Human activity recognition (HAR) is one of the important research areas in pervasive computing. Among HAR, sensor-based refers to acquiring a high-level knowledge about human activities from readings many low-level sensor. In recent years, although traditional methods deep learning (DL) have been widely used for HAR with some good performance, they still face such challenges as feature extraction and characterization, continuous action segmentation dealing time series problems. this study,...

Event Detection (ED) aims to identify instances of specified types events in text, which is a crucial component the overall task event extraction.The commonly used features consist lexical, syntactic, and entity information, but knowledge encoded Abstract Meaning Representation (AMR) has not been utilized this task.AMR semantic formalism meaning sentence as rooted, directed, acyclic graph.In paper, we demonstrate effectiveness AMR capture represent deeper contexts trigger words...

Abstract Human Activity Recognition (HAR) is an important research area in human–computer interaction and pervasive computing. In recent years, many deep learning (DL) methods have been widely used for HAR, due to their powerful automatic feature extraction capabilities, they achieve better recognition performance than traditional are applicable more general scenarios. However, the problem that DL increase computational cost of system take up resources while achieving higher accuracy, which...

We examine the efficacy of machine learning in a central task fundamental analysis: forecasting corporate earnings. find that models not only generate significantly more accurate and informative out-of-sample forecasts than state-of-the-art literature but also perform better compared to analysts' consensus forecasts. This superior performance appears attributable ability uncover new information through identifying economically important predictors capturing nonlinear relationships. The...

The global health crisis due to the fast spread of coronavirus disease (Covid-19) has caused great danger all aspects healthcare, economy, and other aspects. highly infectious insidious nature new greatly increases difficulty outbreak prevention control. early rapid detection Covid-19 is an effective way reduce Covid-19. However, detecting accurately quickly in large populations remains be a major challenge worldwide. In this study, A CNN-transformer fusion framework proposed for automatic...

This paper contributes a joint embedding model for predicting relations between pair of entities in the scenario relation inference. It differs from most stand-alone approaches which separately operate on either knowledge bases or free texts. The proposed simultaneously learns low-dimensional vector representations both triplets repositories and mentions texts, so that we can leverage evidence resources to make more accurate predictions. We use NELL evaluate performance our approach,...

Mixed Poisson–Gaussian noise exists in the star images and is difficult to be effectively suppressed via maximum likelihood estimation (MLE) method due its complicated function. In this article, MLE incorporated with a state-of-the-art machine learning algorithm order achieve accurate restoration results. By applying mixed function as reward of reinforcement algorithm, an agent able form restored image that achieves value complex through Markov Decision Process (MDP). provide appropriate...

Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual aims of reaching peak before 2030 and achieving neutrality 2060. Currently, the primary means monitoring ship exhaust are sniffing method non-imaging optical remote sensing; however, these methods suffer a low prediction efficiency high cost. We developed predicting CO2 content uses convolutional neural network mid-infrared spectral images. First, bench experiment was performed...

Cyclone detection is a classical topic and researchers have developed various methods of cyclone based on sea-level pressure, cloud image, wind field, etc. In this article, deep-learning algorithm incorporated with modern remote-sensing technology forms global-scale cyclone/anticyclone model. Instead using optical images, field data obtained from Mean Wind Field-Advanced Scatterometer (MWF-ASCAT) utilized as the dataset for model training testing. The vectors are reconstructed fed to model,...

The SO2 discharged by ships causes serious pollution to the atmosphere. Therefore, International Maritime Organization has set strict requirements on sulfur content of marine fuel. For first time, this study investigates optimal detection wavelength based imaging technology realize an accurate monitoring concentration in ship exhaust. First, a simulation analysis model (SAMID) exhaust is proposed and analyzed study. Next, bench experiment designed. values range gas required for are obtained....

With the increasing popularity of computer-aided technology applied in medicine, great achievements have been made certain diseases. However, due to similarity clinical and histological features, problem disease classification has not well resolved, especially Crohn's (CD) intestinal tuberculosis (ITB). In this paper, a novel sample connection driven framework named RFG-GCN is presented overcome this. Firstly, employs random forest based graph generation algorithm (RFG) convert structured...

The emission of SO2 from ships is an important source atmospheric pollution. Therefore, the International Maritime Organization (IMO) has established strict requirements for sulfur content marine fuel oil. In this paper, a new optical noncontact detection technique ship exhaust emissions analysis studied. Firstly, single-band simulation model imaging technology concentration in gas and deep neural network prediction were established. A bench test was designed to monitor tail simultaneously...