DiscoversNet is an adaptive simulation-based learning environment for designing neural networks. It contains a knowledge-based neural network consultant module to provide educational guidance during the neural network design process. The consultant module represents domain knowledge using a knowledge-based neural network and provides advice to users based on their current understanding level. The system allows users to build neural network simulators through interactive manipulation of neural network components and receives feedback to correct misconceptions.
Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
Nature Inspired Reasoning Applied in Semantic Webguestecf0af
1) Neural networks are computational structures inspired by biological neural networks and have been successfully used to solve complex tasks like image recognition and natural language processing.
2) Neural networks consist of interconnected nodes that perform simple mathematical functions to produce outputs. The connections between nodes and their weights can be modified through training to solve problems.
3) Nature inspired algorithms like neural networks are well-suited for semantic web problems because they can process large amounts of information quickly to find good enough solutions.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
The document discusses developing a computerized paper evaluation system using neural networks. It proposes replacing the current manual evaluation system, which is biased, inconsistent, and slow, with an automated system. A neural network would analyze student answers, search reference materials for relevant information, assign marks, and ask follow-up questions to further assess student understanding. The network would learn to accurately evaluate papers through a supervised learning process using example papers. Key chapters address the basic structure of the proposed examination system, the role neural networks could play in automatic language analysis and evaluation, and algorithms that could enable unsupervised learning.
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.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina.
2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain.
3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
This document provides an overview of artificial neural networks (ANNs). It discusses ANN basics such as their structure being inspired by biological neural networks in the brain. The document covers different types of ANNs including feedforward and feedback networks. It also discusses ANN properties like learning strategies, applications, advantages like handling noisy data, and disadvantages like requiring training. The conclusion states that ANNs are flexible and suited for real-time systems due to their parallel architecture.
Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
Nature Inspired Reasoning Applied in Semantic Webguestecf0af
1) Neural networks are computational structures inspired by biological neural networks and have been successfully used to solve complex tasks like image recognition and natural language processing.
2) Neural networks consist of interconnected nodes that perform simple mathematical functions to produce outputs. The connections between nodes and their weights can be modified through training to solve problems.
3) Nature inspired algorithms like neural networks are well-suited for semantic web problems because they can process large amounts of information quickly to find good enough solutions.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
The document discusses developing a computerized paper evaluation system using neural networks. It proposes replacing the current manual evaluation system, which is biased, inconsistent, and slow, with an automated system. A neural network would analyze student answers, search reference materials for relevant information, assign marks, and ask follow-up questions to further assess student understanding. The network would learn to accurately evaluate papers through a supervised learning process using example papers. Key chapters address the basic structure of the proposed examination system, the role neural networks could play in automatic language analysis and evaluation, and algorithms that could enable unsupervised learning.
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.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina.
2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain.
3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
This document provides an overview of artificial neural networks (ANNs). It discusses ANN basics such as their structure being inspired by biological neural networks in the brain. The document covers different types of ANNs including feedforward and feedback networks. It also discusses ANN properties like learning strategies, applications, advantages like handling noisy data, and disadvantages like requiring training. The conclusion states that ANNs are flexible and suited for real-time systems due to their parallel architecture.
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
This document contains 40 questions about soft computing concepts including neural networks, fuzzy systems, evolutionary computation, and hybrid intelligent systems. The questions cover topics such as the differences between hard and soft computing, components of expert systems, applications of artificial neural networks, types of learning in neural networks, perceptrons, adaptive linear neurons, backpropagation networks, and training algorithms for various neural network architectures.
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.
The document is a dissertation submitted to Gujarat University in partial fulfillment of a Master's degree in Computer Application, which discusses character recognition using neural networks. It provides an index of the contents including the introduction to neural networks, their architecture and applications, an introduction to character recognition, the use of Matlab and its neural network toolbox, a literature survey, the proposed work on digit recognition, potential enhancements, and conclusions. The dissertation was submitted by Sachinkumar M. Bharadva and Dhara Solanki under the guidance of their internal guide Mr. Sandeep R. Vasant at the AES Institute of Computer Studies.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
This document discusses neural networks and fuzzy control. It begins by defining neural networks and noting that they can be trained to recall responses learned during training when only input data is provided. Fuzzy logic can be incorporated to add flexibility by allowing vague inputs and general system boundaries. The document then discusses various neural network learning algorithms and applications of neuro-fuzzy systems. It notes some shortcomings of current algorithms and proposes other methods for more efficient control. The document also demonstrates how fuzzy parameters and principles can be added to a neural network to provide user flexibility and robustness.
Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
Artificial neural networks seminar presentation using MSWord.Mohd Faiz
This document provides an overview of artificial neural networks. It discusses neural network architectures including feedforward and recurrent networks. It covers neural network learning methods such as supervised learning, unsupervised learning, and reinforcement learning. Backpropagation is described as a method for training neural networks by calculating partial derivatives of the error function. Higher order learning algorithms and considerations for designing neural networks like choosing the number of hidden layers and activation functions are also summarized.
APPLYING NEURAL NETWORKS FOR SUPERVISED LEARNING OF MEDICAL DATAIJDKP
Constructing a classification model based on some given patterns is a form of learning from the environment perception. This modelling aims to discover new knowledge embedded in the input observations. Learning behaviour of the neural network model enhances the classification properties. This paper considers artificial neural networks for learning two different medical data sets in term of number of instances. The experiment results confirm that the back-propagation supervised learning algorithm has proved its efficiency for such non-linear classification issues.
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Comprehensive Final Exam Accounting 3300 Fall 2009 Prof. Richard ...butest
This document provides a comprehensive final exam for an accounting course. It contains 46 multiple choice questions testing concepts related to purchasing costs, economic order quantity, activity-based costing, budgeting, transfer pricing, and divisional performance evaluation. The exam is a take-home exam to be completed and submitted online by the last day of finals.
Activity Recognition from User-Annotated Acceleration Data Ling ...butest
This document summarizes a study on activity recognition using wearable accelerometers. The study collected data from 20 subjects performing everyday activities using 5 wireless accelerometers placed on the body. Machine learning classifiers like decision trees were able to accurately recognize activities from the acceleration data, achieving up to 84% accuracy with models trained on data from multiple subjects. While more sensors provided better recognition, using only 2 sensors like the thigh and wrist maintained good accuracy. The study demonstrated the potential of using inexpensive wearable sensors for activity recognition and that training data can be collected by subjects themselves without supervision from researchers.
The document discusses the future of computing platforms and how they will change to handle massive amounts of data and machine learning tasks. Some key points:
- Traditional views of performance gains from clock speed increases are over. New architectures enabled by multi-core CPUs will radically change computing.
- "Big data" tasks like search, machine learning, and real-time data analysis will be increasingly important drivers of new computing platforms.
- Simple machine learning models applied to massive amounts of data can produce useful results, even without deep domain expertise. This approach has been demonstrated to work well for tasks like language translation.
- Future platforms may blend CPUs and GPUs differently to best handle both serial and parallel tasks for big data and machine
The document reports on experiments conducted with a multiagent system to understand the roles of self and cooperative learning. In the experiments, one agent started with an initial case base of two cases while others started with 16 cases each. The experiments compared learning using only self-learning versus using both self-learning and cooperative learning. The results showed that combining self-learning and cooperative learning generally led to a wider coverage of problem descriptors than only self-learning. It also introduced more diversity into the case bases. Additionally, cooperative learning was found to bring about higher utility and difference gains to the case bases than self-learning alone.
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
This document contains 40 questions about soft computing concepts including neural networks, fuzzy systems, evolutionary computation, and hybrid intelligent systems. The questions cover topics such as the differences between hard and soft computing, components of expert systems, applications of artificial neural networks, types of learning in neural networks, perceptrons, adaptive linear neurons, backpropagation networks, and training algorithms for various neural network architectures.
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.
The document is a dissertation submitted to Gujarat University in partial fulfillment of a Master's degree in Computer Application, which discusses character recognition using neural networks. It provides an index of the contents including the introduction to neural networks, their architecture and applications, an introduction to character recognition, the use of Matlab and its neural network toolbox, a literature survey, the proposed work on digit recognition, potential enhancements, and conclusions. The dissertation was submitted by Sachinkumar M. Bharadva and Dhara Solanki under the guidance of their internal guide Mr. Sandeep R. Vasant at the AES Institute of Computer Studies.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
This document discusses neural networks and fuzzy control. It begins by defining neural networks and noting that they can be trained to recall responses learned during training when only input data is provided. Fuzzy logic can be incorporated to add flexibility by allowing vague inputs and general system boundaries. The document then discusses various neural network learning algorithms and applications of neuro-fuzzy systems. It notes some shortcomings of current algorithms and proposes other methods for more efficient control. The document also demonstrates how fuzzy parameters and principles can be added to a neural network to provide user flexibility and robustness.
Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
Artificial neural networks seminar presentation using MSWord.Mohd Faiz
This document provides an overview of artificial neural networks. It discusses neural network architectures including feedforward and recurrent networks. It covers neural network learning methods such as supervised learning, unsupervised learning, and reinforcement learning. Backpropagation is described as a method for training neural networks by calculating partial derivatives of the error function. Higher order learning algorithms and considerations for designing neural networks like choosing the number of hidden layers and activation functions are also summarized.
APPLYING NEURAL NETWORKS FOR SUPERVISED LEARNING OF MEDICAL DATAIJDKP
Constructing a classification model based on some given patterns is a form of learning from the environment perception. This modelling aims to discover new knowledge embedded in the input observations. Learning behaviour of the neural network model enhances the classification properties. This paper considers artificial neural networks for learning two different medical data sets in term of number of instances. The experiment results confirm that the back-propagation supervised learning algorithm has proved its efficiency for such non-linear classification issues.
This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional
Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features,
For more topics stay tuned with Learnbay.
Comprehensive Final Exam Accounting 3300 Fall 2009 Prof. Richard ...butest
This document provides a comprehensive final exam for an accounting course. It contains 46 multiple choice questions testing concepts related to purchasing costs, economic order quantity, activity-based costing, budgeting, transfer pricing, and divisional performance evaluation. The exam is a take-home exam to be completed and submitted online by the last day of finals.
Activity Recognition from User-Annotated Acceleration Data Ling ...butest
This document summarizes a study on activity recognition using wearable accelerometers. The study collected data from 20 subjects performing everyday activities using 5 wireless accelerometers placed on the body. Machine learning classifiers like decision trees were able to accurately recognize activities from the acceleration data, achieving up to 84% accuracy with models trained on data from multiple subjects. While more sensors provided better recognition, using only 2 sensors like the thigh and wrist maintained good accuracy. The study demonstrated the potential of using inexpensive wearable sensors for activity recognition and that training data can be collected by subjects themselves without supervision from researchers.
The document discusses the future of computing platforms and how they will change to handle massive amounts of data and machine learning tasks. Some key points:
- Traditional views of performance gains from clock speed increases are over. New architectures enabled by multi-core CPUs will radically change computing.
- "Big data" tasks like search, machine learning, and real-time data analysis will be increasingly important drivers of new computing platforms.
- Simple machine learning models applied to massive amounts of data can produce useful results, even without deep domain expertise. This approach has been demonstrated to work well for tasks like language translation.
- Future platforms may blend CPUs and GPUs differently to best handle both serial and parallel tasks for big data and machine
The document reports on experiments conducted with a multiagent system to understand the roles of self and cooperative learning. In the experiments, one agent started with an initial case base of two cases while others started with 16 cases each. The experiments compared learning using only self-learning versus using both self-learning and cooperative learning. The results showed that combining self-learning and cooperative learning generally led to a wider coverage of problem descriptors than only self-learning. It also introduced more diversity into the case bases. Additionally, cooperative learning was found to bring about higher utility and difference gains to the case bases than self-learning alone.
This document discusses scientific applications of machine learning. It describes supervised vs unsupervised learning and generative vs kernel methods. It also discusses using machine learning techniques for tasks like mixture modeling, stochastic grammars, transcriptional gene regulation networks, and gene regulation and signal transduction networks. Examples of applications areas mentioned include biological imaging, mixture modeling, and systems biology software.
This document provides a summary of Christopher J. Pal's professional background and qualifications. It lists his education, including a Ph.D. in Computer Science from the University of Waterloo in 2005. It details his appointments as a research scientist at UMass Amherst since 2005 and previous research internships at Microsoft and the University of Toronto. It also lists his publications, awards, and professional service activities in the field of computer science.
This document describes research into developing fully adaptive bots for first-person shooter games using machine learning techniques. It outlines experiments using both continuous learning and reinforcement learning to create bots that can learn and adapt their behavior through gameplay experience. Initial experiments focused on inferring static models for tank control in the game BZFlag using machine learning. These models were then extended using continuous learning to allow the tanks to adapt to the playstyles of other players. Reinforcement learning was also explored as a way to generate bots that learn individual behaviors solely through trial and error starting from no experience.
Presentation on Machine Learning and Data Miningbutest
The document discusses the differences between automatic learning/machine learning and data mining. It provides definitions for supervised vs unsupervised learning, what automated induction is, and the base components of data mining. Additionally, it outlines differences in the scientific approach between automatic learning and data mining, as well as differences from an industry perspective, including common data mining techniques used and tips for successful data mining projects.
This document provides an overview and summary of a student project report on simulating a feed forward artificial neural network in C++. The report includes an abstract, table of contents, list of figures, and 5 chapters that discuss the objectives of the project, provide background on artificial neural networks, describe the design and implementation of a 3-layer feed forward neural network using backpropagation, present the results, and provide references. The design section explains the backpropagation algorithm and provides pseudocode for calculating outputs at each layer. The implementation section provides pseudocode for training patterns and minimizing error.
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
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.
This article provides an introduction to artificial neural networks (ANNs) and presents guidelines for designing effective ANN solutions. It discusses the key components of ANNs, including their biological inspiration, history, and different types of learning algorithms. The article emphasizes that successful ANN development requires extensive domain knowledge engineering and following best practices for selecting input variables, learning methods, architecture, and training samples. Specifically, it recommends knowledge-based input selection, choosing appropriate learning algorithms based on the data type, designing network topology based on the algorithm, and selecting optimal training set sizes, especially for time series problems. Overall, the article stresses that incorporating domain expertise at each design step is essential for building ANNs that generalize well to new problems.
This document describes a new technique called "Latent Cross" for incorporating contextual data into recurrent neural network (RNN) recommender systems more effectively. The authors first demonstrate that modeling context as direct features in feed-forward neural networks is inefficient at capturing common feature interactions. They then apply this insight to design an improved RNN recommender system that uses Latent Cross. Latent Cross embeds the context and performs an element-wise product of the context embedding with the RNN's hidden states, allowing the model to better understand how context affects recommendations. The authors evaluate their approach on a large-scale RNN recommender system at YouTube and show that Latent Cross improves recommendation performance over conventional techniques.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses using personalized ontologies to improve web information gathering by representing user profiles. It proposes a model that constructs personalized ontologies by adopting user feedback from a world knowledge base. The model also uses users' local instance repositories to discover background knowledge and populate the ontologies. The proposed ontology model is evaluated against benchmark models through experiments using a large standard dataset.
Prediction of Student's Performance with Deep Neural NetworksCSCJournals
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of today’s and future’s samples have similar characteristics.
IRJET - A Survey on Machine Learning Algorithms, Techniques and ApplicationsIRJET Journal
This document discusses machine learning algorithms, techniques, and applications. It begins with an introduction to machine learning and different types of learning including supervised learning, unsupervised learning, reinforcement learning, and others. It then groups various machine learning algorithms based on similarities and compares the performance of popular algorithms like Naive Bayes, support vector machines, and decision trees. The document concludes that machine learning researchers aim to design more efficient algorithms that can perform better across different domains.
In tech application-of_data_mining_technology_on_e_learning_material_recommen...Enhmandah Hemeelee
The document describes a recommendation system that applies data mining techniques to recommend e-learning materials. It proposes using LDAP for fast searching of materials across systems, JAXB for parsing content, and association rule mining and collaborative filtering to generate recommendations. The system collects user activity data, analyzes it using Apriori algorithm to find related search terms and content, and stores results in an LDAP database to provide recommendations to users.
In tech application-of_data_mining_technology_on_e_learning_material_recommen...Enhmandah Hemeelee
The document describes a recommendation system that applies data mining techniques to recommend e-learning materials. It proposes using LDAP for fast searching of materials across systems, JAXB for parsing content, and association rule mining and collaborative filtering for recommendations. A web spider collects content indexes from learning management systems and stores data in an LDAP directory. Users can search for related materials, and the system mines log data to associate frequently searched terms and recommend additional resources.
This document summarizes research on the design and implementation of an assessment model called SMARTIC based on artificial neural networks to evaluate higher education teachers' use and appropriation of information and communication technologies (ICTs). The SMARTIC model was developed using the topology of a multilayer artificial neural network and applied to evaluate 30 teachers. The model diagnoses ICT use and appropriation on a scale of 0 to 100% based on input data related to teachers' characteristics, training, and ICT factors. The results found a linear relationship between the model's nodes and validated the data using normal distribution.
IRJET- Deep Learning Techniques for Object DetectionIRJET Journal
The document discusses deep learning techniques for object detection in images. It provides an overview of convolutional neural networks (CNNs), the most popular deep learning approach for computer vision tasks. The document describes the basic architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. It then discusses several state-of-the-art CNN models for object detection, including ResNet, R-CNN, SSD, and YOLO. The document aims to help newcomers understand the key deep learning techniques and models used for object detection in computer vision.
The document discusses improving neural network classification of astronomical objects into stars and galaxies. It analyzes the classifier used in the SExtractor software, which uses a multi-layer perceptron neural network trained on simulated data. The authors build their own classifier using WEKA to automatically select features and the neural network topology from real data classified by an expert. Their classifier achieved slightly better results than SExtractor and used fewer computational resources. However, more domain specific information is still needed to build a better star/galaxy separator.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptxJavier Daza
The document discusses reactive systems and neural networks. It defines reactive systems as software that rapidly responds to environmental events and changes. Neural networks are artificial intelligence models inspired by the human brain that consist of interconnected artificial neurons. They are used for tasks like machine learning, pattern recognition, and complex data processing. The document then goes into more detail about the structure of artificial neural networks and the components and functioning of artificial neurons.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
AN ADAPTIVE REUSABLE LEARNING OBJECT FOR E-LEARNING USING COGNITIVE ARCHITECTUREacijjournal
Nowadays, a huge amount of ambiguous e-learning materials are available in World Wide Web
irrespective of various objectives. These digital educational resources can be reused and shared from
centralized online repository and it will avoid the redundant learning material. The main goal is to design
consistent adaptable e-learning course material for web-based education system with emphasis on the
quality of learning. This can be done by organizing learning object in a prescribed manner and it can be
reused in feature. Such reusable learning objects are enhanced further to become adaptive reusable
learning objects that are virtually cognitive and responsive towards the specific needs of the user/customer.
This paper proposes the cognitive architecture to offer an adaptive reusable objects (RLO) based on
individual profile of e-learner besides their cognitive behaviour while learning.
Proposing a new method of image classification based on the AdaBoost deep bel...TELKOMNIKA JOURNAL
Image classification has different applications. Up to now, various algorithms have been presented
for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate
is an issue which many researches have been carried out about it. This research intends to optimize
the problem with hybrid methods and deep learning. The hybrid methods were developed to improve
the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative
probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In
fact, this method is anunsupervised method, in which all layers are one-way directed layers except for
the last layer. So far, various methods have been proposed for image classification, and the goal of this
research project was to use a combination of the AdaBoost method and the deep belief network method to
classify images. The other objective was to obtain better results than the previous results. In this project, a
combination of the deep belief network and AdaBoost method was used to boost learning and the network
potential was enhanced by making the entire network recursive. This method was tested on the MINIST
dataset and the results were indicative of a decrease in the error rate with the proposed method as compared
to the AdaBoost and deep belief network methods.
Soft Computing based Learning for Cognitive Radioidescitation
Over the last decade the world of wireless communications has been undergoing
some crucial changes, which have brought it at the forefront of international research and
development interest, eventually resulting in the advent of a multitude of innovative
technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh
networks and Software Defined Radio. Such a highly varying radio environment calls for
intelligent management, allocation and usage of a scarce resource, namely the radio
spectrum. One of the most prominent emerging technologies that promise to handle such
situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio
technology and utilize intelligent software packages that enrich their transceivers with the
highly attractive properties of self-awareness, adaptability and capability to learn. The
Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing,
learning, and optimization algorithms to control and adapt the radio system from the
physical layer and up the communication stack. The integration of a learning engine can be
very important for improving the stability and reliability of the discovery and evaluation of
the configuration capabilities. To this effect, many different learning techniques are
available and can be used by a Cognitive Radio ranging from pure lookup tables to
arbitrary combinations of soft Computing techniques, which include among others:
Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy
systems, Hidden Markov Models, etc. The proposed work contributes in this direction,
aiming to develop a learning scheme and work towards solving problems related to learning
phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the
performance assessment work, conducted in order to design and use an appropriate
structure, while indicative results need to be presented and discussed in order to showcase
the benefits of incorporating such learning schemes into Cognitive Radio systems.
Subsequently feasibility of such learning schemes could be tested with simulations. In the
near future, such learning schemes are expected to assist a Cognitive Radio system to
compare among the whole of available, candidate radio configurations and finally select the
best one to operate in.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
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3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
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4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
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This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
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1. DiscoverNet: Adaptive Simulation-Based Learning Environment
And Support System for Designing Neural Networks
Safia Belkada, Alexandra I. Cristea and Toshio Okamoto
AI & Knowledge Engineering Lab., Graduate School of Information Systems
University of Electro-Communications
Choufugaoka 1-5-1, Chofu-shi, Tokyo 182-8585, Japan
safia@ai.is.uec.ac.jp
Abstract
For the greatest part, intelligent tutoring systems (ITS) have attempted to implement traditional
methods of learning, teaching, train-and-practice, in which students solve relatively simple problems,
chosen in advance by the teacher. Recently however, adaptive educational systems and flexible learning
systems are being considered, as an alternative, powerful new strategy of teaching/learning. This paper
presents the development of an adaptive simulation-based learning environment for designing neural
networks (DiscoverNet), for discovery and self-learning. We chose ‘neural networks’ (NN) as
application domain, due to the fact that their study is particularly suitable for an adaptive, learner level
adjusted approach, as we will show in the paper. We embedded in the NN Computer Aided Design
(CAD) teaching system also a consultant module that is responsible for the educational orientation
during the design of neural networks. Our consulting function is based on storing and processing the
domain knowledge on NN, as well as the current user’s NN knowledge, in a knowledge based neural
network (KBNN) form. This paper presents our DiscoverNet system, with a special focus on the
adaptive features of the KBNN consultant module. We also present a case study for the beginner user,
as well as the results obtained. We will also discuss the educational methods used, as well as the
advantages we are aiming at with these methods.
1 Introduction
1.1 Background and Related Work
Recently, the trend towards developing interactive learning environments or intelligent tutoring systems is gaining
in strength, and, as McArthur et al. [5] pointed out, there are various examples of such researches now a days.
Simulation-learning environments have been proven to be an effective learning strategy [2,5]. This kind of strategy
allows learning by using simulation, by engaging learners to manipulate, freely and directly, concrete objects in the
experimental environment, and observe the changes induced by their manipulations. The aim of this type of
functionality is to foster the integration of theory and practice. The various possible situations, which are implied
implicitly or explicitly in the environment, build up the learner’s experience and, through trial and error, attract the
1
2. learner and support him/ her in establishing his/ her independence and develop his/ her fundamental comprehension
[1,3,10].
Building support environments using only symbolic, knowledge-based systems often leads to the well-known
knowledge acquisition bottleneck. A sub-symbolic neural network model can be trained to perform reasonably well -
based on the training data sets - but it cannot readily incorporate domain knowledge or provide explanation of the
results. A hybrid system made of a combination of the two approaches cited above has been shown [6] to outperform
both, when used separately. There are different approaches to constructing hybrid intelligent systems, which aim to
exploit the features of knowledge-based systems and neural network methods [6,8,11]. One way [11] is to translate the
domain theory into a neural network, and thereby determine the neural network’s topology, then refine the reformulated
rules, by using, for instance, backpropagation. The KBNN algorithm falls in this category [11]. We are building on the
KBNN approach, but with the difference that we use the KBNN network space as a search space for the correct network
topology, closest to the user designed network, as we will show further on.
1.2 Neural Networks as a Problem Solving Tool
There has been a great interest in artificial neural networks over the last few years, as researchers from diverse
backgrounds have produced theoretical foundations and demonstrated numerous useful applications of this field [9].
Neural networks are tools for solving many types of problems. These problems may be characterized as mapping,
clustering, and constrained optimization. There are several neural networks available for each type of problem. For
many applications, the mapping of the problem on a neural network requires detailed understanding of neural networks,
which can be achieved only by performing calculations by oneself, and by developing some simulators for extremely
simple examples. Most students that have to use neural networks, often have to face great difficulties to even acquire a
working knowledge of the very basics. Such problems, indeed very common in many areas, require however knowledge
about highly abstract concepts. When using neural networks for different domain applications, this involves
constructing simulators, and this can be very difficult to do, for example, during a NN lecture. For the same reasons,
students cannot really be expected to solve anything by trivial exercises during tutorials. This is not so much due to the
conceptual difficulties inherent in the neural networks, but a simple consequence of the representational complexity of
such a model, unrelated to the pedagogical method used. To overcome these problems, we build an adaptive learning
environment, to encourage the student to develop a deep understanding of the mechanisms of training and applying
neural networks.
2
3. 1.3 Our Research Goal and Methodology
In this paper we propose a framework based on a hybrid approach, integrating a knowledge-based system with the
neural network paradigm, to support learning neural networks within the DiscoverNet system. DiscoverNet allows
learners (for the sake of convenience only, we will use in the remaining the word “user” to refer to learner) to build
their neural network simulators, by blending theory and praxis within the environment, and facilitate learning through a
tutoring program. The system is based on the concept that neural networks can be broken down into a fundamental set
of neural components [4]. The user constructs his/her domain theory in the environment where s/he can choose some
components and find the pattern of relationships among these components. We embedded a consultant module in the
system that is responsible for the educational orientation during the design of neural networks. The consultant
reproduces the initial neural network, which is made by the user during the manipulations of neural objects, by using a
set of inference rules, to help determine the network's architecture and initial weight values. The consultant examines
which information is lacking or wrong, and sends it to the user, together with the initial network, showing the user
his/her misconceptions. The system has an object-oriented architecture, in which an adaptive user interface adjusts to the
user's skills, and has the role of the motivator in the discovery learning process. I.e., the interface allows visualizing
neural models as concrete neural objects. A user builds his/her models by making use of the neural objects and methods
provided by the library of objects. The system is based on a network of networks, adapting itself to the current user. The
order of constructing neural networks was chosen to reflect the complexity of the course material. However, the
material at each level is independent, so that the user may enter the design session at any level and in almost any order
s/he desires.
In this paper we will first show the difficulties that appear in teaching neural networks, and thereby justify the need
of an adaptive learning environment for this field. We identify the interaction with the design mechanism and the
visualization of abstract concepts as two different components of the pedagogical tool for teaching neural networks. Our
students are seniors, or graduate students, in science and engineering. It is assumed that the user is familiar with
calculus and some vector-matrix notions and operations. The mathematical treatment has been kept at a minimal level
in the system, allowing the users to build their neural networks by blending theory and praxis within the environment,
without too much delay or overhead.
The paper is organized as follows. Section 2 describes the basic features of the CAD learning system and its
underlying learning environment. In Section 3 we describe the computational aspects of the support system, via the
domain knowledge representation, followed by an illustration of the advice process. Experimental results and their
3
4. evaluation are reported in section 4, highlighting learning capabilities and properties of the system. Finally, we draw
some conclusions and evaluate our system from the educational point of view.
2. Simulation-Based Learning Environment
2.1 Neural Network Models and Preliminary Research
The most distinctive element in neural networks, as opposed to traditional computational structures, is denoted as
learning. Learning represents the adaptation of some characteristic neural network parameters, by using a set of
examples (known as outcomes of the problem for given conditions) so that, for given input patterns, the outputs reflect
the value of specific functions. The final data-modeling goal is to be able to generalize from prior examples (to be able
to estimate the outcomes of the problem under unknown conditions). Neural networks represent the combination of
architecture (topology) and appropriate learning techniques. Most NNs are often classified as single layer or multilayer
NNs. A neural network model is formally defined by a quadruple < S, W, L, F >, where W is a set of weights (wji), L is
a set of learning algorithms (la), F is a set of external activation functions (fact), and S is further defined by a pair (N, C),
where N is a set of units (ni) and C a set of connections (ci). Assume n units in a neural network model. We have the
following definitions:
N = {ni| 1 ≤ n}
C ⊆ {(ni→ nj) | 1 ≤ (i, j) ≤ n}
W ⊆ {(ni→ nj, wji) | 1 ≤ (i, j) ≤ n}
L = {la1, la2…}
F = {fact1, fact2…} (1)
The number of connections between the units defines the number of layers in the NN. Each two connected units have
an associated weight. The weights represent information being used by the model to solve the problem. Each learning
algorithm is a mapping that transforms one neural network state, which is defined by the set of W, to another neural
network state. For the most typical neural network setting, training is accomplished by presenting a sequence of vectors
or patterns. The weights are then adjusted according to a learning algorithm, i.e., in backpropagation, by summing the
weighted inputs and applying an external activation function. Typically, the same activation function is used for all units
in any particular layer. For instance, let F be a set of activation functions F ={f}, and Xi, Yj a sets of input and output
units. Assume that xi ∈ Xi and yj ∈ Yj. The output, yj, is expressed as:
yinj=Σxiwii ( 2)
4
5. yj =fact(yinj) ( 3)
We have based our CAD teaching environment on some knowledge extraction from three domain specialists, teachers
and professors of Neural Networks at university level and higher. The information based on their experience was
refined and used for the implementation of our system.
2.2 Knowledge Representation and Object Manipulation in DiscoverNet
Abstract
Class
neural
Learning algorithms
Backprop
Kohonen
-addNeuronLayer
-createMapLayer
-connectLayers
-getActivationArea
-learn�
.
Weight matrices Layers Units
-changeWeights -activateSigmoid -activation funct
--compOutputErr
Unit map
Comment objects
Specific
objects
pattern Input value
The domain knowledge of neural networks is represented in the system as a KBNN set of neural objects stored in
Inputs objects
the object library (figure 1). The object library consists of a package of classes, which provide meta- representations for
the graphical objects. The architecture of each class has two levels. The object knowledge level is based on objects and
Figure 1: Neural Net Knowledge Base
contains the core of formal and procedural concepts related to the neural network models. The user creates instances of
a class when s/he manipulates objects. For instance (figure 2), in the feedforward neural networks [7], the initial
instances are the creation of activation functions instances, initial matrix weight instances, and control parameters
instances. The user can interactively create instances and specify the values associated with the relevant functions. The
structural knowledge level allows the exploration of the design stage by defining concepts and relations (e.g., learning
algorithm) over the object knowledge. The structural knowledge is a set of rules defined on sets of instances from the
domain knowledge. The modeling of the domain knowledge of neural network consists of hierarchical relations
between various neural network models, as described in Figure 1. We construct models of knowledge for various neural
network models and various design models for a particular knowledge model.
5
6. Each object encapsulates a state and events. Modeling knowledge domain consists of hierarchical relationships of
various neural network models: we construct knowledge models for the various neural network models, and various
design models established for a particular knowledge model. The levels shown in Figure 1 do not represent levels of
abstraction, they only show, which classes are present and what classes have relationships to each other. The abstract
class “Neural” contains generic methods, e.g., a method to set the initial learning rate of a neural net. Note that the
system prototype contains classes suitable for the Backpropagation network and the Kohonen Feature Map network.
These types of neural networks are considered as most powerful, yet basic, models [7]. We chose these models because
they are the most used, and the equations that describe the models are simple compared to other models. Multilayer
perceptrons (MLP) are feedforward networks trained with static backpropagation [7]. These networks are used in many
applications requiring static pattern classification. Backpropagation is a learning algorithm used to change the weights
connected to the network’s hidden layer(s) [7]. The backpropagation uses a computed output error to change the
weights values in backward direction. It is vitally important to choose the correct algorithm, since the choice of the
algorithm can affect the final neural network performance. For the first steps in the neural network domain, a novice
user can try to handle similar problems of growing complexity.
Figure 2 shows a simplified user action model constructed by the consultant. Each node represents a design
object instance. Each design object tree can be decomposed into components required to accomplish the higher-level
node restrictions. Actions are at the lowest level in the graph. The decomposition may lead to nodes that are sequential.
Sequential nodes represent actions that must be performed in the prescribed order. Arcs represent events that describe
the initiating and terminating state of an action.
Major learner actions
Supervised Unsupervise
learning d learning
Actions to design a specific neural network
Learning Algorithm,
e.g.,backpropagation Input data
Architectu (training/testing data)
re
Parameters’
specification
Related components actions
6
7. Weight matrix
Number of layers/ Learning rate
units
Threshold
specifications
Connections’ type
e.g., full_connection
Figure2: User’s Design Model
In the following, the state and event concepts that we used will be explained.
2.2.1 State
A state, X, is a class variable which is a numerical attribute (e.g., an initial weight matrix), a characteristic of a
neural object (e.g., activation function) or a process (e.g., learning algorithm). It can be computed from predefined
formulas.
2.2.2 Event
Our definition of events is analogue to the definition of events in object-oriented computation. The event handling
determines the communication between interactive objects. Our communication model is a connective model, which
allows any object to communicate with any other one. However, for instance, in order for the “full_connection” object
to communicate with a unit object, it must have a pointer to that object. In such a case, the “full_ connection” object
can invoke some method on the unit object that handles the communication. The neural network model is constructed
from a collection of objects and the appropriate relationship between them. The overall design is provided by the object
interaction. However, although breaking down neural models into a set of neural objects reduces their complexity, it
sometimes leads to larger sets of objects. To manage these objects we determine the relationship existing between them
by using the inheritance mechanism.
Figure 3 shows an example of object interaction mechanism via messages that lead to object state transition.
Message
corresponding to
the trigging
simulation stage Simulation trace file
Message
analyzer
Message object transmission
Activate object
7
Figure 3: objects interactions
8. 2.3 DiscoverNet System Architecture
With DiscoverNet the user designs a neural network model through different manipulations of objects existing in the
library and different requests to the system for helps and hints. Figure 4 shows a simplified control flow of DiscoverNet,
which consists of the following parts:
(i) Adaptive user interface: it consists of the learning and authoring environment related to the design of the neural
network models. It is in this part also, where the user model representing the individual’s understanding level is first
analyzed and built.
(ii) Knowledge based neural network: it accumulates neural network models. Each neural model consists of the
respective architectures, learning algorithm and set of parameters [7]. The selection of the appropriate parameters and
architecture for a given application is made after many trials.
(iii) Consultant module: it manages the communication between different parts and gives assistance to the user
whenever it is needed.
(iv) In Figure 4, the design database stores representations of the design description made by the user of the solving
procedures of one specific application.
(v) The object library contains components of neural networks.
(vi) The system gradually builds a user model, and uses it for the analysis and understanding of the real user’s needs.
The consultant module uses this model, in particular, to generate useful guidance and correct the user mistakes.
User Model
Object Library
Adaptive user Interface
User
Knowledge Base
CONSULTANTMODULE
Neural Network
(KBNN)
Advisor
Design
Database
8
9. Figure 4. Architecture of DiscoverNet System
3. Discovery Learning Support System
In this section, we analyze the approach used for the consultant. The consultant module gives advice to the user
considering different possible concept descriptions, by integrating a knowledge-based system with a neural network that
helps the former to refine the initial domain knowledge. The consultant has two main parts: the rule base and the
advice algorithm.
3.1 The Rule Base System
A rule-based system captures the decision-making of the design. The decision-making rules consist of packages of
classes that attempt to identify objects or decide on actions:
(i) Design classes contain a set of rules, which describe the construction of a neural network model according to what
constraints should be given in the design stage and what information is desired ( An example can be found in figure 5).
(ii) For each Design class we define a set of rules that describe a specific neural network model and we name it
Specification class (Please find and example in figure 6).
(iii) Advisor classes manage the communication between the user and the whole system.
Rule MLFFwithBP = new Rule(rb, "MLFF_with_BP",
new Clause(supervisedType,cEquals, "Norecurrent") ,
new Clause(activfunc,cEquals, "nolinear"),
new Clause(learning,cEquals, "Backpropagation"),
new Clause(training,cEquals, 巴 atch_training")) ;
�
Figure 5. Definition of rules in the Design class
RuleVariable supervisedType = new RuleVariable("Supervised_Type") ;
RuleVariable learning = new RuleVariable("learning") ;
RuleVariable activfunc = new RuleVariable("ActivFunc") ;
learning.setLabels("Delta_rule Error_correction Competitive_learning Backpropagation") ;
Figure 6. Definition of rule 痴 variables in the specification class
3.2 The Advice Process
In the following we examine the computational approach of the advice process by using our algorithm, which has
9
10. two phases:
1) Searching through the space of neural net architectures H.
2) Training the neural network and providing feedback information.
Figure 7 shows the advice process in the consultant. The consultant diagnoses a neural model made by the user by
constructing a script file during the user's manipulations of objects (considered as initial domain knowledge). The
algorithm uses the initial knowledge to help guide the search through the space H and determine a starting point in the
weights space. The algorithm then tries to translate the script file into a set of units represented as an AND/OR
dependency tree. From the tree, the consultant chooses a proper index, related to the learning algorithm, and examines
whether the architecture and the corresponding parameters can be approximated in the space H. The following
algorithm describes the searching method:
(i) Assume I <S2, W2,L2,F2> is a set of instances representing the initial knowledge (user input).
(ii) Select h <S1,W1,L1,F1> with q layers in H,
(iii) I= I+ U I-: I+ is the set of instances satisfying h, and I-,those that do not satisfy h,
(iv) Map h <S1,W1,L1,F1> with I <S2,W2,L2,F2>
(v) Compute the probability P to choose the best initial weights in h to be included in I+,
∑ w ji
( j ,i )∈I
(vi) Pi = , Where wji ∈ I is weight value from unit i to unit j. The probability of belonging to the
∑ w ji + ∑ w ji
( j ,i )∈I ( j , i )∈H
instance I- is (1-this probability). The consultant tends to assign most of the nodes that are heavily linked together. This
helps to minimize the destruction of the design model I made by the user, since nodes belonging to the designed model
are connected by heavily linked weights.
(vii) I can be mapped to h if card (I+)= card (h)
(viii)
(ix)and
Pi ≠ 0.
That is, h can induce all possible partitions of I+ instance if the dimension of h is equal to the one of I+.
: File transformation
: Algorithm 10
NN made by the system and
: Input/output Candidates of best parameters for
Script :file: Analyzer algorithm
Learner
Rulebase the neural model presented to the
Domain
Actions trace file transformation learner
Searcher/
Knowledge : Database the NN) H)
(Train
Algorithm (space
11. Design/specification
classes
Rule1: {MLP,
superv}
Rule2:
{MLP,Bp,input/outpu
Initial neural model
made by a user
AND/OR Tree
Consultant Module
Constructed Neural
Model: graphical
representation
Figure 7 Advice Process in the Consultant Module
The induced network is a fully connected neural network with L ={l}, where l is the learning algorithm chosen by the
user, F = {f1, f2…fq}, where F is the set of activation functions. The weight values are confined to the range from –1 to –1;
that is, -1 ≤wji ≤ 1 for all wji, 1≤(i, j) ≤n, where wji is the weight associated with the connection pointing from unit i to
unit j. The consultant optimizes the induced neural network as follows:
(i) Neurons chosen by the user are initialized with heavy weight connections,
(ii) Lightweight connections are added to the network to facilitate the correction of the user's NN,
(iii) Additional input neurons may be automatically added by the system to the network, to incorporate features,
which do not appear in the network made by the user,
(iv) The consultant initializes the weights and biases if needed (i.e., if not already initialized by the user).
The consultant examines which information is lacking or wrong and sends the result to the user together with the initial
network showing the user’s misconceptions.
4. Implementation and Results
11
12. The experimental results are organized into three subsections. The first part explains the functionality of the learning
environment while the second part illustrates an application example for parameter computation, and the last part
represents a case study of parameter settings for the novice user training. For the experimental purpose, we have used
simple logic functions as problem applications.
4.1 Sample Assistance Process
At the start of the session, the main screen pops up as shown by Figure 8. The user builds his/her network by
assembling objects. The system constrains the network to be physically possible, by graphical representation restrictions
(see figure 10). When the design is satisfactorily completed, the user is required to train the network by specifying a
learning algorithm accompanied with its specific parameters. The environment shows the output data of the network
and the corresponding graph. In fact, to build a neural model, starting with the data, the user constructs the topology of
his/her neural model and specifies its initial weight matrices and learning parameters. Each layer of the constructed
model has to contain vectors of data for input/output units. When the learning algorithm is selected from a list of
options contained within menus, the training session can be started. At the end of the training session, the output pattern
of the data and the corresponding graph are shown to the user as final results.
Since the selection of appropriate parameters is crucial for a fast convergence of the algorithm [8], the user may
impose some constraints on the parameters. It is practical that the user specifies how much correction should be applied
to the weights by tuning the parameters.
Figure 8. Neural Network Design Session
12
13. 4.2 A MLP Weight Computation Example
This section presents a weight computation case study. Suppose that we have the following 3-layered MLP:
Input layer Hidden layer Patterns to be learned: Input
target
Output layer 01 0
Input Values
11 1
Output value
Weight matrix 2
Weight matrix 1 0.35
0.62 0.42 0.81
− 0.17 0.55
Figure 9. Weight change according to input patterns
First, the weight values are set to random values in weight matrix 1 and weight matrix 2. The learning rate of the net
is set to 0.25. Next, the values of the first input pattern (0 1) are set to the neurons of the input layer (the output of the
input layer is the same as its input). The neurons in the hidden layer are activated:
Input of the hidden neuron 1 : 0 × 0.62 + 1 × 0.52 = 0.55
Input of the hidden neuron 2 : 0 × 0.42 + 1 * (-0.17) = -0.17
Output of the hidden neuron 1 : 1 1 + e −0.55 = 0.63
Output of the hidden neuron 2 : 1 1 + e +0.17 = 0.46 (4)
The neuron in the output layer are activated:
Input of output neuron : 0.35 × 0.63 + 0.81 × 0.46 = 0.59
Output of output neuron : 1 1 + e -0.6 = 0.64 (5)
Computation final error : - 0.64
0.32659
By applying the backpropagation algorithm, the weight matrix2 is changed to: , and the weight matrix1
0.79311
0.62 0.42
is changed to .
− 0.21 0.5126
The first input pattern has been therefore propagated through the net. The same procedure is used to for the next input
pattern, but with the changed weight values. After forward and backward propagation of the second pattern, one
learning step is complete and the net error can be calculated by adding up the square output errors of each pattern. By
13
14. performing this procedure repeatedly, this error value gets smaller and smaller. The algorithm is successfully finished,
if the net error value reaches zero or is below some error margin (threshold).
4.3 The Configuration Menu as a Key to Embedding NNs’ Simulators
Most of NNs have a number of learning parameters that have to be appropriately specified. The selection of the
appropriate parameters for large-scale applications is an important experiment problem for a fast convergence of the
algorithm [12]. The user may impose some constraints on the parameters. Consequently, in training novice users, it is
necessary to develop techniques to properly set these parameters. In our system, from the perspective of the user, the
differences that appear between various models are reflected in the NN initialization and configuration menu. As
previously mentioned, this menu depends on the architecture and the learning algorithm selected in the network item
menu. The parameter settings for the backpropagation learning algorithm are as follows: (i) the parameter panel
allows the setting of the specific backpropagation learning parameters, known as learning rate, momentum, and
threshold. (ii) the external activation function menu choice allows the setting of the units to either a sigmoid, or
hyperbolic tangent function. (iii) the threshold input allows a brute force elimination of training examples with the
outputs above or below a certain level. (iv) the train panel allows the selection of the training data, as well as of the
number of training epochs (the number of repetitive passes through the set of examples). (v) from the test panel, the
selection of the testing data set is possible, to test the generalization ability of the NN. As can be seen in the left part of
Figure 10, the user can specify parameters such as external activation function, error threshold, and/or iterations,
learning rate, I/O pairs of values. Figure 10 displays the simulation for a beginner user, simulating the XOR problem.
The architecture of the network is presented on the right side of figure 10. The weights and their dynamic change during
training are displayed next to the network architecture.
14
15. Figure 10: Parameters Setting’s Screen
From these first tests and results, after a first feedback from users, we have concluded that some of the layout
proprieties of the display should be changed, in order to improve the understanding, especially for the beginner user.
For instance, the representation of weights next to the network architecture could reflect the layer structure of the
network, enhancing comprehension and making the following of the learning procedure easier for the inexperienced
user. Moreover, the consultant functions should be refined, especially with regard to the dialogue model between
system (represented by the consultant) and the user.
5. Discussions and Conclusion
In this paper, we have presented the DiscoverNet system, which automatically generates a user-specific neural
network environment, and interacts to support the user in the designing process. The system is based on two main
components: the graphical CAD representation of an initial network and its outputs, and an interactive tutor that checks
the user’s actions during the design sessions. The methodology that underlies the design of the system is based on
constructing assistant script classes to follow the user's design steps; a script defines the sequence of assistance. An
initial neural network is projected in the space of neural network architectures H, only if it matches approximately some
rules of the space H; otherwise the initial neural network cannot be trained. We backed up our approach with
convincing results given by the knowledge based neural network approach. In this latter approach, prior domain
knowledge is used, and complete initial knowledge is not required; the initial knowledge is translated into a neural
network, and then refined into a correct knowledge representation. We presented an object-oriented approach for the
design of an adaptive learning environment for discovery and self-learning learning. The system is extended with a
consultant module that contributes in the learning stage. We focused on the description of the advice process algorithm
in the consultant. As the first step, in order to study the feasibility of such a system, we considered the typical
components and building blocks of the feedforward neural network and Kohonen Feature Map network. We chose
these models because they are the most used, and the equations that describe the models are simple compared to other
models.
15
16. From some first tests and results, we have concluded that some of the layout proprieties of the display should be
changed, in order to improve the understanding, especially for the beginner user. Moreover, the consultant functions
should be refined, especially with regard to the dialogue model between the consultant and the user.
From educational point of view, the system implements the “learning-by-discovery”, “learning-by-doing”,
“exploratory learning” and “reinforcement learning” teaching strategies. In our implementation, discovery learning
means to search for the appropriate neural network for a specific application, based on the information provided by
samples in the knowledge base. Our consultant function is based on gathering NN knowledge in a KBNN meta-
knowledge structure.
This tool is aimed both at students, as well as at in-service workers and researchers in different fields, who need a
quick tutorial of NN design and usage. Therefore, our ultimate goal of DiscoverNet development is to assist senior and
graduate students or engineers in designing NN, and performing NN experiments on different applications.
The DiscoverNet prototype has been successfully tested, however, more extensive in-class tests are needed. The
system is intended for the use in courses at the Graduate School of Information Systems at the University of Electro-
Communications, Japan. From these diverse groups of students we expect to gain valuable suggestions on future
extensions to the current version of DiscoverNet. Embedding additional NNs would be extremely beneficial for the
improvement of the current implementation.
As the system is implemented in Java, it can be easily transferred to the Internet, for long-distance and life-long-
based education purposes. Especially, with the so-called high-capacity, high-speed Japan Gigabit Network introduced
between several national universities, as well as private companies in Japan, this system will be just one of several
distance teaching tools that our laboratory will be offering to the research and education community involved in this
Gigabit project, which is supported by the Japanese Ministry of Posts and Telecommunications, and implemented by the
Telecommunications Advancement Organization (TAO).
We believe that that with this system, we are tackling the problem and serving the need of a systematic, automatic and
adaptive approach to neural network teaching.
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