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20,116 results

2. Characterization of Household Electricity Consumption in Uruguay

Author

Llagueiro, Pablo, Porteiro, Rodrigo, Nesmachnow, Sergio, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nesmachnow, Sergio, editor, and Hernández Callejo, Luis, editor

Published
2024
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3. Parallel-Distributed Implementation of the Lipizzaner Framework for Multiobjective Coevolutionary Training of Generative Adversarial Networks

Author

Nesmachnow, Sergio, Toutouh, Jamal, Ripa, Guillermo, Mautone, Agustín, Vidal, Andrés, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barrios H., Carlos J., editor, Rizzi, Silvio, editor, Meneses, Esteban, editor, Mocskos, Esteban, editor, Monsalve Diaz, Jose M., editor, and Montoya, Javier, editor

Published
2024
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4. Artificial life and intelligent agents: second international symposium, ALIA 2016, Birmingham, UK, June 14-15, 2016, revised selected papers.

Author

Battle, Steve, Headleand, Christopher J., Lewis, Peter R, and Ritsos, Panagiotis D.

Subjects
Artificial intelligence, Computational intelligence, Computer simulation, Genetic algorithms, Robotics
Abstract

Summary: This book constitutes the refereed proceedings of the Second International Symposium on Artificial Life and Intelligent Agents, ALIA 2016, held in Birmingham, UK, in June 2016. The 8 revised full papers and three revised short papers presented together with two demo papers were carefully reviewed and selected from 25 submissions. The papers are organized in topical sections on modelling; robotics; bio-inspired problem solving; human-like systems; applications and games.

Published
2018

5. Interpretability of AI Systems in Electronic Governance

Author

Carbonaro, Antonella, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Ortiz-Rodríguez, Fernando, editor, Tiwari, Sanju, editor, Sicilia, Miguel-Angel, editor, and Nikiforova, Anastasija, editor

Published
2022
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7. Human–Robot Intermittency and Invariants

Author

Heiden, Bernhard, Tonino-Heiden, Bianca, Alieksieiev, Volodymyr, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Marín-Raventós, Gabriela, editor, and Mishra, Durgesh Kumar, editor

Published
2022
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8. Computational Intelligence for Analysis of Traffic Data

Author

Winter, Hernán, Serra, Juan, Nesmachnow, Sergio, Tchernykh, Andrei, Shepelev, Vladimir, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nesmachnow, Sergio, editor, and Hernández Callejo, Luis, editor

Published
2021
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15. Channel Assignment with Ant Colony Optimization

Author

Peras, Marko, Ivkovic, Nikola, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zamuda, Aleš, editor, Das, Swagatam, editor, Suganthan, Ponnuthurai Nagaratnam, editor, and Panigrahi, Bijaya Ketan, editor

Published
2020
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16. Implicit Cognition and Understanding Unobserved Human Mind States by Machines

Author

Ding, Amy Wenxuan, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zeng, An, editor, Pan, Dan, editor, Hao, Tianyong, editor, Zhang, Daoqiang, editor, Shi, Yiyu, editor, and Song, Xiaowei, editor

Published
2019
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17. Computational Intelligence for Detecting Pedestrian Movement Patterns

Author

Chavat, Juan P., Nesmachnow, Sergio, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Founding Editor, Washio, Takashi, Founding Editor, Yuan, Junsong, Founding Editor, Nesmachnow, Sergio, editor, and Hernández Callejo, Luis, editor

Published
2019
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18. Monitoring for the Evaluation Process On-Line Prototype Based on OpenFace Algorithm

Author

Ruiz-Vivanco, Omar, Gonzalez-Eras, Alexandra, Cordero, Jorge, Barba-Guaman, Luis, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Botto-Tobar, Miguel, editor, Pizarro, Guillermo, editor, Zúñiga-Prieto, Miguel, editor, D’Armas, Mayra, editor, and Zúñiga Sánchez, Miguel, editor

Published
2019
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19. Computational Intelligence for Locating Garbage Accumulation Points in Urban Scenarios

Author

Toutouh, Jamal, Rossit, Diego, Nesmachnow, Sergio, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Battiti, Roberto, editor, Brunato, Mauro, editor, Kotsireas, Ilias, editor, and Pardalos, Panos M., editor

Published
2019
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21. A Multidimensional Adaptive Growing Neuro-Fuzzy System and Its Online Learning Procedure

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Shakhovska, Natalia, editor, and Stepashko, Volodymyr, editor

Published
2018
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22. Extended Selected Papers of the 14th International Conference on Information, Intelligence, Systems, and Applications : IISA2023

Author

Nikolaos Bourbakis, George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain, Nikolaos Bourbakis, George A. Tsihrintzis, Maria Virvou, and Lakhmi C. Jain

Subjects
Computational intelligence, Artificial intelligence, Engineering—Data processing
Abstract

This book includes extended versions of selected research works that were presented at the 14th International Conference on Information, Intelligence, Systems, and Applications (IISA2023), Volos, Greece, 10–12 July 2024 (https://www.easyacademia.org/iisa2023) along with additional relevant papers by invited authors. Information is widely available and accessible but frequently leads to information overload and overexposure and the effort for coding, storing, hiding, securing, transmitting, and retrieving it may be excessive. Intelligence, inspired by biological and other paradigms, is required to manage information and extract knowledge from it. Information and multimedia systems, with an increasing level of intelligence, are being developed that incorporate these advances. As a result, new technologies, protocols, and novel applications are emerging. In turn, the novel applications lead to the collection of new information, and the “Information à Intelligence à Systems à Applications” circle is repeated at ever higher levels. These works cover a broad spectrum of topics from both novelties in theory and advanced methodologies as well as novel applications in energy, agriculture, medicine, forensics, healthcare, education, tourism, inspection, traffic, and software engineering. Targeted towards academicians, researchers, practitioners, and students in computer science and artificial intelligence, this work is also accessible to individuals from other disciplines interested in the cutting-edge developments of information, intelligence, systems, and applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into the application areas of interest to them.

Published
2024

23. Model Development and Incremental Learning Based on Case-Based Reasoning for Signal and Image Analysis

Author

Perner, Petra, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Barneva, Reneta P., editor, Brimkov, Valentin E., editor, and Tavares, João Manuel R.S., editor

Published
2017
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24. Reasoning with Information Granules to Support Situation Classification and Projection in SA

Author

Gaeta, Angelo, Loia, Vincenzo, Orciuoli, Francesco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Petrosino, Alfredo, editor, Loia, Vincenzo, editor, and Pedrycz, Witold, editor

Published
2017
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28. Intelligent Financial Fraud Detection Practices: An Investigation

Author

West, Jarrod, Bhattacharya, Maumita, Islam, Rafiqul, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Tian, Jin, editor, Jing, Jiwu, editor, and Srivatsa, Mudhakar, editor

Published
2015
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29. A Survey on Short-Term Electricity Price Prediction Models for Smart Grid Applications

Author

Vardakas, John S., Zenginis, Ioannis, Akan, Ozgur, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Bellavista, Paolo, Series editor, Mumtaz, Shahid, editor, Rodriguez, Jonathan, editor, Katz, Marcos, editor, Wang, Chonggang, editor, and Nascimento, Alberto, editor

Published
2015
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30. Modeling and Simulation Interoperability Concepts for Multidisciplinarity, Interdisciplinarity, and Transdisciplinarity – Implications for Computational Intelligence Enabling Autonomous Systems

Author

Tolk, Andreas, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Hodicky, Jan, editor

Published
2015
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31. TRUST THE "SCIENCE"... THAT JUST RETRACTED 11,000 "PEER-REVIEWED" PAPERS.

Subjects
ACADEMIC fraud, ARTIFICIAL intelligence, COMPUTATIONAL intelligence, LIVER disease diagnosis, ANT colonies
Abstract

This article discusses the recent revelation that the science publisher Wiley has retracted over 11,000 peer-reviewed papers that were determined to be fake. The papers were described as "naked gobbledygook sandwiches" and were created by professional cheating services using AI to shuffle words and create seemingly original academic papers. The misuse of terminology and lack of rigorous human oversight allowed these fraudulent papers to pass peer review. As a result, Wiley has shuttered 19 of its journals and the industry is developing AI tools to detect fake papers. The article argues for a shift from traditional peer review to rigorous live debates to ensure accountability and prevent academic fraud. [Extracted from the article]

Published
2024

33. Towards Integrating Emotion into Intelligent Context

Author

Moore, Philip, Evans, Cain, Pham, Hai V., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Haller, Armin, editor, Huang, Guangyan, editor, Huang, Zhisheng, editor, Paik, Hye-young, editor, and Sheng, Quan Z., editor

Published
2013
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34. Tuning of a PID controller using evolutionary multi objective optimization methodologies and application to the pulp and paper industry

Author

B. Nagaraj, K. Nisi, and A. Agalya

Subjects
0209 industrial biotechnology, Computer science, PID controller, Particle swarm optimization, Computational intelligence, 02 engineering and technology, Ziegler–Nichols method, Pulp and paper industry, Multi-objective optimization, 020901 industrial engineering & automation, Artificial Intelligence, Control theory, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Software, Evolutionary programming
Abstract

Proportional–Integral–Derivative controller technique continues to provide the easiest and effective solutions to most of the industrial applications in recent years. However PID controller is poorly tuned in practice compared to most other tuning methods and is complicated with poor performance. This research presents a multi objective optimization approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization and Bacterial foraging optimization. The proposed multi objective optimization algorithm is used to tune the PID controller parameters and their performances have been compared with the conventional methodologies like Ziegler Nichols method. The results proved that the use of multi objective optimization approach based controller tuning improves the performance of process in terms of time domain specifications and performance index, set point tracking and regulatory changes and also provides stability. This paper describes the various multi objective optimization algorithms and its implementation to tune the PID Controller used in paper machine DCS as real time processing of a Pulp and paper industry processes.

Published
2018

35. The Use of Computational Intelligence in the Design of Polymers and in Property Prediction

Author

Chen, Xi, Sztandera, Les M., Cartwright, Hugh M., Granger-Bevan, Stephen, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoff, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Jia, Xiaohua, Series editor, Zomaya, Albert, Series editor, Di Caro, Gianni A., editor, and Theraulaz, Guy, editor

Published
2014
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37. Application of a Method Based on Computational Intelligence for the Optimization of Resources Determined from Multivariate Phenomena

Author

Kuri-Morales, Angel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Batyrshin, Ildar, editor, and Mendoza, Miguel González, editor

Published
2013
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40. Role-Based Management and Matchmaking in Data-Mining Multi-Agent Systems

Author

Kazík, Ondřej, Neruda, Roman, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Cao, Longbing, editor, Zeng, Yifeng, editor, Symeonidis, Andreas L., editor, Gorodetsky, Vladimir I., editor, Yu, Philip S., editor, and Singh, Munindar P, editor

Published
2013
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41. Review paper on research direction towards cancer prediction and prognosis using machine learning and deep learning models

Author

Nimmagadda Satyanarayana Murthy and Chaitanya Bethala

Subjects
General Computer Science, Artificial neural network, Computer science, business.industry, Deep learning, Decision tree, Bayesian network, Computational intelligence, Machine learning, computer.software_genre, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Recurrent neural network, Categorization, Artificial intelligence, business, computer
Abstract

Cancer is characterized as a heterogeneous disease of various types. The early detection and prognosis of a cancer type have turned into a major requirement, as it facilitates successive medical treatment of patients. The research team has classified the cancer patients into high or low-risk groups. This makes it a significant task for the medical teams to study the application of deep learning and machine learning models. As a result, such techniques have been employed for modeling the development and treatment of cancer conditions. Additionally, the machine learning tools can have the ability the significant detection features from complex datasets. Numerous techniques like Support Vector Machines (SVM), Bayesian Networks (BN), Decision Trees (DT), Artificial Neural Networks (ANN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) has been broadly utilized in cancer research. As per the current survey, the detection rate is about 99.89%, which shows the prediction models' efficiency and precise decision making. However, it is proven that deep learning and machine learning approaches can enhance cancer progression. An adequate level of estimation is required for such approaches for considering the daily medical practice. This survey analyzes and learns the diverse contributions of cancer prediction models using intelligent approaches. Further, the paper tries to categorize the different algorithms, the utilized datasets, and utilized environments. Along with this, various performance measures evaluated in each contribution is sorted out. An extensive search is conducted relevant to machine learning and deep learning methods in cancer susceptibility, recurrence, and survivability prediction, and the existing challenges in this area are clearly described. However, ML models are still in the testing as well as the experimentation phase for cancer prognoses. As the datasets are getting larger with higher quality, researchers are building increasingly accurate models. Moreover, ML models have a long way to go, and most of the models still lack sufficient data and suffer from bias.

Published
2021

42. Landau Theory of Meta-learning

Author

Plewczynski, Dariusz, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bouvry, Pascal, editor, Kłopotek, Mieczysław A., editor, Leprévost, Franck, editor, Marciniak, Małgorzata, editor, Mykowiecka, Agnieszka, editor, and Rybiński, Henryk, editor

Published
2012
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43. Chaotic Patterns in Crowd Simulation

Author

Cases, Blanca, Olasagasti, Francisco Javier, Moujahid, Abdelmalik, D’Anjou, Alicia, Graña, Manuel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Kampis, George, editor, Karsai, István, editor, and Szathmáry, Eörs, editor

Published
2011
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44. Bio-inspired computing tools and applications: position paper

Author

Tom Gedeon

Subjects
Computer Networks and Communications, Computer science, Computational intelligence, 02 engineering and technology, Machine learning, computer.software_genre, Autonomic computing, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, End-user computing, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Soft computing, business.industry, Natural computing, Applied Mathematics, Human-centered computing, Data science, Computer Science Applications, Computational Theory and Mathematics, Position paper, 020201 artificial intelligence & image processing, Artificial intelligence, Bio-inspired computing, business, computer, 030217 neurology & neurosurgery, Information Systems
Abstract

The confluence of significant computational power and inexpensive sensors provides the opportunity to reliably collect large volumes of information from the world and extract humanly useful information resources. This paper reviews a coherent body of work over the last 20+ years focused on development of advanced bio-inspired computing techniques, and their applications primarily for human related data in behaviour and human centered computing. We close with a synthesis proposing an experiment analysis methodology combining these tools.

Published
2017

45. Effective Black-Box Testing with Genetic Algorithms

Author

Last, Mark, Eyal, Shay, Kandel, Abraham, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Ur, Shmuel, editor, Bin, Eyal, editor, and Wolfsthal, Yaron, editor

Published
2006
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46. 6G in the sky: On‐demand intelligence at the edge of 3D networks (Invited paper)

Author

Sergio Barbarossa, Antonio Pietrabissa, Emanuele De Santis, Alessandro Giuseppi, Ilgyu Kim, Josep Vidal, Taesang Choi, Zdenek Becvar, Emilio Calvanese Strinati, Francesca Costanzo, Thomas Haustein, Junhyeong Kim, Nicolas Cassiau, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, and Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions

Subjects
General Computer Science, Non-terrestrial communications, satellite, lcsh:TK7800-8360, Virtual computer systems, Library science, 3D connectivity, 02 engineering and technology, 3D services, lcsh:Telecommunication, Intel·ligència computacional, lcsh:TK5101-6720, Ordinadors, Xarxes d' -- Disseny i construcció, Political science, On demand, unmanned aerial vehicle, 0202 electrical engineering, electronic engineering, information engineering, media_common.cataloged_instance, non‐terrestrial communications, Electrical and Electronic Engineering, European union, 3d services, 5g, Computer networks -- Design and construction, Sistemes virtuals (Informàtica), 6G, media_common, Computational intelligence, lcsh:Electronics, High-altitude platform stations, 020206 networking & telecommunications, Unmanned aerial vehicle, high‐altitude platform stations, b5g, Enginyeria de la telecomunicació::Processament del senyal [Àrees temàtiques de la UPC], B5G, Electronic, Optical and Magnetic Materials, 3d connectivity, Satellite, Information and Communications Technology, Mobile edge computing, Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], 020201 artificial intelligence & image processing, mobile edge computing, 5G, 6g
Abstract

Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on-demand edge cloud services in three-dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low-orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space-borne MEC, enabling intelligent, personalized, and distributed on-demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence-based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes. European Union in the Horizon 2020 EU-Korea project 5G-ALLSTAR, GA no. 815323, by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT No. 2018-0-00175) and by Grant No. P102-18-27023S, funded by the Czech Science Foundation.

Published
2020

48. On the Application of Computational Intelligence Methods on Active Networking Technology

Author

Jalili-Kharaajoo, Mahdi, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Li, Minglu, editor, Sun, Xian-He, editor, Deng, Qianni, editor, and Ni, Jun, editor

Published
2004
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49. Position Paper: The Usefulness of Data-driven, Intelligent Agent-Based Modelling for Transport Infrastructure Management

Author

Peer-Olaf Siebers, Brendan Ryan, Olusola Theophilus Faboya, and Grazziela P. Figueredo

Subjects
050210 logistics & transportation, Computer science, 05 social sciences, Transport network, Computational intelligence, computer.software_genre, Transport Pathway, Data-driven, Intelligent agent, Cognitive work analysis, Risk analysis (engineering), 0502 economics and business, Position paper, 0501 psychology and cognitive sciences, computer, 050107 human factors
Abstract

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

Published
2018

50. Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2020.

Author

Beyerer, Jürgen, Beyerer, Jürgen, Maier, Alexander, and Niggemann, Oliver

Subjects
Communications engineering / telecommunications, Computer networking & communications, Electrical engineering, Artificial Intelligence, Cognitive Robotics, Communications Engineering, Networks, Computational intelligence, Computer Engineering and Networks, Computer Systems Organization and Communication Networks, Computer-based algorithms, Cyber-Physical Systems, Cyber-physical systems, IoT, Cybernetics & systems theory, Industry 4.0, Internet of Things, Machine Learning, Open Access, Smart grid
Abstract

Summary: This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.