Shin Kamada

A generation method of filtering rules of Twitter via smartphone based Participatory Sensing system for tourist by interactive GHSOM and C4.5

Mobile Phone based Participatory Sensing (MPPS) systems involve a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. We have developed the tourist subjective data collection system with Android smartphone. The tourist can tweet the information of sightseeing spots by using the application. The application can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM and C4.5.

An adaptive learning method of Restricted Boltzmann Machine by neuron generation and annihilation algorithm

Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties to determine an optimal network structure to analyze big data. In order to evade the problem, we investigated the variance of parameters to find an optimal structure during learning. For the reason, we should check the variance of parameters to cause the fluctuation for energy function in RBM model. In this paper, we propose the adaptive learning method of RBM that can discover an optimal number of hidden neurons according to the training situation by applying the neuron generation and annihilation algorithm. In this method, a new hidden neuron is generated if the energy function is not still converged and the variance of the parameters is large. Moreover, the inactivated hidden neuron will be annihilated if the neuron does not affect the learning situation. The experimental results for some benchmark data sets were discussed in this paper.

An adaptive learning method of Deep Belief Network by layer generation algorithm

Deep Belief Network (DBN) has a deep architecture that represents multiple features of input patterns hierarchically with the pre-trained Restricted Boltzmann Machines (RBM). A traditional RBM or DBN model cannot change its network structure during the learning phase. Our proposed adaptive learning method can discover the optimal number of hidden neurons and weights and/or layers according to the input space. The model is an important method to take account of the computational cost and the model stability. The regularities to hold the sparse structure of network is considerable problem, since the extraction of explicit knowledge from the trained network should be required. In our previous research, we have developed the hybrid method of adaptive structural learning method of RBM and Learning Forgetting method to the trained RBM. In this paper, we propose the adaptive learning method of DBN that can determine the optimal number of layers during the learning. We evaluated our proposed model on some benchmark data sets.

Fine tuning method by using knowledge acquisition from Deep Belief Network

We developed an adaptive structure learning method of Restricted Boltzmann Machine (RBM) which can generate/annihilate neurons by self-organizing learning method according to input patterns. Moreover, the adaptive Deep Belief Network (DBN) in the assemble process of pre-trained RBM layer was developed. The proposed method presents to score a great success to the training data set for big data benchmark test such as CIFAR-10. However, the classification capability of the test data set, which are included unknown patterns, is high, but does not lead perfect correct solution. We investigated the wrong specified data and then some characteristic patterns were found. In this paper, the knowledge related to the patterns is embedded into the classification algorithm of trained DBN. As a result, the classification capability can achieve a great success (97.1% to unknown data set).

Adaptive learning method of recurrent temporal deep belief network to analyze time series data

Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Such architecture is well known to represent higher learning capability compared with some conventional models if the best set of parameters in the optimal network structure is found. We have been developing the adaptive learning method that can discover the optimal network structure in Deep Belief Network (DBN). The learning method can construct the network structure with the optimal number of hidden neurons in each Restricted Boltzmann Machine and with the optimal number of layers in the DBN during learning phase. The network structure of the learning method can be self-organized according to given input patterns of big data set. In this paper, we embed the adaptive learning method into the recurrent temporal RBM and the self-generated layer into DBN. In order to verify the effectiveness of our proposed method, the experimental results are higher classification capability than the conventional methods in this paper.

Early discovery of chronic non-attenders by using NFC attendance management system

Near Field Communication (NFC) standards cover communications protocols and data exchange formats. They are based on existing radio-frequency identification (RFID) standards. In Japan, Felica card is a popular way to identify the unique ID. Recently, the attendance management system (AMS) with RFID technology has been developed as a part of Smart University, which is the educational infrastructure using high technologies, such as ICT. However, the reader/writer for Felica is too expensive to build the AMS. NFC technology includes not only Felica but other type of IC chips. The Android OS 2.3 and the later can provide access to NFC functionality. Therefore, we developed AMS for university with NFC on Nexus 7. Because Nexus 7 is a low cost smart tablet, a teacher can determine to use familiarly. Especially, this paper describes the method of early discovery for chronic non-attenders by using the AMS system on 2 or more Nexus 7 which is connected each other via peer-to-peer communication. The attendance situation collected from different Nexus 7 is merged into a SQLite file and then, the document is reported to operate with the trunk system in educational affairs section.

Clustering and retrieval method of immunological memory cell in clonal selection algorithm

The clonal selection principle explains the basic features of an adaptive immune response to a antigenic stimulus. It established the idea that only those cells that recognize the antigens are selected to proliferate and differentiate. This paper explains a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. Antibodies generated by the clonal selection algorithm are clustered in some categories according to the affinity maturation, so that immunological memory cells which respond to the specified pathogen are created. Experimental results to classify the medical database of Coronary Heart Disease databases are reported. For the dataset, our proposed method shows the 99.6% classification capability of training data.

Recommendation system of Grants-in-Aid for researchers by using JSPS keyword

The grant for the research gives the researcher the important opportunity to make fruitful research results. Recently, the notification of the government grants and some Foundation grants in the various fields informs the researchers through Internet. However, the notification provided through Internet includes ambiguous and complex. The researchers will fail to notice the grant information which he/she should submit the application form. The department of research support at the university should avoid to miss the researchers opportunity. The support staff classifies the notifications into some categories related to the research fields and provides to the researchers the application form best matched to the researchers current research field. We have developed recommendation system of Grant-in-Aid system for researchers by using JSPS (Japan Society for the Promotion of Science) keywords. The system can determine some rules associated between the characteristic keywords extracted from the website and the keywords from the researchers current research field. The system recommends to the researcher the best matched application form. The researcher can surely know the information without failing to notice it by using the developed system.

A Clonal Selection Algorithm with Levenshtein Distance Based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM

Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and Clonal Selection Algorithm with Immunological Memory Cells in this paper.

Adaptive structure learning method of deep belief network using neuron generation–annihilation and layer generation

Recently, deep learning is receiving renewed attention in the field of artificial intelligence. Deep belief network (DBN) has a deep network architecture that can represent multiple features of input patterns hierarchically, using pre-trained restricted Boltzmann machines (RBMs). Such deep network architectures enable extremely high classification accuracy in many tasks compared to previous methods. However, determining various parameters to design effective deep network architectures is a difficult task even for experienced designers, since traditional RBM and DBN cannot change their network structure during the training. The adaptive structure learning method has been previously proposed for finding the optimum number of hidden neurons in multilayered neural networks. The method employs the neuron generation–annihilation algorithm by observing the variance of weight decays. We develop the adaptive structure learning method of RBM and DBN using the neuron generation–annihilation and layer generation algorithm by observing the variance of some parameters. The effectiveness of our proposed model was verified by tenfold cross-validation on benchmark data sets CIFAR-10 and CIFAR-100. The adaptive DBN achieved the highest classification accuracy (97.4% for CIFAR-10, 81.2% for CIFAR-100) among several latest DBN- and CNN-based methods.