The posterity of Zadeh's 50-year-old paper: A retrospective in 101 Easy Pieces – and a Few More

International audienceThis article was commissioned by the 22nd IEEE International Conference of Fuzzy Systems (FUZZ-IEEE) to celebrate the 50th Anniversary of Lotfi Zadeh's seminal 1965 paper on fuzzy sets. In addition to Lotfi's original paper, this note itemizes 100 citations of books and papers deemed “important (significant, seminal, etc.)” by 20 of the 21 living IEEE CIS Fuzzy Systems pioneers. Each of the 20 contributors supplied 5 citations, and Lotfi's paper makes the overall list a tidy 101, as in “Fuzzy Sets 101”. This note is not a survey in any real sense of the word, but the contributors did offer short remarks to indicate the reason for inclusion (e.g., historical, topical, seminal, etc.) of each citation. Citation statistics are easy to find and notoriously erroneous, so we refrain from reporting them - almost. The exception is that according to Google scholar on April 9, 2015, Lotfi's 1965 paper has been cited 55,479 times

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies

Molecular Mechanisms of Leaf Morphogenesis

Leaf morphology is obviously determined in a plant. By contrast, its morphology is often changeable when the plant copes with various environmental changes. To update our understanding of the regulatory mechanisms of leaf morphogenesis with robustness and flexibility, this book provides a series of academic papers that cover molecular mechanism of leaf morphogenesis and offers readers' opportunities to find beautiful mechanisms that plants develop

Optimum Average Silhouette Width Clustering Methods

Cluster analysis is the search for groups of alike instances in the data. The two major problems in cluster analysis are: how many clusters are present in the data? And how can the actual clustering solution be found? We have developed a unified approach to estimate number of clusters and clustering solution mutually. This work is about theory, methodology and algorithm developed of newly proposed approach. // Average silhouette width (ASW) is a well-known index for measuring the clustering quality and for the estimation of the number of clusters. The index is in wide use across disciplines as standard practice for these tasks. In this work the clustering methodolo- gies is proposed that can itself estimate number of clusters on the fly, as well as produce the clustering against this estimated number by optimizing the ASW index. The performance of the ASW index for these two tasks are meticulously investigated. // ASW based clustering functions are proposed for the two most popular clustering domains i.e., hierarchical and non-hierarchical. The performance comparison for clustering solutions obtained from the proposed methods with a range of clustering methods has been done for the quality evaluation. // The performance comparison for the estimation of the number of clusters of the proposed methods has been made using a wide spectrum of cluster estimation indices and methods. For this, large scale studies for the estimation of the number of clusters have been conducted with well-reputed clustering methods to find out each method’s estimation performance with different indices/methods for various kinds of clustering structures. // Developing mathematical and theoretical aspects for clustering is a relatively new and challenging avenue. Recently this research domain has received considerable attention due to the present need and importance of theory of clustering. The purpose behind the theory development for clustering is to make the general nature of clustering more understandable without assuming particular data generating structures and independently from any clustering algorithm/functions. Lastly, a considerable amount of attention has been drawn towards the theory development of the ASW index in the latter part of the thesis

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed