Journal of Intelligent Computing and Modeling for Data Analytics

Special focus on : Applications in Language, Vision and Control

Volume : Issue 1 of Volume 1 about to be published

Next issue : October 2020

Subject domains : Data analytics, Machine learning, Natural language processing, Computer vision, Modeling and Distributed/Parallel computing

Objectives and Scope
Editorial panel
Paper submission
Call for papers
Objectives and Scope
  • To enable sharing of novel ideas in intelligent computing and applications with a specific focus on language, vision and control related applications.
  • To disseminate the state-of-the-art research methods adopted in Data Analytics.
  • To promote interdisciplinary research across statistics, machine learning, data analytics with intelligent computing as the unifying theme and enabler.
  • To empower research scholars to obtain feedback from expert researchers working in this domain.


With the advent of novel applications like autonomous driving vehicles, recommender systems, chatbots, Question & Answering systems which are empowered by the recent advancements in statistical machine learning & inference, probabilistic graphical modeling, high performance computing and data analytics, there is a justified need to research on the techniques and methods underlying these emerging domains.Further, the techniques involved are interdisciplinary in nature, thus necessitating the creation of a forum to exchange state-of-the-art research methodologies across these domains to empower the focused research groups.


The data processed in the applications cited above are multi-modal and stream-based in nature. Further, multiplescans of the data set are typically required to construct formal non-linear models that impart intelligence to these applications thus mimicking a human expert’s decisions. The computing techniques adopted in building models must be tractable and intelligent to cater to the functional and non-functional requirements of theapplication being realized.


Such computing techniques are a culmination of the state-of-the-art research methods evolved in allied interdisciplinary domains like model learning, parallel processing, real-time data analytics, statistical verification and validation of the assumptions underlying the model. This Journal can be a forum to exchange best practices followed to solve open complex engineering problems faced while building intelligent applications. This forum can enable the formation of focused research cliques to facilitate effective sharing of ideas and subsequently can facilitate collaborative interdisciplinary research to address societal problems.


Typically, such intelligent models are built using iterative techniques that learns the latent parameters of the model based on a training dataset. The quality of the model and its predictive accuracy are dependent on the quality and bias of the training data.The training datasets are generally voluminous and does not fit into the main memory of a single workstation computer. Hence, to execute training algorithms the training data is sharded across distributed computing workstations and a distributed learning strategy is followed. Several challenges and architectural issues arise while distributing the computations to learn a model. Such a distribution must leverage the underlying parallel processing facilities at the hardware level and should facilitate efficient data locality optimization.


With the introduction of new architectural features at the hardware level and new distributed programming frameworks like Map Reduce there is an imminent need to port the sequential intelligent model building algorithms to their distributed counterparts. Hence the discipline of building intelligent systems and intelligent computing techniques is a confluence of multiple disciplines which requires a forum to facilitate the best minds working in niche sub-domains to share and critically evaluate one another’s ideas with an eye to adapt these ideas to further each other’s research work.


A decade back there was a restriction imposed on the perplexity and the degree of non-linearity of a model which can be built. However, with the advent of Graphics Processing Unit (GPU) and Multi-core architectures, this limitation no longer holds and deep-learning models and Long-Short-Term-Memory Models are gaining traction to solve complex problems in Natural Language, Speech and image processing domains.


The above-mentioned sub-domains like model building, distributed intelligent algorithms, Parallelization of sequential intelligent algorithms, applications of intelligent algorithms to problems in Vision, Language and Speech are within the scope of this journal.

Practice papers and position papers:


Review papers on the contemporary models, tools, APIs and AI frameworks to disseminate the state-of-the-art practices adopted in building intelligent systems are welcome. The journal also solicits position papers which are proof-of-concepts or promising ideas that are yet to be implemented. The journal also welcomes research posters which are short contributions in niche sub-domains which typically cannot be considered as full research papers.

Editorial panel

Chief Advisor:
Prof. C. S. P. Rao, Director, NIT Andhra Pradesh


(i) Dr. Karthick Seshadri, Assistant Professor, Department of CSE, NIT Andhra Pradesh

Associate Editor:
(i) Dr. Nagesh Bhattu S, Assistant Professor, Department of CSE, NIT Andhra Pradesh



Editorial Advisory Board:


Internal Members:

(i) Dr. K. Himabindu, Assistant Professor, Department of CSE, NIT Andhra Pradesh
(ii) Dr. A. Goutham Reddy, Assistant Professor, Department of CSE, NIT Andhra Pradesh
(iii) Dr. Srilatha Chebrolu, Assistant Professor, Department of CSE, NIT Andhra Pradesh


Tentative List of External Members:
(i) Prof. Balaraman Ravindran, Professor, Department of CSE, IIT Madras
(ii) Prof. K. Viswanathan Iyer, Professor, Department of CSE, NIT Trichy
(iii) Dr. P. Balamurugan, Assistant Professor, Industrial Engineering and Operations Research (IEOR), IIT Bombay
(iv) Mr. Chid Kollengode, Director Analytics, LinkedIn, Bangalore 



Paper submission


Manuscript Submission and Author Instructions:


Peer Review

Your submission will be subject to a stringent peer review process by a panel of reviewers and domain experts before a decision is rendered.


Publication fee


No publication fee will be charged.


Journal Template

The manuscript has to be prepared in MS Word or Latex. While preparing the paper in MS Word, make sure that all the equations are typeset using the equation editor and are not in the image format.


Instructions to prepare manuscript in MS Word:


Use a normal, plain font (e.g., 10-point Times Roman) for text.
Use italics for emphasis.
Use the automatic page numbering function to number the pages.
Do not use field functions.
Use tab stops or other commands for indents, not the space bar.
Use the table function, not spreadsheets, to make tables.
Use the equation editor or MathType for equations.
Save your file in docx format (Word 2007 or higher) or doc format (older Word versions).


LaTex TemplateClick here to download the file


Copyright Agreement


You agree to the following terms while submitting your manuscript:

(i) The manuscript is an original work done by authors.

(ii) The manuscript was not previously published.

(iii) The manuscript is not under consideration for potential publication in any other journal/conference proceedings.

(iv) The manuscript does not contain anything illegal or contains material that violates copyright of others or any other legal rights.

(v) The corresponding author confirms that all the co-authors have reviewed the manuscript and agree to its contents.

(vi) Addition or deletion of authors once the manuscript has been submitted will not be permitted.

(vii) Copyright of the manuscript once published will be with the authors and the authors can post or disseminate the article along with a citation to the published article posted on the journal’s website.


Article Submission

Your manuscript can be submitted through this submission link. You need to have a valid mail id for your submission to be processed. Any non-descriptive mail ids that resembles a spam source will not be accepted while submitting the manuscript.




Manuscripts must be typeset in English.


Conflict of Interest


Submissions by faculty or research scholars that pose a conflict of interest due to the authors’ affiliation will not be accepted.

Call for papers
  • The editorial panel calls for original high quality research work within the scope of this journal for its first issue.
  • The submission deadline for the first issue is 31st July 2020.

About the Publisher:


This journal will be published by the Department of Computer Science and Engineering at National Institute of Technology, Andhra Pradesh. We are in the process of liaising with reputed publishers to bring this journal under their aegis.


The Issues of the journal will be indexed in the Institute's website and library.


About the Institute:


National Institute of Technology, Andhra Pradesh is the 31st institution among the chain of NITs started by the Government of India. NIT Andhra Pradesh is established in the state of Andhra Pradesh recently in the academic year 2015 – 2016. The institute offers B.Tech., M.Tech., M.S. (by research) and Ph.D. programmes in major Engineering disciplines. The institute is about to launch M.Tech. programmes across thrust Engineering domains.


About the Department of Computer Science and Engineering:


The department offers B. Tech, M. Tech (Computer Science and Data Analytics) and Ph.D. programmes. The department has qualified, dedicated, experienced faculty with deep sense of commitment towards the students and institution. Faculty proficiency in thrust areas of Computer Science motivates students to participate in research activities and skill development programs. The department has a very good placement record. Faculty research interests include, but are not limited to the following areas; Artificial Intelligence, Machine Learning, Pattern Recognition, Big data Analytics, NLP, Deep Learning, Cyber Security, Cryptography.