Neural Network Principles and Applications

Zayegh, Amer; Bassam, Nizar Al

doi:10.5772/intechopen.80416

Cited by 36 publications

(21 citation statements)

References 4 publications

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“…Some of them, referred to as input units, are intended to receive different types of data from the outside world, which the network will try to learn about, identify, or otherwise process. Other units, known as output units, are located in the network's contrary direction and signal how the network reacts to the information it receives [22]. The majority of deep neural networks are fully connected, which means that each concealed unit and output unit is linked to every other unit in the layers on either side.…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

Chowdhury

Das

Saha

et al. 2021

Preprint

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Objectives: The COVID-19 pandemic is among the most serious global threats, and it is still a significant concern. The people of Bangladesh are undergoing one of the world's largest vaccination drive. With the recent launch and introduction of the COVID-19 vaccines, many of us are curious about the general opinion or view of the vaccine. While the vaccine has ignited new hope in the battle against COVID-19, it has also sparked militant anti-vaccine campaigns, so the need to analyze public opinion on the COVID-19 vaccine has emerged. Methods: Traditional machine learning methods were used to obtain a benchmark result for the experiment. The recurrent neural network (RNN) algorithm was used next. Several different types of recurrent neural networks were used, including simple RNNs, Gated Recurrent Units (GRUs), and LSTMs. Finally, to achieve a more optimal result, small BERT models (Bidirectional Encoder Representations from Transformers) were used. Results: Upon study and testing on several models and methods, it can be seen that BERT model was the most accurate of the bunch, which was 84%. On the other hand, Naive Bayes was able to obtain an accuracy of 81%. Naive Bayes and BERT produced similar results in F1- Score, but the performance of Naive Bayes can improve as the dataset size grows. Conclusion: Knowing about public opinions on the COVID-19 vaccine is critical, and action must be taken to ensure that everybody understands the value of vaccination and that everybody receives the COVID-19 vaccine. Vaccination may help to develop immunity, which lowers the likelihood of contracting the disease and its consequences.

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Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

Chowdhury

Das

Saha

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…This activation function decides whether the information is relevant or should not pass to the subsequent unit. The whole process of learning is based on altering the values of weights and biases depending on the calculated loss function between the actual and desired output ( Zayegh and Bassam, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

Bodzas

Kodytek

Žídek

2020

Front. Bioeng. Biotechnol.

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Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.

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“…Although, there are many neural network classification techniques, in most RS image analyses, a neural network method uses back-propagation for supervised learning, selecting a number of hidden layers. The users can choose different activation functions (Zayegh, Bassam, 2018). Learning occurs by adjusting the weights in the node to minimize the difference between the output node activation and the output.…”

Section: Feature Selection and A Supervised Classification Methodsmentioning

confidence: 99%

Advanced Classification of Optical and Sar Images for Urban Land Cover Mapping

Amarsaikhan

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

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Neural Network Principles and Applications

Cited by 36 publications

References 4 publications

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

WITHDRAWN: Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

Advanced Classification of Optical and Sar Images for Urban Land Cover Mapping

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