Greece

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.

EANN 2020

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

unstructured text mining

disease prediction

clinical recommendation systems

knowledge base

deep learning

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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. 

This paper presents a self-sufficient solar-powered real-time Deep Learning (DL) based system that runs inference 100% on solar energy and which is composed of an Nvidia Jetson TX2 board and a dual-axis solar tracker based on the cast-shadow principle. In order to have a higher energy being generated by the solar tracker as well as a lower energy consumption by the real-time DL-based system, we have: a) updated our solar tracker’s panel with a higher number of polycrystalline photovoltaic (PV) cells and connected it to a chain of two inverters, one accumulator and one solar charge controller; b) implemented a motion detection method that triggers the inference process only when there is substantial movement in webcam frame. Experimental results show that our solar tracker generates sufficient and constant solar energy for all the 4 DL models (VGG-19, Incep-tionV3, ResNet-50 and MobileNetV2) that are running in real-time on the Nvidia Jetson TX2 platform and which requires more than 5 times less energy when compared to a laptop having a Nvidia GTX 1060 GPU, proving that real-time DL-based systems can be powered by solar trackers without the need of traditional power plugs or need to pay for electricity bills.

Efficient Implementation of a Self-Sufficient Solar-Powered Real-Time Deep Learning-Based System

The main aim of this paper is to study the stability problem for neutral-type Hopﬁeld neural networks possessing multiple time delays in the states of the neurons and multiple neutral delays in time derivative of states of the neurons. By using a suitable Lyapunov functional, a novel suﬃcient stability condition is obtained for global asymptotic stability of neutral-type neural networks with multiple delays. The derived stability criterion can be expressed in terms of the parameters of the neural network model which totally relies on some simple relationships established between the network parameters and it is completely independent of time delays and neutral delays. Hence, this new global asymptotic stability condition can be easiliy tested and veriﬁed by using some algebraic mathematical properties. We will also make a comparision between the result of this paper and previously published corresponding results. This comparision will indicate the advanteges of our proposed stability condition over the previously reported stability conditions. Since obtaining stability conditions for neutral type neural networks with multiple delays is a diﬃcult task to achieve due to the insuﬃcient mathematical methods and techniques, the result given in this paper can be considered an important and alternative result for this class of neutral type neural systems.

Stability Analysis of Neutral-Type Hopﬁeld Neural Networks with Multiple Delays

Radar sensors, unlike lidars or cameras, can measure objects' instantaneous velocity and composition. However, the ability to process and retrieve spatial information from radar point clouds has not yet been fully established. In this work, we propose a key technique for improving the performance of standard machine learning point-wise processing methods on radar point clouds. We show that a network can learn to extract object-related signatures for every point using automotive radar measurements. In addition, we propose RadarPCNN, a novel architecture for performing semantic segmentation, specifically designed for radar point clouds. RadarPCNN uses PointNet++, aided with mean shift as feature extractor module and an attention mechanism to fuse information from different neighborhood levels. We show that our model outperforms state-of-the-art solutions on our dataset.

Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks

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.

Classification of Coseismic Landslides Using Fuzzy and Machine Learning Techniques

Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.

A compact sequence encoding scheme for online human activity recognition in HRI applications

Recent advances in optical sensor technologies and those of Geoinformatics can now support very large scale high definition multispectral and panchromatic images. This capability allows the use of remote sensing for the observation of complex ecosystems on earth. It supports sustaining biodiversity, precision agriculture, the management of land, crops, and parasites. It also supports advanced quantitative studies of biophysical and biogeochemical cycles in coastal or inland waters. One of the key applications that will allow the development of new types of decision support systems, is that of precise and effective scene classification. This will offer considerable advantages to business, science, and engineering. This work proposes a novel and very effective system based on geographic object-based scene classification in remote sensing images. More specifically the proposal includes a deep systems architecture of the Residual Neural Network (ResNet) type. This, omitting some layers in the early stages of training, achieves an effective simplification of the network by eliminating the Vanishing Gradient Problem (VGP) which troubles other deep learning architectures. Furthermore, the proposed system uses the softmax activation instead of the sigmoid function in the last level and the fully residual network was trained using the novel AdaBound algorithm that employs dynamic bounds on their learning rates. This achieves a smooth transition to stochastic gradient descent, tackling the noise dispersed points of misclassification with great precision. This is something that other spectral classiﬁcation methods cannot handle. The proposed system has been tested, very successfully, in scene identification from remote sensing images. This confirms that it could be further used in advanced level processes for Large-Scale Geospatial Data Analysis such as cross-border classification, the recognition, and monitoring of certain patterns and multisensor data fusion.

Large-Scale Geospatial Data Analysis: Geographic Object-based Scene Classification in Remote Sensing Images by GIS and Deep Residual Learning

Critical Care Units (CCU) in a hospital treat the severely sick patients that need constant monitoring and close medical attention. Feeding patients, enteral feeding in particular, is a critical and continu- ous process. Monitoring patients, managing their feeding and referring to a dietician is a key factor in CCUs. Screening patients for referral to a dietician in a CCU is an error-prone and complicated task. One of the main challenges in this regard is that the data needed to screen patients is scattered among many different variables and textual forms. The number of patients being treated in the CCU is also a significant problem since it becomes difficult for the staff to keep track of the needs of all patients. Therefore, an automated screening tool can support effectively the feeding process and contribute considerably towards improving the quality and consistency of patient care. In this paper we present early stages of a project that aims at using machine learning techniques to help CCU consultants to automatically screen patients for dietician referral.

Automated Screening of Patients for Dietician Referral

Received a bachelor's (2016) and master's degree (2018) from ITMO University, specialization: software engineering. Currently, I am a research programmer at the ITMO University machine learning laboratory and DeepPavlov laboratory at MIPT.

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Efficient Implementation of a Self-Sufficient Solar-Powered Real-Time Deep Learning-Based System

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JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation

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