An optimal sensor placement algorithm (O-SPA) for improving tracking precision of human activity in real-world healthcare systems

Alarifi, Abdulaziz; AlZubi, Ahmad Ali; Al‐Maitah, Mohammed; Al-Kasasbeh, Basil

doi:10.1016/j.comcom.2019.09.001

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…A deep learning technique appears to be an improvement in computer‐aided diagnosis (CAD) (Zhu et al, 2019). Convolutional neural network (CNN) is a widely known deep learning method that achieves the highest success in medical fields, such as neuroimaging, natural language processing, and speech recognition (Alarifi et al, 2019). Furthermore, many researchers developed several models based on CNN mostly useful for AD diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images

2022

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Alzheimer's disease (AD) is a neurodegenerative disease that causes memory loss and is considered the most common type of dementia. In many countries, AD is commonly affecting senior citizens having an aged more than 65 years. Machine learningbased approaches have some limitations due to data pre-processing issues. We propose a health care intelligent system based on a deep convolutional neural network (DCNN) in this research work. It classifies normal control (NC), mild cognitive impairment (MCI), and AD. The proposed model is employed on white matter (WM), and grey matter (GM) tissues with more cognitive decline features. In the experimental process, we used 375 Magnetic Resonance Image (MRI) subjects collected from Alzheimer's disease neuroimaging initiative (ADNI), including 130 NC people, 120 MCI patients, and 125 AD patients. We extract three major regions during preprocessing, that is, WM, GM and cerebrospinal fluid (CSF). This study shows promising classification results for NC versus AD 97.94%, MCI versus AD 92.84%, and NC versus MCI 88.15% on GM images. Furthermore, our proposed model attained 95.97%, 90.82%, and 86.87% on the same three binary classes on WM tissue, respectively. When comparing existing studies in terms of accuracy and other evaluation parameters, we found that our proposed approach shows better results than those approaches based on the CNN method.

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Section: Introductionmentioning

confidence: 99%

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images

2022

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“…The MESP has been widely applied to healthcare (Alarifi et al 2019), power system (Li et al 2012, manufacturing (Wang et al 2019), data science (Charikar et al 2000, Song and Liò 2010, Zilly et al 2017, etc. Specifically, suppose that the n random variables follow a Gaussian distribution and their covariance matrix C ∈ R n×n has a rank d ≤ n. Then the goal of MESP is to seek a size-s (s ≤ d) principal submatrix of C with the largest determinant, i.e., MESP can be formulated as…”

Section: Introductionmentioning

confidence: 99%

Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees

Li¹,

Xie²

2020

Preprint

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This paper studies a classic maximum entropy sampling problem (MESP), which aims to select the most informative principal submatrix of a prespecified size from a covariance matrix. MESP has been widely applied to many areas, including healthcare, power system, manufacturing and data science. By investigating its Lagrangian dual and primal characterization, we derive a novel convex integer program for MESP and show that its continuous relaxation yields a near-optimal solution. The results motivate us to study an efficient sampling algorithm and develop its approximation bound for MESP, which improves the best-known bound in literature. We then provide an efficient deterministic implementation of the sampling algorithm with the same approximation bound. By developing new mathematical tools for the singular matrices and analyzing the Lagrangian dual of the proposed convex integer program, we investigate the widely-used local search algorithm and prove its first-known approximation bound for MESP. The proof techniques further inspire us with an efficient implementation of the local search algorithm. Our numerical experiments demonstrate that these approximation algorithms can efficiently solve medium-sized and large-scale instances to near-optimality. Our proposed algorithms are coded and released as open-source software. Finally, we extend the analyses to the A-Optimal MESP (A-MESP), where the objective is to minimize the trace of the inverse of the selected principal submatrix.

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Human activity recognition based on multienvironment sensor data

Yang

et al. 2023

Information Fusion

108

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An optimal sensor placement algorithm (O-SPA) for improving tracking precision of human activity in real-world healthcare systems

Cited by 6 publications

References 23 publications

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images

Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees

Human activity recognition based on multienvironment sensor data

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