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Prodotti della tipologia (ordinati per Data di deposito in Decrescente ordine): 1 a 20 di 36.180
Tipologia Anno Titolo Titolo inglese Autore File
Lauree magistrali 2025 Variable Influence aNd Contribution: a methodology for Explainable AI in Neural Networks Variable Influence aNd Contribution: a methodology for Explainable AI in Neural Networks VINCO, RICCARDO
Lauree magistrali 2025 Sales Analysis and Forecasting: Time Series Modeling for Business Decision Making Sales Analysis and Forecasting: Time Series Modeling for Business Decision Making VENDRAMINETTO, RICCARDO
Lauree magistrali 2025 Enhancing Innovation Diffusion Models with Neural Residual Modeling for Improved Forecasting Enhancing Innovation Diffusion Models with Neural Residual Modeling for Improved Forecasting TÁMARA GLÜCK, NICOLÁS MATÍAS
Lauree magistrali 2025 Expanding Latent Ideology Estimation via Machine Learning for User Classification in Online Social Media Expanding Latent Ideology Estimation via Machine Learning for User Classification in Online Social Media SUSLA, DIANA
Lauree magistrali 2025 An evolutionary analysis of Autism risk genes An evolutionary analysis of Autism risk genes SOLTANTOUYEH, ANAHITA
Lauree magistrali 2025 Detection of Bombina Vocalizations in Environmental Recordings Using Convolutional Neural Networks Detection of Bombina Vocalizations in Environmental Recordings Using Convolutional Neural Networks SMIRNOV, ILIA
Lauree magistrali 2025 An automated framework for converting Reactome signaling pathways into bioinformatics analysis-ready gene–gene networks An automated framework for converting Reactome signaling pathways into bioinformatics analysis-ready gene–gene networks SIGNOR, VALENTINA
Lauree magistrali 2025 Analysis and Optimization of Material Flows in a Steel Plant Warehouse. Analysis and Optimization of Material Flows in a Steel Plant Warehouse. PREGNOLATO, NICOLA
Lauree magistrali 2025 Modeling Spatio-Temporal Data via Learnable Axonal Delays in Convolutional Recurrent Spiking Neural Networks Modeling Spatio-Temporal Data via Learnable Axonal Delays in Convolutional Recurrent Spiking Neural Networks PLANTAMURA, DOMENICO
Lauree magistrali 2025 Automated pattern recognition and JSON extraction for ETL development Automated pattern recognition and JSON extraction for ETL development PASSARO, GIOVANNI
Lauree magistrali 2025 Benchmarking Personalization Capabilities in Embodied AI Agents Benchmarking Personalization Capabilities in Embodied AI Agents NARDELLA, GAIA
Lauree magistrali 2025 Subspace Identification Methods for Extracting Behaviorally Relevant Neural Dynamics Subspace Identification Methods for Extracting Behaviorally Relevant Neural Dynamics MESAGLIO, ELEONORA
Lauree magistrali 2025 Adaptive Density-Aware Sampling For High-Dimensional Datasets Adaptive Density-Aware Sampling For High-Dimensional Datasets MARZOLA, DEVIS
Lauree magistrali 2025 AI-Enhanced Monitoring for Data Warehouses AI-Enhanced Monitoring for Data Warehouses MANOCCHIO, ANDREA
Lauree magistrali 2025 Development and Evaluation of Deep Learning Models for Forecasting Water Levels in Sewage Pumping Stations Development and Evaluation of Deep Learning Models for Forecasting Water Levels in Sewage Pumping Stations LOZANO DIBILDOX, JOSE CARLOS
Lauree magistrali 2025 Neural Relational Associative Memories Neural Relational Associative Memories LOLLATO, FRANCESCO
Lauree magistrali 2025 Enhancing Browser Automation with Multimodal LLM's Enhancing Browser Automation with Multimodal LLM's KAYA, ZEYNEP SILA
Lauree magistrali 2025 Scalable Content Analysis of Social Media Videos Using Multimodal Large Language Models: A Video-to-Text Pipeline for Large-Scale Analysis Scalable Content Analysis of Social Media Videos Using Multimodal Large Language Models: A Video-to-Text Pipeline for Large-Scale Analysis GORNI SILVESTRINI, MATTEO
Lauree magistrali 2025 Empirical Exploration of the Explanation-Guided Learning for Causal Effect Estimation Empirical Exploration of the Explanation-Guided Learning for Causal Effect Estimation GLADO, ANNA
Lauree magistrali 2025 Efficient On-Premise AI: Benchmarking Quantized SLMs on CPU-only Infrastructure Efficient On-Premise AI: Benchmarking Quantized SLMs on CPU-only Infrastructure GASTALDON, SIMONE
Prodotti della tipologia (ordinati per Data di deposito in Decrescente ordine): 1 a 20 di 36.180
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Prodotti recenti

Variable Influence aNd Contribution: a methodology for Explainable AI in Neural Networks

Explainability in Artificial Intelligence is a recent and important field of research and application. The powerfulness of AI models needs to meet the human necessity to understand how a decision was made. For this reason, many explainability methodologies are being developed. In this dissertation, a new methodology over neural networks is developed and compared to the already existing ones. It is called VINC, which stands for Variable Influence aNd Contribution, and it is able to separate in...

Sales Analysis and Forecasting: Time Series Modeling for Business Decision Making

This thesis investigates the analysis and modeling of a monthly revenue time series with the objective of understanding its underlying dynamics and improving forecasting accuracy. Revenue time series are typically characterized by trends, seasonal patterns, and irregular fluctuations, which make their modeling both challenging and essential for effective business decision-making. The analysis is based on ARIMA-type models, whose main properties are introduced and discussed. Model performance ...

Enhancing Innovation Diffusion Models with Neural Residual Modeling for Improved Forecasting

Innovation diffusion models such as the Bass model are widely used to describe long-term adoption patterns but often fail to capture short-term fluctuations and volatility. This thesis proposes a hybrid forecasting approach that enhances diffusion models through residual modeling, combining structural diffusion with statistical and neural correction models. Classical and dynamic market potential Bass models are paired with SARIMAX and neural residual models, including Temporal Convolutional N...

Expanding Latent Ideology Estimation via Machine Learning for User Classification in Online Social Media

Predicting political ideology from social media is an important task in computational social science. Many users do not openly state their political views, so their ideology must be inferred from online behavior. This work presents a machine learning approach that combines tweet text with information from retweet networks to predict latent user ideology. Textual features capture what users say, while network features capture who they interact with and how influence spreads. Supervised models ...

An evolutionary analysis of Autism risk genes

This thesis examines whether common genetic variation associated with autism spectrum condition (ASC) is structured with respect to two evolutionary genomic features: introgression deserts and human accelerated regions (HARs). A genome-wide segmentation framework classified the autosomal genome into five categories based on introgression signal and Neanderthal-specific SNP density, providing a map against which ASC variants were analysed. ASC variants are strongly enriched for derived allele...

Detection of Bombina Vocalizations in Environmental Recordings Using Convolutional Neural Networks

This thesis studies the automatic detection of Bombina sounds in environmental audio recordings. The task is formulated as a binary classification problem on short audio segments derived from long field recordings. The approach is based on convolutional neural networks trained on log-mel spectrograms. Model performance is evaluated using date-based 5-fold cross-validation to prevent data leakage and to account for strong variability between recording days. Additional robustness is achieved t...

An automated framework for converting Reactome signaling pathways into bioinformatics analysis-ready gene–gene networks

The topological analysis of biological pathways represents a central component in the study of genetic and molecular mechanisms. In particular, representing pathways in a graph-based format enables automatic analyses ranging from high-level network science approaches to bioinformatics-specific analyses. However, the conversion of signaling pathways into networks is a non-trivial task, due both to the high complexity of the biological reactions, represented as network edges, and to the strong ...

Analysis and Optimization of Material Flows in a Steel Plant Warehouse.

Efficient material handling and warehouse management are critical aspects of steel production plants, where a large number of billet movements are performed daily across internal and external storage areas. Inefficient logistics decisions can lead to unnecessary relocations, increased handling costs, and reduced operational performance. This thesis presents a data-driven analysis of billet movements in the warehouse of an industrial steel plant. The objective is to understand the behavior of ...

Modeling Spatio-Temporal Data via Learnable Axonal Delays in Convolutional Recurrent Spiking Neural Networks

Spiking Neural Networks (SNNs) have emerged as a promising and more energy-efficient paradigm for spatiotemporal processing compared to standard Artificial Neural Networks (ANNs), as they process information through sparse, binary discrete events called "spikes", whereas ANNs rely on dense, continuous real-valued computations that are typically more energy-intensive. Despite these advantages, effectively capturing complex temporal dynamics remains a significant challenge. In this work, we in...

Automated pattern recognition and JSON extraction for ETL development

Transforming technical and functional specifications into ETL workflows is a complex and time consuming process that requires careful interpretation and manual coding. This research explores an AI-driven approach to automate and optimize ETL development by leveraging Natural Language Processing (NLP) and Generative AI. The proposed methodology focuses on extracting meaningful patterns from documentation and generating structured code templates to accelerate development while maintaining flexi...

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