• Milestones in this area have shown huge improvements in recognition accuracy using various methods to build acoustic models like Hidden Markov Model (HMM), Support Vector Machine (SVM), Gaussian Mixture Models and Artificial Neural Networks (ANN). (iieta.org)
  • Dr Mumtaz Ali is a leading AI and cross-disciplinary researcher in Knowledge & Data Engineering, Deep Learning, Machine Learning, Artificial Intelligence (AI), Advance Fuzzy, and Decision Support Systems. (edu.au)
  • With the rapid development of artificial intelligence, neural network models based on Deep Learning (DL) technology emerge and are widely used in advanced fields such as automatic driving and medical diagnosis. (google.com)
  • Classification and Watermarking of Brain Tumor using Artificial and Convolutional Neural Networks. (stanford.edu)
  • Additionally, the course also provides students with the opportunity to network with industry professionals and gain valuable insights into the field of Artificial Intelligence. (4achievers.com)
  • These sub-fields are based on technical considerations, such as particular goals e.g. "robotics" or "machine learning", the use of particular tools "logic" or artificial neural networks, or deep philosophical differences. (w3we.com)
  • Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. (w3we.com)
  • To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Mining (OM), which exploits the ad-vantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). (analytixon.com)
  • We develop a conceptually novel approach for the detection of MCs based on the use of deep convolutional neural networks (DCNNs). (hse.ru)
  • Droj G. The applicability of fuzzy theory in remote sensing image classification . (ad-astra.ro)
  • Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification. (korea.ac.kr)
  • Density Based Fuzzy Support Vector Machines for Multicategory Pattern Classification. (korea.ac.kr)
  • As a result, we used the Ensembling of Neuro-Fuzzy (E-NF) method to perform a high-level classification of medical diseases. (techscience.com)
  • Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. (uni-trier.de)
  • Re-entry of neural networks in many clustering, classification and pattern recognition problems have triggered current researchers to focus in making use of its power in the area of speech recognition. (iieta.org)
  • This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. (biomedcentral.com)
  • Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. (biomedcentral.com)
  • We use machine learning to synthesize algorithms (e.g., controllers in cyber-physical systems) and for the identification of system parameters (e.g., in bio-chemical reaction networks). (lmf-lab.fr)
  • Adaptive fuzzy backstepping output feedback control for strict feedback nonlinear systems with unknown sign of high-frequency gain. (uni-trier.de)
  • Nonlinear system identification with continuous piecewise linear neural network. (uni-trier.de)
  • An adaptive wavelet differential neural networks based identifier and its stability analysis. (uni-trier.de)
  • A deep learning inference engine test method based on differential evaluation comprises a model import inspection module, an intermediate representation acquisition module and a result evaluation module. (google.com)
  • N. Popescu-Bodorin, V.E. Balas, I.M. Motoc 8-Valent Fuzzy Logic for Iris Recognition and Biometry . (ad-astra.ro)
  • Fuzzy Logic as the Logic of Natural Languages. (korea.ac.kr)
  • I: Type-2 Fuzzy Logic: Theory and Applications. (korea.ac.kr)
  • A Method for Response Integration in Modular Neural Networks with Type-2 Fuzzy Logic for Biometric Systems. (korea.ac.kr)
  • Adaptive Type-2 Fuzzy Logic for Intelligent Home Environment. (korea.ac.kr)
  • Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BP. (korea.ac.kr)
  • An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications. (korea.ac.kr)
  • Building Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox. (korea.ac.kr)
  • We study logical formalisms that have applications in planning, synthesis, or formalizing the strategic behavior of intelligent agents (e.g., description logics, strategy logics, fuzzy logic, and dynamic logics). (lmf-lab.fr)
  • II: Fuzzy Clustering: Theory and Applications. (korea.ac.kr)
  • Incorporation of Non-euclidean Distance Metrics into Fuzzy Clustering on Graphics Processing Units. (korea.ac.kr)
  • A clustering algorithm for radial basis function neural network initialization. (uni-trier.de)
  • The approach utilized at TOPO can best be summarized in two steps, the first being a coordinate regression by means of a deep neural network followed by the extraction of the pose by a PnP solver (Figure 1). (epfl.ch)
  • Evolutionary Computing for Topology Optimization of Type-2 Fuzzy Systems. (korea.ac.kr)
  • 2003 ). The widespread of smart devices like electronic blackboards and intelligent tutoring systems with the combination of innovative online technologies like the Internet of things (IoT) and social networking stimulates mobile learning towards smart learning (Kim et al. (springeropen.com)
  • Enabling state estimation for fault identification in water distribution systems under large disasters. (readthedocs.io)
  • Dynamic seismic damage assessment of distributed infrastructure systems using graph neural networks and semi-supervised machine learning. (readthedocs.io)
  • A Decision-making Framework for Water Distribution Systems using Fuzzy Inference and Centrality Analysis. (readthedocs.io)
  • Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems such as chess and Go, autonomously operating cars, intelligent routing in content delivery networks, and military simulations. (w3we.com)
  • Moreover, training of input data was done using four types of NF techniques: Fuzzy Adaptive Learning Control Network (FALCON), Adaptive Network-based Fuzzy Inference System (ANFIS), Self Constructing Neural Fuzzy Inference Network (SONFIN) and/Evolving Fuzzy Neural Network (EFuNN). (techscience.com)
  • Second, we design a new deep neural network, which has better performance on AMP recognition than existing methods. (biomedcentral.com)
  • This article compares the performance of Bernoulli-Bernoulli Deep Belief Networks (BBDBN) and Gaussian-Bernoulli Deep Belief Networks (GBDBN) on phoneme recognition of spoken speech in Tamil. (iieta.org)
  • The invention belongs to the field of software engineering and machine learning, and particularly relates to model processing aiming at a deep learning inference engine. (google.com)
  • And evaluating the support of the inference engine on a specific deep learning framework for the intermediate process and the output result of the deep learning model processing. (google.com)
  • Intuitionistic Fuzzy Methods in Optimal Software Reliability Allocation. (ad-astra.ro)
  • Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. (waset.org)
  • To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. (waset.org)
  • Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure. (waset.org)
  • In this paper we investigate the influence of external noise on the inference of network structures. (waset.org)
  • Both inference and analysis of this kind of model are difficult tasks, thus global, (high-level), analysis of the network, has its attractions. (biomedcentral.com)
  • This paper concentrates on quantitative modelling of gene regulatory networks (GRNs) using DNA microarray data, as this is more informative than qualitative analysis of biological data. (biomedcentral.com)
  • Analysis and design of associative memories based on recurrent neural network with discontinuous activation functions. (uni-trier.de)
  • Identifying Intrusions in Computer Networks with Principal Component Analysis. (auth.gr)
  • Building Evidence Graphs for Network Forensics Analysis. (auth.gr)
  • Network Forensics Analysis with Evidence Graphs. (auth.gr)
  • Network Performance Measurement Methodologies in PGMS. (auth.gr)
  • The literature reports the detection and identification of life-threatening arrhythmias and, particularly, congestive heart failure, ventricular and atrial fibrillation, and ventricular tachycardia. (hindawi.com)
  • Machine learning model and strategy for fast and accurate detection of leaks in water supply network. (readthedocs.io)
  • Co-Simulating Physical Processes and Network Data for High-Fidelity Cyber-Security Experiments. (readthedocs.io)
  • However, due to the high sensitivity of data and service transaction within the RFID network, security consideration must be addressed. (waset.org)
  • Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. (waset.org)
  • More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. (waset.org)
  • The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. (waset.org)
  • Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay. (waset.org)
  • Intuitionistic Fuzzy Approaches for Quality Evaluation of Learning Objects. (ad-astra.ro)
  • Learning the mean: A neural network approach. (uni-trier.de)
  • SQL Generation from Natural Language using Supervised Learning and Recurrent Neural Networks. (stanford.edu)
  • In particular, we exploit model learning to extract structural information from recurrent neural networks. (lmf-lab.fr)
  • Aquaeis: Middleware support for event identification in community water infrastructures. (readthedocs.io)
  • and identification of carbohydrates metabolic related enzymes toward lipid production. (uthm.edu.my)
  • Network Application Functions. (ntu.edu.sg)
  • The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. (waset.org)
  • 5. Intelligent approach for retinal disease identification / Rajyaguru, Vipul C. / Vithalani, Chandresh H. / Thanki, Rohit M. (stanford.edu)
  • Network Components. (ntu.edu.sg)
  • Improved delay-dependent stability criteria for neural networks with two additive time-varying delay components. (uni-trier.de)