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.
- Object detection-based surgical instrument tracking in laparoscopy videos(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Guerrero Ramírez, Cuauhtemoc Alonso; Ochoa Ruiz, Gilberto; emipsanchez; González Mendoza, Miguel; Hinojosa Cervantes, Salvador Miguel; Falcón Morales, Luis Eduardo; School of Engineering and Sciences; Campus Monterrey; Medina Pérez, Miguel ÁngelMinimally invasive surgery (MIS) has transformed surgery by offering numerous advantages over traditional open surgery, such as reduced pain, minimized trauma, and faster recovery times. However, endoscopic MIS procedures remain highly operator-dependent, demanding significant skill from the surgical team to ensure a positive postoperative outcome for the patient. The implementation of computer vision techniques such as reliable surgical instru ment detection and tracking can be leveraged for applications such as intraoperative decision support, surgical navigation assistance, and surgical skill assessment, which can significantly improve patient safety. The aim of this work is to implement a Multiple Object Tracking (MOT) benchmark model for the task of surgical instrument tracking in laparoscopic videos. To this end, a new dataset is introduced, m2cai16-tool-tracking, based on the m2cai16-tool locations dataset, specifically designed for surgical instrument tracking. This dataset includes both bounding box annotations for instrument detection and unique tracking ID annotations for multi-object tracking. This work employs ByteTrack, a state-of-the-art multiple-object tracking algorithm that follows the tracking-by-detection paradigm. ByteTrack predicts tool positions and associates object detections across frames, allowing consistent tracking of each instrument. The object detection step is performed using YOLOv4, a state-of-the-art object detection model known for real-time performance. YOLOv4 is first trained on the m2cai16-tool-locations dataset to establish a baseline performance and then on the custom m2cai16-tool-tracking dataset, al lowing to compare the detection performance of the custom dataset with an existing object detection dataset. YOLOv4 generates bounding box predictions for each frame in the laparo scopic videos. The bounding box detections serve as input for the ByteTrack algorithm, which assigns unique tracking IDs to each instrument to maintain their trajectories across frames. YOLOv4 achieves robust object detection performance on the m2cai16-tool-locations dataset, obtaining a mAP50 of 0.949, a mAP75 of 0.537, and a mAP50:95 of 0.526, with a real-time inference speed of 125 fps. However, detection performance on the m2cai16-tool tracking dataset is slightly lower, with a mAP50 of 0.839, mAP75 of 0.420, and mAP50:95 of 0.439, suggesting that differences in data partitioning impact detection accuracy. This lower detection accuracy for the tracking dataset likely affects the tracking performance of ByteTrack, reflected in a MOTP of 76.4, MOTA of 56.6, IDF1 score of 22.8, and HOTAscore of 23.0. Future work could focus on improving the object detection performance to enhance tracking quality. Additionally, including appearance-based features into the track ing step could improve association accuracy of detections across frames and help maintain consistent tracking even in challenging scenarios like occlusions. Such improvements could enhance tracking reliability to support surgical tasks better.
- Machine translation for suicide detection: validating spanish datasetsusing machine and deep learning models(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Arenas Enciso, Francisco Ariel; Zareel, Mahdi; emipsanchez; García Ceja, Enrique Alejandro; Roshan Biswal, Rajesh; School of Engineering and Sciences; Sede EGADE MonterreySuicide is a complex health concern that affects not only individuals but society as a whole. The application of traditional strategies to prevent, assess, and treat this condition has proven inefficient in a modern world in which interactions are mainly made online. Thus, in recent years, multidisciplinary efforts have explored how computational techniques could be applied to automatically detect individuals who desire to end their lives on textual input. Such methodologies rely on two main technical approaches: text-based classification and deep learning. Further, these methods rely on datasets labeled with relevant information, often sourced from clinically-curated social media posts or healthcare records, and more recently, public social media data has proven especially valuable for this purpose. Nonetheless, research focused on the application of computational algorithms for detecting suicide or its ideation is still an emerging field of study. In particular, investigations on this topic have recently considered specific factors, like language or socio-cultural contexts, that affect the causality, rationality, and intentionality of an individual’s manifestation, to improve the assessment made on textual data. Consequently, problems like the lack of data in non-Anglo-Saxon contexts capable of exploiting computational techniques for detecting suicidal ideation are still a pending endeavor. Thus, this thesis addresses the limited availability of suicide ideation datasets in non-Anglo-Saxon contexts, particularly for Spanish, despite its global significance as a widely spoken language. The research hypothesizes that Machine- Translated Spanish datasets can yield comparable results (within a ±5% performance range) to English datasets when training machine learning and deep learning models for suicide ideation detection. To test this, multiple machine translation models were evaluated, and the two most optimal models were selected to translate an English dataset of social media posts into Spanish. The English and translated Spanish datasets were then processed through a binary classification task using SVM, Logistic Regression, CNN, and LSTM models. Results demonstrated that the translated Spanish datasets achieved scores in performance metrics close to the original English set across all classifiers, with limited variations in accuracy, precision, recall, F1-score, ROC AUC, and MCC metrics remaining within the hypothesized ±5% range. For example, the SVM classifier on the translated Spanish sets achieved an accuracy of 90%, closely matching the 91% achieved on the original English set. These findings confirm that machine-translated datasets can serve as effective resources for training ML and DL models for suicide ideation detection in Spanish, thereby supporting the viability of extending suicide detection models to non-English-speaking populations. This contribution provides a methodological foundation for expanding suicide prevention tools to diverse linguistic and cultural contexts, potentially benefiting health organizations and academic institutions interested in psychological computation.
