A5043904334- Prostate Cancer Treatment and Research
- Radiopharmaceutical Chemistry and Applications
- DNA and Biological Computing
- Advanced Memory and Neural Computing
- Topic Modeling
- Cancer, Lipids, and Metabolism
- Advanced biosensing and bioanalysis techniques
- Natural Language Processing Techniques
- Neural Networks and Reservoir Computing
- Advanced Image Processing Techniques
- Advanced Breast Cancer Therapies
- Image and Signal Denoising Methods
- Robotic Mechanisms and Dynamics
- Stochastic Gradient Optimization Techniques
- Generative Adversarial Networks and Image Synthesis
- Cancer Genomics and Diagnostics
- Multimodal Machine Learning Applications
- Cellular Automata and Applications
- Image Processing Techniques and Applications
- Financial Markets and Investment Strategies
- Stock Market Forecasting Methods
- Neural dynamics and brain function
- Machine Learning and ELM
- Digital Media Forensic Detection
- Modular Robots and Swarm Intelligence
Emory University
2024-2025
Yunnan Metallurgical Group (China)
2025
Xihua University
2019-2024
Zhejiang University
2015-2024
The University of Sydney
2022-2024
Chengdu Medical College
2024
Duke University
2019-2024
Hainan University
2024
Guizhou Minzu University
2024
Soochow University
2020-2024
With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition importance time series forecasting in market operation management. In this paper, we propose a new model based on deep learning ensemble model. The is constructed by taking advantage convolutional neural network (CNN), long short-term memory (LSTM) network, autoregressive moving average (ARMA) CNN-LSTM introduced...
The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage medical resources. To aid and accelerate diagnosis process, automatic via deep learning models recently been explored by researchers across world. While different data-driven have developed mitigate COVID-19, data itself is still scarce patient privacy concerns. Federated Learning (FL) natural solution because it allows organizations cooperatively learn an effective model without sharing raw data. However,...
Spiking neural P (SNP) systems are a class of neural-like computing models, abstracted by the mechanism spiking neurons. This article proposes new variant SNP systems, called gated (GSNP) which composed Two mechanisms introduced in nonlinear GSNP consisting reset gate and consumption gate. The two gates used to control updating states Based on neurons, prediction model for time series is developed, known as model. Several benchmark univariate multivariate evaluate proposed compare several...
Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models inspired from the nonlinear mechanism neurons. NSNP have distinguishing feature: mechanism. To handle edge detection images, this paper proposes variant, with two outputs (TO), termed as NSNP-TO systems. Based on system, an framework is developed, ED-NSNP detector. The ability detector relies convolutional kernels. obtain good performance, particle swarm optimization (PSO) used to optimize parameters...
Nonlinear spiking neural P (NSNP) systems are a recently developed theoretical model, which is abstracted by nonlinear mechanism of biological neurons. NSNP have structure and the potential to describe dynamic systems. Based on systems, novel time series forecasting approach in this paper. During training phase, first converted frequency domain using redundant wavelet transform, then according data, an system automatically constructed adaptively trained domain. Then, well-trained can...
Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing models that inspired by the mechanism spiking neurons 3rd-generation networks. Chaotic time series forecasting is one most challenging problems for machine learning models. To address this challenge, we first propose nonlinear version SNP systems, called with autapses (NSNP-AU systems). In addition to consumption generation spikes, NSNP-AU have three gate functions, which related states outputs...
Cyber risk is an important emerging source of in the economy. To estimate its impact on asset market, we use machine learning techniques to develop a firm-level measure cyber risk. The aggregates information from rich set firm characteristics and shows superior ability forecast future cyberattacks individual firms. We find that firms with higher earn average stock returns. When these underperform, cybersecurity experts tend have concerns about risk, exchange-traded funds outperform. Further...
The fully autonomous harvesting of oyster mushrooms in the greenhouse requires development a reliable and robust robot. In this paper, we propose an oyster-mushroom-harvesting robot, which can realize operations entire greenhouse. two crucial components robot are perception module end-effector. Intel RealSense D435i is adopted to collect RGB images point cloud real time; improved SSD algorithm proposed detect mushrooms, finally, existing soft gripper manipulated grasp mushrooms. Field...
Highways consume enormous electric power and therefore contribute to heavy economic costs due the operation of auxiliary road facilities including lighting, displays, health-monitoring systems for tunnels bridges, etc. We here propose a new strategy supply highways by harvesting mechanical energy from reciprocating deformation pavements. A series wheel tracking tests are performed demonstrate possibility using piezoelectric elements transform stored in pavements vehicular load into...
Evaluating the selection of content in a summary is important both for human-written summaries, which can be useful pedagogical tool reading and writing skills, machine-generated are increasingly being deployed information management. The pyramid method assesses by aggregating units from summaries wise crowd (a form crowdsourcing). It has proven highly reliable but largely depended on manual annotation. We propose PEAK, first to automatically assess using that also generates models. PEAK...
Abstract We explored a new artificial intelligence-assisted method to assist junior ultrasonographers in improving the diagnostic performance of uterine fibroids and further compared it with senior confirm effectiveness feasibility intelligence method. In this retrospective study, we collected total 3870 ultrasound images from 667 patients mean age 42.45 years ± 6.23 [SD] for those who received pathologically confirmed diagnosis 570 women 39.24 5.32 without lesions Shunde Hospital Southern...
Abstract Purpose: In men with metastatic castration-resistant prostate cancer (mCRPC), prostate-specific membrane antigen (PSMA)-targeted radioligand therapy has drastically improved clinical outcomes. A liquid biopsy characterizing PSMA expression could be useful in guiding optimal therapy. Experimental Design: We conducted a retrospective analysis of the prospective multicenter PROPHECY (Prospective CiRculating PrOstate Cancer Predictors HighEr Risk mCRPC StudY) trial (n = 118) treated...
We have previously developed and externally validated a prognostic model of overall survival (OS) in men with metastatic, castration-resistant prostate cancer (mCRPC) treated docetaxel. sought to validate this broader group docetaxel-naïve mCRPC specific subgroups (White, Black, Asian patients, different age groups, treatments) classify patients into two three risk groupings on the basis model.