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| 1 | Novel RAG2 mutation in a patient with T-B-severe combined immunodeficiency and disseminated BCG disease显示文摘 | Sadeghi-Shabestari M Vesal S Jabbarpour-Bonyadi M | 2009 | J Investig AUergol Clin Immunol2009，19，6： | 1 |
| 2 | Nutritional status and nutrition risk screening in hospitalized children in N ew Z ealand显示文摘 | Vesal Moeeni Tony Walls Andrew S Day | 2013 | Acta Paediatr2013，，9： | 1 |
| 3 | Cognitive Power Management in Wireless Sensor Networks显示文摘在无线传感器节点的动态电源管理(DPM ) 是为减少闲散精力消费的一种著名技术。DPM 由动态地基于事件出现的预言翻转它的单位的开/关地位控制一个节点操作模式。然而,后来,每个模式变化在它的自己的权利导致一些开销,保证 DPM 效率不是在展出有未知统计的非宿命论和无常的环境的吝啬的功绩。我们在这份报纸的解决方案套房,一起指了同样认知的电源管理(CPM ) ,是向在统计上未知的设置的创新 DPM 的一次原则性的尝试并且给二不同分析保证。我们的第一个图案在面临非静止的事件过程时基于学习自动机和保证 better-than-pure-chance DPM。我们的第二个解决方案迎合事件出现可以在雇用一个对手的人物的甚至更一般的设置。在这种情况中,我们以节点在依靠一个 no-external-regret 过程以一种联机方式学习它的得最高分的战略策略的一场重复零和的比赛与它的环境提出单个尘埃的相互作用。我们进行数字实验以网络一生和事件损失百分比测量我们的计划的表演。 | Seyed Mehdi Tabatabaei Vesal Hakami Mehdi Dehghan | 2015 | Journal of Computer Science & Technology2015，30，6： | 1 |
| 4 | Novel RAG2 mutation in a patien! with T-B-severe combined immunodeficiency and disseminated BCG disease显示文摘 | Sadeghi-SM Vesal S Jabbarpour-BM | 2009 | J Investig Allergol Clin Immunnl2009，19，6： | 1 |
| 5 | Novel RAG2 ruination in a patient with T B severe combined immunodeficiency and disseminated BCG disease显示文摘 | Sadeghi - Shabestari M Vesal S Jabbarpour - Bonyadi M | 2009 | J Investig AI lergol Clin Immunol2009，19，6： | 1 |
| 6 | Application and preventive maintenance of neurology medical equipment in isfahan alzahra hospital显示文摘 | Alikhani P Vesal S Kashefi P | 2013 | Int Prey Med2013，4，2： | 1 |
| 7 | Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework显示文摘Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation.The authors present computer vision techniques for detecting and classifying fruit leaf diseases.Examples of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and classification.The following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy.This paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature selection.In the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced dataset.The next step is to use a pre-trained deep model named Darknet53 and fine-tune it.Next,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling layer.Finally,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning classifiers.The experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry diseases.The overall average achieved accuracy is 99.7%,higher than previous techniques. | Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Fayadh Alenezi Abdullah Alqahtani Khean Vesal Yunyoung Nam | 2023 | Computers, Materials & Continua2023，，4： | 0 |
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