排序方式： 共有25条查询结果，搜索用时 15 毫秒
1.
《Information processing & management》2022,59(1):102816
Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models. 相似文献
2.
[目的/意义]以汽车论坛例，提出一种针对专业社交媒体文本的主题知识元抽取方法。[方法/过程]首先，通过LDA模型提取出汽车论坛中文本的主题，并进行去重，形成主题列表；其次，基于融合主题特征的深度学习模型T-LSTM模型构建适于汽车论坛本文的情感分析模型；然后，通过计算各词汇在图模型TextRank中的重要性与各词汇的Word2Vec主题相似度，抽取情感关键词与关键句，用于对文本主题与情感倾向的解释与补充；最后，对上述方法进行集成，输出结构化的主题知识元。[结果/结论]实验结果中，抽取得到的主题知识元合格率达到69.1%，表明本文提出的主题知识元抽取方法，能够围绕知识主题较为准确地抽取知识元，实现知识的结构化转换。 相似文献
3.
Aspect-based sentiment analysis aims to predict the sentiment polarities of specific targets in a given text. Recent researches show great interest in modeling the target and context with attention network to obtain more effective feature representation for sentiment classification task. However, the use of an average vector of target for computing the attention score for context is unfair. Besides, the interaction mechanism is simple thus need to be further improved. To solve the above problems, this paper first proposes a coattention mechanism which models both target-level and context-level attention alternatively so as to focus on those key words of targets to learn more effective context representation. On this basis, we implement a Coattention-LSTM network which learns nonlinear representations of context and target simultaneously and can extracts more effective sentiment feature from coattention mechanism. Further, a Coattention-MemNet network which adopts a multiple-hops coattention mechanism is proposed to improve the sentiment classification result. Finally, we propose a new location weighted function which considers the location information to enhance the performance of coattention mechanism. Extensive experiments on two public datasets demonstrate the effectiveness of all proposed methods, and our findings in the experiments provide new insight for future developments of using attention mechanism and deep neural network for aspect-based sentiment analysis. 相似文献
4.
在线动作检测对安防和人机交互等应用非常重要,该问题要求模型在动作刚开始时就能检测到,而不是等待整个事件完整结束。由于在线动作检测只能基于观测到的部分视频进行判断,因此相比动作识别和动作检测等任务,模型需要挖掘更多信息辅助决策。基于在线动作检测问题中常用的长短时记忆网络(LSTM)模型,构建双流LSTM模型(2S-LSTM),并将在图像领域中被广泛使用的自监督学习思想引入到在线动作检测问题中。首先,双流网络2S-LSTM模型分别对RGB流与光流的时序信息采用LSTM建模。同时基于自监督学习的思想构建出2个新型的损失函数——时序相似度损失与光流稳定损失用于模型的训练。实验表明,与过去的在线动作检测方法RED、TRN、IDN相比,本文的模型在TVSeries与THUMOS’14这2个数据集上都取得了较好的结果。 相似文献
5.
基于机器学习算法的研究热点趋势预测模型对比与分析——BP神经网络、支持向量机与LSTM模型 总被引：2，自引：0，他引：2
[目的/意义]细粒度分析学科领域热点主题发展脉络并对利用机器学习算法对未来发展趋势进行准确预测研究。[方法/过程]提出一种基于机器学习算法的研究热点趋势预测方法与分析框架，以基因工程领域为例利用主题概率模型识别WOS核心集中论文摘要数据研究热点主题并进行主题演化关联构建，然后选取BP神经网络、支持向量机及LSTM模型等3种典型机器学习算法进行预测分析，最后利用RE指标和精准度指标评价机器学习算法预测效果并对基因工程领域在医药卫生、农业食品等方面研究趋势进行分析。[结果/结论]实验表明基于LSTM模型对热点主题未来发展趋势预测准确度最高，支持向量机预测效果次之，BP神经网络预测效果较差且预测稳定性不足，同时结合专家咨询和文献调研表明本文方法可快速识别基因领域研究主题及发展趋势，可为我国学科领域大势研判和架构调整提供决策支持和参考。 相似文献
6.
为通过光电容积脉搏波信号获取动脉血压参数,并将其作为判断个人健康状况的依据,基于 Tensorflow框架训练 LSTM 网络模型与传统 RNN 模型,使用 625 000 条光电容积脉搏波数据序列通过忘记、选择记忆、输出阶段得出符合生理规律的血压参数,将两种模型放在 125 000 条样本的测试集中进行有效性验证。实验结果表明,训练后的 LSTM 模型对血压的预测比传统 RNN 模型更准确,LSTM 预测评价指标 MAE、RMSE、STD 和 R2_score 分别为 4.05、8.78、7.42 和 0.89,且预测结果符合美国医疗仪器促进协会标准（MAE<5mmHg,STD<8mmHg）,而传统 RNN 模型则为 11.58、17.03、14.54 和 0.73。LSTM 模型能较好地预测血压参数,在生物医学领域有较高的应用价值,其效果优于传统 RNN 模型。 相似文献
7.
作为衡量空气质量的重要指标,准确预测PM2.5浓度变化尤为重要。提出Prophet和长短期记忆（LSTM）相结合的组合预测模型（Prophet-LSTM）。在模型构建过程中,首先利用Prophet模型的可分解方法,将PM2.5日值浓度序列分解成趋势、周期和随机波动分量|然后对趋势和周期分量建立Prophet模型,对随机波动分量建立LSTM模型|最后将各分量的预测值集成得到PM2.5浓度的预测值。以郑州市PM2.5日值浓度数据为例进行实证分析,结果表明,该组合预测模型相较对比模型能够更好地预测PM2.5日值浓度的变化趋势。 相似文献
8.
为了改善传统时间序列方法无法在预测模型中添加相关变量等缺点,并提高股指预测精度,运用LSTM神经网络等深度学习方法对我国上证指数及沪深300指数进行预测分析,并将预测结果与RNN、CNN、ARMA等模型进行比较,然后在模型中加入百度指数测试其对预测精度的影响,最后检验LSTM模型对训练步长的敏感性。研究结果表明,LSTM能够实现对股指的精准预测,其预测评价指标MAE、MAPE、RMSE分别为0.008、0.025、0.011,预测误差低于其它模型,加入百度指数可进一步提升其预测能力,但改变LSTM模型训练步长对结果影响不大。因此,LSTM模型在金融经济预测领域有较高的应用价值。 相似文献
9.
特定目标情感分类不仅依赖于上下文信息,还需结合特定目标的特征信息,是一种细粒度的情感分析。针对特定目标情感分类提出了一种基于深度记忆网络的分类模型。该模型以双向LSTM和注意力机制为主干框架,从双向LSTM中抽取出目标的特征表示,将目标特征信息加入句子表示中,并加入多计算层（Hops）结构,用以挖掘句子和目标更深层次的情感特征信息,每个计算层的结构类似,共享参数。最后在SemEval2014和SemEval2016数据集上进行实验,取得了比其它基准模型更好的效果。 相似文献
10.