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
Objective: To observe effects of Dan Wei Powder (胆胃散 Powder for treating the gall bladder and stomach) Tea Bag (DWSTB) on the aggregation rate of blood platelet in vivo and in vitro. Methods: Increase of the platel...Objective: To observe effects of Dan Wei Powder (胆胃散 Powder for treating the gall bladder and stomach) Tea Bag (DWSTB) on the aggregation rate of blood platelet in vivo and in vitro. Methods: Increase of the platelet aggregation rate in the rat in vivo was induced by carrageenin, and increase of the rabbit platelet aggregation rate in vitro was induced by adenosine diphosphate (ADP) and collagen, respectively. The effects of DWSTB on the platelet aggregation rate were investigated in vivo and in vitro, respectively. Results: The maximum in vivo platelet aggregation rate in the rat was significantly decreased after administration of 2.0 and 4.0 g·kg-1 DWSTB (P<0.05.P<0.01). The maximum rabbit platelet aggregation rate induced by ADP and collagen in vitro were suppressed significantly by 2.0-16.0 mg·mL-1 and 2.0-8.0 mg·mL-1 DWSTB, respectively (P<0.05.P<0.01). And the effect of DWSTB on platelet aggregation was raised with increase ofits dose. Conclusion: Dan Wei Powder Tea Bag can restrain the aggregation of platelet in vivo and in vitro.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Objective: To observe effects of Dan Wei Powder (胆胃散 Powder for treating the gall bladder and stomach) Tea Bag (DWSTB) on the aggregation rate of blood platelet in vivo and in vitro. Methods: Increase of the platelet aggregation rate in the rat in vivo was induced by carrageenin, and increase of the rabbit platelet aggregation rate in vitro was induced by adenosine diphosphate (ADP) and collagen, respectively. The effects of DWSTB on the platelet aggregation rate were investigated in vivo and in vitro, respectively. Results: The maximum in vivo platelet aggregation rate in the rat was significantly decreased after administration of 2.0 and 4.0 g·kg-1 DWSTB (P<0.05.P<0.01). The maximum rabbit platelet aggregation rate induced by ADP and collagen in vitro were suppressed significantly by 2.0-16.0 mg·mL-1 and 2.0-8.0 mg·mL-1 DWSTB, respectively (P<0.05.P<0.01). And the effect of DWSTB on platelet aggregation was raised with increase ofits dose. Conclusion: Dan Wei Powder Tea Bag can restrain the aggregation of platelet in vivo and in vitro.
文摘选择性集成通过选择部分基分类器参与集成,从而提高集成分类器的泛化能力,降低预测开销.但已有的选择性集成算法普遍耗时较长,将数据挖掘的技术应用于选择性集成,提出一种基于FP-Tree(frequent pattern tree)的快速选择性集成算法:CPM-EP(coverage based pattern mining for ensemble pruning).该算法将基分类器对校验样本集的分类结果组织成一个事务数据库,从而使选择性集成问题可转化为对事务数据集的处理问题.针对所有可能的集成分类器大小,CPM-EP算法首先得到一个精简的事务数据库,并创建一棵FP-Tree树保存其内容;然后,基于该FP-Tree获得相应大小的集成分类器.在获得的所有集成分类器中,对校验样本集预测精度最高的集成分类器即为算法的输出.实验结果表明,CPM-EP算法以很低的计算开销获得优越的泛化能力,其分类器选择时间约为GASEN的1/19以及Forward-Selection的1/8,其泛化能力显著优于参与比较的其他方法,而且产生的集成分类器具有较少的基分类器.
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.
