(s):  
Pratic Chakraborty
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Power Consumption ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abstract Machine ◽  
Native Languages ◽  
Processing Power ◽  
Server Computer

Abstract: Machine learning is the buzz word right now. With the machine learning algorithms one can make a computer differentiate between a human and a cow. Can detect objects, can predict different parameters and can process our native languages. But all these algorithms require a fair amount of processing power in order to be trained and fitted as a model. Thankfully, with the current improvement in technology, processing power of computers have significantly increased. But there is a limitation in power consumption and deployability of a server computer. This is where “tinyML” helps the industry out. Machine Learning has never been so easy to access before!

Machine Learning Based Heat Transfer Optimization of Nano-fluid flow in a Helically Coiled Pipe

2021 ◽  
Vol 9 (12) ◽  
pp. 1717-1731
Author(s):  
Awnish Kumar
Keyword(s):  
Heat Transfer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Abstract Machine ◽  
Nano Fluid ◽  
Heat Transfer Efficiency ◽  
Manufacturing Sectors ◽  
Heat Transfer Optimization

Abstract: Machine Learning algorithms are widely used in various fields such as energy sectors, manufacturing sectors and aerospace sectors. These algorithms are used mainly in predictive and optimization purpose. The present study deals with the application of two machine learning algorithms i.e. Random Forest algorithm and Support Vector Machine Algorithm to predict the heat transfer efficiency of a flowing nano-fluid in a helically coiled pipe. Keywords: Machine Learning; Optimization; Nano-fluid; Heat Transfer

scholarly journals Edge Machine Learning: Enabling Smart Internet of Things Applications

2018 ◽  
Vol 2 (3) ◽  
pp. 26 ◽  
Author(s):  
Mahmut Yazici ◽  
Shadi Basurra ◽  
Mohamed Gaber
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Power Consumption ◽  
High Performance ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Support Vector ◽  
Processing Power ◽  
Chip Technology ◽  
Speed Training

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.

scholarly journals Machine Learning-Based Detection of Spam Emails

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zeeshan Bin Siddique ◽  
Mudassar Ali Khan ◽  
Ikram Ud Din ◽  
Ahmad Almogren ◽  
Irfan Mohiuddin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
South Asia ◽  
Social Communication ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Native Languages ◽  
Social Media Platforms ◽  
E Mail

Social communication has evolved, with e-mail still being one of the most common communication means, used for both formal and informal ways. With many languages being digitized for the electronic world, the use of English is still abundant. However, various native languages of different regions are emerging gradually. The Urdu language, coming from South Asia, mostly Pakistan, is also getting its pace as a medium for communications used in social media platforms, websites, and emails. With the increased usage of emails, Urdu’s number and variety of spam content also increase. Spam emails are inappropriate and unwanted messages usually sent to breach security. These spam emails include phishing URLs, advertisements, commercial segments, and a large number of indiscriminate recipients. Thus, such content is always a hazard for the user, and many studies have taken place to detect such spam content. However, there is a dire need to detect spam emails, which have content written in Urdu language. The proposed study utilizes the existing machine learning algorithms including Naive Bayes, CNN, SVM, and LSTM to detect and categorize e-mail content. According to our findings, the LSTM model outperforms other models with a highest score of 98.4% accuracy.

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