Deep Learning and Image Recognition

Li, Chaoyang; Li, Xiaohan; Chen, Manni; Sun, Xinyao

doi:10.1109/iceict57916.2023.10245041

Cited by 18 publications

(6 citation statements)

References 28 publications

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“…Automated code generation and testing, personalized user experiences, and autonomous IT operations are other notable areas where AI enhances efficiency [31]. Additionally, AI contributes to data management and analysis, image and speech recognition, and robotic process automation in IT processes [32]. These advancements collectively optimize workflows, improve security, and elevate the overall user experience in diverse IT applications.…”

Section: Artificial Intelligence and Societymentioning

confidence: 99%

Exploring the Relationship between the Coverage of AI in WIRED Magazine and Public Opinion Using Sentiment Analysis

Moriniello,

Martí-Testón,

Muñoz

et al. 2024

Applied Sciences

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The presence and significance of artificial intelligence (AI) technology in society have been steadily increasing since 2000. While its potential benefits are widely acknowledged, concerns about its impact on society, the economy, and ethics have also been raised. Consequently, artificial intelligence has garnered widespread attention in news media and popular culture. As mass media plays a pivotal role in shaping public perception, it is crucial to evaluate opinions expressed in these outlets. Understanding the public’s perception of artificial intelligence is essential for effective public policy and decision making. This paper presents the results of a sentiment analysis study conducted on WIRED magazine’s coverage of artificial intelligence between January 2018 and April 2023. The objective of the study is to assess the prevailing opinions towards artificial intelligence in articles from WIRED magazine, which is widely recognized as one of the most reputable and influential publications in the field of technology and innovation. Using two sentiment analysis techniques, AFINN and VADER, a total of 4265 articles were analyzed for positive, negative, and neutral sentiments. Additionally, a term frequency analysis was conducted to categorize articles based on the frequency of mentions of artificial intelligence. Finally, a linear regression analysis of the mean positive and negative sentiments was performed to examine trends for each month over a five-year period. The results revealed a leading pattern: there was a predominant positive sentiment with an upward trend in both positive and negative sentiments. This polarization of sentiment suggests a shift towards more extreme positions, which should influence public policy and decision making in the near future.

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Section: Artificial Intelligence and Societymentioning

confidence: 99%

Exploring the Relationship between the Coverage of AI in WIRED Magazine and Public Opinion Using Sentiment Analysis

Moriniello,

Martí-Testón,

Muñoz

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Two pooling methods are commonly used for CNN models: one is max-pooling, which adopts the maximum value of the local neuron clusters in the feature map, and the other is average pooling, which calculates the mean value in the map [ 68 ]. Fully connected layers are put in to connect all the neuron clusters in one layer to those in another layer [ 69 ]. Flattened matrices pass through these layers to classify the images for the final output ( Figure 1 ).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models

Kitaya,

Yasuo,

Yamaguchi

2024

Medicina

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Chronic endometritis (CE) is an inflammatory pathologic condition of the uterine mucosa characterized by unusual infiltration of CD138(+) endometrial stromal plasmacytes (ESPCs). CE is often identified in infertile women with unexplained etiology, tubal factors, endometriosis, repeated implantation failure, and recurrent pregnancy loss. Diagnosis of CE has traditionally relied on endometrial biopsy and histopathologic/immunohistochemistrical detection of ESPCs. Endometrial biopsy, however, is a somewhat painful procedure for the subjects and does not allow us to grasp the whole picture of this mucosal tissue. Meanwhile, fluid hysteroscopy has been recently adopted as a less-invasive diagnostic modality for CE. We launched the ARCHIPELAGO (ARChival Hysteroscopic Image-based Prediction for histopathologic chronic Endometritis in infertile women using deep LeArninG mOdel) study to construct the hysteroscopic CE finding-based prediction tools for histopathologic CE. The development of these deep learning-based novel models and computer-aided detection/diagnosis systems potentially benefits infertile women suffering from this elusive disease.

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“…Convolutional Neural Network technology serves as an example of deep learning [6], [7]. Four main layers make up the Convolutional Neural Network architecture: the convolutional layer define characteristics from input; nonlinear activation layer improves the CNN's nonlinear representations; the pooling layer, which chooses and filters features; and fully connected layer generates final predicted result [8], [9]. CNN is capable of representation learning in addition to extracting features from images [10,11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The employed three CNN models, thus e=3. The voting system for proposed work describes as in equation (8,9):…”

Section: Proposed Ensemble Modelmentioning

confidence: 99%

An Ensemble Novel Deep Learning Technique for Chest Radiograph-Based Pneumonia Prediction

2023

Preprint

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Pneumonia continues to be a prominent treatable cause of global mortality, stressing the importance of early identification to enable prompt intervention. Chest X-rays (CXRs) are an essential diagnostic tool, however determining their exact interpretation is still very difficult. By addressing both medical experts and individuals who are new to the area, the proposed work aims to improve prediction of pneumonia. The Synthetic Minority Over-sampling Technique has been utilised to cope with imbalanced dataset because the used dataset does not have balanced distribution among all classes. A pneumonia prediction model that makes use of convolutional neural networks including CustomVGG19, CustomResNet-50 and CustomDenseNet121 and the proposed ensemble model to improve diagnosis of pneumonia has been proposed. These models are trained and improved in experiments. The optimization of each model's performance was achieved through the systematic exploration of diverse configurations and hyperparameters. The ultimate outcomes were derived by employing the ensemble technique, which involved amalgamating the predictions of CNN models during the analysis. Results demonstrate the superiority of the proposed model, which achieved a 97.68% prediction accuracy.

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Deep Learning and Image Recognition

Cited by 18 publications

References 28 publications

Exploring the Relationship between the Coverage of AI in WIRED Magazine and Public Opinion Using Sentiment Analysis

Exploring the Relationship between the Coverage of AI in WIRED Magazine and Public Opinion Using Sentiment Analysis

Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models

An Ensemble Novel Deep Learning Technique for Chest Radiograph-Based Pneumonia Prediction

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