According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferen...According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.展开更多
The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to st...The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.展开更多
针对导弹装备维修保障效能系统复杂庞大,指标间相互关联影响、数据信息获取困难且易丢失扭曲的现状,提出一种基于灰色—改进二元语义的方法对维修保障效能进行评估。该方法利用改进G1法和改进CRITIC(criteria importance though intercr...针对导弹装备维修保障效能系统复杂庞大,指标间相互关联影响、数据信息获取困难且易丢失扭曲的现状,提出一种基于灰色—改进二元语义的方法对维修保障效能进行评估。该方法利用改进G1法和改进CRITIC(criteria importance though intercrieria correlation)法分别得出主客观权重,依据最小信息鉴别法获取综合权重,利用灰色聚类分析法将定量数据转化为二元语义形式,运用改进二元语义将定性语言评估信息进行转化并对完整系统进行评估。获取的各指标权重更加科学合理,避免了信息的丢失扭曲,评估结果更加准确。该模型评估结果与实际情况基本相符,能真实科学反映导弹装备维修保障效能水平并可针对性查找薄弱环节,可为导弹装备维修保障系统的管理和完善提供依据。展开更多
文摘According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.
文摘The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.
文摘针对导弹装备维修保障效能系统复杂庞大,指标间相互关联影响、数据信息获取困难且易丢失扭曲的现状,提出一种基于灰色—改进二元语义的方法对维修保障效能进行评估。该方法利用改进G1法和改进CRITIC(criteria importance though intercrieria correlation)法分别得出主客观权重,依据最小信息鉴别法获取综合权重,利用灰色聚类分析法将定量数据转化为二元语义形式,运用改进二元语义将定性语言评估信息进行转化并对完整系统进行评估。获取的各指标权重更加科学合理,避免了信息的丢失扭曲,评估结果更加准确。该模型评估结果与实际情况基本相符,能真实科学反映导弹装备维修保障效能水平并可针对性查找薄弱环节,可为导弹装备维修保障系统的管理和完善提供依据。
