Greece

Texture offers an effective characterization of image shape and orientation. Thus, a predominant task is to detect and extract texture features that discriminate accurately images within different semantic classes. The challenge resides in making these features invariant to several changes, such as affine transformation and viewpoint change, in order to ensure their robustness. Besides, the training phase requires a large number of images. To deal with these issues, Genetic Programming (GP) is adopted in this work with the intention of classifying precisely texture images using some training images per class. In fact, in order to automatically generate a descriptor that is invariant to illumination, rotation and scale; the proposed method combines GP with the scale extraction technique involved by SIFT. The performance of the proposed method is validated on five challenging datasets of non-scaled as well as scaled texture images. Results show that the method is robust not only to scale but also to rotation, while achieving significant performance compared to the state of the art methods.

EANN 2020

A Genetic Programming Method for Scale-Invariant Texture Classification

texture

genetic programming

sift

image classification

scale invariance

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

technical paper

21st edition of EANN (International Conference on Engineering Applications of Neural Networks) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually and Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference. 

In this paper, a method to access the location of a steel strip in the rolling process was developed. The method consists of a hybrid system composed of a CNN-based semantic segmentation followed by morphological operation and outlier removal. The proposed method was capable of estimating the position of the strip with high precision and low computational burden, making it suitable for the application. The implementation of automatic estimation for the steel strip positioning, replacing the current human operation, can yield substantial costs saving. Future work will be carried out for the and integration of automatic control in the process.

Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation

When convolutional neural networks are applied to imagesegmentation results depend greatly on the data sets used to train thenetworks. Cloud providers support multi GPU and TPU virtual machinesmaking the idea of cloud-based segmentation as service attractive. In thispaper we study the problem of building a segmentation service, whereimages would come from different acquisition instruments, by training ageneralized U-Net with images from a single or several datasets. We alsostudy the possibility of training with a single instrument and performquick retrains when more data is available. As our example we performsegmentation of Optic Disc in fundus images which is useful for glau-coma diagnosis. We use two publicly available data sets (RIM-One V3,DRISHTI) for individual, mixed or incremental training. We show thatmultidataset or incremental training can produce results that are simi-lar to those published by researchers who use the same dataset for bothtraining and validation

Multidataset incremental Training for OpticDisc Segmentation

Performance evaluation an image data set on a CNN from scratch and on a pre-trained network.

Factors affecting accuracy of Convolutional Neural Network using VGG-16

Hyperspectral satellite imagery is now widely being used for accurate disaster prediction and terrain feature classification. However, in such classification tasks, most of the present approaches use only the spectral information contained in the images. Therefore, in this paper, we present a novel framework that takes into account both the spectral and spatial information contained in the data for land cover classification. For this purpose, we use the Gaussian Maximum Likelihood (GML) and Convolutional Neural Network (CNN) methods for the pixel-wise spectral classification and then, using segmentation maps generated by the Watershed algorithm, we incorporate the spatial contextual information into our model with a modified majority vote technique. The experimental analyses on two benchmark datasets demonstrate that our proposed methodology performs better than the earlier approaches by achieving an accuracy of 99.52% and 98.31% on the Pavia University and the Indian Pines datasets respectively. Additionally, our GML based approach, a non-deep learning algorithm, shows comparable performance to the state-of-the-art deep learning techniques, which indicates the importance of the proposed approach for performing a computationally efficient classification of hyperspectral imagery.

A Novel Spatial-Spectral Framework for the Classification of Hyperspectral Satellite Imagery

Exercise Recognition is relevant in many high impact domains, from healthcare to recreational activities to sports sciences, which faces many challenges when deployed in the real world. 
For instance, typical lab performances of Machine Learning~(ML) models, are hard to replicate, due to differences in personal nuances, traits and ambulatory rhythms.
Thus effective transferability of a trained model depends on its ability to adapt and personalise to a new user or a user group.
We look at person-agnostic and person-aware methods of evaluation to identify transferability of generic deep learning models and introduce a personalised algorithm to improve performance in different user groups. 
Our findings show that Exercise Recognition, when compared to results with other HAR tasks, to be a far more challenging personalisation problem.

Transferability of Personalised Exercise Recognition Models

In this presentation I introduce my paper Instance Weighted Clustering: Local Outlier Factor and K-Means. I briefly explain the area, inspirations, methodology and findings. Instance Weighted clustering is easy to understand and produces robust clustering results, the technique can be applied to variety of k-means like algorithms.

Instance Weighting Local Outlier Factor based Approach

There has been increasing popularity in medical text mining due to its vast applications in the field of disease prediction and clinical Recommendation systems. Radiology reports possess rich information depicting radiologists investigations on the health conditions of the patients in associated radiology images. However, radiology reports exist in a free-text unstructured format consisting of valuable information for disease prediction. This information cannot be easily retrieved and utilized for prediction without suitable text mining techniques. The medical dataset available in the current procedure is small, domain-specific and restricted to the institution. However, data is one of the critical factors to power Machine Learning (ML) and Deep Learning (DL) models. To overcome the above challenge of predicting disease in the low data condition, we present a practical Deep Learning framework that combines a Knowledge Base (KB) with the Deep Learning for accurate text mining and predicting the lung diseases from the unstructured radiology free-text reports. We adopt Glove word embeddings with the KB trained on large corpus for effective text modelling. Further, we incorporate Convolutional Neural Network-based Discriminative Dimensionality Reduction (CNN-DDR) to obtain the most discriminative feature vector. Finally, a fully connected Deep Neural Network (DNN) is leveraged as the prediction model to detect the diseases. We applied the proposed framework to predict the lung diseases on radiology reports from both publicly available Indiana University (IU) dataset and data collected from the private hospital. We benchmark the performance of the proposed framework, which outperforms against the standard ML Techniques.

Medical Knowledge-based Deep Learning Framework for Disease Prediction on Unstructured Radiology Free-Text Reports under Low Data condition

In this study, artificial bee colony-based feature selection methods are employed in order to overcome above mentioned problems which is observed in Parkinson’s Disease classification datasets. The fitness values were computed using the k-NN and the algorithms were tested on a public data set. At the end of the study, it is observed that binary versions of artificial bee colony quite successful in feature selection and improves the results in the current literature.

Feature selection with artificial bee colony algorithms for classifying Parkinson’s Diseases

The main purpose of this paper is to find the appropriate fuzzy implication
among the parametric and the three known (Kleen-Dienes, Lukasiewicz
and Reichenbach) from the literature implications which will provide the
smallest square error if adapted to our climatic data.

Application of Algorithmic Fuzzy Implications on Climatic Data

Seismically generated landslides represent one of the most damaging hazards associated with earthquakes in countries with high seismicity. The delineation of prone to coseismic landsliding areas is crucial in order to predict the occurrence of earthquake-induced landslides and consequently reduce the relevant risk. The goal of this study is to investigate the correlation of the pattern of coseismic landslides with geological and topographical variables with the volume of landslides based on fuzzy logic and machine learning techniques. For this task, a real dataset of 421 and 767 instances for years 2003 and 2015 respectively from the island of Lefkada was used. A new approach based on Fuzzy C-Means Algorithm and Ensemble Subspace k- Nearest-Neighbors (Ensemble Subspace k-NN) is proposed. Landslides were classified according to their severity with a success rate of 99.5% and 98.7% for 2003 and 2015 respec-tively. The performance of the proposed approach was evaluated using “One Versus All” Strategy, calculating Accuracy, Sensitivity, Specificity, Precision and F-1 Score for each cluster.

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