Calls for Papers (special): Journal of Cases on Information Technology (JCIT)


Special Issue On: Applications of Artificial Intelligence and Machine Learning in new Generation of Computer Communications: Solutions, Trends and Methods

Submission Due Date
5/1/2020

Guest Editors
Dr Osamah Ibrahim Khalaf
Al-Nahrain University, Iraq

Dr. K. Martin Sagayam
Karunya Institute of Technology and Sciences,Coimbatore.

Dr. Abdelli Mohammed El Amine,
Researcher at University of Salamanca , Spain

Dr. M. Manikandan,
Anna University (MIT Campus), Chennai.

Introduction
Artificial Intelligence (AI) and Machine Learning (ML) approaches, well known from IT disciplines, are beginning to emerge in the networking domain. Increasingly integrated with and supporting various aspects of computing and networking, Artificial Intelligence (AI) is anticipated to become increasingly more important in terms of support for digital assets as well as physical infrastructure. These approaches can be clustered into AI/ML techniques for network management; network design for AI/ML applications and system aspects. AI/ML techniques for network management, operations & automation address the design and application of AI/ML techniques to improve the way we address networking today. Recently, networking has become the focus of a huge transformation enabled by new models resulting from virtualization and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and more recently, edge cloud and fog. This development towards enhanced design opportunities along with increased complexity in networking as well as in networked applications has fueled the need for improved network automation in agile infrastructures. Artificial Intelligence techniques are used to execute efficient, rapid, trustworthy management operations. Network design and optimization for AI/ML applications addresses a complementing topic namely the support of AI/ML-based systems through novel networking techniques including new architectures as well as performance models.

Objective
On the other hand, Artificial Intelligence (AI), well known from computer science (CS) disciplines, are beginning to emerge in the wireless communications and have recently received much attention as a key enabler for future 5G and beyond wireless networks. These AI approaches including Machine Learning (ML), Deep Learning (DL) and Deep Reinforcement Learning (DRL) approaches have been gradually applied to wireless communication systems for various purposes, which extensively improve the performance of wireless communication systems and users. Therefore, AI technologies have a great potential to meet the various requirements of seamless wide-area coverage, low-power massive-connections, low latency high-reliability, and many other scenarios.
Due to the new features of future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements, traditional methods are no longer suitable which brings much more potential application of AI. Just as DL technology has become a new hotspot in the research of physical-layer wireless communications and challenges conventional communication theories. Currently DL-based methods show promising performance improvements but lack of solid analytical tools and universal network architectures. In addition to the traditional neural network-based data-driven model, the model-driven deep network model and the DRL model which combined DL with reinforcement learning are more suitable for dealing with future communication systems, which can be modelled with interpretability. Moreover, most of current studies focus on solving old problems such as estimation accuracy and resource allocation optimization in wireless communication systems. However, it is important to distinguish new capabilities created by AI technologies and rethink wireless communication systems based on AI-driven schemes. Therefore, the old theory will be supplemented and updated to a large extent when solving the old problems with the new method of AI. At the same time, the problems brought by the introduction of AI technology into communication, such as how to reduce the complexity of AI algorithm to make it suitable for lightweight devices and so on are also important directions in the future.
In this Special issue, we are going to interrogate about the main roles of Artificial intelligence and Machine learning in new generation of Computer Communications and future of upcoming technologies based on AI/ML. In addition, it will be requested to send high quality and novel research papers, platforms, Softwares and technologies in these fields.

Recommended Topics
• Artificial Intelligence
• Machine Learning
• AI/ML for IoT
• Deep Reinforcement Learning (DRL)
• Computer Communications
• Computer Science (CS)
• 5G and Cellular Networks
• Wireless Communications
• Software-Defined Networking (SDN)
• Network Function Virtualization (NFV)
• Blockchain Technology
• Blockchain Communications
• Networking
• End to end Communication
• Network Security based on AI/ML techniques
• AI/ML for multimedia networking
• Modeling and performance evaluation for Intelligent Network
• Big Data
• Cloud Computing
• Network Management
• Protocol design and optimization using machine learning
• Bio-inspired learning for networking and communications
• Innovative architectures and infrastructures for intelligent networks

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on “Applications of Artificial Intelligence and Machine Learning in new Generation of Computer Communications: Solutions, Trends and Methods” on or before 1 May 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.
Any questions regarding this special issue should be sent to lead guest editor and invited guest editors of this special issue. All submitted papers will be reviewed by at least three reviewers and selected based on their originality, novelty, significance, relevance, and clarity of presentation. The covering letter should indicate the names of the authors and their affiliations, addresses, faxes, and e-mails. The manuscript should be prepared in journal template (Computer Communications) and converted into a PDF for auto-submission system.

All inquiries should be directed to the attention of:
Lead Editor
Dr Osamah Ibrahim Khalaf

usama.ibrahem@coienahrain.edu.iq
usama81818@gmail.com
Co-Guest Editor
Dr. K. Martin Sagayam

martinsagayam.k@gmail.com
Dr. Abdelli Mohammed El Amine,
abdelli.univ@gmail.com
Dr. M. Manikandan,,
maniiz@annauniv.edu

Special Issue On: Networking, Remote Sensing and Machine Learning Applications for Environments

Submission Due Date
5/20/2020

Guest Editors
1. Dr Osamah Ibrahim Khalaf
Al-Nahrain University, Iraq

2. Dr. K. Martin Sagayam
Karunya Institute of Technology and Sciences,Coimbatore.

3. Dr. Abdelli Mohammed El Amine,
University of Salamanca, Spain.

4. Dr. L. Arun Raj,
B. S. Abdur Rahman Cresent Institute of Science and Technology, Chennai.

Introduction
Remote Sensing and Machines Learning Applications for Environment - Special Issue Information. The rapidly increasing availability of multispectral, high spatial resolution imagery, collected by satellites, cubesats, and airborne sensors, presents an opportunity to detect landscape change, landuse landcover, surface temperature, nature disaster with increased spatial detail of research environment and applications. The research environment studies utilizing data from several satellite imagery such as LANDSAT, WORLDVIEW, SPOT, LIDAR data, Sentinel and MODIS other satellite imagery. Today, a new generation of research environment studies using several satellite imagery with different spatial resolution based on research study through the capitalizing on the availability of data from high spatial resolution global monitoring missions. For example, the unprecedented 45-year long global Landsat archive is increasingly used to analyze past and present global land and water changes, and higher temporal frequency global observations from Sentinel are enabling the use of dense high resolution time series for near real time monitoring. In addition to Sentinel and Landsat, data from other global Landsat-class missions are increasingly being integrated into virtual Earth observation constellations that further advances global land and water monitoring.

Objective
These challenges all point to the need for improved image processing approaches specific to multispectral, high spatial resolution imagery. In this Special Issue, the methodological contributions in terms of novel machine learning algorithms as well as the application of innovative techniques to relevant scenarios from hyperspectral data. On the other hand, the environmental modelling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as: processes related to atmosphere, hydrology, and land surface, among others. In fact, environmental models may span a wide spectrum of geographic (i.e., from local to regional to global-levels) and temporal (i.e., diurnal to monthly to annual to decadal-levels) scale. They often integrates various aspects of the environment that can be described upon employing various types of models, such as process-driven, empirical or data-driven, deterministic, stochastic, etc.

Recommended Topics
• agriculture,
• Water,
• Forest fire,
• Flooding,
• Volcano,
• Local/regional warming,
• Urban environment,
• Environmental pollution,
• Change detection using high spatial resolution imagery,
• Integration of high spatial resolution multispectral imagery with other remote measurements (e.g., SAR, lidar, UAVs, Sentinel),
• Integration of high-spatial resolution multispectral imagery with ground-based datasets,
• Spectral data pre-processing,
• Feature extraction and selection from high-dimensional data,
• Machine learning and data mining methodologies for hyperspectral data analysis,
• Methods for image segmentation and classification, change and target detection, multi-temporal analysis,
• Advanced techniques for characterization of natural ecosystems, coastal systems, agricultural, or urban areas
• Networking

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on “Networking, Remote Sensing and Machine Learning Applications for Environment” on or before20 May 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Lead Editor
Dr Osamah Ibrahim Khalaf
Al-Nahrain University, Iraq
usama.ibrahem@coienahrain.edu.iq
usama81818@gmail.com

Co-Guest Editor
Dr. K. Martin Sagayam
Karunya Institute of Technology and Sciences, Coimbatore.
martinsagayam.k@gmail.com

Dr. Abdelli Mohammed El Amine,
University of Salamanca, Spain.
abdelli.univ@gmail.com

Dr. L. Arun Raj,
B. S. Abdur Rahman Cresent Institute of Science and Technology, Chennai.
arunraj@crescent.education

Special Issue On: Artificial Intelligence in Data Science (AIDS)

Submission Due Date
9/30/2020

Guest Editors
Neetu Sardana,
Jaypee Institute of Information Technology, India

Vikas Saxena,
Jaypee Institute of Information Technology, India

Introduction
Artificial Intelligence (AI) has become a dominant form of intelligence which is impacting virtually every business. AI is acting as a driver for all emerging technology like big data , IOT, robotics etc. Data Science is a field that makes use of AI to generate predictions. It focuses on transforming data for analysis and visualizations. Advancements in AI and growth in data-driven techniques has resulted in wide research opportunities in a variety of areas like e-commerce, businesses, science, healthcare and social networks. AI based systems promote data driven analytics in all these areas which can be beneficial in understanding the current practices, trends and patterns. AI in data science can provide promising solutions to many challenging problems through learning and intelligent decision making. Mining the data relevant functionality with AI has received a substantial attention both in academia and business communities.

Objective
The special issue on “Artificial Intelligence in Data Science” focuses on recent research and advancements related to the impact of Artificial Intelligence in varied applications. In this special issue, we invite technical papers describing original and unpublished results of foundational, theoretical, empirical, and applied Artificial Intelligence research.

Recommended Topics
• Natural Language processing for data analytics
• Social network analysis
• Knowledge representation and reasoning
• Machine learning in medicine and healthcare informatics
• Network/graph Mining
• Automated Credit Fraud Detection
• Recommendation and ranking engines
• Web Intelligence and search
• Bioinformatics
• Security surveillance in online business
• Data Analytics for Virality detection
• Data-driven services and applications

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Artificial Intelligence in Data Science on or before September 30, 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Neetu Sardana,
neetu.sardana@jiit.ac.in

Vikas Saxena
vikas.saxena@jiit.ac.in

Guest Editor
Journal of Cases on Information Technology (JCIT)