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Potential of Plant Growth Regulators on Modulating Rooting of <i>Rosa centifolia</i>

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American Journal of Plant Sciences 2015年第5期6卷 659-665页

作者： Gulzar Akhtar Ahsan Akram Yasar Sajjad Rashad Mukhtar Balal muhammad adnan shahid Hasan Sardar Kaleem Naseem shahid Masood Shah Department of Horticulture University College of Agriculture University of Sargodha Pakistan Department of Environmental Sciences COMSATS Institute of Information Technology Abbottabad Pakistan Institute of Horticultural Sciences University of Agriculture Faisalabad Pakistan

Rosa centifolia is grown for its flowers which are used for essential oil extraction. It is vegetatively propagated through its cuttings which are difficult to root. This study was designed to increase the rooting percentage of cuttings by applying plant growth regulators (PGRs) with different levels. In total 31 treatments with different concentrations (450 ppm, 700 ppm and 950 ppm) of indole butyric acid (IBA), indole acetic acid (IAA), naphthalene acetic acid (NAA) alone, in combination and with same concentrations of 6-benzylamino purine (BAP) were used. Healthy similar sized cutting of R. centifolia was treated in solution of PGRs by quick dip method and planted in polythene bags. The data for shoot length, shoot dry weight, number of roots, root length and root dry weight were recorded. All three levels (450 ppm, 700 ppm and 950 ppm) of IBA alone produced maximum results in case of all parameters as compared to all other treatments. Among three levels of IBA, 450 ppm concentration produced maximum shoot length (10.67 cm), shoot dry weight (3.02 g), number of roots (14.00), root length (11.90 cm) and root dry weight (0.50 g). Lower concentrations of plant growth regulators produced better results as compared to higher concentrations.

关键词： Growth Regulator Rosa centifolia Auxins Rooting Shooting

IDSSCNN-XgBoost:Improved Dual-Stream Shallow Convolutional Neural Network Based on Extreme Gradient Boosting Algorithm for Micro Expression Recognition

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Computers, Materials & Continua 2025年第1期82卷 729-749页

作者： adnan Ahmad Zhao Li Irfan Tariq Zhengran He School of Information Science and Engineering Southeast UniversityNanjing210016China

Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial *** deep neural networks(DNNs)with convolutional structures have been ***,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small ***,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective *** address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME *** proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective *** outputs are then fused,concatenated,and classified using the XgBoost *** comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of *** proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent *** accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,*** proposed method has the advantage of operational efficiency and less computational time.

关键词： ME recognition dual stream shallow convolutional neural network euler video magnification TV-L1 XgBoost

Review of Techniques for Integrating Security in Software Development Lifecycle

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Computers, Materials & Continua 2025年第1期82卷 139-172页

作者： Hassan Saeed Imran Shafi Jamil Ahmad adnan Ahmed Khan Tahir Khurshaid Imran Ashraf College of Electrical and Mechanical Engineering National University of Sciences and Technology(NUST)Rawalpindi46604Pakistan Department of Computer Science Abasyn University Islamabad CampusIslamabad45510Pakistan Military College of Signals National University of Sciences and Technology(NUST)Rawalpindi46600Pakistan Department of Electrical Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Republic of Korea

Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our *** conduct research in this field,periodic comparative analysis is needed with the new techniques coming up *** purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way *** review follows Kitchenham’s review *** review has been divided into three main stages including planning,execution,and *** the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation *** risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation *** a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat *** review reveals that threat analysis and security testing are needed to develop automated tools for the *** estimation of effort required to prioritize potential security risks is a big challenge in software *** accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep *** is also imperative to validate these effort estimation methods to ensure all potential security threats are *** challenge is selecting the right model for each specific security *** achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.

关键词： Software development lifecycle systematic literature review critical software security risks national institute of standards and technology DevSecOps open web application security project McGraw’s touch points

Automatic Unusual Activities Recognition Using Deep Learning in Academia

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Computers, Materials & Continua 2022年第1期70卷 1829-1844页

作者： muhammad Ramzan adnan Abid shahid Mahmood Awan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Electronics Computing and MathematicsUniversity of DerbyDerbyUnited Kingdom

In the current era,automatic surveillance has become an active research problem due to its vast real-world applications,particularly for maintaining law and order.A continuous manual monitoring of human activities is a tedious *** use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few *** computer vision techniques have been applied for observation and surveillance of real-world *** research study focuses on detecting and recognizing unusual activities in an academic situation such as examinationhalls,which may help the invigilators observe and restrict the students from cheating or using *** the best of our knowledge,this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual *** proposed model has been named Automatic Unusual Activity Recognition(AUAR),which employsmotion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse *** evaluation using standard performancemeasures confirmthat theAUARmodel outperforms the already proposed approaches for unusual activity *** from evaluating the proposed model on the examination dataset,we also apply AUAR on Violent and Movies datasets,widely used in the relevant literature to detect suspicious *** results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

关键词： Deep learning unusual activities examination CNN surveillance human activity recognition

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

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Computers, Materials & Continua 2022年第6期71卷 5603-5618页

作者： shahid Mehmood Imran Ahmad muhammad adnan Khan Faheem Khan T.Whangbo Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Education sector has witnessed several changes in the recent *** changes have forced private universities into fierce competition with each other to get more students *** competition has resulted in the adoption of marketing practices by private universities similar to commercial *** get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their *** study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the *** techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore *** results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local *** on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

关键词： Social media higher education sentiment analysis content analysis competitive analysis text mining

Modelling and Verification of Context-Aware Intelligent Assistive Formalism

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Computers, Materials & Continua 2022年第5期71卷 3355-3373页

作者： shahid Yousaf Hafiz Mahfooz Ul Haque Abbas Khalid muhammad adnan Hashmi Eraj Khan Department of Computer Science&IT The University of LahoreLahore54000Pakistan Department of Software Engineering The University of LahoreLahore54000Pakistan Higher Colleges of Technology Abu Dhabi25026UAE

Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life activities. This trend has been rapidly advancing towards the newgeneration of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquirecontextual information from the surrounding environment autonomously,perform reasoning on it, and then adapt their behaviors accordingly. With theproliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However,it is often challenging because the imperfect nature of context can cause theinconsistent behavior of the system. In this paper, we propose a contextaware intelligent decision support formalism to assist cognitively impairedpeople in managing their routine life activities. For this, we present a semanticknowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language(SWRL) rules. The set of contextualized information and the set of rulesacquired from the ontology can be used to model Context-aware Multi-AgentSystems (CMAS) in order to autonomously plan all activities of the users andnotify users to act accordingly. To illustrate the use of the proposed formalism,we model a case study of Mild Cognitive Impaired (MCI) patients usingColored Petri Nets (CPN) to show the reasoning process on how the contextaware agents collaboratively plan activities on the user’s behalf and validatethe correctness properties of the system.

关键词： Context-awareness multi-agents colored petri net ontology

De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning(MCR)

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Computer Systems Science & Engineering 2023年第5期45卷 1167-1186页

作者： Kazim Ali adnan N.Qureshi muhammad shahid Bhatti Abid Sohail muhammad Hijji Atif Saeed Department of Computer Science Faculty of Information TechnologyUniversity of Central Punjab LahoreLahore54000Pakistan Department of Computer Science COMSAT University IslamabadLahore CampusLahore54000Pakistan Faculty of Computers and Information Technology Computer Science DepartmentUniversity of TabukTabuk47711Saudi Arabia

Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor *** quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition *** the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR ***,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising *** proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI *** MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian *** experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result *** Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean ***,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original *** proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%*** the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.

关键词： MR brain images median filter wiener filter concatenation learning residual learning MCR de-noising method

Defending Adversarial Examples by a Clipped Residual U-Net Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2237-2256页

作者： Kazim Ali adnan N.Qureshi muhammad shahid Bhatti Abid Sohail Mohammad Hijji Department of Computer Science Faculty of Information TechnologyUniversity of Central Punjab Lahore54000Pakistan Department of Computer Science COMSAT University IslamabadLahore CampusLahore54000Pakistan Faculty of Computers and Information Technology Computer Science DepartmentUniversity of TabukTabuk47711Saudi Arabia

Deep learning-based systems have succeeded in many computer vision ***,it is found that the latest study indicates that these systems are in danger in the presence of adversarial *** attacks can quickly spoil deep learning models,e.g.,different convolutional neural networks(CNNs),used in various computer vision tasks from image classiﬁcation to object *** adversarial examples are carefully designed by injecting a slight perturbation into the clean *** proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense,Generative Adversarial Net-work Defense,Deep Regret Analytic Generative Adversarial Networks Defense,Deep Denoising Sparse Autoencoder Defense,and Condtional Generattive Adversarial Network *** have experimentally proved that our approach is better than previous defensive *** proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial *** proposed defensive approach is based on residual and U-Net *** experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer visionﬁ*** have also reported similarity(SSIM and PSNR)between the original and restored clean image examples by the proposed CRU-Net defense model.

关键词： Adversarial examples adversarial attacks defense method residual learning u-net cgan cru-et model

Numerical investigation for the suitable choice of bubble diameter correlation for EMMS/bubbling drag model

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Chinese Journal of Chemical Engineering 2022年第7期35卷 254-270页

作者： Nouman Ahmad Jianqiang Deng muhammad adnan Shaanxi Key Laboratory of Energy Chemical Process Intensification Xi’an 710049China School of Chemical Engineering and Technology Xi’an Jiaotong UniversityXi’an 710049China

Mesoscale bubbles exist inherently in bubbling fluidized beds and hence should be considered in the constitutive modeling of the drag *** energy minimization multiscale bubbling(EMMS/bubbling)drag model takes the effects of mesoscale structures(i.e.,bubbles)into the modeling of drag coefficient and thus improves the coarse-grid simulation of bubbling and turbulent fluidized ***,its dependence on the bubble diameter correlation has not been thoroughly *** hydrodynamic disparity between homogeneous and heterogeneous fluidization is accounted for by the heterogeneity index,H_(d),which can be affected by choice of bubble diameter *** this choice of bubble diameter correlation influences the model prediction calls for further fundamental *** article incorporated seven different bubble diameter correlations into EMMS/bubbling drag model and studied their effects on H_(d).The performance of these correlations has been compared with the correlation used previously by EMMS/bubbling drag *** found that some of the correlations predicted lower Hd by order of a magnitude than the correlation used by the original EMMS/bubbling *** on such analysis,we proposed a modification in the EMMS drag model for bubbling and turbulent fluidized beds.A computational fluid dynamics(CFD)simulation using two-fluid model with the modified EMMS/bubbling drag model was performed for two bubbling and one turbulent fluidized *** distribution,time averaged solid concentration and axial solid concentration profiles were studied and compared with the previous version of the EMMS/bubbling drag model and experimental *** found that the right choice of bubble diameter correlations can significantly improve the results for CFD simulations.

关键词： Mesoscale Bubbles Energy minimization multiscale(EMMS) Heterogeneity index Bubbling fluidized bed