NavTalks
From Navigators
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<td style="width:300px">Miracle Aniakor</td> | <td style="width:300px">Miracle Aniakor</td> | ||
| - | <td style="width:600px"><span style="border-bottom: dashed 1px #000" title=""> | + | <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Deep Learning is a relatively new method in the machine learning industry that has been used for a variety of tasks; it has sparked considerable attention among academics worldwide. Deep learning has been applied to complicated problems that need a high degree of human intelligence, most notably in the healthcare industry. One of the medical sectors that deep learning is widely used is radiology. In radiology, different radiographic techniques are used to identify brain tumors, which are among the most severe and fatal types of tumors, with a limited survival rate if not treated at its early stage. While classifying tumors in radiographic images is a critical task in the health sector, it is a hard and time-consuming procedure that radiologists must undertake, with the accuracy of their analysis entirely depending on their knowledge. Today's radiological diagnostic, such as magnetic resonance (MR) tests, is mostly subjective and may be inadequately accurate, posing a significant danger to patients. As a result, harnessing Artificial Intelligence (AI) technologies to decrease diagnostic mistakes is critical. This work used deep learning and radiomics to develop an automated approach for identifying cancers in patients using magnetic resonance imaging (MRI) datasets from kaggle third party API database. The suggested approach employs a Convolutional Neural Network (CNN) with transfer learning as our deep learning model for performing binary classification on our magnetic resonance images. In other words, this study used a pre-trained AlexNet model and moved it to a CNN architecture, and then assessed the model's efficacy and performance using an image dataset that the model had never seen after training, achieving an accuracy of 93.1 percent.">BRAIN TUMOR CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK</span></td> |
<td style="width:30px"> </td> | <td style="width:30px"> </td> | ||
</tr> | </tr> | ||
Revision as of 11:45, 9 December 2021
The NavTalks is a series of informal talks given by Navigators members or some special guests about every two-weeks at Ciências, ULisboa.
Leave mouse over title's presentation to read the abstract.
Contents |
Upcoming presentations
December 2021
| 16 | Paulo Antunes | TBD | |
| 16 | Bruno Matos | TBD |
January 2022
| 13 | Carlos Mão de Ferro | TBD | |
| 13 | Daniel Ângelo | TBD | |
| 27 | Robin Vassantlal | TBD | |
| 27 | João Loureiro | TBD |
February 2022
| 10 | Rohit Kumar | TBD | |
| 10 | David Silva | TBD | |
| 24 | Adriano Mão de Ferro | TBD |
March 2022
| 10 | Miracle Aniakor | BRAIN TUMOR CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK | |
| 10 | Diogo Duarte | TBD | |
| 24 | Nuno Dionísio | TBD |
April 2022
| 7 | Samaneh Shafee | TBD | |
| 7 | Diogo Pires | TBD | |
| 21 | Žygimantas Jasiūnas | TBD |
May 2022
| 5 | Allan Espíndola | TBD | |
| 5 | Gabriel Freitas | TBD | |
| 19 | Tiago R. N. Carvalho | TBD | |
| 19 | Gonçalo Reis | TBD |
June 2022
| 2 | Rafael Ramires | TBD | |
| 2 | Inês Sousa | TBD | |
| 16 | Pedro Rosa | TBD | |
| 16 | Jorge Martins | TBD | |
| 30 | Pedro Alves | TBD | |
| 30 | Lívio Rodrigues | TBD |
July 2022
| 14 | Miguel Oliveira | TBD |
Past presentations
September 2018
| 20 | Alysson Bessani | SMaRtChain: A Principled Design for a New Generation of Blockchains | |
| 20 | Rui Miguel | Named Data Networking with Programmable Switches |
October 2018
| 4 | Bruno Vavala (Research Scientist in Intel Labs) | Private Data Objects | |
| 4 | Marcus Völp (Research Scientist, CritiX, SnT, Univ. of Luxembourg) | Reflective Consensus | |
| 18 | Yair Amir (Professor, Johns Hopkins University) | Timely, Reliable, and Cost-Effective Internet Transport Service using Structured Overlay Networks |
November 2018
| 13 | Salvatore Signorello | The Past, the Present and some Future of Interest Flooding Attacks in Named-Data Networking | |
| 13 | Tiago Oliveira | Vawlt - Privacy-Centered Cloud Storage | |
| 27 | Nuno Neves | Segurança de Software - Como Encontrar uma Agulha num Palheiro? | |
| 27 | Ricardo Mendes | Vawlt - The Zero-knowledge End-to-end Encryption Protocol |
December 2018
| 11/12 | António Casimiro | AQUAMON: Dependable Monitoring with Wireless Sensor Networks in Water Environments | |
| 11/12 | Carlos Nascimento | Review of wireless technology for AQUAMON: Lora, sigfox, nb-iot |
January 2019
| 15/01 | Fernando Alves | A comparison between vulnerability publishing in OSINT and Vulnerability Databases | |
| 15/01 | Ibéria Medeiros | SEAL: SEcurity progrAmming of web appLications | |
| 29/01 | Fernando Ramos | Networks that drive themselves…of the cliff | |
| 29/01 | Miguel Garcia | Some tips before rushing into LaTeX (adapted from: How (and How Not) to Write a Good Systems Paper) |
February 2019
| 19/02 | Ana Fidalgo | Conditional Random Fields and Vulnerability Detection in Web Applications | |
| 19/02 | João Sousa | Towards BFT-SMaRt v2: Blockchains and Flow Control |
March 2019
| 13/03 | Fernando Ramos | How to give a great -- OK, at least a good -- research talk | |
| 13/03 | Ricardo Morgado | Automatically correcting PHP web applications |
March 2019
| 27/03 | Nuno Dionísio | Cyberthreat Detection from Twitter using Deep Neural Networks | |
| 27/03 | Fernando Ramos | My network protocol is better than yours! |
April 2019
| 10/04 | Adriano Serckumecka | SIEMs | |
| 10/04 | Tulio Ribeiro | BFT Consensus & PoW Consensus (blockchain). |
May 2019
| 08/05 | Miguel Garcia | Diverse Intrusion-tolerant Systems | |
| 29/05 | Pedro Ferreira | The concept of the next navigators cybersecurity H2020 project | |
| 29/05 | Vinicius Cogo | Auditable Register Emulations |
June 2019
| 05/06 | Diogo Gonçalves | Network coding switch | |
| 05/06 | Francisco Araújo | Generating Software Tests To Check For Flaws and Functionalities | |
| 26/06 | Joao Pinto | Implementation of a Protocol for Safe Cooperation Between Autonomous Vehicles | |
| 26/06 | Tiago Correia | Design and Implementation of a Cloud-based Membership System for Vehicular Cooperation | |
| 26/06 | Robin Vassantlal | Confidential BFT State Machine Replication |
March 2021
| 24 | Ana Fidalgo | Machine Learning approaches for vulnerability detection |
April 2021
| 7 | Vasco Leitão | Discovering Association Rules Between Software System Requirements and Product Specifications | |
| 21 | João Caseirito | Improving Web Application Vulnerability Detection Leveraging Ensemble Fuzzing |
May 2021
| 5 | Paulo Antunes | Web Vulnerability Discovery at an Intermediate Language Level | |
| 19 | Frederico Apolónia | Building Occupancy Assessment |
June 2021
| 2 | Bernardo Portela | Secure Conflict-free Replicated Data Types | |
| 16 | Žygimantas Jasiūnas and Vasco Ferreira | Monitoring and Integration of heterogeneous building IoT platforms and smart systems | |
| 30 | João Inácio | Automatic Removal of Flaws in Embedded System Software |
July 2021
| 14 | André Gil | Platform Architecture and data management for cloud-based buildings energy self-assessment and optimization | |
| 28 | João Valente | Data quality and dependability of IoT platform for buildings energy assessment |
