scholarly journals Quasar: Easy Machine Learning for Biospectroscopy

Cells ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2300
Author(s):  
Marko Toplak ◽  
Stuart T. Read ◽  
Christophe Sandt ◽  
Ferenc Borondics
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Open Source ◽  
Machine Learning Techniques ◽  
User Friendliness ◽  
Access Problem ◽  
Learning Techniques ◽  
Data Rates ◽  
Ease Of Access ◽  
User Friendly

Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques.

scholarly journals The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques

2021 ◽  
Vol 1804 (1) ◽  
pp. 012133
Author(s):  
Mahmood Shakir Hammoodi ◽  
Hasanain Ali Al Essa ◽  
Wial Abbas Hanon
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Machine Learning Techniques ◽  
Learning Techniques

Big Mac: A Distributed PaaS Framework for on Demand Big Data Processing Using Machine Learning Techniques

2020 ◽  
Vol 17 (1) ◽  
pp. 92-100
Author(s):  
Balanand Jha ◽  
Kumar Abhishek ◽  
Akshay Deepak ◽  
Prakhar Shrivastav ◽  
Suraj Thakre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Techniques ◽  
Computing Power ◽  
On Demand ◽  
Learning Techniques ◽  
Ease Of Access ◽  
Start Ups

In the age of start-ups and technical research, the demand for high-end computing power and loads of space is ever increasing. Machine learning techniques have become an inseparable part of the big data analytics. Setting up one’s own infrastructure to deal with all this vastness is usually not feasible due to high expenses and lack of desired expertise. As a solution to this problem, this paper proposes a system for Big-Data Analytics and Machine Learning based on Hadoop and Spark frameworks that also supports Operating System (OS) Rental Services. Machine Learning (ML) services provide option to use both existing inbuilt popular models or create one’s own model. OS Rental services provide users with high end infrastructure on their low-end devices on rent. The entire implementation has been made open source for ease of access and facilitating extensibility.

scholarly journals Applying Machine Learning Techniques to Predict the Maintainability of Open Source Software

2019 ◽  
Vol 8 (5S3) ◽  
pp. 192-195
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Software Applications ◽  
Learning Techniques ◽  
Software Maintainability ◽  
Quality Aspect

Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.

scholarly journals Energy Meter Data Analysis Using Machine Learning Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 08-13
Author(s):  
Anitha Kumari K ◽  
Indusha M ◽  
Abarna Devi D ◽  
Dheva Dharshini S
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Use Cases ◽  
Machine Learning Techniques ◽  
Energy Usage ◽  
Energy Meter ◽  
Learning Techniques ◽  
Significant Interest ◽  
F Measure

With the advancement of technology, existence of energy meters are not merely to measure energy units. The proliferation of energy meter deployments had led to significant interest in analyzing the energy usage by the machines. Energy meter data is often difficult to analyzeowing to the aggregation of many disparate and complex loads. At utility scales, analysis is further complicated by the vast quantity of data and hence industries turn towards applying machine learning techniques for monitoring and measuring loads of the machines. The energy meter data analysis aims at analyzing the behavior of the machine and providing insights on usage of the energy. This will help the industries to identify the faults in the machine and to rectify it.Two use cases with two different motor specifications is considered for evaluation and the efficiency is proved by considering accuracy, precision, F-measure and recall as metrics.

scholarly journals Developing an Open-Source Lightweight Game Engine with DNN Support

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1421
Author(s):  
Haechan Park ◽  
Nakhoon Baek
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Programming Languages ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Learning Technology ◽  
Game Engine ◽  
Learning Techniques ◽  
Technical Issues

With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a light-weight game engine for quick prototyping. Our game engine is implemented in a cost-effective way, in comparison to well-known commercial proprietary game engines, by utilizing open source products. Due to its simple internal architecture, our game engine is especially beneficial for modifying and reviewing the new functions through quick and repetitive tests. In addition, the game engine has a DNN (deep neural network) module, with which the proposed game engine can apply deep learning techniques to the game features, through applying deep learning algorithms in real-time. Our DNN module uses a simple C++ function interface, rather than additional programming languages and/or scripts. This simplicity enables us to apply machine learning techniques more efficiently and casually to the game applications. We also found some technical issues during our development with open sources. These issues mostly occurred while integrating various open source products into a single game engine. We present details of these technical issues and our solutions.

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