Cognitive Infocommunications, Theory and Applications

التفاصيل البيبلوغرافية
مؤلف مشترك: SpringerLink (Online service)
مؤلفون آخرون: Klempous, Ryszard (المحرر), Nikodem, Jan (المحرر), Baranyi, Péter Zoltán (المحرر)
الملخص:XIII, 462 p. 190 illus., 89 illus. in color.
text
اللغة:الإنجليزية
منشور في: Cham : Springer International Publishing : Imprint: Springer, 2019.
الطبعة:1st ed. 2019.
سلاسل:Topics in Intelligent Engineering and Informatics, 13
الموضوعات:
الوصول للمادة أونلاين:https://doi.org/10.1007/978-3-319-95996-2
التنسيق: الكتروني كتاب
جدول المحتويات:
  • Using deep rectifier neural nets and probabilistic sampling for topical unit classification
  • Monte Carlo methods for real-time driver workload estimation using a cognitive architecture
  • Cognitive data visualization - a new field with a long history
  • Executive functions and personality from a systemic-ecological perspective
  • Mirroring and prediction of gestures from interlocutor’s behavior
  • Automatic labeling affective scenes in spoken conversations
  • Tracking the expression of annoyance in call centers
  • Modeling of filled pauses and prolongations to improve Slovak spontaneous speech recognition
  • Enhancing air traffic management security by means of conformance monitoring and speech analysis
  • Compassion cluster expression features in affective robotics from a cross-cultural perspective
  • Understanding human sleep behaviour by machine learning
  • Electroencephalogram-based brain-computer interface for Internet of Robotic Things
  • CogInfoCom-driven surgical skill training and assessment
  • Cognitive cloud-based telemedicine system
  • Pilot application of eye-tracking to analyze a computer exam test
  • The edu-coaching method in the service of efficient teaching of disruptive technologies
  • 3D modeling and printing interpreted in terms of cognitive infocommunication
  • Constraints programming driven decision support system for rapid production flow planning
  • Improving adaptive gameplay in serious games through interactive deep reinforcement learning
  • A study on a protocol for ad hoc network based on Bluetooth Low Energy.