Deep Learning Application Pros And Cons Over Algorithm

Moshayedi, Ata Jahangir; Roy, Atanu Shuvam; Kolahdooz, Amin; Yang, Shuxin

doi:10.4108/airo.v1i.19

Cited by 63 publications

(44 citation statements)

References 79 publications

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“…Some commonly used ML models were also fit to the database, using standard specifications. Among those, deep learning [ 16 ] using a convolutional neural network (CNN) resulted in the best performance. Specifically, a three-layered fully connected CNN was applied to this dataset and attained an AUROC of 0.74.…”

Section: Resultsmentioning

confidence: 99%

An evolutionary machine learning algorithm for cardiovascular disease risk prediction

et al. 2022

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Introduction This study developed a novel risk assessment model to predict the occurrence of cardiovascular disease (CVD) events. It uses a Genetic Algorithm (GA) to develop an easy-to-use model with high accuracy, calibrated based on the Isfahan Cohort Study (ICS) database. Methods The ICS was a population-based prospective cohort study of 6,504 healthy Iranian adults aged ≥ 35 years followed for incident CVD over ten years, from 2001 to 2010. To develop a risk score, the problem of predicting CVD was solved using a well-designed GA, and finally, the results were compared with classic machine learning (ML) and statistical methods. Results A number of risk scores such as the WHO, and PARS models were utilized as the baseline for comparison due to their similar chart-based models. The Framingham and PROCAM models were also applied to the dataset, with the area under a Receiver Operating Characteristic curve (AUROC) equal to 0.633 and 0.683, respectively. However, the more complex Deep Learning model using a three-layered Convolutional Neural Network (CNN) performed best among the ML models, with an AUROC of 0.74, and the GA-based eXplanaible Persian Atherosclerotic CVD Risk Stratification (XPARS) showed higher performance compared to the statistical methods. XPARS with eight features showed an AUROC of 0.76, and the XPARS with four features, showed an AUROC of 0.72. Conclusion A risk model that is extracted using GA substantially improves the prediction of CVD compared to conventional methods. It is clear, interpretable and can be a suitable replacement for conventional statistical methods.

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Section: Resultsmentioning

confidence: 99%

An evolutionary machine learning algorithm for cardiovascular disease risk prediction

et al. 2022

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“…The applications of robotic could be extended on various potential works such as environmental, energy, hybrid materials, biomaterials, etc [43][44][45][46][47][48][49][50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Review On: The Service Robot Mathematical Model

Moshayedi

Roy

Sambo

et al. 2022

EAI Endorsed Trans AI Robotics

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After nearly 30 years of development, service robot technology has made important achievements in the interdisciplinary aspects of machinery, information, materials, control, medicine, etc. These robot types have different shapes, and mainly in some are shaped based on application. Till today various structure are proposed which for the better analysis’s need to have the mathematical equation that can model the structure and later the behaviour of them after implementing the controlling strategy. The current paper discusses the various shape and applications of all available service robots and briefly summarizes the research progress of key points such as robot dynamics, robot types, and different dynamic models of the differential types of service robots. The current review study can be helpful as an initial node for all researchers in this topic and help them to have the better simulation and analyses. Besides the current research shows some application that can specify the service robot model over the application.

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“…e experimentation was performed on Pavia University (PU), Pavia Center (PC), and Kennedy Space Center (KSC) datasets, and the results show that the proposed methods achieve better accuracy with low computational cost [58]. It has many applications in different fields of life like speech recognition systems, search engines, and other AI-based applications like robotics [59]. ere are many ML techniques available in literature like K-means clustering and PCA for classification tasks, and to perform regression, there are techniques like SVM, decision trees, ANN, ensemble methods, random forest, and so on [60,61].…”

Section: Machine Learningmentioning

confidence: 99%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

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Deep Learning Application Pros And Cons Over Algorithm

Cited by 63 publications

References 79 publications

An evolutionary machine learning algorithm for cardiovascular disease risk prediction

An evolutionary machine learning algorithm for cardiovascular disease risk prediction

Review On: The Service Robot Mathematical Model

Remote Sensing Image Classification: A Comprehensive Review and Applications

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