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Este libro presenta la importancia de la información y la manera de utilizarla para el proceso de ciencia de datos, para luego introducir los fundamentos teóricos de ambas disciplinas y presentar su aplicación en ocho...

Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varyin...

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in...

The “one-to-many” problem is a typical challenge that faced by many machine learning aided inverse nanophotonics designs where one target optical response can be achieved by many solutions (designs). Although novel t...

With the rapid growth of Internet of Vehicles (IoV) technology, the attack surface has expanded significantly in recent years. Traditional intrusion detection systems, such as signature-based and rule-based approaches, a...

Traditional malware detection techniques frequently fail to detect and stop malicious activity in an era where cyber threats are becoming more complex. Any software that enters a computer system without the administrator...

Abstract The design of multi-principal element alloys (MPEAs) is challenged by data scarcity and the complexity of optimizing multiple mechanical properties simultaneously. To address these challenges, we investigate an ...

Malware represents the most critical threat in cybersecurity, meant to compromise the security for any individual or any organization. These are covert software, designed to perform malicious act like data theft, data al...

One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced cl...

Abstract Heat dissipation performance is critical to the design of high-end equipment, such as integrated chips and high-precision machine tools. Owing to the advantages of artificial intelligence in solving complex task...

IntroductionWe evaluated whether a generative adversarial network (GAN)-based super-resolution (SR) radiomics pipeline could improve the pre-fine-needle aspiration (FNA) prediction of nondiagnostic (Bethesda I) outcomes ...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

Subjects / keywords: machine learning security, adversarial machine learning, ai security, anonymization and de-identification technologies, cloud computing and security and privacy threats in the cloud, information-theoretic security; Techn...

To fully exploit the advantages of GNSS-R small satellites (large quantity, short revisit cycle) and address key challenges of multisource GNSS-R data—track-wise distribution, uneven spatial coverage, and observat...

Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn th...

Subjects / keywords: Discriminator; Generator (circuit theory); Generative grammar; Adversarial system; Set (abstract data type); Computer science; Artificial intelligence; Artificial neural network; Order (exchange); Machine learning; Econo...

Photonic crystals (PhCs) have demonstrated great potential for use in integrated photonic systems. However, traditional design methods often struggle with low efficiency and limited flexibility. While deep learning appro...

Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The pur...

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a n...

Subjects / keywords: Discriminative model; Computer science; Artificial intelligence; Domain (mathematical analysis); Backpropagation; Artificial neural network; Context (archaeology); Test data; Transfer of learning; Identification (biology...