Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

Saberi-Movahed, Farshad; Mohammadifard, Mahyar; Mehrpooya, Adel; Rezaei-Ravari, Mohammad; Berahmand, Kamal; Rostami, Mehrdad; Karami, Saeed; Najafzadeh, Mohammad; Hajinezhad, Davood; Jamshidi, Mina; Abedi, Farshid; Mohammadifard, Mahtab; Farbod, Elnaz; Safavi, Farinaz; Dorvash, Mohammadreza; Mottaghi-Dastjerdi, Negar; Vahedi, Shahrzad; Eftekhari, Mohammad; Saberi-Movahed, Farid; Alinejad‐Rokny, Hamid; Band, Shahab S.; Tavassoly, Iman

doi:10.1016/j.compbiomed.2022.105426

Cited by 57 publications

(28 citation statements)

References 96 publications

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“…In this regard, computer vision play an important role to dimensionality reduction with Matrix Factorization (MF) has valuable framework to treat against COVID-19 [44]. Additionally, Feature Selection and Prognosis Classification used to develop machine learning based intelligent system for COVID-19 disease [45]. The spectral clustering [46] and gene selection technique [47] has been presented to map to a low-dimensional space by merging node centrality and community detection.Due to increase spread of COVID-19 outbreak cause serious condition to the global education systems.…”

Section: Social Distance Measurementmentioning

confidence: 99%

Face mask detection and social distance monitoring system for COVID-19 pandemic

Javed

Butt

Khalid

et al. 2022

Multimed Tools Appl

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Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learningbased visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillancebased dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10, 000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy 1 .

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Section: Social Distance Measurementmentioning

confidence: 99%

Face mask detection and social distance monitoring system for COVID-19 pandemic

Javed

Butt

Khalid

et al. 2022

Multimed Tools Appl

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“…Through the technical skill of aircraft cockpit, a good comfortable experience may develop like the happiness in driving [5]. Saberi-Movahed et al [6] conducted a high-dimensional reduction of blood biomarker space in a cohort of COVID-19 patients based on matrix factor-mediated feature selection, and the results showed that arterial blood gas oxygen saturation and C-reactive protein (CRP) were the most important clinical biomarkers for poor prognosis in these patients. Najafzadeh et al [7] used group data processing (GMDH) to predict the 3 d free-span expansion rate around the pipe induced by waves.…”

Section: Introductionmentioning

confidence: 99%

Study on Aircraft Cockpit Function Based on Neural Network

You-chao

2022

Wireless Communications and Mobile Computing

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With the rapid development of numerous airlines, various trend designs have been implemented to develop cockpit interiors for pilots. When communicating with most pilots, they express their emotional feelings about the cockpit. The performance-enhanced cockpit provides the pilot with a better emotional experience, further enhancing the comfort and pleasure of driving. Therefore, it is necessary to provide a cockpit with good performance. Therefore, this paper proposes a novel Neural Network-based Balanced Optimization Algorithm (NN-EOA) for cockpit emotion recognition. The proposed NN-EOA technique simulates quantitative computation with high accuracy and minimizes the error rate during evaluation. Here, images of the interior cockpit design were assessed on four emotional terms, namely, user-friendly (interactive), precise (precise), traditional (traditional), and neat (tidy). Finally, experimental results are performed on various parameters, namely, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) for various techniques. From the experimental evaluations, it can be seen that the proposed NN-EOA provides high agreement rates in the experimental group with the smallest MAE, MAPE, and RMSE.

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“…For example, the researchers applied modified matrix-factorization-based feature selection methods to determine two genes that can predict the cell’s response to cancer treatment [ 65 ]. Further, these techniques were applied to determine the clinical biomarkers that predict health outcomes in COVID-19 patients [ 66 ]. Although the nature of the data in these domains is different from textual Reddit data, these techniques can be examined in future research, as a similar non-negative matrix factorization dimensionality reduction method was used by one of the included studies [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

Yeskuatov

Chua

Foo

2022

IJERPH

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Suicide is a major public-health problem that exists in virtually every part of the world. Hundreds of thousands of people commit suicide every year. The early detection of suicidal ideation is critical for suicide prevention. However, there are challenges associated with conventional suicide-risk screening methods. At the same time, individuals contemplating suicide are increasingly turning to social media and online forums, such as Reddit, to express their feelings and share their struggles with suicidal thoughts. This prompted research that applies machine learning and natural language processing techniques to detect suicidality among social media and forum users. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. To achieve this objective, we conducted a literature review of the recent articles detailing machine learning and natural language processing techniques applied to Reddit data to detect the presence of suicidal ideations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we selected 26 recent studies, published between 2018 and 2022. The findings of the review outline the prevalent methods of data collection, data annotation, data preprocessing, feature engineering, model development, and evaluation. Furthermore, we present several Reddit-based datasets utilized to construct suicidal ideation detection models. Finally, we conclude by discussing the current limitations and future directions in the research of suicidal ideation detection.

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Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

Cited by 57 publications

References 96 publications

Face mask detection and social distance monitoring system for COVID-19 pandemic

Face mask detection and social distance monitoring system for COVID-19 pandemic

Study on Aircraft Cockpit Function Based on Neural Network

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

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