Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Voice fraud mitigation: developing a deep learning system for detecting cloned voices in telephonic communications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Villicaña Ibargüengoyti, José Rubén; Montesinos Silva, Luis Arturo; emimmayorquin; Santos Díaz, Alejandro; Mantilla Caeiros, Alfredo Víctor; School of Engineering and Sciences; Campus Ciudad de MéxicoThis study addresses the increasing threat in recent years of voice fraud by cloned voices in phone calls. This problem can compromise personal security in many aspects. The primary goal of this work is to develop a deep learning-based detection system for distinguishing between real and cloned voices in Spanish, focusing on calls made over telephone lines. To achieve this, a dataset was generated from real and cloned audio samples in Spanish. The audios captured were simulated under various telephone codecs and noise levels. Two deep learning models, a convolutional neural network (which in this project is named Vanilla CNN) and a transfer learning (MobileNetV2) approach, were trained using spectrograms derived from the audio data. The results indicate a high accuracy in identifying real and cloned voices, reaching up to 99.97% accuracy. Also, many validations were performed under different types of noise and codecs included in the dataset. These findings highlight the effectiveness of the proposed architectures. Additionally, an ESP32 audio kit was integrated with Amazon Web Services to implement voice detection during phone calls. This study contributes to voice fraud detection research focused on the Spanish language.
- Smart camera FPGA hardware implementation for semantic segmentation of wildfire imagery(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-13) Garduño Martínez, Eduardo; Rodriguez Hernández, Gerardo; mtyahinojosa, emipsanchez; Gonzalez Mendoza, Miguel; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoIn the past few years, the more frequent occurrence of wildfires, which are a result of climate change, has devastated society and the environment. Researchers have explored various technologies to address this issue, including deep learning and computer vision solutions. These techniques have yielded promising results in semantic segmentation for detecting fire using visible and infrared images. However, implementing deep learning neural network models can be challenging, as it often requires energy-intensive hardware such as a GPU or a CPU with large cooling systems to achieve high image processing speeds, making it difficult to use in mobile applications such as drone surveillance. Therefore, to solve the portability problem, an FPGA hardware implementation is proposed to satisfy low power consumption requirements, achieve high accuracy, and enable fast image segmentation using convolutional neural network models for fire detection. This thesis employs a modified UNET model as the base model for fire segmentation. Subsequently, compression techniques reduce the number of operations performed by the model by removing filters from the convolutional layers and reducing the arithmetic precision of the CNN, decreasing inference time and storage requirements and allowing the Vitis AI framework to map the model architecture and parameters onto the FPGA. Finally, the model was evaluated using metrics utilized in prior studies to assess the performance of fire detection segmentation models. Additionally, two fire datasets are used to compare different data types for fire segmentation models, including visible images, a fusion of visible and infrared images generated by a GAN model, fine-tuning of the fusion GAN weights, and the use of visible and infrared images independently to evaluate the impact of visible-infrared information on segmentation performance.
- Harnessing machine learning for short-to-long range weather forecasting: a Monterrey case study(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Machado Guillén, Gustavo de Jesús; Cruz Duarte, Jorge Mario; mtyahinojosa, emimmayorquin; Filus, Katarzyna; Falcón, Jesús Guillermo; Ibarra, Gerardo; Departamento de Ciencias Computacionales; Campus Monterrey; Conant, Santiago EnriqueWeather forecasting is crucial in adapting and integrating renewable energy sources, particularly in regions with complex climatic conditions like Monterrey. This study aims to provide reliable weather prediction methodologies by evaluating the performance of various traditional Machine Learning models, including Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Recurrent Neural Networks (RNN) such as SimpleRNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Cascade LSTM, Bidirectional RNNs, and a novel Convolutional LSTM/LSTM architecture that handles spatial and temporal data. The research employs a dataset of historical weather data from Automatic Weather Stations and Advanced Baseline Imager Level 2 GOES-16 products, including key weather features like air temperature, solar radiation, wind speed, relative humidity, and precipitation. The models were trained and evaluated across different predictive ranges by combining distinct sampling and model output sizes. This study’s findings underscore the effectiveness of the Cascade LSTM models, achieving a Mean Absolute Error of 1.6 °C for 72-hour air temperature predictions and 85.79 W/m2 for solar radiation forecasts. The ConvLSTM/LSTM model also significantly improves short-term predictions, particularly for solar radiation and humidity. The main contribution of this work is a comprehensive methodology that can be generalized to other regions and datasets, supporting the nationwide implementation of localized machine-learning forecasting models. This methodology includes steps for data collection, preprocessing, creation of lagged features, and model implementation, as well as applying distinct approaches to forecasting by using autoregressive and fixed window models. This framework enables the development of accurate, region-specific forecasting models, facilitating better weather prediction and planning nationwide.
- Caption generation with transformer models across multiple medical imaging modalities(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Vela Jarquin, Daniel; Santos Díaz, Alejandro; dnbsrp; Soenksen, Luis Ruben; Montesinos Silva, Luis Arturo; Ochoa Ruiz, Gilberto; School of Engineering and Sciences; Campus Monterrey; Tamez Peña, José GerardoCaption generation is the process of automatically providing text excerpts that describe relevant features of an image. This process is applicable to very diverse domains, including healthcare. The field of medicine is characterized by the vast amount of visual information in the form of X-Rays, Magnetic Resonances, Ultrasound and CT-scans among others. Descriptive texts generated to represent this kind of visual information can aid medical professionals to achieve a better understanding of the pathologies and cases presented to them and could ultimately allow them to make more informed decisions. In this work, I explore the use of deep learning to face the problem of caption generation in medicine. I propose the use of a Transformer model architecture for caption generation and evaluate its performance on a dataset comprised of medical images that range across multiple modalities and represented anatomies. Deep learning models, particularly encoder-decoder architectures have shown increasingly favorable results in the translation from one information modality to another. Usually, the encoder extracts features from the visual data and then these features are used by the decoder to iteratively generate a sequence in natural language that describes the image. In the past, various deep learning architectures have been proposed for caption generation. The most popular architectures in the last years involved recurrent neural networks (RNNs), Long short-term memory (LSTM) networks and only recently, the use of Transformer type architectures. The Transformer architecture has shown state-of-the art performance in many natural language processing tasks such as machine translation, question answering, summarizing and not long ago, caption generation. The use of attention mechanisms allows Transformers to better grasp the meaning of words in a sentence in a particular context. All these characteristics make Transformers ideal for caption generation. In this thesis I present the development of a deep learning model based on the Transformer architecture that generates captions for medical images of different modalities and anatomies with the ultimate goal to aid professionals improve medical diagnosis and treatment. The model is tested on the MedPix online database, a compendium of medical imaging cases and the results are reported. In summary, this work provides a valuable contribution to the field of automated medical image analysis
- The identification of DoS and DDoS attacks to IoT devices in software defined networks by using machine learning and deep learning models(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05) Almaraz Rivera, Josué Genaro; PEREZ DIAZ, JESUS ARTURO; 31169; Pérez Díaz, Jesús Arturo; puelquio/mscuervo; Trejo Rodríguez, Luis Ángel; Botero Vega, Juan Felipe; School of Engineering and Sciences; Campus Monterrey; Cantoral Ceballos, José AntonioThis thesis project explores and improves the current state of the art about detection techniques for Distributed Denial of Service (DDoS) attacks to Internet of Things (IoT) devices in Software Defined Networks (SDN), which as far as is known, is a big problem that network providers and data centers are still facing. Our planned solution for this problem started with the selection of strong Machine Learning (ML) and Deep Learning (DL) models from the current literature (such as Decision Trees and Recurrent Neural Networks), and their further evaluation under three feature sets from our balanced version of the Bot-IoT dataset, in order to evaluate the effects of different variables and avoid the dependencies produced by the Argus flow data generator. With this evaluation we achieved an average accuracy greater than 99% for binary and multiclass classifications, leveraging the categories and subcategories present in the Bot-IoT dataset, for the detection and identification of DDoS attacks based on Transport (UDP, TCP) and Application layer (HTTP) protocols. To extend the capacity of this Intrusion Detection System (IDS) we did a research stay in Colombia, with Universidad de Antioquia and in collaboration with Aligo (a cybersecurity company from Medellín). There, we created a new dataset based on real normal and attack traffic to physical IoT devices: the LATAM-DDoS-IoT dataset. We conducted binary and multiclass classifications with the DoS and the DDoS versions of this new dataset, getting an average accuracy of 99.967% and 98.872%, respectively. Then, we did two additional experiments combining our balanced version of the Bot-IoT dataset, applying transfer learning and a datasets concatenation, showing the differences between both domains and the generalization level we accomplished. Finally, we deployed our extended IDS (as a functional app built in Java and connected to an own cloud-hosted Python REST API) into a real-time SDN simulated environment, based on the Open Network Operating System (ONOS) controller and Mininet. We got a best accuracy of 94.608%, where 100% of the flows identified as attackers were correctly classified, and 91.406% of the attack flows were detected. This app can be further enhanced with the creation of an Intrusion Prevention System (IPS) as mitigation management strategy to stop the identified attackers.