Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates...Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.展开更多
Objective:The purpose of the study was to evaluate listening effort in adults who experience varied annoyance towards noise.Materials and methods:Fifty native Kannada-speaking adults aged 41e68 years participated.We e...Objective:The purpose of the study was to evaluate listening effort in adults who experience varied annoyance towards noise.Materials and methods:Fifty native Kannada-speaking adults aged 41e68 years participated.We evaluated the participant's acceptable noise level while listening to speech.Further,a sentence-final wordidentification and recall test at 0 dB SNR(less favorable condition)and 4 dB SNR(relatively favorable condition)was used to assess listening effort.The repeat and recall scores were obtained for each condition.Results:The regression model revealed that the listening effort increased by 0.6%at 0 dB SNR and by 0.5%at 4 dB SNR with every one-year advancement in age.Listening effort increased by 0.9%at 0 dB SNR and by 0.7%at 4 dB SNR with every one dB change in the value of Acceptable Noise Level(ANL).At 0 dB SNR and 4 dB SNR,a moderate and mild negative correlation was noted respectively between listening effort and annoyance towards noise when the factor age was controlled.Conclusion:Listening effort increases with age,and its effect is more in less favorable than in relatively favorable conditions.However,if the annoyance towards noise was controlled,the impact of age on listening effort was reduced.Listening effort correlated with the level of annoyance once the age effect was controlled.Furthermore,the listening effort was predicted from the ANL to a moderate degree.展开更多
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results i...In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.展开更多
网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的...网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的最优努力水平选择及广告定价策略。研究发现:CPW(cost per watch)定价模式下,广告商承担了消费者是否购买的不确定性风险,当消费者敏感性系数偏低时,广告商会提交较低的出价,且B/D两类广告商赢得竞拍的概率相等;对比CPW模式,在CPA(cost per action)定价模式下广告商的努力水平更低,且CPA定价模式中B型(品牌型)广告商赢得竞拍的概率更大,但赢得竞拍的广告商边际利润往往较低;与广告商相反,主播在CPA定价模式下的收益大于CPW,且随消费者敏感性系数的增加,两种定价模式下的收益差逐渐增大;CPW定价模式下预期观看直播的用户量和购买率均高于CPA,网络直播市场倾向于从CPW广告定价合同中获得较大收益。展开更多
目的探究言语刺激强度对健听者听配能的影响。方法选取38例健听成人,采用语速x刺激强度的言语理解测试范式,将瞳孔扩张听配能测量和听觉任务完成准确度及反应速度相结合,观察阈上范围内20 dB SPL的强度变化对听配能和任务表现的影响。...目的探究言语刺激强度对健听者听配能的影响。方法选取38例健听成人,采用语速x刺激强度的言语理解测试范式,将瞳孔扩张听配能测量和听觉任务完成准确度及反应速度相结合,观察阈上范围内20 dB SPL的强度变化对听配能和任务表现的影响。结果语速对平均瞳孔扩张有显著影响(P<0.001,η_(p)^(2)=0.618),但刺激强度对平均瞳孔扩张无显著影响(P=0.213,η_(p)^(2)=0.040)。增加言语刺激强度能够显著提升任务反应速度(P=0.043,η_(p)^(2)=0.099),在正常语速下,还能够显著提升言语理解准确度(P<0.001,η_(p)^(2)=0.374)。结论健听人群听觉舒适阈范围内将言语声刺激强度增加20 dB SPL能够提高聆听效率,但该效应可能并不是通过改变可听度或唤醒度,从而影响听配能结果,可能是改变听觉处理速度。展开更多
文摘Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.
文摘Objective:The purpose of the study was to evaluate listening effort in adults who experience varied annoyance towards noise.Materials and methods:Fifty native Kannada-speaking adults aged 41e68 years participated.We evaluated the participant's acceptable noise level while listening to speech.Further,a sentence-final wordidentification and recall test at 0 dB SNR(less favorable condition)and 4 dB SNR(relatively favorable condition)was used to assess listening effort.The repeat and recall scores were obtained for each condition.Results:The regression model revealed that the listening effort increased by 0.6%at 0 dB SNR and by 0.5%at 4 dB SNR with every one-year advancement in age.Listening effort increased by 0.9%at 0 dB SNR and by 0.7%at 4 dB SNR with every one dB change in the value of Acceptable Noise Level(ANL).At 0 dB SNR and 4 dB SNR,a moderate and mild negative correlation was noted respectively between listening effort and annoyance towards noise when the factor age was controlled.Conclusion:Listening effort increases with age,and its effect is more in less favorable than in relatively favorable conditions.However,if the annoyance towards noise was controlled,the impact of age on listening effort was reduced.Listening effort correlated with the level of annoyance once the age effect was controlled.Furthermore,the listening effort was predicted from the ANL to a moderate degree.
基金This work was supported by the Technology development Program of MSS[No.S3033853].
文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.
文摘网络直播广告作为一种新型营销方式快速发展,优化直播广告运营主体努力水平及定价策略是一项值得深入研究的课题。本文基于广告投放效果的两种定价模式,构建了包含两个广告商和一个主播的网络直播广告定价决策模型,探索广告商与主播的最优努力水平选择及广告定价策略。研究发现:CPW(cost per watch)定价模式下,广告商承担了消费者是否购买的不确定性风险,当消费者敏感性系数偏低时,广告商会提交较低的出价,且B/D两类广告商赢得竞拍的概率相等;对比CPW模式,在CPA(cost per action)定价模式下广告商的努力水平更低,且CPA定价模式中B型(品牌型)广告商赢得竞拍的概率更大,但赢得竞拍的广告商边际利润往往较低;与广告商相反,主播在CPA定价模式下的收益大于CPW,且随消费者敏感性系数的增加,两种定价模式下的收益差逐渐增大;CPW定价模式下预期观看直播的用户量和购买率均高于CPA,网络直播市场倾向于从CPW广告定价合同中获得较大收益。
文摘目的探究言语刺激强度对健听者听配能的影响。方法选取38例健听成人,采用语速x刺激强度的言语理解测试范式,将瞳孔扩张听配能测量和听觉任务完成准确度及反应速度相结合,观察阈上范围内20 dB SPL的强度变化对听配能和任务表现的影响。结果语速对平均瞳孔扩张有显著影响(P<0.001,η_(p)^(2)=0.618),但刺激强度对平均瞳孔扩张无显著影响(P=0.213,η_(p)^(2)=0.040)。增加言语刺激强度能够显著提升任务反应速度(P=0.043,η_(p)^(2)=0.099),在正常语速下,还能够显著提升言语理解准确度(P<0.001,η_(p)^(2)=0.374)。结论健听人群听觉舒适阈范围内将言语声刺激强度增加20 dB SPL能够提高聆听效率,但该效应可能并不是通过改变可听度或唤醒度,从而影响听配能结果,可能是改变听觉处理速度。
