Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Get best thesis topics in machine learning from Experienced Ph.D. Writers at Techsparks with 100% Plagiarism Free Work & Affordable price. Our goal is to make students free from their assignments burden, by providing the best thesis assistance. For more details call us at-9465330425 or Visit at: https://bit.ly/3zRB3vN
How to create your own artificial neural networksAgrata Shukla
See how to create your own neural networks.Artificial neural networks are used to simulate the functioning of the human brain.The machine could not think but it predicts.These ANN’s are inspired from the nervous system of the human brain.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Clustering and Classification of Cancer Data Using Soft Computing Technique IOSR Journals
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
Towards Automatic Composition of Multicomponent Predictive SystemsManuel Martín
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
Get best thesis topics in machine learning from Experienced Ph.D. Writers at Techsparks with 100% Plagiarism Free Work & Affordable price. Our goal is to make students free from their assignments burden, by providing the best thesis assistance. For more details call us at-9465330425 or Visit at: https://bit.ly/3zRB3vN
How to create your own artificial neural networksAgrata Shukla
See how to create your own neural networks.Artificial neural networks are used to simulate the functioning of the human brain.The machine could not think but it predicts.These ANN’s are inspired from the nervous system of the human brain.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Clustering and Classification of Cancer Data Using Soft Computing Technique IOSR Journals
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
Towards Automatic Composition of Multicomponent Predictive SystemsManuel Martín
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Neural network based numerical digits recognization using nnt in matlabijcses
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
A neural is a system hardware or software that is patterned to function and was named after the neurons in the brains of humans. A neural network is known to involve several huge processors that are arranged and work in the parallel format for effectiveness. Applications include: handwriting recognition, oil data analysis, weather forecast prediction and face recognition.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
In the present study, the abilities of three classification methods of data mining namely artificial
neural networks with feed-forward back propagation algorithm, J48 decision tree method and
logistic regression analysis are compared in a medical real dataset. The prediction of
malignancy in suspected thyroid tumour patients is the objective of the study. The accuracy of
the correct predictions (the minimum error rate), the amount of time consuming in the
modelling process and the interpretability and simplicity of the results for clinical experts are
the factors considered to choose the best method
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2. A particular application, that network is
ready to be trained. To start this process
the initial weights are chosen randomly.
There are two approaches to training
supervised
unsupervised
3. Supervised training involves a mechanism of
providing the network with the desired output
either by manually "grading" the network's
performance or by providing the desired outputs
with the inputs.
In supervised training, both the inputs and the
outputs are provided. The network then
processes the inputs and compares its resulting
outputs against the desired outputs.
4. This process occurs over and over as the weights
are continually tweaked. The set of data which
enables the training is called the "training set."
During the training of a network the same set of
data is processed many times as the connection
weights are ever refined.
The current commercial network development
packages provide tools to monitor how well an
artificial neural network is converging on the
ability to predict the right answer.
5. This could be because the input data does not
contain the specific information from which the
desired output is derived. Networks also don't
converge if there is not enough data to enable
complete learning. Ideally, there should be
enough data so that part of the data can be held
back as a test.
Many layered networks with multiple nodes are
capable of memorizing data. To monitor the
network to determine if the system is simply
memorizing its data in some non significant way,
supervised training needs to hold back a set of
data to be used to test the system after it has
undergone its training.
6. The designer then has to review the input and
outputs, the number of layers, the number of
elements per layer, the connections between the
layers, the summation, transfer, and training
functions, and even the initial weights themselves.
Those changes required to create a successful
network constitute a process wherein the "art" of
neural networking occurs.
There are many laws (algorithms) used to implement
the adaptive feedback required to adjust the weights
during training. The most common technique is
backward-error propagation, more commonly known
as back-propagation. These various learning
techniques are explored in greater depth later in this
report.
7. It involves a "feel," and conscious analysis, to
insure that the network is not overtrained.
Initially, an artificial neural network configures
itself with the general statistical trends of the
data. Later, it continues to "learn" about other
aspects of the data which may be spurious from a
general viewpoint.
The system has been correctly trained, and no
further learning is needed, the weights can, if
desired, be "frozen." In some systems this
finalized network is then turned into hardware so
that it can be fast. Other systems don't lock
themselves in but continue to learn while in
production use.
8. In unsupervised training, the network is
provided with inputs but not with desired
outputs. The system itself must then decide what
features it will use to group the input data. This
is often referred to as self-organization or
adaption.
unsupervised learning is not well understood.
This adaption to the environment is the promise
which would enable science fiction types of
robots to continually learn on their own as they
encounter new situations and new environments.
9. . Some of these situations involve military
action where new combat techniques and
new weapons might be encountered.
Because of this unexpected aspect to life
and the human desire to be prepared, there
continues to be research into, and hope for,
this field.
Yet, at the present time, the vast bulk of
neural network work is in systems with
supervised learning. Supervised learning is
achieving results.
10. Topology is a branch of mathematics that
studies how to map from one space to
another without changing the geometric
configuration.
The three-dimensional groupings often
found in mammalian brains are an
example of topological ordering.
11. Neural systems do exact their own
demands. They do require their
implementer to meet a number of
conditions. These conditions include:
A data set which includes the information
which can characterize the problem.
An adequately sized data set to both train
and test the network.
12. an understanding of the basic nature of the
problem to be solved so that basic first-cut
decision on creating the network can be made.
These decisions include the activization and
transfer functions, and the learning methods.
an understanding of the development tools.
adequate processing power (some applications
demand real-time processing that exceeds what is
available in the standard, sequential processing
hardware. The development of hardware is the
key to the future of neural networks).