ICS-SVM: A user retweet prediction method for hot topics based on improved SVM

In social networks, many complex factors affect the prediction of user forwarding behavior. This paper proposes an improved SVM prediction method for user forwarding behavior of hot topics to improve prediction accuracy. Firstly, we consider that the improved Cuckoo Search algorithm can select the optimal penalty parameters and kernel function parameters to optimize the SVM and thus predict the user's forwarding behavior. Secondly, this paper considers the factors that affect the user forwarding behavior comprehensively from the user's own factors and external factors. Finally, based on the characteristics of the user's forwarding behavior changing over time, the time-slicing method is used to predict the trend of hot topics. Experiments show that the method can accurately predict the user's forwarding behavior and can sense the trend of hot topics.     

一种基于信息老化的协同过滤推荐算法

协同过滤推荐算法中存在推荐信息低时效性问题,该文针对此问题,结合信息老化理论,提出一种基于信息老化的协同过滤推荐算法。该算法利用用户的点击记录,构建项目的时效性评价模型来预测项目当前时刻被点击的概率;将模型与基于项目协同过滤推荐算法结合,综合考虑用户的兴趣和项目的时效性来发现项目的最近邻居,从而进行高时效性的推荐。实验结果表明,与传统基于项目的协同过滤推荐算法相比,该算法提高了推荐结果的时效性。     

基于随机森林回归分析的PM2.5浓度预测模型

针对神经网络算法在当前PM2.5浓度预测领域存在的易过拟合、网络结构复杂、学习效率低等问题,引入RFR(random forest regression,随机森林回归)算法,分析气象条件、大气污染物浓度和季节所包含的22项特征因素,通过调整参数的最优组合,设计出一种新的PM2.5浓度预测模型—RFRP模型.同时,收集了西安市2013-2016年的历史气象数据,进行模型的有效性实验分析.实验结果表明,RFRP模型不仅能有效预测PM2.5浓度,还能在不影响预测精度的同时,较好地提升模型的运行效率,其平均运行时间为0.281 s,约为BP-NN(back propagation neural network,BP神经网络)预测模型的5.88％.     

Driver fatigue detection based on deeply-learned facial expression representation

Driver fatigue detection is a significant application in smart cars. In order to improve the accuracy and timeliness of driver fatigue detection, a fatigue detection algorithm based on deeply-learned facial expression analysis is proposed. Specifically, the face key point detection model is first trained by multi block local binary patterns (MB-LBP) and Adaboost classifier. Subsequently, the eyes and mouth state are detected by using the trained model to detect the 24 facial features. Afterwards, we calculate the number of two parameters that can describe the driver's fatigue state and the proportion of the closed eye time within the unit time (PERCLOS) and yawning frequency. Finally, the fuzzy inference system is utilized to deduce the driver's fatigue state (normal, slight fatigue, severe fatigue). Experimental results show that the proposed algorithm can detect driver fatigue degree quickly and accurately.     

Predicting group partitions in mobile ad hoc networks

Group mobility is prevalent in many mobile ad hoc network applications such as disaster recovery, military actions, etc., and group partitions are unavoidable in such kinds of dynamic networks. Group partition may occur when mobile nodes move in diverse mobility patterns and causes the network to be partitioned into disconnected components. It may result in severe link disconnections, which then interrupt intergroup communications. By examining the group mobility pattern, we can predict the possibility of network partitions, and thus minimize the amount of communication disruptions. In this paper, we introduce a system for predicting potential group partitions in mobile ad hoc networks. On the basis of historical group mobility information, a quadratic regression model is formulated to predict the direction and speed of a group's movement. A group's micromovement is ignored and is replaced by significant linear displacement to measure its mobility to improve the prediction accuracy. We also discuss the notion of safe distance between adjacent groups, based on which the time duration before the occurrence of group partitioning can be measured. Experimental results show that our proposed prediction method demonstrates its effectiveness and efficiency in group partition predictions such that remedial actions can be taken in advance to avoid network disconnection. Copyright © 2011 John Wiley & Sons, Ltd.     

基于小波多分辨分析的线性预测

提出了一种对非平稳时间序列预测的新方法。通过小波多分辨分析把某些非平稳时间序列分解为若干层近似意义上的平稳时间序列，然后再用自回归模型对每层的单支重构信号进行预测，最后综合每层的预测值可得到原时间序列的预测值。仿真实验表明了该方法的优越性在于比传统的方法精度高。     

基于数据同化的飞机尾流行为预测

飞机尾流是飞机飞行时在其后方产生的一对反向旋转的强烈湍流,对后续飞机飞行以及机场安全起降影响极大,其演化趋势的预测已成为空中交通安全管制的瓶颈,亟需发展基于实时探测数据的飞机尾流行为预测技术.在雷达探测反演得到的尾流涡心位置和速度环量等特征参数基础上,开展飞机尾流行为预测分析,能够预知飞机尾流危害区域,为机场安全起降动...     

IoT-based systemic lupus erythematosus prediction model using hybrid genetic algorithm integrated with ANN

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.     

制造云服务组合中支持服务关联的QoS感知评估模型

为了更好地从大量候选云服务集中选出满足制造需求且服务质量较优的制造云服务协同完成制造任务,针对QoS准确性给组合筛选带来的影响,基于接口关联、业务实体关联、统计关联,提出了一种支持服务关联的QoS感知评估模型。引入企业合作时间、KULC和IR参数,利用历史信息进行预测对统计关联QoS变化量进行动态调整,保证了时效性。通过算例分析验证了所提方法的可行性,实验结果表明该方法能为服务需求方提供更切合实际的解决方案。     

基于时间序列自适应建模的粒子滤波动态目标跟踪

在目标跟踪中为达到目标的运行模型与实际轨迹相符,本文提出基于时间序列自适应建模的粒子滤波算法（TS_PF）。采用时间序列方法动态构建预测模型,并将粒子滤波算法中一系列加权粒子以该模型进行状态转移,运用粒子滤波重采样技术,使预测误差进一步减小,预测精度逼近最优估计。仿真实验表明在粒子滤波算法中采用时间序列自适应建模,能够...     

基于时空演变多重特性建模的近海叶绿素浓度时序预测

近海环境是沿海地区社会经济发展的关键支撑系统，近海环境的持续恶化对海洋经济的可持续发展带来了巨大挑战。叶绿素浓度的反映了水体理化性质的演变规律，对近海生态环境保护具有重要意义。尽管现有时序叶绿素浓度预测方法能从时空数据中挖掘有效信息，揭示时空数据的发展趋势和变化规律，但忽略了时空数据的结构化特征以及外界因素/突发因素对叶绿素浓度的影响。因此，本文提出基于时空演变多重特性建模的近海叶绿素浓度时序预测模型，并由四部分构成:自相关时序预测模块预测叶绿素浓度时序变化规律；多视角空间融合预测模块在构建预测点与其他位置叶绿素浓度空间关联性基础上，考虑海域气象状况，提高了空间叶绿素浓度预测的可靠性；基于环境上下文的突变模块通过对极端因素建模，挖掘突变因素与的叶绿素浓度变化的关联；时空动态聚合模块利用结构化模式，结合时间、空间叶绿素预测结果，实现不同圈层全要素近海叶绿素浓度建模。在渤海叶绿素浓度数据上的实验结果表明，该算法模型极大程度提升了近海叶绿素预测模型的准确性与可靠性。      

Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter

Pedestrian trajectory prediction plays an important role in bothadvanced driving assistance system (ADAS) and autonomous vehicles. An algorithm for pedestrian trajectory prediction in crossing scenario is proposed. To obtain features of pedestrian motion, we develop a method for data labelling and pedestrian body orientation regression. Using the hierarchical features as domain of discourse, fuzzy logic rules are built to describe the transition between different pedestrian states and motion models. With derived probability of each type of motion model we further predict the pedestrian trajectory in the next 1.5 s using switching Kalman filter (KF). The proposed algorithm is further verified in our dataset, and the result indicates that the proposed algorithm successfully predicts pedestrian' s crossing behavior 0.4 s earlier before pedestrian moves. Meanwhile, the precision of predicted trajectory surpasses other methods including interacting multi-model KF and dynamic Bayesian network (DBN).     

Dual‐Phase Approach to Improve Prediction of Heart Disease in Mobile Environment

In this paper, we propose a dual‐phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease — in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self‐organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.     

基于混合编码和长短时记忆网络的机场延误预测方法

为充分利用机场延误状态信息的时间相关性,提高机场延误预测精度,提出一种基于混合编码和长短时记忆网络（Long Sort Term Memory, LSTM）的机场延误预测方法。该方法首先将机场信息、航班信息和气象信息进行数据预处理,得到机场延误数据;然后,利用LSTM网络对机场延误数据进行特征提取;最后,构建Softmax分类器对机场延误分类预测。实验结果表明,本文基于机场延误数据在数据预处理阶段提出的混合编码方法,可使预测准确率提高约5%。同时,利用LSTM网络来提取数据的时间相关特征信息,网络模型的预测准确率最终可达94.01%。并且由不同机场数据对网络的普适性分析结果表明,该算法更适合于原始数据量大的中大型枢纽机场。      

智能集成VBR MPEG视频流量预测模型

作为数字媒体网络视频通信的主要方式,VBR MPEG视频流量的预测能力是直接关系缓冲区设计、动态带宽分配及拥塞控制等提高网络服务质量的关键因素.因此针对MPEG视频流的复杂特性,充分利用人工智能方法的优势,提出并建立了基于模糊神经网络的智能集成VBR MPEG 视频流量预测模型.采用模糊预测模型提高预测精度,利用神经网络解决预测的实时性问题.实验结果表明,与标准AR预测模型相比,该模型预测的准确度和可靠性显著提高,且算法简单易于推广到其他方法中使用.     

输电线路在线监测系统可靠性提升方法研究

根据输电线路在线监测系统的特点和实际运行情况,对其常见故障进行了分类与分析,并建立了可靠性模型。在此基础上,提出了针对电路设计、机械设计、电源设计、标准化设计、实验验证等各个环节的可靠性提升方法。最后提出了针对输电线路在线监测系统的可靠性预计方法,并对所优化的系统进行了可靠性预计,得到了系统平均无故障工作时间（MTBF）的预计值,在一定程度上验证了可靠性提升方法的应用效果。     

基于粒子滤波的动态自回归预测模型方法

针对自回归模型以固定历史观测序列建模,模型不能随时间序列新的观测值实时更新,导致预测中对序列趋势变化适应性差,预测精度低的问题,提出以粒子滤波动态优化调整自回归模型的方法,通过对模型参数蒙特卡洛采样得到粒子,以粒子描述模型状态变量的演变,采用递推贝叶斯方法估计粒子权重,由粒子及其权重近似模型参数的后验滤波值,从而随观测序列的动态获得不断更新模型参数,提高了模型预测结果的精确性,并能给出预测结果的置信区间。最后以NASA艾姆斯中心锂离子电池试验数据为例,验证了该方法的有效性。     

基于兴趣和行为预测的移动社交网络动态资源发现机制

针对时延容忍的移动社交网络中的资源发现问题,在三维环境下提出一种基于兴趣和行为预测的动态资源发现机制(IBRD)。IBRD首先从用户的文件资源和信息表中提取兴趣向量,然后通过节点间的余弦相似度计算构造初始的虚拟兴趣社区。之后,通过对移动社交数据的分析,建立半马尔可夫链模型以预测节点的行为和运动轨迹。依据模型的预测结果,实现虚拟兴趣社区的动态维护。最后,基于动态的虚拟兴趣社区构建高效的资源发现策略。IBRD机制在随机网络仿真器(ONE)上得以实现,实验结果表明,IBRD与同类模型相比,具有较高的资源发现成功率、较低的平均时延和通信开销。     

基于灰色模型和混沌时间序列的卫星钟差预测算法

为了提高非线性卫星钟差预测的精度,降低单一钟差预测模型对钟差预测的风险,提出了一种组合模型的卫星钟差预测算法.该算法首先采用db1小波对卫星钟差序列进行3层多分辨率分解和单支重构,得到一个趋势分量和三个细节分量,然后运用灰色预测模型对重构后的趋势分量和混沌一阶加权局域预测法对重构后的细节分量分别进行预测,最后将各分量预测结果相加后得到总的钟差预测值.以GPS卫星钟差数据做算例分析,在6小时的钟差预测中,算法绝对误差最大值比单一的灰色预测模型误差小1.3ns以上.将该组合预测模型用于非线性卫星钟差预测中,可以提高钟差预测的精度和可靠性.     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.