Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the au...Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.展开更多
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
Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of ...Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of images.Since the work is done offshore,these agreements or images must be shared through the Internet or stored over the cloud.The breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of danger.This article proposes a novel algorithm for securing the NDAs in the form of images.As an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the Internet.As the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of time.The confusion effects have been realized through an XOR operation between the confused image,and chaotic data.Besides,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it has.The machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.展开更多
基金supported by Researchers Supporting Program(TUMAProject-2021-14)AlMaarefa University,Riyadh,Saudi Arabia.Mohd Anul Haq would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-173.
文摘Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.
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This research is fully funded by Universiti Teknologi Malaysia under the UTM Fundamental Research Grant(UTMFR)with Cost Center No Q.K130000.2556.21H14.
文摘Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of images.Since the work is done offshore,these agreements or images must be shared through the Internet or stored over the cloud.The breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of danger.This article proposes a novel algorithm for securing the NDAs in the form of images.As an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the Internet.As the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of time.The confusion effects have been realized through an XOR operation between the confused image,and chaotic data.Besides,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it has.The machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.
