Abstract: Amman, the capital of Jordan, is the main political, economic, social and cultural center of Jordan and beyond. The city faces multitude demographic challenges related to the unstable political situation in the surrounded countries. It has regional and local migrants who left their homes to find better life in the capital. This resulted with random and unequaled population distribution. Some districts have high population and pressure on the infrastructure and services more than other districts.Government works to resolve this challenge in compliance with 100 Cities Resilience Framework (CRF). Amman participated in this framework as a member in December 2014 to work in achieving the four goals: health and welfare, infrastructure and utilities, economy and education as well as administration and government. Previous research studies lack in studying Amman resilient work in neighborhood scale and the population growth as resilient challenge. For that, this study focuses on Al-Thahabiah neighborhood in Shafa Badran district in Amman. This paper studies the reasons and drivers behind this population growth during the selected period in this area then provide strategies to improve the resilient work in neighborhood scale. The methodology comprises of primary and secondary data. The primary data consist of interviews with chief officer in the executive part in Great Amman Municipality and resilient officer. The secondary data consist of papers, journals, newspaper, articles and book’s reading. The other part of data consists of maps and statistical data which describe the infrastructural and social situation in the neighborhood and district level during the studying period. Based upon those data, more detailed information will be found, e.g., the centralizing position of population and the provided infrastructure for them. This will help to provide these services and infrastructure to other neighborhoods and enhance population distribution. This study develops an analytical framework to assess urban demographical time series in accordance with the criteria of CRF to make accurate detailed projections on the requirements for the future development in the neighborhood scale and organize the human requirements for affordable quality housing, employment, transportation, health and education in this neighborhood to improve the social relations between its inhabitants and the community. This study highlights on the localization of resilient work in neighborhood scale and spread the resilient knowledge related to the shortage of its research in Jordan. Studying the resilient work from population growth challenge perspective helps improve the facilities provide to the inhabitants and improve their quality of life.
Abstract: Tumor is an uncontrolled growth of tissues in any part
of the body. Tumors are of different types and they have different
characteristics and treatments. Brain tumor is inherently serious and
life-threatening because of its character in the limited space of the
intracranial cavity (space formed inside the skull). Locating the tumor
within MR (magnetic resonance) image of brain is integral part of the
treatment of brain tumor. This segmentation task requires
classification of each voxel as either tumor or non-tumor, based on
the description of the voxel under consideration. Many studies are
going on in the medical field using Markov Random Fields (MRF) in
segmentation of MR images. Even though the segmentation process
is better, computing the probability and estimation of parameters is
difficult. In order to overcome the aforementioned issues, Conditional
Random Field (CRF) is used in this paper for segmentation, along
with the modified artificial bee colony optimization and modified
fuzzy possibility c-means (MFPCM) algorithm. This work is mainly
focused to reduce the computational complexities, which are found in
existing methods and aimed at getting higher accuracy. The
efficiency of this work is evaluated using the parameters such as
region non-uniformity, correlation and computation time. The
experimental results are compared with the existing methods such as
MRF with improved Genetic Algorithm (GA) and MRF-Artificial
Bee Colony (MRF-ABC) algorithm.
Abstract: Many approaches to pattern recognition are founded on probability theory, and can be broadly characterized as either generative
or discriminative according to whether or not the distribution of the image features. Generative and discriminative models have
very different characteristics, as well as complementary strengths and weaknesses. In this paper, we study these models to recognize the patterns of alphabet characters (A-Z) and numbers (0-9). To handle isolated pattern, generative model as Hidden Markov Model (HMM) and discriminative models like Conditional Random Field (CRF), Hidden Conditional Random Field (HCRF) and Latent-Dynamic Conditional Random Field (LDCRF) with different number of window size are applied on extracted pattern features. The gesture recognition rate is improved initially as the window size increase, but degrades as window size increase further. Experimental results show that the LDCRF is the best in terms of results than CRF, HCRF and HMM at window size equal 4. Additionally, our results show that; an overall recognition rates are 91.52%, 95.28%, 96.94% and 98.05% for CRF,
HCRF, HMM and LDCRF respectively.
Abstract: Noise level has critical effects on the diagnostic
performance of signal-averaged electrocardiogram (SAECG), because
the true starting and end points of QRS complex would be masked by
the residual noise and sensitive to the noise level. Several studies and
commercial machines have used a fixed number of heart beats
(typically between 200 to 600 beats) or set a predefined noise level
(typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform
SAECG analysis. However different criteria or methods used to
perform SAECG would cause the discrepancies of the noise levels
among study subjects. According to the recommendations of 1991
ESC, AHA and ACC Task Force Consensus Document for the use of
SAECG, the determinations of onset and offset are related closely to
the mean and standard deviation of noise sample. Hence this study
would try to perform SAECG using consistent root-mean-square
(RMS) noise levels among study subjects and analyze the noise level
effects on SAECG. This study would also evaluate the differences
between normal subjects and chronic renal failure (CRF) patients in
the time-domain SAECG parameters.
The study subjects were composed of 50 normal Taiwanese and 20
CRF patients. During the signal-averaged processing, different RMS
noise levels were adjusted to evaluate their effects on three time
domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS
voltage of the last QRS 40 ms (RMS40), and (3) duration of the low
amplitude signals below 40 μV (LAS40). The study results
demonstrated that the reduction of RMS noise level can increase
fQRSD and LAS40 and decrease the RMS40, and can further increase
the differences of fQRSD and RMS40 between normal subjects and
CRF patients. The SAECG may also become abnormal due to the
reduction of RMS noise level. In conclusion, it is essential to establish
diagnostic criteria of SAECG using consistent RMS noise levels for
the reduction of the noise level effects.
Abstract: Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.
Abstract: In this present study, experimental work was
conducted to study the effectiveness of newly innovated steel-CFRP
composite (CFRP laminates sandwiched between two steel strips) as
stirrups. A total numbers of eight concrete beams were tested under
four point loads. Each beam measured 1600 mm long, 160mm width
and 240 mm depth. The beams were reinforced with different shear
reinforcements; one without stirrups, one with steel stirrups and six
with different types and numbers of steel-CRFR stirrups. Test results
indicated that the steel-CFRP stirrups had enhanced the shear
strength capacity of beams. Moreover, the tests revealed that steel-
CFRP stirrups reached to their ultimate tensile strength unlike FRP
stirrups which rupture at much lower level than their ultimate
strength as werereported in various researches.
Abstract: Nowadays predicting political risk level of country
has become a critical issue for investors who intend to achieve
accurate information concerning stability of the business
environments. Since, most of the times investors are layman and
nonprofessional IT personnel; this paper aims to propose a
framework named GECR in order to help nonexpert persons to
discover political risk stability across time based on the political
news and events.
To achieve this goal, the Bayesian Networks approach was
utilized for 186 political news of Pakistan as sample dataset.
Bayesian Networks as an artificial intelligence approach has been
employed in presented framework, since this is a powerful technique
that can be applied to model uncertain domains. The results showed
that our framework along with Bayesian Networks as decision
support tool, predicted the political risk level with a high degree of
accuracy.