Abstract: Automation of airport operations can greatly improve
ground movement efficiency. In this paper, we study the speed profile
design problem for advanced airport ground movement control and
guidance. The problem is constrained by the surface four-dimensional
trajectory generated in taxi planning. A decomposed approach of two
stages is presented to solve this problem efficiently. In the first stage,
speeds are allocated at control points, which ensure smooth speed
profiles can be found later. In the second stage, detailed speed profiles
of each taxi interval are generated according to the allocated control
point speeds with the objective of minimizing the overall fuel
consumption. We present a swarm intelligence based algorithm for the
first-stage problem and a discrete variable driven enumeration method
for the second-stage problem, since it only has a small set of discrete
variables. Experimental results demonstrate the presented
methodology performs well on real world speed profile design
problems.
Abstract: The Multiple Intelligences theory characterizes human
intelligence as a multifaceted entity that exists in all human beings
with varying degrees. The most important contribution of this theory
to the field of English Language Teaching (ELT) is its role in
identifying individual differences and designing more learnercentered
programs. The present study aims at investigating the
relationship between different elements of multiple intelligence and
grammar scores. To this end, 63 female Iranian EFL learner selected
from among intermediate students participated in the study. The
instruments employed were a Nelson English language test, Michigan
Grammar Test, and Teele Inventory for Multiple Intelligences
(TIMI). The results of Pearson Product-Moment Correlation revealed
a significant positive correlation between grammatical accuracy and
linguistic as well as interpersonal intelligence. The results of
Stepwise Multiple Regression indicated that linguistic intelligence
contributed to the prediction of grammatical accuracy.
Abstract: Despite the advances made in various new
technologies, application of these technologies for agriculture still
remains a formidable task, as it involves integration of diverse
domains for monitoring the different process involved in agricultural
management. Advances in ambient intelligence technology represents
one of the most powerful technology for increasing the yield of
agricultural crops and to mitigate the impact of water scarcity,
climatic change and methods for managing pests, weeds and diseases.
This paper proposes a GPS-assisted, machine to machine solutions
that combine information collected by multiple sensors for the
automated management of paddy crops. To maintain the economic
viability of paddy cultivation, the various techniques used in
agriculture are discussed and a novel system which uses ambient
intelligence technique is proposed in this paper. The ambient
intelligence based agricultural system gives a great scope.
Abstract: The aim of this research was to reveal the link
between mental variables, such as spatial abilities, memory, intellect
and professional experience of drivers.
Participants were allocated to four groups: no experience,
inexperienced, skilled and professionals (total 85 participants). The
level of ability for spatial navigation and indicator of nonverbal
memory grow along the process of accumulation of driving
experience. At high levels of driving experience, this tendency is
especially noticeable. The professionals having personal
achievements in driving (racing) differ from skilled drivers in better
feeling of direction, which is specific for them not just in a short-term
situation of an experimental task, but also in life-size perspective.
The level of ability of mental rotation does not grow with the growth
of driving experience, which confirms the multiple intelligence
theory according to which spatial abilities represent specific, other
than logical intelligence type of intellect. The link between spatial
abilities, memory, intellect and professional experience of drivers
seems to be different relating spatial navigation or mental rotation as
different kinds of spatial abilities.
Abstract: In this paper, the actuality of the study, and the role of
subjective well-being problem in modern psychology and the
comprehending of subjective well-being by current students is
defined. The purpose of this research is to educe peculiarities of
comprehending of subjective well-being by students with various
levels of emotional intelligence. Methods of research are adapted
Russian-Language questionnaire of K. Riff 'The scales of
psychological well-being'; emotional intelligence questionnaire of D.
V. Lusin. The research involved 72 students from different
universities and disciplines aged between 18 and 24. Analyzing the
results of the studies, it can be concluded that the understanding of
happiness in different groups of students with high and low levels of
overall emotional intelligence is different, as well as differentiated by
gender. Students with a higher level of happiness possess more
capacity and higher need to control their emotions, to cause and
maintain the desired emotions and control something undesirable.
Abstract: Aim of this research study is to investigate and
establish the characteristics of brain dominances (BD) and multiple
intelligences (MI). This experimentation has been conducted for the
sample size of 552 undergraduate computer-engineering students. In
addition, mathematical formulation has been established to exhibit
the relation between thinking and intelligence, and its correlation has
been analyzed. Correlation analysis has been statistically measured
using Pearson’s coefficient. Analysis of the results proves that there
is a strong relational existence between thinking and intelligence.
This research is carried to improve the didactic methods in
engineering learning and also to improve e-learning strategies.
Abstract: This article presents two methods for the
compensation of harmonics generated by a nonlinear load. The first is
the classic method P-Q. The second is the controller by modern
method of artificial intelligence specifically fuzzy logic. Both
methods are applied to a shunt Active Power Filter (sAPF) based on a
three-phase voltage converter at five levels NPC topology. In
calculating the harmonic currents of reference, we use the algorithm
P-Q and pulse generation, we use the intersective PWM. For
flexibility and dynamics, we use fuzzy logic. The results give us clear
that the rate of Harmonic Distortion issued by fuzzy logic is better
than P-Q.
Abstract: This research study aims to present a retrospective
study about speech recognition systems and artificial intelligence.
Speech recognition has become one of the widely used technologies,
as it offers great opportunity to interact and communicate with
automated machines. Precisely, it can be affirmed that speech
recognition facilitates its users and helps them to perform their daily
routine tasks, in a more convenient and effective manner. This
research intends to present the illustration of recent technological
advancements, which are associated with artificial intelligence.
Recent researches have revealed the fact that speech recognition is
found to be the utmost issue, which affects the decoding of speech. In
order to overcome these issues, different statistical models were
developed by the researchers. Some of the most prominent statistical
models include acoustic model (AM), language model (LM), lexicon
model, and hidden Markov models (HMM). The research will help in
understanding all of these statistical models of speech recognition.
Researchers have also formulated different decoding methods, which
are being utilized for realistic decoding tasks and constrained
artificial languages. These decoding methods include pattern
recognition, acoustic phonetic, and artificial intelligence. It has been
recognized that artificial intelligence is the most efficient and reliable
methods, which are being used in speech recognition.
Abstract: Entrepreneurs face different sort of difficulties
especially with customers, organizations and employees. Emotional
intelligence which is the ability to understand and control the
emotions is an important factor to help entrepreneurs end up
challenges to the result they prefer. So it is assumed that
entrepreneurs especially those who have passed the first challenging
years of starting a new business, have high emotional intelligence. In
this study the Iranian established entrepreneurs have been surveyed.
According to Iran Gem 2014 report the percentage of established
entrepreneur in Iran is 10.92%. So by using Cochran sample formula
(1%) 96 Iranian established entrepreneurs have been selected and
Emotional intelligence appraisal questionnaire distributed to them.
The SPSS19 result shows high emotional intelligence in Iranian
established entrepreneurs.
Abstract: Moving into a new era of healthcare, new tools and
devices are developed to extend and improve health services, such as
remote patient monitoring and risk prevention. In this concept,
Internet of Things (IoT) and Cloud Computing present great
advantages by providing remote and efficient services, as well as
cooperation between patients, clinicians, researchers and other health
professionals. This paper focuses on patients suffering from bipolar
disorder, a brain disorder that belongs to a group of conditions
called affective disorders, which is characterized by great mood
swings. We exploit the advantages of Semantic Web and Cloud
Technologies to develop a patient monitoring system to support
clinicians. Based on intelligently filtering of evidence-knowledge and
individual-specific information we aim to provide treatment
notifications and recommended function tests at appropriate times or
concluding into alerts for serious mood changes and patient’s nonresponse
to treatment. We propose an architecture as the back-end
part of a cloud platform for IoT, intertwining intelligence devices
with patients’ daily routine and clinicians’ support.
Abstract: Traditional document representation for classification
follows Bag of Words (BoW) approach to represent the term weights.
The conventional method uses the Vector Space Model (VSM) to
exploit the statistical information of terms in the documents and they
fail to address the semantic information as well as order of the terms
present in the documents. Although, the phrase based approach
follows the order of the terms present in the documents rather than
semantics behind the word. Therefore, a semantic concept based
approach is used in this paper for enhancing the semantics by
incorporating the ontology information. In this paper a novel method
is proposed to forecast the intraday stock market price directional
movement based on the sentiments from Twitter and money control
news articles. The stock market forecasting is a very difficult and
highly complicated task because it is affected by many factors such
as economic conditions, political events and investor’s sentiment etc.
The stock market series are generally dynamic, nonparametric, noisy
and chaotic by nature. The sentiment analysis along with wisdom of
crowds can automatically compute the collective intelligence of
future performance in many areas like stock market, box office sales
and election outcomes. The proposed method utilizes collective
sentiments for stock market to predict the stock price directional
movements. The collective sentiments in the above social media have
powerful prediction on the stock price directional movements as
up/down by using Granger Causality test.
Abstract: Nature is the immense gifted source for solving
complex problems. It always helps to find the optimal solution to
solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide
research area of networks which has set of independent nodes. The
characteristics involved in MANET’s are Dynamic, does not depend
on any fixed infrastructure or centralized networks, High mobility.
The Bio-Inspired algorithms are mimics the nature for solving
optimization problems opening a new era in MANET. The typical
Swarm Intelligence (SI) algorithms are Ant Colony Optimization
(ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization
(PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf
Search Algorithm (WSA) and so on. This work mainly concentrated
on nature of MANET and behavior of nodes. Also it analyses various
performance metrics such as throughput, QoS and End-to-End delay
etc.
Abstract: Recently, the green architecture becomes a
significant way to a sustainable future. Green building designs
involve finding the balance between comfortable homebuilding and
sustainable environment. Moreover, the utilization of the new
technologies such as artificial intelligence techniques are used to
complement current practices in creating greener structures to keep
the built environment more sustainable. The most common objectives
in green buildings should be designed to minimize the overall impact
of the built environment that effect on ecosystems in general and in
particularly human health and natural environment. This will lead to
protecting occupant health, improving employee productivity,
reducing pollution and sustaining the environmental. In green
building design, multiple parameters which may be interrelated,
contradicting, vague and of qualitative/quantitative nature are
broaden to use. This paper presents a comprehensive critical state- ofart-
review of current practices based on fuzzy and its combination
techniques. Also, presented how green architecture/building can be
improved using the technologies that been used for analysis to seek
optimal green solutions strategies and models to assist in making the
best possible decision out of different alternatives.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: Artificial intelligence applications are commonly used
in industry in many fields in parallel with the developments in the
computer technology. In this study, a fire room was prepared for the
resistance of wooden construction elements and with the mechanism
here, the experiments of polished materials were carried out. By
utilizing from the experimental data, an artificial neural network
(ANN) was modelled in order to evaluate the final cross sections of
the wooden samples remaining from the fire. In modelling,
experimental data obtained from the fire room were used. In the
developed system, the first weight of samples (ws-gr), preliminary
cross-section (pcs-mm2), fire time (ft-minute), and fire temperature
(t-oC) as input parameters and final cross-section (fcs-mm2) as output
parameter were taken. When the results obtained from ANN and
experimental data are compared after making statistical analyses, the
data of two groups are determined to be coherent and seen to have no
meaning difference between them. As a result, it is seen that ANN
can be safely used in determining cross sections of wooden materials
after fire and it prevents many disadvantages.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: A knowledge-based expert system with the acronym
RASPE is developed as an application tool to help decision makers in
construction companies make informed decisions about managing
risks in pipeline construction projects. Choosing to use expert
systems from all available artificial intelligence techniques is due to
the fact that an expert system is more suited to representing a
domain’s knowledge and the reasoning behind domain-specific
decisions. The knowledge-based expert system can capture the
knowledge in the form of conditional rules which represent various
project scenarios and potential risk mitigation/response actions. The
built knowledge in RASPE is utilized through the underlying
inference engine that allows the firing of rules relevant to a project
scenario into consideration. Paper provides an overview of the
knowledge acquisition process and goes about describing the
knowledge structure which is divided up into four major modules.
The paper shows one module in full detail for illustration purposes
and concludes with insightful remarks.
Abstract: Job Scheduling plays an important role for efficient
utilization of grid resources available across different domains and
geographical zones. Scheduling of jobs is challenging and NPcomplete.
Evolutionary / Swarm Intelligence algorithms have been
extensively used to address the NP problem in grid scheduling.
Artificial Bee Colony (ABC) has been proposed for optimization
problems based on foraging behaviour of bees. This work proposes a
modified ABC algorithm, Cluster Heterogeneous Earliest First Min-
Min Artificial Bee Colony (CHMM-ABC), to optimally schedule
jobs for the available resources. The proposed model utilizes a novel
Heterogeneous Earliest Finish Time (HEFT) Heuristic Algorithm
along with Min-Min algorithm to identify the initial food source.
Simulation results show the performance improvement of the
proposed algorithm over other swarm intelligence techniques.
Abstract: The classroom of the 21st century is an ever changing
forum for new and innovative thoughts and ideas. With increasing
technology and opportunity, students have rapid access to
information that only decades ago would have taken weeks to obtain.
Unfortunately, new techniques and technology are not the cure for
the fundamental problems that have plagued the classroom ever since
education was established. Class size has been an issue long debated
in academia. While it is difficult to pin point an exact number, it is
clear that in this case more does not mean better. By looking into the
success and pitfalls of classroom size the true advantages of smaller
classes will become clear. Previously, one class was comprised of 50
students. Being seventeen and eighteen- year- old students,
sometimes it was quite difficult for them to stay focused. To help
them understand and gain much knowledge, a researcher introduced
“The Theory of Multiple Intelligence” and this, in fact, enabled
students to learn according to their own learning preferences no
matter how they were being taught. In this lesson, the researcher
designed a cycle of learning activities involving all intelligences so
that everyone had equal opportunities to learn.
Abstract: The Smart Help for persons with disability (PWD) is a
part of the project SMARTDISABLE which aims to develop relevant
solution for PWD that target to provide an adequate workplace
environment for them. It would support PWD needs smartly through
smart help to allow them access to relevant information and
communicate with other effectively and flexibly, and smart editor
that assist them in their daily work. It will assist PWD in knowledge
processing and creation as well as being able to be productive at the
work place. The technical work of the project involves design of a
technological scenario for the Ambient Intelligence (AmI) - based
assistive technologies at the workplace consisting of an integrated
universal smart solution that suits many different impairment
conditions and will be designed to empower the Physically disabled
persons (PDP) with the capability to access and effectively utilize the
ICTs in order to execute knowledge rich working tasks with
minimum efforts and with sufficient comfort level. The proposed
technology solution for PWD will support voice recognition along
with normal keyboard and mouse to control the smart help and smart
editor with dynamic auto display interface that satisfies the
requirements for different PWD group. In addition, a smart help will
provide intelligent intervention based on the behavior of PWD to
guide them and warn them about possible misbehavior. PWD can
communicate with others using Voice over IP controlled by voice
recognition. Moreover, Auto Emergency Help Response would be
supported to assist PWD in case of emergency. This proposed
technology solution intended to make PWD very effective at the
work environment and flexible using voice to conduct their tasks at
the work environment. The proposed solution aims to provide
favorable outcomes that assist PWD at the work place, with the
opportunity to participate in PWD assistive technology innovation
market which is still small and rapidly growing as well as upgrading
their quality of life to become similar to the normal people at the
workplace. Finally, the proposed smart help solution is applicable in
all workplace setting, including offices, manufacturing, hospital, etc.