• LSTM combined with convolutional neural networks (CNNs) improved automatic image captioning. (wikipedia.org)
  • Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to phase-change memory (PCM) devices. (nature.com)
  • Currently, my students and I are working on efficient execution of trained deep convolutional neural networks (an important technique in machine learning), and embedded systems for selected health applications, such as lung function evaluation and transabdominal fetal oximetry. (citris-uc.org)
  • The proposed research concludes that deep learning and convolutional neural networks give a most appropriate solution retaining discriminative and dynamic information of the input action. (techscience.com)
  • Dewangan, D.K., Sahu, S.P. RCNet: road classification convolutional neural networks for intelligent vehicle system. (iiitm.ac.in)
  • Methodology and Computing in Applied Probability , Vol. 24, p. 963-990 (2022). (uclouvain.be)
  • Thus the network can maintain a sort of state, allowing it to perform such tasks as sequence-prediction that are beyond the power of a standard multilayer perceptron. (wikipedia.org)
  • The second methodology is vision-based dynamics prediction. (isi.edu)
  • The favorable results prevale especially with the regression trees and neural networks, where locally weighted regression was used as a model for predicting the prediction error. (cai.sk)
  • In these experiments the prediction accuracy increased in 60% of experiments with regression trees and in 50% of experiments with neural networks, while the increase of the prediction error did not occur in any experiment. (cai.sk)
  • Neural network models are also used weather prediction, facial recognition, oil exploration data analysis, and text-to-speech transcriptions. (smartdatacollective.com)
  • 2010. Hybrid intelligent methodology to design translation invariant morphological operators for Brazilian stock market prediction. (lu.se)
  • 2010. Mobile ad hoc network proactive routing with delay prediction using neural network. (lu.se)
  • An interpretable neural network for outcome prediction in traumatic brain injury. (cdc.gov)
  • Video surveillance can benefit greatly by advances in Internet of Things (IoT) and cloud computing. (techscience.com)
  • Advances in Intelligent Systems and Computing, vol 1408. (iiitm.ac.in)
  • Advances in computing power, as well as techniques like machine learning, neural networks, and computer vision, have allowed researchers to ask questions and make discoveries that were not possible even ten years ago. (virginia.edu)
  • Furthermore, as technology advances and computing power and computing speed increases, data analysts will be able to improve on the type, caliber, and quality of the statistical information that is derived from the raw data. (smartdatacollective.com)
  • In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. (nature.com)
  • Platforms for deploying the trained model of such networks and performing inference in an energy-efficient manner are highly attractive for edge computing applications. (nature.com)
  • In this approach, the network weights are encoded as the analog charge state or conductance state of these devices organized in crossbar arrays, and the matrix-vector multiplications during inference can be performed in-situ in a single time step by exploiting Kirchhoff's circuit laws. (nature.com)
  • The methodology involves an Adaptive Neuro Fuzzy Inference System (ANFIS) using the Fuzzy C Means clustering (FCM) and Subtractive Clustering (SC) technique to compute the software effort. (iajit.org)
  • There are three of them exposed in this book, namely: fuzzy logic, evolutionary algorithms and artificial neural networks - all inspired by nature. (osiander.de)
  • The book Soft Computing: Integrating Evolutionary, Neural and Fuzzy Systems provides a comprehensive introduction to the area of soft computing addressing three of the main constituents of this discipline: fuzzy logic, neural computing and evolutionary computing. (osiander.de)
  • Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. (osiander.de)
  • This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. (osiander.de)
  • Many computational intelligence and learning methods, such as expert systems, neural networks, genetic algorithms and so on have found success in a variety of control and automation fields. (conference-service.com)
  • I stayed there at the Institut für Programmstrukturen und Datenorganisation and got my Ph.D. in 1995 with a thesis revolving around constructive neural network learning algorithms and compiler construction for parallel computers . (fu-berlin.de)
  • This first led me to create a benchmark collection called Proben1 for neural network learning algorithms in 1994 [ Proben1 , NNbench ], which still appears to be somewhat popular . (fu-berlin.de)
  • In Bartlomiej Beliczynski and Andrzej Dzielinski and Marcin Iwanowski and Bernardete Ribeiro editors , 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, Part I, volume 4431, pages 276-285, Warsaw, Poland, 2007. (ucl.ac.uk)
  • In our supply chain, neural networks are the main drivers behind the inventory management system recommending specific vehicle configurations to dealers, and evolutionary computing algorithms (in conjunction with dynamic semantic network-based expert systems) are deployed in support of resource management in assembly plants. (oreilly.com)
  • The drawback of these approaches is that every neural network would have to be trained on each individual chip before deployment. (nature.com)
  • Intelligent Control, which differs from conventional approaches is based on methodologies derived from Artificial Intelligence[AI] and soft computing techniques is capable of dealing with problems in diverse computational and communication domains and, more recently, those related to renewable energy and mobile autonomous systems, where conventional methods were unsuccessful. (conference-service.com)
  • Grigore ALBEANU, Florin Popentiu-VLADICESCU Recent Soft Computing Approaches in Digital Learning Object Evaluation . (ad-astra.ro)
  • The special issue seeks contributions addressing the different challenges of chip-scale nanocommunications and networking, putting emphasis on emerging technologies (e.g., wireless, RF interconnects, optics), new approaches (e.g., approximate computing, machine-learning-based design) and disruptive applications (e.g., quantum computers). (wikicfp.com)
  • In contrast to the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. (wikipedia.org)
  • A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled. (wikipedia.org)
  • This is also called Feedforward Neural Network (FNN). (wikipedia.org)
  • From artificial intelligence to networking and cybersecurity, ISI conducts basic and applied research and development across a wide range of advanced information processing, computer, and communications technologies. (isi.edu)
  • Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. (nature.com)
  • Increasing processing requirements in the Artificial Intelligence (AI) realm has led to the emergence of domain-specific architectures for Deep Neural Network (DNN) applications. (mdpi.com)
  • In Andre de Leon F. de Carvalho and Sara Rodriguez-Gonzalez and Juan De Paz Santana and Juan Rodriguez editors , 7th International Symposium Distributed Computing and Artificial Intelligence, volume 79, pages 357-364, Valencia, Spain, 2010. (ucl.ac.uk)
  • Reservoir computing (RC) is a branch of AI that offers a highly efficient framework for processing temporal inputs at a low training cost compared to conventional Recurrent Neural Networks (RNNs). (frontiersin.org)
  • It is clear that conventional Von-Neumann architecture is no longer sufficient for the ubiquitous AI and many newly-arrived complex computing tasks. (usc.edu)
  • Soft computing as it was defined by Professor Lotfi Zadeh is a collection of methodologies that cope with the main disadvantage of the conventional (hard) computing the poor performances when working in uncertain conditions. (iospress.com)
  • Elman and Jordan networks are also known as "Simple recurrent networks" (SRN). (wikipedia.org)
  • Deep neural networks (DNNs) are a type of machine learning model that are inspired by the structure and function of the human brain. (meta-guide.com)
  • We provide an open platform to efficiently compute semantic distance, including tutorials and documentation ( https://osf.io/gz4fc/ ). (springer.com)
  • VLDNet: Vision-based lane region detection network for intelligent vehicle system using semantic segmentation. (iiitm.ac.in)
  • In 1993, a neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent layers in an RNN unfolded in time. (wikipedia.org)
  • We propose to use Deep Neural Networks to solve data-driven stochastic optimization problems. (ssrn.com)
  • Amrina is a current Computing Ph.D. student with Data Science emphasis at Boise State who is currently involved with working on tensor, inversion, tensor Neural Network, and deep Neural Network. (boisestate.edu)
  • The commonly known problem of exploding and vanishing gradients, arising in very deep FNNs and from cyclic connections in RNNs, results in network instability and less effective learning, making the training process complex and expensive. (frontiersin.org)
  • In the present scenario, most of the deep learning models rely on pre-processing, extracting features, and network topology but still are not sufficient to provide satisfactory accuracy for the small and noisy database. (researchsquare.com)
  • Dewangan, D.K., Sahu, S.P. Lane detection in intelligent vehicle system using optimal 2- tier deep convolutional neural network. (iiitm.ac.in)
  • In this talk, I will first discuss our recent developments of a special \"neural CPU\" processor at the conjunction of Von-Neumann and deep learning architectures to establish a new computing platform where general-purpose computing is incorporated into the framework of deep learning accelerators achieving significant end-to-end performance enhancement and data movement reduction. (usc.edu)
  • ACM provides independent, nonpartisan, and technology-neutral research and resources to policy leaders, stakeholders, and the public about public policy issues, drawn from the deep technical expertise of the computing community. (acm.org)
  • New York, NY, March 27, 2019 - ACM, the Association for Computing Machinery, today named Yoshua Bengio, Geoffrey Hinton, and Yann LeCun recipients of the 2018 ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. (acm.org)
  • It is a type of deep learning technology that, when utilised in the in the corporate world, tends to focus on solving complex predictive analyses problems using pattern recognition methodologies. (smartdatacollective.com)
  • Driverless cars aren't the only application for deep learning on the road: neural networks have begun to make their way into every corner of the automotive industry, from supply-chain management to engine controllers. (oreilly.com)
  • Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases. (cdc.gov)
  • Recent years have witnessed the emergence of computing architectures that integrate up to a thousand processor cores and memory on a single die as a result of relentless semiconductor device scaling. (wikicfp.com)
  • While the scope of the special issue revolves around communications and networking aspects, submissions discussing frontier aspects such as memory architectures, 2.5D/3D packages, or application mapping are also welcome. (wikicfp.com)
  • An Elman network is a three-layer network (arranged horizontally as x, y, and z in the illustration) with the addition of a set of context units (u in the illustration). (wikipedia.org)
  • Jordan networks are similar to Elman networks. (wikipedia.org)
  • The methodology is compared with the effort estimated using an Elman neural network. (iajit.org)
  • In 2009, a Connectionist Temporal Classification (CTC)-trained LSTM network was the first RNN to win pattern recognition contests when it won several competitions in connected handwriting recognition. (wikipedia.org)
  • It should be noted at this juncture, that for a neural network pattern recognition model to predict or forecast future trends as accurately as possible, substantial amounts of data are needed to train the model. (smartdatacollective.com)
  • 8 Natural Parallel (Soft) Computing. (osiander.de)
  • What's the Difference Between Distributed & Parallel Computing? (hitechnectar.com)
  • This has given rise to many computing methodologies - parallel computing and distributed computing are two of them. (hitechnectar.com)
  • What is Parallel Computing? (hitechnectar.com)
  • Parallel computing is a model that divides a task into multiple sub-tasks and executes them simultaneously to increase the speed and efficiency. (hitechnectar.com)
  • Distributed computing is different than parallel computing even though the principle is the same. (hitechnectar.com)
  • Parallel computing generally requires one computer with multiple processors. (hitechnectar.com)
  • In parallel computing, the tasks to be solved are divided into multiple smaller parts. (hitechnectar.com)
  • We can also say, parallel computing environments are tightly coupled. (hitechnectar.com)
  • In parallel computing environments, the number of processors you can add is restricted. (hitechnectar.com)
  • In systems implementing parallel computing, all the processors share the same memory. (hitechnectar.com)
  • Parallel computing is often used in places requiring higher and faster processing power. (hitechnectar.com)
  • GPGPU parallel computing is supported. (u-tokyo.ac.jp)
  • The system combines with Convolutional Neural Network (CNN) structure based on cloud computing to effectively identify and create music scores. (hindawi.com)
  • Today, cloud computing is not a strange word. (hindawi.com)
  • Cloud computing is a service model related to the Internet [ 1 ]. (hindawi.com)
  • Cloud computing is very fast, and it can reach 100,000 computing cycles per second, which allows users to use this computing power to meet their needs. (hindawi.com)
  • For example, cloud computing can be used to simulate the changing trend of the market economy. (hindawi.com)
  • The Moodle teaching platform is deeply studied based on traditional disciplines with modern network technology and cloud computing technology. (hindawi.com)
  • First, cloud computing technology is used to summarize and analyze the current music teaching situation in all colleges. (hindawi.com)
  • Then, the data of online music playing, downloading, and online karaoke songs from computers, TV, mobile phones, and other media are analyzed using cloud computing technology. (hindawi.com)
  • We deploy a network of multilayer perceptrons (also known as artificial neural networks or ANNs) for 'learning' the correct value of the dissipation coefficient. (wias-berlin.de)
  • In literature, we often find the methodology adopted by researchers for selection of hy- perparameters of ANNs somewhat heuristic. (wias-berlin.de)
  • The ALCF is committed to providing training and outreach opportunities that prepare researchers to efficiently use its leadership computing systems, while also cultivating a diverse and skilled HPC workforce for the future. (anl.gov)
  • Ford researchers developed and implemented, in mass-produced cars, an innovative misfire detection system-a neural-net-based classifier of crankshaft acceleration patterns for diagnosing engine misfire (undesirable combustion failure that has a negative impact on performance and emissions). (oreilly.com)
  • We are confident that this issue will offer good information to graduated students, professors, and researchers working in the realm of soft computing and intelligent systems. (iospress.com)
  • This is the most general neural network topology because all other topologies can be represented by setting some connection weights to zero to simulate the lack of connections between those neurons. (wikipedia.org)
  • The illustration to the right may be misleading to many because practical neural network topologies are frequently organized in "layers" and the drawing gives that appearance. (wikipedia.org)
  • The conductance tuning linearity is an important parameter of analog RRAM for neuromorphic computing. (elsevierpure.com)
  • My research interests lie in the development of nanotechnology for applications in electronics and neuromorphic computing. (lu.se)
  • We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline model for argumentative relation classification. (dagstuhl.de)
  • Reservoir computing is a recently proposed methodology from the field of machine learning and neural networks, which has been used successfully in several pattern classification problems, like speech and image recognition. (ugent.be)
  • Although there are a few studies which try to address this issue for the general classification or regression problems, when it comes to data-driven scientific computing, a void remains. (wias-berlin.de)
  • Second, I will discuss efficient data processing solutions for domain-specific computing using examples of a sparse convolutional neural network accelerator for 3D/4D point-cloud image classification and efficient data processing for wirelessly powered human machine interface System-on-Chip (SoC) with embedded machine learning capabilities. (usc.edu)
  • Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1997 and set accuracy records in multiple applications domains. (wikipedia.org)
  • In this presentation, she compared two different approac hes: Inverse Methods and Neural Networks and explained how data collection practitioners could design useful methods that could substantially advance knowledge in a variety of applications, such as industry, academic, government institutions etc. (boisestate.edu)
  • In this study, a soft computing-based methodology which synergistically combines Artificial Neural Networks and Genetic Algorithm (GA) applications, is proposed as an alternative for calibration methodology that considerably reduces the computation time in comparison to other commonly used methods. (concordia.ca)
  • 16] Kalichanin-Balich I. and Lopez-Martin C., Applying a Feed Forward Neural Network for Predicting Software Development Effort of Short-Scale Projects, in Proceeding of Eighth ACIS International Conference on Software Engineering Research Management and Applications, Montrea, pp. 269-275, 2010. (iajit.org)
  • One of the more popular applications is to build a predictive analysis model or a neural network that will answer questions about the future. (smartdatacollective.com)
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications, 13(3):291-305, 2009. (ucl.ac.uk)
  • About 15 years ago, Ford Motor Company introduced one of the first large-scale industrial applications of neural networks. (oreilly.com)
  • Given the historical data of the observed covariate, taken decision, and the realized cost in past periods, we train a neural network to predict the objective value as a function of the decision and the covariate. (ssrn.com)
  • The neural network is trained with data that allows it to predict the movement of the objects. (isi.edu)
  • Finally, a genetic-algorithm uses the trained neural-network in its fitness function to determine the appropriate set of values for the calibration parameters. (concordia.ca)
  • However, the proposed methodology can work efficiently for any type of video. (techscience.com)
  • This was a joint AI Lund/COMPUTE seminar. (lu.se)
  • Once trained, for a given covariate, we optimize the neural network over the decision variable using gradient-based methods because the gradient and the Hessian matrix can be analytically computed. (ssrn.com)
  • Amrina summarized her talk stating, "Inverse Methods and Neural Networks are used to estimate causal factors from a set of observations. (boisestate.edu)
  • Recent soft computing methods in software reliability engineering. (ad-astra.ro)
  • Albeanu G., Duda G. Recent soft computing methods in software reliability engineering . (ad-astra.ro)
  • The same year, I participated in an assessment of the amount of empirical evaluation performed in the software engineering literature [ Expeval ] and performed an analogous one for articles about neural network learning methods [ NNeval ]. (fu-berlin.de)
  • Approximate computing for NoC and NoC-based systems: approximate communication in on-chip networks, approximate computing-communications interplay, adaptive error control. (wikicfp.com)
  • Evaluation of computer systems and networks is needed at every stage in the life cycle of the product including design, manufacturing, sales/purchase, use, upgrade, tuning, etc. (emerald.com)
  • I am interested in design methodologies for embedded and cyber-physical systems (CPS), which utilize computing to monitor, service and control various application-specific processes, including those in the physical world. (citris-uc.org)
  • The main theme of the reviewed book is integration and synergistic co-operation of a few soft-computing methodologies that have their roots in neural systems. (osiander.de)
  • Lecture Notes in Networks and Systems, vol 528. (iiitm.ac.in)
  • Distributed computing is a field that studies distributed systems. (hitechnectar.com)
  • In distributed computing, several computer systems are involved. (hitechnectar.com)
  • Extreme embedded nano-systems: real-time, mission-critical, intermittent computing, energy-harvesting-based embedded networks. (wikicfp.com)
  • Soft Computing (SC) techniques are oriented towards the analysis and design of intelligent systems covering an interdisciplinary methodological framework. (iospress.com)
  • For this assignment, you are required to do a Part 1 5-10 minutes presentation and Part 2 report on a recent academic paper on a topic related to Software Engineering or Software Engineering Methodologies. (mybestassignmenthelp.com)
  • One methodology models movement using physics. (isi.edu)
  • This relies on neural networks - computing models used in AI to identify relationships in datasets - for learning physics, and the underlying mechanism is approximated through data. (isi.edu)
  • We test the proposed methodology using four regression models: locally weighted regression, linear regression, regression trees and neural networks. (cai.sk)
  • The proposed methodology allows for the calibration of microscopic traffic models with fewer computational resources than is commonly used. (concordia.ca)
  • 10] Heiat A., Comparison of Artificial Neural Network and Regression Models for Estimating Software Development Effort, Information and Software Technology, vol. 44, no.15, pp. 911- 922, 2002. (iajit.org)
  • By eliminating explicit sensitivity calculations and enabling real-time optimization, this methodology enhances efficiency and accuracy in achieving lightweight designs. (anl.gov)
  • The main challenge is for these chip-scale nanonetworks to provide the efficiency, versatility, scalability and reliability necessary to tackle the growing technological, architectural and workload heterogeneity in this new era of computing. (wikicfp.com)
  • As space heating represents a large share of total energy use, thermal networks, i.e. district cooling or heating networks, would be able to increase the efficiency of the energy system in an economic way. (lu.se)
  • February 29 th through March 1 st , 2020 Computing Ph.D. student Amrina Ferdous attended and presented her current research at the "Data Science and Image Analysis Conference of the Pacific Northwest" at Washington State University in Pullman, WA. (boisestate.edu)
  • This enables real-time optimization, with the neural network representing the material layout. (anl.gov)
  • The Argonne Leadership Computing Facility enables breakthroughs in science and engineering by providing supercomputing resources and expertise to the research community. (anl.gov)
  • 4 Evolutionary Design of Artificial Neural Networks. (osiander.de)
  • Dewangan, D.K., Sahu, S.P. Towards the design of vision-based intelligent vehicle system: methodologies and challenges. (iiitm.ac.in)
  • Dewangan, D.K. and Sahu, S.P. (2021), PotNet: Pothole detection for autonomous vehicle system using convolutional neural network. (iiitm.ac.in)
  • Starting from a Siamese neural network that classifies pairs of argument units into support vs. attack relations, we extend this system with a set of features that encode a variety of features extracted from two complementary background knowledge resources: ConceptNet and DBpedia. (dagstuhl.de)
  • Optimized Convolutional Neural Network for Road Detection with Structured Contour and Spatial Information for Intelligent Vehicle System. (iiitm.ac.in)
  • Moodle system is used to build a music teaching network system based on the analysis of previous studies and the existing music teaching network platform. (hindawi.com)
  • In simple terms, a neural network or artificial neural network is "a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. (smartdatacollective.com)
  • This was also called the Hopfield network (1982). (wikipedia.org)
  • Computing (2021), 103, 2867-2892 (2021). (iiitm.ac.in)
  • Member of UN GGIM Academic Network, since March 2021. (lu.se)
  • Inversion sees the physics through the mathematical model while the Neural Networks learn the physics from the training data. (boisestate.edu)
  • The widely used convolutional neural network (CNN), a type of FNN, is mainly used for static (non-temporal) data processing. (frontiersin.org)
  • Abstract: In this new era of data-driven domain-specific computing, the integrated circuits, serving as the cornerstones of modern electronic devices, are facing tremendous challenges in meeting the ever-growing data processing demand under staggering technology improvement. (usc.edu)
  • As a result, it is critical to look for new computing architecture that delivers the most efficient computing and data processing solutions. (usc.edu)
  • To address this, processors integrate interconnection networks that manage the movement of data in a scalable and cost-effective manner at the chip scale, i.e., for ranges between hundreds of nanometers to a few millimeters. (wikicfp.com)
  • For this component you will be required to do a 5-10 minute presentation on a recent academic paper on a topic related to Software Engineering or Software Engineering Methodologies. (urgenthomework.com)
  • That work led to my Habilitation in 2000 based on a book on empirical methodology in software engineering . (fu-berlin.de)
  • The research presents Intelligent dynamic gesture recognition (IDGR) using a Convolutional neural network (CNN) empowered by edit distance for video recognition. (techscience.com)
  • Wireless local area networks, Bluetooth, and intelligent transmission channels based on specific frequency can replace wired audio transmission and are widely used in the digital music classroom. (hindawi.com)
  • In recent years though, new methodologies in brain research have made significant impacts. (virginia.edu)
  • The reviewed book is intended both for undergraduate students in soft computing disciplines as well as for engineers, practitioners, and problem solvers in many areas of application. (osiander.de)
  • ACM's educational activities, conducted primarily through our Education Board and Advisory Committee, range from the K-12 space (CSTA) and two-year programs to undergraduate, graduate, and doctoral-level education, and professional development for computing practitioners at every stage of their career. (acm.org)