Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Saeed, Jwan Najeeb; Zeebaree, Subhi R. M.

doi:10.38094/jastt20189

Cited by 59 publications

(42 citation statements)

References 70 publications

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“…Researchers began to shift the research focus to the multiperson skeleton extraction scheme [ 11 ]. On this basis, many scholars improved and optimized the scheme [ 12 , 13 ]. In addition to bottom-up, fruitful research results have been achieved in the top-down field, such as local extraction method [ 14 ], mask CNN [ 15 ], and cascade pyramid model [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

2021

Computational Intelligence and Neuroscience

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With the significant increase of social informatization, the emerging information technology represented by machine vision has been applied to more and more scenes. Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. However, the existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Therefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Through the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. Among them, on all MPII data sets, the average accuracy of PSO-LSTM proposed in this paper is 3.9% higher than that of other optimal algorithms; on the PoseTrack data set, the average accuracy of detection and extraction is improved by 2.3%. The above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

2021

Computational Intelligence and Neuroscience

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“…In formula (12), K d is the concentration of the text to d. e definition of clustering result concentration is shown in the following formula:…”

Section: Optimization Of Algorithm Based On Cnn and Feedbackmentioning

confidence: 99%

“…Based on the public data of Iowa, Saeed and Zeebaree used the improved k-prototype clustering algorithm combined with BP neural network to build the prediction model of the recidivism rate after the criminals were released from prison. e research results show that the prediction accuracy of the model is as high as 87.9% [12]. Çolak used hierarchical clustering algorithm and principal component analysis to classify multiple carbon sources and then studied the effect of different fermentation conditions on the fatty acid composition of Trichosporon F1-2 single cell oil [13].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network

Chen

2021

Computational Intelligence and Neuroscience

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Chinese language is also an important way to understand Chinese culture and an important carrier to inherit and carry forward Chinese traditional culture. Chinese language teaching is an important way to inherit and develop Chinese language. Therefore, in the era of big data, data mining and analysis of Chinese language teaching can effectively sum up experience and draw lessons, so as to improve the quality of Chinese language teaching and promote Chinese language culture. Text clustering technology can analyze and process the text information data and divide the text information data with the same characteristics into the same category. Based on big data, combined with convolutional neural network and K-means algorithm, this paper proposes a text clustering method based on convolutional neural network (CNN), constructs a Chinese language teaching data mining analysis system, and optimizes it so that the system can better mine Chinese character data in Chinese language teaching data in depth and comprehensively. The results show that the optimized k-means algorithm needs 683 iterations to achieve the target accuracy. The average K-measure value of the optimized system is 0.770, which is higher than that of the original system. The results also show that K-means algorithm can significantly improve the clustering effect, optimize the data mining analysis system of Chinese language teaching, and deeply mine the Chinese data in Chinese language teaching, so as to improve the quality of Chinese language teaching.

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“…A practical method in the decision-making and analysis of physical processes is optimization. In mathematical terms, optimizing the solution from among all possible solutions lies in finding the best solution [54]. Optimization is executed by evaluating different solutions iteratively before an optimal or satisfactory solution is found.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Yahia

Zeebaree

Sadeeq

et al. 2021

AJRCoS

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Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.

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Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Cited by 59 publications

References 70 publications

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

[Retracted] Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

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