The adoption of machine-learning-enabled systems in the healthcare domain is on the rise. While the use of ML in healthcare has several benefits, it also expands the threat surface of medical systems. We show that the...
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ISBN:
(纸本)9798350345025;9798350345018
The adoption of machine-learning-enabled systems in the healthcare domain is on the rise. While the use of ML in healthcare has several benefits, it also expands the threat surface of medical systems. We show that the use of ML in medical systems, particularly connected systems that involve interfacing the ML engine with multiple peripheral devices, has security risks that might cause life-threatening damage to a patient's health in case of adversarial interventions. These new risks arise due to security vulnerabilities in the peripheral devices and communication channels. We present a case study where we demonstrate an attack on an ML-enabled blood glucose monitoring system by introducing adversarial data points during inference. We show that an adversary can achieve this by exploiting a known vulnerability in the Bluetooth communication channel connecting the glucose meter with the ML-enabled app. We further show that state-of-the-art risk assessment techniques are not adequate for identifying and assessing these new risks. Our study highlights the need for novel risk analysis methods for analyzing the security of AI-enabled connected health devices.
Software programmers may get gradually precise at expecting consequences without being clearly coded using machinelearning techniques. machinelearning is based on the idea that models and algorithms may collect inpu...
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Wind energy is now a pivotal part of renewable energy systems, where precise forecasting of wind turbine power output is crucial for effective wind farm management. This paper proposes a new method that integrates Sup...
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Wind energy is now a pivotal part of renewable energy systems, where precise forecasting of wind turbine power output is crucial for effective wind farm management. This paper proposes a new method that integrates Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP), along with an ensemble model merging Long Short-Term Memory (LSTM) and Random Forest. Meteorological data, such as wind speed, direction, temperature, and failure time, collected from wind farms, form the dataset. These parameters are used as input features for machinelearning models, while the output represents the wind turbine's power output. Our findings demonstrate that the ensemble approach, combining LSTM and Random Forest, surpasses individual SVR and MLP models. This technique advances predictive maintenance strategies and optimizes renewable energy systems, promoting the transition to a sustainable energy future.
AI and robotics have emerged as revolutionary technologies influencing the future of different sectors and our daily lives. If robotics is to be intelligent, artificial intelligence must be at the forefront of the fie...
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To meet the demand of modern application, modern machinelearning and adaptive signal processing techniques are needed. With the help of revolutionary advancements in mobile communication such as 5G and 6G, integratio...
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Mortality risk prediction of ICU patients is a valuable and challenging task due to limited clinical data. Accurate mortality risk prediction can improve the utilization of resources. In this work, we explore the use ...
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Transport Mode Detection (TMD) is a crucial component in the field of Intelligent Transportation systems (ITS), taking advantage of current advancements in Artificial Intelligence and the Internet of Things (IoT). Thi...
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Data management is the most challenging aspect of building machinelearning (ML) systems. ML systems can read large volumes of historical data when training models, but inference workloads are more varied, depending o...
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ISBN:
(纸本)9798400704222
Data management is the most challenging aspect of building machinelearning (ML) systems. ML systems can read large volumes of historical data when training models, but inference workloads are more varied, depending on whether it is a batch or online ML system. The feature store for ML has recently emerged as a single data platform for managing ML data throughout the ML lifecycle, from feature engineering to model training to inference. In this paper, we present the Hopsworks feature store for machinelearning as a highly available platform for managing feature data with API support for columnar, row-oriented, and similarity search query workloads. We introduce and address challenges solved by the feature stores related to feature reuse, how to organize data transformations, and how to ensure correct and consistent data between feature engineering, model training, and model inference. We present the engineering challenges in building high-performance query services for a feature store and show how Hopsworks outperforms existing cloud feature stores for training and online inference query workloads.
Epilepsy is a threatening neurological disease which causes sudden bursts in the electrical activities of the brain known as epileptic seizures. Due to its less availability in terms of treatment, and the data is also...
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This study investigates the viability of using machinelearning (ML) to improve valve management and water delivery, focusing on soil hydration in the root zones. The System ensures that the appropriate amount of wate...
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