This document summarizes a research paper that models attention shift impairment in autistic individuals using an artificial neural network called Self Organizing Feature Map (SOFM). SOFM is an unsupervised learning algorithm that can train autistic people to recognize and learn objects. The document provides background on autism as a neurodevelopmental disorder characterized by social and communication difficulties. It describes research showing differences in brain structure and function in autistic individuals, pointing to the cortex being differently wired. It also summarizes treatment approaches and long-term outcomes for autism.
Kindly donated to Dyslexia International to disseminate, Dr Duncan Milne's book 'Teaching the brain to read' presents the elements of neuropsychology in relation to dyslexia. It explains the process of reading and examines the implications for teaching literacy. Teaching the brain to read is a book for teachers, parents and reading specialists, which uses brain research to support theory and practice. As well as recent developments in brain imaging research, the book discusses parallel developments in theories underlying the teaching of literacy and best educational practice.
For more material visit the "e-Campus" at www.dyslexia-international.org
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
Kindly donated to Dyslexia International to disseminate, Dr Duncan Milne's book 'Teaching the brain to read' presents the elements of neuropsychology in relation to dyslexia. It explains the process of reading and examines the implications for teaching literacy. Teaching the brain to read is a book for teachers, parents and reading specialists, which uses brain research to support theory and practice. As well as recent developments in brain imaging research, the book discusses parallel developments in theories underlying the teaching of literacy and best educational practice.
For more material visit the "e-Campus" at www.dyslexia-international.org
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
Artificial intelligence turns brain activity into speechusmsystems
Artificial intelligence turns brain activity into speech for most people who are frozen and unable to speak, the signals for what they want to say hide in their brains. No one could interpret those signs directly. But three research groups have recently made progress in transforming data from electrodes placed on the brain into computer-generated speech. Using computational models called neural networks, they have, in some cases, reconstructed words and sentences that are understandable to human listeners.
Asamblare calculator 1. INTRODUCERE
2. LEGĂTURI PROCESOARE
3. LEGĂTURI RĂCITOARE
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8. LEGĂTURI DISPOZITIVE MULTIMEDIA
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Through this store we bring to you a lot of products that are sold by different furniture stores in Charlotte NC. This is one reason why you may no longer have to run specific searches like ‘Ikea Charlotte NC’ or ‘Ashley Furniture Charlotte NC.’
http://www.wellsource.com/home.html | By actively providing wellness activities and developing a culture of health at your organization, you are investing in the greatest asset in your company – the health and well-being of your staff. It will pay rich dividends in goodwill, increased productivity, and ultimately lower healthcare costs.
Autism is group of child developmental disabilities that can cause social, communication and behavioural challenges in Kids. Giostar provides Stem Cell Treatment for Autism call us at +91 7043008890.
President of MSSA Prof. Dr. Vladimir Trajkovski prsented the his topic: "Association between cerebral palsy and autism spectrum disorders" at 8th interdisciplinary congress: "Cerebral palsy and other movement disorders" on 1-2 of November 2018 in Moscow, Russia.
Artificial intelligence turns brain activity into speechusmsystems
Artificial intelligence turns brain activity into speech for most people who are frozen and unable to speak, the signals for what they want to say hide in their brains. No one could interpret those signs directly. But three research groups have recently made progress in transforming data from electrodes placed on the brain into computer-generated speech. Using computational models called neural networks, they have, in some cases, reconstructed words and sentences that are understandable to human listeners.
Asamblare calculator 1. INTRODUCERE
2. LEGĂTURI PROCESOARE
3. LEGĂTURI RĂCITOARE
4. LEGĂTURI PLĂCI VIDEO
5. LEGĂTURI PLĂCI DE BAZĂ
6. LEGĂTURI STOCARE
7. LEGĂTURI MEMORIE
8. LEGĂTURI DISPOZITIVE MULTIMEDIA
9. LEGĂTURI CARCASE ŞI SURSE DE ALIMENTARE
10. LEGĂTURI MONITOARE
11. LEGĂTURI IMPRIMANTE
12. LEGĂTURI PERIFERICE
Through this store we bring to you a lot of products that are sold by different furniture stores in Charlotte NC. This is one reason why you may no longer have to run specific searches like ‘Ikea Charlotte NC’ or ‘Ashley Furniture Charlotte NC.’
http://www.wellsource.com/home.html | By actively providing wellness activities and developing a culture of health at your organization, you are investing in the greatest asset in your company – the health and well-being of your staff. It will pay rich dividends in goodwill, increased productivity, and ultimately lower healthcare costs.
Autism is group of child developmental disabilities that can cause social, communication and behavioural challenges in Kids. Giostar provides Stem Cell Treatment for Autism call us at +91 7043008890.
President of MSSA Prof. Dr. Vladimir Trajkovski prsented the his topic: "Association between cerebral palsy and autism spectrum disorders" at 8th interdisciplinary congress: "Cerebral palsy and other movement disorders" on 1-2 of November 2018 in Moscow, Russia.
MobiManager helps you to manage, control, and secure company-owned Android devices. It allows you to distribute apps and contents across a wide range of mobile devices from the cloud.
MobiManager platform consists of overlapping defense and security mechanisms that protect against intrusion, malware, and more malicious threats.
Business benefit of Mobile device managementSyeful Islam
MobiManager - Cloud-based mobile device management solution which helps small and large businesses to
• Secure
• Monitor
• Manage
• Support mobile devices, deployed across organization for work purpose.
INfor- Mobile cloud Solution
Bring your essentials wherever your Mobile takes you. INforcloud puts your data at your fingertips, under your control. Store your documents, calendar, contacts and photos on a server at home, at one of our providers or in a data center you know.
A New Approach: Automatically Identify Proper Noun from Bengali Sentence for ...Syeful Islam
More than hundreds of millions of people of almost all levels of education and attitudes from different country communicate with
each other for different using various languages. Machine translation is highly demanding due to increasing the usage of web
based Communication. One of the major problem of Bengali translation is identified a naming word from a sentence, which is
relatively simple in English language, because such entities start with a capital letter. In Bangla we do not have concept of small
or capital letters and there is huge no. of different naming entity available in Bangla. Thus we find difficulties in understanding
whether a word is a proper noun or not. Here we have introduce a new approach to identify proper noun from a Bengali sentence
for UNL without storing huge no. of naming entity in word dictionary. The goal is to make possible Bangla sentence conversion
to UNL and vice versa with minimal storing word in dictionary.
A New Approach: Automatically Identify Naming Word from Bengali Sentence for ...Syeful Islam
More than hundreds of millions of people of almost all levels of education and attitudes from
different country communicate with each other for different using various languages. Machine
translation is highly demanding due to increasing the usage of web based Communication. One of
the major problem of Bengali translation is identified a naming word from a sentence, which is
relatively simple in English language, because such entities start with a capital letter. In Bangla we
do not have concept of small or capital letters and there is huge no. of different naming entity
available in Bangla. Thus we find difficulties in understanding whether a word is a naming word
(proper noun) or not. Here we have introduced a new approach to identify naming word from a
Bengali sentence for UNL without storing huge no. of naming entity in word dictionary. The goal is
to make possible Bangla sentence conversion to UNL and vice versa with minimal storing word in
dictionary.
Disordered Brain Modeling Using Artificial Network SOFMSyeful Islam
Autism is known as a neurobiological developmental disorder which affects language,
communication, and cognitive skill. In the case of autism attention shift impairment and
strong familiarity preference are considered to be prime deficiencies. Attention shift
impairment is one of the most seen behavioral disorders found in autistic patients. We
have model this behavior by employing self-organizing feature map (SOFM).
It covers:
- What is jQuery?
- Why jQuery?
- How include jQuery in your web page
- Creating and manipulating elements
- Events
- Animations and effects
- Talking to the server
- jQuery UI
- Writing plugins
- Breaking news around new releases
- Using the CDN
An Algorithm for Electronic Money Transaction Security (Three Layer Security)...Syeful Islam
In the era ofinternet, most ofthe people all over the world completed their transaction
on internet. Though the user of electronic transaction or E-money transaction system
increase rapidly but the majority person are concern about the security of this system.
The growth in online transactions has resulted in a greater demand for fast and accurate
user identification and authentication. Conventional method of identification based on
possession of ID cards or exclusive knowledge like a social security number or a
password are not all together reliable. Identification and authentication by individuals'
biometric characteristics is becoming an accepted procedure that is slowly replacing the
most popular identification procedure – passwords. Among all the biometrics, fingerprint
based identification is one of the most mature and proven technique. Along with the
combination of conventional system, biometric security, Global positioning system(GPS)
and mobile messaging we have design an algorithm which increase security ofelectronic
transaction and more reliable to user. A three layer security model to enhancing security
ofelectronic transaction is proposed in this paper.
A New Approach: Automatically Identify Naming Word from Bengali Sentence for ...Syeful Islam
More than hundreds of millions of people of almost all levels of education and attitudes from different country communicate with each other for different purposes using various languages. Machine translation is highly demanding due to increasing the usage of web based Communication. One of the major problem of Bengali translation is identified a naming word from a sentence, which is relatively simple in English language, because such entities start with a capital letter. In Bangla we do not have concept of small or capital letters and there is huge no. of different naming entity available in Bangla. Thus we find difficulties in understanding whether a word is a naming word or not. Here we have introduced a new approach to identify naming word from a Bengali sentence for machine translation system without storing huge no. of naming entity in word dictionary. The goal is to make possible Bangla sentence conversion with minimal storing word in dictionary.
Design Analysis Rules to Identify Proper Noun from Bengali Sentence for Univ...Syeful Islam
Abstract—Now-a-days hundreds of millions of people of
almost all levels of education and attitudes from different
country communicate with each other for different
purposes and perform their jobs on internet or other
communication medium using various languages. Not all
people know all language; therefore it is very difficult to
communicate or works on various languages. In this
situation the computer scientist introduce various inter
language translation program (Machine translation). UNL
is such kind of inter language translation program. One of
the major problem of UNL is identified a name from a
sentence, which is relatively simple in English language,
because such entities start with a capital letter. In Bangla
we do not have concept of small or capital letters. Thus
we find difficulties in understanding whether a word is a
proper noun or not. Here we have proposed analysis rules
to identify proper noun from a sentence and established
post converter which translate the name entity from
Bangla to UNL. The goal is to make possible Bangla
sentence conversion to UNL and vice versa. UNL system
prove that the theoretical analysis of our proposed system
able to identify proper noun from Bangla sentence and
produce relative Universal word for UNL.
Performance Evaluation of Finite Queue Switching Under Two-Dimensional M/G/1...Syeful Islam
Abstract—In this paper we consider a local area network (LAN) of dual mode service
where one is a token bus and the other is a carrier sense multiple access with a collision
detection (CSMA/CD) bus. The objective of the paper is to find the overall cell/packet
dropping probability of a dual mode LAN for finitelength queue M/G/1(m) traffic. Here, the
offered traffic of the LAN is taken to be the equivalent carried traffic of a one-millisecond
delay. The concept of a tabular solution for two-dimensional Poisson’s traffic of circuit
switching is adapted here to find the cell dropping probability of the dual mode packet
service. Although the work is done for the traffic of similar bandwidth, it can be extended
for the case of a dissimilar bandwidth of a circuit switched network.
This tutorial will help you to understand about Java OOP’S concepts with examples. Let’s discuss about what are the features of Object Oriented Programming. Writing object-oriented programs involves creating classes, creating objects from those classes, and creating applications, which are stand-alone executable programs that use those objects.
Emergency Notification:
It is an emergency case to notify some important person or other concern person when we fall in any problem such as accident or attacked by criminal or any other incidence. On other hands it also needs to track victim location to help him from incidence.
Already some concept grows about emergency notification some people try to give solution. But they can’t think the whole scenario or their solution is not realistic or they use this concept to resolve other purpose.
To resolve this problem here I proposed an interactive, user friendly and more efficient solution by smartphone. I have given an idea about emergency notification system in Samsung phone, which can solve this.
Here I want to mention some common incidence and how our phone helps us in that incident. In our regular life we can fall into various incidents such as
- Accident
- Attacked by snatcher, criminal or terrorist
- Suddenly fall into sick (Heart attack, stroke or others) etc.
In the case of accident if victim fatally injured or there is any situation that, victim can’t get enough time to call or notify someone or group of people to help him. This situation can happen if anyone fall in sudden sick and he/she want to call his/her relatives to help him. But they cannot capable to talk. If anyone attacked by snatcher, criminal or terrorist, then it is emergency to informed police or other concern person. But it is impossible to inform police by call.
To overcome this problem I proposed a system which can notify concern person automatically if user fall any incident. There is also need to track the location of victims because the location of victims can be changed and it is emergency to know the latest location where victim exist otherwise it is difficult to find out the victim and help him.
Business Value:.
- This is more realistic feature and more users friendly and efficient.
- I hope this also minimize our life risk or our tension when we are out of home or away from family members or there’s.
Development of analysis rules to identify proper noun from bengali sentence f...Syeful Islam
- Today the regional economies, societies, cultures and educations are integrated through a globe-spanning network of communication and trade.
- This globalization trend evokes for a homogeneous platform so that each member of the platform can apprehend what other intimates and perpetuates the discussion in a mellifluous way.
- However the barriers of languages throughout the world are continuously obviating the whole world from congregating into a single domain of sharing knowledge and information.
- Therefore researcher works on various languages and tries to give a platform where multi lingual people can communicate through their native language.
- Researcher analyze the language structure and form structural grammar and rules which used to translate one language to other.
- From the last few years several language-specific translation systems have been proposed.
- Since these systems are based on specific source and target languages, these have their own limitations.
- As a consequence United Nations University/Institute of Advanced Studies (UNU/IAS) were decided to develop an inter-language translation program .
- The corollary of their continuous research leads a common form of languages known as Universal Networking Language (UNL) and introduces UNL system.
- UNL system is an initiative to overcome the problem of language pairs in automated translation. UNL is an artificial language that is based on Interlingua approach.UNL acts as an intermediate form computer semantic language whereby any text written in a particular language is converted to text of any other forms of languages.
- UNL system consists of major three components:
- language resources
- software for processing language resources (parser) and
- supporting tools for maintaining and operating language processing software or developing language resources.
- The parser of UNL system take input sentence and start parsing based on rules and convert it into corresponding universal word from word dictionary.
- The challenge in detection of named is that such expressions are hard to analyze using UNL because they belong to the open class of expressions, i.e., there is an infinite variety and new expressions are constantly being invented.
- Bengali is the seventh popular language in the world, second in India and the national language of Bangladesh.
- So this is an important problem since search queries on UNL dictionary for proper nouns while all proper nouns(names) cannot be exhaustively maintained in the dictionary for automatic identification.
In this research project we do this task , Proper noun detection and conversion.
Development of analysis rules to identify proper noun from bengali sentence f...
Disordered Brain Modeling Using Artificial Network SOFM
1. International Journal of Artificial Intelligence and Applications for Smart Devices
Vol.3, No.1 (2015), pp.1-14
http://dx.doi.org/10.14257/ijaiasd.2015.3.1.01
ISSN: 2288-6710 IJAIASD
Copyright ⓒ 2015 SERSC
Disordered Brain Modeling Using Artificial Network SOFM
Md. Syeful Islam1
, Ruhul Abedin2
and Fakrul Hasan3
1
Samsung R&D Institute Bangladesh Ltd.
2
BJIT Ltd.
3
Dynamic Solution Innovators Ltd.
syefulislam@yahoo.com, ruhul.abedin@yahoo.com, razib.fh@gmail.com
Abstract
Autism is known as a neurobiological developmental disorder which affects language,
communication, and cognitive skill. In the case of autism attention shift impairment and
strong familiarity preference are considered to be prime deficiencies. Attention shift
impairment is one of the most seen behavioral disorders found in autistic patients. We
have model this behavior by employing self-organizing feature map (SOFM).
Keywords: Autism, Self Organizing Feature Map, Autism Spectrum Disorder, Feature
Map, Neural Network
1. Introduction
Autism is a disorder of neural development characterized by impaired social interaction
and communication by restricted and repetitive behavior. These signs all begin before a
child is three years old [1]. Autism affects information processing in the brain by altering
how nerve cells and their synapses connect and organize; how this occurs is not well
understood [2].
Autism has a strong genetic basis, although the genetics of autism are complex and it is
unclear whether ASD is explained more by rare mutations, or by rare combinations of
common genetic variants [3]. In rare cases, autism is strongly associated with agents that
cause birth defects. Controversies surround other proposed environmental causes, such as
heavy metals, pesticides or childhood vaccines; the vaccine hypotheses are biologically
implausible and lack convincing scientific evidence. The prevalence of autism is about 1–
2 per 1,000 people worldwide; however, the Centers for Disease Control and Prevention
(CDC) reports approximately 9 per 1,000 children in the United States are diagnosed with
ASD. The number of people diagnosed with autism has increased dramatically since the
1980s, partly due to changes in diagnostic practice; the question of whether actual
prevalence has increased is unresolved.
Currently, there is no reliable evidence as to exactly what are the neural bases for
autism. It is clear that autism is a heterogeneous disorder, even it is genetic. There is no
accepted animal model condition, although infant monkeys with selective brain lesions
show behavioral, features suggestive of autism. Our research goal is to model autism in
such way that is helpful to improve the learning and training process of autistic people
.We used unsupervised learning algorithm that can train autistic people to recognize and
learn object. This model has been implemented using Self Organizing Feature Map
(SOFM) [4].
2. Understanding Autism
Autism is a term used for a number of developmental disabilities called Autism
Spectrum Disorder (ASD). ASD is a life-long neurobiological disorder that affects
2. International Journal of Artificial Intelligence and Applications for Smart Devices
Vol. 3, No. 1 (2015)
2 Copyright ⓒ 2015 SERSC
how the person perceives and interprets their world, particularly the social
environment. [5]
The symptoms of ASD vary and can range from mild to severe, but most children
on the spectrum show difficulties with:
1) Social Interaction
2) Verbal and non-verbal communication
3) Repetitive behaviors or limited interests
2.1. Possible Reason of Autism
It has long been presumed that there is a common cause at the genetic, cognitive,
and neural levels for autism's characteristic triad of symptoms [6]. However, there is
increasing suspicion that autism is instead a complex disorder whose core aspects
have distinct causes that often co-occur.
Autism has a strong genetic basis, although the genetics of autism are complex
and it is unclear whether ASD is explained more by rare mutations with major
effects, or by rare multi-gene interactions of common genetic variants. Complexity
arises due to interactions among multiple genes, the environment, and epigenetic
factors which do not change DNA but are heritable and influence gene expression.
Studies of twins suggest that heritability is 0.7 for autism and as high as 0.9 for
ASD, and siblings of those with autism are about 25 times more likely to be autistic
than the general population. However, most of the mutations that increase autism
risk have not been identified. Typically, autism cannot be traced to a Mendelian
(single-gene) mutation or to a single chromosome abnormality like fragile X
syndrome, and none of the genetic syndromes associated with ASDs have been
shown to selectively cause ASD. Numerous candidate genes have been located, with
only small effects attributable to any particular gene. The large number of autistic
individuals with unaffected family members may result from copy number
variations—spontaneous deletions or duplications in genetic material during
meiosis. Hence, a substantial fraction of autism cases may be traceable to genetic
causes that are highly heritable but not inherited: that is, the mutation that causes
the autism is not present in the parental genome.
2.2. Over view of Human Brain
The human brain is the center of the human nervous system [8]. Enclosed in the
cranium, the human brain has the same general structure as that of other mammals,
but is over three times larger than the brain of a typical mammal with an equivalent
body size. Most of the spatial expansion comes from the cerebral cortex, a
convoluted layer of neural tissue which covers the surface of the forebrain.
Especially expanded are the frontal lobes, which are associated with executive
functions such as self-control, planning, reasoning, and abstract thought. The
portion of the brain devoted to vision, the occipital lobe, is also greatly enlarged in
human beings.
3. International Journal of Artificial Intelligence and Applications for Smart Devices
Vol. 3, No. 1 (2015)
Copyright ⓒ 2015 SERSC 3
Brain evolution, from the earliest shrew-like mammals through primates to
hominids, is marked by a steady increase in en-cephalization, or the ratio of brain to
body size. Estimates vary for the number of neuronal and non-neuronal cells
contained in the brain, ranging from 80 or 90 billion (~85 109) non-neuronal cells
(glial cells) and an approximately equal number of (~86 109) neurons, of which
about 10 billion (1010) are cortical pyramidal cells, to over 120 billion neuronal
cells, with an approximately equal number of non-neuronal cells. These cells pass
signals to each other via as many as 1000 trillion (1015, 1 quadrillion) synaptic
connections. Due to evolution and synaptic pruning, however, the modern human
brain has been shrinking over the past 28,000 years.
Figure 1. Overview of Human Brain
The brain monitors and regulates the body's actions and reactions. It continuously
receives sensory information, and rapidly analyzes this data and then responds
accordingly by controlling bodily actions and functions. The brainstem controls
breathing, heart rate, and other autonomic processes that are independent of
conscious brain functions. The neo-cortex is the center of higher-order thinking,
learning, and memory. The cerebellum is responsible for the body's balance,
posture, and the coordination of movement.
Despite being protected by the thick bones of the skull, suspended in
cerebrospinal fluid, and isolated from the bloodstream by the blood-brain barrier,
the human brain is susceptible to many types of damage and disease. The most
common forms of physical damage are closed head injuries such as a blow to the
head, a stroke, or poisoning by a wide variety of chemicals that can act as
neurotoxins. Infection of the brain, though serious, is rare due to the biological
barriers which protect it. The human brain is also susceptible to degenerative
disorders, such as Parkinson's disease, multiple sclerosis, and Alzheimer's disease.
A number of psychiatric conditions, such as schizophrenia and depression, are
widely thought to be associated with brain dysfunctions, although the nature of such
brain anomalies is not well understood.
4. International Journal of Artificial Intelligence and Applications for Smart Devices
Vol. 3, No. 1 (2015)
4 Copyright ⓒ 2015 SERSC
2.3. Overview of Autistic Brain
Autism is a disorder of neural development characterized by impaired social
interaction and communication, and by restricted and repetitive behavior. These
signs all begin before a child is three years old. Autism affects information
processing in the brain by altering how nerve cells and their synapses connect and
organize; how this occurs is not well understood. It is one of three recognized
disorders in the autism spectrum (ASDs), the other two being Asperger syndrome,
which lacks delays in cognitive development and language, and Pervasive
Developmental Disorder-Not Otherwise Specified (commonly abbreviated as PDD-
NOS), which is diagnosed when the full set of criteria for autism or Asperger
syndrome are not met.
Figure 2. Parts of the Brain Affected by Autism
Over the past few years, a number of studies have been published linking
differences in brain structure and function to autism spectrum disorders. For
example, scientists have noted that:
1) At a certain point in post-natal development, autistic brains are larger.
2) Testosterone may be linked to autism.
3) Certain portions of the brain, such as the amygdala, may be enlarged in
autistic brains.
4) Certain parts of the brain may function differently in autistic people.
5) "Minicolumns" in the brain may be formed differently and be more
numerous in autistic brains.
6) The entire brain may function differently in autistic people.
To better understand which of these findings is legitimate and which is most
significant, I interviewed Dr. Nancy Minshew of the University of Pittsburgh.
Minshew is one of the most prolific and best-known researchers in the field of
autism and the brain. According to Dr. Minshew, "These different theories are not
all so different."
5. International Journal of Artificial Intelligence and Applications for Smart Devices
Vol. 3, No. 1 (2015)
Copyright ⓒ 2015 SERSC 5
2.4. The Autistic Brain Is Differently Wired
What all of these brain findings have in common, Dr. Minshew explains, is that
they point to autism as a disorder of the cortex. The cortex is the proverbial "gray
matter": the part of the brain which is largely responsible for higher brain functions,
including sensation, voluntary muscle movement, thought, reasoning, and memory.
Figure 3. Children’s Brain (Autistic Brain vs. Normal Brain)
Figure 4. Major Brain Structure Implicated in Autism
In many autistic people, the brain develops too quickly beginning at about 12
months. By age ten, their brains are at a normal size, but "wired" atypically. "The
brain is most complex thing on the planet," says Dr. Minshew. "So its wiring has to
be very complex and intricate. With autism there's accelerated growth at the wrong
time, and that creates havoc. The consequences, in terms of disturbing early
development, include problems within the cortex and from the cortex to other
regions of the cortex in ways that compromise language and reasoning abilities."
Minicolumns, which are small structures within the cortex, are also different
among autistic people. Dr. Manuel Casanova, a researcher at the University of
6. International Journal of Artificial Intelligence and Applications for Smart Devices
Vol. 3, No. 1 (2015)
6 Copyright ⓒ 2015 SERSC
Kentucky, has found that autistic people have more minicolumns which include a
greater number of smaller brain cells. In addition, the "insulation" between these
minicolumns is not as effective as it is among typically developing people. The
result may be that autistic people think and perceive differently and have less of an
ability to block sensory input.
2.5. Treatment of Autism
No cure is known. Children recover occasionally, so that they lose their diagnosis
of ASD; this occurs sometimes after intensive treatment and sometimes not. It is not
known how often recovery happens; reported rates in unselected samples of children
with ASD have ranged from 3% to 25%. Most autistic children can acquire language
by age 5 or younger, though a few have developed communication skills in later
years. Most children with autism lack social support, meaningful relationships,
future employment opportunities or self-determination. Although core difficulties
tend to persist, symptoms often become less severe with age. Few high-quality
studies address long-term prognosis. Some adults show modest improvement in
communication skills, but a few decline; no study has focused on autism after
midlife. Acquiring language before age six, having an IQ above 50, and having a
marketable skill all predict better outcomes; independent living is unlikely with
severe autism. A 2004 British study of 68 adults who were diagnosed before 1980 as
autistic children with IQ above 50 found that 12% achieved a high level of
independence as adults, 10% had some friends and were generally in work but
required some support, 19% had some independence but were generally living at
home and needed considerable support and supervision in daily living, 46% needed
specialist residential provision from facilities specializing in ASD with a high level
of support and very limited autonomy, and 12% needed high-level hospital care. A
2005 Swedish study of 78 adults that did not exclude low IQ found worse prognosis;
for example, only 4% achieved independence. A 2008 Canadian study of 48 young
adults diagnosed with ASD as preschoolers found outcomes ranging through poor
(46%), fair (32%), good (17%), and very good (4%); 56% of these young adults had
been employed at some point during their lives, mostly in volunteer, sheltered or
part-time work. Changes in diagnostic practice and increased availability of
effective early intervention make it unclear whether these findings can be
generalized to recently diagnosed children [9].
3. Modeling of Authentic Learning
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of
artificial neural network that is trained using unsupervised learning to produce a low-
dimensional (typically two-dimensional), discredited representation of the input space of
the training samples, called a map. Self-organizing maps are different from other artificial
neural networks in the sense that they use a neighborhood function to preserve the
topological properties of the input space. This makes SOMs useful for visualizing low-
dimensional views of high-dimensional data, akin to multidimensional scaling. The model
was first described as an artificial neural network by the Finnish professor Teuvo
Kohonen, and is sometimes called a Kohonen map [11, 12].
For our working purpose the self-organizing map is described by dividing it into 3
parts:
1) p-dimensional input space
2) l-dimensional feature space and
3) Competitive net for measuring minimum distance.
7. International Journal of Artificial Intelligence and Applications for Smart Devices
Vol. 3, No. 1 (2015)
Copyright ⓒ 2015 SERSC 7
There a SOM is discussed about where m neurons, each with p synapses are organized
in an l-dimensional lattice (grid) representing the feature space. Such a neural network
performs mapping of a p-dimensional input space into the l-dimensional feature space. In
Figure 5 we present an example of a self-organizing map consisting of m =12 neurons in
which the input space is 3-dimensional (p = 3) and the feature space is 2-dimensional (l =
2). The first section of the network is a distance-measure layer consisting of m = 12
dendrites each containing p = 3 synapses ex-cited by p–dimensional stimuli x and
characterized by the p–dimensional weight vector wi, i =1,...,m. The distance-measure
layer calculates the distances d¬i between each input vector x and every weight vector wi.
This distance vector di = [d1,...,dm)] is passed to the competition layer, the Min
Net,which calculates the minimal distance di = min di in order to establish the position of
the winning neuron k.
Figure 5. A 2-D SOM with p=3; m=[3 4]; l=2
3.1. Detail of the SOM Learning Algorithm
The complete algorithm can be described as consisting of the following steps-
Initialize:
(a) The weight matrix W with a random sample of m input vectors.
(b) The learning gain and the spread of the neighborhood function.
For every input vector, x(n), n = 1, . . . ,N:
(a) Determine the winning neuron, k(n), and its position V (k, :) as
k (n) = argmin = |xT(n) −W(j, :)|
(b) Calculate the neighborhood functions -
Λ(n, j) = exp(−ρ2(j)/2σ2)
; Where ρ (j) = |V (j, :) − V (k(n), :)| for j = 1, . . . ,m.
(c) Update the weight matrix as -
∆W = η (n) • Λ(n) • (xT(n) −W(j, :))
8. International Journal of Artificial Intelligence and Applications for Smart Devices
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8 Copyright ⓒ 2015 SERSC
0 0.5 1 1.5 2 2.5 3 3.5 4
0.5
1
1.5
2
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4
w11
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w31
w12
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w13
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x1
x2
All neurons (unlike in the simple competitive learning) have their weights modified
with a strength proportional to the neighborhood function and to the distance of their
weight vector from the current input vector (as in competitive learning).The step (2) is
repeated E times, where E is the number of epochs.
3.2. Feature Map
Self-Organizing Feature Maps (SOFM or SOM) also known as Kohonen maps or
topographic maps were first introduced by von der Malsburg (1973) and in its present
form by Kohonen (1982) [7]. A typical Feature Map is a plot of synaptic weights in the
input space in which weights of the neighboring neurons are joined by lines and illustrates
the mapping from the input space to the feature spaces. For simplicity, we restrict our
attention here to two-dimensional input and feature spaces (p, l = 2).As an illustrative
example let us consider a SOM with p = 2 inputs and m = 12 neurons organized on a 3 ×
4 lattice. An example of the weight W and position V matrices and the resulting feature
map is given in Figure 6.
Figure 6. Example of Weight and Position Matrix and their Feature Map for
p, l =2
In our modeling we will be using similar SOMs with p, l = 2 and neurons organized in
either a 2 × 2, or 3 × 3 mesh.
3.3. The Autistic Learning Model
Based on the SOM neural network a model of a learning autistic model can be build.
The block diagram of the learning model is described below:
Figure 7. A Block Diagram of the Model of Autistic Learning
K W V
1
2
3
4
5
6
7
8
9
10
11
12
0.73 0.87
0 .62 1.01
0.18 2.93
3.07 2.06
1.83 2.81
1.47 2.28
3.38 1.27
0.6 2.27
3.51 0.61
3.26 1.85
2.92 3.05
3.16 3.90
1 1
2 1
3 1
1 2
2 2
3 2
1 3
2 3
3 3
1 4
2 4
3 4
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The block-diagram of the model of autistic learning which includes source familiarity
filter and attention shift mechanism is presented in Figure 7. The central part is the SOM
neural network as presented in Figure 5, together with the learning section implementing
the learning law, or map formation algorithm. At each learning step a stimulus is
randomly generated from one of the sources, S1...Sc. The attention shifting mechanism
determines if that stimulus is presented to the map for learning. For modeling autistic
learning we have used two learning mode:
In the normal, or novelty seeking learning mode, attention is shifted to another source
if the new stimulus originates from that source.
(a) (b)
Figure 8. A 4×4 Feature Map in the 2-D Input Space Developed in the: (a)
Novelty Seeking, and (b) Attention Shifting Restricted by Familiarity
Preference Learning Modes
In the attention shifting restricted by familiarity preference learning mode attention is
shifted to another source if that source presents the next new stimulus, but conditionally,
depending on the map’s familiarity with that source. The map familiarity to a particular
source is measured by the time averaged value of the distance between map nodes and the
stimuli. When both sources are unfamiliar to the map, i.e., in the beginning stage of self-
organization, attention is shifted to an alternate source if that source presented the next
stimulus as in the novelty seeking mode. As the map develops some familiarity with the
sources, i.e., the node weights begin to resemble the data; attention is shifted with a higher
probability to the source which is most familiar to the map. If the map becomes familiar
to two or more sources (the average deference between node weights and the data from
the sources becomes smaller than a predetermined small value) then attention is
unconditionally shifted.
The feature map presented in Figure 8 (b) is the result of learning when the attention
shifting is restricted by familiarity preference.
4. Result Analysis
In order to model autistic behavior we arrange the two-dimensional training data
and plot them into feature map using SOFM. In this section I have taken training
data for random behavioral neuron and we presented some of experimental output of
training data plot into feature map.
Here Training data (synaptic weights in the input space in which weights of the
neighboring neurons are joined by lines) is plot into map which illustrates the
mapping from p-dimensional input space to l-dimensional feature space. In training
data K denotes the serial no. of neuron and V is the position and W represent the
10. International Journal of Artificial Intelligence and Applications for Smart Devices
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10 Copyright ⓒ 2015 SERSC
1 1.5 2 2.5 3 3.5 4 4.5
1.5
2
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3
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4
4.5
w11
w21
w31
w12 w22
w32
w13
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w33
x1
1 1.5 2 2.5 3 3.5 4
1.5
2
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3
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4
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x1
weight of corresponding neuron. Each neuron, yv in the feature map is characterized
by its position in the lattice specified by a 2-D vector v = [v1 v2], and by a 3-D
weight vector wv = [w1v w2v w3v]. In the feature map the point representing the
weight vector, wk, is joined by line segments with points representing weights wk −
1 and wk +1 an so no because neurons wk − 1, k, and wk + 1 are located in the
adjacent positions of the 1-D neuronal lattice.
4.1. Second-order Headings
(a) (b)
Figure 9. (a) Table Containing Weight and Position Vector of a 3x3
Matrices, (b) Feature Map of a 3x3 Matrices
In Figure 9 (a) denote the no. of nine neurons and the weight W and position V of
corresponding neurons. After that we plot them into 2-D input space into a 2-D
neuronal space. Here 9 neurons are organized into 3×3 grid and it should be plot
into 3×3 output lattice. Consider a neuron 5 which located at the central vertex of
the 3×3 neuronal output lattice. The neuron has four neighbors: 4, 6, 2 and 8.
Therefore, in the feature maps the nodes w12, w32, w21 and w23 will all be joint
with a line to the node w22.
Similarly we have plot several neurons and there behavioral training data those
are organized at 4×4, 4×5 and 5×5 grid and map into corresponding feature map
[Figure 10-13].
4.2. Computing Result of 3x4 Matrices
(a) (b)
Figure 10. (a) Table Containing Weight and Position Vector of a 3x4
Matrices, (b) Feature Map of a 3x4 Matrices
k W V
1
2
3
4
5
6
7
8
9
1.2112 1.9029
2.9771 2.1452
3.5297 1.9243
2.2040 2.4788
3.1951 2.4056
3.8310 2.4777
1.6952 3.7477
3.2597 4.0180
4.1503 3.4330
1.0000 1.0000
2.0000 1.0000
3.0000 1.0000
1.0000 2.0000
2.0000 2.0000
3.0000 2.0000
1.0000 3.0000
2.0000 3.0000
3.0000 3.0000
1
2
3
4
5
6
7
8
9
10
11
12
1.0214 1.9538
3.0455 1.5313
3.6231 2.1645
2.3045 2.7039
2.6524 2.9933
3.5861 2.6004
2.1847 3.4265
2.7352 3.2655
3.2837 3.2708
1.9410 4.9551
3.1734 4.4239
3.0275 4.7583
1.0000 1.0000
2.0000 1.0000
3.0000 1.0000
1.0000 2.0000
2.0000 2.0000
3.0000 2.0000
1.0000 3.0000
2.0000 3.0000
3.0000 3.0000
1.0000 4.0000
2.0000 4.0000
3.0000 4.0000
12. International Journal of Artificial Intelligence and Applications for Smart Devices
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12 Copyright ⓒ 2015 SERSC
1 2 3 4 5 6 7
1
2
3
4
5
6
7
w11
w21
w31
w41 w51
w12 w22
w32 w42
w52
w13
w23
w33
w43
w53
w14
w24
w34
w44
w54
w15
w25
w35
w45
w55
x1
x2
4.5. Computing Results of 5x5 Matrices
(a) (b)
Figure 13. (a) Weight and Position Vector of a 5x5 Matrices, (b) Feature
Map of a 5x5 Matrices
5. Conclusion
Our works do not help autistic children to learn but it only focuses on the
modeling of one of the autistic behavioral problem, attention shift impairment, with
the conjunction of familiarity preference. In our resulting feature maps for the
attention shift impairment restricted by familiarity preference, the map shrinks by it
area because of the reduced learning capability. This shrinkage of the map
represents the reduced learning capability due to attention shift impairment.
Self-organization feature map shows if attention shift is very low (for the case of
autism) and the learning rate is very low too. And hence the autistic patient whose
attention shift is restricted by familiarity preference shows the behavior of doing
something repetitively.
References
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pp. 305-19.
[3] E. B. Caronna, J. M. Milunsky and H. Tager-Flusberg, “Autism spectrum disorders: clinical and
research frontiers”, Arch Dis Child, vol. 93, no. 6, pp. 518-23.
[4] T. Kohonen, “Self-Organisation and Associative Memory”, Berlin: Springer-Verlag, 3rd ed., (2001).
[5] E. B. Caronna, J. M. Milunsky and H. Tager-Flusberg, “Autism spectrum disorders: clinical and
research frontiers”, Arch Dis Child, vol. 93, no. 6, (2008), pp. 518-23.
[6] Diagnostic and statistical manual of mental disorders. 4th ed. American Psychiatric Association, (1994).
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pp. 5-20.
[8] D. G. Amaral, C. M. Schumann and C. W. Nordahl, “Neuroanatomy of autism”, Trends Neurosci., vol.
31, no. 3, pp. 137–45.
K W V
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
2.1951 2.2322
2.8310 1.2421
3.6952 2.3716
5.2597 1.3800
6.1503 1.3533
1.9029 3.2260
3.1452 3.0322
3.9243 2.1911
4.4788 2.0165
5.4056 3.2515
1.4777 3.2788
2.7477 3.4182
4.0180 3.9260
4.4330 3.3982
6.1739 3.6569
1.7953 4.0907
2.5186 5.3837
3.9838 4.8159
4.7652 4.5929
5.6228 4.7217
1.9724 5.4675
2.8698 5.6061
4.1127 5.3163
5.3396 5.8117
5.7316 6.0645
1.0000 1.0000
2.0000 1.0000
3.0000 1.0000
4.0000 1.0000
5.0000 1.0000
1.0000 2.0000
2.0000 2.0000
3.0000 2.0000
4.0000 2.0000
5.0000 2.0000
1.0000 3.0000
2.0000 3.0000
3.0000 3.0000
4.0000 3.0000
5.0000 3.0000
1.0000 4.0000
2.0000 4.0000
3.0000 4.0000
4.0000 4.0000
5.0000 4.0000
1.0000 5.0000
2.0000 5.0000
3.0000 5.0000
4.0000 5.0000
5.0000 5.0000
13. International Journal of Artificial Intelligence and Applications for Smart Devices
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Copyright ⓒ 2015 SERSC 13
[9] H. Cass, “Visual impairment and autism: current questions and future research”, Autism, vol. 2, no. 2,
(1998), pp. 117–38.
[10] S. Geman, E. Bienenstock and R. Doursat, “Neural networks and the bias/variance dilemma”, Neural
Computation, vol. 4, (1992), pp. 1-58.
[11] T. Kohonen and T. Honkela, “Kohonen Network”, (2007).
[12] M. Oja, S. Kaski and T. Kohonen, “Bibliography of SelfOrganizing Map (SOM) Papers: 1998-2001
Addendum”, Neural Computing Surveys, vol. 3, (2003), pp. 1-156.
Authors
Md. Syeful Islam, he obtained his B.Sc. and M.Sc. in Computer
Science and Engineering from Jahangirnagar University, Dhaka,
Bangladesh in 2010 and 2011 respectively. He is now working as a
Senior Software Engineer at Samsung R&D Institute Bangladesh.
Previously he worked as a software consultant in the Micro-Finance
solutions Department of Southtech Ltd. in Dhaka, Bangladesh. His
research interests are in Natural Language processing, AI,
embedded computer systems and sensor networks, distributed
Computing and big data analysis.
Ruhul Abedin, he obtained his B.Sc. in Computer Science and
Engineering from Jahangirnagar University, Dhaka, Bangladesh in
2010. He is now working as a Software Engineer at BJIT ltd.
previously he worked as Software Engineer Sonali Polaris Ltd. His
research interests are in Natural Language processing, AI and
Neural Network.
Fakrul Hasan, he obtained his B.Sc. in Computer Science and
Engineering from Jahangirnagar University, Dhaka, Bangladesh in
2010. He is now working as a Software Engineer at Dynamic
Solution Innovators Ltd. His research interests are in Natural
Language processing, AI and Neural Network.
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14 Copyright ⓒ 2015 SERSC