Presented a Framework of Computational Modeling to Identify the Patient Admission Scheduling Problem in the Healthcare System

Rad, Reza Hosseini; Baniasadi, Sahba; Yousefi, Parisa; Heravi, Hakimeh Morabbi; Al-Ani, Muzhir Shaban; Ilani, Mohsen Asghari

doi:10.1155/2022/1938719

Cited by 10 publications

(7 citation statements)

References 70 publications

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“…Deep Neural Networks (DNNs) represent a class of artificial neural networks distinguished by their numerous hidden layers positioned between the input and output layers. These networks excel at capturing intricate patterns and correlations within data, particularly in scenarios involving high-dimensional input data such as images, audio, and text [10]. Leveraging deep architectures, DNNs autonomously learn hierarchical representations of features, enabling them to effectively discern between various classes within the dataset.…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

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Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach

Asghari Ilani,

Moftakhar Tehran,

Kavei

et al. 2024

Preprint

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This study delved into the application of various machine learning (ML) models for the classification of lung cancer levels. Through meticulous monitoring of parameters such as minimum child weight and learning rate, efforts were made to mitigate overfitting while optimizing model performance. The Deep Neural Network (DNN) emerged as a standout performer, showcasing robust performance across training, validation, and testing stages. Ensemble methods like voting and bagging also demonstrated promising results. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges in achieving satisfactory performance. Overall, the investigation sheds light on the efficacy of different ML models in lung cancer level classification and underscores the importance of parameter tuning to address overfitting concerns.

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Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

“…Combining machine learning with metabolomics has shown promise in identifying biomarkers for earlystage lung cancer diagnosis [9]. By integrating these approaches, we strive to contribute to the advancement of early detection methods, ultimately improving patient outcomes in the fight against lung cancer [6,10].…”

Section: Introductionmentioning

confidence: 99%

Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach

Asghari Ilani,

Moftakhar Tehran,

Kavei

et al. 2024

Preprint

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“…Compared to other machine learning models, a convolutional neural network (CNN) is better equipped for AM surface classification due to its better efficiency, robustness, and generalization capabilities. CNN is a deep learning method which uses inter-connected neurons to classify the images into various groups or categories [13][14][15][16]. Each neuron is a computational unit which passes its output to the next layer and so on.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

CNN-based Labelled Crack Detection for Image Annotation

Asghari Ilani,

Amini,

Karimi

et al. 2024

Preprint

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Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

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“…Machine learning techniques offer promising avenues for real-time tracking and diagnosis of COVID patients [10]. By analyzing symptom patterns, machine learning algorithms can classify individuals at risk of infection [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning

Asghari Ilani,

Moftakhar Tehran,

Kavei

et al. 2024

Preprint

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Despite the availability of COVID-19 vaccines, the global pandemic remains a significant health challenge. Timely detection of the virus is crucial for effective containment efforts, prompting the exploration of machine learning (ML) models for COVID-19 detection. highlights the ongoing significance of COVID-19 as a global health crisis despite the availability of vaccines. To address the need for timely virus detection, the study explores the efficacy of machine learning (ML) models, including DNN, Voting, Bagging, SVC_rbf, SVC_linear, SVC_polynomial, and SVC_sigmoid, in predicting COVID-19 infection probability. With a dataset comprising 4000 samples split into 3200 for training and 800 for testing, the analysis reveals that DNN and Voting models demonstrate the highest performance, achieving an impressive accuracy of 89%. These results underscore the potential of ML techniques, particularly DNN and ensemble methods like Voting, in facilitating early COVID-19 detection and contributing to effective pandemic management. The study emphasizes the importance of continued research to further refine and optimize ML-based COVID-19 detection systems.

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Presented a Framework of Computational Modeling to Identify the Patient Admission Scheduling Problem in the Healthcare System

Cited by 10 publications

References 70 publications

Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach

Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach

CNN-based Labelled Crack Detection for Image Annotation

A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning

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