ISSN (Print): 2213-2759
ISSN (Online): 1874-4796
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ISSN (Print): 2213-2759
ISSN (Online): 1874-4796
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Special Issue Submission
43 Articles Ahead of Print are available electronically
The special issue contains research papers elaborating advancements in Swarm Intelligence and Machine learning for optimizing
problems in image processing and data analytics. Swarm Intelligence, is an artificial intelligence technique, involving
the study of collective behavior in decentralized systems [1-3]. Swarm Intelligence refers to the problem-solving behavior that
emerges from the interaction between individuals of such systems, and computational SI refers to algorithmic models of such
behaviours. These algorithmic models have shown to be able to adapt well in changing environments, and are immensely flexible
and robust. The last decade has shown an exaggerated amount of research interests in Swarm Intelligence, by a significant
increase in the number of research publications on various techniques of SI, especially on Ant Colony Optimization (ACO),
Bee Colony Optimization (BCO), Firefly Swarm, Cat Swarm and many more. Lots of research is undergoing in many realworld
applications in the range from scientific research to engineering tasks.
In this special issue, we aim to bring interaction of two important areas- Swarm Intelligence and Machine Learning as fusion
towards solving complex problems in the area of Image Processing and Data Analytics.
The special issue contains 8 papers, selected after a vigorous reviewing process conducted by a team of renowned Swarm
Intelligence experts. These papers deal with applying SI techniques in the area of Image Processing, Healthcare and Pattern
Artificial intelligence is getting significance in our daily life. Everyday, new ideas are proposed by
scientists, researchers, academicians and event students to develop new apps, tools or machines to
make our livelihood earlier. Therefore, artificial intelligence and machine learning remain one of the
sprightliest extents of discussion and attentiveness in current technology developments. Machine learning
and artificial intelligence contribute to an operative elucidation in engineering applications. They
cover pattern recognition, computer vision, artificial neural network, image processing, biometric systems,
fuzzy systems, reasoning, evolutionary algorithms, and quantum computing, amid others. Such
practices are supplementary to social intellect for managing uncertainty and individual vagueness in
the course of making decisions. Another innovative research era in the development of machine learning
and artificial intelligence models is data analytics and optimization which play a significant role in
many research directions. Accordingly, the fast growth of computer science research has elevated the importance of in-depth
junction of machine learning and artificial intelligence computing models. Additionally, applying machine learning and artificial
intelligence coordination for engineering applications is viable and rigorous. Another latest research direction now is deep
learning which is more steps ahead to create a human computer interface for reaching new goals in artificial intelligence. This
is providing enormous opportunities for researchers.
Therefore, this thematic issue has a primary goal to circulate the innovations and applications of machine learning and artificial
intelligence methodologies. The different engineering systems include various sub-branches as pattern recognition, computer
vision, artificial neural network, image processing, biometric systems, swarm intelligence, computational intelligence,
fuzzy systems, reasoning, evolutionary algorithms, and quantum computing. There are many manuscripts submitted by researchers
for this thematic issue. After double blind review of submitted articles, it is not possible to accept all the papers for
publication due many reasons like, out of scope of Thematic Issue, novelty of research work etc. This is “Part I” of Thematic
Issue - “Artificial Intelligence and Machine Learning: Recent Advances and Applications”, which has 5 accepted articles.
This thematic issue starts with an article from Sekhar et al.  in which, they developed a method to predict essential proteins
by using the topological feature, and biological features. In this proposed solution, two methodologies, Mean Weighted
Average and Recursive Feature Elimination are used to predict essential proteins and compared to select the best one. In Mean
Weighted Average, consecutive slot data is to be taken into aggregated count, to get the nearest value which is considered as a
prescription for the best proteins for the slot, whereas in Recursive Feature Elimination method, whole data is spilt into different
slots and the essential protein for each slot is determined. Once the prediction is done, the prescribed proteins have to correlate.
The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentage superior when compared
to Mean Weighted Average and Between-ness Centrality.
The authors Prasanna et al.  proposed an IoT tool to increase the performance of the postures of the Yogis, through yoga
assistant kit with prediction intelligence which will assist in performing suitable yoga postures. This will help the Yogis to
achieve more positive results in the practice of Yoga, with the highest quality of meditation. The developed IoT kit consists of a
hardware module (embedded in wrist band) and a mobile application. The yogi should wear the wrist band while practising
yoga. The wrist band consists of various sensors like temperature sensor, pressure sensor, humidity sensor etc. which sense
body parameters and store them in a central database. Using neural networks and embedded intelligence, our system aims to
predict the number of sun salutations a person (yogi) should perform based on the parameters collected from the kit. The results
showed that our system works as a virtual trainer which suggests the yogi with the appropriate asana to be performed based on
present body conditions.
Chowdhary  highlighted novel techniques on 3D object recognition system with local shape descriptors and depth data
analysis. The proposed work is applied to RGBD and COIL100 datasets and this is of four-fold including pre-processing, feature
generation, dimensionality reduction, and classification. The first stage of pre-processing is smoothing by 2D median filtering
on the depth (Z-value) and registration by orientation correction on 3D object data. The next stage is of feature generation,
having two phases of shape map generation with shape index map and SIFT/SURF descriptors. The dimensionality reduction is
the third stage of this proposed work where linear discriminant analysis and principal component analysis are used. The final
stage is fused on classification. Here, calculation of the discriminative subspace for the training set, testing of object data and
classification are done by comparing target and query data with different aspects for finding proper matching tasks.
Parimala  discussed recent advances in the field of information and social network which led to the problem of community
detection that has received much attention among the researchers. This paper focuses on community discovery, a fundamental
task in network analysis by balancing both attribute and structural similarity. The attribute similarity is evaluated using the
Jaccard coefficient and structural similarity is achieved through modularity. The proposed algorithm, Structural Attribute Graph
Clustering, is based on multiphase approach and has proved to be more scalable and efficient when compared with other stateof-
the-art algorithms. The extensive analysis is performed on real world datasets like Facebook, DBLP, Twitter and Flickr with
different sizes that demonstrate the effectiveness and efficiency of the SAGC algorithm over other algorithms. Additionally, the
clusters are detected based on structural and attribute similarity by achieving high intracluster similarity and low inter cluster
The study by Vincent et al.  aims to develop a predictive model for diagnosing the Major Depressive Disorder among the
IT professionals by reducing the feature dimension using feature selection techniques and evaluate them by implementing three
machine learning classifiers such as Naïve Bayes, Support Vector Machines and Decision Tree. Major Depressive Disorder
(MDD) is in simple terms a psychiatric disorder may be indicated by having mood disturbances which are consistent for more
than a few weeks. It is considered a serious threat to psychophysiology which when left undiagnosed, may even lead to the
death of the victim so it is more important to have an effective predictive model. Major Depressive disorder is often termed as
comorbid (medical condition that co-occurs with another) medical condition, it is hardly possible for the physicians to predict
that the victim is under depression, timely diagnosis of MDD may help in avoiding other comorbidities. Machine learning is a
branch of artificial intelligence which makes the system capable of learning from the past and with that experience it improves
the future results even without programming explicitly. As in recent days because of high dimensionality of features, the accuracy
of the predictions is comparatively low. In order to get rid of redundant and unrelated features from the data and improve
the accuracy, relevant features must be selected using effective feature selection methods.
The new era of engineering is revolving around advance technology and there development. At the same time making these
technologies smart in terms of their sustainable development is a challenge. Due to this from last decade the development of
Smart Technologies in Engineering for communication is gaining a lot of interest in the research community. The main objective
of Smart Technologies in Engineering comprise a dynamic new interdisciplinary research field that encompasses a wide
spectrum of engineering applications including, but not limited to, intelligent structures and materials, actuators, sensors, control
systems and software tools for the design of adaptive structures.
The lead article in this special issue is titled as “An era of Micro Irrigation - A priority driven approach to enhance the optimum
utilization of Water and a way towards “intelligent farming”, by Santosh R. Durugkar et al. develop a priority-driven algorithm
based on the soil moisture values executes itself and find out the lowest one to start the irrigation to increase the moisture
at plant area. This system has additional advantages such as testing the quality of water utilization in terms of TDS, Conductivity
and pH of the water.
The second article entitled “Fast and Efficient Recovery of Root Node Failure in Spanning Tree Routing Protocol” by Vijay
Nunia et al. In this paper, the spanning tree routing protocol is introduced into the network, a storm is developed in the network,
thereby, blocking all the network traffic. The Spanning Tree Protocol (STP) was developed to prevent this broadcasting storm.
It builds a topology, or a map, in order to identify any loop occurring in the network. The paper finds the definitive solution for
the root failure problem in an algorithmic form.
GA-QMR: Genetic Algorithm oriented MANET QoS Multicast Routing by Sukhvinder Singh et al. the paper presents genetic
oriented QoS Multicast Routing (GA-QMR) algorithm. The routes are required to satisfy the end to end delay, jitter,
packet loss rate, packet success rate, bandwidth, etc. In the similar context, simulation experiments are performed for 8 mobile
nodes and compared with two well known algorithms to predict the proposed algorithm performance.
The last article is titled as “A Two Way Synchronized Quantum Channel Quantum Key Distribution Protocol” by Manish
Kalra, In this paper presents QKD protocol, which is a variation over two basic protocols i.e. BB84 and B92 and uses two-way
quantum channel instead of the one-way quantum channel and it uses the classical channel for reconciliation phase. This protocol
is synchronized in a manner that both parties who want to share key start communication at same point of time and also
given the design for proposed protocols and their comparison with basic protocols.
Big data, as a new ubiquitous term, is transforming science, engineering, medicine, healthcare, finance, business, and ultimately
society itself. Big Data has become an emerging paradigm for the practitioners and researchers to explore the value of
datasets whose size is beyond the ability of commonly used software tools. As such, big data management is spurring on tremendous
amounts of research and development of related technologies and applications.
This special issue is intended to foster the dissemination of recent advances in cloud computing and big data. It is including
the theory, architecture and utility, particularly related to the use of cloud computing technologies to deal with big data. We
invite the submissions of high quality papers describing future potentials or on-going work.
This special issue aims to foster the dissemination of state-of-the-art research in the area of recent advances in big data
management. This includes design, architecture and utility, particularly related to the use of big data processing technologies
and the presentation of future trends.
The issue comprises revised and substantially extended versions of selected papers presented at the 2016 Second International
Conference of Cloud Computing Technologies and Applications (CLOUDTECH’2016), but we have also strongly received
some papers out of this conference.
Editors received 15 submissions that were peer-reviewed by top experts in the field. Based on the reviews and our reading
of the papers, editors selected 4 high-quality ones to be published. The acceptance rate is 26.7%. Contributions of these papers
are summarized as follows.
In the first paper , the authors present a caching system using column access frequency strategy. It orginates from Patent
Publication Number CN103631972A. The caching process includes judging cache hit or miss, updating column access frequency,
and change data capture. The effect of this system increases the cache hit rate.
In the second paper , the authors present the analysis of parallel SVM based classification technique on healthcare. In the
current scenario, Cardio Vascular Disease is the major cause for human mortality across the world. This analysis is the hardcore
need in today’s medical research for prediction of Cardio Vascular Disease.
In the third paper , the authors present a global analysis of Cloud computing model that applies the Taguchi concept to
highlight the parameters which have the greatest impact on the system performance. The applied and the proposed ant colony
algorithm can improve the performance of the Cloud environment.
In the last paper , the authors propose a scheme, based on cipher-text policy attribute based encryption (CP-ABE). This
method is able to achieve the desired level of security while having a reduced computation overhead. The efficient revocation process
can achieve both forward and backward security while maintaining a low overhead on both the data owner and the users.
It is expected that this collection will contribute to advance knowledge in the evolving field of big data management.
The guest editors of this special issue wish to thank the referees for their valuable input, as well as the authors, who at all
times pursued the best quality. The authors hope that the readers will find this special issue useful in their research. Special
thanks to Professor Hamid Mcheick (Editor-in-chief of the Recent Patents on Computer Science) for his guidance.
The first editor Prof. Kun Ma acknowledges the support of the National Natural Science Foundation of China (61772231),
the Shandong Provincial Natural Science Foundation (ZR2017MF025), the Shandong Provincial Key R&D Program of China
(2015GGX106007 & 2018CXGC0706), the Project of Shandong Province Higher Educational Science and Technology Program
(J16LN13), the Science and Technology Program of University of Jinan (XKY1734 & XKY1828), and the Project of
Shandong Provincial Social Science Program (18CHLJ39).
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