Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioural patterns and modification of the behaviour. A significant part of this process is influenced by the theory of representational systems which equates to the five main senses. The preferred representational system of an individual can explain a large part of exhibited behaviours and characteristics. There are different methods to recognise the representational systems, one of which is to investigate the sensory based words in the used language during the conversation. However, there are difficulties during this process since there is not a single reference method used for identification of representational systems and existing ones are subject to human interpretations. Some human errors like lack of experience, personal judgment, different levels of skill and personal mistakes may also affect the accuracy and reliability of the existing methods. This research aims to apply a new approach that is to automate the identification process in order to remove human errors thereby increasing the accuracy and precision. Natural Language Processing has been used for automating this process and an intelligent software has been developed able to identify the preferred representational system with increased accuracy and reliability. This software has been tested and compared to human identification of representational systems. The results of the software are similar to a NLP practitioner and the software responds more accurately than a human practitioner in various parts of the process. This novel methodology will assist the NLP practitioners to obtain an improved understanding of their clients’ behavioural patterns and the associated cognitive and emotional processes

Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®

Neuro Linguistic Programming (NLP) is a collection of techniques for personality development. Meta programmes, which are habitual ways of inputting, sorting and filtering the information found in the world around us, are a vital factor in NLP. Differences in meta programmes result in significant differences in behaviour from one person to another. Personality types can be recognized through utilizing and analysing meta programmes. There are different methods to predict personality types based on meta programmes. The Myers–Briggs Type Indicator® (MBTI) is currently considered as one of the most popular and reliable methods. In this study, a new machine learning method has been developed for personality type prediction based on the MBTI. The performance of the new methodology presented in this study has been compared to other existing methods and the results show better accuracy and reliability. The results of this study can assist NLP practitioners and psychologists in regards to identification of personality types and associated cognitive processes

A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data

In criminal records, intentional manipulation of data is prevalent to create ambiguous identity and mislead authorities. Registering data electronically can result in misspelled data, variations in naming order, case sensitive data and inconsistencies in abbreviations and terminology. Therefore, trying to obtain the true identity (or identities) of a suspect can be a challenge for law enforcement agencies. We have developed a targeted approach to identity resolution which uses a rule-based scoring system on physical and official identity attributes and a graph-based analysis on social identity attributes to interrogate policing data and resolve whether a specific target is using multiple identities. The approach has been tested on an anonymized policing dataset, used in the SPIRIT project, funded by the European Union’s Horizon 2020. The dataset contains four ‘known’ identities using a total of five false identities. 23 targets were inputted into the methodology with no knowledge of how many or which had false identities. The rule-based scoring system ranked four of the five false identities with the joint highest score for the relevant target name with the remaining false identity holding the joint second highest score for its target. Moreover, when using graph analysis, 51 suspected false identities were found for the 23 targets with four of the five false identities linked through the crimes they had been involved in. Therefore, an identity resolution approach using both a rule-based scoring system and graph analysis, could be effective in facilitating the investigation process for law enforcement agencies and assisting them in finding criminals using false identities

Assessment of equity in public health care financing in 2013

زمینه و هدف: تأمین مالی در نظام سلامت زمانی عادلانه خواهد بود که هزینه‌های مربوط به مراقبت‌های سلامت برای خانوارها برحسب توان پرداخت آن‌ها نه بر‌اساس خطر خود بیماری توزیع شده باشد. ﻫﺪف از اﻧﺠﺎم اﯾﻦ ﭘﮋوﻫﺶ ارزﯾﺎﺑﯽ عدالت در تأمین مالی مراقبت‌های دولتی سلامت براساس دو منبع اصلی تأمین مالی شامل پرداخت‌های مستقیم خانوارها برای ﻣﺮاﻗﺒﺖﻫﺎی دولتی و ﺗﻮزﯾﻊ بودجه دولتی ﻣﯽﺑﺎﺷﺪ. روش‌ بررسی: اﯾﻦ ﻣﻄﺎﻟﻌﻪ از ﻧﻮع ﺗﻮﺻﯿﻔﯽ- ﺗﺤﻠﯿلی اﺳﺖ. در این مطالعه از آﻣﺎرﻫﺎی ﺛﺒﺘﯽ وزارت بهداشت درمان و آموزش پزشکی و مرکز آمار ایران، در ﺳﺎل 1392 اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻣﺘﻐﯿﺮﻫﺎی اﺻﻠﯽ ﻣﻮرد ﻣﻄﺎﻟﻌﻪ ﺷﺎﻣﻞ پرداخت‌های مستقیم خانوارها برای ﻣﺮاﻗﺒﺖﻫﺎی دولتی ﺳﻼﻣﺖ، ﺗﻮزﯾﻊ بودجه دولتی سلامت و درآمد خانوارها ﺑﻪ ﺗﻔﮑﯿﮏ اﺳﺘﺎنﻫﺎی ﮐﺸﻮر ﺑﻮده‌ اﺳﺖ. برای تحلیل داده‌ها و انجام محاسبات از نرم‌افزار Excel و شاخص‌های ضریب ‌جینی، شاخص‌ تمرکز و شاخص پیش‌رونده کاکوانی برای سنجش عدالت در تأمین مالی هزینه‌های سلامت استفاده شده است. یافته‌ها: در سال 1392، محاسبه شاخص ضریب جینی توزیع درآمد بین خانوارها، معادل 387/0 و شاخص تمرکز پرداخت‌های مستقیم خانوارها برای مراقبت‌های سلامت در استان‌های کشور معادل 056/0 به‌دست آمد. شاخص پیش‌رونده کاکوانی معادل 331/0-، شاخص تمرکز توزیع بودجه سلامت در استان‌های کشور معادل 05/0- و شاخص پیش‌رونده کاکوانی معادل 337/0- محاسبه گردید. نتیجه‌گیری: ارزیابی به عمل آمده براساس شاخص‌های ضریب جینی، شاخص تمرکز و شاخص پیش‌رونده کاکوانی نشان می‌دهد که توزیع بودجه دولتی سلامت در استان‌های کشور و پرداخت‌های مستقیم خانوارها برای مراقبت‌های دولتی سلامت براساس متغیر درآمد خانوارها، غیرعادلانه می‌باشد

Natural Language Processing approach to NLP Meta model automation

Neuro Linguistic Programming (NLP) is one of the most utilised approaches for personality development and Meta model is one of the most important techniques in this process. Usually, when one speaks about a problem or a situation, the words that one chooses will delete, distort or generalize portions of their experience. Meta model, which is a set of specific questions or language patterns, can be used to understand and recover the information hidden behind the words used. This technique can be adopted to understand other people’s problems or enable them to understand their own issues better. Applying the Meta Model, however, requires a great level of skill and experience for correct identification of deletion, distortion and generalization. Using the appropriate recovery questions is challenging for NLP practitioners and Psychologists. Moreover, the efficiency and accuracy of existing methods on the Meta model can potentially be hindered by human errors such as personal judgment or lack of experience and skill. This research aims to automate the process of using the Meta Model in conversation in order to eliminate human errors, thereby increasing the efficiency and accuracy of this method. An intelligent software has been developed using Natural Language Processing, with the ability to apply the Meta model techniques during conversation with its user. Comparisons of this software with performance of an established NLP practitioner have shown increased accuracy in identification of the deletion and generalization processes. Recovery of information has also been more efficient in the software in comparison to an NLP practitioner

Genome surveyor 2.0: cis-regulatory analysis in Drosophila

Genome Surveyor 2.0 is a web-based tool for discovery and analysis of cis-regulatory elements in Drosophila, built on top of the GBrowse genome browser for convenient visualization. Genome Surveyor was developed as a tool for predicting transcription factor (TF) binding targets and cis-regulatory modules (CRMs/enhancers), based on motifs representing experimentally determined DNA binding specificities. Since its first publication, we have added substantial new functionality (e.g. phylogenetic averaging of motif scores from multiple species, and a novel CRM discovery technique), increased the number of supported motifs about 4-fold (from approximately 100 to approximately 400), added provisions for evolutionary comparison across many more Drosophila species (from 2 to 12), and improved the user-interface. The server is free and open to all users, and there is no login requirement. Address: http://veda.cs.uiuc.edu/gs

Morphological variability of the Aspius aspius taeniatus (Eichwald, 1831) in the Southern Caspian Sea Basin

Traditional morphometric measurements and meristic counts were used to investigate the hypothesis of population fragmentation of Mash mahi, Aspius aspius taeniatus (Eichwald, 1831) among two fishing areas in southern Caspian Sea basin (Tonekabon:32 specimens and Sari:34 specimens ). Univariate analysis of variance showed significant differences between the means of the two groups for 12 out of 26 standardized morphometric measurement and three out of nine meristic counts. In discriminant function analysis (DFA), the proportion of individuals correctly classified into their original groups was 82.1% and 61.2% for morphometric and meristic characteristics, respectively. Clustering based on Euclidean distances among groups of centroids using an UPGMA and also principal component analysis’ results (PCA) for morphometric and meristic data indicated that two samples of Mash mahi were distinct from each other in these regions, while there were a relatively high degree of overlap between two locations

Comparison of quinolone and β-lactam resistance among Escherichia coli strains isolated from urinary tract infections

The growing frequency of antibiotic resistances is now a universal problem. Increasing resistance to new generations of β-lactam and quinolone antibiotics in multidrug- resistant Enterobacteriaceae isolates is considered an emergency health issue worldwide. The aim of this study was to evaluate plasmid-mediated quinolone resistance genes in ESBL-producing Escherichia coli isolated from urinary tract infections (UTIs). In our study ESBL- producing isolates were assessed by screening methods. After determination of antimicrobial susceptibility, detection of ESBLs and quinolone resistance genes was performed. A total of 97 ESBL-producing E. coli were determined. The blaTEM, blaSHV and blaCTX-M genes were detected in 90 isolates. The blaTEM was the most frequent- ly detected gene (46.4), followed by blaSHV (31.9) and blaCTX-M (14.4). The most prevalent quinolone resistance gene among ESBL-producing isolates was oqxAB which found in 67 isolates (69.1). The frequencies of the aac(6�)-Ib-cr, qnr and qepA were 65 (67), 8 (8.2) and 6 (6.2), respectively. Our data indicate that the prevalence of plasmid-mediated quinolone resistance genes in ESBL-positive isolates is increasing. The co-dissemination of PMQR and ESBL genes among E. coli isolates can be considered a threat to public health. Therefore, prescription of antibiotics against infectious disease should be managed carefully. © 2016, International Society of Musculoskeletal and Neuronal Interactions. All rights reserved

Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism

Characterization of cell type specific regulatory networks and elements is a major challenge in genomics, and emerging strategies frequently employ high-throughput genome-wide assays of transcription factor (TF) to DNA binding, histone modifications or chromatin state. However, these experiments remain too difficult/expensive for many laboratories to apply comprehensively to their system of interest. Here, we explore the potential of elucidating regulatory systems in varied cell types using computational techniques that rely on only data of gene expression, low-resolution chromatin accessibility, and TF-DNA binding specificities (\u27motifs\u27). We show that static computational motif scans overlaid with chromatin accessibility data reasonably approximate experimentally measured TF-DNA binding. We demonstrate that predicted binding profiles and expression patterns of hundreds of TFs are sufficient to identify major regulators of approximately 200 spatiotemporal expression domains in the Drosophila embryo. We are then able to learn reliable statistical models of enhancer activity for over 70 expression domains and apply those models to annotate domain specific enhancers genome-wide. Throughout this work, we apply our motif and accessibility based approach to comprehensively characterize the regulatory network of fruitfly embryonic development and show that the accuracy of our computational method compares favorably to approaches that rely on data from many experimental assays. Acids Research