Neural Networks - Science topic

Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research...

Deterministic wave elevation prediction is crucial for improving the power generation efficiency of offshore energy structures (OESs). Although phase-resolved wave models may predict deterministic wave information, a comprehensive understanding of wave theory and mathematics is necessary to guarantee their accuracy. Inspired by the capability of ph...

This paper proposes a novel multimodal deep weakly-supervised learning framework, SinkholeNet, to classify and localize sinkhole(s) in high-resolution RGB-slope aerial images. The SinkholeNet first employs a multimodal Convolutional Neural Network (CNN) architecture that simultaneously extracts features from the input RGB image and ground slope map...

Recently, interpretable machine learning has re-explored concept bottleneck models (CBM), comprising step-by-step prediction of the high-level concepts from the raw features and the target variable from the predicted concepts. A compelling advantage of this model class is the user's ability to intervene on the predicted concept values, consequently...

Average treatment effect has a very important role to measure the differences in mean outcomes between units assigned to the treatment and units assigned to the control and it has applications in different scientific domains such as molecular biology, econometrics, machine learning. Accordingly, from previous studies in biological networks, it has...

Robust feed-forward neural network is one of the recent network structures that is based on the combinationof the classical robust regression and the deep neural network. Moreover, the conditional randomizationtest (CRT) is another well-known approach that is based on the comparison of the known conditionaldistribution with the distribution of the...

Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or R^N , N ∈ N, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We research also the case of approximation by iterated operators of the last four types, th...

Input-Convex Neural Networks (ICNNs) are networks that guarantee convexity in their input-output mapping. These networks have been successfully applied for energy-based modelling, optimal transport problems and learning invariances. The convexity of ICNNs is achieved by using non-decreasing convex activation functions and non-negative weights. Beca...

Sensor-rich smartphones have facilitated a lot of services and applications. Indoor/Outdoor (IO) status serves as a critical foundation for various upstream tasks, including seamless pedestrian navigation, power management, and activity recognition. Nevertheless, achieving robust, efficient, and accurate IO detection remains challenging due to envi...

Deep learning-based methods have proved useful for adversarial attack detection. However, conventional detection algorithms exploit crisp set theory for classification boundary. Therefore, representing vague concepts is not available. Motivated by the recent success in fuzzy systems, we propose a fuzzy rule-based neural network to improve adversari...

Predictive maintenance (PdM) is a cost-cutting method that involves avoiding breakdowns and production losses. Deep learning (DL) algorithms can be used for defect prediction and diagnostics due to the huge amount of data generated by the integration of analog and digital systems in manufacturing operations. To improve the predictive maintenance st...

Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computa...

As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image dete...

Seasonal time series with trends are the most common data sets used in forecasting. This work focuses on the automatic processing of a non-pre-processed time series by studying the efficiency of recurrent neural networks (RNN), in particular both long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM) extensions, for model...

In this paper, we propose a sufficient condition at which a neural network can approximate a set of optimization algorithm solutions; we establish under which conditions a neural network can replace an optimization algorithm to solve a problem with the objective of safely deploying that network in a system where online solutions are necessary to si...

In this paper, we propose a sufficient condition at which a neural network can approximate a set of optimization algorithm solutions; we establish under which conditions a neural network can replace an optimization algorithm to solve a problem with the objective of safely deploying that network in a system where online solutions are necessary to si...

Facial landmark detection is a widely researched field of deep learning as this has a wide range of applications in many fields. These key points are distinguishing characteristic points on the face, such as the eyes centre, the eye's inner and outer corners, the mouth centre, and the nose tip from which human emotions and intent can be explained....

The fault-tolerant tracking control problem is studied for the discrete-time systems with actuator faults. To lessen adverse impacts of actuator fault, a PPD information-driven fault estimation algorithm is established to adaptively estimate actuator fault information online, which avoids the additional construction and training process of neural n...

The poster focuses on employing Convolutional Neural Networks (CNNs) for the purpose of animal classification. The main goal is to create a deep learning model that can effectively and accurately classify new, unfamiliar images as either cats or dogs. Additionally, the aim is to evaluate the performance and accuracy of the classifier to assess the...

The safety and stability of a wind turbine is determined by the health condition of its gearbox. The temperature variation, compared with other characteristics of the gearbox, can directly and sensitively reflect its health conditions. However, the existing deep learning models (including the single model and the hybrid model) have their limitation...

In a hyperspectral image classification (HSIC) task, manually labeling samples requires a lot of manpower and material resources. Therefore, it is of great significance to use small samples to achieve the HSIC task. Recently, convolutional neural networks (CNNs) have shown remarkable performance in HSIC, but they still have some areas for improveme...

The optical reflection characteristics of asphalt pavement have an important influence on road-lighting design, and the macrotexture and microtexture of asphalt pavement significantly affect its reflection characteristics. To investigate the impact of texture parameters on the retroreflection coefficient of asphalt pavement, the texture indices of...

Fractional-order neural network models have become an active research subject and have attracted increasing attention in many fields [...]

This article is concerned with fixed-time synchronization and preassigned-time synchronization of Cohen–Grossberg quaternion-valued neural networks with discontinuous activation functions and generalized time-varying delays. Firstly, a dynamic model of Cohen–Grossberg neural networks is introduced in the quaternion field, where the time delay succe...

Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures. During the meta-training process, ZeroBind formulates t...

Financial fraud detection poses a critical challenge in the contemporary digital economy due to its potential to inflict substantial harm on individuals, businesses, and financial institutions. In this research, we introduce an innovative approach that combines Genetic Programming (GP) with Convolutional Neural Network (CNN) optimization to enhance...

Entity alignment plays an essential role in the integration of knowledge graphs (KGs) as it seeks to identify entities that refer to the same real-world objects across different KGs. Recent research has primarily centred on embedding-based approaches. Among these approaches, there is a growing interest in graph neural networks (GNNs) due to their a...

The reduction of various nitrogen oxide (NOx) emissions from diesel engines is an important environmental issue due to their negative impact on air quality and public health. Selective catalytic reduction (SCR) has emerged as an effective technology to mitigate NOx emissions, but predicting the performance of SCR systems remains a challenge due to...

Deep neural networks have achieved remarkable results across various tasks. However, they are susceptible to ad-versarial examples, which are generated by adding adver-sarial perturbations to original data. Adversarial training (AT) is the most effective defense mechanism against adver-sarial examples and has received significant attention. Recent...

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-c...

The control of large-scale industrial systems has several criteria, such as ensuring high productivity, low production costs and the lowest possible environmental impact. These criteria must be established for all subsystems of the large-scale system. This study is devoted to the development of a hierarchical control system that meets several of th...

Recently, deep convolutional neural networks (CNNs) have shown significant advantages in improving the performance of single image super-resolution (SISR). To build an efficient network, multi-scale convolution is commonly incorporated into CNN-based SISR methods via scale features with different perceptive fields. However, the feature correlations...

The relationship between ratios of excitatory to inhibitory neurons and the brain's dynamic range of cortical activity is crucial. However, its full understanding within the context of cortical scale-free dynamics remains an ongoing investigation. To provide insightful observations that can improve the current understanding of this impact, and base...

The calculation of a resistive sensor is considered as the load of an unstable communication line using a neural network. In the corresponding approximation or regression problem, the feedforward neural network is trained using training data and additional control data. Such data are calculated from a mathematical model of the communication line in...

Detecting respiration in a non-intrusive manner is beneficial not only for convenience but also for cases where the traditional ways cannot be applied. This paper presents a novel simple low-cost system where ambient Wi-Fi signals are acquired by a third-party tool (Nexmon) installed in a Raspberry Pi and is able to detect the respiration time doma...

The optimized radial basis function network is a kind of neural network that utilizes a step size inside of the gradient strategy for the modeling, where a small step size will spend much time to reach a minimum, while a big step size will jump over the minimum; hence, it needs an acceptable step size. The genetic optimizer is one option to seek an...

To what extent can evolution be considered as the sole first principle that explains all properties of nervous systems? This paper proposes an innovative, mathematically rigorous perspective on understanding nervous systems from an evolutionary perspective, leveraging methods of nonequilibrium statistical physics. This approach allows for modeling...

Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of co...

The automatic segmentation of brain tumours is a critical task in patient disease management. It can help specialists easily identify the location, size, and type of tumour to make the best decisions regarding the patients' treatment process. Recently, deep learning methods with attention mechanism helped increase the performance of segmentation mo...

With the rapid development of GPU (graphics processing unit) technologies and neural networks, we can explore more appropriate data structures and algorithms. Recent progress shows that neural networks can partly replace traditional data structures. In this paper, we proposed a novel DNN (deep neural network)-based learned locality-sensitive hashin...

In urban areas, utilizing traffic lights to prioritize vehicles at the intersection is a solution to control traffic. Among the smart traffic light methods, the methods based on machine learning are particularly important due to their simplicity and performance. In this paper, Q-learning with deep neural network are combined and used in two differe...

Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably in the form of diminished public trust and safety concerns from long-tail unforeseen driving scenarios. This predicament is due to the limitation of deep neural networks in AD software, which struggle with interpretability and exhibit poor generalization capabilities in ou...

El diseño de medicamentos se ha beneficiado significativamente de los avances en la química computacional y las redes neuronales. En este artículo, exploramos el papel fundamental que desempeñan técnicas de la química computacional como la Teoría del Funcional de la Densidad (DFT), el Docking Molecular y la Dinámica Molecular (MD) en la comprensión...

Remaining useful life (RUL) prediction of bearings is significantly important to ensure reliable operation of bearings. In practice, it is routinely impossible to obtain the full life cycle degradation data of bearings that needs to be used in prediction. The accuracy of the RUL prediction of bearings is often affected by incomplete degradation dat...

This study aims to address the mental health challenges brought about by the diversified development and rapid changes in society, with special attention to the psychological status of the student population. By using the SCL-90 mental health testing tool, collecting students' mental health data, and applying the fuzzy comprehensive evaluation meth...

Since the current remote sensing pre-trained models trained on optical images are not as effective when applied to SAR image tasks, it is crucial to create sensor-specific SAR models with generalized feature representations and to demonstrate with evidence the limitations of optical pre-trained models in downstream SAR tasks. The following aspects...

Neuromorphic computing offers a promising solution to overcome the von Neumann bottleneck, where the separation between the memory and the processor poses increasing limitations of latency and power consumption. For this purpose, a device with analog switching for weight update is necessary to implement neuromorphic applications. In the diversity o...

After the development of the Versatile Video Coding (VVC) standard, research on neural network-based video coding technologies continues as a potential approach for future video coding standards. Particularly, neural network-based intra prediction is receiving attention as a solution to mitigate the limitations of traditional intra prediction perfo...

Neural networks have evolved into strong and dependable machine learning systems. However, training these systems requires human intervention in selecting neural network parameters and evaluating results. This human intervention exposes the training of a neural network to human bias. One key task in neural network learning success is selecting opti...

基于机载LiDAR测深ALB（Airborne LiDAR Bathymetry）技术的海底底质分类能够为浅海水域的海洋资源开发利用、海洋环境保护、海洋工程建设等提供基础数据，对海洋活动与海洋科学研究具有重要意义。针对ALB海底底质分类存在的特征冗余问题，本文提出了一种顾及波形和地形特征优选的底质分类算法。在提取波形和地形特征的基础上，构建Relief-F特征优选模型，通过计算各特征在底质分类中的贡献率，实现多元特征优选；然后，利用随机森林RF（Random Forest）、支持向量机SVM（Support Vector Machine）、BP神经网络BPNN（Back Propagation Neural Network）3种分类器进行监督分类，提取珊瑚礁、砾石、砂、植被、海岸带5类底质。...

We propose a sparse computation method for optimizing the inference of neural networks in reinforcement learning (RL) tasks. Motivated by the processing abilities of the brain, this method combines simple neural network pruning with a delta-network algorithm to account for the input data correlations. The former mimics neuroplasticity by eliminatin...

Inverse halftoning is a technology that converts a binary image into a continuous tone image. Due to the wide application of inverse halftoning, many scholars have proposed several deep convolutional neural networks (DCNN) to optimize their performance. According to the observation, there is still room for improvement in content generation and deta...

In this work, we build upon a simple model of a primitive nervous system presented in a prior companion paper. Within this model, we formulate and solve an optimization problem, aiming to mirror the process of evolutionary optimization of the nervous system. The formally derived predictions include the emergence of sharp peaks of neural activity ('...

p>It's important to note that the classification of epileptic seizures can be complex, and an individual may experience more than one type of seizure. Additionally, advancements in understanding epilepsy may lead to refinements in the classification system over time. If someone is experiencing seizures, it's crucial for them to consult with a healt...

In this article, an innovative offline deep imitation learning algorithm for optimal trajectory planning is proposed. While many state‐of‐the‐art works achieved optimal trajectory planning, their systems were stable or quasistable, and their approaches rarely optimized the system's initial conditions (ICs). Here, a new unstable dynamic system task...

This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line voltage regulators. The analyzed study-case represents a real-li...

p>It's important to note that the classification of epileptic seizures can be complex, and an individual may experience more than one type of seizure. Additionally, advancements in understanding epilepsy may lead to refinements in the classification system over time. If someone is experiencing seizures, it's crucial for them to consult with a healt...

Achieving accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) is important for clinical diagnosis and accurate treatment, and the efficient extraction and analysis of MRI multimodal feature information is the key to achieving accurate segmentation. In this paper, we propose a multimodal information fusion method for brain tumo...

Knowledge about the subtypes of a disease critically affects clinical decisions ranging from the choice of therapeutic options to patient management. If the understanding of a disease is partial and the subtypes of the disease are not yet known, a traditional supervised approach becomes untenable for disease subtype classification. In these context...

PixelKey represents a novel approach to authentication, leveraging the capabilities of Convolutional Neural Networks (CNNs) and Artificial Intelligence (AI). This whitepaper delves into the core principles and technological underpinnings of our system, demonstrating its potential to revolutionize the field of authentication. We elaborate on CNN arc...

This brief paper analyzes the positivity and asymptotic stability of incommensurate fractional-order coupled neural networks (FOCNNs) with time-varying delays. Under a reasonable assumption about the activation functions of neurons, a sufficient and necessary condition is proposed to guarantee that FOCNNs are positive systems. Furthermore, the suff...