Slovakia

 Cluster Labeling Models apply Artificial Intelligence techniques to extract the main characteristics of data partitioned into clusters to provide a tool for interpretation of the clustering. For this purpose, different techniques, such as Machine Learning, Fuzzy Logic, and Data Discretization are used to identify essential attributes for forming clusters and the ranges of values associated with them. This paper presents a Cluster Labeling Model based on regression that delimits the attributes ranges of values that best summarize the groups. In the proposed model, the importance of an attribute to clustering is determined by the capacity of its most accurate range of values -- according to regression-- to represent the clusters. The results achieved in the experiments have shown that the model is useful, providing specific labels for each cluster that represents between 84% and 100% of the elements of the clusters for the databases used.

IWSSIP 2021

Automatic Cluster Labeling Based on Regression Error Analysis

clustering analysis

cluster labeling

unsupervised learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

technical paper


IWSSIP is an international conference that brings together researchers and developers from both academia and industry reporting the latest scientific and theoretical advances to discuss and debate major issues and state-of-the-art aspects on systems, signals and image processing.

###### For the session discussion, each author will be seated at individual breakout tables.  Please find which table they will be at here:
###### [QA table assignments in Gather.town](https://drive.google.com/file/d/1hBHOD_KF7NSsaTcpYcHCZslXJvQVxJQe/view?usp=sharing)

"The main aim of this article is to compare U-Net models
with different methods of medical image segmentation in ophthalmology when searching for the optic disc and vessels on the retina. We
used freely available databases to analyze the results. DRIVE, STARE
and MESSIDOR were used for blood vessel segmentation and DRION
database was used for optic disc segmentation. In vascular segmentation,
we compared the method based on morphological operations with U-Net
convolutional network. The best results were obtained by neural network, where we achieved accuracy of 96.7% on DRIVE dataset. Methods
based on morphological operations, Attentional U-Net network and Fast
Radial Symmetry Transform were used for optic disc localization, where
the U-net network achieved the best sensitivity of 91.6%. We show that
in our tests U-Net architectures achieve better results in vessel and optic disc segmentation even compared to standard methods, that do not
depend on the amount of available data."

Segmentation of signiﬁcant regions in retinal images: perspective of U-Net network through a comparative approach

In the presentation we show the main aspects of the work. We made a brief introduction to the research and related works. We explain the methodology step by step and briefly discuss the results and conclusion.

A New Approach for Facial Recognition based on Three-dimensional Point Cloud and Kinect Sensor using Machine Learning

This work presents the computer vision and machine learn-

Simultaneous localization and mapping in 3Dspace

ing techniques used in an implementation of a prototype system to aid

Advanced Scene Sensing for Virtual Teleconference

"Coronavirus (COVID-19) has been causing severe pneumonia worldwide since its emergence in 2019. Considering the scarcity of efficient resources for performing pre-diagnostic imaging, the computed tomography (CT) exam is used as an auxiliary resource for medical diagnosis by means of specialist doctors. Aiming to add solutions for the Computer Assisted Diagnosis (CAD), this work proposes an approach that uses the Internet of Things concept to dispose an API that provides the diagnosis of COVID-19 through Artificial Intelligence (AIoT) methods. The methodology was developed and divided into three stages, which are: 1) segmentation of the region of interest (lung); 2) deep extraction of attributes from the segmented region; and 3) patient classification using machine learning techniques. The Clean-CC-CCII dataset was used in the proposed approach of the experiment to validate the results. The experiment achieved excellent results with Accuracy of (91.12%), Sensitivity (93.45%), Precision (92.13%), Specificity (87.45%), F1-Score (92.79%), and AUC (0.910) in its best model thus surpassing works of
literature."

New AIoT-Based Approach for Automatic Detection of COVID-19 Using Deep Learning Techniques in CT Images

With the arrival of the New Coronavirus 2020 pandemic, dif-ferent realities had to adapt in the face of the difficulties that plaguedworld society. creening and pre-diagnosis measures are extremely impor-tant to contain the growing advance with peaks of alarming contagions.Within this perspective of designing efficient solutions and quick resultsfor rapid pre-diagnosis, screening is extremely important to contain thespread of contagion. This study proposes an effective IoT-based solutionfor  the  classification  and  detection  of  coughs,  with  the  aim  of  identi-fying  COVID-19  and  non-COVID-19  cough  patterns  in  a  patient  withsymptoms  of  the  disease.  The  cloud  system  uses  a  framework  capableof  capturing  the  previously  acquired  coughing  sounds  and  classifyingthem on the spectrograms through different deep learning networks us-ing transfer learning techniques. The proposed study was able to obtain 96.16% Accuracy in its best MobileNet + MLP model, with a Precisionof 97.47%, demonstrating an excellent performance for pre-diagnosis ofCOVID-19 in patients for possible screening through invasive tests.

New Approach Detection of COVID-19 by Cough Spectrogram Using IoT and Learning Transfer Technique for Classification

"The paper is focused on evaluating the security of using the Border
Gateway Protocol (BGP) as an alternative to the OpenFlow protocol for communication between the Software-Defined Networking (SDN) switch and the SDN controller. BGP is the main routing protocol used on the Internet. For this reason, it is considered as a possible alternative or replacement for the OpenFlow protocol used in SDN networks. In our paper, we present the identified security risks
resulting from the use of BGP in SDN in the standard way of BGP implementation."

Security of using Border Gateway Protocol in Software-Defined Networking

An Optimized Approach to COVID-19 Detection via Cough Audio Signals and Dimensionality Reduction

Clustering and classification techniques are frequently used to extract patterns and classify new data. Those techniques can be applied in data sets where the label information is not provided: the clustering interpretation identifies a pattern to assist the classification process. This interpretation often requires a specialist analysis since it depends on the approached problem but can also be performed by an automatic cluster labeling model. These models select the clusters' main characteristics to create labels that can be used as a summary of the clusters. In the literature papers in which these approaches are proposed, the labeling models present labels as attributes and ranges of values representing most clusters' elements. This paper proposes a cluster prediction model that uses cluster labels to generate rules and membership functions of a Fuzzy System to determine the cluster for new samples. The proposed model was evaluated by comparing accuracy, Kappa coefficient, and training time with other well-known classification algorithms. Also, we evaluated the number of rules generated. For different data sets used in the experiments, all available on the UCI repository, the results showed a Kappa coefficient above 0.8, which is considered excellent, and accuracy rate and training time similar to the well-known algorithms.

Cluster Prediction Model based on Automatic Cluster Labeling and Fuzzy System

An Automated Fingerprint Identification System (AFIS) is the cornerstone of many modern identity-driven applications, ranging from device authentication and law enforcement to security and borderline control. As the population in urban centers grows and the digitalization of services grows, so does the demand for more effective and efficient fingerprint recognition systems. Singular Points (SP), such as core and delta, are important landmarks that help to tackle this challenge. This paper proposes and evaluates an effective approach for SP detection based on a single-stage deep convolutional neural network model for object detection. We show that YOLOv4 detector with customized output layers is effective for handling cores and deltas patterns as patches in fingerprint images, using their center as coordinates. Experimental results were carried out on the challenging SPD2010 dataset to evaluate the proposed SP detector under different configurations. The best result is 60.34% of correctly detected fingerprints. In particular, compared to the state-of-the-art methods, our approach achieves an improvement up to 12% in correct detections, 8% in core detection rate, and 10% in delta detection rate. Core and delta miss rates are also reduced by 8% and 10%, respectively.

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Automatic Cluster Labeling Based on Regression Error Analysis

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Segmentation of signiﬁcant regions in retinal images: perspective of U-Net network through a comparative approach

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