Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is availabl...Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.展开更多
Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their preferences.These systems analyze users...Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their preferences.These systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide recommendations.To improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music popularity.This paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as input.Initially,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural network.This network then predicts ratings for unreviewed music by users.Additionally,the system analyses user music listening behaviour and music popularity.Music popularity can help to address cold start users as well.Finally,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening habits.The proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each user.Among these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across users.The number of recommended tracks is aligned with each user’s typical listening rate.The experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain(nDCG)of 82.3%,and a prediction accuracy of new ratings at 99.388%.展开更多
Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based access.This model offers several advantages for Internet of Healthcare Things(I...Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based access.This model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and scalability.However,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and delays.These issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous monitoring.To address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based IoHT.The SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop selection.This approach reduces broadcast storms,optimizes overall energy consumption,and extends network lifetime.The system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed strategy.The SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare scenarios.Results demonstrated that SEF significantly enhanced NDN-based IoHT performance.Specifically,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated areas.This forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.展开更多
Language information output plays an important role in students＇ language acquisition process and is an important way to improve students＇ comprehensive language using ability and language level. Because college stu...Language information output plays an important role in students＇ language acquisition process and is an important way to improve students＇ comprehensive language using ability and language level. Because college students＇ English foundation is weak in our country at present stage, there are many problems in the process of language information output. In this case, we can increase the application of Intemet technology as a way to expand language output in English language teaching. This article is to explore college English teaching language information output ways in the Internent age.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.
基金funded by the National Nature Sciences Foundation of China with Grant No.42250410321。
文摘Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their preferences.These systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide recommendations.To improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music popularity.This paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as input.Initially,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural network.This network then predicts ratings for unreviewed music by users.Additionally,the system analyses user music listening behaviour and music popularity.Music popularity can help to address cold start users as well.Finally,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening habits.The proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each user.Among these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across users.The number of recommended tracks is aligned with each user’s typical listening rate.The experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain(nDCG)of 82.3%,and a prediction accuracy of new ratings at 99.388%.
基金funded by the King Salman Center for Disability Research through Research Group No.KSRG-2023-335.
文摘Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based access.This model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and scalability.However,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and delays.These issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous monitoring.To address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based IoHT.The SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop selection.This approach reduces broadcast storms,optimizes overall energy consumption,and extends network lifetime.The system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed strategy.The SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare scenarios.Results demonstrated that SEF significantly enhanced NDN-based IoHT performance.Specifically,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated areas.This forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.
文摘Language information output plays an important role in students＇ language acquisition process and is an important way to improve students＇ comprehensive language using ability and language level. Because college students＇ English foundation is weak in our country at present stage, there are many problems in the process of language information output. In this case, we can increase the application of Intemet technology as a way to expand language output in English language teaching. This article is to explore college English teaching language information output ways in the Internent age.
