2,059 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Predicting the Future is like Completing a Painting!
This article is an introductory work towards a larger research framework
relative to Scientific Prediction. It is a mixed between science and philosophy
of science, therefore we can talk about Experimental Philosophy of Science. As
a first result, we introduce a new forecasting method based on image
completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time
series forecasting is transformed into fully images- and signal-based
processing procedures. After transforming a time series data into its
corresponding image, the problem of data forecasting becomes essentially a
problem of image inpainting problem, i.e., completing missing data in the
image. An extensive experimental evaluation is conducted using a large dataset
proposed by the well-known M3-competition. Results show that FM2I represents an
efficient and robust tool for time series forecasting. It has achieved
prominent results in terms of accuracy and outperforms the best M3 forecasting
methods.Comment: 25 pages, 12 figure
Applicability of Artificial Neural Network for Automatic Crop Type Classification on UAV-Based Images
Recent advances in optical remote sensing, especially with the development of machine learning models have made it possible to automatically classify different crop types based on their unique spectral characteristics. In this article, a simple feed-forward artificial neural network (ANN) was implemented for the automatic classification of various crop types. A DJI Mavic air drone was used to simultaneously collect about 549 images of a mixed-crop farmland belonging to Federal University of Technology Minna, Nigeria. The images were annotated and the ANN algorithm was implemented using custom-designed Python programming scripts with libraries such as NumPy, Label box, and Segmentation Mask, for the classification. The algorithm was designed to automatically classify maize, rice, soya beans, groundnut, yam and a non-crop feature into different land spectral classes. The model training performance, using 70% of the dataset, shows that the loss curve flattened down with minimal over-fitting, showing that the model was improving as it trained. Finally, the accuracy of the automatic crop-type classification was evaluated with the aid of the recorded loss function and confusion matrix, and the result shows that the implemented ANN gave an overall training classification accuracy of 87.7% from the model and an overall accuracy of 0.9393 as computed from the confusion matrix, which attests to the robustness of ANN when implemented on high-resolution image data for automatic classification of crop types in a mixed farmland. The overall accuracy, including the user accuracy, proved that only a few images were incorrectly classified, which demonstrated that the errors of omission and commission were minimal
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
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions
Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research
JefiAtten: An Attention Based Neural Network Model for Solving Maxwell's Equations with Charge and Current Sources
We present JefiAtten, a novel neural network model employing the attention
mechanism to solve Maxwell's equations efficiently. JefiAtten uses
self-attention and cross-attention modules to understand the interplay between
charge density, current density, and electromagnetic fields. Our results
indicate that JefiAtten can generalize well to a range of scenarios,
maintaining accuracy across various spatial distribution and handling amplitude
variations. The model showcases an improvement in computation speed after
training, compared to traditional integral methods. The adaptability of the
model suggests potential for broader applications in computational physics,
with further refinements to enhance its predictive capabilities and
computational efficiency. Our work is a testament to the efficacy of
integrating attention mechanisms with numerical simulations, marking a step
forward in the quest for data-driven solutions to physical phenomena.Comment: 29 pages, 11 figure
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