Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the d...Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format.However,physiological recordings often exhibit irregular and unordered patterns,posing a significant challenge in conceptualising them as matrices.As a result,GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system.Additionally,our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain.Subsequently,demographic data are included,and a multi-task learning architecture is devised,integrating classification and regression tasks.The trials used an authentic dataset,including 800 brain x-ray pictures,consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases.Based on empirical evidence,our methodology demonstrates superior performance in classification,surpassing other comparison methods with a notable achievement of 92.27%in terms of area under the curve as well as a correlation coefficient of 0.62.展开更多
The cactus is a plant that grows in many rural areas,widely used as a hedge,and has multiple benefits through the manufacture of various cosmetics and other products.However,this crop has been suffering for some time ...The cactus is a plant that grows in many rural areas,widely used as a hedge,and has multiple benefits through the manufacture of various cosmetics and other products.However,this crop has been suffering for some time from the attack of the carmine scaleDactylopius opuntia(Hemiptera:Dactylopiidae).The infestation can spread rapidly if not treated in the early stage.Current solutions consist of regular field checks by the naked eyes carried out by experts.The major difficulty is the lack of experts to check all fields,especially in remote areas.In addition,this requires time and resources.Hence the need for a system that can categorize the health level of cacti remotely.To date,deep learning models used to categorize plant diseases from images have not addressed the mealy bug infestation of cacti because computer vision has not sufficiently addressed this disease.Since there is no public dataset and smartphones are commonly used as tools to take pictures,it might then be conceivable for farmers to use them to categorize the infection level of their crops.In this work,we developed a system called CactiVIT that instantly determines the health status of cacti using the Visual image Transformer(ViT)model.We also provided a new image dataset of cochineal infested cacti.1 Finally,we developed a mobile application that delivers the classification results directly to farmers about the infestation in their fields by showing the probabilities related to each class.This study compares the existing models on the new dataset and presents the results obtained.The VIT-B-16 model reveals an approved performance in the literature and in our experiments,in which it achieved 88.73%overall accuracy with an average of+2.61%compared to other convolutional neural network(CNN)models that we evaluated under similar conditions.展开更多
文摘Recently,numerous estimation issues have been solved due to the developments in data-driven artificial neural networks(ANN)and graph neural networks(GNN).The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format.However,physiological recordings often exhibit irregular and unordered patterns,posing a significant challenge in conceptualising them as matrices.As a result,GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system.Additionally,our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain.Subsequently,demographic data are included,and a multi-task learning architecture is devised,integrating classification and regression tasks.The trials used an authentic dataset,including 800 brain x-ray pictures,consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases.Based on empirical evidence,our methodology demonstrates superior performance in classification,surpassing other comparison methods with a notable achievement of 92.27%in terms of area under the curve as well as a correlation coefficient of 0.62.
基金PHC Toubkal/21/121-Campus France:45942UG who funded the work and the mobility fee between Morocco and France.
文摘The cactus is a plant that grows in many rural areas,widely used as a hedge,and has multiple benefits through the manufacture of various cosmetics and other products.However,this crop has been suffering for some time from the attack of the carmine scaleDactylopius opuntia(Hemiptera:Dactylopiidae).The infestation can spread rapidly if not treated in the early stage.Current solutions consist of regular field checks by the naked eyes carried out by experts.The major difficulty is the lack of experts to check all fields,especially in remote areas.In addition,this requires time and resources.Hence the need for a system that can categorize the health level of cacti remotely.To date,deep learning models used to categorize plant diseases from images have not addressed the mealy bug infestation of cacti because computer vision has not sufficiently addressed this disease.Since there is no public dataset and smartphones are commonly used as tools to take pictures,it might then be conceivable for farmers to use them to categorize the infection level of their crops.In this work,we developed a system called CactiVIT that instantly determines the health status of cacti using the Visual image Transformer(ViT)model.We also provided a new image dataset of cochineal infested cacti.1 Finally,we developed a mobile application that delivers the classification results directly to farmers about the infestation in their fields by showing the probabilities related to each class.This study compares the existing models on the new dataset and presents the results obtained.The VIT-B-16 model reveals an approved performance in the literature and in our experiments,in which it achieved 88.73%overall accuracy with an average of+2.61%compared to other convolutional neural network(CNN)models that we evaluated under similar conditions.
