关键词： Predictive model for electricity demand   Climate and ECMWF Reanalysis variables   ANN   MARS   MLR   ARIMA   Hybrid ANN   Bootstrapping   Sustainable energy management systems  

摘要： Reliable models that can forecast energy demand (G) are needed to implement affordable and sustainable energy systems that promote energy security. In particular, accurate G models are required to monitor and forecast local electricity demand. However, G forecasting is a multivariate problem, and thus models must employ robust pattern recognition algorithms that can detect subtle variations in G due to causal factors, such as climate variables. Therefore, this study developed an artificial neural network (ANN) model that used climatic variables for 6-hour (h) and daily G forecasting. The input variables included the six most relevant climate variables from Scientific Information for Land Owners (SILO) and 51 Reanalysis variables obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) models. This information was used to forecast G data obtained from the energy utility (Energex) at 8 stations in southeast Queensland, Australia, by utilizing statistically significant lagged cross-correlations of G with its predictor variables. The developed ANN model was then benchmarked against multivariate adaptive regression spline (MARS), multiple linear regression (MLR), and autoregressive integrated moving average (ARIMA) models using various statistical metrics, such as relative root-mean square error (RRMSE%). Additionally, this study developed a hybrid ANN model by combining the forecasts of the ANN, MARS, and MLR models. The bootstrap (B) technique was also used with the hybrid ANN model, creating the B-hybrid ANN, to estimate the forecast uncertainty. According to both forecast horizons, the results indicated that the ANN model was more accurate than the ARIMA, MARS, and MLR models for G forecasting. Furthermore, the hybrid ANN was the most accurate model developed in this research study. For example, at the best site (Redcliffe), the hybrid ANN model generated an RRMSE of 3.85% and 4.37% for the 6-h and daily horizons, respectively. This study found tha

关键词： Global energy forecasting competition   Quantile random forest   Gradient boosting   Neural networks   Deep learning   Ensemble forecasting   Probabilistic forecasting  

摘要： This paper describes the preprocessing and forecasting methods used by team Orbuculum during the qualifying match of the Global Energy Forecasting Competition 2017 (GEFCom2017). Tree-based algorithms (gradient boosting and quantile random forest) and neural networks made up an ensemble. The ensemble prediction quantiles were obtained by a simple averaging of the ensemble members' prediction quantiles. The result shows a robust performance according to the pinball loss metric, with the ensemble model achieving third place in the qualifying match of the competition. (C) 2019 Published by Elsevier B.V. on behalf of International Institute of Forecasters.

关键词： Reliability   cloud computing   edge computing   distributed systems   placement   consolidation   autoscaling   remediation   machine learning   optimization  

摘要： Large-scale software systems are currently designed as distributed entities and deployed in cloud data centers. To overcome the limitations inherent to this type of deployment, applications are increasingly being supplemented with components instantiated closer to the edges of networks-a paradigm known as edge computing. The problem of how to efficiently orchestrate combined edge-cloud applications is, however, incompletely understood, and a wide range of techniques for resource and application management are currently in use. This article investigates the problem of reliable resource provisioning in joint edge-cloud environments, and surveys technologies, mechanisms, and methods that can be used to improve the reliability of distributed applications in diverse and heterogeneous network environments. Due to the complexity of the problem, special emphasis is placed on solutions to the characterization, management, and control of complex distributed applications using machine learning approaches. The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system consolidation, and application elasticity and remediation. Survey results are presented along with a problem-oriented discussion of the state-of-the-art. A summary of identified challenges and an outline of future research directions are presented to conclude the article.

关键词： machine learning   water saturation   long short-term memory   artificial neural network   computational time  

摘要： Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

关键词： Oncology Head & Neck Surgery   HNSCC   Audit   Complications  

摘要： Most surgical specialties have attempted to address concerns about unfair comparison of outcomes by risk-adjusting data to benchmark specialty-specific outcomes that are indicative of the quality of care. We are building on previous work in head and neck surgery to address the current need for a robust validated means of risk adjustment. A dataset of care episodes, which were recorded as a clinical audit of complications after operations for squamous cell carcinoma (SCC) of the head and neck (n = 1254), was analysed with the Waikarto Environment for Knowledge Analysis (WEKA) machine learning tool. This produced 4 classification models that could predict complications using data on the preoperative demographics of the patients, operation, functional status, and tumour stage. Three of them performed acceptably: one that predicted any complication within 30 days (area under the receiver operating characteristic curve (AUROC) 0.72), one that predicted severe complications (Clavien-Dindo grade 3 or above) within 30 days (AUROC 0.70), and one that predicted a prolonged duration of hospital stay of more than 15 days, (AUROC 0.81). The final model, which was developed on a subgroup of patients who had free tissue transfer (n = 443), performed poorly (AUROC 0.59). Subspecialty groups within oral and maxillofacial surgery are seeking metrics that will allow a meaningful comparison of the quality of care delivered by surgical units in the UK. For these metrics to be effective they must show variation between units and be amendable to change by service personnel. Published baseline data must also be available. They should be modelled effectively so that meaningful comparison, which takes account of variations in the complexity of the patients needs or care, is possible. (C) 2019 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

关键词： accuracy measure   effort estimation   machine learning   SLR   software engineering  

摘要： Machine learning (ML) techniques have been widely investigated for building prediction models, able to estimate software development effort as well as to improve the accuracy of other estimation techniques. The objective of this paper is to systematically review the recent studies which used and discussed the software effort estimation models built using ML techniques. The performed literature review is based on the empirical studies published in the time period of January 1991 to December 2017, by employing widely used guidelines. The review has selected a total of 75 primary studies after the careful filtering of inclusion/exclusion and quality assessment criteria. The performed analysis reveals that artificial neural network (ANN) as ML model, NASA as dataset, and mean magnitude of relative error (MMRE) as accuracy measure are widely used in the selected studies. ANN and support vector machine (SVM) are the two techniques which have outperformed other ML techniques in more studies. Regression techniques are the mostly used among the non-ML techniques, which outperformed other ML techniques in about 19 studies. Moreover, SVM and regression techniques in combination are characterized by better predictions when compared with other ML and non-ML techniques.

关键词： biological markers   carcinogenesis   cervical cancer   cervical lesions   copy number polymorphism   genes   genome   human papillomavirus   machine learning   mitochondria   mutation   somatic   stratification  

摘要： From initial human papillomavirus (HPV) infection and precursor stages, the development of cervical cancer takes decades. High-sensitivity HPV DNA testing is currently recommended as primary screening method for cervical cancer, whereas better triage methodologies are encouraged to provide accurate risk management for HPV-positive women. Given that virus-driven genomic variation accumulates during cervical carcinogenesis, we designed a 39 Mb custom capture panel targeting 17 HPV types and 522 mutant genes related to cervical cancer. Using capture-based next-generation sequencing, HPV integration status, somatic mutation and copy number variation were analyzed on 34 paired samples, including 10 cases of HPV infection (HPV+), 10 cases of cervical intraepithelial neoplasia (CIN) grade and 14 cases of CIN2+ (CIN2: n = 1;CIN2-3: n = 3;CIN3: n = 9;squamous cell carcinoma: n = 1). Finally, the machine learning algorithm (Random Forest) was applied to build the risk stratification model for cervical precursor lesions based on CIN2+ enriched biomarkers. Generally, HPV integration events (11 in HPV+, 25 in CIN1 and 56 in CIN2+), non-synonymous mutations (2 in CIN1, 12 in CIN2+) and copy number variations (19.1 in HPV+, 29.4 in CIN1 and 127 in CIN2+) increased from HPV+ to CIN2+. Interestingly, 'common' deletion of mitochondrial chromosome was significantly observed in CIN2+ (P = 0.009). Together, CIN2+ enriched biomarkers, classified as HPV information, mutation, amplification, deletion and mitochondrial change, successfully predicted CIN2+ with average accuracy probability score of 0.814, and amplification and deletion ranked as the most important features. Our custom capture sequencing combined with machine learning method effectively stratified the risk of cervical lesions and provided valuable integrated triage strategies.

关键词： 水声被动定位   机器学习   深度学习   神经网络   监督学习  

摘要： 本文对基于机器学习方法的水声被动定位研究进展进行了综述。所涉及的机器学习方法有多层感知机(前馈神经网络)、支持向量机、随机森林及以卷积网络层和全连接层为主要组成单元的深度神经网络。本文通过重点引述近几年发表在国际期刊和会议上的相关前沿研究工作,详细论述了将机器学习方法应用于水声被动定位的关键理论基础、单水听器和阵列前端信号预处理算法设计及几种典型的机器学习模型。此外,还指出了现有算法在推向实际应用中面临的困难及挑战。最后,基于作者的思考,文章展望了未来基于机器学习的水声定位算法的几个潜在的研究方向。

关键词： 信号处理方法   智能化处理   大数据   征稿通知   机器学习方法   信息技术   社会生活   学习能力  

摘要： 随着信息技术的高速发展,在现代社会生活中每天都有着各式各样的大量数据产生。大数据时代已然来临,而如何合理地存储、处理以及利用这些数据就成为了受到广泛关注的焦点性问题。一方面,大数据更加依赖具有学习能力、推理能力和优化能力的智能信号处理方法;另一方面,大数据为智能信号处理方法的发展提供了条件。大数据时代的智能信号处理是以信号模型为驱动,以大样本为基础,通过机器学习方法提取信号特征,进而实现对信号的智能化处理和利用。本次征文的主要目的是给大数据时代下的智能信号处理提供新的视角和思路。

关键词： 质谱   蛋白质组学   生物标志物   机器学习   特征选择   深度学习  

摘要： 随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据'高维度'及'稀疏性'问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。