Deep learning approach for facial age classification: a survey of the state-of-the-art

2020 ◽  
Author(s):  
Olatunbosun Agbo-Ajala ◽  
Serestina Viriri
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Facial Age

scholarly journals Deep Learning Approach for Automatic Microaneurysms Detection

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 542
Author(s):  
Muhammad Mateen ◽  
Tauqeer Safdar Malik ◽  
Shaukat Hayat ◽  
Musab Hameed ◽  
Song Sun ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Vision Loss ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Manual Analysis

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely “E-Ophtha” and “DIARETDB1”, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

scholarly journals A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paramita Ray ◽  
Amlan Chakrabarti
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Learning Method ◽  
Rule Based ◽  
Aspect Extraction ◽  
Art Methods ◽  
Rule Based Approach

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

scholarly journals A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 80 ◽  
Author(s):  
Rania M. Ghoniem
Keyword(s):  
Deep Learning ◽  
Liver Cancer ◽  
State Of The Art ◽  
The State ◽  
Convergence Time ◽  
Computational Time ◽  
Learning Approach ◽  
Liver Lesions ◽  
Learning Models ◽  
Abc Algorithm

Current research on computer-aided diagnosis (CAD) of liver cancer is based on traditional feature engineering methods, which have several drawbacks including redundant features and high computational cost. Recent deep learning models overcome these problems by implicitly capturing intricate structures from large-scale medical image data. However, they are still affected by network hyperparameters and topology. Hence, the state of the art in this area can be further optimized by integrating bio-inspired concepts into deep learning models. This work proposes a novel bio-inspired deep learning approach for optimizing predictive results of liver cancer. This approach contributes to the literature in two ways. Firstly, a novel hybrid segmentation algorithm is proposed to extract liver lesions from computed tomography (CT) images using SegNet network, UNet network, and artificial bee colony optimization (ABC), namely, SegNet-UNet-ABC. This algorithm uses the SegNet for separating liver from the abdominal CT scan, then the UNet is used to extract lesions from the liver. In parallel, the ABC algorithm is hybridized with each network to tune its hyperparameters, as they highly affect the segmentation performance. Secondly, a hybrid algorithm of the LeNet-5 model and ABC algorithm, namely, LeNet-5/ABC, is proposed as feature extractor and classifier of liver lesions. The LeNet-5/ABC algorithm uses the ABC to select the optimal topology for constructing the LeNet-5 network, as network structure affects learning time and classification accuracy. For assessing performance of the two proposed algorithms, comparisons have been made to the state-of-the-art algorithms on liver lesion segmentation and classification. The results reveal that the SegNet-UNet-ABC is superior to other compared algorithms regarding Jaccard index, Dice index, correlation coefficient, and convergence time. Moreover, the LeNet-5/ABC algorithm outperforms other algorithms regarding specificity, F1-score, accuracy, and computational time.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

scholarly journals Unsupervised Deep Learning via Affinity Diffusion

2020 ◽  
Vol 34 (07) ◽  
pp. 11029-11036
Author(s):  
Jiabo Huang ◽  
Qi Dong ◽  
Shaogang Gong ◽  
Xiatian Zhu
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
General Purpose ◽  
Training Data ◽  
Learning Approach ◽  
Model Learning ◽  
Feature Representations ◽  
Discriminative Feature ◽  
Training Samples ◽  
Unsupervised Deep Learning

Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployability in real-world scenarios without any labelling budget. In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. It is based on self-discovering semantically consistent groups of unlabelled training samples with the same class concepts through a progressive affinity diffusion process. Extensive experiments on object image classification and clustering show the performance superiority of the proposed method over the state-of-the-art unsupervised learning models using six common image recognition benchmarks including MNIST, SVHN, STL10, CIFAR10, CIFAR100 and ImageNet.

Ensemble-based deep meta learning for medical image segmentation

2021 ◽  
pp. 1-7
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
The State ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Meta Learning ◽  
Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

Fauce

2021 ◽  
Vol 14 (11) ◽  
pp. 1950-1963
Author(s):  
Jie Liu ◽  
Wenqian Dong ◽  
Qingqing Zhou ◽  
Dong Li
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
The State ◽  
Light Weight ◽  
Critical Problem ◽  
Estimation Errors ◽  
Cardinality Estimation ◽  
Complex Queries ◽  
Uncertainty Information ◽  
Deep Learning Model

Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.

Sign in / Sign up

Export Citation Format

Share Document