دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش بیست و چهارم

منطق فازی (Fuzzy Logic) اولین بار در پی تنظیم نظریه مجموعه‌های فازی به وسیله پروفسور لطفی زاده (۱۹۶۵ میلادی) در صحنه محاسبات نو ظاهر شد. در واقع منطق فازی از منطق ارزش‌های «صفر و یک» نرم‌افزارهای کلاسیک فراتر رفته و درگاهی جدید برای دنیای علوم نرم‌افزاری و رایانه‌ها می‌گشاید، زیرا فضای شناور و نامحدود بین اعداد صفر و یک را نیز در منطق و استدلال‌های خود به کار می‌گیرد. در ادامه مقالات علمی انتشارات بین المللی اشپرینگر (Springer) در زمینه منطق فازی (Fuzzy Logic) برای دانلود آمده است. می توانید برای دانلود هر یک از مقالات از سرور دانلود متلب سایت، بر روی لینک دانلود هر یک از آن ها، کلیک کنید.

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دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — فهرست اصلی

عنوان اصلی مقاله Uncovering transcriptional interactions via an adaptive fuzzy logic approach
نوع مقاله مقاله ژورنال
نویسندگان Cheng-Long Chuang, Kenneth Hung, Chung-Ming Chen, Grace S Shieh
چکیده / توضیح To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.
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عنوان اصلی مقاله A method to improve protein subcellular localization prediction by integrating various biological data sources
نوع مقاله مقاله ژورنال
نویسندگان Thai Quang Tung, Doheon Lee
چکیده / توضیح Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.
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عنوان اصلی مقاله Identifying promoter features of co-regulated genes with similar network motifs
نوع مقاله مقاله ژورنال
نویسندگان Oscar Harari, Coral del Val, Rocío Romero-Zaliz, Dongwoo Shin, Henry Huang, Eduardo A Groisman, Igor Zwir
چکیده / توضیح A large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined.
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عنوان اصلی مقاله Gene regulatory networks modelling using a dynamic evolutionary hybrid
نوع مقاله مقاله ژورنال
نویسندگان Ioannis A Maraziotis, Andrei Dragomir, Dimitris Thanos
چکیده / توضیح Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.
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عنوان اصلی مقاله Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images
نوع مقاله مقاله ژورنال
نویسندگان Sinan Kockara, Mutlu Mete, Bernard Chen, Kemal Aydin
چکیده / توضیح Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density –greater than certain number of points- around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.
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عنوان اصلی مقاله Assessment of predictive models for chlorophyll-a concentration of a tropical lake
نوع مقاله مقاله ژورنال
نویسندگان Sorayya Malek, Sharifah Mumtazah Syed Ahmad, Sarinder Kaur Kashmir Singh, Pozi Milow, Aishah Salleh
چکیده / توضیح This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.
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عنوان اصلی مقاله Unsupervised fuzzy pattern discovery in gene expression data
نوع مقاله مقاله ژورنال
نویسندگان Gene PK Wu, Keith CC Chan, Andrew KC Wong
چکیده / توضیح Discovering patterns from gene expression levels is regarded as a classification problem when tissue classes of the samples are given and solved as a discrete-data problem by discretizing the expression levels of each gene into intervals maximizing the interdependence between that gene and the class labels. However, when class information is unavailable, discovering gene expression patterns becomes difficult.
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عنوان اصلی مقاله Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
نوع مقاله مقاله ژورنال
نویسندگان Siow-Wee Chang, Sameem Abdul-Kareem, Amir Feisal Merican, Rosnah Binti Zain
چکیده / توضیح Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.
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عنوان اصلی مقاله Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides
نوع مقاله مقاله ژورنال
نویسندگان Jerzy Stanislawski, Malgorzata Kotulska, Olgierd Unold
چکیده / توضیح Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods.
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عنوان اصلی مقاله Introduction
نوع مقاله مقاله ژورنال
نویسندگان Riccardo Rizzo, Paulo JG Lisboa
چکیده / توضیح The International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) is a small but active conference where a group of researchers share their ideas and visions. The proceedings of the conference are usually published in a book and a group of selected papers are collected in a post proceedings book published by Springer.
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