Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
TOP 1 CITED PAPER - International Journal of Artificial Intelligence & Appli...gerogepatton
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Then, it is employed for training feedforward neural networks for two benchmark classification problems. Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search. Simulation results demonstrate the effectiveness of the proposed algorithm.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
MOST READ ARTICLES IN ARTIFICIAL INTELLIGENCE - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.
TOP CITED UBICOMPUTING ARTICLES IN 2013 - International Journal of Ubiquitous...ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field
Genetic Algorithm for optimization on IRIS Dataset REPORT pdfSunil Rajput
Apply the Genetic Algorithm for optimization on a dataset obtained from UCI ML repository.
For Example: IRIS Dataset
Genetic Algorithm Optimization, Iris Dataset, Machine Learning, Python.
TOP 1 CITED PAPER - International Journal of Artificial Intelligence & Appli...gerogepatton
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Then, it is employed for training feedforward neural networks for two benchmark classification problems. Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search. Simulation results demonstrate the effectiveness of the proposed algorithm.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
MOST READ ARTICLES IN ARTIFICIAL INTELLIGENCE - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.
TOP CITED UBICOMPUTING ARTICLES IN 2013 - International Journal of Ubiquitous...ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field
Genetic Algorithm for optimization on IRIS Dataset REPORT pdfSunil Rajput
Apply the Genetic Algorithm for optimization on a dataset obtained from UCI ML repository.
For Example: IRIS Dataset
Genetic Algorithm Optimization, Iris Dataset, Machine Learning, Python.
Genetic Algorithm for optimization on IRIS Dataset presentation pptSunil Rajput
Apply the Genetic Algorithm for optimization on a dataset obtained from UCI ML repository.
For Example: IRIS Dataset
Genetic Algorithm Optimization, Iris Dataset, Machine Learning, Python.
Approaching Rules Induction CN2 Algorithm in Categorizing of Biodiversityijtsrd
Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn from data, without being explicitly programmed. Machine learning applications are classification, regression, clustering, density estimation and dimensionality reduction. The CN2 algorithm is a classification technique designed for the efficient induction of simple, comprehensible rules of form “if cond then predict classâ€, even in domains where noise may be present. Biodiversity means biological diversity, the variety of life found in a place on Earth or, often, the total variety of life on Earth. This research used butterflies as biological dataset for categorizing biodiversity and passed it to CN2 Rule Induction. In this research, “The Fauna of British India, Ceylon and Burma. Butterflies. Vol. I and Vol. II†written by C.T Bingham are used as the required knowledge for resource and categorizing biodiversity of butterfly families by rules induction with CN2 algorithm system has developed. In this system, MS Visual Studio as a programming tool and MS SQL Server as for database development are used. Su Myo Swe | Khin Myo Sett ""Approaching Rules Induction: CN2 Algorithm in Categorizing of Biodiversity"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25153.pdf
Paper URL: https://www.ijtsrd.com/computer-science/computer-architecture/25153/approaching-rules-induction-cn2-algorithm-in-categorizing-of-biodiversity/su-myo-swe
AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORKijsc
Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on
the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years
the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning
algorithm.
An overview on Advanced Research Works on Brain-Computer InterfaceWaqas Tariq
A brain–computer interface (BCI) is a proficient result in the research field of human- computer synergy, where direct articulation between brain and an external device occurs resulting in augmenting, assisting and repairing human cognitive. Advanced works like generating brain-computer interface switch technologies for intermittent (or asynchronous) control in natural environments or developing brain-computer interface by Fuzzy logic Systems or by implementing wavelet theory to drive its efficacies are still going on and some useful results has also been found out. The requirements to develop this brain machine interface is also growing day by day i.e. like neuropsychological rehabilitation, emotion control, etc. An overview on the control theory and some advanced works on the field of brain machine interface are shown in this paper.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
Hybrid deep learning model using recurrent neural network and gated recurrent...IJECEIAES
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
OPTIMAL CLUSTERING AND ROUTING FOR WIRELESS SENSOR NETWORK BASED ON CUCKOO SE...ijassn
ABSTRACT
In this research work, the egg laying radius of cuckoo search algorithm is used to create a cluster and then search for the optimum node based on multiobjective genetic algorithm with pareto ranking, so that the data can be forwarded to the sink.The primary focus is onthe two performance metrics parameters,one is the maximization of network lifetime and other is the minimization of delay. For maximizing the network
lifetime parameter, the overlapped target sensing by many sensors is wastage of energy by two or more sensors, where the same task can be done by one sensor. To overcome this problem, the sequence set cover methodology is used.For minimization of delay parameter, the sleep-wake scheduling mechanism will be considered, but substantial delays are introduced as transmitting node needs to wait for its next-hop relay node to wake up. These delays can be taken care by developing any cast based packet forwarding schemes
where individual node forwards a packet to the first neighboring node that wakes up among multiple candidate nodes. This any cast forwarding schemes minimizes the expected packet-delivery delays from the sensor nodes to the sink node. The introduced work will perform energy proficient routing with an objective to improve the network life, packet loss ratio and overall network throughput. The proposed algorithm was
simulated in MATLAB and compared with LEACH algorithm. The results show that our proposed algorithm issuperiorfor prolonging the network lifetime, minimizing the packet loss and increasing the
throughput.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
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.
A Survey of Various Intrusion Detection Systemsijsrd.com
In this paper, we present an overview of existing intrusion detection techniques. All these algorithms are described more or less on their own. Intrusion detection system is a very popular and computationally expensive task. We also explain the fundamentals of intrusion detection system. We describe today's approaches for intrusion detection system. From the broad variety of efficient techniques that have been developed we will compare the most important ones. We will systematize the techniques and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected.
Top cited articles 2020 - Advanced Computational Intelligence: An Internation...aciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
New Research Articles 2019 October Issue Signal & Image Processing An Interna...sipij
Signal & Image Processing: An International Journal (SIPIJ)
ISSN: 0976 – 710X [Online]; 2229 - 3922 [Print]
http://www.airccse.org/journal/sipij/index.html
Current Issue; October 2019, Volume 10, Number 5
Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction
Besma Sadou1, Atidel Lahoulou2, Toufik Bouden1, Anderson R. Avila3, Tiago H. Falk3 and Zahid Akhtar4, 1Non Destructive Testing Laboratory, University of Jijel, Algeria, 2LAOTI laboratory, University of Jijel, Algeria, 3University of Québec, Canada and 4University of Memphis, USA
Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi1, Reza Anvari1, Ali Forghani1 and Behrouz Minaei2, 1Malek-Ashtar University of Technology, Iran and 2Iran University of Science and Technology, Iran
Robust Image Watermarking Method using Wavelet Transform
Omar Adwan, The University of Jordan, Jordan
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
Itaru Kaneko1, Yutaka Yoshida2 and Emi Yuda3, 1&2Nagoya City University, Japan and 3Tohoku University, Japan
http://www.airccse.org/journal/sipij/vol10.html
Cellular computing is a discipline that deals with the analysis and modelling of real cellular processes for the purpose of computation. It essentially uses engineering principles to study and manipulate cells. This paper provides a short introduction to cellular computing. We also highlight underlying challenges and avenues of implementations of cellular computing. Matthew N. O. Sadiku | Nana K. Ampah | Sarhan M. Musa "Cellular Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23385.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23385/cellular-computing/matthew-n-o-sadiku
Top Cited Articles in Advanced Computational Intelligence : October 2020aciijournal
Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, omputational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.
Genetic Algorithm for optimization on IRIS Dataset presentation pptSunil Rajput
Apply the Genetic Algorithm for optimization on a dataset obtained from UCI ML repository.
For Example: IRIS Dataset
Genetic Algorithm Optimization, Iris Dataset, Machine Learning, Python.
Approaching Rules Induction CN2 Algorithm in Categorizing of Biodiversityijtsrd
Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn from data, without being explicitly programmed. Machine learning applications are classification, regression, clustering, density estimation and dimensionality reduction. The CN2 algorithm is a classification technique designed for the efficient induction of simple, comprehensible rules of form “if cond then predict classâ€, even in domains where noise may be present. Biodiversity means biological diversity, the variety of life found in a place on Earth or, often, the total variety of life on Earth. This research used butterflies as biological dataset for categorizing biodiversity and passed it to CN2 Rule Induction. In this research, “The Fauna of British India, Ceylon and Burma. Butterflies. Vol. I and Vol. II†written by C.T Bingham are used as the required knowledge for resource and categorizing biodiversity of butterfly families by rules induction with CN2 algorithm system has developed. In this system, MS Visual Studio as a programming tool and MS SQL Server as for database development are used. Su Myo Swe | Khin Myo Sett ""Approaching Rules Induction: CN2 Algorithm in Categorizing of Biodiversity"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25153.pdf
Paper URL: https://www.ijtsrd.com/computer-science/computer-architecture/25153/approaching-rules-induction-cn2-algorithm-in-categorizing-of-biodiversity/su-myo-swe
AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORKijsc
Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on
the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years
the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning
algorithm.
An overview on Advanced Research Works on Brain-Computer InterfaceWaqas Tariq
A brain–computer interface (BCI) is a proficient result in the research field of human- computer synergy, where direct articulation between brain and an external device occurs resulting in augmenting, assisting and repairing human cognitive. Advanced works like generating brain-computer interface switch technologies for intermittent (or asynchronous) control in natural environments or developing brain-computer interface by Fuzzy logic Systems or by implementing wavelet theory to drive its efficacies are still going on and some useful results has also been found out. The requirements to develop this brain machine interface is also growing day by day i.e. like neuropsychological rehabilitation, emotion control, etc. An overview on the control theory and some advanced works on the field of brain machine interface are shown in this paper.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
Hybrid deep learning model using recurrent neural network and gated recurrent...IJECEIAES
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
OPTIMAL CLUSTERING AND ROUTING FOR WIRELESS SENSOR NETWORK BASED ON CUCKOO SE...ijassn
ABSTRACT
In this research work, the egg laying radius of cuckoo search algorithm is used to create a cluster and then search for the optimum node based on multiobjective genetic algorithm with pareto ranking, so that the data can be forwarded to the sink.The primary focus is onthe two performance metrics parameters,one is the maximization of network lifetime and other is the minimization of delay. For maximizing the network
lifetime parameter, the overlapped target sensing by many sensors is wastage of energy by two or more sensors, where the same task can be done by one sensor. To overcome this problem, the sequence set cover methodology is used.For minimization of delay parameter, the sleep-wake scheduling mechanism will be considered, but substantial delays are introduced as transmitting node needs to wait for its next-hop relay node to wake up. These delays can be taken care by developing any cast based packet forwarding schemes
where individual node forwards a packet to the first neighboring node that wakes up among multiple candidate nodes. This any cast forwarding schemes minimizes the expected packet-delivery delays from the sensor nodes to the sink node. The introduced work will perform energy proficient routing with an objective to improve the network life, packet loss ratio and overall network throughput. The proposed algorithm was
simulated in MATLAB and compared with LEACH algorithm. The results show that our proposed algorithm issuperiorfor prolonging the network lifetime, minimizing the packet loss and increasing the
throughput.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
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.
A Survey of Various Intrusion Detection Systemsijsrd.com
In this paper, we present an overview of existing intrusion detection techniques. All these algorithms are described more or less on their own. Intrusion detection system is a very popular and computationally expensive task. We also explain the fundamentals of intrusion detection system. We describe today's approaches for intrusion detection system. From the broad variety of efficient techniques that have been developed we will compare the most important ones. We will systematize the techniques and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected.
Top cited articles 2020 - Advanced Computational Intelligence: An Internation...aciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
New Research Articles 2019 October Issue Signal & Image Processing An Interna...sipij
Signal & Image Processing: An International Journal (SIPIJ)
ISSN: 0976 – 710X [Online]; 2229 - 3922 [Print]
http://www.airccse.org/journal/sipij/index.html
Current Issue; October 2019, Volume 10, Number 5
Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction
Besma Sadou1, Atidel Lahoulou2, Toufik Bouden1, Anderson R. Avila3, Tiago H. Falk3 and Zahid Akhtar4, 1Non Destructive Testing Laboratory, University of Jijel, Algeria, 2LAOTI laboratory, University of Jijel, Algeria, 3University of Québec, Canada and 4University of Memphis, USA
Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi1, Reza Anvari1, Ali Forghani1 and Behrouz Minaei2, 1Malek-Ashtar University of Technology, Iran and 2Iran University of Science and Technology, Iran
Robust Image Watermarking Method using Wavelet Transform
Omar Adwan, The University of Jordan, Jordan
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
Itaru Kaneko1, Yutaka Yoshida2 and Emi Yuda3, 1&2Nagoya City University, Japan and 3Tohoku University, Japan
http://www.airccse.org/journal/sipij/vol10.html
Cellular computing is a discipline that deals with the analysis and modelling of real cellular processes for the purpose of computation. It essentially uses engineering principles to study and manipulate cells. This paper provides a short introduction to cellular computing. We also highlight underlying challenges and avenues of implementations of cellular computing. Matthew N. O. Sadiku | Nana K. Ampah | Sarhan M. Musa "Cellular Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23385.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23385/cellular-computing/matthew-n-o-sadiku
Top Cited Articles in Advanced Computational Intelligence : October 2020aciijournal
Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, omputational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.
An Approach for IRIS Plant Classification Using Neural Network ijsc
Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.
June 2020: Most Downloaded Article in Soft Computing ijsc
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
Top 5 most viewed articles from academia in 2019 - gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
Trends in Advanced Computing in 2020 - Advanced Computing: An International J...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
Feasibility of Artificial Neural Network in Civil Engineeringijtsrd
An Artificial neural network ANN is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network based method was widely applied to the civil engineering because of the strong non linear relationship between known and un known of the problems. They come with good modelling in areas where conventional approaches finite elements, finite differences etc. require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications. Vikash Singh | Samreen Bano | Anand Kumar Yadav | Dr. Sabih Ahmad ""Feasibility of Artificial Neural Network in Civil Engineering"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22985.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/22985/feasibility-of-artificial-neural-network-in-civil-engineering/vikash-singh
A neural network is a series of algorithms that attempts to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates. Neural networks have the ability to adapt to changing input so the network produces the best possible result without the need to redesign the output criteria.
Classifier Model using Artificial Neural NetworkAI Publications
When it comes to AI and ML, precision in categorization is of the utmost importance. In this research, the use of supervised instance selection (SIS) to improve the performance of artificial neural networks (ANNs) in classification is investigated. The goal of SIS is to enhance the accuracy of future classification tasks by identifying and selecting a subset of examples from the original dataset. The purpose of this research is to provide light on how useful SIS is as a preprocessing tool for artificial neural network-based classification. The work aims to improve the input dataset to ANNs by using SIS, which may help with problems caused by noisy or redundant data. The ultimate goal is to improve ANNs' ability to identify data points properly across a wide range of application areas.
ABSTRACT: An artificial neural network (ANN) is an information processing construct inspired by the manner in which the brain processes information and were originally developed to mimic the learning process of the human brain. They have been increasingly used in the chemical industry for data analysis, process control, pattern identification, identification of drug targets, and the prediction of several physicochemical properties. This paper provides a brief introduction on neural networks and their applications to the chemical industry.
Cardiovascular Disease Prediction Using Machine Learning Approaches.pptxTaminul Islam
Cardiovascular Disease Prediction Using Machine Learning Approaches.
Presentation for CISES 2023. Presentation Outline.
Introduction
Objectives
Literature review
Data Collection
Methodology
Result
Challenges & Future work
Conclusion
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.
Automatic Selection of Open Source Multimedia Softwares Using Error Back-Prop...IJERA Editor
Open source opens a new era to provide license of the software for the user at free of cost which is advantage over paid licensed software. In Multimedia applications there are many versions of software are available and there is a problem for the user to select compatible software for their own system. Most of the time while surfing for software a huge list of software opens in response. The selection of particular software which is pretty suitable for the system from a real big list is the biggest challenge that is faced by the users. This work has been done that focuses on the existing open source software that are widely used and to design an automatic system for selection of particular open source software according to the compatibility of users own system. In this work, error back-propagation based neural network is designed in MATLAB for automatic selection of open source software. The system provides the open source software name after taking the information from user. Regression coefficient of 0.93877 is obtained and the results shown are up to the mark and can be utilized for the fast and effective software search.
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
2. Citation Count – 105
APPLICATION OF GENETIC ALGORITHM OPTIMIZED NEURAL
NETWORK CONNECTION WEIGHTS FOR MEDICAL DIAGNOSIS OF PIMA
INDIANS DIABETES
Asha Gowda Karegowda1 , A.S. Manjunath2 , M.A. Jayaram3
1,3 Dept. of Master of Computer Applications ,Siddaganga Institute of
Technology, Tumkur, India 2 Dept. of Computer Science, Siddaganga Institute of
Technology, Tumkur India
ABSTRACT
Neural Networks are one of many data mining analytical tools that can be utilized to
make predictions for medical data. Model selection for a neural network entails various
factors such as selection of the optimal number of hidden nodes, selection of the relevant
input variables and selection of optimal connection weights. This paper presents the
application of hybrid model that integrates Genetic Algorithm and Back Propatation
network(BPN) where GA is used to initialize and optmize the connection weights of
BPN. Significant feactures identified by using two methods :Decision tree and GA-CFS
method are used as input to the hybrid model to diagonise diabetes mellitus. The results
prove that, GA-optimized BPN approach has outperformed the BPN approach without
GA optimization. In addition the hybrid GA-BPN with relevant inputs lead to further
improvised categorization accuracy compared to results produced by GA-BPN alone with
some redundant inputs.
KEYWORDS
Back Propagation Network, Genetic algorithm, connection weight optimisation.
For More Details : http://airccse.org/journal/ijsc/papers/2211ijsc02.pdf
Volume Link : http://airccse.org/journal/ijsc/current2011.html
REFERENCES
[1] S. Haykin,(1994), Neural Networks- A comprehensive foundation, Macmillan Press,
New York.
[2] D.E. Rinehart, G.E. Hinton, and R. J. Williams, (1986), Learning internal
representations by errorpropagation, In D.E. Rumelhart and J.L. McClelland, editors,
Parallel Distributed Processing, Cambridge, MA: MIIT Press.
3. [3] H. Lu, R. Setiono and H. Liu, (1996),”Effective data mining using neural networks”,
IEEE Trans. On Knowledge and Data Engineering.
[4] A. Roy, (2000),Artificial neural networks – a science in trouble, SIGKDD
Explorations.
[5] D. Goldberg, (1989)Genetic Algorithms in Search, Optimization , and Machine
learning, Addison Wesley.
[6] http://www.myreaders.info/09_Genetic_Algorithms.pdf
[7] Berson Alex, Smith Stephen J. (1999) ,Data Warehousing, Data Mining, &OLAP.,
McGraw-Hill Book Co.
[8] Jihoon Yang, Vasant G. Honavar , (1998)”Feature Subset Selection Using a Genetic
Algorithm”, Journal IEEE Intelligent Systems, Volume 13 Issue 2.
[9] Brill, F., Brown, D., & Martin, W. (1992). “Fast Genetic Selection of Features for
Neural Network Classifiers”, IEEE Transactions on Neural Networks, 3(2), pp324-328.
[10] Arena P, Caponetto R, Fortuna L, Xibilia M G (1992), “Genetic algorithm to select
optimal neural network topology”, Proceedings of the 35th Midwest Symposium on
Circuits and Systems 2: pp1381– 1383.
[11] Maniezzo V,(1994) ,Genetic evolution of the topology and weight distribution of
neural networks. IEEE Neural Network. 5: pp39–53
[12] Sexton R S, Dorsey R E, Johnson J D (1998) Toward global optimization of neural
networks: A comparison of the genetic algorithm and back propagation. Decis. Support
Syst. 22: pp171–185
[13] Rajasekaran, S and G. A Vijayalakshmi Pai (1996), Genetic Algorithm based
Weight Determination for Backpropogation Networks, Proc of the Fourth Int Conf on
Advanced Computing, pp 73-79).
[14] Osman Ahmed, Mohd Nord, Suziah Sulaiman, Wan Fatimah,(2009), ”Study of
Genetic Algorithm to Fully-automate the Design and Training of Artificial Neural
Network”,International Journal of Computer Science and Network Security, VOL.9 No.1.
[15] H. Paul S., G. Ben S., T. Thomas G., W. Robert S.,(2004), ” Use of genetic
algorithms for neural networks to predict community-acquired pneumonia”, Artificial
Intelligence in Medicine, Vol. 30, Issue 1, pp.71-84.
4. [16] D.Shanti, G. Sahoo , N. Saravanan, (2009), “ Evolving Connection Weights of ANN
using GA with application to the Prediction of Stroke Disease”, International Journal of
Soft Computing 4(2):pp95- 102, Medwell Publishing.
[17] R.V. Murali, Member, IAENG, A.B.Puri, and G.Prabhakaran ,(2010), “GA-Driven
ANN Model for Worker Assignment into Virtual Manufacturing Cells”,Proceedings of
the World Congress on Engineering 2010 Vol III, London, U.K.
[18] H. Salehi, S. Zeinali Heris*, M. Koolivand Salooki and S. H. Noei,(2011),”
Designing a NN for closed Themosyphon with Nanofluid using a GA”, Brazilian Journal
of Chemical Engineering ,Vol. 28, No. 01, pp. 157 – 168.
[19] Jennifer G. Dy, (2004),Feature Selection for Unsupervised Learning, Journal of
Machine Learning, pp845-889.
[20] M.A.Jayaram, Asha Gowda Karegowda,(2007),” Integrating Decision Tree and
ANN for Categorization of Diabetics Data”, International Conference on Computer
Aided Engineering, IIT Madras, Chennai, India.
[21] Asha Gowda Karegowda and M.A.Jayaram, (2009),”Cascading GA & CFS for
feature subset selection in Medial data mining”, IEEE International Advance Computing
Conference, Patiyala, India
[22] Asha Gowda Karegowda, A. S. Manjunath & M.A.Jayaram,(2010), Comparative
study of attribute selection using Gain ratio and correlation based feature selection,
International Journal of Information Technology and Knowledge Management, Volume
2, No. 2, pp. 271-277.
[23] Editorial, ( 2004),Diagnosis and Classification of Diabetes Mellitus, American
Diabetes Association, Diabetes Care, vol 27, Supplement 1.
5. Citation Count – 49
DETERMINATION OF OVER-LEARNING AND OVER-FITTING PROBLEM
IN BACK PROPAGATION NEURAL NETWORK
Gaurang Panchal1, Amit Ganatra2 , Parth Shah3, Devyani Panchal4
Department of Computer Engineering, Charotar Institute of Technology (Faculty
of Technology and Engineering), Charotar University of Science and Technology,
Changa, Anand-388 421, INDIA
ABSTRACT
A drawback of the error-back propagation algorithm for a multilayer feed forward neural
network is over learning or over fitting. We have discussed this problem, and obtained
necessary and sufficient Experiment and conditions for over-learning problem to arise.
Using those conditions and the concept of a reproducing, this paper proposes methods for
choosing training set which is used to prevent over-learning. For a classifier, besides
classification capability, its size is another fundamental aspect. In pursuit of
high performance, many classifiers do not take into consideration their sizes and contain
numerous both essential and insignificant rules. This, however, may bring adverse
situation to classifier, for its efficiency will been put down greatly by redundant rules.
Hence, it is necessary to eliminate those unwanted rules. We have discussed various
experiments with and without over learning or over fitting problem.
KEYWORDS
Neural Network, learning, Hidden Neurons, Hidden Layers
For More Details : http://airccse.org/journal/ijsc/papers/2211ijsc04.pdf
Volume Link : http://airccse.org/journal/ijsc/current2011.html
REFERENCES
[1] Carlos Gershenson , “Artificial Neural Networks for Beginners”
[2] Vincent Cheung ,Kevin Cannons, “An Introduction to Neural Networks”, Signal &
Data Compression Laboratory, Electrical & Computer Engineering University of
Manitoba, Winnipeg, Manitoba, Canada
[3] “Artificial Neural Networks” ocw.mit.edu
6. [4] Guoqiang Peter Zhang , “Neural Networks for Classification: A Survey”, IEEE
TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C:
APPLICATIONS AND REVIEWS, VOL. 30, NO. 4, NOVEMBER 2000
[5] V.P. Plagianakos, G.D. Magoulas, M.N. Vrahatis, “Learning rate adaptation in
stochastic gradient descent”, ,Department of Mathematics, University of Patras,
[6] Wen Jin-Wei Zhao, Jia-Li Luo Si-Wei and Han Zhen “ The Improvements of BP
Neural Network Learning Algorithm”, Department of Computer Science &
Technology,Northem Jiaotong University ,BeiJing, 100044, P.R.China,
[7] Wenjian Wang, Weizhen Lu, Andrew Y T Leung, Siu-Ming Lo, Zongben Xu,
“Optimal feed-forward neural networks based on the combination of constructing and
pruning by genetic algorithms”, IEEE TRANSACTIONS ON NEURAL NETWORKS
2002
[8] “A Detailed Comparison of Backpropagation Neural Network and Maximum-
Likelihood Classifiers for Urban Land Use Classification”,IEEE TRANSACTIONS ON
GEOSCIENCE AND REMOTE SENSING, VOL. 33, NO. 4, JULY 199.5
[9] Z. J. Liu C. Y. Wang Z. Niu A. X. Liu ”Evolving Multi-spectral Neural Network
Classifier Using a Genetic Algorithm”. Laboratory of Remote Sensing Information
Sciences, the Institute of Remote Sensing Applications,
[10]Fiszelew, A., Britos, P., Ochoa, A., Merlino, H., Fernández, E., García-Martínez
“Finding Optimal Neural Network Architecture Using Genetic Algorithms”, R.Software
& Knowledge Engineering Center. Buenos Aires Institute of Technology.Intelligent
Systems Laboratory. School of Engineering. University of Buenos Aires.
[11]M.P.Craven, “A FASTER LEARNING NEURAL NETWORK CLASSIFIER
USING SELECTIVE BACKPROPAGATION” Proceedings of the Fourth IEEE
International Conference on Electronics, Circuits and Systems
[12]Wenjian Wang, Weizhen Lu, Andrew Y T Leung, Siu-Ming Lo, Zongben Xu,
“Optimal feed-forward neural networks based on the combination of constructing and
pruning by genetic algorithms”, IEEE TRANSACTIONS ON NEURAL NETWORKS
2002
[13]Teresa B. Ludermir, Akio Yamazaki, and Cleber Zanchettin, “An Optimization
Methodology for Neural Network Weights and Architectures” IEEE TRANSACTIONS
ON NEURAL NETWORKS, VOL. 17, NO. 6, NOVEMBER 2006
7. [14]S. Rajasekaran, G.A Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic, and
Genetic Algorithms Synthesis and Applications” International Journal on Soft Computing
( IJSC ), Vol.2, No.2, May 2011 51
[15]Mrutyunjaya Panda and Manas Ranjan Patra, “NETWORK INTRUSION
DETECTION USING NAÏVE BAYES” IJCSNS International Journal of Computer
Science and Network Security, VOL.7 No.12, December 2007
[16]S. SELVAKANI1 and R.S.RAJESH2, “Escalate Intrusion Detection using GA –
NN”, Int. J. Open Problems Compt. Math., Vol. 2, No. 2, June 2009
[17]Nathalie Villa*(1,2) and Fabrice Rossi(3), Recent advances in the use of SVM for
functional data classification, First International Workshop on Functional and Operatorial
Statistics. Toulouse, June 19-21, 2008
[18] KDD Cup’99 Data set , http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
8. Citation Count – 28
AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL
NETWORK
Madhusmita Swain1 , Sanjit Kumar Dash2, Sweta Dash3 and Ayeskanta
Mohapatra4
1, 2 Department of Information Technology, College of Engineering and
Technology,
Bhubaneswar, Odisha, India
3Department of Computer Science and Engineering, Synergy Institute of
Engineering and Technology, Dhenkanal, Odisha, India
4Department of Computer Science and Engineering, Hi-tech Institute of
Technology,
Bhubaneswar, Odisha, India
ABSTRACT
Classification is a machine learning technique used to predict group membership for data
instances. To simplify the problem of classification neural networks are being introduced.
This paper focuses on IRIS plant classification using Neural Network. The problem
concerns the identification of IRIS plant species on the basis of plant attribute
measurements. Classification of IRIS data set would be discovering patterns from
examining petal and sepal size of the IRIS plant and how the prediction was made from
analyzing the pattern to form the class of IRIS plant. By using this pattern and
classification, in future upcoming years the unknown data can be predicted more
precisely. Artificial neural networks have been successfully applied to problems in
pattern classification, function approximations, ptimization, and associative memories.
In this work, Multilayer feed- forward networks are trained using back propagation
learning algorithm.
KEYWORDS
IRIS dataset, artificial neural networks, Back-propagation algorithm
For More Details : http://airccse.org/journal/ijsc/papers/2112ijsc07.pdf
Volume Link : http://airccse.org/journal/ijsc/current2012.html
9. REFERENCES
[1] Aqel, M.M., Jena, R.K., Mahanti, P.K. and Srivastava (2009) ‘Soft Computing
Methodologies in Bioinformatics’, European Journal of Scientific Research, vol.26, no 2,
pp.189-203.
[2] Avcı Mutlu, Tülay Yıldırım(2003) ‘Microcontroller based neural network realization
and IRIS plant classifier application’, International XII. Turkish Symposium on Artificial
Intelligence and Neural Network
[3] Cho, Sung-Bae.and Dehuri, Satchidananda (2009) ‘A comprehensive survey on
functional link neural network and an adaptive PSO–BP learning for CFLNN, Neural
Comput & Applic’ DOI 10.1007/s00521-009-0288-5.
[4] Fisher, A. W., Fujimoto, R. J. and Smithson, R. C.A. (1991) ‘A Programmable
Analog Neural Network Processor’, IEEE Transactions on Neural Networks, Vol. 2, No.
2, pp. 222-229.
[5] Fu, L.(1991) ‘ Rule learning by searching on adapted nets. In Proceedings of National
Conference on Artificial Intelligence’ Anaheim, CA, USA, pp. 590-595.
[6] Han, J. and Kamber, M. (2000)‘ Data Mining: Concepts and Techniques’ , 2nd ed.
Morgan Kaufmann.
[7] Dr.Hapudeniya, Muditha M. MBBS(2010),‘Artificial Neural Networks in
Bioinformatics’ Medical Officer and Postgraduate Trainee in Biomedical Informatics ,
Postgraduate Institute of Medicine, University of Colombo ,Sri Lanka, vol.1, no
2,pp.104-111. International Journal on Soft Computing ( IJSC ) Vol.3, No.1, February
2012 89
[8] Kavitha Kannan ‘Data Mining Report on IRIS and Australian Credit Card Dataset’,
School of Computer Science and Information Technology, University Putra Malaysia,
Serdang, Selangor, Malaysia.
[9] Marček D., ‘Forecasting of economic quantities using fuzzy autoregressive models
and fuzzy neural networks’, Vol.1, pp.147-155.1
[10] Pai, G. V and Rajasekaran, S, (2006), ‘Neural Networks, Fuzzy Logic and Genetic
Algorithms Synthesis and Applications’, 6th ed, Prentice Hall of India Pvt. Ltd.
[11] Rath, Santanu and Vipsita, Swati (2010) ‘An Evolutionary Approach for Protein
Classification Using Feature Extraction by Artificial Neural Network’, Int’l Conf. on
Computer & Communication TechnologyıICCCT’10.
11. Citation Count – 28
Decision Support System for the Intelligient Identification of Alzheimer using Neuro
Fuzzy logic
Obi J.C1. and Imainvan A.A2.
Department of Computer Science, University of Benin, Benin City. Nigeria1 & 2.
ABSTRACT
Alzheimer Disease (AD) is a form of dementia; it is a progressive, degenerative disease.
Alzheimer is a brain disease that causes problems with memory, thinking and behavior. It
is severe enough to interfere with daily activities. Alzheimer symptoms are characterized
by memory loss that affects day-to-day function, difficulty performing familiar tasks,
problems with language, disorientation of time and place, poor or decreased judgment,
problems with abstract thinking, misplacing things, changes in mood and behavior,
changes in personality and loss of initiative. Neuro-Fuzzy Logic explores
approximation techniques from neural networks to find the parameter of a fuzzy system.
In this paper, the traditional procedure for the medical diagnosis of Alzheimer employed
by physician is analyzed using neuro-fuzzy inference procedure. The proposed system is
a useful decision support approach for the diagnosis of Alzheimer.
KEYWORDS
Neural Network, Fuzzy logic, Neuro Fuzzy System & Alzeimer
For More Details : http://airccse.org/journal/ijsc/papers/2211ijsc03.pdf
Volume Link : http://airccse.org/journal/ijsc/current2011.html
REFERENCES
[1]. Alzheimier Association (2010), “What is Alzheimer”, retrieved
from http://www.alz.org/alzheimers_disease_what_is_alzheimers.asp
[2]. Akinyokun O.C. (2002), “Neuro-fuzzy expert system for evaluation of human
Resource performance”, First Bank of Nigeria Endowment Fund lecture Federal
University of technology, Akure, Nigeria.
12. [3].Aleksander I. and Morton H. (1998), “An introduction to neural computing”2nd
Edition Computer Science press.
[4]. Andreas N. (2001), “Neuro-Fuzzy system”, retrieved from http//:Neuro-Fuzzy
System.html.
[5].Bart K. and Satoru I. (1993), “Fuzzy Logic”, retrieved
from http//:Fortunecity.com/emachines/e11/86/fuzzylog.html.
[6].Beth R. (2002), “Alzheimer's Disease: A Brief History and Avenues for Current
Research”, retrieved from http
://www.jyi.org/volumes/volume6/issue2/features/reger.html -Cached - Similar
[7]. Bishop C.M. (1995), “Neural Networks for pattern Recognition”, Oxford University
Press, United Kingdom.
[8].Christos S. and Dimitros S. (2008), “Neural Network” retrieved
from http//:docs.toc.com/doc/1505/neural-networks.
[9].CWS.(2000),“Tuberculosis Research Paper”, retrieved
from http://writing4students.blogspot.com/2009/12/tuberculosis-research-paper.html.
[10]. Edward C.H. (2010), “Article: The gorilla Connection” retrieved
from http//:Nature.com/nature/journal/v467/n7314/full/467404a.html.
[11]. Eklund D. and Fuller R. (1993), “A Neural-Fuzzy Approach to medical
Diagnostic”Gedemedic project, Abo Academy University, Development Centres heisnki,
pp.210-225.
[12]. Gary R. and George P.E. (2002), “Application of Neuro System to
behavior Representation in Computer generated forces”, retrieved http//:Cuil.com.
[13]. Johnson R.C. (1993), “Making the Neural-Fuzzy Connection”, Electronic
Engineering
Times, Cmp Publications, Manhasset, New York.
[14]. Kosaka M. (1991), “Application of Fuzzy Logic/Neural Network to Securities
Trading
Decision Support”, Conference Proceeding of the 1991 IEEE International Conference on
Systems, man and Cybernetics, Vol.3, pp.1913 – 1918.
[15]. Leondes C. (2010), “The Technology of Fuzzy Logic Algorithm retrieved
From Suite101.com/examples-of-expert-System-application-in-artificial Intelligience.
13. [16]. MedicineNet (2011), “Alzheimer” retrieved
from http://www.medicinenet.com/script/main/art.asp?articlekey=505&page=4
[17]. Nauck K. (1996), “Fuzzy Neural Network”, http//:Wikipedia.org.
[18]. Neil and Janet C. (2005), “History of Tuberculosis”, retrieved from http://
www.micklebring.com/Oakwood/ch18.html
[19]. Pao Y.H. (1989), “Adaptive Pattern Recognition and Neural Network”, Addison
Wesley. International Journal on Soft Computing ( IJSC ), Vol.2, No.2, May 2011 38
[20]. Peter V. R.; Deborah B.; Berry W.R.; Teresa R.; Lon S.S.Pierre N.T., David M.B.
(2011),
“Practice guideline for the treatment of patient with Alzheimer’s and other
Dementia”retrieved
from http://www.psychiatryonline.com/pracGuide/PracticePDFs/AlzPG101007.pdf
[21]. Ponniyin S.K (2009), “Neural Network”, Icann2007.org/neural.networks.
[22]. Otuorimuo O. (2006), “Prototype of Fuzzy System for the Formulation and
Classification of Poultry Feed”, Bachelor of Science (Computer Science) Project,
University of Benin, Benin City, Nigeria.
[23]. Rudolf K. (2008), “Article: Institute of Information and Communication System”,
OttoVan-Guericke, University of Magdebury, Germany.
[24]. Rumelhert D.E.,Windrow B., and Lehr M.A (1994), “Neural Networks: Application
in Industry, Business and Science”, Communication of ACM,37(1994), 93-105.
[25]. Stathacopoulou R.,Magoulas G.D.,Grigoriadou M., and Samarakou M.(2004),
“NeuralFuzzy knowledge processing in Intelligent learning Environment for Improved
Student Diagnosis” DOI Information 10.1016/j.ins.2004.02.026.
[26]. Statsoft Incorporated (2008), “Neural Network” retrieved from http//:google.com.
[27]. Tom S.; Justin B.; Daan W.; Yi Sun; Martin F.; Frank S.; Thomas R and Jurgen
S(2010), “PyBrain” Journal of Machine Learning Research 1 (2010) 999-1000 retrieved
from http://www.idsia.ch/~sun/pybrain.pdf
[28]. Vahid K. and Gholam A.M. (2009), “Artificial Intelligence in medicines”,V47 ,
Issues 1
Information Technology Department, School of Engineering, Terbiat Moderas University
Tehran,Iran.
14. [29]. Wikipedia (2010), “Artificial Neural Network” retrieved from http//:
en.Wikipedia.org/wiki/Artificial-neural-network.
[30]. Wong K., Fung C and Myers D. (2002), “An Integrated Neural Fuzzy Approach
With reduced rules for well log analysis”, International Journal of Fuzzy Systems 4(1)
592-599.
[31]. World Health Organization (2005), “International Publication: Malaria”, retrieved
from Whqlidboc.Who.Int/Publication/2005/9241580364-Chapter 7.pdf.
[32]. WrongDiagnosis, (2011), “Alzheimer: diagnosis and prognosis”, retrieved
from http:// wrongdiagnosis.com
[33]. Zadeh L.A. (1965), “Fuzzy sets. Information and control, Vol.8, pp.338-353.
[34]. Zimmermann H.J. (1993), “Fuzzy sets, Decision making and expert system”
International series in Management Science/Operation Research, University of Houston,
U.S.A.
15. Citation Count – 18
A Directional Feature with Energy based Offline Signature Verification Network
Minal Tomar* and Pratibha Singh
Department of Electrical & Electronics*, Malwa Institute of Technology,
Indore (M.P.) 452016 Department of Electronics and Instrumentation,Institute of
Engineering and Technology,Devi Ahilya Vishwavidyalaya, Indore (M.P.) 452017
ABSTRACT
Signature used as a biometric is implemented in various systems as well as every
signature signed by each person is distinct at the same time. So, it is very important to
have a computerized signature verification system. In offline signature verification
system dynamic features are not available obviously, but one can use a signature as an
image and apply image processing techniques to make an effective offline signature
verification system. Author proposes a intelligent network used directional feature and
energy density both as inputs to the same network and classifies the signature.
Neural network is used as a classifier for this system. The results are compared with both
the very basic energy density method and a simple directional feature method of offline
signature verification system and this proposed new network is found very effective as
compared to the above two methods, specially for less number of training samples, which
can be implemented practically.
KEY WORDS
Neural Network, Directional Feature, Energy Density, Neuron, Back propagation,
AR, FRR
For More Details : http://airccse.org/journal/ijsc/papers/2111ijsc05.pdf
Volume Link : http://airccse.org/journal/ijsc/current2011.html
REFERENCES
1. Bai-ling Zhang, “Off-Line Signature Recognition and Verification by Kernel Principal
Component SelfRegression,” Fifth International Conference on Machine Learning and
Applications(ICMLA'06) 10.1109/ICMLA.2006.37, p. 28 to 33.
2. Deepthi Uppalapati, “Integration of Offline and Online Signature Verification
systems,” Department of Computer Science and Engineering, I.I.T., Kanpur, July 2007.
16. 3. Rejean Plamondon and Sargur N. Srihari, “On-Line and Off-Line Handwriting
Recognition: A Comprehensive Survey,” IEEE transections on pattern analysis and
machine intelligence, Vol. 12 No. 1, January 2000, pp. 63 - 84.
4. The IEEE website. [Online]. Available: http://www.ieee.org
5. Department of Computer Science, IIT, Kanpur for precious informations. Available:
http://www.cse.iitk.ac.in
6. H. N. Prakash and D. S. Guru, “Off-line signature verification: an approach based on
score level fusion,” International journal of computer application, Vol. 1-No.18, 2010.
7. Samaneh Ghandali and Mohsen Ebrahimi Moghaddan, “Off-line Persian Signature
identification and verification based on image registration and fusion,” Journal of
Multimedia, Vol.-4 No.3, june 2009. International Journal on Soft Computing ( IJSC ),
Vol.2, No.1, February 2011 57
8. Imran Siddiqi and Nicole vincent, “A set of chain-code based features for writer
recognition,”10th International Conference on document analysis and recognition, 2009.
9. Stephane Armand, Michael Blumenstein and Vallipuram Muthukkumarasamy, “Off-
line signature verification using enhanced modified direction feature and neural based
classification,”
10. Andrew T. Wilson, “Off-line handwriting recognition using neural network,”
11. Miguel A. Ferrer, Gesus B. Alonso and Carlos M. Travieso, “Off-line Geometric
parameters for automatic signature verification using fixed point arithematic,” IEEE
transections on pattern analysis and machine intelligence, Vol. 27 No.6, June 2005,
pp.993-997.
12. Reena Bajaj and Shantanu Choudhari, “Signature Verification using multiple neural
classifiers,” Pattern recognition, Vol. 30 No. 1, 1997, pp. 1-7.
13. Emre Ozgunduz, Tulin Sentruk & M. Elif karsligil, “Off-line signature verification
and recognition by support vector machine”.
14. Thomas M. Breuel, “Representations and metrics for off-line handwriting
segmentation.
15. M. Blumenstein and B. Verma, “An artificial neural network based segmentation
algorithm for off-line handwriting recognition”.
17. 16. Jamal Fathi and Abuhasna, “Signature recognition using conjugate gradient neural
network”.
17. Minal Tomar & Pratibha Singh, “A Simpler Energy Density method for Off-line.
Signature Verification using Neural Network”.
18. Minal Tomar & Pratibha Singh, “An Intelligent network for offline signature
verification system using chain code” published in proceeding of The First International
Conference on Computer Science and Information Technology.
18. Citation Count – 17
HASH FUNCTION IMPLEMENTATION USING ARTIFICIAL NEURAL
NETWORK
V. R. Kulkarni1, Shaheen Mujawar2 and Sulabha Apte3
1Department of Information Science and Engineering, Gogte Institute of
Technology, VTU University, Belgaum, Karnataka, India
2Department of Computer science and Engineering, Gogte Institute of Technology,
VTU University, Belgaum, Karnataka, India
3Department of Computer science and Engineering, Walchand Institute of
Technology, Solapur
ABSTRACT
In this paper an algorithm for one-way hash function construction based on a two layer
feed forward neural network along with the piece-wise linear (pwl) chaotic map is
proposed. Based on chaotic neural networks, a Hash function is constructed, which
makes use of neural networks' diffusion property and chaos' confusion property. This
function encodes the plaintext of arbitrary length into the hash value of fixed length
(typically, 128-bit, 256-bit or 512-bit). Theoretical analysis and experimental results
show that this hash function is one-way, with high key sensitivity and plaintext
sensitivity, and secure against birthday attacks or meet-in-the-middle attacks. These
properties make it a suitable choice for data signature or authentication.
KEYWORDS
One-way Hash function, Neural network, Chaotic map, Plaintext Sensitivity
For More Details : http://airccse.org/journal/ijsc/papers/1110ijsc01.pdf
Volume Link : http://airccse.org/journal/ijsc/current2010.html
REFERENCES
[1] Shiguo Lian, Zhongxuan Liu, Zhen Ren, Haila Wang, “Hash Function Based on
Chaotic Neural Networks” IEEE, 2006.
[2] Shiguo Lian,Jinsheng Sun,Zhiquan Wang, “ One-way Hash Function Based on
Neural Network” Journal of Information Assurance and Security,2006
19. [3] Yi Du, Detang Lu, Daolun Li,“An Effective Hash-based Method for Generating
Synthetic Well Log” 2008 IEEE.
[4] Qun-ting Yang,Tie-gang Gao,Li Fan,Qiao-lun Gu, “ Analysis of One-way Alterable
Length Hash Function Based on Cell Neural Network” Fifth Intenational Conference on
Information Assurance and Security,2009
[5] Qinghua Zhang,Han Zhang and Zhaohui Li,”One-way Hash Function Construction
Based on Conservative Chaotic Systems” Journal of Information Assurance and Security,
5, pp.171-178, 2010
[6] M.K. Rachel, K. Einat, K. Ido, Wolfgang, "Public Channel 012. Cryptography
by Synchronization of Neural Networks and ChaoticMaps," Physical Review Letters,
Vol. 91, No.11, Sep 12, 2003: 118701/1-118701/4. International Journal on Soft
Computing ( IJSC ), Vol.1, No.1, November 2010 8
[7] L.P. Yee, D. L.C. Silva, "Application of Multilayer Perceptron Network as a One-way
Hash Function” International Joint Conference on Neural Networks, Vol. 2,2002.
[8] Li, C., S. Li, D. Zhang and G. Chen,”Cryptanalysis of a chaotic neural network
based multimedia encryption scheme”. Advances in Multimedia Information Processing
PCM ,2004 .
[9] Khalil Shihab, “A Backpropagation Neural Network for Computer Network Security”
Journal of Computer Science 2 (9): 710-715, 2006.
[10] C.-K. Chan and L.M. Cheng. The convergence properties of a clipped Hopfield
network and its application in the design of key stream generator, IEEE Transactions on
Neural Networks, Vol.12, No. 2,pp. 340-348, March 2001.
[11] D.A. Karras and V. Zorkadis. On neural network techniques in the secure
management of communication systems through improving and quality assessing
pseudorandom stream generators.Neural Networks, Vol. 16, No. 5-6, June - July, 2003:
899-905
[12] S.G. Lian, G.R. Chen, A. Cheung, Z.Q. Wang. A Chaotic-Neural-Network-Based
Encryption Algorithm for JPEG2000 Encoded Images. In: Processing of 2004 IEEE
Symposium on Neural Networks (ISNN2004), Dalian, China, Springer LNCS, 3174
(2004) 627-632.
[13] Liew Pol Yee and L.C. De Silva. Application of multilayer perception networks in
symmetric block ciphers. Proceedings of the 2002 International Joint Conference on
Neural Networks, Honolulu, HI, USA, Vol. 2, 12-17 May 2002: 1455 – 1458.
20. [14] N. Masuda, K. Aihara, "Cryptosystems With Discretized Chaotic Maps," IEEE
Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 49,
No. 1, Jan 2002.
[15] H.P. Lu, S.H. Wang, G. Hu, "Pseudo-random number generator based on coupled
map lattices,"International Journal of Modern Physics B 2004; 18(17-19): 2409-2414.
[16] William Stallings. Cryptography and Network Security, Third Edition.
[17] NIST.Secure Hash Standard. Federal Information Processing Standard. FIPS-180-
1.April 1995
[18] S Sivanandam .S Sumathi Introduction to Neural Networks Using MATLAB 6. 0
(Computer Science Series)
[19] Rudra Pratap Getting Started With MATLAB 7 - A Quick Introduction For
Scientists And Engineers
[20] www.mathwork.com
21. Citation Count – 15
ACCURACY DRIVEN ARTIFICIAL NEURAL NETWORKS IN STOCK
MARKET PREDICTION
Selvan Simon1 and Arun Raoot2
1Asst. Professor, P. G. Dept. of Computer Sci., SNDT Women’s University, Mumbai
2Professor, National Institute of Industrial Engineering, Vihar Lake, Mumbai
ABSTRACT
Globalization has made the stock market prediction (SMP) accuracy more challenging
and rewarding for the researchers and other participants in the stock market. Local and
global economic situations along with the company’s financial strength and prospects
have to be taken into account to improve the prediction accuracy. Artificial Neural
Networks (ANN) has been identified to be one of the dominant data mining techniques in
stock market prediction area. In this paper, we survey different ANN models that
have been experimented in SMP with the special enhancement techniques used with them
to improve the accuracy. Also, we explore the possible research strategies in this
accuracy driven ANN models.
KEYWORDS
Artificial Neural Networks, Multilayer Perceptron, Back Propagation, Stock market
prediction & Prediction accuracy.
For More Details : http://airccse.org/journal/ijsc/papers/3211ijsc03.pdf
Volume Link : http://airccse.org/journal/ijsc/current2012.html
REFERENCES
[1] M. Majumder and A. Hussian, MD, “Forcasting of Indian Stock Market Index Using
Artificial Neural Network,” nseindia.com, pp. 1-21, 2010.
[2] D. V. Setty, T. M. Rangaswamy, and K. N. Subramanya, “A review on Data Mining
Applications to the Performance of Stock Marketing,” International Journal of Computer
Applications, vol. 1, no. 3, pp. 33-43, Feb. 2010.
[3] P. Falinouss, “Stock trend prediction using news articles,” Master’s thesis, Lulea
University of Technology, pp. 1653–0187, 2007.
22. [4] D. Zhang and L. Zhou, “Discovering golden nuggets: data mining in financial
application,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE
Transactions on, vol. 34, no. 4, pp. 513–522, 2004.
[5] J. Kutsurelis, “Forecasting financial markets using neural networks: An analysis of
methods and accuracy,” 1998.
[6] R. Dase and D. Pawar, “Application of Artificial Neural Network for stock market
predictions: A review of literature,” International Journal of Machine Intelligence, ISSN,
vol. 2, no. 2, pp. 0975– 2927, 2010.
[7] E. W. Saad, D. V. Prokhorov, and D. C. Wunsch, “Comparative study of stock trend
prediction using time delay, recurrent and probabilistic neural networks,” Neural
Networks, IEEE Transactions on, vol. 9, no. 6, pp. 1456–1470, 1998.
[8] T. Quah, “DJIA stock selection assisted by neural network,” Expert Systems with
Applications, vol. 35, no. 1-2, pp. 50-58, Jul. 2008.
[9] P. R. Charkha, “Stock Price Prediction and Trend Prediction Using Neural
Networks,” 2008 First International Conference on Emerging Trends in Engineering and
Technology, pp. 592-594, 2008.
[10] M. Naeini, H. Taremian, and H. Hashemi, “Stock market value prediction using
neural networks,” 2010 International Conference on Computer Information Systems and
Industrial Management Applications (CISIM), pp. 132-136, Oct. 2010.
[11] J. H. Wang and J. Y. Leu, “Stock market trend prediction using ARIMA-based
neural networks,” in Neural Networks, 1996., IEEE International Conference on, 1996,
vol. 4, pp. 2160–2165.
[12] M. Thenmozhi, “Forcasting Stock Index Returns Using Neural Networks,”
dbr.shtr.org, vol. 7, no. 2, pp. 59-69, 2006.
[13] A. Al-Luhaib, K. Al-Ghoneim, and Y. Al-Ohali, “Dynamic Targets for Stock Market
Prediction,” in Signal Processing and Communications, 2007. ICSPC 2007. IEEE
International Conference on, 2007, no. November, pp. 1019–1022.
[14] Q. Zhao, X. Zhao, and F. Duan, “Prediction Model of Stock Prices Based on
Correlative Analysis and Neural Networks,” 2009 Second International Conference on
Information and Computing Science, pp. 189-192, 2009.
[15] L. Hsieh, S. Hsieh, and P. Tai, “Enhanced stock price variation prediction via DOE
23. and BPNN-based optimization,” Expert Systems with Applications, vol. 38, no. 11, pp.
14178-14184, May 2011.
[16] S. H. Lee and J. S. Lim, “Forecasting KOSPI by weighted average defuzzification
based on NEWFM,” and Advanced Systems, 2007. ICIAS 2007., pp. 66–70, 2007.
[17] F. Li and C. Liu, “Application Study of BP Neural Network on Stock Market
Prediction,” 2009 Ninth International Conference on Hybrid Intelligent Systems, pp. 174-
178, 2009. International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012 44
[18] S. O. Olatunji, M. S. Al-Ahmadi, M. Elshafee, and Y. A. Fallatah, “Saudi Arabia
stock prices forecasting using artificial neural networks,” Applications of Digital, pp. 81–
86, 2011.
[19] Y. Li and W. Ma, “Applications of Artificial Neural Networks in Financial
Economics: A Survey,” 2010 International Symposium on Computational Intelligence
and Design, pp. 211-214, Oct. 2010.
[20] A. M. Safer, “Predicting abnormal stock returns with a nonparametric nonlinear
method,” in Neural Networks, 2001. Proceedings. IJCNN’01. International Joint
Conference on, 2001, vol. 3, no. 1993, pp. 1833–1837.
[21] M. Schumann and T. Lohrbach, “Comparing artificial neural networks with
statistical methods within the field of stock market prediction,” in System Sciences, 1993,
Proceeding of the Twenty-Sixth Hawaii International Conference on, 1993, vol. 4, pp.
597–606.
[22] J. Boston, “A measure of uncertainty for stock performance,” in Computational
Intelligence for Financial Engineering (CIFEr), 1998. Proceedings of the
IEEE/IAFE/INFORMS 1998 Conference on, 1998, pp. 161–164.
[23] H. White, “Economic prediction using neural networks: The case of IBM daily stock
returns,” in Neural Networks, 1988., IEEE International Conference on, 1988, pp. 451–
458.
[24] C. Ornes, “A neural network that explains as well as predicts financial market
behavior,” Computational Intelligence for Financial, pp. 43–49, 1997.
[25] A. Refenes, A. Zapranis, and Y. Bentz, “Modelling stock returns with neural
networks,” in Neural Network Applications and Tools. Workshop on (1993), 1993, pp.
39–50.
[26] P. Sutheebanjard and W. Premchaiswadi, “Stock Exchange of Thailand Index
24. Prediction Using Back Propagation Neural Networks,” 2010 Second International
Conference on Computer and Network Technology, pp. 377-380, 2010.
[27] M. Mehrara, A. Moeini, M. Ahrari, and A. Ghafari, “Using Technical Analysis with
Neural Network for Forecasting Stock Price Index in Tehran Stock Exchange,” Middle
Eastern Finance and Economics, no. 6, 2010.
[28] J. Roman and A. Jameel, “Backpropagation and recurrent neural networks in
financial analysis of multiple stock market returns,” in System Sciences, 1996.,
Proceedings of the Twenty-Ninth Hawaii International Conference on,, 1996, vol. 2, pp.
454–460.
[29] Y. JingTao and L. T. Chew, “Guidelines for financial forecasting with neural
networks,” . Neural Information Processing, Shanghai,, 2001.
[30] S. Walczak, “Gaining competitive advantage for trading in emerging capital markets
with neural networks,” Journal of Management Information Systems, vol. 16, no. 2, pp.
177–192, 1999.
25. Citation Count – 10
Solution of Inverse Kinematics for SCARA Manipulator Using Adaptive Neuro-
Fuzzy Network
Wesam Mohammed Jasim
College of Computer, University of Anbar, Iraq
ABSTRACT
Solution of inverse kinematic equations is complex problem, the complexity comes from
the nonlinearity of joint space and Cartesian space mapping and having multiple solution.
In this work, four adaptive neurofuzzy networks ANFIS are implemented to solve the
inverse kinematics of 4-DOF SCARA manipulator. The implementation of ANFIS is
easy, and the simulation of it shows that it is very fast and give acceptable error.
KEYWORDS
Inverse Kinematics, Adaptive Neuro-Fuzzy, SCARA Manipulator.
For More Details : http://airccse.org/journal/ijsc/papers/2411ijsc06.pdf
Volume Link: http://airccse.org/journal/ijsc/current2011.html
REFERENCES
[1] Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, 2006, “ Robot Modeling and
Control “ John Wiley & Sons, New York.
[2] E. Sariyildiz, and H. Temeltas, 14-17 July, 2009, “ Solution of Inverse Kinematic
Problem for Serial Robot Using Dual Quaterninons and Plucker Coordinates “
IEEE/ASME, Singapore, pp.338-343.
[3] P. Kalra, P.B. Mahapatra, and D.K. Aggarwal, 2003, “ On the Solution of Multimodel
Robot Inverse Kinematic Functions using Real-coded Genetic Algorithms “ IEEE, pp.
1840-1845.
[4] YangshengXu and Michael C. Nechgba, 1993,“ Fuzzy Inverse Kinematic Mapping “
IROS Conference.
[5] RamakrishnanMukundan, , 2008, “ A Fast Inverse Kinematics Solution for an n-link
Joint Chain” ICITA, pp. 349-354.
26. [6] Victor H. and etc., October 2010,“ Kinematics for the SCARA and the Cylindrical
Manipulators “ICIC, Vol. 4, No. 5, pp. 1-6.
[7] Samuel R. Buss, October, 2009, “ Introduction to inverse Kinematics with Jacobian
Transpose, Pseudoinverse and Damped Least Squares methods “ University of California
press, pp. 1-19.
[8] Takehiko O. and H. Canada, 2010, “Solution for Ill-Posed Inverse Kinematics of
Robot
ArmbyNetwork Inversion “ Hindawi Publishing Corporation, Journal of Robotics,
Volume 2010, doi:10.1155/2010/870923.
[9] Panchanand J., 2009, “Novel Artificial Neural Network Application for Prediction of
Inverse Kinematics of Manipulator” M.Sc. Thesis, National Institute of Technology,
India.
[10] Dr. Bob John “Adaptive Network Based Fuzzy Inference Systems (ANFIS)
“.www.cse.dmu.ac.uk/~hseker/ANFISnotes.doc
27. Citation Count – 06
An Artificial Neural Network Model for Classification of Epileptic Seizures Using
HuangHilbert Transform
Shaik.Jakeer Husain1 and Dr.K.S.Rao 2
1Dept. of Electronics and Communication Engineering , Vidya Jyothi Institute of
Technology, Hyderabad India
ABSTRACT
Epilepsy is one of the most common neurological disorders characterized by transient and
unexpected electrical disturbance in the brain. In This paper the EEG signals are
decomposed into a finite set of band limited signals termed as Intrinsic mode functions.
The Hilbert transom is applied on these IMF’s to calculate instantaneous frequencies. The
2nd,3rd and 4th IMF's are used to extract features of epileptic signal. A neural network
using back propagation algorithm is implemented for classification of epilepsy. An
overall accuracy of 99.8% is achieved in classification.
KEYWORDS
Electroencephalogram(EEG),Hilbert-Huang transform(HHT), Instantaneous frequency
(ifs), intrinsic mode function (IMF)
For More Details : http://airccse.org/journal/ijsc/papers/5314ijsc03.pdf
Volume Link : http://airccse.org/journal/ijsc/current2014.html
REFERENCES
[1] J. Gotman., “Automatic recognition of epileptic seizures in the EEG,” Clinical
Neurophysiology, vol. 54, pp. 530–540, 1982
[2] J.Gotman., “Automatic seizure detection: improvements and evaluation,” Clinical
Neurophysiology, vol. 76, pp. 317–324, 1990
[3] N.E. Huang, Z. Shen, S.R. Long, M.L. Wu, H.H. Shih,Q. Zheng, N.C. Yen, C.C.
Tung, and H.H. Liu, “TheEmpirical Mode Decomposition and Hilbert Spectrumfor
Nonlinear and Nonstationary Time Series Analysis,” Proc. Roy. Soc., vol. 454, pp. 903 –
995, 1998.
[4] Y.U. Khan, J. Gotman, “Electroencephalogram Wavelet based automatic seizure
detection iintracerebral”, Clinical Neurophysiology, vol. 114, pp. 899-908, 2003
28. [5] Güler NF, Übeyli ED, Güler.”Recurrent neural networksemploying Lyapunov
exponents for EEG signal classification”,Expert Syst Appl. 2005; 29(3):506-14
[6] Varun Bajaj, Ram Bilas Pachori “Epileptic Seizure Detection Based on the
Instantaneous Area of Analytic Intrinsic Mode Functions of EEG Signals,” Biomed Eng
Lett, vol. 3, pp. 17-21, 2013
[7] EEG time timeseries (epilepticdata)(2005,Nov.) [Online],
http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html
[8] Hedi Khammari , Ashraf Anwar, “A Spectral Based Forecasting Tool of Epileptic
Seizures ” IJCSI International.Journal of Computer Science Issues, Vol. 9, Issue 3, No 3,
May 2012
[9] Rami J Oweis and Enas W Abdulhay., “Seizure classification in EEG signals utilizing
Hilbert- Huang transform” BioMedical Engineering OnLine 2011, 10:38
[10] lajos losonczi, lászló bakó, sándor-tihamér ,Brassai and lászló-ferenc Márton.,
“Hilbert-huang transform used for eeg signal analysis ,” The 6th edition of the
Interdisciplinarity in Engineering International Conference , “Petru Maior” University of
Tîrgu Mure, Romania, 2012
29. Citation Count – 05
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR
FACE RECOGNITION
A.S.Raja1 and V. JosephRaj2
1Research Scholar, Sathyabama University, Jeppiar Nagar, Chennai,Tamil Nadu,
India
2Professor, Kamaraj College, Thoothukudi, Tamil Nadu, India
ABSTRACT
The word biometrics refers to the use of physiological or biological characteristics of
human to recognize and verify the identity of an individual. Face is one of the human
biometrics for passive identification with uniqueness and stability. In this manuscript we
present a new face based biometric system based on neural networks supervised self
organizing maps (SOM). We name our method named SOM-F. We show that
the proposed SOM-F method improves the performance and robustness of recognition.
We apply the proposed method to a variety of datasets and show the results.
KEYWORDS
Biometrics, Face, Supervised Self Organizing Maps (SOM).
For More Details : http://airccse.org/journal/ijsc/papers/3312ijsc03.pdf
Volume Link : http://airccse.org/journal/ijsc/current2012.html
REFERENCES
[1] Jain AK, Bolle R, Pankanti S (eds) (1999) "Biometrics: Personal identification in
networked society" . Kluwer Academic Publishers, Boston/Dordrecht/London
[2] Wechsler H, Phillips JP, Bruce V, Folgeman Soulie F, Huang TS (eds) (1997)" Face
recognition – From theory to applications" . ASI NATO series, vol 163, Springer, Berlin
Heidelberg New York.
[3] Zhao WY, Chellappa R, Rosenfeld A, Philips PJ (2000) "Face recognition: A
literature survey". UMD CfAR technical report CAR-TR-948
30. [4] Turk M, Pentland A (1991)" Eigenfaces for face recognition". J Cognitive Neurosci
3(1):71–86 [5] Jain A, Flynn P, Ross AA, Handbook of Biometrics, Springer, Heidelberg
(2008)
[6] Wiskott L, Fellous JM, Kru¨ ger N, von der Malsburg C (1997) "Face recognition by
elastic bunch graph matching". IEEE T Pattern Anal 19(7):775–779
[7] Kotropoulos CL, Tefas A, Pitas I (2000) "Morphological elastic graph matching
applied to frontal face authentication under well-controlled and real conditions". Pattern
Recogn 33(12):1935–1947
[8] Penev PS, Atick J (1996) "Local feature analysis: A general statistical theory for
object representation". Network–Comp Neural 7(3):477–500
[9] de Vel O, Aeberhard S (1999) "Line-based face recognition under varying pose".
IEEE T Pattern Anal 21(10):1081–1088
[10] Alvarado GJ, Pedrycz W, Reformat M, Kwak KC (2006) " Deterioration of visual
information in face classification using eigenfaces and fisherfaces". Int J Mach Vis Appl
17(1):68–82
[11] Karande KJ, Talbar SN (2009) "Independent component analysis of edge
information for face recognition". Int J Image Proc 3(3): 120–130
[12] Liu C, Wechsler H (1999) " Comparative assessment of independent component
analysis (ICA) for face recognition ". In: Proceedings of the 2nd international conference
on audio- and video-based biometric person authentication, pp 211–216
[13] Li J, Zhao B, Zhang H (2009) "Face recognition based on PCA and LDA
combination feature extraction". In: Proceedings of the 1st IEEE international conference
on information science and engineering, pp 1240–1243
[14] Zhang H, Deng W, Guo J, Yang J (2010) "Locality preserving and global
discriminant projection with prior information". Int J Mach Vis Appl 21:577–585
[15] Jarillo G, Pedrycz W, Reformat M (2008) "Aggregation of classifiers based on
image transformations in biometric face recognition". Int J Mach Vis Appl 19:125–140
[16] Lu J, Plataniotis KN, Venetsanopoulos AN (2003)" Face recognition using kernel
direct discriminant analysis algorithms". IEEE Trans Neural Netw 14(1):117–126
[17] Bach FR, Jordan MI (2002) "Kernel independent component analysis". Int J Mach
Learn Res 3:41–48.
31. [18] Shan S, Gao W, Zhao D (2003)" Face recognition based on facespecific subspace".
Int J Imag Syst Technol 13:23–32
[19] Delac K, Grgic M, Grgic S (2005) "Independent comparative study of PCA, ICA,
and LDA on the FERET data set". Int J Imag Syst Technol 15:252–260
[20] Karande KJ, Talbar SN (2009)" Independent component analysis of edge
information for face recognition". Int J Image Proc 3(3): 120–130
[21] Jiang X, Mandal B, Kot A (2009) "Complete Discriminant evaluation and feature
extraction in kernel space for face recognition". Int J Mach Vis Appl 20:35–46
[22] Alvarado GJ, Pedrycz W, Reformat M, Kwak K (2006)" Deterioration of visual
information in face classification using eigenfaces and fisherfaces". Int J Mach Vis Appl
17(1):68–82 39
[23] Oravec M, Pavlovicˇova´ J (2007) "Face Recognition methods based on feedforward
neural networks,principal component analysis and self-organizing map". Radio Eng
16(1):51–57
[24] http://sourceforge.net/projects/opencvlibrary/
[25] Viola, P., Jones, M.:" Rapid object detection using boosted cascade of simple
features". In: Proceedings of IEEE Computer Vision and Pattern Recognition (2001)
[26] Lienhart, R., Maydt, J.:" An extended set of Haar-like features for rapid object
detection". In: Proceedings of IEEE International Conference on ISOMEe Processing, pp.
900–903 (2002)
[27] Face Database Source Link, http://www4.comp.polyu.edu.hk/ csajaykr/
[28] Fischer, M. M. (2001). "Computational neural networks: Tools for spatial data
analysisComputational neural networks: Tools for spatial data analysis". In M.
M. Fischer & Y. Leung (Eds.), Geocomputational modelling: Techniques and
applications (pp. 79–102). Heidelberg: Springer.