Recent years, reinforcement learning has been developed in many area and achieve high performance in many application. However, for huge amount data and training will make hard leaning for the agent of reinforcement l...
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This research work aims to analyze the nutritional value of different cereals available in the market through various machinelearning models. This analysis is supplemented with the visualization of data also for enha...
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In this study we aim to forecast monetary contributions using three machinelearning methods based on geographical location, state, required donation amount and number of supporters. data from Milaap portal between Ja...
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Models produced by machinelearning are not guaranteed to be free from bias, particularly when trained and tested with data produced in discriminatory environments. The bias can be unethical, mainly when the data cont...
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ISBN:
(纸本)9781450394666
Models produced by machinelearning are not guaranteed to be free from bias, particularly when trained and tested with data produced in discriminatory environments. The bias can be unethical, mainly when the data contains sensitive attributes, such as sex, race, age, etc. Some approaches have contributed to mitigating such biases by providing bias metrics and mitigation algorithms. The challenge is users have to implement their code in general/statistical programming languages, which can be demanding for users with little programming and fairness in machinelearning experience. We present FairML, a model-based approach to facilitate bias measurement and mitigation with reduced software development effort. Our evaluation shows that FairML requires fewer lines of code to produce comparable measurement values to the ones produced by the baseline code.
Everyone in the modern era, where the internet is widely used, relies on a range of online sources for news channels. Because more people are using Facebook, Instagram, and other social media platforms, news has sprea...
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Due to its achievements in recent years, machinelearning (ML) is now used in a wide variety of domains. Educating ML has hence become an important factor in academia and industry. We argue that students learning abou...
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ISBN:
(纸本)9781665444347
Due to its achievements in recent years, machinelearning (ML) is now used in a wide variety of domains. Educating ML has hence become an important factor in academia and industry. We argue that students learning about machinelearning will need, in addition to theoretical knowledge, approaches to interactively explore machinelearning models and their parameters. This paper introduces EduML - an interactive approach for lecturers to teach and for students or professionals to study and explore the fundamentals of machinelearning. EduML allows users to experiment with data preparation, dimensionality reduction and a wide range of classifiers on different data sets. These data sets can be analysed in order to understand the complexity of the classification problem. The classifiers can be autonomously fitted to the training data or the effect of manually altering model hyperparameters can be explored. Additionally, to get started with programming own ML pipelines, Python and R source code of configured ML pipelines can be extracted. EduML has been used in a lecture as an interactive demo or by students in lab sessions. Roth scenarios were evaluated with a user survey.
The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical grou...
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Plasmonic biosensors offer a unique opportunity to precisely control light-matter coupling in surface-enhanced infrared absorption (SEIRA) spectroscopy. However, the broadband nature of infrared spectra and the comple...
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The goal of object detection is to recognize the position and category of all objects in an image, allowing for machine vision understanding. Many approaches have been developed to solve this problem, primarily based ...
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Intrusion in an IoT (Internet of Things) device or an IoT based data is quite common but the data being shared in a secured manner is the point to be analysed. Any data that has a connection to the internet has a chan...
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