Hierarchical Self Research Articles

Pain suffering and the self. An active allostatic inference explanation.

Distributed processing that gives rise to pain experience is anchored by a multidimensional self-model. I show how the phenomenon of pain asymbolia and other atypical pain-related conditions (Insensitivity to Pain, Chronic Pain, 'Social' Pain, Insensitivity to Pain, Chronic Pain, 'Social' Pain, empathy for pain and suffering) can be explained by this idea. It also explains the patterns of association and dissociation among neural correlates without importing strong modular assumptions. It treats pain processing as a species of allostatic active inference in which the mind co-ordinates its processing resources to optimize basic bodily functioning at different time scales. The self is inferred to be source and target of regulation in this process. The self-modelling account reconciles conflicting deaffectualization and depersonalization accounts of pain asymbolia by showing how depersonalization and pain asymbolia arise at different levels of hierarchical self modelling.

Hierarchical self attention based sequential labelling model for Bhojpuri, Maithili and Magahi languages

Sequential labelling plays a vital role in solving numerous Natural Language Processing (NLP) applications such as Machine Translation and Information Extraction etc. One of these is Part-of-Speech (POS) tagging, which assigns a sequence of grammatical categories to the given sentence, and Chunking which groups them into ‘chunks’ or what can be called minimal phrases. Bhojpuri, Maithili and Magahi are low resource languages and widely spoken in central north-eastern India, belonging to the Indo-Aryan language family. The creation of an annotated corpus for POS tagging and Chunking, and then building an initial automatic tool for these problems is the first attempt towards building language technology tools for these languages. The annotated corpus used to develop POS Taggers and Chunkers, based on various machine learning algorithms (TnT, CRF, MEMM and Structured SVM) and state-of-the-art LSTM-CNN-CRF model, and then these compared with the obtained results on two new proposed deep learning-based models, Self-Attention Hierarchical Bi-LSTM CRF (SAHBiLC) and a fine-tuned version of it, Fine-SAHBiLC. The SAHBiLC and Fine-SAHBiLC models outperform on Bhojpuri (Accuracy for POS and Chunking is 0.86% and 0.94%, respectively) and Maithili (Accuracy for POS and Chunking is 0.86% and 0.95%, respectively) and Magahi (Accuracy for POS is 0.86%).

Information Visualization Using Clustering and Predictive Model

This paper aims to reveal the impact of rainfall on tea export from India, an issue that remained unexplored in the existing literature. This study explores a new model to predict India's tea export more accurately that would be helpful for Indian tea planters and exporters to plan their production as well as the inventory holding for deriving maximum value from tea export. A two-stage modelling approach has been developed. Firstly, an artificial intelligence-based growing hierarchical self-organising map algorithm is employed on the monthly time series of monthly frequency spreading over April 2005 to December 2013 to segregate India's monthly tea export data series into visual clusters of recognized pattern. Further, a predictive model using support vector machine has been developed and applied to forecast the tea export and then the importance of the predictor variables of the tea export have been identified. Finally, using the appropriate measures of performance a comparative analysis has been performed for each of the model. The newness of the study pertains to the two facts revealed from the study: firstly, India's tea export is embedded of complexity and nonlinearity, which could receive a successful clustering through growing hierarchical self organizing map that would make a deeper analysis easier with a further application of rich statistical techniques. Secondly, the analysis of prediction errors and the relative importance of the predictor variables establish rainfall as one of the most significant variable in predicting India's tea export, insight that has never surfaced in the literature developed thus far.

A MODIFIED FUZZY C-MEANS BASED ALGORITHM FOR SEGMENTATION OF MRI IMAGES

Segmentation is an important process to extract suspicious region from complex medical images. In this research work a new method is proposed to diagnose liver fibrosis and brain tumor through MRI Images using image processing clustering algorithm such as Fuzzy Cognitive Means along with intelligent optimization tools, such as Hierarchical Self Organizing Map (HSOM), Bacteria Foraging Optimization (BFO). The experimental results of the proposed method are analyzed and compared with other existing methods.

Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers

Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. In this study, the case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features from a ground based hyperspectral imaging system. Hyperspectral images of healthy and diseased plant canopies were taken at Rothamsted Research, UK by a Specim V10 spectrograph. Five wavebands of 20 nm width were utilized for accurate identification of each of the stress and healthy plant conditions. The technique that was developed used a hybrid classification scheme consisting of hierarchical self organizing classifiers. Three different architectures were considered: counter-propagation artificial neural networks, supervised Kohonen networks (SKNs) and XY-fusion. A total of 12 120 spectra were collected. From these 3 062 (25.3%) were used for testing. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95% for all three architectures. The proposed approach aimed at sensor based detection of diseased and stressed plants so that can be treated site specifically contributing to a more effective and precise application of fertilizers and fungicides according to specific plant’s needs.

Women in the neighbourhood: Reinstating the European Union’s civilising mission on the back of gender equality promotion?

The European Union has launched its New European Neighbourhood Policy as a reaction to a ‘changing neighbourhood’. A key novelty in the New European Neighbourhood Policy is the special role attributed to gender equality promotion as an important ingredient of Europeanisation. The literature has so far focused on assessing whether and to what extent neighbourhood countries adopt and implement European Union gender equality norms. Bringing together the feminist and postcolonial literature on gender equality promotion and European identity formation, this article resituates the New European Neighbourhood Policy within the broader debate regarding processes of European identity formation and Europe’s relations with Others. We combine the concept of delineating gendered and racialised coding with the concept of contrapuntal reading to analyse key official European Union documents alongside the voices expressing themselves through new (social) media. This allows us to highlight silences and exclusions within New European Neighbourhood Policy narratives, to resituate these narratives in their historical context, and to render visible the diversity of competing and interrelated narratives related to gender equality promotion. We read the recent focus on gender equality promotion in the New European Neighbourhood Policy as an expression of the ambivalence of European Union identity building: at a moment when neighbouring countries move closer to Europe, either adopting the acquis communautaire or going through democratisation processes, they are placed at a spatial and temporal distance outside Europe. Our analysis highlights the persistence of colonial practices of Othering and hierarchical Self–Other definitions that are reproduced through current New European Neighbourhood Policy policies. Yet, we suggest that this moment might also present an opportunity to render visible and take seriously the co-constitutive relationship between the European Union and its Others, which could point to alternative forms of interaction and identity building.

Tailoring local density of optical states to control emission intensity and anisotropy of quantum dots in hybrid photonic-plasmonic templates

We report results of controlled tuning of the local density of states (LDOS) in versatile, flexible, and hierarchical self assembled plasmonic templates. Using 5 nm diameter gold (Au) spherical nanoantenna within a polymer template randomly dispersed with quantum dots, we show how the photoluminescence intensity and lifetime anisotropy of these dots can be significantly enhanced through LDOS tuning. Finite difference time domain simulations corroborate the experimental observations and extend the regime of enhancement to a wider range of geometric and spectral parameters bringing out the versatility of these functional plasmonic templates. It is also demonstrated how the templates act as plasmonic resonators for effectively engineer giant enhancement of the scattering efficiency of these nano antenna embedded in the templates. Our work provides an alternative method to achieve spontaneous emission intensity and anisotropy enhancement with true nanoscale plasmon resonators.

Cooperative Growing Hierarchical Recurrent Self Organizing Model for Phoneme Recognition

In this paper, we propose a system of a tree evolutionary recurrent self-organizing models. Inherited from the Growing Hierarchical Self-Organizing Map GHSOM. The proposed GHSOM variants are characterized by a hierarchical model, composed of independent RSOMs (recurrent Self-Organizing Map). The case study of the proposed system is phoneme recognition in continuous speech and speaker independent context. GHSOM variants serve az tools for developing intel- ligent systems and proposed artificial intelligence application.

Performance Evaluation of Basic Segmented Algorithms for Brain Tumor Detection

In the field of computers segmentation of image plays a very important role. By this method the required portion of object is traced from the image. In medical image segmentation, clustering is very famous method . By clustering, an image is divided into a number of various groups or can also be called as clusters. There are various methods of clustering and thresholding which have been proposed in this paper such as otsu , region growing , K Means , fuzzy c means and Hierarchical self organizing mapping algorithm. Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. As process of fuzzy c mean is too slow, this drawback is then removed. In this paper by experimental analysis and performance parameters the segmentation of hierarchical self organizing mapping method is done in a better way as compared to other algorithms. The various parameters used for the evaluation of the performance are as follows: segmentation accuracy (Sa) , area (A), rand index (Ri),and global consistency error (Gce) .

Supramolecular assembly in side-chain conjugated thiophene copolymers

Supramolecular assembly of side chain-conjugated polythiophenes were brought about by a interplay of dipole–dipole interactions and hydrogen bonding. Copolymers of (E)-3,5-dimethyl-4-(2-(thiophene-3-yl)vinyl) isoxazole (DTVI) and (E)-2-(2-(thiophen-3-yl) vinyl)-1H-benzo[d] imidazole (TVB) were synthesized and their ability to form self assemblies in solution as well as in thin film were explored at device conditions over ITO substrates. The twisted hierarchical assembly of polyTVB was found to improve the charge carrier mobility and is explained by a possible mechanism in which, an innate hierarchical self assembly through J-aggregates is formed in thin film prepared under device conditions. The copolymers of DTVI and TVB were studied for change in morphology using AFM, and GIXRD.

Identification of Novel RNA-Protein Contact in Complex of Ribosomal Protein S7 and 3’-Terminal Fragment of 16S rRNA in E. coli

For prokaryotes in vitro, 16S rRNA and 20 ribosomal proteins are capable of hierarchical self- assembly yielding a 30S ribosomal subunit. The self-assembly is initiated by interactions between 16S rRNA and three key ribosomal proteins: S4, S8, and S7. These proteins also have a regulatory function in the translation of their polycistronic operons recognizing a specific region of mRNA. Therefore, studying the RNA-protein interactions within binary complexes is obligatory for understanding ribosome biogenesis. The non-conventional RNA-protein contact within the binary complex of recombinant ribosomal protein S7 and its 16S rRNA binding site (236 nucleotides) was identified. UV-induced RNA-protein cross-links revealed that S7 cross-links to nucleotide U1321 of 16S rRNA. The careful consideration of the published RNA- protein cross-links for protein S7 within the 30S subunit and their correlation with the X-ray data for the 30S subunit have been performed. The RNA - protein cross-link within the binary complex identified in this study is not the same as the previously found cross-links for a subunit both in a solution, and in acrystal. The structure of the binary RNA-protein complex formed at the initial steps of self-assembly of the small subunit appears to be rearranged during the formation of the final structure of the subunit.

Three Steps of Hierarchical Self Assembly Toward a Stable and Efficient Surface Enhanced Raman Spectroscopy Platform

We report an innovative application of a true “bottom-up” approach for preparation of functional material. Three consecutive self-assembly steps were utilized for formation of a complex surface enhanced Raman spectroscopy (SERS) platform. First the Langmuir–Blodgett technique was used to deposit gold nanoparticles on a solid substrate. Thus prepared surfaces were afterward used as substrates in the chemical vapor deposition process of gallium nitride (GaN) nanowire growth. On such scaffolding, a third step of material fabrication was performed. Gold microflowers (Au MFs) deposited from solution preferably appeared at the top of the GaN nanowires and not in the cavities in between. The obtained morphology of the final material was controlled at each step of the preparation process to tailor its properties for desired purposes. Prepared surfaces were tested as SERS platforms. The enhancement factor was around 107 in case of p-mercaptobenzoic acid (p-MBA). The platforms were also suitable for biological and biomedical applications. We demonstrated the label free detection of DNA. The substrates gave reproducible SERS spectra both across a single platform and between different platforms. The average spectral correlation coefficients (Γ) was 0.87. Moreover, the obtained material proved to be very stable. The presented complex structure demonstrated therefore had the advantages of the two surface functionalization concepts it comprised: (1) GaN nanowire growth and (2) Au MF deposition, eliminating their major drawbacks. Presented material combined high SERS enhancement factor of Au MFs deposited on a flat surface and good durability of microflowers deposited on a surface completely covered with nanowires, which were almost SERS inactive. The final product provided truly exceptional stability and repeatability of SERS results, maintaining an enhancement factor comparable to the best commercially available platforms.

A-GHSOM: An adaptive growing hierarchical self organizing map for network anomaly detection

The growing hierarchical self organizing map (GHSOM) has been shown to be an effective technique to facilitate anomaly detection. However, existing approaches based on GHSOM are not able to adapt online to the ever-changing anomaly detection. This results in low accuracy in identifying intrusions, particularly “unknown” attacks. In this paper, we propose an adaptive GHSOM based approach (A-GHSOM) to network anomaly detection. It consists of four significant enhancements: enhanced threshold-based training, dynamic input normalization, feedback-based quantization error threshold adaptation, and prediction confidence filtering and forwarding. We first evaluate the A-GHSOM approach for intrusion detection using the KDD’99 dataset. Extensive experimental results demonstrate that compared with eight representative intrusion detection approaches, A-GHSOM achieves significant overall accuracy improvement and significant improvement in identifying “unknown” attacks while maintaining low false-positive rates. It achieves an overall accuracy of 99.63%, and 94.04% accuracy in identifying “unknown” attacks while the false positive rate is 1.8%. To avoid drawing research results and conclusions solely based on experiments with the KDD dataset, we have also built a dataset (TD-Sim) that consists of a mixture of live trace data from the Lawrence Berkeley National Laboratory and simulated traffic based on our testbed network, ensuring adequate coverage of a variety of attacks. Performance evaluation with the TD-Sim dataset shows that A-GHSOM adapts to live traffic and achieves an overall accuracy rate of 97.12% while maintaining the false positive rate of 2.6%.

Research of new strategies for improving CBR system

Case based reasoning (CBR), as an important AI technology, has gained popularity for its unique means of problem solving, which solves a new problem by remembering previous similar situations and reusing knowledge from the solutions to these situations. To construct a CBR system, two key issues have to be considered: one is feature selection, through which important features are extracted from the whole experience case and make up a case; the other is case retrieval, through which most appropriate case is retrieved for reuse. In order to further improve the accuracy of CBR system, this paper proposes a new feature selection method called Calculating Differences based on Growing Hierarchical Self Organizing Map clustering (CD-GHSOM) and a new case retrieval method called Growing Hierarchical Self Organizing Map based Case Retrieval (GHSOM-CR). Lots of experiments are implemented to validate the effectiveness of the proposed methods by comparing them with other recent researches.

Hierarchical self assembly of patterns from the Robinson tilings: DNA tile design in an enhanced Tile Assembly Model

We introduce a hierarchical self assembly algorithm that produces the quasiperiodic patterns found in the Robinson tilings and suggest a practical implementation of this algorithm using DNA origami tiles. We modify the abstract Tile Assembly Model, (aTAM), to include active signaling and glue activation in response to signals to coordinate the hierarchical assembly of Robinson patterns of arbitrary size from a small set of tiles according to the tile substitution algorithm that generates them. Enabling coordinated hierarchical assembly in the aTAM makes possible the efficient encoding of the recursive process of tile substitution.

Research on CBR system based on data mining

Case based reasoning (CBR) is a popular problem solving methodology which solves a new problem by remembering previous similar situations and reusing knowledge from the solutions to these situations. Aiming at traditional CBR system's too much dependence upon experts or engineers, this paper introduces data mining technology into CBR system and GHSOM (Growing Hierarchical Self Organizing Map), an excellent data mining tool with ANN (artificial neural network) technology, is integrated with it. After principal features are selected from numerous initial features to represent a case, through GHSOM cases are organized and managed in case base and while case retrieval is conducted, new case is guided into corresponding sub-case base, which greatly raises system's accuracy and efficiency. At last, experiments are implemented to validate the effectiveness of the proposed methods by comparing the proposed methods with other recent researches.

An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map

An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map

An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing

Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map(HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization

Multiscale cancer modeling: In the line of fast simulation and chemotherapy

Although Multiscale Cancer Modeling has a realistic view in the process of tumor growth, its numerical algorithm is time consuming. Therefore, it is problematic to run and to find the best treatment plan for chemotherapy, even in case of a small size of tissue. Using an artificial neural network, this paper simulates the multiscale cancer model faster than its numerical algorithm. In order to find the best treatment plan, it suggests applying a simpler avascular model called Gompertz. By using these proposed methods, multiscale cancer modeling may be extendable to chemotherapy for a realistic size of tissue. In order to simulate multiscale model, a hierarchical neural network called Nested Hierarchical Self Organizing Map (NHSOM) is used. The basis of the NHSOM is an enhanced version of SOM, with an adaptive vigilance parameter. Corresponding parameter and the overall bottom-up design guarantee the quality of clustering, and the embedded top-down architecture reduces computational complexity. Although by applying NHSOM, the process of simulation runs faster compared with that of the numerical algorithm, it is not possible to check a simple search space. As a result, a set containing the best treatment plans of a simpler model (Gompertz) is used. Additionally, it is assumed in this paper, that the distribution of drug in vessels has a linear relation with the blood flow rate. The technical advantage of this assumption is that by using a simple linear relation, a given diffusion of a drug dosage may be scaled to the desired one. By extracting a proper feature vector from the multiscale model and using NHSOM, applying the scaled-best treatment plans of Gompertz model is done for a small size of tissue. In addition, simulating the effect of stress reduction on normal tissue after chemotherapy is another advantage of using NHSOM, which is a kind of “emergent”.

An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique

Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher value of tumor pixels by this neuro-fuzzy approach. The computation speed of the proposed method is also studied. The multilayer segmentation results of the neuro fuzzy are shown to have interesting consequences from the viewpoint of clinical diagnosis. Neuro fuzzy technique shows that MRI brain tumor segmentation using HSOM-FCM also perform more accurate one.