A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival.However,there is not yet a valid prediction model available...A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival.However,there is not yet a valid prediction model available.An efficient feature extraction technique is also required to increase a prediction model’s precision.CDAS(cancer data access system)program is a great place to look for cancer along with images or biospecimens.In this study,we look at data from the CDAS system,specifically bowel cancer(colorectal cancer)datasets.This study suggested a survival prediction method for rectal cancer.In addition,this determines which deep learning algorithm works best by comparing their performance in terms of prediction accuracy.The initial job that leads to correct findings is corpus cleansing.Moving forward,the data preprocessing activity will be performed,which will comprise“exploratory data analysis and pruning and normalization or experimental study of data,which is required to obtain data features to design the model for cancer detection at an early stage.”Aside from that,the data corpus is separated into two sub-corpora:training data and test data,which will be utilized to assess the correctness of the constructed model.This study will compare our autoencoder accuracy to that of other deep learning algorithms,such as artificial neural network,convolutional neural network,and restricted Boltzmann machine,before implementing the suggested methodology and displaying the model’s accuracy graphically after the suggested new methodology or algorithm for patients with rectal cancer.Various criteria,including true positive rate,receiver operating characteristic(ROC)curve,and accuracy scores,are used in the experiments to determine the model’s high accuracy.In the end,we determine the accuracy score for each model.The outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction models.It is shown that variational deep encoders have excellent accuracy of 94%in this cancer prediction and 95%for ROC curve regions.The findings demonstrate that automated prediction algorithms are capable of properly estimating rectal cancer patients’chances of survival.The best results,with 95%accuracy,were generated by deep autoencoders.展开更多
There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices...There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices.To maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a relay.Based on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot scheduling.As a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised.We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion.The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay.However,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance enhancement.Through simulation,the proposed approach is successfully shown.These improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.展开更多
Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical imaging.However,existing models struggle to efficiently extract features from medical images and may squander a...Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical imaging.However,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness classification.To address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is proposed.An upgraded primary C3D network is utilised to create rougher low‐level feature maps.It introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map output.Then,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification task.The other two outputs employ backpropagation during training to improve the primary classification job.Experimental findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative dataset.The authors demonstrate promise for future disease classification studies.展开更多
文摘A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival.However,there is not yet a valid prediction model available.An efficient feature extraction technique is also required to increase a prediction model’s precision.CDAS(cancer data access system)program is a great place to look for cancer along with images or biospecimens.In this study,we look at data from the CDAS system,specifically bowel cancer(colorectal cancer)datasets.This study suggested a survival prediction method for rectal cancer.In addition,this determines which deep learning algorithm works best by comparing their performance in terms of prediction accuracy.The initial job that leads to correct findings is corpus cleansing.Moving forward,the data preprocessing activity will be performed,which will comprise“exploratory data analysis and pruning and normalization or experimental study of data,which is required to obtain data features to design the model for cancer detection at an early stage.”Aside from that,the data corpus is separated into two sub-corpora:training data and test data,which will be utilized to assess the correctness of the constructed model.This study will compare our autoencoder accuracy to that of other deep learning algorithms,such as artificial neural network,convolutional neural network,and restricted Boltzmann machine,before implementing the suggested methodology and displaying the model’s accuracy graphically after the suggested new methodology or algorithm for patients with rectal cancer.Various criteria,including true positive rate,receiver operating characteristic(ROC)curve,and accuracy scores,are used in the experiments to determine the model’s high accuracy.In the end,we determine the accuracy score for each model.The outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction models.It is shown that variational deep encoders have excellent accuracy of 94%in this cancer prediction and 95%for ROC curve regions.The findings demonstrate that automated prediction algorithms are capable of properly estimating rectal cancer patients’chances of survival.The best results,with 95%accuracy,were generated by deep autoencoders.
文摘There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices.To maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a relay.Based on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot scheduling.As a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised.We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion.The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay.However,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance enhancement.Through simulation,the proposed approach is successfully shown.These improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.
基金the Deanship of Scientific Research at King Khalid University for funding this work through General Research Project under grant number(GRP/75/44).
文摘Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical imaging.However,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness classification.To address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is proposed.An upgraded primary C3D network is utilised to create rougher low‐level feature maps.It introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map output.Then,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification task.The other two outputs employ backpropagation during training to improve the primary classification job.Experimental findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative dataset.The authors demonstrate promise for future disease classification studies.
