Autosoft Journal

Special Issues


Instructions for Special Issues Proposals


Note: For those guest editors who wish to prepare a proposal, here are a set of pointers to considered:

1. Guest editors are not to submit their own papers

2. All authors from around the world should be able to submit papers.

3. At least 3 DETAILED reviews are needed for each paper.

4. Rejections can be done by guest editor, but acceptance needs to be done by TSI Press - the publisher.

5. Autosoft Journal has the paper charge of $830 and as needed a $100 charge for English and grammar corrections.

6. Each author will receive a hard copy of that issue and an unlimited access to Autosoft online subscription: http://autosoftjournal.net/

7. When all done, guest editor needs to write a detailed editorial for the issue.


Call For Papers


Medical students, researchers, medical professionals, and radiologists always deal with different kinds of data to identify the symptoms and inferences for diagnostic procedures. Due to the tremendous advancement in image acquisition devices, the data is quite large (moving to big data), that makes it challenging and interesting for image analysis. This rapid growth in medical images and modalities require extensive and tedious efforts by a medical expert that is subjective, prone to human error and may have large variations across different expert. Machine learning is one of the fastest- growing and most exciting field, concerned with the study and design of computer algorithms for learning good representations of data, at multiple levels of abstraction. A happy marriage of high-performance computing with machine learning promise the capacity to deal high dimensional medical data for accurate and efficient diagnosis. When made accessible to the right person (clinician) in the right place (point of care) at the right time (real- or near-real time), machine learning-enabled models can give clinicians more pertinent information to support their patient care decisions. By considering all rudimentary insights of the machine learning applications in healthcare, this special issue has been proposed to enable the researchers and medical practitioners to bring alternate solutions for the existing complex issues. Thereby, new approaches and products will make healthcare services into an affordable and promising sector.

Topics of interest for articles include, but are not limited to:

Artificial Intelligence based mathematical approach for modelling and simulation
Bio-inspired algorithms for disease prediction and Analysis
Biomedical imaging and Data Visualization
Biomedical text mining/extraction
Brain Computer Interface for Rehabilitation Engineering
Content based medical Image Retrieval
Deep convolutional Neural Networks for large–scale medical data set
Deep learning algorithms for object detection, image restoration, image classification
Intelligent medical information system
Knowledge based feature engineering
Soft computing based assessment of brain behavior
Sustainable intelligent systems for clinical decision support system
Knowledge discovery for evidence based medicine
Smart wearable systems
Unsupervised/supervised algorithms for medical image/signal classification

Guest editors:

Dr. Vijay Jeyakumar(Corresponding GE), SSN College of Engineering, Tamil Nadu, India, vijayjeyakumar@ieee.org
Dr. Kavitha Anandan, SSN College of Engineering, Tamil Nadu, India. kavithaa@ssn.edu.in
Dr. Hualou Liang, Drexel Univeristy, Philadelphia, USA, hl374@drexel.edu
Dr. Yuvaraj Rajamanickam, Nanyang Technological University, Singapore,ryuvaraj@ntu.edu.sg

Submission Instructions

Authors should follow the manuscript format and the submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site: http://wacong.org/autosoft/auto/index.php. The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusions.

Important Dates

Manuscript Due: July 15, 2019
First Round of Reviews: November 25, 2019
Final Decision: January 15, 2020
Final Submission Deadline: March 21, 2020


In the new era of technologies, the advancement towards soft computing and the AI is becoming significant, which results in promising and outstanding performance addressing the uncertain conditions. Soft Computing methods produce tremendous results, which are significantly adhered towards improving the accuracy of a variety of challenging problems in various fields when compared to the traditional machine learning methods. On the other hand, the requirement towards the need for efficient Signal Processing to achieve the targeted goal is high. However, the high- performance system always needs interaction with its environment through real-time signal processing, since the exchange of information between the systems enables a better learning methodology that results in responsible governance. The developed a fully automated system in the field of signal processing would help in accurate identification of abnormalities and rectify them. The accuracy of the computer-aided systems is highly superior to the manual observations, and hence the physicians significantly prefer automated systems. During the previous decade, soft computing has emerged as potential candidates for solving complex and intricate global optimization problems, which are otherwise difficult to solve by traditional methods. In the present scenario, Signal, signal processing, Industrial optimization, Control system applications, and power system application fields have challenging deeds which are to be unraveled by researchers. Some favorite soft computing techniques for Machine Learning and Global Optimization include Artificial Neural Networks, Fuzzy logic, Genetic Algorithms (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FFA) algorithm, etc., are been successfully applied to a wide range of benchmark and real-world application problems. Day by day the processing of signals with in-depth features has become near impractical due to several reasons. The technology for processing the signal has a lot of challenges and hurdles, which need proper optimization through intelligence. This special issue is an ideal platform for the researchers to come out with innovative ideas and approaches in the area of the signal. This Special issue gains much importance since it directly influences the signal-processing field and provides welfare for the society.

Topics of interest for articles include, but are not limited to:

A deep Boltzmann machine on intelligent signal computing
An evolutionary approach to process the signals
Architectures for the Real-time sensing and intelligent processing
Convolutional Neural Network, Auto-Encoders, Restricted Boltzmann Machines for signal classification
Deep Belief Networks on Real-time Signal Processing
Parallel and distributed algorithm design and implementation in signal sensing
Analytics for multi-dimension data
Intelligent computing on signal for data analysis
Real-time remote sensing Signal, such as hyper spectral signal classification, unmixing, compression, content-based signal indexing, and retrieval, land-use, and land cover classification, target detection/tracking, anomaly detection, monitoring of natural and man-induced disasters, etc.

Guest editors:

Prof. Nagaraj Balakrishnan (Corresponding GE), Dean - Karpagam Innovation Centre, Karpagam College of Engineering, Coimbatore, India nagaraj@kce.ac.in
Prof. Danilo Pelusi, Professor, Dept. of Communication, Engineering, University of Teramo, Italy. dpelusi@unite.it
Prof. Joy I. -Z. Chen, Professor, Depart. Of Communication, Engineering, Dayeh University, Taiwan, jchen@mail.dyu.edu.twc

Submission Instructions

Authors should follow the manuscript format and the submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site: http://wacong.org/autosoft/auto/index.php. The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusions.

Important Dates

Manuscript Due: August 20, 2019
First Round of Reviews: October 20, 2019
Final Submission Deadline: December 25, 2019


The objective of this special issue is According to ABI Research’s research analysis Edge Analytics in Internet of Things (IoT), the total volume of data produced annually by IoT-connected devices is estimated to reach almost 130 million yottabytes (YB) in 2020. With such huge numbers in play, managing the large volume, variety, and velocity of big data becomes one of the major challenges the Internet of Things (IoT) industry has to face today. With the number of IoT-connected devices estimated to grow to tens of billions by the end of the decade, the volume of the data generated by these devices is poised to reach unprecedented proportions. Now, as we collect massive amounts of Internet of Things (IoT) data, our ability as humans to make sense of it becomes quite the challenge. To be more efficient, a process is needed that will automatically and in real time collect data, make predictions, and react. Machine learning and a complete tool chain that supports this model are required. Internet of Things (IoT) is not only about collecting the data, but it’s also focused on obtaining value from the data after we’ve acquired it. Attaching sensors to everything only becomes worthwhile when we can predict, control, and make decisions in response to the data. One of the greatest boons that machine learning and its algorithms have delivered to the Internet of Things (IoT) is how easily it integrates into the IoT’s platforms. Most of the leading Internet of Things (IoT) platforms now offer machine learning capabilities. This enables the Internet of Things (IoT) system to analyze sensor data, look for correlations and determine the best response to take. The system continuously checks to see how well its predictions are working and keeps refining its own algorithm. The rapid proliferation of mobile devices around the globe, for instance, is one of the key drivers of the Internet of Things (IoT), and machine learning often fits neatly into the world of mobile device development, programming, and maintenance. Companies who want to succeed in today’s marketplace understand the valuable potential hidden in machine learning, and are starting to justifiably treat their algorithms as valued parts of their workforce. As billions of more devices spread across the world in the next one or two decades alone, these algorithms and the cost-cutting advances they bring to businesses and consumers alike will only grow more indispensable. As more people sign up on social media platforms, buy smart devices and commute with autonomous vehicles, the vice grip of the Internet of Things (IoT) on society will only grow stronger, powered to a large extend by the wondrous world of machine learning. This huge increase in data will drive great improvements in machine learning, opening countless opportunities for us to reap the benefits. With the right algorithms, the system can be gradually taught to recognize any internal and external production-related factors, optimize the use of consumables, and improve the efficiency of the entire production process.

Topics of interest for articles include, but are not limited to:

Design and Evaluation of Energy Efficient Networks and Services in IoT
Algorithms for Time Series Data and IoT
IoT system architecture and Enabling technologies
Intelligent interfaces for Internet of Things
IoT Sensing Things Technology and Applications
Efficient Resource Management Based on IoT
Knowledge-Based Discovery of Devices in the IoT
Localization in IoT
Crowd-Sourcing and Opportunistic IoT
Security, Trust, Privacy and Identity in the IoT
Performance Evaluation of IoT Technologies
Classification and interpretation of images, text, video
Deep learning and latent variable models
Bayesian machine learning
Classification, regression and prediction
Machine Learning for Web Navigation and Mining
Machine Learning for Information Retrieval
Neural Network Learning
Distributed and Parallel Learning Algorithms and Applications
Structured prediction, relational learning, logic and probability
Machine learning for network slicing optimization
Reinforcement Learning and Planning
Fault-tolerant network protocols using machine learning
Machine learning and big data analytics for network management
State-of-practice, experience reports, industrial experiments, and case studies in the IoT

Guest editors:

Prof.D.Ganesh Gopal (Corresponding GE), , Galgotias University, India dganeshgopal@gmail.com
Prof. Victor Chang, Xi’an Jiaotong-Liverpool University (XJTLU), China victorchang.research@gmail.com
Prof. Bharat S. Rawal Kshatriya, Penn State Abington University, USA bsr17@psu.edu
Prof. Danilo Pelusi, Professor, Dept. of Communication, Engineering, University of Teramo, Italy. dpelusi@unite.it

Submission Instructions

Authors should follow the manuscript format and the submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site: http://wacong.org/autosoft/auto/index.php. The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusions.

Important Dates

Manuscript Due: June 25, 2019
First Round of Reviews: August 15, 2019
Final Submission Deadline: October 25, 2019


Advances in sensor and data-storage technologies have facilitated the wide applications of sensors in modern agricultural systems, such as irrigation and drainage systems, plant protection systems, greenhouse systems, etc. Various sensors allow the collection of large volumes of data related to operations of agricultural systems in different formats, including time-series, images, videos and sound waves. However, traditional methods and strategies lack the ability to make full use of such large amounts of data. Artificial Intelligence (AI) algorithms are emergingly employed to perform high-level big data analytics tasks, such as prediction, classification, object detection, and clustering. The successful applications of AI algorithms based big data have been verified in different domains, such as manufacturing, healthcare, power supply, and energy management. Thus, it is valuable and meaningful to develop and apply data-enabled intelligence algorithms to relevant aspects of the agricultural systems. This special issue aims to attract original research articles that report the latest applications of data-enabled Intelligence algorithms in the field of agricultural systems as well as review papers which describe the current state of the art. The goal is that it provides an opportunity for us to gain a significantly better understanding of the current developments and the future direction of data-enable intelligence in relation to complex agricultural systems.

Topics of interest for articles include, but are not limited to:

Data Mining for Crop Production Prediction and Plant Disease Classification
Machine Learning and Deep Learning for Intelligent Decision Making
Metaheuristic Algorithms for Irrigation Scheduling and Agricultural Land Management
Computer Vision for Crop and Soil Monitoring
Text Mining for Automated Agricultural Knowledge Discovery from Internet
System Science and Engineering for Operations Management of Agricultural Production
Mobile and Edge Computing in Agriculture
Advanced Sensing Systems for Data Collection

Guest editors:

Dr. Long Wang, University of Science and Technology Beijing, Beijing, China and City University of Hong Kong, Kowloon, Hong Kong long.wang@ieee.org
Prof. Zhe Song, Nanjing University, Nanjing, China zsong1@nju.edu.cn
Dr. Chao Huang, University of Science and Technology Beijing, Beijing, China chaohuang@ustb.edu.cn
Dr. Shancheng Jiang, The Hong Kong Polytechnic University, Kowloon, China shancheng.jiang@polyu.edu.hk
Dr. Jenq-Haur Wang, National Taipei University of Technology, Taipei, Taiwan jhwang@ntut.edu.tw

Submission Instructions

Authors should follow the manuscript format and the submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site: http://wacong.org/autosoft/auto/index.php. The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusions.

Important Dates

Manuscript Due: August 25, 2019
First Round of Reviews: December 15, 2019
Final Submission Deadline: February 15, 2020




Past Call For Papers


Smart manufacturing, also known as Industry 4.0, refers to the next-generation manufacturing paradigm that aims to make use of smart sensors, cloud computing infrastructures, artificial intelligence or machine learning, advanced robotics to improve manufacturing productivity and cost efficiency. As one of the key enablers for smart manufacturing, the Internet of Things (IoT) enables integration of physical objects with digital systems by offering connectivity of manufacturing devices and systems through sensors or augmented reality. Due to the arrival of big data, Internet of Things, cyber-physical systems, cloud manufacturing, and so on, manufacturing is in the process of undergoing a significant transformation to become more intelligent and automated. More strikingly, various artificial intelligence techniques, machine learning algorithms, and big data analytics are being researched and deployed into remanufacturing context, e.g., design for remanufacturing, advanced remanufacturing process, robotics in manufacturing, critical failure prediction, inventory forecasting, resilient manufacturing networks, closed-loop supply chain management, etc. The purpose of this SI is to provide a forum for researchers and practitioners to exchange ideas and progress in related areas.

Topics of interest for articles include, but are not limited to:

Emerging sensing technologies for smart manufacturing
Sensor-enabled manufacturing process monitoring and control
Manufacturing intelligence and manufacturing informatics
Advanced diagnostics, prognostics and asset health management
Embedded systems for smart manufacturing
Augmented reality and wearable computing for greater equipment or process awareness
Machine-to-machine communication standards for smart manufacturing
Human machine interactions for smart manufacturing
Smart manufacturing test beds
Cybersecurity for smart manufacturing systems
Smart inspection systems
Cloud-based applications for smart manufacturing
IoT interoperability for smart manufacturing

Guest editors:

Zheng Xu, Shanghai University, Shanghai, China
Neil Yen, University of Aizu, Japan
Junchi Yan, IBM Research, China

Submission Instructions

Authors should follow the manuscript format and the submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site: http://wacong.org/autosoft/auto/index.php. The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusions. Important Dates
Manuscript Due: October 1, 2018
First Round of Reviews: November 30, 2018
Final Decision: December 30, 2018


Today life is being continuously threatened by various harmful diseases, some of which are even incurable. Recently the rate of diseases is increasing rapidly with the increase in the change of symptoms of each disease. The medical industry requires new Intelligent technologies, to successful diagnoses and surgical outcomes depend on the experience and skill of examiners with it the risk of failure. Thus, the Medical industry requires new Intelligent technologies, such as soft computing techniques, to assess information objectively. Soft computing is based on natural as well as artificial ideas. It is referred as a computational intelligence. In fact the role model for soft computing is a human mind. Soft computing techniques have become one of promising tools that can provide practical and reasonable solution. It combines the design and problem solving skills of engineering with medical to advance health care treatment, including diagnosis, monitoring, treatment and therapy.Computers aid in developing a fully automated system which would help in accurate identification of abnormalities in the medical field. The accuracy of the computer aided systems is highly superior to the manual observations and hence automated systems are significantly preferred by the physicians. Most of the automated systems are based on soft computing techniques which includes Artificial neural networks, fuzzy theory, evolutionary algorithms, Artificial intelligence techniques, etc. The Medical Healthcare Systems are integrated with Soft Computing techniques and expert systems to assist the doctors in every possible ways. Modern intelligent Medical Healthcare systems and techniques give access to vast sources of knowledge base as well as virtual database most of which are self-updating. This special issue small effort to present a review of some of the Soft Computing techniques carried out by various researchers in the field of development of Expert systems, new algorithms and tools used for the diagnosis of different disease. Soft computing techniques came into existence to deal effectively with the emerging problems related to medical diagnosis. The purpose of this special session is to demonstrate the potential of several intelligent approaches exploited in medical planning, diagnosis and treatment. This also brings together researchers and practitioners from academia to industry working in multi-disciplinary area and technically converging areas.

Topics of interest for articles include, but are not limited to:

Medical imaging, signal processing and text analysis
Data mining medical data and Clinical Expert Systems
Modelling and simulation of medical processes
Patient-centric care, medical imaging, medical ontology
Rational drug design and personalized medicine
Biomedical text/data mining and visualization
Computer-aided diagnosis, detection and surgery systems
Medical informatics and Healthcare
Medical image/signal analysis and theory/algorithm/systems
Multidimensional data Visualisation
Soft computing for medical Screening
Therapy, Prognosis and MonitoringBiomedical/Biological Analysis and Epidemiological Studies
Hospital Management,Medical Instruction and Training
Pathological signals (ECG, EEG, EMG)
Medical Images (mammograms, ultrasound, X-ray, CT, and MRI)
Neural networks ,fuzzy logic and Genetic algorithms
Intelligent medical imaging systems and Motion Analysis
Wireless Healthcare and Biological image analysis
Biomedical Data, Biomedical Ontology and Bioinformatics
Artificial Neural Networks for scan images such as brain, eye, lungs, blood, bone, etc.
Medical Image denoising, noise removal, etc.
Medical scan images and texture analysis

Guest editors:

Dr. A. Jayanthiladevi, Jain University, Bangalore, India- drjayanthila@ieee.org
Dr. Jenn-Wei Lin, Fu Jen Catholic University, Taiwan- jwlin@csie.fju.edu.tw
Manivel Kandasamy, OptumHealth|United Health Group, USA- manivel.kandasamy@optum.com

Submission Instructions

Authors should follow the manuscript format and the submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site: http://wacong.org/autosoft/auto/index.php. The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusions.

Tentative submission deadline of the Special Section.
Manuscript due: September 25th, 2018
Acceptance/Rejection notification: November 20th, 2018
Final manuscript due: December 25th , 2018


Topics

Network information intelligent systems
Soft computing techniques
Driven modelling and control
Intelligent and knowledge based systems
Web interaction
Machine learning for big data and information processing
Statistical and deep learning methods
Distributed generation systems
Signal feature, fingerprint recognition
Computing on signal and/or image processing

Important Dates

Submission deadline: April 1, 2017
Notification of the first-round review: June 1, 2017
Revised submission due: October 1, 2017
Final notice of acceptance/reject: November 1, 2017
Camera Ready Due: December 1, 2017

Guest Editors

Prof. Wen-Hsiang Hsieh
Department of Automation Engineering, National Formosa University
Taiwan
E-mail: allen@nfu.edu.tw

Prof. Jerzy W Rozenblit
Department of Electrical and Computer Engineering, University of Arizona
Arizona, USA
E-mail: jr@ece.arizona.edu

Prof. Minvydas Ragulskis
Department of Mathematical Modeling, Kaunas University of Technology
Lithuania
E-mail: minvydas.ragulskis@ktu.lt



Data is often considered the crown jewels of an organization. It can be used in myriad ways to run the business, market to customers, forecast sales, measure performance, gain competitive advantage, and discover new business opportunities. In addition, lately, a convergence of new technologies and market dynamics has opened a new frontier for information management and analysis. This new wave of computing involves data with far greater volume, velocity, and variety than ever before. Big Data is being used in ingenious ways to predict customer-buying habits, detect fraud and waste, analyze product sentiment, and react quickly to events and changes in business conditions. It is also a driving force behind new business opportunities. Traditional data analysis technologies are based on the well-structured data from operational systems that conform to pre-determined relationships. Big Data, however, does not follow this structured model. The streams are all different and it is difficult to establish common relationships. However, with its diversity and abundance come opportunities to learn and to develop new ideas which that can help researcher to learn some new knowledge. The architectural challenge is to bring the two paradigms together. So, rather than approach Big Data as a new technology silo, which enables to leverage all types of data, as situations demand, to promptly satisfy kinds of new needs.

Topics of interest for articles include, but are not limited to:

Big data analysis algorithms
Scalable data storage and computation management for Big Data
Resource scheduling, SLA, Fault tolerance and reliability for Big Data
Multiple source streaming data processing and integration
Virtualization and visualization of Big Data
Novel programming models and platforms such as MapReduce or Spark for Big Data
Security and privacy in Big Data processing
Green, energy-efficient models and sustainability issues for Big Data
Innovative Cloud infrastructure for Big Data
Wireless and mobility support in for Big Data
Scalable software platforms for fast Big Data analytics on heterogeneous and hybrid architectures
Big Data applications on heterogeneous architectures such as healthcare, surveillance and sensing, e-commerce, etc.
Guest Editor Information
Arun Kumar Sangaiah, VIT University, India
Corresponding GE: Walter Miller, University of Alberta, Canada (millerwal920@gmail.com)
Ford Lumban Gaol, Bina Nusantara University, Republik Indonesia
KRISHN K. MISHRA, Department of Mathematics and Computer Science, University of Missouri, USA

Guest editors:

Arun Kumar Sangaiah
Dr. Walter Miller
Dr. Ford Lumban Gaol
Dr. K. K. Mishra

Submission Instructions

This special issue solicits original work not under consideration for publication in any other conference or journal. Authors need to prepare the manuscripts according to the rudiments of Intelligent Automation & Soft Computing journal (Autosoft Journal). Authors should submit their papers through the online manuscript portal system (http://wacong.org/autosoft/auto/index.php) and select the right special issue. For more information, please contact the Corresponding Guest Editor Dr. Walter Miller at millerwal920@gmail.com.

Tentative submission deadline of the Special Section.
Manuscript due: December 31, 2017
First round of reviews: March 31, 2018
Revised paper due: May 15, 2018
Final author notification: June 30, 2018
Expected publication: the third quarter of 2018


The cloud-based Internet of Things (IoT) is used to connect a wide range of things such as vehicles, mobile devices, sensors, industrial equipments and manufacturing machines to develop a various smart systems it includes smart city and smart home, smart grid, smart industry, smart vehicle, smart health and smart environmental monitoring. A recent report from Juniper Research has discovered that “the number of IoT (Internet of Things) connected devices will number 38.5 billion in 2020, up from 13.4 billion in 2015: a rise of over 285%”. Similarly, “The Internet of Things: Consumer, Industrial & Public Services 2015-2020”, found that while the number of connected devices already exceeds the number of humans on the planet by over 2 times, for most enterprises, simply connecting their systems and devices remains the first priority. A recent report state that, “The overall Internet of Things market is projected to be worth more than one billion U.S. dollars annually from 2017 onwards”. As a result, data production at this stage will be 44 times greater than that in 2009, indicating a rapid increase in the volume, velocity and variety of data. Hence, IoT based smart systems generate a large volume of data often called big data that cannot be processed by traditional data processing algorithms and applications. Here will therefore, by difficulty in storing, processing and visualizing this huge data generated from IoT based system. However, there is highly useful information and so many potential values hidden in the huge volume of IoT based sensor data. IoT based sensor data has gained much attention from researchers in healthcare, bioinformatics, information sciences, policy and decision makers in governments and enterprises. Nowadays, Artificial intelligence methods play a significant role in various environments including business monitoring, healthcare applications, production development, research and development, share market prediction, business process, industrial applications, social network analysis, weather analysis and environmental monitoring. The Internet of Things (IoT) and Artificial intelligence will play a vital role in numerous ways in the future. There are multiple forces which are driving the growing need for both technologies and more and more industries, governments, engineers, scientists and technologists have started to implement it in manifold circumstances. The potential opportunities and benefits of both AI and IoT can be practiced when they are combined, both at the devices end as well as at server. For example, AI combined with Machine learning can study from the data to analyze and predict the future actions in advance, such as order replacements in marketing and failure of equipment in an industry just in time. Moreover, AI can be used with machine learning in smart-homes to make a truly grand smart home experience. Similarly, AI methods with IoT can be used to analyze the human behavior via Bluetooth signals, motion sensors, or facial-recognition technology and to make the corresponding changes in lighting and room temperatures. This special issue aims to gather recent research works in emerging artificial intelligence methods, intelligent algorithms, machine learning algorithms and multi-agent systems for cloud-based Internet of Things.

Topics include, but are not limited to, the following:

Automated reasoning and inference for cloud-based Internet of Things
Case-based reasoning in cloud-based Internet of Things
Cognitive aspects of AI in cloud-based Internet of Things
Intelligent interfaces for cloud-based Internet of Things
Knowledge representation in cloud-based Internet of Things
Machine learning for cloud-based Internet of Things
Multiagent systems for cloud-based Internet of Things
Natural language processing for cloud-based Internet of Things
Intelligent algorithms for cloud-based Internet of Things
Agent based algorithms for cloud-based Internet of Things
Swarm Intelligence, Nature Inspired algorithms for cloud-based Internet of Things
Artificial intelligence and Genetic algorithms for cloud-based Internet of Things
Machine learning and deep learning for cloud-based Internet of Things
Fuzzy systems for cloud-based Internet of Things
Neural networks for cloud-based Internet of Things

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our information on submitting articles.

If you have any queries concerning this special issue, please email the Guest Editors.

• Submission deadline: July 30, 2018
• Author notification of first round review: Sep 25, 2018
• Revised submission due: Oct 25, 2018
• Final notification: Nov 15, 2018
• Camera-ready due: Dec 25, 2018

Guest Editors:

Dr.Gunasekaran Manogaran, University of California, Davis, USA, gunavit@gmail.com
Dr. Naveen Chilamkurti, Department of Computer Science and Computer Engineering, LaTrobe University, Melbourne, Australia, n.chilamkurti@latrobe.edu.au
Dr. Ching-Hsien Hsu, Department of Computer Science and Information Engineering, Chung Hua University, Taiwan chh@chu.edu.tw


Based-on the rapid advancement of computers, internet, and ICT infrastructures, the fourth industrial revolution has recently begun. In this industrial era, diverse technological innovations that are focused on connectivity and convergence integrate the physical, biological, and digital boundaries and affects all areas of economy and industry. Accordingly, these technologies connect people and thing with things through internet, analyze vast amount of data produced by such connectivity to obtain a certain pattern, and predict human behaviors based on the results of the analysis to create new values. This Special Issue is about emerging ICT and IoT technology breakthroughs that are essential for moving towards fourth industrial revolution. These ICT and IoT innovations enables connectivity of smart things and seamless convergence of diverse technologies to provide productivity and efficiency improvements, better quality of life, and even solutions for environmental issues.

Topics of interests include, but are not limited to:
Artificial Intelligence and Autonomous Systems in Smart Factory
Big Data Integration, Algorithms, Methodology, Analytics and Challenges in IoT
Cognitive Computing, Affective Computing, Machine Learning for IoT
Edge Computing Technologies for IoT
Hybrid Intelligent Models and Applications for IoT and Industrial Applications
Information Coordination and Interaction in IoT
Intelligent and Interactive Interface for IoT
Intelligent Transportation Systems
Machine Learning and Decision Science Models for Data Analysis for Industrial IoT
Meta-Heuristic Algorithms for IoT
Software Engineering Approaches for IoT

Submission Details

Authors should follow the manuscript format and submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site: http://wacong.org/autosoft/auto/index.php Further, the manuscript must be submitted in the form of WORD file to email (edit.kim09@gmail.com). The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusion.

Schedule

Manuscript due: July 5, 2018
Acceptance/rejection notification: November 5, 2018
2nd round check: January 15, 2019
Final manuscript due: January 31, 2019

Guest Editor(s)
Prof. Soo Kyun Kim(Corresponding GE), Paichai University, Korea, email: edit.kim09@gmail.com
Prof. Mario Köppen, Kyushu Institute Technology, Japan
Prof. Ali Kashif Bashir, University of Faroe Islands, Faroe Islands, Denmark
Prof. Yuho Jin, New Mexico State University, USA

For enquiries, please contact edit.kim09@gmail.com.


JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


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