SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

Patro, Kiran Kumar; Prakash, Allam Jaya; Hammad, Mohamed; Tadeusiewicz, Ryszard; Pławiak, Paweł

doi:10.1016/j.bbe.2023.01.005

Cited by 25 publications

(6 citation statements)

References 60 publications

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“…Besides this, Patro et al developed a custom SCovNet based on CNN, which achieved the highest accuracy of 97.99% among the other SOTA models. In contrast, the proposed CNN-PCC-ELM achieved a satisfactory accuracy of 99.55%, almost 2% higher than their model [73] . Azam et al had the best precision and recall results out of all the SOTA models, coming in at 99.02% and 98.26%, respectively [79] .…”

Section: Performance Comparison and Discussionmentioning

confidence: 72%

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Nahiduzzaman

Goni

Islam

et al. 2023

Biocybernetics and Biomedical Engineering

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Section: Performance Comparison and Discussionmentioning

confidence: 72%

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Nahiduzzaman

Goni

Islam

et al. 2023

Biocybernetics and Biomedical Engineering

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“…A review in [23] indicates that machine learning is robust enough to aid doctors in predicting the likelihood of future type 2 diabetes development. Machine learning (ML) was employed in a study [24] to conduct a comprehensive evaluation of predicting methods for diabetes. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluated bias in machine learning models, whereas Meta-DiSc measured variability in a systematic review, demonstrating the greater effectiveness of machine learning compared to traditional methods.…”

Section: Literature Studymentioning

confidence: 99%

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

Panigrahy,

Dash,

Padhy

2024

IJACSA

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Diabetics mellitus has the potential to result in numerous challenges. Based on the increasing morbidity rates in recent years, it is projected that the global diabetic population will surpass 642 million by 2040, indicating that approximately one in every ten individuals will have diabetes. Undoubtedly, this alarming statistic necessitates urgent focus from both academics as well as industry to foster novelty and advancement in prediction of diabetics, with the aim of preserving patients' lives. Deep learning (DL) was employed to forecast a multitude of ailments as a result of its swift advancement. Nevertheless, DL approaches continue to face challenges in achieving optimal prediction performance as a result of the selection of hyperparameters and tuning of parameters. Hence, the careful choice of hyper-parameters plays a crucial role in enhancing classification performance. This paper introduces TSO-DBN, a Tabu Search Optimization method (TSO) that is based on Deep Belief Network (DBN). TSO-DBN has demonstrated exceptional performance in several medical fields. The Tabu Search Optimization algorithm (TSO) has been used to pick hyperparameters and optimize parameters. During the experiment, two problems were tackled in order to improve the findings. The TSO-DBN model exhibited exceptional performance, surpassing other models with an accuracy of 96.23%, an F1-score of 0.8749, and a Matthews Correlation Coefficient (MCC) of 0.88.63.

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“…Deep learning is a rapidly growing subfield of artificial intelligence that has shown a remarkable ability to learn from vast amounts of data and can solve complex problems with high accuracy [3,4]. However, this often requires access to powerful computing resources and significant data storage, making it challenging to deploy deep-learning algorithms in resource-limited environments [5,6].…”

Section: Introductionmentioning

confidence: 99%

Efficient Roundabout Supervision: Real-Time Vehicle Detection and Tracking on Nvidia Jetson Nano

Elmanaa,

Sabri,

Abouch

et al. 2023

Applied Sciences

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In recent years, a significant number of people in Morocco have been commuting daily to Casablanca, the country’s economic capital. This heavy traffic flow has led to congestion and accidents during certain times of the day as the city’s roads cannot handle the high volume of vehicles passing through. To address this issue, it is essential to expand the infrastructure based on accurate traffic-flow data. In collaboration with the municipality of Bouskoura, a neighboring city of Casablanca, we proposed installing a smart camera on the primary route connecting the two cities. This camera would enable us to gather accurate statistics on the number and types of vehicles crossing the road, which can be used to adapt and redesign the existing infrastructure. We implemented our system using the YOLOv7-tiny object detection model to detect and classify the various types of vehicles (such as trucks, cars, motorcycles, and buses) crossing the main road. Additionally, we used the Deep SORT tracking method to track each vehicle appearing on the camera and to provide the total number of each class for each lane, as well as the number of vehicles passing from one lane to another. Furthermore, we deployed our solution on an embedded system, specifically the Nvidia Jetson Nano. This allowed us to create a compact and efficient system that is capable of a real-time processing of camera images, making it suitable for deployment in various scenarios where limited resources are required. Deploying our solution on the Nvidia Jetson Nano showed promising results, and we believe that this approach could be applied in similar traffic-surveillance projects to provide accurate and reliable data for better decision-making.

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SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

Cited by 25 publications

References 60 publications

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

Efficient Roundabout Supervision: Real-Time Vehicle Detection and Tracking on Nvidia Jetson Nano

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