Slovakia

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&#39; 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&#39; 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.

IWSSIP 2021

Cluster Prediction Model based on Automatic Cluster Labeling and Fuzzy System

cluster labeling.

fuzzy

machine learning

classification

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

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.

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)

Fingerprints are widely used for biometric validation worldwide. Since human fingerprints are unique and remain constant over time, it provides an easy-to-use, reliable, and economical authentication method. In addition to that, fingerprint recognition systems are of great importance because of their applicability in our lives. This work presents a classification methodology based on Henry Classification System using Convolutional Neural Networks (CNNs) models, such as Darknet, Alexnet, Resnet, VGG16, and Deep Belief Network. Besides that, we evaluate our proposal by carrying out experiments using grayscale images and pre-processed images as input on the classification step with the combination of the Gabor filter and the morphological thinning operation. We have obtained the highest result accuracy of 95.1% in the NIST Special Database 4, a widespread fingerprint dataset, using the Resnet 34 model in grayscale images. The proposed approach was evaluated with extraction strategies of classic attributes and based on convolutional networks. According to the results, the proposed methodology presents promising results, surpassing the traditional approaches present in the literature.

Fingerprint Classification based on the Henry System via ResNet

"The paper presents a multi-tool environment with an online assistant for Direct Digital Synthesis (DDS). The environment is integrated into the Online CADCOM platform and serves the Moodle-based Computer-Aided Design in Communication course in the Faculty of Telecommunications at the Technical University of Sofia. The design of DDS, as an all-digital signal generator, is one of the topics in the category “Oscillators design”. 
The environment integrates the online tools which can be used for DDS and the MATLAB (Simulink) -Vivado (FPGA) combination for creating an experimental setup.  The online assistant offers multimedia and interactive digital content on DDS, leading students in the implementation of the set of tools integrated for solving DDS design tasks. By combining teaching materials with tasks, quizzes, and forums the online assistant helps students develop critical thinking and encourages collaboration between them. Learning Management Systems, Moodle, and collaborative learning with an open web-based blog enhance students’ analytical skills as they share knowledge and discuss in a group. The new environment is probated with a group of Erasmus+ students on a mobility in TU-Sofia, following the CADCOM course.
"

Multi-tool environment with an online assistant for teaching Direct Digital Synthesis

"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."

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