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Abstract Background Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine. Hypothesis/Objectives To derive a model...

The problem of incomplete data matrices is repeatedly found in large databases; posing a significant obstacle for an effective treatment of data. This paper examines a self-organizing-map (SOM) based method of data imput...

Abstract Background Bacterial diseases include any illness caused by bacteria. Bacteria are tiny microorganisms that can only be seen with a microscope. Other types of microorganisms include viruses, fungi, and some para...

Abstract Background The research shows the impact of land use forms and population density on the spatial variation of COVID-19 cases and deaths. Selected elements of land use that influence the number of COVID-19 cases ...

The identification of suitable models for predicting daily water flow is important for planning and management of water storage in reservoirs of Argentina. Long-term prediction of water flow is crucial for regulating res...

By introducing the artificial intelligence technology, on improved Nelder-Mead acceleration algorithm based on the neural network is proposed to obtain better optimization results. A back analysis method for construction...

Meta-optics have opened new possibilities for portable, high-performance microscopy, offering ultrathin and highly customizable wavefront control in scenarios where bulky optics limit adoption. Here, we use this capabili...

Abstract Purpose This study introduces a new Convolutional Neural Network (CNN) architecture, EyeDiagNet, for the classification of fundus images into four classes: normal, cataract, diabetic retinopathy (DR), and glauco...

Abstract The stability of surrounding rock is a critical challenge in underground engineering. Accurate prediction of Excavation Damaged Zone Thickness (EDZT) in surrounding rock is vital for designing safe underground s...

Jaringan syaraf tiruan merupakan metode yang sangat sering digunakan untuk peramalan. Akurasi jaringan syaraf tiruan dipengaruhi oleh jumlah lapisan tersembunyi dan neuron didalamnya. Salah satu teori yang membahas tenta...

Abstract The primary objective of this study is to develop and validate robust data-driven models for accurately predicting bacterial growth inhibition induced by cerium oxide nanoparticles across different bacterial str...

ABSTRACT Background and Aims De novo drug design is the process of generating novel lead compounds that possess desirable pharmacological activities and optimal physicochemical properties for therapeutic development. In ...

Abstract This model is highly helpful in thermal management and complex energy systems, particularly where precise control over heat and mass transfer is required. Solarized nanofluids can be used to improve heat absorpt...

Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book...

Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical ...

From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques fo...

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solvin...

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up ...

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a sh...

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuni...

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where th...

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of gr...

We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple ...