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| 1 | Mango Leaf Disease Identification Using Fully Resolution Convolutional Network显示文摘Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases. | Rabia Saleem Jamal Hussain Shah Muhammad Sharif Ghulam Jillani Ansari | 2021 | Computers, Materials & Continua2021，，12： | 2 |
| 2 | Prevalence of malig- nant disorders in 50 cases of postmenopausal bleeding 显示文摘 | Jillani K Khero RB Maqsood SA | 2010 | J Pak Med Assoc2010，60，7： | 1 |
| 3 | Techniques of Remote Sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan显示文摘 | Mateeul Haq Memon Akhtar Sher Muhammad Siddiqi Paras Jillani Rahmatullah | 2012 | The Egyptian Journal of Remote Sensing and Space Sciences2012，，： | 1 |
| 4 | 生命源泉:塔尔沙漠建造太阳能水泵显示文摘语音:英式发音适合泛听语速:150词/分钟关键词:Thar Desert,water,filter水是塔尔沙漠地区的珍贵日用品。女人们必须步行至数英里外的地下井取水,所取得的还经常是受到污染的咸水。如今,当地政府在塔尔沙漠这里建造了太阳能淡化水设施,这是世界上规模最大的太阳能滤水设备之一。他们安装了这些从欧洲进口的水泵,先抽取地下水,再把水存在这些大水池里;然后利用管道抽水。 | Shahzeb Jillani 小狐 | 2015 | 疯狂英语(新悦读)2015，0，10： | 0 |
| 5 | A Two Stream Fusion Assisted Deep Learning Framework for Stomach Diseases Classification显示文摘Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling.Such technique is sensitive to these models.Thus,fake samples cause AI and DL model to produce diverse results.Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further.In this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks dramatically.Recently,such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers.Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality.As a result,it took weeks to generate patients test report,which is tiring and strenuous for them.Therefore,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such abnormalities.As far as the classification is concern,the adversarial attacks generate problems in classified images.Now days,to handle this issue machine learning is mainstream defensive approach against adversarial attacks.Hence,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks.Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods. | Muhammad Shahid Amin Jamal Hussain Shah Mussarat Yasmin Ghulam Jillani Ansari Muhamamd Attique Khan Usman Tariq Ye Jin Kim Byoungchol Chang | 2022 | Computers, Materials & Continua2022，，11： | 0 |
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