Results 11 to 20 of about 168,829 (242)
Journal on Interactive Systems, 2022 The way Complex Machine Learning (ML) models generate their results is not fully understood, including by very knowledgeable users. If users cannot interpret or trust the predictions generated by the model, they will not use them.
Bárbara Gabrielle C. O. Lopes +3 more
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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 +2 more
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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
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, 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 ...
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Explainable Mental Health Chat Support using MKO-DEBiGRU with Knowledge Graph and SHAP [PDF]
ITM Web of ConferencesRecently, mental health chat support has become increasingly important for providing timely assistance and guidance to users seeking psychological assistance.
Nagarathna P. +3 more
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Optimizing Distributed Energy Storage Deployment in Smart Grids for Enhanced Grid Performance and Energy Management [PDF]
E3S Web of ConferencesLarge-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 +5 more
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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.
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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 +4 more
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Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.
PLoS Computational Biology, 2021 Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical ...
Qiang Gu +7 more
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PCP-ML: Protein characterization package for machine learning [PDF]
BMC Research Notes, 2014 Machine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction. To speed further development of machine learning based tools and their release to the community, we have developed a package which characterizes several aspects of a protein commonly used for protein prediction tasks with ...
Eickholt, Jesse, Wang, Zheng
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