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A Novel Method for Routing Optimization in Software-Defined Networks

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Computers, Materials & Continua 2022年第12期73卷 6393-6405页

作者： Salem Alkhalaf Fahad Alturise Department of Computer College of Science and Arts in ArRass Qassim UniversityAr RassQassimSaudi Arabia

Software-defined network(SDN)is a new form of network architecture that has programmability,ease of use,centralized control,and protocol *** has received high attention since its *** SDN network architecture,network management becomes more efficient,and programmable interfaces make network operations more flexible and can meet the different needs of various *** mainstream communication protocol of SDN is OpenFlow,which contains aMatch Field in the flow table structure of the protocol,which matches the content of the packet header of the data received by the switch,and completes the corresponding actions according to the matching results,getting rid of the dependence on the protocol to avoid designing a new *** order to effectively optimize the routing forSDN,this paper proposes a novel algorithm based on reinforcement *** proposed technique canmaximize numerous objectives to dynamically update the routing strategy,and it has great generality and is not reliant on any specific network *** control of routing strategy is more complicated than many Q-learning-based algorithms due to the employment of reinforcement *** performance of the method is tested by experiments using the OMNe++*** experimental results reveal that our PPO-based SDN routing control method has superior performance and stability than existing algorithms.

关键词： Reinforcement learning routing algorithm software-defined network optimization

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 1235-1257页

作者： Javed Rashid Imran Khan Ghulam Ali Shafiq ur Rehman Fahad Alturise Tamim Alkhalifah Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Department of CS University of OkaraOkara56310Pakistan Department of Botany University of OkaraOkara56310Pakistan Department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassQassim52571Saudi Arabia

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human *** is cultivated in vast areas of Asian and Non-Asian countries,including *** guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability *** existing plant leaf disease detection techniques can detect only one disease from a ***,a single leaf may contain symptoms of multiple *** study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several ***,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava *** proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder ***,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected *** the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava *** self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and *** proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and *** average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

关键词： Guava leaf diseases guava leaf segmentation guava patches segmentation multiple leaf diseases guava leaf diseases dataset

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

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Computers, Materials & Continua 2022年第3期70卷 4829-4845页

作者： Mohammad Hadwan Basheer M.Al-Maqaleh Fuad N.Al-Badani Rehan Ullah Khan Mohammed A.Al-Hagery Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Faculty of Computer Sciences and Information Systems Thamar UniversityThamarYemen Faculty of Administration Science Ibb UniversityIbbYemen Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medical domains,exchange rates and consumer price *** main problem is insufficient forecasting *** present study proposes a hybrid forecastingmethods to address this *** proposed method includes three *** first model is based on the autoregressive integrated moving average(ARIMA)statistical model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARIMA and BPNN(ARIMA/BPNN)and artificial neural networks and ARIMA(ARIMA/ANN)to gain the benefits of linear and *** forecasting models proposed in this study are used to predict the indices of the consumer price index(CPI),and predict the expected number of cancer patients in the Ibb Province in *** standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage *** on the computational results,the improvement rate of forecasting the CPI dataset was 5%,71%,and 4%for ARIMA/BPNN model,ARIMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARIMA/BPNNmodel,ARIMA/ANN model,and BPNNmodel ***,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data *** ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

关键词： Hybrid model forecasting non-linear data time series models cancer patients neural networks box-jenkins consumer price index

An Enhanced Graphical Authentication Scheme Using Multiple-Image Steganography

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Computer Systems Science & Engineering 2023年第3期44卷 2095-2107页

作者： Khalil Hamdi Ateyeh Al-Shqeerat Department of Computer Science College of ComputerQassim UniversitySaudi Arabia

Most remote systems require user authentication to access ***-based passwords are still widely used as a standard method of user *** conventional text-based passwords are rather hard to remember,users often write their passwords down in order to compromise *** of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by *** user authentication methods have recently been proposed to verify the user ***,the fundamental limitation of a graphi-cal password is that it must have a colorful and rich image to provide an adequate password space to maintain security,and when the user clicks and inputs a pass-word between two possible grids,the fault tolerance is adjusted to avoid this *** paper proposes an enhanced graphical authentication scheme,which comprises benefits over both recognition and recall-based graphical techniques besides image *** combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user *** evaluate the effectiveness of the proposed scheme,peak signal-to-noise ratio and mean squared error parameters have been used.

关键词： Security graphical authentication steganography peak signal to noise ratio mean squared error

Cross Intelligence Evaluation for Effective Emotional Intelligence Estimat

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Computers, Materials & Continua 2022年第2期70卷 2489-2505页

作者： Ibrahim Alsukayti Aman Singh Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Science and Engineering Lovely Professional University144411PunjabIndia

Afamous psychologist or researcher,Daniel Goleman,gave a theory on the importance of Emotional Intelligence for the success of an individual’s *** Goleman quoted in the research that“The contribution of an individual’s Intelligence Quotient(IQ)is only 20%for their success,the remaining 80%is due to Emotional Intelligence(EQ)”.However,in the absence of a reliable technique for EQ evaluation,this factor of overall intelligence is ignored in most of the intelligence evaluation *** research presented an analysis based on basic statistical tools along with more sophisticated deep learning *** proposed cross intelligence evaluation uses two different aspects which are similar,i.e.,EQ and SQ to estimate EQ by using a trained model over SQ *** presented analysis ensures the resemblance between the Emotional and Social Intelligence of an *** research authenticates the results over standard statistical tools and is practically inspected by deep learning *** Emotional Intelligence Questionnaire-Short Form(TEIQue-SF)and Social IQ dataset are deployed over aMulti-layered Long-Short TermMemory(M-LSTM)based deep learning model for accessing the resemblance between EQ and *** M-LSTM based trained deep learning model registered,the high positive resemblance between Emotional and Social Intelligence and concluded that the resemblance factor between these two is more than 99.84%.This much resemblance allows future researchers to calculate human emotional intelligence with the help of social *** flexibility also allows the use of Big Data available on social networks,to calculate the emotional intelligence of an individual.

关键词： Emotional intelligence social intelligence deep learning social IQ dataset multi-layered long short term memory

Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

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Computers, Materials & Continua 2021年第12期69卷 3399-3411页

作者： Suliman Aladhadh Hidayat Ur Rehman Ali Mustafa Qamar Rehan Ullah Khan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Ainfinity Algorythma Abu DhabiUnited Arab Emirates Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

A tremendous amount of vendor invoices is generated in the corporate *** automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is *** paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash *** text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an *** chunk is later passed to the deep learning model,which performs text *** deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the *** model integrates feature extraction,modeling sequence,and transcription into a unified *** handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale ***,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical *** overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed *** model is thus generic and can be used for other similar recognition scenarios.

关键词： Character recognition text spotting long short-term memory recurrent convolutional neural networks

Current updates of CRISPR/Cas9-mediated genome editing and targeting within tumor cells:an innovative strategy of cancer management

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Cancer Communications 2022年第12期42卷 1257-1287页

作者： Khaled S.Allemailem Mohammed A Alsahli Ahmad Almatroudi Faris Alrumaihi Fahd Khaleefah Alkhaleefah Arshad Husain Rahmani Amjad Ali Khan Department of Medical Laboratories College of Applied Medical SciencesQassim UniversityBuraydahSaudi Arabia Department of Laboratory and Blood Bank Al Rass General HospitalAl RassSaudi Arabia Department of Basic Health Sciences College of Applied Medical SciencesQassim UniversityBuraydahSaudi Arabia

Clustered regularly interspaced short palindromic repeats-associated protein(CRISPR/Cas9),an adaptive microbial immune system,has been exploited as a robust,accurate,efficient and programmable method for genome targeting and *** innovative and revolutionary technique can play a significant role in animal modeling,in vivo genome therapy,engineered cell therapy,cancer diagnosis and *** CRISPR/Cas9 endonuclease system targets a specific genomic locus by single guide RNA(sgRNA),forming a heteroduplex with target *** Streptococcus pyogenes Cas9/sgRNA:DNA complex reveals a bilobed architecture with target recognition and nuclease ***/Cas9 assembly can be hijacked,and its nanoformulation can be engineered as a delivery system for different clinical ***,the efficient and safe delivery of the CRISPR/Cas9 system to target tissues and cancer cells is very challenging,limiting its clinical *** delivery strategies of this system may have many advantages,but disadvantages such as immune system stimulation,tumor promotion risk and small insertion size outweigh these ***,there is a desperate need to develop an efficient non-viral physical delivery system based on simple *** delivery strategies of CRISPR/Cas9 by a nanoparticle-based system have shown tremendous potential,such as easy and large-scale production,combination therapy,large insertion size and efficient in vivo *** review aims to provide in-depth updates on Streptococcus pyogenic CRISPR/Cas9 structure and its mechanistic *** addition,the advances in its nanoformulation-based delivery systems,including lipid-based,polymeric structures and rigid NPs coupled to special ligands such as aptamers,TAT peptides and cell-penetrating peptides,are ***,the clinical applications in different cancers,clinical trials and future prospects of CRISPR/Cas9 delivery and genome targeting are also discussed.

关键词： cancer clinical trials CRISPR/Cas9 genome editing mechanism nanoparticles structure

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

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Computer Systems Science & Engineering 2023年第3期44卷 1973-1988页

作者： Aishah Alrashidi Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Electrical load forecasting is very crucial for electrical power systems’planning and *** electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load *** meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi *** factory load and meteorological data used in this study are recorded hourly between 2016 and *** data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in *** applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory *** addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this *** outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning ***,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

关键词： Electricity load forecasting meteorological data machine learning feature selection modeling real-world problems predictive analytics