ISSN (Print): 1872-2121
ISSN (Online): 2212-4047
Volume 15, 6 Issues, 2021
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Special Issue Submission
Intelligent Feature Learning Methods for Mechanical Fault Diagnosis
Guest Editor(s): Changqing Shen
Submit Abstract via Email
Thank you for your letter. Bentham Science Publishers is a high profile publication house. The service quality is good. It’s a pleasure working with you and your team. Hope Bentham Science Publishers will prosper in the years to come.
(College of Electric and Information Engineering, Zhengzhou University of Light Industry, , Zhengzhou, Henan, China)
5 Abstract Ahead of Print are available electronically
42 Articles Ahead of Print are available electronically
Since the past decade, as the miniaturization of Internet of things devices increased, voluminous amounts of data have been
produced. Due to this, there is an undeniable need to adopt big data in IoT applications. Having recognized the fact that in this
smart connected world, “IoT is the senses and Bigdata is the fuel", there is a gripping need to implement these two key technologies
across various Engineering domains in the world. IoT and big data applications in domains such as Social network analysis,
E-governance, NLP, Speech recognition, bioinformatics, Industry, Healthcare, Disaster management, Medical imaging
would have rewarding effects on business, human life style and health. Big data social network analysis has diversified set of
applications and research areas in travel and tourism, defence, security, etc. IoT services in e-governance can produce better
productivity at strategic, tactical and operational levels in the field of agriculture. IoT driven big data analytics automation can
enable better digital decision-making process. IoT is emblematic of so much of big data’s promise and when combined with
machine learning manifestation such as speech recognition, can consistently aid the enterprise in the analytic endeavours associated.
The influence of big data analytics in bio-informatics is vital as the advancement of unparalleled data in bioinformatics
over the years is a major concern for storage and management. Disseminating knowledge from such massive data could be a
key aspect in the field of bioinformatics. Personalised preventive health coaches can be developed by efficiently making use of
IoT and big data analytics. As the application of IoT big data is relatively new in medical imaging and disaster management,
many improvements in the capacity, management and research level will help to gain maximum benefits from this opportunity.
Also, there could be considerable amount of challenges that can arise during the implementation of such applications. Issues
related to data privacy, processing and visualization have to be sorted out using appropriate techniques.
This special issue of Recent Patents on Engineering received many papers. After going through several months of review
process with several levels of blind review, finally 6 papers have been selected for publication.
The first paper by Dr. Sivaram et al.  entitled “Improving Energy Efficiency in the Internet of Things using Artificial Bee
Colony Algorithm” aims to present an Algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption
to discuss their constraints in the IoT scenarios, subsequently, proposing new collaborative approaches for key facilities
to reduce the demands of the current security protocols. This work covers and combines a wide range of concepts linked by
IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem in IoT and
security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of
energy - efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources
has been developed. Because of the low capacity of sensor nodes energy efficiency in WSNs has been an important concern.
Finally, the results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption
and security, which prove the relevance of a proposed ABC approach and a key establishment.
The second paper selected was titled “Bayesian Game Approach to Mitigate DoS Attack in Vehicular ad-hoc Networks”
written by Ilavendhan et al.  which aims to find rapid utilization of the vehicles and the absence of emergency alerts facility
to the vehicle user during natural calamities had led to the number of road accidents over the past few years. VANETs provide
an environment where the vehicles can interact with each other to avoid such accidents. Due to the vulnerabilities present in the
network layer of the VANET, the vehicle user delays the timely traffic data dissemination. Ensuring Security mechanism in
VANET is crucial for robust network construction for data transmission. This paper discusses the methodology for handling the
security issues in VANET common in non-cooperative game theoretic approaches and also the proposed Bayesian Game based
approach to mitigate the DoS attacks. The results are simulated and compared with the existing methods using the metrics chosen.
The proposed Bayesian game provides better Packet drop ratio, Throughput and End to End delay against the DoS Attack.
The third paper that was selected entitled “System Design of MEB in M-IWD Model with Heuristic Function on WSN”
written by Mohamed et al.  who proposed an algorithm M-IWD Algorithm that has been designed in order to achieve the
divergence to find out an optimal MEB tree in WSN. The M-IWD algorithm is incorporated with proposed heuristic function in
order to enhance the characteristics of randomness, individual diversity to minimize the total energy required to broadcast the
data from each sensor node towards sink node in a network. The proposed variant has been evaluated and compared with respect
to contemporary Evolutionary techniques using appropriate performance criteria. In this perspective, a suitable experimental
setup has been designed and experiments are performed on different classes of MEB instances obtained from standard
MEB library [Comopt 2012] in order to validate the proposed M-IWD.
The fourth paper that was selected entitled “Hyperspectral Image Data Classification with Refined Spectral Spatial Features
Based on Stacked Autoencoder Approach” written by the Menezes et al.  discussed a method which aims to condense the
spectral-spatial information through suitable feature extraction and feature selection methods to reduce data dimension to an
appropriate scale. Further, the reduced feature set is processed by SAE for final feature representation and classification. The
proposed method resulted in reduced computation time by ~300s and an improvement in classification accuracy by ~15% as compared to uncondensed spectral-spatial features fed directly to SAE network. The paper concludes stating that future research
could explore the combination of most state-of-the art techniques.
The fifth paper selected was titled “Heart Disease Prediction System using Ensemble of Machine Learning Algorithms”
written by Abirami Raj et al. . The main objective of this paper is to design a robust machine learning algorithm to predict
heart disease. The prediction of heart disease is performed using Ensemble of machine learning algorithms. This is to boost the
accuracy achieved by individual machine learning algorithms. The paper shows how Heart Disease Prediction System is developed
where the user can input the patient details and the prediction for the particular patient is made using the model developed.
The model will predict the output to be either normal or risky. Linear Discriminant Analysis (LDA), Classification and Regression
Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes classifier are used as base
learners. These algorithms are combined using random forest as the meta classifier. The predictions of classifier are combined
using random forest algorithm. The accuracy is lifted from 85.53% to 87.64% which is an impressive improvement on accuracy.
Various techniques were adopted to preprocess the data to suite the requirement of analysis. Feature selections were made
to optimize the performance of machine learning algorithms. Ensemble prediction gave better accuracy when combined using
Random forest algorithm as combiner. Better feature selection techniques can be applied to further improve the accuracy.
The sixth paper selected was “Cluster and Outlier Analysis for Ground Water Quality Data in the Regions of Kadapa District
in Andhra Pradesh” written by Ganga Devi . In this paper, K-means clustering, K-Mediods clustering and Hierarchical
clustering methods are used to group the collected regions of water samples based on the water quality. Later outlier analysis
can be done and various interesting patterns are identified. According to the WQI values calculated, all the collected samples
are suitable for drinking purpose. According to WQI values calculation, for the collected water sample data, it contains 13 Poor
tuples, 13 Good tuples and 31 excellent tuples. According to K-means clustering, 3 clusters are formed with sizes 8, 17, 32.
According to Outlier analysis, the region Pullareddypet (sample No. 7) has highest EC, TH and TDS values among the 57 collected
water samples. The region Veerapalli (Sample No. 37) has highest floride value 3.58 among all 57 samples of collection.
Conclusion: Unsupervised learning methods K-Means Clustering, K-Mediods clustering and Hierarchical clustering methods
are described for the collected water samples physico-chemical parameter data. The cluster analysis results were compared with
WQI values calculated. The three clusters are overlapping with each other with small degree. In the study area, for drinking
purpose, only excellent, good, poor category tuples were found. Later outlier analysis is described using Box plot method and
K-means clustering method. By using outlier analysis using K-means clustering, various interesting hidden patterns from the
data are extracted and it is useful if the data is very large and voluminous.
After a rigorous review, we finalized 6 articles that were suitable and were within the scope of the special issue. Since this is
a special issue, we did not accommodate any papers that were away from the scope of the theme and after further scrutinizing
and several levels of blind review, this has been done. Hope this special issue will gather more citation and improve the impact
factor of the journal. We greet the editor in chief and manager publications for giving us an opportunity to do a special issue
and further we expect to do more special issue with them. Thank You.
Welcome to the special issue on “Cutting edge researches on reliability evaluation and risk management”, in Recent Patents
on Engineering (RPENG). With the advancement of technology, systems in modern life and production process are becoming
more and more complicated. Due to various internal and external causes, systems may fail to function properly, resulting in
economic loss or human casualty. Therefore, it is essential to investigate the state-of-art techniques in reliability modelling and
risk evaluation of systems of different fields, based on which decisions can be made to optimize the systems considering both
risk and cost issues.
RPENG is a peer-reviewed journal featuring both the study of different types of engineering systems, and the interdisciplinary
researches which apply the typical methods in engineering fields. The purpose of the Special Issue on cutting edge researches
on reliability evaluation and risk management is to cover some hot topics or state-of-art techniques in the field of reliability
and risk analysis. This issue contains not only the theoretical models used to evaluate reliability of systems with different
structures, but also the data-driven methods to dynamically predict the system residual useful life. It covers not only the reliability
of some typical hardware systems which are important in model society, but also the risk management in digital systems,
human health, etc.
There are 11 papers in this issue. The first paper reviews the recent advance in the reliability modelling of wireless sensor
systems, which are closely related to the internet of things. The second paper is about accelerated life testing, which is widely
applied in reliability field. In particular, the works on multi-stress accelerated life testing are reviewed. The third paper is about
the reliability modelling of manufacturing system, where both the product quality and the equipment reliability are concerned.
The fourth work is a review of the application of various statistical methods and engineering devices on the risk management of
human beings. The fifth paper reviews the recent works which use multivalued decision diagram to study the reliability of multistate
systems, which have applications on electrical grids, computer networks, etc. The sixth paper reviews recent advances on
the sustainable development of geological exploration technology and risk management.The seventh work reviews the works
related to synthetic oil well cement retarder, discussing the risk and uncertainty. The eighth work reviews the recent advances
in product design techniques. The ninth work reviews the application of stochastic process in remaining life estimation. The
tenth work reviews the advance on applying various engineering methods into investment risk management. The final work is
on the risk control study based on the digital recording technology industry investment projects in "internet +"
Finally, we would like to thank all the reviewers for their excellent work and the authors for their contribution. We expect
that Recent Patents on Engineering provide the best platform for the authors and the readers, with a comprehensive overview of
the most recent developments in the area of engineering.
In recent few years, the enormous growth of IoT objects and cloud-based storage systems has directed towards huge distributed
repositories of data. The heterogeneous IoT data created by devices is stored in different databases. Besides, the open IoT
framework exploits the Incorporation of big data platforms as per the specific application requirements. Notably, in case of extrapolative
methods, the data is continuously gathered through IoT devices and from open data sources. In this perspective, the
convergence of Big Data with IoT needs to address specific technical functionalities. So, the main objective is to collect enormous
data, combined with data analytics and data automation software. Next, the cognitive analysis of data gathered from IoT devices
and sensors. Then, integrating the Big Data and IoT with cloud environment for utilizing various cloud services is offered.
The primary objective of this special issue is to address the various problems identified pertaining to the convergence of Big
Data, IoT, and Cloud computing. The special issue has a wide range of contributions such as (1) Aspect oriented Software Systems
(2) Industrial Internet of Things (3) Magnetic Resonance Image processing though Graphical Processing Unit and (4) Service
level agreement for the cloud computing environment. This special issue will facilitate the research group as a valuable
resource towards advancing the emerging technologies towards real-time applications.
Authors in the paper titled, "A Framework for evaluating extensibility in an aspect- oriented software system and its validation”
address the problem of extensibility in software systems. The authors propose evaluating extensibility and validation for
software systems through the aspect oriented approach. There is growing demand on dynamic and voluminous data through
emerging technologies such as Internet of Things, big data, cloud computing, etc. Hence, this paper's main objective is to present
an aspect-oriented approach that handles code dynamically and extensibility of the software design process. The authors
also proposed a Karl Pearson Product Movement based Correlation method for evaluating and validating the extensibility of
aspect oriented software systems. The proposed framework includes various performance factors that influence software extensibility,
such as design size, complexity, coupling, and cohesion.
Authors in the paper titled, "A Multi-Layered framework for Internet of Everything (IoE) via Wireless Communication and
Distributed Computing in Industry 4.0" targets the role of IoT in Industrial evolution. The convergence of smart emerging technologies
such as IoT, Big data, Cloud computing, Fog computing, and wireless network leads to more sophisticated Industrial
advancements. Here, the authors address the interconnection establishment of various smart components of IIoT. A three-tiered
framework to handle the influencing factors of IIoT such as heterogeneity, voluminous, and variety is proposed. The paper provides
an overview of IIoT components, principles behind IIoT, communication, and internetworking standards.
Authors in a paper titled "GPU Accelerated Bilateral Filter for MR Image Denoising" address the importance of magnetic
resonance imaging as a vital part of computer-aided diagnostic system. Various benefits of a remote healthcare system and enhanced
MRI techniques are explored in this paper. Further, the classification of soft tissues and texture based on the Markov
model is presented in this work. The quality of MR Image is enhanced through CUDA Graphic Processing Unit. The authors
use the accelerated bilateral filter to improve the denoising mechanism of MR images. The experimentation results show that
GPU speed up is 382 times for an image size of 65 megapixel image at the total computation time of 334.01 msec.
Authors in the paper titled "Preserving SLA Parameters for Trusted IaaS Cloud: An Intelligent Monitoring Approach" address
the issues associated with service level agreement between cloud service providers and customers in the cloud computing
environment. The authors address the possible actions, compliance of SLA, penalties for breaching and trust stability. The
SLA's vital components include computing resources, utilization levels, measurement, maintenance, and handling the value
factors detailed by the authors in this work.
The special issue will help students, researchers, professionals, engineers, and developers have a deep insight into the
emerging techniques and their need for convergence to solve the various real-world problems. We hope this special issue will
impact the readers and increase the interest in this field of research.
It gives us immense pleasure to write the editorial for Volume 14, Issue 3 of the “Recent Patents on Engineering
(RPENG)”. The acceptance rate for this issue is 41%. Hence, the target of this issue to provide good quality of research papers
is well met.
In the paper by A. Ahmad et al.,  the massive growth of information has led to the wide usage of recommender systems
for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However,
such methods suffer from two problems; scalability and sparsity. In the proposed research, the two issues of collaborative filtering
are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying
network, the Shapley value concept is used, which divides users into different clusters. After that, the recommendation
algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the
ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead
of computation involved when the algorithm is executed over the entire dataset.
K. Ahmad et al.,  presented the bottleneck review of Mix Networks (MixNets). MixNet was proposed by Chaum in 1996.
MixNet provides the untraceable anonymous communication to the sender and receiver as well as security and privacy of the
messages. It overcomes vulnerabilities and improves the communication and efficiently handles the variable lengths messages
and yields the desirable output. They categorized the MixNets based on cryptography techniques and MixNet classification.
T. Khan et al.,  described multicasting as a robust and efficient one-to-many group transmission technique to reduce
communication cost, bandwidth consumption, processing time, and delays with similar reliability as of regular unicast. This
patent presents a novel and comprehensive congestion control method known as an integrated multicast congestion control approach
(ICMA) to reduce packet loss. The recommended mechanism is based on a leave-join and flows control mechanism
along with a Proportional Integral and Derivative (PID) controller to reduce packet loss, depending on the congestion status.
The PID controller computes the expected incoming rate at each router and feedback this rate to upstream routers of the multicast
network to stabilize their local buffer occupancy. The approach combines three key concepts: first, it uses queue length to
detect congestion; second, the leave-join method is used to reduce congestion when numbers of layers are more than one; third,
if congestion is too high and all the receivers leave enhancement layers and join the base layer then we applied flow control
mechanism. ICMA dynamically adjust sending rate and leverage single rate scheme LIMD in the multi-rate environment depending
upon the feedback received. This approach can be implemented in future systems to provide long term and costefficient
solutions for the delivery of affordable multimedia services to users. The simulation results of NS-2 showed remarkable
performance of the proposed approach in terms of delay, throughput, bandwidth utilization, and packet loss than the other
existing methods. ICMA provides better bandwidth utilization and throughput than other existing approaches.
In paper by M. Ahmed et al., , the idle listening period to acquire the shared channel in the wireless sensor networks is
found to consume much of the battery power. SMAC protocol proposes a static approach to address this issue by assigning
fixed time slots for sleep and awake periods. Every sensor node shall wait for its awake period to transmit the data, thus reducing
the idle listening wastage of energy. This research paper proposed a dynamic approach for the sleep and awake periods. It
proposes to decide the sleep and awake periods by adjusting it according to the data transmission pattern of all the sensor nodes.
In paper by T. Kaur et al., , it is emphasised that for the efficient functioning of Body Area Networks, it is critical to obtain
minimal energy consumption with the required level of Quality of Service as data to be communicated is important. Therefore,
effective and efficient techniques for route selection between data packets are highly required. This paper proposes an
Optimized Energy Efficient and Quality-of-Service aware Routing Protocol (OEEQR) to achieve longer network lifetime, energy
efficiency, lower delay, and high throughput. Routing is done based on the value of the proposed cost function which is
the best combination of residual energy, distance, and path loss as its parameters. The cost function is optimized using Particle
Swarm Optimization (PSO) technique. Node with a minimum value of the cost function is selected as the forwarder node.
Our most sincere thanks go to the Editor in Chief, Editorial Board Members, and reviewers who had reviewed the papers
sincerely and gave the review of paper within time to publish this on the scheduled date. We are also thankful for those authors
whose manuscript could not be accepted due to various issues and would like to suggest them to submit the paper to another
journal or forthcoming issues after modifying the manuscript as per reviewers’ comments.
Welcome to the special issue on “Renewable Energy Research and Applications”, in Recent Patents on Engineering
(RPENG). Diminishing level of conventional energy resources results in the increased focus of researcher in renewable energy
based system. In the past years, a significant amount of research on renewable energy systems has been carried out and is
reported in the form of various patents. Among them, many topics have inspired researchers' interests, such as grid-tied renewable
systems, off-grid/standalone hybrid systems, microgrid, nanogrid, PV based irrigation systems etc.
RPENG is a peer-reviewed journal considers both theoretical and implementation based papers. The demand for electricity
is increasing drastically and impacts of using conventional fuels like pollution, global warming, rising environmental temperature,
shifting of weather cycle etc. are also very prominent. Due to these reasons, renewable energy gained much interest in the
last two decades [1-6]. The purpose of the Special Issue on Renewable Energy Research and Applications is to bring together
researchers, engineers, manufacturers, practitioners and customers from all over the world to share and discuss advances and
developments in renewable energy research and applications. The aims to present significant results in the international renewable
energy community in the form of research, development, applications, design, and technology. It is therefore intended to
assist researchers, scientists, manufacturers, companies, communities, agencies, associations and societies to keep abreast of
new developments in their specialities and to unite in finding alternative energy solutions to current issues such as the greenhouse
effect, sustainable and clean energy issues.
In the first article, authors have suggested Distributed Delay Framework (DDF) mechanism incorporate the delay factor in
the evolution of the membrane potential of a neuron model regarding distributed delay kernel functions. Depending on the parameter
of the investigation, there exist some choices of delay kernel function for a neuron model. Authors have concluded that
the LIFH model is capable of reproducing unimodal, bimodal and multimodal inter-spike- interval distributions which are qualitatively
similar to the experimentally observed ISI distributions.
The second article has performed, a study and analysis of performance parameter of (Bi/Sb)-Te based TE alloy along with
an investigation over a wide range of temperature. A Bi2Te3 based commercial-of-the-shelf (COTS) TEG has been
experimentally tested in controlled temperature environment for the analysis of its efficiency and it has found that maximum
efficiency of 2.12% is achieved at a temperature difference of 60oC. Investigation performed in this article that is useful for the
selection of material for thermal energy harvesting techniques and helps to provide an optimized framework for the research
community to decide the (Bi1-xSbx)2Te3 mixed crystal alloy for their applications.
The third article includes study and analyses of various aggregation techniques applied in Mobile Ad-hoc Network (MANET)
point out the improvements in various QoS parameters resulted due to the application of aggregation on routes, data and
address in the field of MANETs. The Route Aggregation (RA) methodology is a very effective mechanism to improve various
QoS parameters like delay packet delivery ratio etc., that primarily leads to the reduction of the average energy consumed. It
also demonstrates the security enhancements possible in the field of MANET contributed by aggregation.
The next article proposes a computational model of these human memory processes to perform complex tasks in the way
humans do. The proposed model simulates the cognition of the human memory process by incorporating the functionality of
episodic, semantic and procedural memory along with their interaction system. The model uses hippocampus cortical interaction
based biological model and competitive trace theory to mimic the process of episodic as well as semantic memory. A novel
approach has been proposed to encode events in the episodic memory to simulate primacy, recency effect, and Ebbinghaus forgetting
mechanism. The procedural memory has been modelled as per the biological model of basal ganglia to provide functionalities
like reinforcement learning. The proposed model also incorporates the tripartite architecture of the hippocampus,
posterior cortex and frontal cortex with basal ganglia to support human-like decision-making system in novel situations.
The last article explored a Multi-input Dual-output (MIDO) DC-DC converter based stand-alone Solar Photovoltaic (SPV)
system; surrounded with plants for water pumping with the stable flow (not pulsating) along with battery energy storage (BES)
for lighting. The proposed work has two main objectives; first to maximize the available PV power under shadowing and mismatching
condition in case of series/parallel connected PV modules and second is to improve the utilization of available PV
energy with dual loads connected to it. Implementation of the proposed MIDO converter along with BES to address these objectives
has been carried out.
Finally, we would like to thank all the reviewers for their excellent work and the authors for their contribution. We expect
that Recent Patents on Engineering provide the best platform for the authors and the readers, with a comprehensive overview of
the most recent developments in the area of engineering.
Big Data Analysis
It is a matter of pleasure for us to put forth the special issue on “Machine Learning (ML) and Knowledge Mining (KM)” in
Recent Patents on Engineering, Bentham Science. The special issue on Machine Learning (ML) and Knowledge Mining (KM)
invited researchers /Academia to submit the original work in the area of Machine learning and Knowledge Mining. Machine
learning and Knowledge Mining represents the development in the field of Computer Science, Artificial Intelligence, Robotics
& Business Intelligence and played a vital role in information technology and sciences. Basically, Machine learning is the technology
of implementing a system which can act without being explicitly programmed. ML & KM has the ability to adapt to
new circumstances and to detect and extrapolate patterns. One key goal of ML and KM was to be able to generalize a limited
set of data. Machine learning has been used to develop self-driving cars, practical speech recognition system, effective web
search engines, and a greatly improved understanding of the human genome. In general, Machine learning deals with designing
and developing algorithms to evolve behaviors based on empirical data. Machine learning and Knowledge Mining applications
include algorithms to identify spam or to stop credit card fraud, systems that analyze past sales data to predict customer behavior,
extract knowledge from bioinformatics data, images and video, optimize robot behavior so that a task can be completed
using minimum resources, identify listening failures not limited to them but a long list of interesting and extremely useful applications.
Machine learning and Knowledge Mining are so persistent today that we probably use them dozens of times a day
without knowing it.
The main scope of this special issue is to bring together applications of machine learning in the area given below in order to
provide a wide range of techniques that can be effectively applied and also to show how such techniques should be adapted to
each particular domain. This issue presents a compilation of six papers that span a broad variety of research topics in various
emerging areas of Information Technology and Computer Science. Finalizing the constitution of the panel of referees, for
double-blind peer review(s) of the considered manuscripts, was a painstaking process, but it helped us to ensure that the best of
the considered manuscripts are showcased and that too after undergoing multiple cycles of review, as required. The six papers,
that were finally published, were chosen out of twenty-five papers that we received from all over the world for this issue. We
understand that the confirmation of the final acceptance, to the authors / contributors, sometimes is delayed, but we also hope
that you concur with us in the fact that quality review is a time taking process which is further delayed if the reviewers are
senior researchers in their respective fields and hence, are hard pressed for time.
The first paper is “Security of NoSQL Database Against Intruders”. The goal of this paper is to discuss NoSQL features
over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural
data and no complex relationship. In this paper, authors described the security of NoSQL database against intruders which
is growing rapidly. Conclusion: This paper also defines probably the most prominent NoSQL databases and describes their security
aspects and problems .
The second paper is “Energy Efficient Techniques in Wireless Sensor Networks”. The aim is to enhance energy efficiency
that leads to a prolonged lifetime of networks. In this paper, authors have emphasized on energy efficient clustering technique
along with feature wise summary of existing clustering protocols .
The third paper is “A Review on Sentiment Classification: Natural Language Understanding”. The goal is to understand the
basic steps involved in analyzing the text data which can be helpful in determining sentiments behind them. This review provides
a detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment
analysis and classifications have been reviewed to throw some light on the work done related to the field. Results: Sentiment
analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or
determining the behavior of a person. In this paper, authors concluded that this study helps in understanding the basic steps involved
in natural language understanding. At each step, there are multiple techniques that can be applied to data. Different classifiers
provide variable accuracy depending on the data set and classification technique used .
The fourth paper is “QoS Enabled Improved Location Aided Routing (QEILA)”, the objective of this work is to harness the
location aided routing and build a protocol which provides Quality of Service in terms of required battery life and available
bandwidth. Method: In this work, various patented and non-patented location-based routing and Quality of Service methods for
a wireless network have been reviewed. The improved location aided routing protocol has been utilized and equipped with
Quality of Service to check while selecting the next forwarding node for path construction. In this paper, the proposed system
can be utilized for transmitting heavy data traffic in Mobile Ad-hoc net-work with Quality of Service in real time situation .
The fifth paper of this issue is “A Hybrid Filtering Approach for an Improved Context-aware Recommender System”. The
goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the
current movie recommender systems. Method: Post evaluation of various patents related to recommender systems, the proposed
approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the
importance of contextual attribute provided by the user. Results: The performance of the proposed system is measured in terms
of the precision of the system and ranking of the recommended movies to the user. In this paper, authors concluded with the
help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach. The proposed system will be vital for movie ticketing
brands for promotional purposes and various online content providers to recommend accurate movies to their users .
The sixth paper is “Model for Gestational Diabetes on web-based parameters”; the goal of this paper is to depict a
differential equation-based model for gestational diabetes i.e. glucose concentration, insulin concentration, placental volume,
beta-cell mass and haemoglobin alc. Further in this work, the stability of the model is discussed by routh-hurwitz stability criterion.
In this paper, authors have taken different parameters associated with diabetes mellitus into consideration for the mathematical
model which shows the effects on glucose level and thus helpful in under-standing the role of these parameters in
diabetes. In future, by understanding the role of these parameters, a necessary action can be taken as precautions to avoid/treat
gestational diabetes mellitus .
We give sincere thanks to Dr, Togay Ozbakkaloglu, Editor-in-Chief, Recent Patents on Engineering for giving us an
opportunity to convene the special issue in his esteemed publishing Journal and Mr. M. Shahid Nisar, Assistant Manager
Publication, Bentham Science for his kind cooperation in completion of this issue. We thank our esteemed authors for having
shown confidence in the special issue and considering Recent Patents on Engineering as a platform to showcase and share their
original research work. We would also wish to thank the authors whose papers were not published in this issue of the Journal,
probably because of the minor shortcomings.
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