arXiv:2511.16848v1 Announce Type: new 
Abstract: Monitoring aquatic species, especially elusive ones like lobsters, presents challenges. This study focuses on Homarus gammarus (European lobster), a key species for fisheries and aquaculture, and leverages non-invasive Passive Acoustic Monitoring (PAM). Understanding lobster habitats, welfare, reproduction, sex, and age is crucial for management and conservation. While bioacoustic emissions have classified various aquatic species using Artificial Intelligence (AI) models, this research specifically uses H. gammarus bioacoustics (buzzing/carapace vibrations) to classify lobsters by age (juvenile/adult) and sex (male/female).
  The dataset was collected at Johnshaven, Scotland, using hydrophones in concrete tanks. We explored the efficacy of Deep Learning (DL) models (1D-CNN, 1D-DCNN) and six Machine Learning (ML) models (SVM, k-NN, Naive Bayes, Random Forest, XGBoost, MLP). Mel-frequency cepstral coefficients (MFCCs) were used as features.
  For age classification (adult vs. juvenile), most models achieved over 97% accuracy (Naive Bayes: 91.31%). For sex classification, all models except Naive Bayes surpassed 93.23%. These strong results demonstrate the potential of supervised ML and DL to extract age- and sex-related features from lobster sounds. This research offers a promising non-invasive PAM approach for lobster conservation, detection, and management in aquaculture and fisheries, enabling real-world edge computing applications for underwater species.

استخدمت دراسة حديثة الذكاء الاصطناعي والمراقبة الصوتية الحيوية لتحديد جنس وعمر جراد البحر الأوروبي، Homarus gammarus، في جونشافن، اسكتلندا. من خلال تحليل الانبعاثات الصوتية الحيوية، صنف الباحثون جراد البحر إلى فئات يافعة/بالغة وذكر/أنثى باستخدام نماذج متقدمة من التعلم العميق والتعلم الآلي، مما يعزز فهم هذه الأنواع الرئيسية في مصايد الأسماك وتربية الأحياء المائية.

Un estudio reciente ha utilizado inteligencia artificial y monitoreo bioacústico para determinar el sexo y la edad de la langosta europea, Homarus gammarus, en Johnshaven, Escocia. Al analizar las emisiones bioacústicas, los investigadores clasificaron las langostas en categorías de juvenil/adulto y macho/hembra utilizando modelos avanzados de aprendizaje profundo y aprendizaje automático, mejorando así la comprensión de esta especie clave para la pesca y la acuicultura.

Une étude récente a utilisé l'intelligence artificielle et la surveillance bioacoustique pour déterminer le sexe et l'âge du homard européen, Homarus gammarus, à Johnshaven, en Écosse. En analysant les émissions bioacoustiques, les chercheurs ont classé les homards en catégories juvéniles/adultes et mâles/femelles à l'aide de modèles avancés d'apprentissage profond et d'apprentissage automatique, améliorant ainsi la compréhension de cette espèce clé pour la pêche et l'aquaculture.

A recent study has utilized Artificial Intelligence and bioacoustic monitoring to determine the sex and age of the European lobster, Homarus gammarus, in Johnshaven, Scotland. By analyzing the bioacoustic emissions, researchers classified lobsters into juvenile/adult and male/female categories using advanced Deep Learning and Machine Learning models, enhancing understanding of this key species for fisheries and aquaculture.

Sex and age determination in European lobsters using AI-Enhanced bioacoustics

arXiv:2511.16856v1 Announce Type: new 
Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder that, in addition to directly impairing functional mobility, is frequently associated with vocal impairments such as hypophonia and dysarthria, which typically manifest in the early stages. The use of vocal biomarkers to support the early diagnosis of PD presents a non-invasive, low-cost, and accessible alternative in clinical settings. Thus, the objective of this cross-sectional study was to consistently evaluate the effectiveness of a Deep Neural Network (DNN) in distinguishing individuals with Parkinson's disease from healthy controls, in comparison with traditional Machine Learning (ML) methods, using vocal biomarkers. Two publicly available voice datasets were used. Mel-frequency cepstral coefficients (MFCCs) were extracted from the samples, and model robustness was assessed using a validation strategy with 1000 independent random executions. Performance was evaluated using classification statistics. Since normality assumptions were not satisfied, non-parametric tests (Kruskal-Wallis and Bonferroni post-hoc tests) were applied to verify whether the tested classification models were similar or different in the classification of PD. With an average accuracy of $98.65\%$ and $92.11\%$ on the Italian Voice dataset and Parkinson's Telemonitoring dataset, respectively, the DNN demonstrated superior performance and efficiency compared to traditional ML models, while also achieving competitive results when benchmarked against relevant studies. Overall, this study confirms the efficiency of DNNs and emphasizes their potential to provide greater accuracy and reliability for the early detection of neurodegenerative diseases using voice-based biomarkers.

أظهرت دراسة حديثة إمكانية استخدام المؤشرات الصوتية في الكشف المبكر عن مرض باركنسون (PD) من خلال مقارنة فعالية شبكة عصبية عميقة (DNN) مع نماذج التعلم الآلي التقليدية. استخدمت الدراسة مجموعتين من بيانات الصوت المتاحة للجمهور لاستخراج معاملات ميل التردد (MFCC) وتقييم متانة النموذج من خلال استراتيجيات التحقق الواسعة.

Un estudio reciente ha demostrado el potencial de los biomarcadores vocales en la detección temprana de la enfermedad de Parkinson (EP) al comparar la efectividad de una Red Neuronal Profunda (DNN) con modelos de aprendizaje automático tradicionales. La investigación utilizó dos conjuntos de datos de voz disponibles públicamente para extraer coeficientes cepstrales en frecuencia Mel (MFCC) y evaluar la robustez del modelo a través de estrategias de validación extensas.

Une étude récente a démontré le potentiel des biomarqueurs vocaux dans la détection précoce de la maladie de Parkinson (MP) en comparant l'efficacité d'un réseau de neurones profond (DNN) aux modèles d'apprentissage automatique traditionnels. La recherche a utilisé deux ensembles de données vocales disponibles publiquement pour extraire les coefficients cepstraux en fréquence Mel (MFCC) et évaluer la robustesse du modèle à travers des stratégies de validation étendues.

A recent study has demonstrated the potential of vocal biomarkers in the early detection of Parkinson's disease (PD) by comparing the effectiveness of a Deep Neural Network (DNN) against traditional machine learning models. The research utilized two publicly available voice datasets to extract Mel-frequency cepstral coefficients (MFCCs) and assess model robustness through extensive validation strategies.

The use of vocal biomarkers in the detection of Parkinson's disease: a robust statistical performance comparison of classic machine learning models

arXiv:2511.07827v2 Announce Type: replace-cross 
Abstract: The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterized by dilated cerebral ventricles of the fetal brain and is important to diagnose early, as it can be associated with an increased risk for fetal aneuploidies and/or underlying genetic syndromes. An Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), recently developed by our group, was fine-tuned for a binary classification task to distinguish fetal brain ultrasound images as either normal or showing ventriculomegaly. The USF-MAE incorporates a Vision Transformer encoder pretrained on more than 370,000 ultrasound images from the OpenUS-46 corpus. For this study, the pretrained encoder was adapted and fine-tuned on a curated dataset of fetal brain ultrasound images to optimize its performance for ventriculomegaly detection. Model evaluation was conducted using 5-fold cross-validation and an independent test cohort, and performance was quantified using accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed USF-MAE model reached an F1-score of 91.76% on the 5-fold cross-validation and 91.78% on the independent test set, with much higher scores than those obtained by the baseline models by 19.37% and 16.15% compared to VGG-19, 2.31% and 2.56% compared to ResNet-50, and 5.03% and 11.93% compared to ViT-B/16, respectively. The model also showed a high mean test precision of 94.47% and an accuracy of 97.24%. The Eigen-CAM (Eigen Class Activation Map) heatmaps showed that the model was focusing on the ventricle area for the diagnosis of ventriculomegaly, which has explainability and clinical plausibility.

أظهرت دراسة حديثة تطوير نموذج تعلم عميق يهدف إلى الكشف عن حالة توسع البطينين في صور الموجات فوق الصوتية قبل الولادة. تُعتبر هذه الحالة، التي تتميز بتوسع البطينين الدماغيين في الجنين، مهمة للتشخيص المبكر نظرًا لارتباطها بزيادة خطر حدوث تشوهات صبغية جنينية ومتلازمات وراثية. تم ضبط النموذج، المعروف باسم نموذج الأساس الذاتي المراقب بالموجات فوق الصوتية مع تشفير تلقائي مقنع (USF-MAE)، لمهمة تصنيف ثنائية لصور الموجات فوق الصوتية للدماغ الجنيني.

Un estudio reciente ha desarrollado un modelo de aprendizaje profundo destinado a detectar la ventriculomegalia en imágenes de ultrasonido prenatal. Esta condición, caracterizada por ventrículos cerebrales dilatados en el cerebro fetal, es crucial de diagnosticar tempranamente debido a su asociación con un mayor riesgo de aneuploidías fetales y síndromes genéticos. El modelo, conocido como Modelo de Fundación Auto-Supervisado de Ultrasonido con Auto-Codificación Enmascarada (USF-MAE), fue ajustado para la clasificación binaria de imágenes de ultrasonido cerebral fetal.

Une étude récente a développé un modèle d'apprentissage profond visant à détecter la ventriculomégalie dans les images d'échographie prénatale. Cette condition, caractérisée par des ventricules cérébraux dilatés dans le cerveau fœtal, est cruciale à diagnostiquer tôt en raison de son association avec des risques accrus d'aneuploïdies fœtales et de syndromes génétiques. Le modèle, connu sous le nom de Modèle de Fondation Auto-Supervisé en Échographie avec Auto-Encodage Masqué (USF-MAE), a été affiné pour la classification binaire des images d'échographie cérébrale fœtale.

A recent study has developed a deep learning model aimed at detecting ventriculomegaly in prenatal ultrasound images. This condition, characterized by dilated cerebral ventricles in the fetal brain, is crucial to diagnose early due to its association with increased risks for fetal aneuploidies and genetic syndromes. The model, known as the Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), was fine-tuned for binary classification of fetal brain ultrasound images.

Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly

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Google ha negado las acusaciones de que analiza los correos electrónicos de los usuarios para fines de entrenamiento de IA, aclarando que los usuarios pueden optar por no participar en tales funciones si tienen preocupaciones. Esta respuesta se produce en medio de un creciente escrutinio sobre las prácticas de privacidad en la industria tecnológica.

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Google has denied allegations that it analyzes users' emails for AI training purposes, clarifying that users can opt out of such features if they have concerns. This response comes amid growing scrutiny over privacy practices in the tech industry.

Sex and age determination in European lobsters using AI-Enhanced bioacoustics

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