Guest Editors
Dr. Mohammad Tabrez Quasim，University of Bisha, Saudi Arabia.
Dr. Asadullah Shaikh, Najran University, Saudi Arabia.
Dr. Basit Shahzad, National University of Modern Languages, Islamabad.
Dr. Fahad Alqarni, University of Bisha, Saudi Arabia.
Dr. Kapal Dev, university of johannesburg South Africa.
Summary
Rapidly changing human needs and increased
data generation by information, use of different communication modes and
technological systems give rise to the need of ‘Anticipatory Computing’
paradigm. Due to advances in Information Technology (IT) and domain of
Anticipatory Computing, every facet of the public and private sectors is
getting effective. The Computing paradigm indicates a technology associated
field which is designed to anticipate specific user’s needs and for the
well-being of human society in a way to achieve the idea of “serve before you
ask.” This phenomenon will create an opportunity as well as challenges in the
field of computer science at same time. The recent research direction and rapid
development of anticipating intelligence from social networks data bring
changes not only to our daily lives but to global business also. Particularly,
social network’s big data analysis will reshape the practices of anticipating
intelligence from the data and can be effectively used for discovery and
management of knowledge.
Social
groups are an important part of social networks also considered as a strong pool
of smart
Keywords
• Education and social networks
• Software engineering for social networks
• Internet of things and cloud computing
• Social network mining and data storage
• Social networks for health care
• Marketing and social networks
• Social networks for communication & Collaboration
• Human interaction in social networks
• Mobile and smartphone applications
• Information and knowledge management
• Benchmark, tools, and empirical studies
• Digital humanities
• Behavioral and economic Computing
• User modeling, privacy, and ethics
Published Papers
-
Open Access
ARTICLE
A Perfect Knob to Scale Thread Pool on Runtime
Faisal Bahadur, Arif Iqbal Umar, Insaf Ullah, Fahad Algarni, Muhammad Asghar Khan, Samih M. Mostafa
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1483-1493, 2022, DOI:10.32604/cmc.2022.024936
（This article belongs to this Special Issue:
Social Networks Analysis and Knowledge Management)
Abstract Scalability is one of the utmost nonfunctional requirement of server applications, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) has been used extensively as a middleware service in server applications. The size of thread pool is the most significant factor, that affects the overall performance of servers. Determining the optimal size of thread pool dynamically on runtime is a challenging problem. The most widely used and simple method to tackle this problem is to keep the size of thread pool equal to the request…
More >
-
Open Access
ARTICLE
Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage
Zeyad Ghaleb Al-Mekhlafi, Ebrahim Mohammed Senan, Taha H. Rassem, Badiea Abdulkarem Mohammed, Nasrin M. Makbol, Adwan Alownie Alanazi, Tariq S. Almurayziq, Fuad A. Ghaleb
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 775-796, 2022, DOI:10.32604/cmc.2022.024492
（This article belongs to this Special Issue:
Social Networks Analysis and Knowledge Management)
Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in…
More >
Login
Register
Submit a Paper
Propose a Special lssue