Results 71 to 80 of about 9,482,218 (334)

A low-cost neural sorting network with O(1) time complexity [PDF]

, 2011
[[abstract]]In this paper, we present an O(1) time neural network with O(n1 + var epsilon) neurons and links to sort n data, var epsilon > 0. For large-size problems, it is desirable to have low-cost hardware solutions.
Lin, Shun-Shii;Hsu, Shen-Hsuan
core  

The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?

Advanced Robotics Research, EarlyView.
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley   +1 more source

Deep Cognitive Neural Network (DCNN) [PDF]

, 2019
Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and ...
Hussain, Amir, Howard, Newton, Adeel, Ahsan, Gogate, Mandar   +3 more
core   +2 more sources

Drought Forecasting Using Artificial Wavelet Neural Network Integrated Model (WA-ANN) and Time Series Model (ARIMA) [PDF]

علوم و مهندسی آبیاری, 2018
Drought prediction in water resources systems plays an important role in reducing drought damage. In recent decades, Traditional methods including: fitting and mathematical models have been widely used to predict droughts.
mahbobeh younesi, Nadiya Shahraki, Safar Marofi, Hamed Nozari   +3 more
doaj   +1 more source

Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences

Advanced Science, EarlyView.
This study presents DeepMutDTA, a deep learning framework aimed at predicting mutation‐induced changes in protein‐drug interactions and prioritizing variants potentially linked to drug resistance. Trained on large‐scale data, it incorporates SimSiam‐MuTF, a label‐aware contrastive fine‐tuning strategy that encourages separation between WT and MT ...
Xiaowen Hu, Pan Zhang, Shangqian Wu, Hao Sun, Minwei Li, Sophia Tsoka, Zizhang Sheng, Lei Deng   +7 more
wiley   +1 more source

Modeling and Simulation of Water Softening by Nanofiltration Using Artificial Neural Network [PDF]

Iranian Journal of Chemistry & Chemical Engineering, 2006
An artificial neural network has been used to determine the volume flux and rejections of Ca2+ , Na+ and Cl¯, as a function of transmembrane pressure and concentrations of Ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration
Mahmoud Mousavi, Akram Avami
doaj  

Improving land use land cover mapping of a neural network with three optimizers of multi-verse optimizer, genetic algorithm, and derivative-free function

Egyptian Journal of Remote Sensing and Space Sciences, 2021
For land management and planning, information on the Land Use Land Cover (LULC) is vital. In this research, three optimizers of the Multi-Verse Optimizer (MVO), Genetic Algorithm (GA), and Derivative-free Function (DF) are developed in MATLAB programming
Ali Jamali
doaj   +1 more source

Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction

, 2023
Urgent transition from the dependence on fossil fuels towards renewable energies requires more solar photovoltaic power to be connected to the electricity grids, with reliable supply through accurate solar radiation forecasting systems.
Sujan Ghimire, Deo, Ravinesh C., Casillas-Perez, David, Sancho Salcedo-Sanz, Bhandari, Binayak, David Casillas-Pérez, Ghimire, Sujan, Prasad, Ramendra, Ravinesh C. Deo, Thong Nguyen-Huy, Nguyen-Huy, Thong, Ramendra Prasad, Salcedo-sanz, Sancho, Binayak Bhandari   +13 more
core   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

Detecting attacks to computer networks using a multi-layer perceptron artificial neural network [PDF]

, 2011
In this paper, we present concepts in artificial neural networks (ANN) to help detect intrusion attacks against network computers, and introduce and compare a multi-layer perceptron ANN (MLPANN) with Snort, an open-source tool for intrusion detection ...
Araújo, Genival Mariano de, Amaral, Dino Macedo, Romariz, Alexandre Ricardo Soares   +2 more
core   +1 more source