Results 21 to 30 of about 1,710,361 (207)

Machine Learning: Algorithms, Real-World Applications and Research Directions

SN Computer Science, 2021
In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc.
Iqbal H. Sarker
semanticscholar   +1 more source

Stock Market Prediction Using Machine Learning(ML)Algorithms

Advances in Distributed Computing and Artificial Intelligence Journal, 2020
Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks.
Muhammad UMER, Muhammad AWAIS, Muhammad MUZAMMUL   +2 more
doaj   +1 more source

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Sensors, 2023
The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ...
Arshad Farhad, Jae-Young Pyun
doaj   +1 more source

Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application

, 2020
Artificial Intelligence (AI) and especially machine learning (ML) become increasingly more frequently applicable in factory operations. This paper presents a systematic review of today’s applications of ML techniques in the factory environment.
S. Fahle, Christopher Prinz, B. Kuhlenkötter   +2 more
semanticscholar   +1 more source

Machine Learning (ML)

, 2023
Machine learning (ML) is important in many industries like healthcare, finance, retail, marketing, and autonomous vehicles. It helps with things like diagnosing diseases, personalizing medicine, detecting fraud, and making recommendations. However, ML also has some challenges like making sure the data is decent quality, avoiding biases, and being able ...
openaire   +2 more sources

Explainable Mental Health Chat Support using MKO-DEBiGRU with Knowledge Graph and SHAP [PDF]

ITM Web of Conferences
Recently, mental health chat support has become increasingly important for providing timely assistance and guidance to users seeking psychological assistance.
Nagarathna P., Thalla Srujana, Patil Sujata, Reddy K Phanindra   +3 more
doaj   +1 more source

Optimizing Distributed Energy Storage Deployment in Smart Grids for Enhanced Grid Performance and Energy Management [PDF]

E3S Web of Conferences
Large-scale hydroelectric is the most mature kind of energy storage, but medium- and small-scale plants are used widely with renewable energy sources that are likely to be integrated in the next generation electrical distribution system or smart grid ...
Reddy K. Jyothsna, G Veeresh, Kansal Lavish, Sharma Saurabh, Al-Rubaye Taqi, Arun Vanya   +5 more
doaj   +1 more source

ML Confidential: Machine Learning on Encrypted Data [PDF]

, 2013
We demonstrate that by using a recently proposed somewhat homomorphic encryption (SHE) scheme it is possible to delegate the execution of a machine learning (ML) algorithm to a compute service while retaining confidentiality of the training and test data.
Graepel, T., Lauter, K., Naehrig, M.
openaire   +2 more sources

Multilayer Fisher extreme learning machine for classification

Complex & Intelligent Systems, 2022
As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years.
Jie Lai, Xiaodan Wang, Qian Xiang, Jian Wang, Lei Lei   +4 more
doaj   +1 more source

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting [PDF]

, 2019
The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age.
Andrejková G., Ashmall J., Bergstra J., E. Camporeale, Fasshauer G. E., Gelman A., Goodfellow I., Murphy K. P., Parnowski A., Pedregosa F., Pesnell W. D., Russell S. J., Semeniv O., Stepanova M., Stringer G., Sutton R. S., Turner D., Valach F., Vapnik V., Vega‐Jorquera P.   +19 more
core   +2 more sources