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A Fused Machine Learning Approach for Intrusion Detection System

Computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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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

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Rational design on photoelectrodes and devices to boost photoelectrochemical performance of solar-driven water splitting: a mini review

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Frontiers of Chemical Science and Engineering 2022年第6期16卷 777-798页

作者： Siliu Lyu Muhammad adnan Younis Zhibin Liu Libin Zeng Xianyun Peng Bin Yang Zhongjian Li Lecheng Lei Yang Hou Key Laboratory of Biomass Chemical Engineering of Ministry of Education College of Chemical and Biological EngineeringZhejiang UniversityHangzhou 310027China Institute of Zhejiang University-Quzhou Quzhou 324000China

As an eco-friendly,efficient,and low-cost technique,photoelectrochemical water splitting has attracted growing interest in the production of clean and sustainable hydrogen by the conversion of abundant solar *** the photoelectrochemical system,the photoelectrode plays a vital role in absorbing the energy of sunlight to trigger the water splitting process and the overall efficiency depends largely on the integration and design of photoelectrochemical *** recent years,the optimization of photoelectrodes and photoelectrochemical devices to achieve highly efficient hydrogen production has been extensively *** this paper,a concise review of recent advances in the modification of nanostructured photoelectrodes and the design of photoelectrochemical devices is ***,the general principles of structural and morphological factors in altering the photoelectrochemical performance of photoelectrodes are ***,the performance indicators and first principles to describe the behaviors of charge carriers are analyzed,which will be of profound guiding significance to increasing the overall efficiency of the photoelectrochemical water splitting ***,current challenges and prospects for an in-depth understanding of reaction mechanisms using advanced characterization technologies and potential strategies for developing novel photoelectrodes and advanced photoelectrochemical water splitting devices are demonstrated.

关键词： photoelectrochemical water splitting photoelectrodes hydrogen production charge separation catalytic mechanism

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Projection of precipitation extremes over South Asia from CMIP6 GCMs

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Journal of Arid Land 2023年第3期15卷 274-296页

作者： adnan ABBAS Asher S BHATTI Safi ULLAH Waheed ULLAH Muhammad WASEEM ZHAO Chengyi DOU Xin Gohar ALI Land Science Research Center Nanjing University of Information Science&TechnologyNanjing 210044China Department of Geology Bacha Khan UniversityCharsadda 24420Pakistan Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China Center of Excellence in Water Resources University of Engineering and TechnologyLahore 54890Pakistan Pakistan Meteorological Department Sector H-8/2Islamabad 44000Pakistan

Extreme precipitation events are one of the most dangerous hydrometeorological disasters,often resulting in significant human and socio-economic losses *** is therefore important to use current global climate models to project future changes in precipitation *** present study aims to assess the future changes in precipitation extremes over South Asia from the Coupled Model Intercomparison Project Phase 6(CMIP6)Global Climate Models(GCMs).The results were derived using the modified Mann-Kendall test,Sen's slope estimator,student's t-test,and probability density function *** extreme precipitation indices were assessed,including wet days(RR1mm),heavy precipitation days(RR10mm),very heavy precipitation days(RR20mm),severe precipitation days(RR50mm),consecutive wet days(CWD),consecutive dry days(CDD),maximum 5-day precipitation amount(RX5day),and simple daily intensity index(SDII).The future changes were estimated in two time periods for the 21^(st) century(i.e.,near future(NF;2021-2060)and far future(FF;2061-2100))under two Shared Socioeconomic Pathway(SSP)scenarios(SSP2-4.5 and SSP5-8.5).The results suggest increases in the frequency and intensity of extreme precipitation indices under the SSP5-8.5 scenario towards the end of the 21^(st) century(2061-2100).Moreover,from the results of multimodel ensemble means(MMEMs),extreme precipitation indices of RR1mm,RR10mm,RR20mm,CWD,and SDII demonstrate remarkable increases in the FF period under the SSP5-8.5 *** spatial distribution of extreme precipitation indices shows intensification over the eastern part of South Asia compared to the western *** probability density function of extreme precipitation indices suggests a frequent(intense)occurrence of precipitation extremes in the FF period under the SSP5-8.5 scenario,with values up to 35.00 d for RR1mm and 25.00-35.00 d for *** potential impacts of heavy precipitation can pose serious challenges to the study area regarding floodin

关键词： precipitation extremes extreme precipitation indices climate change Coupled Model Intercomparison Project 6(CMIP6) Global Climate Model(GCM) South Asia

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University 4. Faculty of Science Agronomy Department Hydraulics Division University Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM)

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm(ANFIS-FCM-PSOGSA) in improving the estimation accuracy of river suspended sediment loads(SSLs) is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization(ANFIS-FCM-PSO), ANFIS-FCM, and sediment rating curve(SRC) models. Various input combinations involving lagged river flow(Q) and suspended sediment(S) values were used for model development. The effect of Q and S on the model’s accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error(RMSE), mean absolute error(MAE), Nash-Sutcliffe Efficiency(NSE), and coefficient of determination(R~2) and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO(or ANFIS-FCM) models by 8.14%(1.72%), 14.7%(5.71%), 12.5%(2.27%), and 25.6%(1.86%),in terms of the RMSE, MAE, NSE and R~2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Reducing phosphorylation of nitrate reductase improves nitrate assimilation in rice

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Journal of Integrative Agriculture 2022年第1期21卷 15-25页

作者： HAN Rui-cai LI Chen-yan adnan RASHEED PAN Xiao-hua SHI Qing-hua WU Zi-ming Key Laboratory of Crop Physiology Ecology and Genetic BreedingMinistry of Education/College of AgronomyJiangxi Agricultural UniversityNanchang 330045P.R.China Rice Research Institute Jiangxi Academy of Agricultural Sciences/Jiangxi Provincial Key Laboratory for Physiology and Genetics of RiceNanchang 330200P.R.China

Nitrate reductase(NR) is an important enzyme for nitrate assimilation in plants, and post-translational phosphorylation regulates NR activity. To evaluate the impact of the dephosphorylation of nitrate reductase 1(NIA1) protein on NR activity,nitrogen metabolism and plant growth, NIA1 phosphorylation site directed mutant lines(S532 D and S532 A) and an OsNia1 over-expression line(OE) were constructed, and the phenotype, NIA1 protein and its phosphorylation level, NR activity,nitrate metabolism and reactive oxygen metabolism of the transgenic lines were analysed. Exogenous NIA1 protein was not phosphorylated in S532 D and S532 A mutant lines, and their NR activities, activity states of NR and assimilation efficiencies of NO3–-N were higher than those in Kitaake(WT) and OE. The changes in these physiological and biochemical indexes in the OE line were less than in S532 D and S532 A compared to WT. These results suggest that the removal of transcriptional level control had little effect on nitrogen metabolism, but the removal of post-translational modification had a profound effect on it. With the removal of NIA1 phosphorylation and the improvement in the nitrate assimilation efficiency, the plant height and chlorophyll content of S532 D and S532 A decreased and the hydrogen peroxide and malondialdehyde contents of rice seedlings increased, which may be related to the excessive accumulation of nitrite as an intermediate metabolite. These results indicated that the phosphorylation of NR may be a self-protection mechanism of rice. The reduced phosphorylation level of nitrate reductase improved the assimilation of nitrate, and the increased phosphorylation level reduced the accumulation of nitrite and prevented the toxic effects of reactive oxygen species in rice.

关键词： rice(Oryza sativa L.) nitrate reductase phosphorylation directed mutation nitrogen metabolism

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IBCIS:Intelligent blood cell identification system

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Progress in Natural Science:Materials International 2008年第10期18卷 1309-1314页

作者： adnan Khashman Electrical and Electronic Engineering Department Near East UniversityLefkosaMersin 10Turkey

The analysis of blood cells in microscope images can provide useful information concerning the health of *** are three major blood cell types,namely,erythrocytes (red),leukocytes (white),and *** classification is time consuming and suscep- tible to error due to the different morphological features of the *** paper presents an intelligent system that simulates a human visual inspection and classification of the three blood cell *** proposed system comprises two phases:The image preprocessing phase where blood cell features are extracted via global pattern averaging,and the neural network arbitration phase where training is the first and then classification is carried *** results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes,sizes and orientation,thus providing a fast,simple and efficient rotational and scale invariant blood cell identification system which can be used in automating laboratory reporting.

关键词： Blood cells Intelligent identification Neural network Pattern averaging

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Single-walled carbon nanotubes coated D-shaped fiber for aqueous ethanol detection

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Optoelectronics Letters 2022年第7期18卷 430-433页

作者： Huda adnan Zain Malathy Batumalay Hazli Rafis Abdul Rahim Moh Yasin Sulaiman Wadi Harun Department of Electrical Engineering University of MalayaKuala Lumpur 50603Malaysia Faculty of Information Technology INTI International UniversityNilai 71800Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelakaMalaysia Department of Physics Faculty of Science and TechnologyAirlangga UniversitySurabayaIndonesia

In this letter,single-walled carbon nanotubes(SWCNT)coating was used on the D-shaped silica fiber for ethanol sensing in aqueous *** performance of this structure as an ethanol sensor was studied here by monitoring output power variation and wavelength shift with changing ethanol *** the concentration range of 5%—50%of ethanol,the SWCNT coated structure showed an improved sensitivity compared to the uncoated *** sensitivity is improved from 0.0135 dB/%to 0.0409 dB/%with the *** SWCNT coated sample also showed a peak wavelength shift of 0.1 nm with the change of ethanol concentration in the same range.

关键词： shaped fiber walled

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Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

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Computers, Materials & Continua 2023年第11期77卷 2157-2178页

作者： Gul Nawaz Muhammad Junaid adnan Akhunzada Abdullah Gani Shamyla Nawazish Asim Yaqub Adeel Ahmed Huma Ajab Department of Information Technology The University of HaripurHaripur22060Pakistan Faculty of Computing and Informatics University Malaysia SabahSabah88400Malaysia Department of Environmental Sciences COMSATS University Abbottabad CampusAbbottabad22010Pakistan Department of Chemistry COMSATS University Abbottabad CampusAbbottabad22010Pakistan

Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate *** proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)*** centralizes the control plane and separates it from the data *** simplifies a network and eliminates vendor specification of a *** of this open nature and centralized control,SDN can easily become a victim of DDoS *** proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate *** Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)*** proposed DNN model scans the data to find the correlated features and delivers high-quality *** model enhances the security of SDN and has better accuracy as compared to previously proposed *** choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing *** model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss *** accuracy increases to 99.80%as we increase the number of epochs to 100 *** proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed *** can handle a huge amount of structured and unstructured data and can easily solve complex problems.

关键词： Distributed denial of service(DDoS)attacks software-defined networking(SDN) classification deep neural network(DNN)

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