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Exploiting Data Science for Measuring the Performance of Technology Stocks

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Computers, Materials & Continua 2023年第9期76卷 2979-2995页

作者： Tahir Sher Abdul Rehman Dongsun Kim Imran Ihsan Department of creative technologies Air UniversityIslamabad44230Pakistan school of Computer Science and Engineering Kyungpook National UniversityDaegu41566Korea

The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses.In our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology stocks.Under discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 samples.As already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other models.In this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of 99.98.Other statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data.Thus,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.

关键词： Machine learning data science smart environments stocks movement deep learning stock marketing

Enhanced Temporal Correlation for Universal Lesion Detection

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 3051-3063页

作者： Muwei Jian Yue Jin Hui Yu school of Computer Science and Technology Shandong University of Finance and EconomicsJinanChina school of Information Science and Technology Linyi UniversityLinyiChina school of creative technologies University of PortsmouthPortsmouthUK

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

关键词： Universal lesion detection computational biology medical computing deep learning enhanced temporal correlation

Predicting and Curing Depression Using Long Short Term Memory and Global Vector

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Computers, Materials & Continua 2023年第3期74卷 5837-5852页

作者： Ayan Kumar Abdul Quadir Md J.Christy Jackson Celestine Iwendi school of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India school of creative technologies University of BoltonBoltonUK

In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.

关键词： Emotion dynamics depression heart rate internet of things global vector long short term memory machine learning sentiment analysis

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

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Computers, Materials & Continua 2023年第1期74卷 1561-1574页

作者： Tahir Sher Abdul Rehman Dongsun Kim Department of creative technologies Air UniversityIslamabad44230Pakistan school of Computer Science and Engineering Kyungpook National UniversityDaegu41566Korea

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well.Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world.However,with the advancement of technology,the Internet of Things(IoT)and social IoT(SIoT),the versatile data produced by smart devices helped a lot in overcoming this lethal disease.Data mining is a technique that could be used for extracting useful information from massive data.In this study,we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset“COVID-19 Symptoms and Presence.”RapidMiner Studio ML software was used to apply the Decision Tree(DT),Random Forest(RF),K-Nearest Neighbors(K-NNs)and Naive Bayes(NB),Integrated Decision Tree(ID3)algorithms.To develop the model,the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures,Cohan’s kappa statistics,properly or mistakenly categorized cases and root means square error.The results demonstrate that DT outperforms other methods,with an accuracy of 98.42%and a root mean square error of 0.11.In the future,a devisedmodel will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets.

关键词： COVID-19 prediction COVID-19 analysis machine learning(ML) algorithms internet of things(IoT) social IoT(SIoT)

A Study on Half-Vehicle Active Suspension Control Using Sampled-Data Control

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A Study on Half-Vehicle Active Suspension Control Using Samp...

Accurate and Robust Eye Center Localization via Fully Convolutional Networks

2011 Chinese Control and Decision Conference（CCDC）

作者： Steve Hand Chris Hilton school of creative technologies University of Portsmouth

This paper is concerned with the problem of sampled-data H∞ control for a half-car active suspension system.First of all,by regarding the heave and the pitch accelerations,and the suspension defiection as the optimization objectives,the vehicle suspension system will be established.Then,an input delay approach is employed to transform the resulting active vehicle suspension system with sampling measurements into a continuous-time system with a delay in the state.Thirdly,by constructing a novel Lyapunov functional,a sufficient condition for the existence of sampled-data H∞ controller is given to ensure asymptotical stability of the closed-loop system and also satisfy the constraint performance.The corresponding condition can be converted into a convex optimization problem and verified easily by means of standard software.Finally,a design example is exploited to demonstrate the effectiveness of the proposed design method.

关键词： A half-car model Active suspension system H∞ control Sampled-data control.

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1127-1138页

作者： Yifan Xia Hui Yu Fei-Yue Wang the School of Creative Technologies University of PortsmouthPortsmouthPO1 2DJUK the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network(FCN),which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning.Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

关键词： Deep learning eye center localization eye gaze estimation eye tracking fully convolutional network (FCN) humancomputer interaction

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 565-581页

作者： Shiming Liu Yifan Xia Zhusheng Shi Hui Yu Zhiqiang Li Jianguo Lin Department of Mechanical Engineering Imperial College LondonLondonSW72AZUK school of creative technologies University of PortsmouthPortsmouthPO12DJUK Manufacturing Technology Institute AVICBeijing 100024China

Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components.To surmount the springback occurring in sheet metal forming processes,numerous studies have been performed to develop compensation methods.However,for most existing methods,the development cycle is still considerably time-consumptive and demands high computational or capital cost.In this paper,a novel theory-guided regularization method for training of deep neural networks(DNNs),implanted in a learning system,is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter,e.g.,loading stroke,in sheet metal bending processes.By directly bridging the workpiece shape to the process parameter,issues concerning springback in the process design would be circumvented.The novel regularization method utilizes the well-recognized theories in material mechanics,Swift’s law,by penalizing divergence from this law throughout the network training process.The regularization is implemented by a multi-task learning network architecture,with the learning of extra tasks regularized during training.The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base,respectively.One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface.In this research,the neural models were found to outperform a traditional machine learning model,support vector regression model,in experiments with different amount of training data.Through a series of studies with varying conditions of training data structure and amount,workpiece material and applied bending processes,the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs,especially when only scarce and scattered experiment data are available for training which is often the cas

关键词： Data-driven deep learning deep learning deep neural network(DNN) intelligent manufacturing machine learning sheet metal forming springback theory-guided deep learning theoryguided regularization

Requirements model driven adaption and evolution of Internetware

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Science China(Information Sciences) 2014年第6期57卷 241-259页

作者： LIU Lin YANG Chen WANG JianMin YE XiaoJun LIU YingBo YANG HongJi LIU XiaoDong school of Software Tsinghua University creative Computing Cluster Bath Spa University school of Computing Edinburgh Napier University

Today’s software systems need to support complex business operations and processes.The development of the web-based software systems has been pushing up the limits of traditional software engineering methodologies and technologies as they are required to be used and updated almost real-time,so that users can interact and share the same applications over the internet as needed.These applications have to adapt quickly to the diversified and dynamic changing requirements in the physical,technological,economical and social environments.As a consequence,we are expecting a major paradigm shift in software engineering to reflect such changes in computing environment in order to better address the fundamental needs of organisations in this new era.Existing software technologies,such as model driven development,business process engineering,online(re-)configuration,composition and adaptation of managerial functionalities are being repurposed to reduce the time taken for software development by reusing software codes.The ability to dynamically combine contents from numerous web sites and local resources,and the ability to instantly publish services worldwide have opened up entirely new possibilities for software development.In retrospect to the ten years applied research on Internetware,we have witnessed such a paradigm shift,which brings about many changes to the developmental experience of conventional web applications.Several related technologies,such as cloud computing,service computing,cyber-physical systems and social computing,have converged to address this emerging issue with emphasis on different aspects.In this paper,we first outline the requirements that the Internetware software paradigm should meet to excel at web application adaptation;we then propose a requirement model driven method for adaptive and evolutionary applications;and we report our experiences and case studies of applying it to an enterprise information system.Our goal is to provide high-level guidelines to researchers and practitioners to meet th

关键词： requirements information system Internetware adaptation evolution

Experimental study on NO_x emission correlation of fuel staged combustion in a LPP combustor at high pressure based on NO-chemiluminescence

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Chinese Journal of Aeronautics 2020年第2期33卷 550-560页

作者： Zhichao WANG Yuzhen LIN Jianchen WANG Chi ZHANG Zhijun PENG National Key Laboratory of Science and Technology on Aero-Engine Aero-thermodynamics Collaborative Innovation Center for Advanced Aero-EngineSchool of Energy and Power EngineeringBeihang UniversityBeijing 100083China creative Arts Technologies and ScienceUniversity of BedfordshireLuton LU13JUUK

Experimental investigations on NOx emissions of a single-cup,Lean Premixed Prevaporized(LPP),module combustor were carried out at elevated inlet temperature and pressure up to810 K and 2.0 MPa,close to the real operating conditions of aero-engine combustors.This LPP combustor adopts centrally staged fuel injections which could produce separated stratified swirling spray flame.In the NOx emissions measurements,the ranges of dome equivalence ratio and fuel stage ratio were from 0.55 to 0.58 and 8%to 24%,respectively.The optical diagnosis on separated stratified swirling spray flame were carried out with fuel stage ratio changing from 15%to 30%.Therefore,NO*and OH*chemiluminescence images were obtained.The results show that NOx emissions increase with the increase of the fuel stage ratio.And from the chemiluminescence images,the main flame and pilot flame are found weakly coupled.The pilot flame plays a significant role in NOx emission production because of its higher adiabatic flame temperature.Based on the results of chemiluminescence optical tests,a new NOx emission prediction model is proposed based on the Lefebvre’s single flame model The estimate of local equivalence ratio of the pilot stage’s nonpremixed flame is modified considering the characteristics of spray combustion,and a"PLUS"emission prediction model suitable for separated stratified swirling spray flame is obtained.Compared to the experimental data,the"PLUS"model exhibits a good prediction in a range of±13%of deviation.

关键词： Combustors Lean staged combustion Low emissions NOx correlation Optical diagnosis