Aboul Ella Hassanien (Abo)

The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

Abstract—This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water
quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed,
along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of
water pollutants.

This paper presents a bio-inspired optimized classification model for
assessing water quality. As fish gills histopathology is a good biomarker for indicating
water pollution, the proposed classification model uses fish gills microscopic
images in order to asses water pollution and determine water quality.
The proposed model comprises five phases; namely, case representation for
defining case attributes via pre-processing and feature extraction steps, retrieve,
reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms
have been utilized for feature extraction stage. Case-based reasoning
(CBR) has been employed, along with the bio-inspired Gray Wolf Optimization
(GWO) algorithm, for optimizing feature selection and the k case retrieval parameters
in order to asses water pollution. The datasets used for conducted experiments
in this research contain real sample microscopic images for fish gills
exposed to copper and water pH in different histopathlogical stages. Experimental
results showed that the average accuracy achieved by the proposed GWO-CBR
classification model exceeded 97.2% considering variety of water pollutants.

This paper proposes a recommender system to predict and suggest a
set of rehabilitation methods for patients with spinal cord injuries (SCI). The proposed
system automates, stores and monitors the heath conditions of SCI patients.
The International Classification of Functioning, Disability and Health classification
(ICF) is used to stores and monitors the progress in health status. A set of
similarity measures are utilized in order to get the similarity between patients and
predict the rehabilitation recommendations. Experimental results showed that the
proposed recommender system has obtained an accuracy of 98% via implementing
the cosine similarity measure.

Enzymes are important in our life due to its importance in the most biological processes. Thus, classification of the enzyme’s function is vital to save efforts and time in the labs. In this paper, we propose an approach based on sequence alignment to compute the similarity between any two sequences. In the proposed approach, two different sequence alignment methods are used, namely, local and global sequence alignment. There are different score matrices such as BLOSUM and PAM are used in the local and global alignment to calculate the similarity between the unknown sequence and each sequence of the training sequences. The results which obtained were acceptable to some extent compared to previous studies that have surveyed.

Multidimensionality in social networks is a great issue that
came out into view as a result of that most social media sites such as
Facebook, Twitter, and YouTube allow people to interact with each other
through dierent social activities. The community detection in such mul-
tidimensional social networks has attracted a lot of attention in the recent
years. When dealing with these networks the concept of community de-
tection changes to be, the discovery of the shared group structure across
all network dimensions such that members in the same group interact
with each other more frequently than those outside the group. Most of
the studies presented on the topic of community detection assume that
there is only one kind of relation in the network. In this paper, we propose
a multi-objective approach, named MOGA-MDNet, to discover commu-
nities in multidimensional networks, by applying genetic algorithms. The
method aims to nd community structure that simultaneously maximizes
modularity, as an objective function, in all network dimensions. This
method does not need any prior knowledge about number of communi-
ties. Experiments on synthetic and real life networks show the capability
of the proposed algorithm to successfully detect the structure hidden
within these networks.

This paper intends to enhance the image for the next usage
of region growing technique for segmenting the region of liver away from
other organs. The approach depends on a preprocessing phase to enhance
the appearance of the boundaries of the liver. This is performed using
contrast stretching and some morphological operations to prepare the
image for next segmentation phase. The approach starts with combining
Otsu's global thresholding with dilation and erosion to remove image
annotation and machine's bed. The second step of image preparation
is to connect ribs, and apply lters to enhance image and deepen liver
boundaries. The combined lters are contrast stretching and texture l-
ters. The last step is to use a simple region growing technique, which has
low computational cost, but ignored for its low accuracy. The proposed
approach is appropriate for many images, where liver could not be sep-
arated before, because of the similarity of the intensity with other close
organs. A set of 44 images taken in pre-contrast phase, were used to test
the approach. Validating the approach has been done using similarity
index. The experimental results, show that the overall accuracy oered
by the proposed approach results in 91.3% accuracy.

Water pollution by organic materials or metals is one of the
problems that threaten humanity, both nowadays and over the next decades.
Morphological changes in Nile Tilapia "Oreochromis niloticus'' fish liver
and gills can also represent the adaptation strategies to maintain some
physiological functions or to assess acute and chronic exposure to
chemicals found in water and sediments. This paper presents an automatic
system for assessing water quality; in Sharkia Governorate - Egypt, based
on microscopic images of fish gills and liver. The proposed system used
fish gills and liver as hybrid-biomarker in order to detect water
pollution. It utilized case-based reasoning (CBR) for indicating the
degree of water quality based on the different histopathological changes
in fish gills and liver microscopic images. Various performance
evaluation metrics; namely, retrieval accuracy, Receiver Operating
Characteristic (ROC) curves, F-measure, and G-mean, have been used in
order to objectively indicate the true performance of the system
considering the unbalanced data. Experimental results showed that the
proposed hybrid-biomarker CBR based system achieved water quality
prediction accuracy of 97.9% using cosine distance similarity measure.
Also, it outperformed both SVMs and LDA classifiers for the tested
microscopic images dataset.

Geotagged photos carry hidden data about the surrounding area, and the owner of the photo. Moreover; Geotagged photos have background information about the user, where the alternative resources of Geo-spatial data lack background information. In this study, we propose identification for the circles of relations of the smartphone user from Geotagged photos. The proposed solution mainly depends on a framework, which is based on smartphone photo gallery. The framework extracts a degree of relation between smartphone user and circles of relations entities. Circles of relations incorporate closest people, places, where the participant visits, and interests. The circles of relations are represented in a social graph, which shows the clusters of social relations and interests of smartphone user. The social graph clarifies the nature and the degree of the relations for the participants. The results of framework introduced the relation between the level of variety of participant social relations, and the degree of relations.

In this paper, a novel binarization approach based on neutrosophic sets and sauvola’s approach is presented.
This approach is used for historical Arabic manuscript images which have problems with types of noise. The input RGB image is changed into the NS domain, which is shown using three subsets, namely, the percentage of indeterminacy in a subset, the percentage of falsity in a subset and the percentage of truth in a subset. The entropy in NS is used for evaluating the indeterminacy with the most important operation ”λ mean” operation in order to minimize indeterminacy which can be used to reduce noise. Finally, the manuscript is binarized using an adaptive thresholding technique. The main advantage of the proposed approach is that it preserves weak connections and provides smooth and continuous strokes. The performance of the proposed approach is evaluated both objectively and subjectively against standard databases and manually collected data base. The proposed method gives high results compared with other famous binarization approaches