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Comparative Study for ECHAM5 and SDSM in Downscaling Temperature for a Geo-Climatically Diversified Region, Pakistan

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Applied Mathematics 2014年第1期5卷 137-143页

作者： dildar hussain kazmi Ghulam Rasul Jianping Li Suhail Babar Cheema Research & Development Division Pakistan Metrological Department Islamabad Pakistan State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

Pakistan is a country with diversified features in terms of geography and climate. It is an agriculture based country, mainly dependent on Indus water system. In Pakistan, there are loftyplateaus to the north and Arabian Sea in the south, while the interior portion is covered with plateaus or agriculture plains. For such a region, any attempt to monitor/analyze climatic data requires some more specific details. A statistical software “SDSM” is utilized for downscaling daily temperature data of Pakistan and the results generated are compared with the output of a recommended model “ECHAM5”. After analysis, it revealed that comparatively SDSM produced much better results. The outputs from both the approaches were correlated with the observed data;SDSM-observed gave values for correlation coefficient R2 in the range of 81% - 94% whereas ECHAM5-observed produced 73% - 87% for different meteorological stations of Pakistan. On the basis of this study, SDSM can be recommended for future scenario generation of temperature data of Pakistan as well.

关键词： Dynamic Statistical Future Scenario Generation

Impact of climate change on agriculture during winter season over Pakistan

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Agricultural Sciences 2012年第8期3卷 1007-1018页

作者： Khalid M. Malik Arif Mahmood dildar hussain kazmi Jan Muhammad Khan Director General Pakistan Meteorological Department Islamabad Pakistan Director National Agromet Center Pakistan Meteorological Department Islamabad Pakistan Director Pakistan Meteorological Department Islamabad Pakistan Meteorologist National Agromet Center Pakistan Meteorological Department Islamabad Pakistan

This study has been carried out to investigate the impact of climate change over Pakistan and its surrounding areas (60° - 80°E and 20°- 40° N) during winter seasons (December-February). Variability in three meteorological parameters such as: rainfall;air temperature;and moisture transport, has been investigated. Global Pre- cipitation Climatology Center (GPCC) data for precipitation and National Centre for Environ- mental Prediction (NCEP) reanalysis data for computation of Moisture Flux Convergence (MFC) and temperature have been used for the period of 49 years (1961 to 2009). The study period has been divided into three phases on basis of pre- cipitation anomaly i.e., before climate change scenario (1961-1985), transition period (1986- 1999) and after climate change scenario (2000- 2009).Variability in precipitation has been ob- served in three different ways such as, slightly increase in magnitudes, decrease in rainy days and shifting of precipitation pattern towards south of the country. Moisture transport from the surrounding has decreased with increase in precipitation which is indirectly associated with decreases in mass deposit on the glaciers. In- crease in temperature is more prominent over upper and lower part as compared to the central parts of the country. Uncertainty in precipitation has also been observed. Shift of precipitation over southern parts showed positive impact over agriculture sector. As a result, Rabi crop yield has increased during last decade over southern parts of the country.

关键词： Cloud Burst Seasonal Temperature Moisture Transport Shift in Precipitation

Agrometeorological wheat yield prediction in rainfed Potohar region of Pakistan

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Impact of climate change on agriculture during winter season over Pakistan

Agricultural Sciences（农业科学） 2012年第2期

作者： dildar hussain kazmi Ghulam Rasul

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Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications

Agricultural Sciences（农业科学） 2012年第8期

作者： Khalid M. Malik Arif Mahmood dildar hussain kazmi Jan Muhammad Khan

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Computers, Materials & Continua 2021年第1期66卷 715-732页

作者： Rizwan Ali Naqvi dildar hussain Woong-Kee Loh Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea School of Computational Science Korea Institute for Advanced Study(KIAS)Seoul02455Korea Department of Software Gachon UniversitySeongnam13120Korea

Multiple ocular region segmentation plays an important role in different applications such as biometrics,liveness detection,healthcare,and gaze ***,segmentation techniques focus on a single region of the eye at a *** the number of obvious advantages,very limited research has focused on multiple regions of the ***,accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur,ghost effects low resolution,off-angles,and unusual ***,the available segmentation methods cannot address these *** this paper,to address the accurate segmentation of multiple eye regions in unconstrainted scenarios,a lightweight outer residual encoder-decoder network suitable for various sensor images is *** proposed method can determine the true boundaries of the eye regions from inferior-quality images using the high-frequency information flow from the outer residual encoder-decoder deep convolutional neural network(called ORED-Net).Moreover,the proposed ORED-Net model does not improve the performance based on the complexity,number of parameters or network *** proposed network is considerably lighter than previous state-of-theart *** experiments were performed,and optimal performance was achieved using SBVPI and UBIRIS.v2 datasets containing images of the eye *** simulation results obtained using the proposed OREDNet,with the mean intersection over union score(mIoU)of 89.25 and 85.12 on the challenging SBVPI and UBIRIS.v2 datasets,respectively.

关键词： Semantic segmentation ocular regions biometric for healthcare sensors deep learning

Deep Learning in DXA Image Segmentation

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Computers, Materials & Continua 2021年第3期66卷 2587-2598页

作者： dildar hussain Rizwan Ali Naqvi Woong-Kee Loh Jooyoung Lee School of Computational Science Korea Institute for Advanced Study(KIAS)85 HoegiroDongdaemun-guSeoul02455South Korea Department of Unmanned Vehicle Engineering Sejong University209Neungdong-roGwangjin-guSeoul05006South Korea Department of Software Gachon UniversitySeongnam13120South Korea

Many existing techniques to acquire dual-energy X-ray absorptiometry(DXA)images are unable to accurately distinguish between bone and soft *** the most part,this failure stems from bone shape variability,noise and low contrast in DXA images,inconsistent X-ray beam penetration producing shadowing effects,and person-to-person *** work explores the feasibility of using state-of-the-art deep learning semantic segmentation models,fully convolutional networks(FCNs),SegNet,and U-Net to distinguish femur bone from soft *** investigated the performance of deep learning algorithms with reference to some of our previously applied conventional image segmentation techniques(i.e.,a decision-tree-based method using a pixel label decision tree[PLDT]and another method using Otsu’s thresholding)for femur DXA images,and we measured accuracy based on the average Jaccard index,sensitivity,and *** learning models using SegNet,U-Net,and an FCN achieved average segmentation accuracies of 95.8%,95.1%,and 97.6%,respectively,compared to PLDT(91.4%)and Otsu’s thresholding(72.6%).Thus we conclude that an FCN outperforms other deep learning and conventional techniques when segmenting femur bone from soft tissue in DXA *** femur segmentation improves bone mineral density computation,which in turn enhances the diagnosing of osteoporosis.

关键词： Segmentation deep learning osteoporosis dual-energy X-ray absorptiometry

Exploring polyamines:Functions in embryo/fetal development

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Animal Nutrition 2017年第1期3卷 7-10页

作者： Tarique hussain Bi'e Tan Wenkai Ren Najma Rahu dildar hussain Kalhoro Yulong Yin National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production Institute of Subtropical AgricultureKey Laboratory of Agro-EcologyInstitute of Subtropical AgricultureThe Chinese Academy of Sciences University of Chinese Academy of Sciences Hunan Collaborative Innovation Center for Utilization of Botanical Functional Ingredients Hunan Collaborative Innovation Center of Animal Production Safety Department of Veterinary Microbiology Faculty of Animal Husbandry and Veterinary SciencesSindh Agriculture University

Polyamines such as putrescine, spermidine, spermine and agmatine are aliphatic polycationic compounds present in all living cells, and are derived from amino acids, intestinal bacteria, exfoliated enterocytes and supported from diet. Polyamines as the key compounds play essential role in cell proliferation, growth and differentiation. They also exert significant effects on embryonic development,implantation, embryonic diapause, placentation, angiogensis and fetal development. This review paper summarizes the functions of polyamines and embryo/fetus development and its regulatory mechanism which should help to provide some evidences for clinic.

关键词： Polyamines Polyamine biosynthesis Embryo/fetus development

Roman Urdu News Headline Classification Empowered with Machine Learning

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Computers, Materials & Continua 2020年第11期65卷 1221-1236页

作者： Rizwan Ali Naqvi Muhammad Adnan Khan Nauman Malik Shazia Saqib Tahir Alyas dildar hussain Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computational Sciences Korea Institute for Advanced StudySeoul02455Korea

Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak *** from the subcontinent may speak the same Urdu language but they might be using different scripts for *** communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information *** Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the *** is due to the numerous complexities involved in the processing of Roman Urdu *** complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured *** have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be *** author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),Long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different *** preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the *** applying above mentioned different machine learnin

关键词： Roman urdu news headline classification long short term memory recurrent neural network logistic regression multinomial naïve Bayes random forest k neighbor gradient boosting classifier

Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

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Computers, Materials & Continua 2021年第4期67卷 1033-1049页

作者： Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad Faheem Mushtaq Rizwan Ali Naqvi dildar hussain Amir Haider School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan School of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea School of Computational Sciences Korea Institute for Advanced Study(KIAS)85 HoegiroDongdaemunguSeoul02455Korea Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality *** to clinical statistics,one woman out of eight is under the threat of breast *** and inheritance patterns may be a reason behind its spread among ***,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances *** diagnosis and initial stage treatment can help increase the survival *** that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)*** study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based *** multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%***,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.

关键词： Fusion feature breast cancer prediction deep learning convolutional neural network multi-modal medical image fusion decision-based fusion