Výsledky vyhľadávania - 68T05 Learning and adaptive systems in artificial intelligence

The role of diversity and ensemble learning in credit card fraud detection Autor Paldino, Gian Marco, Lebichot, Bertrand, Le Borgne, Yann-Aël, Siblini, Wissam, Oblé, Frédéric, Boracchi, Giacomo, Bontempi, Gianluca

“… The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component of the detection process…”
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Machine learning approach for phishing website detection : A literature survey Autor Patil, Rutuja R., Kaur, Gagandeep, Jain, Himank, Tiwari, Ayush, Joshi, Soham, Rao, Keshav, Sharma, Amit

“… Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection…”
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Artificial Intelligence: A Child’s Play Autor Kashyap, Ravi

“…•Conceptual modifications to Turing Test and Searle's Chinese room argument. We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative…”
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Learning customized and optimized lists of rules with mathematical programming Autor Rudin, Cynthia, Ertekin, Şeyda

“…We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules…”
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Creating herd behavior by virtual agents using neural networks Autor Markowska-Kaczmar, Urszula, Slimak, Adrian

“…The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory…”
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Utterance Style Transfer Using Deep Models Autor Popek, Daniel, Markowska-Kaczmar, Urszula

“… Siamese network is responsible for learning the content embeddings. The models can perform the style transfer between any two speakers with a satisfactory result…”
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An Intelligent System for Identifying Influential Words in Real-Estate Classifieds Autor Abdallah Sherief

“…This paper focuses on the problem of quantifying how certain words in a text affect, positively or negatively, some numeric signal. These words can lead to…”
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Mining Dynamics: Using Data Mining Techniques to Analyze Multi-agent Learning Autor Sherief, Abdallah

“…Analyzing the learning dynamics in multi-agent systems (MASs) has received growing attention in recent years…”
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When unlearning helps Autor Baliga, Ganesh, Case, John, Merkle, Wolfgang, Stephan, Frank, Wiehagen, Rolf

“…Overregularization seen in child language learning, for example, verb tense constructs, involves abandoning correct behaviours for incorrect ones and later reverting to correct behaviours…”
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Stability of Neural Networks for Slightly Perturbed Training Data Sets Autor Berhane, Indrias, Srinivasan, C.

“…In learning models of artificial neural networks, that randomness comes from the distribution of the training data…”
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Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration Autor Metzger, Andreas, Quinton, Clément, Mann, Zoltán Ádám, Baresi, Luciano, Pohl, Klaus

“… To realize self-adaptive systems in the presence of design time uncertainty, online machine learning, i.e…”
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Deep Reinforcement Learning from Self-Play in No-limit Texas Hold'em Poker Autor Pricope, T.-V.

“… This particular set of problems is challenging due to the random factor that makes even adaptive methods fail to correctly model the problem and find the best solution…”
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Adaptive vertical federated learning via feature map transferring in mobile edge computing Autor Li, Yuanzhang, Sha, Tianchi, Baker, Thar, Yu, Xiao, Shi, Zhiwei, Hu, Sikang

“…To bring more intelligence to edge systems, Federated Learning (FL) is proposed to provide a privacy-preserving mechanism to train a globally shared model by utilizing a massive amount of user-generated data on devices…”
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An adaptive online reinforcement learning approach for optimizing computation offloading in connected vehicle networks Autor Mohammadi, Laya, Khajehvand, Vahid, Nobandegani, Khashayar Salehi, Zeinali, Esmaeil

“… This paper proposes a model-free, adaptive online reinforcement learning approach based on the Q-learning algorithm for computation offloading in connected vehicle networks (CVNs…”
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An AI and 6G-IoT enabled computational framework for intelligent medical resource allocation and adaptive personalized healthcare Autor Almadhor, Ahmad, Ayari, Mohamed, Alqahtani, Abdullah, Al Hejaili, Abdullah, Bouallegue, Belgacem, Juanatas, Roben A., Sampedro, Gabriel Avelino

“…The integration of sixth-generation (6G) networks with the Internet of Things (IoT) is transforming smart healthcare by enabling ultra-low latency, high bandwidth, and intelligent connectivity across medical systems…”
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Scene-adaptive radar tracking with deep reinforcement learning Autor Stephan, Michael, Servadei, Lorenzo, Arjona-Medina, José, Santra, Avik, Wille, Robert, Fischer, Georg

“… In this context, scene-adaptive radar processing refers to algorithms that can sense, understand and learn information related to detected targets as well as the environment and adapt its tracking…”
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Robot motion adaptation through user intervention and reinforcement learning Autor Jevtić, Aleksandar, Colomé, Adrià, Alenyà, Guillem, Torras, Carme

“…•Three versions of an interactive framework for robot motion learning are proposed…”
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Probabilistic-based electricity demand forecasting with hybrid convolutional neural network-extreme learning machine model Autor Ghimire, Sujan, Deo, Ravinesh C., Casillas-Pérez, David, Salcedo-Sanz, Sancho, Pourmousavi, S. Ali, Acharya, U. Rajendra

“… as part of their strategic planning, regulating and supplying electricity to consumers. This paper proposes hybrid artificial intelligence models combining convolutional neural networks (CNN…”
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Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters Autor Tobar, Felipe, Castro, Iván, Silva, Jorge, Orchard, Marcos

“…•Novel kernel adaptive filter outperforms existing ones in electric bicycle case study…”
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On the antiderivatives of xp/(1 − x) with an application to optimize loss functions for classification with neural networks Autor Knoblauch, Andreas

“…Supervised learning in neural nets means optimizing synaptic weights W such that outputs y ( x ; W…”
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