This research delves into quantum machinelearning (QML) in the context of computer vision analysis by exploring the progress made in quantum computing and its impact on machinelearning applications such as managing ...
详细信息
Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they...
详细信息
The education sector is poised for a significant shift propelled by Artificial Intelligence (AI) technologies. This paper explores the various uses of AI in education, such as data-driven decision assistance for instr...
详细信息
The surge in heart-related issues is linked to lifestyle changes and increased fast food consumption. The convergence of cloud computing, IoT and machinelearning for the prediction of cardio vascular disease is exami...
详细信息
Depression is a common mental health condition impacting millions worldwide, yet its complexities often hinder accurate diagnosis and effective treatment. This paper examines the role of machinelearning in analyzing ...
详细信息
As datasets continue to expand, the significance of feature selection in identifying influential features for classification becomes increasingly apparent. Meanwhile, the performance of a classifier has a great impact...
详细信息
As the size of modern datasets exceeds the disk and memory capacities of a single computer, machinelearning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pil...
详细信息
With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machinelearning applications, which are the cornerstone of urgent analyt...
详细信息
ISBN:
(纸本)9783031506833;9783031506840
With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machinelearning applications, which are the cornerstone of urgent analytics in distributed computing. Quantum machinelearning could be a solution for the increasing demand of urgent analytics, providing potential theoretical speedups and increased space efficiency. However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machinelearning for urgent analytics. In this work, we investigate the use of Edge computing for the integration of quantum machinelearning into a distributed computing continuum, identifying the main challenges and possible solutions. Furthermore, exploring the data encoding and hyperparameter tuning challenges, we present preliminary results for quantum machinelearning analytics on an IoT scenario.
Crop Yield Prediction (CYP) is crucial for optimizing agricultural practices globally. This study conducts an in-depth review of machinelearning (ML) techniques applied to multivariate datasets for crop yield forecas...
详细信息
Routs are usual tragedies that can cause significant damage to the environment and pose a serious danger to humanoid lives and infrastructure. Early detection and identification of potential landslide-prone areas are ...
详细信息
暂无评论