Signal & Image Processing : An International Journal (SIPIJ) - WJCI Indexed (original) (raw)

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11th International Conference on Signal, Image Processing and Multimedia (SPM 2024) is a forum fo... more 11th International Conference on Signal, Image Processing and Multimedia (SPM 2024) is a
forum for presenting new advances and research results in the fields of Signal, Image Processing and
Multimedia. The conference will bring together leading researchers, engineers and scientists in the
domain of interest from around the world. Authors are solicited to contribute to the conference by
submitting articles that illustrate research results, projects, surveying works and industrial
experiences.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in the
following areas, but are not limited to.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal inte... more Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

SIPIJ, 2023

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

SIPIJ

Papers by Signal & Image Processing : An International Journal (SIPIJ) - WJCI Indexed

Color Filter Array (CFA) has been widely used in digital cameras. There are many variants of CFAs... more Color Filter Array (CFA) has been widely used in digital cameras. There are many variants of CFAs in the
literature. Recently, a new CFA known as CFA 3.0 was proposed by us and has been shown to yield
reasonable performance as compared to some standard ones. In this paper, we investigate the use of
inpainting algorithms to further improve the demosaicing performance of CFA 3.0. Six conventional and
deep learning based inpainting algorithms were compared. Extensive experiments demonstrated that one
algorithm improved over other approaches.

Simple features extracted from the thermal infrared images of the persons' face are proposed for ... more Simple features extracted from the thermal infrared images of the persons' face are proposed for gender
discrimination. Two different types of thermal features are used. The first type is actually based on the
mean value of the pixels of specific locations on the face. All cases of persons from the used database,
males and females, are correctly distinguished based on this feature. Classification results are verified
using two conventional approaches, namely: a. the simplest possible neural network so that generalization
is achieved along with successful discrimination between all persons and b. the leave-one-out approach to
demonstrate the classification performance on unknown persons using the simplest classifiers possible. The
second type takes advantage of the temperature distribution on the ear of the persons. It is found that for
the men the cooler region on the ear is larger as percentage compared to that of the women.

Signal&ImageProcessing:An International Journal (SIPIJ) ***WJCIIndexed***

June, 2024

Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of compute... more Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision,
enabling computers or other computing devices to identify and categorize objects within images. Among
numerous fields of life, food processing is an important area, in which image processing plays a vital role,
both for producers and consumers. This study focuses on the binary classification of strawberries, where
images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we
aim to determine the effectiveness of different models in identifying whether an image contains
strawberries. This research has practical applications in fields such as agriculture and quality control. We
compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks
(CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by
MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis,
our results demonstrate that CNN outperforms the other models in this task. In the future, the deep
learning models can be evaluated on a richer and larger number of images (datasets) for better/improved
results.

Histopathological images are widely used to diagnose diseases including skin cancer. As digital h... more Histopathological images are widely used to diagnose diseases including skin cancer. As digital
histopathological images are typically of very large size, in the order of several billion pixels, automated
identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning
algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the
abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a
Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The
segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine
classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy.
The proposed technique has a low computational complexity.

In computer vision, image segmentation is defined as process of a partition of an image in a numb... more In computer vision, image segmentation is defined as process of a partition of an image in a number of
regions with homogeneous features. The region of our interest here is the liver. Prior to the deep learning
revolution traditional handcrafted features were used for liver segmentation but with deep learning the
features are obtained automatically. There are many semiautomatic and fully automatic approaches have
been proposed to improve the liver segmentation procedure some of them use deep learning techniques for
Segmentation and other one use a Classical Based method for Segmentation. In this paper we aim to
enhance our previous work which we were proposed a Batch Normalization After All - Convolutional
Neural Network (BATA-Convnet) model to segment the liver, where the Dice is equal to 0.91% when
implement our BATA Convnet using MICCA dataset and Dice is equal to 0.84% when implement it using
3D-IRCAD dataset. Here in this paper we propose BATA-Unet model for liver segmentation, it's based on
Unet architecture as backbone but differ in we added a batch-normalization layer an after each
convolution layer in both construction path and expanding path. The proposed method was able to achieve
highest dice similarity coefficient than the previous work where for MICCA dataset Dice =0.97% and for
3D-IRCAD dataset =0.96%. Also our proposed model outperformed other state-of-the-art model when we
compare it with them.

This paper proposes a deep learning method for facial verification of aging subjects. Facial agin... more This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a
texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand
to develop robust methods to verify facial images when they age. In this paper, a deep learning method
based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG)
and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and
classification. The experiments are based on the facial images collected from MORPH and FG-Net
benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature
vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted
on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is
99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art
methods.

In modern digital cameras, the Bayer color filter array (CFA) has been widely used. It is also wi... more In modern digital cameras, the Bayer color filter array (CFA) has been widely used. It is also widely known
as CFA 1.0. However, Bayer pattern is inferior to the red-green-blue-white (RGBW) pattern, which is also
known as CFA 2.0, in low lighting conditions in which Poisson noise is present. It is well known that
demosaicing algorithms cannot effectively deal with Poisson noise and additional denoising is needed in
order to improve the image quality. In this paper, we propose to evaluate various conventional and deep
learning based denoising algorithms for CFA 2.0 in low lighting conditions. We will also investigate the
impact of the location of denoising, which refers to whether the denoising is done before or after a critical
step of demosaicing. Extensive experiments show that some denoising algorithms can indeed improve the
image quality in low lighting conditions. We also noticed that the location of denoising plays an important
role in the overall demosaicing performance.

With the huge innovative improvement in all lifestyles, it has been important to build up the cli... more With the huge innovative improvement in all lifestyles, it has been important to build up the clinical fields,
remembering the finding for which treatment is done; where the fruitful treatment relies upon the
preoperative. Models for the preoperative, for example, planning to understand the complex internal
structure of the liver and precisely localize the liver surface and its tumors; there are various algorithms
proposed to do the automatic liver segmentation. In this paper, we propose a Batch Normalization After All
- Convolutional Neural Network (BATA-Convnet) model to segment the liver CT images using Deep
Learning Technique. The proposed liver segmentation model consists of four main steps: pre-processing,
training the BATA-Convnet, liver segmentation, and the postprocessing step to maximize the result
efficiency. Medical Image Computing and Computer Assisted Intervention (MICCAI) dataset and 3DImage Reconstruction for Comparison of Algorithm Database (3D-IRCAD) were used in the
experimentation and the average results using MICCAI are 0.91% for Dice, 13.44% for VOE, 0.23% for
RVD, 0.29mm for ASD, 1.35mm for RMSSD and 0.36mm for MaxASD. The average results using 3DIRCAD dataset are 0.84% for Dice, 13.24% for VOE, 0.16% for RVD, 0.32mm for ASD, 1.17mm for
RMSSD and 0.33mm for MaxASD.

Graph convolutional networks (GCNs) have been proven to be effective for processing structured da... more Graph convolutional networks (GCNs) have been proven to be effective for processing structured data, so
that it can effectively capture the features of related nodes and improve the performance of model. More
attention is paid to employing GCN in Skeleton-Based action recognition. But there are some challenges
with the existing methods based on GCNs. First, the consistency of temporal and spatial features is ignored
due to extracting features node by node and frame by frame. We design a generic representation of
skeleton sequences for action recognition and propose a novel model called Temporal Graph Networks
(TGN), which can obtain spatiotemporal features simultaneously. Secondly, the adjacency matrix of graph
describing the relation of joints are mostly depended on the physical connection between joints. We
propose a multi-scale graph strategy to appropriately describe the relations between joints in skeleton
graph, which adopts a full-scale graph, part-scale graph and core-scale graph to capture the local features
of each joint and the contour features of important joints. Extensive experiments are conducted on two
large datasets including NTU RGB+D and Kinetics Skeleton. And the experiments results show that TGN
with our graph strategy outperforms other state-of-the-art methods.

Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it... more Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is
appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access
control, and electronic customer relationship management. Current deep learning-based methods have
displayed encouraging performance in age estimation field. Males and Females have a variable type of
appearance aging pattern; this results in age differently. This fact leads to assuming that using gender
information may improve the age estimator performance. We have proposed a novel model based on
Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then
Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task.
Bayesian Optimization reduces the classification error on the validation set for the pre-trained model.
Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The
experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian
Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute
Error (MAE) of 1.2 and 2.67 respectively.

The goal of multi-focus image fusion is to integrate images with different focus objects in order... more The goal of multi-focus image fusion is to integrate images with different focus objects in order to obtain a
single image with all focus objects. In this paper, we give a new method based on neighbour local
variability (NLV) to fuse multi-focus images. At each pixel, the method uses the local variability calculated
from the quadratic difference between the value of the pixel and the value of all pixels in its
neighbourhood. It expresses the behaviour of the pixel with respect to its neighbours. The variability
preserves the edge function because it detects the sharp intensity of the image. The proposed fusion of each
pixel consists of weighting each pixel by the exponential of its local variability. The quality of this fusion
depends on the size of the neighbourhood region considered. The size depends on the variance and the size
of the blur filter. We start by modelling the value of the neighbourhood region size as a function of the
variance and the size of the blur filter. We compare our method to other methods given in the literature.
We show that our method gives a better result.

The main aim of this study is the assessment and discussion of a model for hand-written Arabic th... more The main aim of this study is the assessment and discussion of a model for hand-written Arabic through
segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and
evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in
written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting
images to binary type. In the segmentation step, first removed the small diacritics then bounded a
connected component to segment offline words. Huge data was utilized in the proposed model for applying
a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on
the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then
segmented into sub-words. After small gaps been connected, the model performance evaluation had been
reached 88% against the standard ground truth of the database. The proposed model achieved the highest
accuracy when compared with the related works.

SIPIJ, 2023

SIPIJ

Signal&ImageProcessing:An International Journal (SIPIJ) ***WJCIIndexed***

June, 2024

Image processing technologies may be employed for quicker and accurate diagnosis in analysis and ... more Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.

Long range infrared videos such as the Defense Systems Information Analysis Center (DSIAC) videos... more Long range infrared videos such as the Defense Systems Information Analysis Center (DSIAC) videos usually
do not have high resolution. In recent years, there are significant advancement in video super-resolution
algorithms. Here, we summarize our study on the use of super-resolution videos for target detection and
classification. We observed that super-resolution videos can significantly improve the detection and
classification performance. For example, for 3000 m range videos, we were able to improve the average
precision of target detection from 11% (without super-resolution) to 44% (with 4x super-resolution) and the
overall accuracy of target classification from 10% (without super-resolution) to 44% (with 2x superresolution).

Digital image tampering detection has been an active area of research in recent times due to the ... more Digital image tampering detection has been an active area of research in recent times due to the ease with
which digital image can be modified to convey false or misleading information. To address this problem,
several studies have proposed forensics algorithms for digital image tampering detection. While these
approaches have shown remarkable improvement, most of them only focused on detecting a specific type of
image tampering. The limitation of these approaches is that new forensic method must be designed for
each new manipulation approach that is developed. Consequently, there is a need to develop methods
capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose
image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of
detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep
learning techniques which used constrained pre-processing layers to suppress the effect of image content
in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue
the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish
among different types of image tampering. Through a number of detailed experiments, our results
demonstrate that the proposed general purpose image tampering method can achieve high detection
accuracies in individual and multiclass image tampering detections respectively and a comparative
analysis of our results with the existing state of the arts reveals that the proposed model is more robust
than most of the exiting methods.

5 th International Conference on VLSI & Embedded Systems (VLSIE 2024) will provide an excellent i... more 5
th International Conference on VLSI & Embedded Systems (VLSIE 2024) will provide an
excellent international forum for sharing knowledge and results in theory, methodology and
applications of VLSI and Embedded Systems. Original, unpublished papers, describing research
in the general area of VLSI and Embedded Systems are solicited. Both theoretical and
experimental research results are welcome in the following areas, but are not limited to.

13 th International Conference on Advanced Computer Science and Information Technology (ICAIT 202... more 13
th International Conference on Advanced Computer Science and Information Technology
(ICAIT 2024) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Advanced Information Technology. The Conference
looks for significant contributions to all major fields of the Computer Science and Information
Technology in theoretical and practical aspects. The aim of the conference is to provide a
platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field

2nd International Conference on Embedded Systems and VLSI (EMVL 2024) will provide an excellent I... more 2nd International Conference on Embedded Systems and VLSI (EMVL 2024) will
provide an excellent International forum for sharing knowledge and results in theory,
methodology and applications of Embedded Systems. The goal of this Conference is to bring
together researchers and practitioners from academia and industry to focus on understanding
Modern Embedded Systems concepts and establishing new collaborations in these areas.

2 nd International Conference on NLP & Signal Processing (NLPSIG 2024) will provide an excellent ... more 2
nd International Conference on NLP & Signal Processing (NLPSIG 2024) will provide an
excellent international forum for sharing knowledge and results in theory, methodology and
applications of Signal and Natural Language Processing (NLP).

13th International Conference on Digital Image Processing and Vision (ICDIPV 2024) is a forum for... more 13th International Conference on Digital Image Processing and Vision (ICDIPV 2024) is
a forum for presenting new advances and research results in the fields of Digital Image
Processing and vision. The conference will bring together leading researchers, engineers and
scientists in the domain of interest from around the world.

10th International Conference on VLSI and Applications (VLSIA 2024) will provide an excellent int... more 10th International Conference on VLSI and Applications (VLSIA 2024) will provide an
excellent international forum for sharing knowledge and results in theory, methodology and
applications of VLSI. Original, unpublished papers, describing research in the general area
of VLSI are solicited. Both theoretical and experimental research results are welcome in the
following areas, but are not limited to.

8th International Conference on Electrical and Electronics Engineering (EEEN 2024) aims to bring ... more 8th International Conference on Electrical and Electronics Engineering (EEEN 2024) aims to bring together researchers and practitioners from academia and industry to focus on recent systems and techniques in the broad field of Electrical, Electronics, Instrumentation and Communication Engineering. Original research papers, state-of-the-art reviews are invited for publication in all areas of Electrical, Electronics and Instrumentation Engineering.

8 th International Conference on Electrical & Computer Engineering (E& C 2024) will provide an e... more 8
th International Conference on Electrical & Computer Engineering (E& C 2024) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications impacts and challenges of Electrical & Computer Engineering.
The conference documents practical and theoretical results which make a fundamental
contribution for the development of Electrical & Computer Engineering. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to:

10th International Conference on Signal and Image Processing (SIGI 2024) is a forum for presentin... more 10th International Conference on Signal and Image Processing (SIGI 2024) is a forum for
presenting new advances and research results in the fields of Digital Image Processing. The
conference will bring together leading researchers, engineers and scientists in the domain of
interest from around the world. The scope of the conference covers all theoretical and practical
aspects of the Signal, Image Processing & Pattern Recognition. Authors are solicited to contribute
to the conference by submitting articles that illustrate research results, projects, surveying works
and industrial experiences.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to.

11th International Conference on Signal Processing (CSIP 2024) will provide an excellent internat... more 11th International Conference on Signal Processing (CSIP 2024) will provide an
excellent international forum for sharing knowledge and results in theory, methodology
and applications of Signal and Image Processing. The Conference looks for significant
contributions to all major fields of the Signal and Image Processing in theoretical and
practical aspects. The aim of the conference is to provide a platform to the researchers
and practitioners from both academia as well as industry to meet and share cutting-edge
development in the field.
Authors are solicited to contribute to this conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to.

International Conference on Vision and Computational Intelligence (VCOI 2023) will provide an ex... more International Conference on Vision and Computational Intelligence (VCOI 2023) will
provide an excellent international forum for sharing knowledge and results in theory, methodology
and applications of Vision Computing and Computational Intelligence. The goal of this conference
is to bring together researchers and practitioners from academia and industry to focus on
understanding advances in vision computing & Computational Intelligence and establishing new
collaborations in these areas. Authors are solicited to contribute to the conference by submitting
articles that illustrate research results, projects, surveying works and industrial experiences that
describe significant advances in the areas of vision computing & Computational Intelligence.

14th International Conference on VLSI (VLSI 2023) will provide an excellent international forum f... more 14th International Conference on VLSI (VLSI 2023) will provide an excellent international
forum for sharing knowledge and results in theory, methodology and applications of VLSI.
Original, unpublished papers, describing research in the general area of VLSI are solicited.
Both theoretical and experimental research results are welcome in the following areas, but are
not limited to.

International Conference on Embedded Systems and VLSI (EMVL 2023) will provide an excellent Inter... more International Conference on Embedded Systems and VLSI (EMVL 2023) will provide an excellent International forum for sharing knowledge and results in theory, methodology and applications of Embedded Systems. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern Embedded Systems concepts and establishing new collaborations in these areas.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science, Engineering and Applications.

9thInternational Conference on VLSI and Applications (VLSIA 2023) will provide an excellent inter... more 9thInternational Conference on VLSI and Applications (VLSIA 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of VLSI. Original, unpublished papers, describing research in the general area of VLSI are solicited. Both theoretical and experimental research results are welcome in the following areas, but are not limited to.

10th International Conference on Signal Processing (CSIP 2023) will provide an excellent internat... more 10th International Conference on Signal Processing (CSIP 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Signal and Image Processing. The Conference looks for significant contributions to all major fields of the Signal and Image Processing in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

11th International Conference on Signal, Image Processing and Pattern Recognition (SIPP 2023) is ... more 11th International Conference on Signal, Image Processing and Pattern Recognition (SIPP 2023) is a forum for presenting new advances and research results in the fields of Signal and Image Processing. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.

9th International Conference on Signal Processing and Pattern Recognition (SIPR 2023) is a forum ... more 9th International Conference on Signal Processing and Pattern Recognition (SIPR 2023) is a forum for presenting new advances and research results in the fields of Digital Processing and Pattern Recognition. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world.

11th International Conference on Soft Computing (SCOM 2023) provides a forum for researchers who ... more 11th International Conference on Soft Computing (SCOM 2023) provides a forum for researchers who
address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute
to the conference by submitting articles that illustrate research results, projects, surveying works and
industrial experiences that describe significant advances in the following areas, but are not limited to
these topics only.
We expect that the conference and its publications will be a trigger for further related research and
technology improvements in this important subject. The topics suggested by this conference can be
discussed in term of concepts, surveys, state of the art, research, standards, implementations, running
experiments, applications and industrial case studies. Authors are invited to submit complete unpublished
papers, which are not under review in any other conference or journal in the following, but not limited to..

SIPIJ, 2023

International Conference on Vision and Computational Intelligence (VCOI 2023) will provid... more International Conference on Vision and Computational Intelligence (VCOI 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Vision Computing and Computational Intelligence. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding advances in vision computing & Computational Intelligence and establishing new collaborations in these areas. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of vision computing & Computational Intelligence.

14th International Conference on Internet Engineering & Web Services (InWeS 2023) will provide an... more 14th International Conference on Internet Engineering & Web Services (InWeS 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Internet Engineering & Web services environment. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Internet Engineering & Web services present a rather arduous requirement of robustness, reliability and availability to the end user. Internet Engineering & Web services has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.