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Research on stock trend prediction method based on optimized random forest

CAAI Transactions on Intelligence technology 2023年第1期8卷 274-284页

作者： Lili Yin Benling Li Peng Li Rubo Zhang School of Computer and Science and Technology Harbin University of Science and TechnologyHarbinChina Department of Computer Science and Engineering Dalian Nationalities UniversityDalianChina

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

关键词： ensemble learning finance random forest random search technical indicator

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Severe plastic deformation (SPD) of biodegradable magnesium alloys and composites: A review of developments and prospects

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Journal of Magnesium and Alloys 2022年第4期10卷 938-955页

作者： M.Kasaeian-Naeini M.Sedighi R.Hashemi School of Mechanical Engineering Iran University of Science and TechnologyTehranIran

The use of magnesium in orthopedic and cardiovascular applications has been widely attracted by diminishing the risk of abnormal interaction of the implant with the body tissue and eliminating the second surgery to remove it from the ***,the fast degradation rate and generally inhomogeneous corrosion subsequently caused a decline in the mechanical strength of Mg during the healing *** researches have been conducted on the influences of various severe plastic deformation(SPD)processes on magnesium bioalloys and *** paper strives to summarize the various SPD techniques used to achieve magnesium with an ultrafine-grained(UFG)***,the effects of various severe plastic deformation methods on magnesium microstructure,mechanical properties,and corrosion behavior have been ***,this review intends to clarify the different potentials of applying SPD processes to the magnesium alloys and composites to augment their usage in biomedical applications.

关键词： Magnesium Severe plastic deformation Biodegradable implant Mechanical properties Corrosion

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Modification of Ti3C2Tx nanostructure with KH2PO4 and chitosan for effective removal of strontium from nuclear waste

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Environmental science and Pollution Research 2024年第40期31卷 53329-53347页

作者： Nezami, Shanli Ghaemi, Ahad Yousefi, Taher School of Chemical Petroleum and Gas Engineering Iran University of Science and Technology Narmak Tehran16846 Iran Nuclear Fuel Cycle Research School Nuclear Science and Technology Research Institute Tehran Iran

Nanostructure titanium carbide MXene (Ti3C2Tx) was modified with KH2PO4 and chitosan to effectively remove strontium from nuclear wastewater. Nuclear waste includes radionuclides of uranium, thorium, strontium, and cesium, which are classified depending on the concentration of radionuclides. Nuclear waste with a high strontium concentration is the production waste of radiopharmaceutical production centers. Ti3C2Tx was synthesized from Ti3AlC2 using HF40% and HF in situ (MILD-Ti3C2Tx) in 24 h at 313.15 and 333.15 K. Morphology, structure, and functional groups were investigated using the XRD, SEM, EDS, FTIR, and BET analyses. The Sr(II)’s adsorption capacity on Ti3C2Tx-HF and Ti3C2Tx-HF in situ was obtained as 61.9 and 253.5 mg g−1, respectively (temperature, 298.15 K;pH, 7.00;contact time, 180 min;and Sr(II) concentration, 150 mg l−1). Ti3C2Tx-HF in situ showed fourfold adsorption due to more hydroxyl functional groups and larger interlayer spacing. Ti3C2Tx was modified with KH2PO4 and chitosan to investigate the mechanism of change of Sr(II)’s adsorption capacity, which increased to 370 and 284 mg g−1, respectively. The structural results of modified Ti3C2Tx showed that the surface functional groups increased when modified with chitosan. In addition, modification with KH2PO4, through encapsulating large amounts of KH2PO4 between Ti3C2Tx layers, increased the possibility of Sr(II) diffusion between layers and electrochemical interactions with hydroxyl groups, and thus, increased its adsorption. Some experiments were designed to investigate the effect of parameters like initial concentration of Sr(II), contact time, temperature, and pH solution, as well as modified- and unmodified-Ti3C2Tx on adsorbent. The results revealed that the adsorption process of Sr(II) with pristine and modified-Ti3C2Tx follows pseudo-second-order kinetics and Freundlich heterogeneous isotherm model. Freundlich model isotherm indicates the presence of various functional groups on the surface

关键词： Radioactive waste encapsulation

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Diabetic Retinopathy Image Segmentation Method Based on Fusion DenseNet and U-Net Network

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engineering Letters 2025年第2期33卷 418-428页

作者： Zhang, Yaxuan Xu, Yang Zhai, Jinlong University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Diabetic Retinopathy (DR) is a common and significant complication in patients with diabetes, and severely affecting their quality of life. Image segmentation plays a crucial role in the early diagnosis and treatment of DR. However, traditional methods are limited in terms of segmentation accuracy and generalization capability. This paper proposes a novel image segmentation method for diabetic retinopathy based on the integration of DenseNet and U-Net networks. Firstly, DenseNet is utilized to replace the encoder part of U-Net. The dense connection mechanism enhances the efficiency of feature propagation and improves the feature extraction capability of the encoder. Secondly, we introduce Omni-Dimensional Dynamic Convolution (ODConv) to replace traditional convolutions. ODConv is used to handle diverse input features effectively. The model's adaptability and segmentation accuracy for various samples is improved. Finally, we integrate the Convolutional Block Attention Module (CBAM) into the decoder. By leveraging channel and spatial attention, the ability to capture key features is enhanced, and the segmentation accuracy and boundary recognition capabilities are improved. To validate the effectiveness and feasibility of the model. This paper conducts experiments on the DDR dataset and the IDRID dataset. The experimental results show that the proposed method improved the mDice score by 0.53% compared to traditional methods. It also increased the mIoU score by 1.12% over traditional methods. This indicates that the proposed method has better performance in segmenting diabetic retinopathy images. It also demonstrates better generalization ability. © 2025, International Association of Engineers. All rights reserved.

关键词： Image segmentation

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Privacy-Enhanced Federated Expanded Graph Learning for Secure QoS Prediction

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IEEE Transactions on Services Computing 2025年第03期18卷 1641-1654页

作者： Zou, Guobing Yan, Zhi Hu, Shengxiang Gan, Yanglan Zhang, Bofeng Chen, Yixin Shanghai University School of Computer Engineering and Science Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai201620 China Shanghai Polytechnic University School of Computer and Information Engineering Shanghai201209 China Washington University Department of Computer Science and Engineering St. LouisMO63130 United States

Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimization, which causes privacy breaches. While some federated learning-based methods consider user privacy in a distributed way, they either directly upload local trained parameters or use simple encryption for global aggregation at the central server, thus failing to truly protect user privacy. Secondly, existing federated learning-based methods neglect distributed user-service topology and latent behavior-attribute correlations, compromising QoS prediction accuracy. To address these limitations, we propose a novel framework named Privacy-Enhanced Federated Expanded Graph Learning (PE-FGL) for secure QoS prediction. It first conducts user-service expansion on the invocation graph with advanced privacy-preserving techniques, upgrading first-order local QoS invocations to high-order interaction relationships. Then, it extracts hybrid features from the expanded invocation graph via deep learning and graph residual learning. Finally, a two-layer secure mechanism of federated parameters aggregation is designed to enable collaborative learning among users through local parameter segmentation and global aggregation, achieving effective and secure QoS prediction. Extensive experiments on WS-DREAM demonstrate effective QoS prediction across multiple metrics while preserving privacy in user-service invocations. © 2025 IEEE.

关键词： Federated learning

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Enhanced Multi-Scale Object Detection Algorithm for Foggy Traffic Scenarios

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computers, Materials & Continua 2025年第2期82卷 2451-2474页

作者： Honglin Wang Zitong Shi Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical&Computer Engineering University of Illinois at Urbana ChampaignUrbanaIL 61801USA

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.

关键词： Deep learning object detection foggy scenes traffic detection YOLOv8

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Energy-Aware Dynamic Digital Twin Placement in Mobile Edge Computing 13

Energy-Aware Dynamic Digital Twin Placement in Mobile Edge C...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Hematyar, Mahdi Movahedi, Zeinab Iran University of Science & Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350330151

5G and beyond networks promise to revolutionize device connectivity and interaction with seamless connectivity, fast response times, and intelligent features. With the development of IoT and its need for high-speed, low-latency connectivity, these networks will play a critical role in enabling the next generation of smart and connected systems. Digital twin technology will further enhance the capabilities of these systems by providing accurate and dynamic simulations of physical assets, enabling organizations to make better decisions and optimize their operations. Despite the prior research conducted in the field of optimal placement of digital twins, a few of these studies have overlooked the impact of user transmission delay, while none have addressed user energy consumption. The focus is on optimizing the positioning of digital twins at the edge servers while taking into account the energy consumption of users and ensuring reduced latency between physical devices and their corresponding digital twins. Additionally, the paper explores the potential for social-aware arrangements of digital twins to streamline the process of data exchange and service discovery across the Social Internet of Things (SIoT) network. This work utilized the CPLEX optimization tool to obtain the optimal placement of IoT digital twins at the edge servers, taking into account both user transmission delay and energy consumption. The evaluation results indicate that the proposed model enables optimal placement of digital twins in edge servers, resulting in 8% reduction in latency and 10% decrease in energy consumption, compared to alternative work. © 2023 IEEE.

关键词： Mobile edge computing

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Temperature Sensor Based on a Plasmonic MIM Waveguide with a Grill-Shaped Cavity 6

Temperature Sensor Based on a Plasmonic MIM Waveguide with a...

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6th iranian International Conference on Microelectronics, IICM 2024

作者： Ahadi, Mahsa Ghanavati, Mohammad Vahed, Hamid Karami, Mohammad Azim Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Electrical Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331511371

This paper presents the design and simulation of a plasmonic refractive index (RI) sensor and a temperature sensor. The suggested design comprises a MIM waveguide integrated with a grill-shaped rectangular cavity. Simulations were performed for two configurations: one with a solitary cavity and another with two cavities. The RI sensitivity was determined to be 1681.42 nm/RIU. In addition, temperature sensitivity was evaluated by employing two dielectric materials, namely ethanol and polydimethylsiloxane, which exhibited temperature sensitivities of 0.475 nm/°C and 0.67 nm/°C, respectively. The plasmonic sensor being suggested exhibits promising capabilities for utilization in nanosensing and industrial environments. © 2024 IEEE.

关键词： MIM devices

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

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Acta Oceanologica Sinica 2024年第12期43卷 13-33页

作者： Xiao Yin Taoxing Wu Jie Yu Xiaoyu He Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China

Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,these methods do not adapt well to dynamic seasonal variations in wave *** this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural *** method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic ***,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern ***,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple ***,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three *** experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value ***,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.

关键词： significant wave height forecasting dynamic seasonal variation dynamic graph neural networks

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