NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of servic...NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.展开更多
Direct soil temperature(ST)measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning(ML)tools is helpful.In this study,ML approaches,including KStar,instance-based K-nearest l...Direct soil temperature(ST)measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning(ML)tools is helpful.In this study,ML approaches,including KStar,instance-based K-nearest learning(IBK),and locally weighted learning(LWL),coupled with resampling algorithms of bagging(BA)and dagging(DA)(BA-IBK,BA-KStar,BA-LWL,DA-IBK,DA-KStar,and DA-LWL)were developed and tested for multi-step ahead(3,6,and 9 d ahead)ST forecasting.In addition,a linear regression(LR)model was used as a benchmark to evaluate the results.A dataset was established,with daily ST time-series at 5 and 50 cm soil depths in a farmland as models’output and meteorological data as models’input,including mean(T_(mean)),minimum(Tmin),and maximum(T_(max))air temperatures,evaporation(Eva),sunshine hours(SSH),and solar radiation(SR),which were collected at Isfahan Synoptic Station(Iran)for 13 years(1992–2005).Six different input combination scenarios were selected based on Pearson’s correlation coefficients between inputs and outputs and fed into the models.We used 70%of the data to train the models,with the remaining 30%used for model evaluation via multiple visual and quantitative metrics.Our?ndings showed that T_(mean)was the most effective input variable for ST forecasting in most of the developed models,while in some cases the combinations of variables,including T_(mean)and T_(max)and T_(mean),T_(max),Tmin,Eva,and SSH proved to be the best input combinations.Among the evaluated models,BA-KStar showed greater compatibility,while in most cases,BA-IBK and-LWL provided more accurate results,depending on soil depth.For the 5 cm soil depth,BA-KStar had superior performance(i.e.,Nash-Sutcliffe efficiency(NSE)=0.90,0.87,and 0.85 for 3,6,and 9 d ahead forecasting,respectively);for the 50 cm soil depth,DA-KStar outperformed the other models(i.e.,NSE=0.88,0.89,and 0.89 for 3,6,and 9 d ahead forecasting,respectively).The results con?rmed that all hybrid models had higher prediction capabilities than the LR model.展开更多
Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration.Though,the wet-lab experiments for the inve...Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration.Though,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP”)are usually very costly and remain time-consuming.Therefore,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process.Several anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and efficiency.An exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor”),by incorporation of encoded features and probing machine learning algorithms including deep learning.The proposed model performs best in conjunction with deep learning.Rigorous testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set testing.The proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and stability.Subsequently,the proposed model was also extensively probed in comparison with other existing models.The performance of the proposed model also out-performs other existing models.These outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based examinations.Subsequently,it has the potential to assiduously support medical and bioinformatics research.展开更多
This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications.The proposed model is based on an Ensemble boosting Neu...This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications.The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chestX-ray images through Two Step-As clustering algorithm with rich filter families,abstraction and weight-sharing properties.In contrast to the generally used transformational learning approach,the proposed model was trained before and after clustering.The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group,with each subject group displayed as a distinct category.The retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method,which was then utilised to classify the instances.The Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray findings.Models forMultiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they include.The testswere carried out using theCOVID-19 public radiology database,and a cross-validationmethod ensured accuracy.The proposed classifier with an accuracy of 98.02%percent was found to provide the most efficient outcomes possible.The result is a lowcost,quick and reliable intelligence tool for detecting COVID-19 infection.展开更多
基金the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabiafundedby Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia。
文摘NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.
文摘Direct soil temperature(ST)measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning(ML)tools is helpful.In this study,ML approaches,including KStar,instance-based K-nearest learning(IBK),and locally weighted learning(LWL),coupled with resampling algorithms of bagging(BA)and dagging(DA)(BA-IBK,BA-KStar,BA-LWL,DA-IBK,DA-KStar,and DA-LWL)were developed and tested for multi-step ahead(3,6,and 9 d ahead)ST forecasting.In addition,a linear regression(LR)model was used as a benchmark to evaluate the results.A dataset was established,with daily ST time-series at 5 and 50 cm soil depths in a farmland as models’output and meteorological data as models’input,including mean(T_(mean)),minimum(Tmin),and maximum(T_(max))air temperatures,evaporation(Eva),sunshine hours(SSH),and solar radiation(SR),which were collected at Isfahan Synoptic Station(Iran)for 13 years(1992–2005).Six different input combination scenarios were selected based on Pearson’s correlation coefficients between inputs and outputs and fed into the models.We used 70%of the data to train the models,with the remaining 30%used for model evaluation via multiple visual and quantitative metrics.Our?ndings showed that T_(mean)was the most effective input variable for ST forecasting in most of the developed models,while in some cases the combinations of variables,including T_(mean)and T_(max)and T_(mean),T_(max),Tmin,Eva,and SSH proved to be the best input combinations.Among the evaluated models,BA-KStar showed greater compatibility,while in most cases,BA-IBK and-LWL provided more accurate results,depending on soil depth.For the 5 cm soil depth,BA-KStar had superior performance(i.e.,Nash-Sutcliffe efficiency(NSE)=0.90,0.87,and 0.85 for 3,6,and 9 d ahead forecasting,respectively);for the 50 cm soil depth,DA-KStar outperformed the other models(i.e.,NSE=0.88,0.89,and 0.89 for 3,6,and 9 d ahead forecasting,respectively).The results con?rmed that all hybrid models had higher prediction capabilities than the LR model.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University(https://www.kau.edu.sa/),Jeddah,under Grant No.(D-49-611-1441).
文摘Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration.Though,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP”)are usually very costly and remain time-consuming.Therefore,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process.Several anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and efficiency.An exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor”),by incorporation of encoded features and probing machine learning algorithms including deep learning.The proposed model performs best in conjunction with deep learning.Rigorous testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set testing.The proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and stability.Subsequently,the proposed model was also extensively probed in comparison with other existing models.The performance of the proposed model also out-performs other existing models.These outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based examinations.Subsequently,it has the potential to assiduously support medical and bioinformatics research.
基金This work was funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia,under Grant No.(DF-770830-1441)The author,there-fore,gratefully acknowledge the technical and financial support from the DSR.
文摘This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications.The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chestX-ray images through Two Step-As clustering algorithm with rich filter families,abstraction and weight-sharing properties.In contrast to the generally used transformational learning approach,the proposed model was trained before and after clustering.The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group,with each subject group displayed as a distinct category.The retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method,which was then utilised to classify the instances.The Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray findings.Models forMultiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they include.The testswere carried out using theCOVID-19 public radiology database,and a cross-validationmethod ensured accuracy.The proposed classifier with an accuracy of 98.02%percent was found to provide the most efficient outcomes possible.The result is a lowcost,quick and reliable intelligence tool for detecting COVID-19 infection.
