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Conformal predictions for probabilistically robust scalable machine learning classification
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an...
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Scalable classifier-agnostic channel selection for multivariate time series classification
Accuracy is a key focus of current work in time series classification. However, speed and data reduction are equally important in many applications,...
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Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography
Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer...
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Fast and robust video-based exercise classification via body pose tracking and scalable multivariate time series classifiers
Recent technological advancements have spurred the usage of machine learning based applications in sports science and healthcare. Using wearable...
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Scalable AI Algorithms and Models
Artificial intelligence (AI) is becoming increasingly prevalent. AI is changing the way we live and work, from chatbots that assist with customer...
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Distributed and Scalable Healthcare Data Storage Using Blockchain and KNN Classification
Medical data from patients is saved electronically in the healthcare industry. The paper suggests storing medical data on a distributed on-chain...
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Scalable AI Deployment and Productionization
Scalable AI deployment and productionization are all about bringing artificial intelligence's enormous potential into practice. It's like owning a...
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A scalable and real-time system for disease prediction using big data processing
The growing chronic diseases patients and the centralization of medical resources cause significant economic impact resulting in hospital visits,...
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Scalable decoupling graph neural network with feature-oriented optimization
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph neural networks (GNNs), being an...
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A novel apache spark-based 14-dimensional scalable feature extraction approach for the clustering of genomics data
Feature extraction is essential in bioinformatics because it transforms genomics sequences into feature vectors, which are needed for clustering to...
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Provably accurate and scalable linear classifiers in hyperbolic spaces
Many high-dimensional practical data sets have hierarchical structures induced by graphs or time series. Such data sets are hard to process in...
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Joint graph and reduced flexible manifold embedding for scalable semi-supervised learning
Recently, graph-based semi-supervised learning (GSSL) has received much attention. On the other hand, less attention has been paid to the problem of...
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Sparse classification: a scalable discrete optimization perspective
We formulate the sparse classification problem of n samples with p features as a binary convex optimization problem and propose a outer-approximation...
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SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
Novel artificial intelligence (AI) technology has expedited various scientific research, e.g., cosmology, physics, and bioinformatics, inevitably...
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SHPIA 2.0: An Easily Scalable, Low-Cost, Multi-purpose Smart Home Platform for Intelligent Applications
Sensors, electronic devices, and smart systems have invaded the market and our daily lives. As a result, their utility in smart home contexts to...
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Stochastic analysis of fog computing and machine learning for scalable low-latency healthcare monitoring
In recent years, healthcare monitoring systems (HMS) have increasingly integrated the Internet of Medical Things (IoMT) with cloud computing, leading...
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Scalable model for segmenting Cells’ Nuclei using the U-NET architecture
Medical image segmentation significantly influences medicine for diagnostic purposes where higher precision and accuracy are demanded. One of the...
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Scalable image coding with enhancement features for human and machine
The past decade has seen significant advancements in computer vision technologies, resulting in an increasing consumption of images and videos by...
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Scalable and custom-precision floating-point hardware convolution core for using in AI edge processors
AI algorithms such as CNNs devices have necessitated the design of lightweight, low-power, and fast hardware in edge processors. In this paper, a...
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SNR scalable coding of depth maps using contour-centric SHVC enhancement sublayers
Being a scalable extension of the High Efficiency Video Coding (HEVC), Scalable High Efficiency Video Coding (SHVC) standard makes it possible to...
