Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction

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.

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Classification and Concentration Prediction of VOC Gases Based on Sensor Array with Machine Learning Algorithms

2020 IEEE 15th International Conference on Nano/Micro Engineered and Molecular System (NEMS) ◽

10.1109/nems50311.2020.9265606 ◽

2020 ◽

Author(s):

Yingming Liu ◽

Changhui Zhao ◽

Junqi Lin ◽

Huimin Gong ◽

Fei Wang

Keyword(s):

Machine Learning ◽

Sensor Array ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Concentration Prediction

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The success of machine learning algorithms developed with radiomic features obtained from preoperative contrast-enhanced MRI in the prediction of short-term survival in patients with glioblastoma

Cukurova Medical Journal ◽

10.17826/cumj.904688 ◽

2021 ◽

Vol 46 (2) ◽

pp. 706-713

Author(s):

Okan DILEK ◽

Emin DEMIREL ◽

Emre BİLGİN ◽

Berna BOZKURT DUMAN ◽

Bozkurt GÜLEK

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Short Term ◽

Term Survival ◽

Enhanced Mri ◽

Contrast Enhanced ◽

Contrast Enhanced Mri ◽

Short Term Survival

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Short-term Wind Speed Forecasting using Machine Learning Algorithms

2021 IEEE Madrid PowerTech ◽

10.1109/powertech46648.2021.9494848 ◽

2021 ◽

Author(s):

Sebastiao B. Fonseca ◽

Roberto Celio L. de Oliveira ◽

Carolina M. Affonso

Keyword(s):

Machine Learning ◽

Wind Speed ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Short Term ◽

Wind Speed Forecasting

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Anticipating Cryptocurrency Prices Using Machine Learning

Complexity ◽

10.1155/2018/8983590 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 34

Author(s):

Laura Alessandretti ◽

Abeer ElBahrawy ◽

Luca Maria Aiello ◽

Andrea Baronchelli

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Trading Strategies ◽

Short Term ◽

The Past ◽

Term Evolution ◽

Daily Data ◽

Abnormal Profits

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

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