scholarly journals A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4267
Author(s):  
John Oyekan ◽  
Windo Hutabarat ◽  
Christopher Turner ◽  
Ashutosh Tiwari ◽  
Hongmei He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Learning Algorithms ◽  
Cognitive Architecture ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Experimental Methodology ◽  
Small Subset ◽  
Satellite Network ◽  
Knowledge Based

Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.

scholarly journals SEO Ministration : Content Related Tag Suggestion Using One vs Rest

2021 ◽  
pp. 245-253
Author(s):  
Dyapa Sravan Reddy ◽  
Lakshmi Prasanna Reddy ◽  
Kandibanda Sai Santhosh ◽  
Virrat Devaser
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

SEO Analyst pays a lot of time finding relevant tags for their articles and in some cases, they are unaware of the content topics. The current proposed ML model will recommend content-related tags so that the Content writers/SEO analyst will be having an overview regarding the content and minimizes their time spent on unknown articles. Machine Learning algorithms have a plethora of applications and the extent of their real-life implementations cannot be estimated. Using algorithms like One vs Rest (OVR), Long Short-Term Memory (LSTM), this study has analyzed how Machine Learning can be useful for tag suggestions for a topic. The training of the model with One vs Rest turned out to deliver more accurate results than others. This Study certainly answers how One vs Rest is used for tag suggestions that are needed to promote a website and further studies are required to suggest keywords required.

scholarly journals An Intelligent and Data-Driven Mobile Platform for Youth Volunteer Management using Machine Learning and Predictive Analytics

2020 ◽  
Author(s):  
Alyssa Huang ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
High School ◽  
High School Students ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
School Students ◽  
Best Fit

Volunteering is very important to high school students because it not only allows the teens to apply the knowledge and skills they have acquired to real-life scenarios, but it also enables them to make an association between helping others and their own joy of fulfillment. Choosing the right volunteering opportunities to work on can influence how the teens interact with that cause and how well they can serve the community through their volunteering services. However, high school students who look for volunteer opportunities often do not have enough information about the opportunities around them, so they tend to take whatever opportunity that comes across. On the other hand, as organizations who look for volunteers usually lack effective ways to evaluate and select the volunteers that best fit the jobs, they will just take volunteers on a first-come, firstserve basis. Therefore, there is a need to build a platform that serves as a bridge to connect the volunteers and the organizations that offer volunteer opportunities. In this paper, we focus on creating an intelligent platform that can effectively evaluate volunteer performance and predict best-fit volunteer opportunities by using machine learning algorithms to study 1) the correlation between volunteer profiles (e.g. demographics, preferred jobs, talents, previous volunteering events, etc.) and predictive volunteer performance in specific events and 2) the correlation between volunteer profiles and future volunteer opportunities. Two highest-scoring machine learning algorithms are proposed to make predictions on volunteer performance and event recommendations. We demonstrate that the two highest-scoring algorithms are able to make the best prediction for each query. Alongside the practice with the algorithms, a mobile application, which can run on both iPhone and Android platforms is also created to provide a very convenient and effective way for the volunteers and event supervisors to plan and manage their volunteer activities. As a result of this research, volunteers and organizations that look for volunteers can both benefit from this data-driven platform for a more positive overall experience.

scholarly journals AUTOMATED ROOT CAUSE ANALYSIS OF NON-CONFORMITIES WITH MACHINE LEARNING ALGORITHMS

2018 ◽  
Vol 18 (4) ◽  
pp. 60-72 ◽  
Author(s):  
Tobias MUELLER ◽  
Jonathan GREIPEL ◽  
Tobias WEBER ◽  
Robert H. SCHMITT
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Learning Algorithms ◽  
Root Cause Analysis ◽  
Machine Learning Algorithms ◽  
Decision Tree Algorithm ◽  
Process Data ◽  
Cause Analysis ◽  
Root Cause ◽  
High Level

To detect root causes of non-conforming parts - parts outside the tolerance limits - in production processes a high level of expert knowledge is necessary. This results in high costs and a low flexibility in the choice of personnel to perform analyses. In modern production a vast amount of process data is available and machine learning algorithms exist which model processes empirically. Aim of this paper is to introduce a procedure for an automated root cause analysis based on machine learning algorithms to reduce the costs and the necessary expert knowledge. Therefore, a decision tree algorithm is chosen. A procedure for its application in an automated root cause analysis is presented and simulations to prove its applicability are conducted. In this paper influences affecting the success of detection are identified and simulated e.g. the necessary amount of data dependent on the amount of variables, the ratio between categories of non-conformities and OK parts as well as detectable root causes. The simulations are based on a regression model to determine the roughness of drilling holes. They prove the applicability of machine learning algorithms for an automated root cause analysis and indicate which influences have to be considered in real scenarios.

scholarly journals Prediction Approaches for Smart Cultivation: A Comparative Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Amitabha Chakrabarty ◽  
Nafees Mansoor ◽  
Muhammad Irfan Uddin ◽  
Mosleh Hmoud Al-adaileh ◽  
Nizar Alsharif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Crop Production ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Knowledge Based ◽  
The Neural Network ◽  
Long Time ◽  
Agriculture Extension

Crop cultivation is one of the oldest activities of civilization. For a long time, crop production was carried out based on knowledge passed from generation to generation. However, due to the rapid growth in the human population of the world, human knowledge-based cultivation is not enough to meet the demanding need. To address this issue, the usage of machine learning-based tools has been studied in this paper. An experiment has been carried out over 0.3 million data. This dataset identifies 46 prominent parameters for cultivation, which is collected from the Department of Agriculture Extension, Bangladesh. Comparison between neural networks and numbers of machine learning algorithms has been carried out in this research. It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively.

Self-learning and self-organization as tools for speech research

1998 ◽  
Vol 21 (2) ◽  
pp. 262-263
Author(s):  
R. I. Damper
Keyword(s):  
Machine Learning ◽  
Scientific Inquiry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Self Organization ◽  
Knowledge Based ◽  
Automatic Discovery ◽  
Perceptual Invariance ◽  
Speech Research ◽  
Self Learning

Locus equations offer promise for an understanding of at least some aspects of perceptual invariance in speech, but they were discovered almost fortuitously. With the present availability of powerful machine learning algorithms, ignorance-based automatic discovery procedures are starting to supplant knowledge-based scientific inquiry. Principles of self-learning and self-organization are powerful tools for speech research but remain somewhat under-utilized.

scholarly journals Adversarial Machine Learning on Social Network: A Survey

2021 ◽  
Vol 9 ◽  
Author(s):  
Sensen Guo ◽  
Xiaoyu Li ◽  
Zhiying Mu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Research Progress ◽  
Future Research

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

scholarly journals Stock Price Prediction using Machine Learning Algorithms

2022 ◽  
Vol 10 (1) ◽  
pp. 667-673
Author(s):  
Shreya Pawaskar
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Stock Market Returns ◽  
Finance Industry ◽  
Stock Price Prediction

Abstract: Machine learning has broad applications in the finance industry. Risk Analytics, Consumer Analytics, Fraud Detection, and Stock Market Predictions are some of the domains where machine learning methods can be implemented. Accurate prediction of stock market returns is extremely difficult due to volatility in the market. The main factor in predicting a stock market is a high level of accuracy and precision. With the introduction of artificial intelligence and high computational capacity, efficiency has increased. In the past few decades, the highly theoretical and speculative nature of the stock market has been examined by capturing and using repetitive patterns. Various machine learning algorithms like Multiple Linear Regression, Polynomial Regression, etc. are used here. The financial data contains factors like Date, Volume, Open, High, Low Close, and Adj Close prices. The models are evaluated using standard strategic indicators RMSE and R2 score. Lower values of these two indicators mean higher efficiency of the trained models. Various companies employ different types of analysis tools for forecasting and the primary aim is the accuracy to obtain the maximum profit. The successful prediction of the stock will be an invaluable asset for the stock market institutions and will provide real-life solutions to the problems of the investors. Keywords: Stock prices, Analysis, Accuracy, Prediction, Machine Learning, Regression, Finance

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