1,934 Results Found

Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and dig...

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem....

The analysis of site seismic amplification characteristics is one of the important tasks of seismic safety evaluation. Owing to the high computational cost and complex implementation of numerical simulations, significant differences exist in the pred...

Many industrial accidents occur at construction sites. Several countries are instating safety management measures to reduce industrial accidents at construction sites. However, there are few technical measures relevant to this task, and there are saf...

Advances in information technology have harnessed the application of Quick Response (QR) codes in day-to-day activities, simplifying information exchange. QR codes are witnessed almost everywhere, on consumables, newspapers, information bulletins, et...

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural n...

In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes,...

In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it ofte...

Accurate and fine-grained prediction of PM_2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional ne...

Object detection is a fundamental task in computer vision, which is usually based on convolutional neural networks (CNNs). While it is difficult to be deployed in embedded devices due to the huge storage and computing consumptions, binary neural netw...

This paper proposes a slope stability prediction model based on deep learning and digital twinning methods. To establish a reliable slope database, 30 actual slopes were collected, and 100 digital twin (DT) models were generated for each actual slope...

Deep Neural Networks are state-of-the-art in a large number of challenges in machine learning. However, to reach the best performance they require a huge pool of parameters. Indeed, typical deep convolutional architectures present an increasing numbe...

The forecasting of electricity sales is directly related to the power generation planning of power enterprises and the progress of the generation tasks. Aiming at the problem that traditional forecasting methods cannot properly deal with the actual d...

Landslides pose a great threat to the safety of people’s lives and property within disaster areas. In this study, the Zigui to Badong section of the Three Gorges Reservoir is used as the study area, and the land use (LU), land use change (LUC)...

The sigma ($\sigma$) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. ${\sigma }^{54}$ promoters carried out various ancillary methods and environmentally responsive procedures;...

Aiming at the problems of insufficient detection accuracy and high false detection rates of traditional pest detection models in the face of small targets and incomplete targets, this study proposes an improved target detection network, I-YOLOv10-SC....

Predicting in vivo protein–DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been id...

Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breakin...

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important feat...

This study proposes a double-track method for the classification of fruit varieties for application in retail sales. The method uses two nine-layer Convolutional Neural Networks (CNNs) with the same architecture, but different weight matrices. The fi...

Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementa...

This paper proposes a sound event detection (SED) method in tunnels to prevent further uncontrollable accidents. Tunnel accidents are accompanied by crashes and tire skids, which usually produce abnormal sounds. Since the tunnel environment always ha...

The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genom...

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networ...

This paper proposes an innovative single-channel supervised speech enhancement (SE) method based on UNET, a convolutional neural network (CNN) architecture that expands on a few changes in the basic CNN architecture. In the training phase, short-time...

The standard matrix factorization methods for recommender systems suffer from data sparsity and cold-start problems. Thus, in real-world scenarios where items are commonly associated with textual data such as reviews, it becomes necessary to build a...

The identification of drug–drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional netwo...

Under variable working conditions, the tool status signal is affected by changing machine processing parameters, resulting in a decreased prediction accuracy of the remaining useful life (RUL). Aiming at this problem, a method based on multi-sensor f...

Supervised learning for Unmanned Aerial Vehicle (UAVs) visual-based navigation raises the need for reliable datasets with multi-task labels (e.g., classification and regression labels). However, current public datasets have limitations: (a) Outdoor d...

In deeper layers, ResNet heavily depends on skip connections and Relu. Although skip connections have demonstrated their usefulness in networks, a major issue arises when the dimensions between layers are not consistent. In such cases, it is necessar...

In this paper, some optimized encoding and decoding schemes are proposed for the generalized LDPC convolutional codes (GLDPC–CCs). In terms of the encoding scheme, a flexible doping method is proposed, which replaces multiple single parity chec...

Gaining a deep understanding of precipitation patterns is beneficial for enhancing Australia’s adaptability to climate change. Driven by this motivation, we present a specific spatiotemporal deep learning model that well integrates matrix facto...

Hyperspectral images can capture subtle differences in reflectance of features in hundreds of narrow bands, and its pixel-wise classification is the cornerstone of many applications requiring fine-grained classification results. Although three-dimens...

Carbon emission estimation for power systems is essential for identifying emission responsibilities and formulating effective mitigation measures. Current carbon emission prediction methods for power systems exhibit limited computational efficiency a...

Landslides are among the most destructive geological hazards, necessitating precise landslide susceptibility mapping (LSM) for effective risk management. This study focuses on the northeastern region of Leshan City and investigates the influence of v...

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence...

Background: Gastrointestinal (GI) disorders present significant healthcare challenges, requiring rapid, accurate, and effective diagnostic methods to improve treatment outcomes and prevent complications. Wireless capsule endoscopy (WCE) is an effecti...

The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model...

Wind power forecasting is challenging because of complex, nonlinear relationships between inherent patterns and external disturbances. Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose a...

For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automat...

Objectives: Subarachnoid Hemorrhage (SAH) is a serious neurological emergency case with a higher mortality rate. An automatic SAH detection is needed to expedite and improve identification, aiding timely and efficient treatment pathways. The existenc...

Although the numerical models can estimate the significant influence of local site conditions on the seismic propagation characteristics near the surface in many studies, they cannot feasibly predict the seismic ground motion amplification in regular...

Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address...

The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigu...

This study aims to verify the wood classification performance of convolutional neural networks (CNNs), such as VGG16, ResNet50, GoogLeNet, and basic CNN architectures, and to investigate the factors affecting classification performance. A dataset fro...

In order to solve the problem of the inaccuracy of the traditional online operation risk assessment model based on a physical mechanism and the inability to adapt to the actual operation of massive online operation monitoring data, this paper propose...

The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional...

To enhance the precision of regional agricultural drought resilience evaluation, a convolutional neural network optimized with Adam with weight decay (AdamW–CNN) was constructed. Based on local agricultural economic development regulations and...

The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (C...

Deep generative models are becoming a tool of choice for exploring the molecular space. One important application area of deep generative models is the reverse design of drug compounds for given attributes (solubility, ease of synthesis, etc.). Altho...