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• LDR

  00000cam c22002058c 4500

• 001

  000005153924

• 005

  20251013091111

• 007

  ta

• 008

  251010s2024 flu b 001c0 eng

• 020

  ▼a  9781032632032
  ▼q  (hbk)

• 020

  ▼a  9781032648279
  ▼q  (pbk)

• 020

  ▼z  9781032648309
  ▼q  (ebk)

• 035

  ▼a  (KERIS)REF000020510505

• 040

  ▼a  DLC
  ▼b  eng
  ▼c  DLC
  ▼e  rda
  ▼d  211070

• 042

  ▼a  pcc

• 050

  ▼a  TK5105.8857

• 090

  ▼a  TK5105.8857
  ▼b  U41

• 245

  ▼a  Future communication systems using artificial intelligence, internet of things and data science /
  ▼d  Inam Ullah,
  ▼e  Inam Ullah Khan,
  ▼e  Mariya Ouaissa,
  ▼e  Mariyam Ouaissa,
  ▼e  Salma EL Hajjami

• 250

  ▼a  1st ed.

• 260

  ▼a  Boca Raton :
  ▼b  CRC Press,
  ▼c  2024

• 300

  ▼a  235 p. ;
  ▼c  26 cm

• 504

  ▼a  Includes bibliographical references and index

• 520

  ▼a  "The goal of the Artificial Intelligence (AI), Internet of Things (IoT), and Data Science for future communications systems is to create a venue for industry and academics to collaborate on the development of network and system solutions based on data science, AI, and IoT. Recent breakthroughs in IoT, mobile and fixed communications, and computation have paved the way for a data-centric society of the future. New applications are increasingly reliant on machine-to-machine connections, resulting in unusual workloads and the need for more efficient and dependable infrastructures. Such a wide range of traffic workloads and applications will necessitate dynamic and highly adaptive network environments capable of self-optimization for the task at hand while ensuring high dependability and ultra-low latency. Networking devices, sensors, agents, meters, and smart vehicles/systems generate massive amounts of data, necessitating new levels of security, performance, and dependability. Such complications necessitate the development of new tools and approaches for providing successful services, management, and operation. Predictive network analytics will play a critical role in insight generation, process automation required for adapting and scaling to new demands, resolving issues before they impact operational performance (e.g., prevent network failures, anticipate capacity requirements), and overall network decision making. To increase user experience and service quality, data mining and analytic techniques for inferring quality of experience (QoE) signals are required. AI, IoT, machine learning, reinforcement learning, and network data analytics innovations open new possibilities in areas such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi-agent systems, and network ultra-broadband deployment prioritization. These new analytic platforms will aid in the transformation of our networks and user experience. Future networks will enable unparalleled automation and optimization by intelligently gathering, analyzing, learning, and controlling huge volumes of information"--
  ▼c  Provided by publisher

• 650

  ▼a  Internet of things
  ▼x  Forecasting

• 650

  ▼a  Artificial intelligence
  ▼x  Forecasting

• 650

  ▼a  Big data
  ▼x  Social aspects

• 700

  ▼a  Ullah, Inam

• 950

  ▼b  ￡110

• 991

  ▼a  단행본