Precision

摘要： Precision Planting公司的两项革命性的种子放置优化技术旨在提供更高的种子放置精度,提高作物出苗率和间距均匀度,并提高增产潜力——所有这些都来自拖拉机驾驶室的舒适性。现在可以通过Precision Planting经销商获得的Smart Depth新技术。

摘要： 北京冬奥会的超高清电视转播,精彩纷呈,进一步体现了科技进步对行业发展的巨大推动作用。建设超高清系统,以往支持高清的相关IT设备需要全部更新换代。DELL科技公司有幸参与了国内许多大型超高清项目,获得了很多经验,提供了从客户端到后台核心存储设备的完整解决方案,本文从技术角度解析这套方案,供读者参考。

摘要： Machine learning algorithms have been deployed in numerous optimization,prediction and classification problems.This has endeared them for application in fields such as computer networks and medical diagnosis.Although these machine learning algorithms achieve convincing results in these fields,they face numerous challenges when deployed on imbalanced dataset.Consequently,these algorithms are often biased towards majority class,hence unable to generalize the learning process.In addition,they are unable to effectively deal with high-dimensional datasets.Moreover,the utilization of conventional feature selection techniques from a dataset based on attribute significance render them ineffective for majority of the diagnosis applications.In this paper,feature selection is executed using the more effective Neighbour Components Analysis(NCA).During the classification process,an ensemble classifier comprising of K-Nearest Neighbours(KNN),Naive Bayes(NB),Decision Tree(DT)and Support Vector Machine(SVM)is built,trained and tested.Finally,cross validation is carried out to evaluate the developed ensemble model.The results shows that the proposed classifier has the best performance in terms of precision,recall,F-measure and classification accuracy.

摘要： 近年来,随着我国养殖业及饲料业发展与行业格局的转变,精准营养(Precision nutrition)成为整个养殖与饲料业关注与讨论的焦点与热点,这也是我国养殖业及饲料工业进一步发展的方向与契机。在美国国家生物技术信息中心(National Center for Biotechnology Information,NCBI)网站中以“Precision nutrition”为关键词进行搜索,文献数量从2005年的129篇增加到了2021年的1740篇(如图1),也间接反映了国内外同时都在关注与推动精准营养的研究与应用。关于精准营养在养猪生产中的应用,笔者与国内营养、养殖、科研及设备等各个领域专家都进行了深入讨论与交流,借此与行业同仁分享探讨。

摘要： Purpose-Virtually unlimited amounts of data collection by cybersecurity systems put people at risk of having their privacy violated.Social networks like Facebook on the Internet provide an overplus of knowledge concerning their users.Although users relish exchanging data online,only some data are meant to be interpreted by those who see value in it.It is now essential for online social network(OSN)to regulate the privacy of their users on the Internet.This paper aims to propose an efficient privacy violation detection model(EPVDM)for OSN.Design/methodology/approach-In recent months,the prominent position of both industry and academia has been dominated by privateness,its breaches and strategies to dodge privacy violations.Corporations around the world have become aware of the effects of violating privacy and its effect on them and other stakeholders.Once privacy violations are detected,they must be reported to those affected and it’s supposed to be mandatory to make them to take the next action.Although there are different approaches to detecting breaches of privacy,most strategies do not have a functioning tool that can show the values of its subject heading.An EPVDM for Facebook,based on a deep neural network,is proposed in this research paper.Findings-The main aim of EPVDM is to identify and avoid potential privacy breaches on Facebook in the future.Experimental analyses in comparison with major intrusion detection system(IDS)to detect privacy violation show that the proposed methodology is robust,precise and scalable.The chances of breaches or possibilities of privacy violations can be identified very accurately.Originality/value-All the resultant is compared with well popular methodologies like adaboost(AB),decision tree(DT),linear regression(LR),random forest(RF)and support vector machine(SVM).It’s been identified from the analysis that the proposed model outperformed the existing techniques in terms of accuracy(94%),precision(99.1%),recall(92.43%),f-score(95.43%)and violation detection rate(>98.5%).

摘要： The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

摘要： The accurate prediction of formation pressure is important in oil/gas exploration and development.However,the achievement of this goal remains challenging,due to insufficient logging data and the low predictive data accuracy from seismic data.In this work,a case study was carried out in the Baima area of Wulong,in order to develop a workflow for accurately predicting shale gas formation pressure.The multi-channel stack method was first used,as well as the inversion of single-channel seismic data,to construct velocity and density models of the formation.Combined with the existing welllogging data,the velocity and density models of the whole well section were established.The shale gas formation pressure was then estimated using the Eaton method.The results show that the multi-channel seismic stacking method has a higher accuracy than the inversion of the formation velocity obtained by the single-channel seismic method.The discrepancies between our predicted formation pressure and the actual formation pressure measurement are within an acceptable range,indicating that our workflow is effective.

摘要： 艾迈斯半导体(ams AG)与加拿大领先的病毒检测装置领导品牌Precision Biomonitoring共同宣布建立全球合作伙伴关系,精诚合作,共享技术,携手开发可快速检测COVID 19(SARS CoV 2)病毒的创新检测装置。 通过此次合作,艾迈斯半导体的创新光谱传感器技术与Precision Biomonitoring的侧向层析及数字化能力实现理想融合,从而有望开发针对COVID 19(SARS CoV 2)的大规模创新检测装置。 针对灭活病毒颗粒的初步结果表明,此方案在31周期时间(CT)时具备高灵敏度,可帮助专业人员识别无症状感染者。

摘要： 对于常用机器学习分类算法在不均衡数据上分类性能较差的问题,提出了一种基于生成对抗网络(GAN)的不平衡数据分类策略。所提出的策略首先利用生成对抗网络通过训练生成少数类样本,改善样本不均衡问题。使用机器学习分类器对改善后的数据集进行分类,提升该模型的分类性能。实验使用了商业银行客户数据集,采用Precision、F-mean等作为度量指标,并与SMOTE等传统数据不均衡处理方法进行对比。实验结果表明:对于不平衡数据集的二分类问题,提出的GAN模型在银行客户流失分类问题中具有可行性和适应性。最终取得了良好的度量效果,有很强的适用性及应用价值。

摘要： 对于常用机器学习分类算法在不均衡数据上分类性能较差的问题,提出了一种基于生成对抗网络(GAN)的不平衡数据分类策略.所提出的策略首先利用生成对抗网络通过训练生成少数类样本,改善样本不均衡问题.使用机器学习分类器对改善后的数据集进行分类,提升该模型的分类性能.实验使用了商业银行客户数据集,采用Precision、F-mean等作为度量指标,并与SMOTE等传统数据不均衡处理方法进行对比.实验结果表明:对于不平衡数据集的二分类问题,提出的GAN模型在银行客户流失分类问题中具有可行性和适应性.最终取得了良好的度量效果,有很强的适用性及应用价值.