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Priori information and sliding window based prediction algorithm for energy-efficient storage systems in cloud

SIMULATION MODELLING PRACTICE AND THEORY 2013年第0期39卷 3-19页

作者： Du, Zhihui Fan, Wenjun Chai, Yunpeng Chen, Yinong Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Dept Comp Sci & Technol Beijing 100084 Peoples R China Univ Politecn Madrid Dept Ingn Sistemas Telemat E-28040 Madrid Spain Renmin Univ China Sch Informat Minist Educ Key Lab Data Engn & Knowledge Engn Beijing 100872 Peoples R China Arizona State Univ Sch Comp Informat & Decis Syst Eng Tempe AZ 85287 USA

One of the major challenges in cloud computing and data centers is the energy conservation and emission reduction. Accurate prediction algorithms are essential for building energy efficient storage systems in cloud computing. In this paper, we first propose a Three-State Disk Model (3SDM), which can describe the service quality and energy consumption states of a storage system accurately. Based on this model, we develop a method for achieving energy conservation without losing quality by skewing the workload among the disks to transmit the disk states of a storage system. The efficiency of this method is highly dependent on the accuracy of the information predicting the blocks to be accessed and the blocks not be accessed in the near future. We develop a priori information and sliding window based prediction (PISWP) algorithm by taking advantage of the priori information about human behavior and selecting suitable size of sliding window. The PISWP method targets at streaming media applications, but we also check its efficiency on other two applications, news in webpage and new tool released. Disksim, an established storage system simulator, is applied in our experiments to verify the effect of our method for various users' traces. The results show that this prediction method can bring a high degree energy saving for storage systems in cloud computing environment. 2013 Elsevier B.V. All rights reserved.

关键词： prediction algorithm Energy-efficient cloud computing Conventional disk Energy conservation

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Reality mining: A prediction algorithm for disease dynamics based on mobile big data

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INFORMATION SCIENCES 2017年 379卷 82-93页

作者： Chen, Yuanfang Crespi, Noel Ortiz, Antonio M. Shu, Lei Telecom SudParis Inst Mines Telecom Paris France R&D Dept Montimage France Guangdong Univ Petrochem Technol Maoming Guangdong Peoples R China

Predicting disease dynamics during an epidemic is an important aspect of e-Health applications. In such prediction, Realistic Contact Networks (RCNs) have been widely used to characterize disease dynamics. The structure of such networks is dynamically changed during an epidemic. Capturing such kind of dynamic structure is the basis of prediction. With the popularity of mobile devices, it is possible to capture the dynamic change of the network structure. On this basis, in this study, we evaluate the impact of the network structure on disease dynamics, by analyzing massive spatiotemporal data collected by mobile devices. These devices are carried by the volunteers of Ebola outbreak areas. Based on the results of this evaluation, a model is designed to recognize the dynamic structure of RCNs. On the basis of this model, we propose a prediction algorithm for disease dynamics. By extensive experiments, we show that our algorithm improves the accuracy of the disease prediction. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Reality mining Disease dynamics prediction algorithm Mobile big data

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Communication Network Time Series prediction algorithm Based on Big Data Method

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 1041-1056页

作者： Wang, Tao Wang, Minghui Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Hubei Univ Nationalities Sch Sci Enshi Hubei Peoples R China

In order to solve the low prediction accuracy and prediction rate of traditional time series prediction algorithm in the analysis of massive data, according to the characteristics of communication network performance index, time series prediction algorithm based on the big data method is proposed. In addition, based on the traditional three components of time series feature extraction, the concept of emergency component is introduced, and the outlier detection and processing as well as prediction analysis are carried out on the basis of the extraction results. The results show that the algorithm based on big data, through the analysis, fitting, modelling, and prediction of massive data, smaller granularity decomposition of the time series value is conducted, which significantly improved the credibility and accuracy of prediction. Based on the above findings, it is summarized that the time series prediction algorithm has good performance.

关键词： Mobile communication network Time series Big data prediction algorithm

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CoRSeq_V3-C: a novel HIV-1 subtype C specific V3 sequence based coreceptor usage prediction algorithm

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RETROVIROLOGY 2013年第1期10卷 1-10页

作者： Cashin, Kieran Gray, Lachlan R. Jakobsen, Martin R. Sterjovski, Jasminka Churchill, Melissa J. Gorry, Paul R. Burnet Inst Ctr Virol Melbourne Vic 3004 Australia Univ Melbourne Dept Microbiol & Immunol Parkville Vic 3052 Australia Monash Univ Dept Biochem Melbourne Vic 3004 Australia Monash Univ Dept Mol Biol Melbourne Vic 3004 Australia Monash Univ Dept Microbiol Melbourne Vic 3004 Australia Monash Univ Dept Med Melbourne Vic 3004 Australia Monash Univ Dept Infect Dis Melbourne Vic 3004 Australia

Background: The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm. Results: We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeq(V3-C)), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno([coreceptor]) and WebPSSM(SINSI)-C, which has been designed specifically for C-HIV. Conclusions: CoRSeq(V3-C) is now openly available for public use at ***/coreceptor. Our results show that CoRSeq(V3-C) is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without

关键词： HIV-1 subtype C gp120 V3 CCR5 CXCR4 Coreceptor prediction algorithm

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Development of antigen-prediction algorithm for personalized neoantigen vaccine using human leukocyte antigen transgenic mouse

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CANCER SCIENCE 2022年第4期113卷 1113-1124页

作者： Charneau, Jimmy Suzuki, Toshihiro Shimomura, Manami Fujinami, Norihiro Mishima, Yuji Hiranuka, Kazushi Watanabe, Noriko Yamada, Takashi Nakamura, Norihiro Nakatsura, Tetsuya Natl Canc Ctr Exploratory Oncol Res & Clin Trial Ctr Div Canc Immunotherapy 6-5-1 Kashiwanoha Kashiwa Chiba 2778577 Japan Teikyo Univ Sch Med Dept Pharmacol Tokyo Japan BrightPath Biotherapeut Co Ltd Chiyoda Ku Tokyo Japan

Immunotherapy is currently recognized as the fourth modality in cancer therapy. CTL can detect cancer cells via complexes involving human leukocyte antigen (HLA) class I molecules and peptides derived from tumor antigens, resulting in antigen-specific cancer rejection. The peptides may be predicted in silico using machine learning-based algorithms. Neopeptides, derived from neoantigens encoded by somatic mutations in cancer cells, are putative immunotherapy targets, as they have high tumor specificity and immunogenicity. Here, we used our pipeline to select 278 neoepitopes with high predictive "SCORE" from the tumor tissues of 46 patients with hepatocellular carcinoma or metastasis of colorectal carcinoma. We validated peptide immunogenicity and specificity by in vivo vaccination with HLA-A2, A24, B35, and B07 transgenic mice using ELISpot assay, in vitro and in vivo killing assays. We statistically evaluated the power of our prediction algorithm and demonstrated the capacity of our pipeline to predict neopeptides (area under the curve = 0.687, P < 0.0001). We also analyzed the potential of long peptides containing the predicted neoepitopes to induce CTLs. Our study indicated that the short peptides predicted using our algorithm may be intrinsically present in tumor cells as cleavage products of long peptides. Thus, we empirically demonstrated that the accuracy and specificity of our prediction tools may be potentially improved in vivo using the HLA transgenic mouse model. Our data will help to design feedback algorithms to improve in silico prediction, potentially allowing researchers to predict peptides for personalized immunotherapy.

关键词： epitope peptide HLA transgenic mice HLA-class I neoantigen prediction algorithm

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Development and validation of a risk prediction algorithm for the recurrence of suicidal ideation among general population with low mood

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JOURNAL OF AFFECTIVE DISORDERS 2016年第0期193卷 11-17页

作者： Liu, Y. Sareen, J. Bolton, J. M. Wang, J. L. Univ Calgary Mathison Ctr Mental Hlth Res & Educ Hotchkiss Brain Inst Dept PsychiatCumming Sch Med TRW Bldg3280 Hosp Dr NW Calgary AB T2N 4Z6 Canada Univ Calgary Mathison Ctr Mental Hlth Res & Educ Hotchkiss Brain Inst Dept Community Hlth SciCumming Sch Med TRW Bldg3280 Hosp Dr NW Calgary AB T2N 4Z6 Canada Univ Manitoba Fac Med Dept Psychiat PZ430-771 Bannatyne Ave Winnipeg MB R3E 3N4 Canada

Background: Suicidal ideation is one of the strongest predictors of recent and future suicide attempt. This study aimed to develop and validate a risk prediction algorithm for the recurrence of suicidal ideation among population with low mood Methods: 3035 participants from U.S National Epidemiologic Survey on Alcohol and Related Conditions with suicidal ideation at their lowest mood at baseline were included. The Alcohol Use Disorder and Associated Disabilities Interview Schedule, based on the DSM-IV criteria was used. Logistic regression modeling was conducted to derive the algorithm. Discrimination and calibration were assessed in the development and validation cohorts. Results: In the development data, the proportion of recurrent suicidal ideation over 3 years was 19.5 (95% CI: 17.7, 21.5). The developed algorithm consisted of 6 predictors: age, feelings of emptiness, sudden mood changes, self -harm history, depressed mood in the past 4 weeks, interference with social activities in the past 4 weeks because of physical health or emotional problems and emptiness was the most important risk factor. The model had good discriminative power (C statistic=0.8273, 95% CI: 0.8027, 0.8520). The C statistic was 0.8091 (95% CI: 0.7786, 0.8395) in the external validation dataset and was 0.8193 (95% CI: 0.8001, 0.8385) in the combined dataset. Limitations: This study does not apply to people with suicidal ideation who are not depressed. Conclusions: The developed risk algorithm for predicting the recurrence of suicidal ideation has good discrimination and excellent calibration. Clinicians can use this algorithm to stratify the risk of recurrence in patients and thus improve personalized treatment approaches, make advice and further intensive monitoring. (C) 2016 Elsevier B.V. All rights reserved.

关键词： prediction algorithm Recurrence Suicidal ideation

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External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study

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BMC PSYCHIATRY 2016年第1期16卷 1-8页

作者： Nigatu, Yeshambel T. Liu, Yan Wang, JianLi Univ Calgary Cumming Sch Med Dept Psychiat Calgary AB Canada Univ Calgary Mathison Ctr Mental Hlth Res & Educ Hotchkiss Brain Inst Calgary AB Canada Univ Calgary Cumming Sch Med Dept Community Hlth Sci Calgary AB Canada

Background: Multivariable risk prediction algorithms are useful for making clinical decisions and for health planning. While prediction algorithms for new onset of major depression in the primary care attendees in Europe and elsewhere have been developed, the performance of these algorithms in different populations is not known. The objective of this study was to validate the PredictD algorithm for new onset of major depressive episode (MDE) in the US general population. Methods: Longitudinal study design was conducted with approximate 3-year follow-up data from a nationally representative sample of the US general population. A total of 29,621 individuals who participated in Wave 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and who did not have an MDE in the past year at Wave 1 were included. The PredictD algorithm was directly applied to the selected participants. MDE was assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule, based on the DSM-IV criteria. Results: Among the participants, 8 % developed an MDE over three years. The PredictD algorithm had acceptable discriminative power (C-statistics = 0.708, 95 % CI: 0.696, 0.720), but poor calibration (p < 0.001) with the NESARC data. In the European primary care attendees, the algorithm had a C-statistics of 0.790 (95 % CI: 0.767, 0.813) with a perfect calibration. Conclusions: The PredictD algorithm has acceptable discrimination, but the calibration capacity was poor in the US general population despite of re-calibration. Therefore, based on the results, at current stage, the use of PredictD in the US general population for predicting individual risk of MDE is not encouraged. More independent validation research is needed.

关键词： Depression prediction algorithm Validation Risk factors

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Validation of a venous thromboembolism prediction algorithm for pediatric trauma: A national trauma data bank (NTDB) analysis* , **

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JOURNAL OF PEDIATRIC SURGERY 2020年第6期55卷 1127-1133页

作者： Cunningham, Aaron J. Dewey, Elizabeth Hamilton, Nicholas A. Schreiber, Martin A. Krishnaswami, Sanjay Jafri, Mubeen A. Oregon Hlth & Sci Univ Dept Surg 3181 SW Sam Jackson Pk RdCDW7 Portland OR 97239 USA Oregon Hlth & Sci Univ Div Pediat Surg Portland OR 97201 USA Oregon Hlth & Sci Univ Div Trauma Crit Care & Acute Care Surg Portland OR 97201 USA Randall Childrens Hosp Legacy Emanuel Div Pediat Surg Portland OR USA

Purpose We sought to validate a risk model to predict venous thromboembolism (VTE) in pediatric trauma through an analysis of a contemporary cohort in the National Trauma Data Bank (NTDB).Study design Prospective internal validation was performed in 10 randomly stratified samples of children (age 0-17 years) from the NTDB 2013-2016. Model discrimination was determined by calculation of the c-statistic (AUC), and calibration was evaluated through analysis of observed to expected (O:E) ratio. Recalibration was performed with application of a mixed-effects logistic regression. Model parameters were reestimated based on *** Retrospective review identified 481,485 pediatric trauma patients with 729 (0.2%) episodes of VTE. Discriminatory ability of the model in all random cohorts was significant with AUC > 0.93 (p < 0.001). Inadequate calibration was noted in 4 of 10 cohorts and the entire dataset (p < 0.001) with an O:E ratio of 1.79. Model recalibration resulted in similar discrimination (AUC = 0.95) with improved calibration (O:E ratio = 1.33, p < 0.0001).Conclusion Pediatric trauma prediction models can provide useful data for VTE risk stratification in injured children, but these models must be validated and calibrated prior to use. Recalibration of the model in question resulted in improved accuracy in a contemporary NTDB dataset. These data provide an appropriately calibrated and validated model for clinical *** of evidence II — Prospective internal validation of a multivariable prediction model.

关键词： Venous thromboembolism Pediatric trauma prediction algorithm Validation

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Development of a real-time-position prediction algorithm for under-actuated robot manipulator by using of artificial neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2011年第C8期225卷 1991-1998页

作者： Al-Assadi, H. M. A. A. Isa, A. A. Mat Hasan, Ali T. Rahman, Z. A. Heimann, B. Univ Teknol MARA UiTM Fac Mech Engn Shah Alam 40450 Selangor Malaysia Minist Higher Educ & Sci Res Baghdad Iraq Leibniz Univ Hannover Inst Robot Hannover Ctr Mechatron Hannover Germany

An adaptive learning algorithm using an artificial neural network (ANN) has been proposed to predict the passive joint position of under-actuated robot manipulator. In this approach, a specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torque and angular position for the active joint over a certain period of time. Trying to overcome the disadvantages of many used techniques in the literature, the ANNs have a significant advantage of being a model-free method. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters and backlashes in gear trains. An ANN was trained using the experimentally obtained data and then used to predict the path of the passive joint that is positioned by the dynamic coupling of the active joint. The generality and efficiency of the proposed algorithm are demonstrated through simulations of an under-actuated robot manipulator;finally, the obtained results were successfully verified experimentally.

关键词： under-actuated robot artificial neural network prediction algorithm

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Social Network Information Dissemination and prediction algorithm based on Computer Pattern Modelling and Node Estimation 5

Social Network Information Dissemination and Prediction Algo...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Gao, Wei Chen, Zhen Sichuan Univ Media & Commun Chengdu 611730 Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Social network information dissemination and the prediction algorithm based on computer pattern modelling and node estimation is discussed in this research. According to our proposed algorithm, each node should have a shortcut node cache table, and the new node will sort the time of the response message received. The earlier the response message is received, the closer to the core node. Then, the proposed study considers the topology optimization model, where the algorithm divides the nodes on a potential topological tree into terminal nodes and internal nodes. And call those nodes that have copied the multicast packets in the multicast session and send them downwards. With this improvement, the nodes are modelled and the experimental analysis shows the performance of the system.

关键词： Social Network Information Dissemination prediction algorithm Pattern Modelling Node Estimation

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