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Optimized Resource Allocation and Queue Management for Traffic Control in MANET

Computer Systems Science & Engineering 2023年第5期45卷 1323-1342页

作者： I.Ambika Surbhi Bhatia shakila basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information College of Computer Science and Information TechnologyKing Faisal University36362Saudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks(MANETs).Offering better communication services among the users in a centralized organization is the primary objective of the *** to the features of MANET,this can directly End-to-End Delay(EED)the Quality of Service(QoS).Hence,the implementation of resource management becomes an essential issue in *** paper focuses on the efficient Resource Allocation(RA)for many types of Traffic Flows(TF)in *** Mobile Ad hoc Networks environments,the main objective of Resource Allocation(RA)is to process consistently available resources among terminals required to address the service requirements of the *** three categories improve performance metrics by varying transmission rates and simulation *** solving that problem,the proposed work is divided into Queue Management(QM),Admission Control(AC)and *** effective QM,this paper develops a QM model for elastic(EL)and inelastic(IEL)Traffic *** research paper presents an AC mechanism for multiple TF for effective *** work presents a Resource Allocation Using Tokens(RAUT)for various priority TF for effective ***,nodes have three cycles which are:Non-Critical Section(NCS),Entry Section(ES)and Critical Section(CS).When a node requires any resources,it sends Resource Request Message(RRM)to the *** and inelastic TF priority is determined using Fuzzy Logic(FL).The token holder selects the node from the inelastic queue with high priority for allocating the *** Network Simulator-2(NS-2),simulations demonstrate that the proposed design increases Packet Delivery Ratio(PDR),decrease Packet Loss Ratio(PLR),minimise the Fairness and reduce the EED.

关键词： MANET resource allocation end-to-end delay fuzzy logic QoS

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Improved Multi-Path Routing for QoS on MANET

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Computer Systems Science & Engineering 2023年第6期45卷 2521-2536页

作者： M.Vargheese Surbhi Bhatia shakila basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A Mobile Ad hoc NETwork(MANET)is a self-configuring network that is not reliant on *** paper introduces a new multipath routing method based on the Multi-Hop Routing(MHR)*** is the consecutive selection of suitable relay nodes to send information across nodes that are not within direct range of each *** to ensure good MHR leads to several negative consequences,ultimately causing unsuccessful data transmission in a *** research work consists of three *** first to attempt to propose an efficient MHR protocol is the design of Priority Based Dynamic Routing(PBDR)to adapt to the dynamic MANET environment by reducing Node Link Failures(NLF)in the *** is achieved by dynamically considering a node’s mobility parameters like relative velocity and link duration,which enable the next-hop *** method works more efficiently than the traditional *** the second stage is the Improved Multi-Path Dynamic Routing(IMPDR).The enhancement is mainly focused on further improving the Quality of Service(QoS)in MANETs by introducing a QoS timer at every node to help in the QoS routing of *** QoS is the most vital metric that assesses a protocol,its dynamic estimation has improved network performance *** method uses distance,linkability,trust,and QoS as the four parameters for the next-hop *** is compared against traditional routing *** Network Simulator-2(NS2)is used to conduct a simulation analysis of the protocols under *** proposed tests are assessed for the Packet Delivery Ratio(PDR),Packet Loss Rate(PLR),End-to-End Delay(EED),and Network Throughput(NT).

关键词： Multi-path routing quality of service node-link failure packet delivery ratio

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Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

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Intelligent Automation & Soft Computing 2023年第5期36卷 1767-1782页

作者： M.P.Thiruvenkatasuresh Surbhi Bhatia shakila basheer Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia Corresponding

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient *** a novel ML algorithm speciﬁc to Diabetic Retinopathy(DR)is a chal-lenge and need of the *** images include several challenges,including relevant feature selection,class variations,and robust classiﬁ*** the cur-rent research in DR has yielded favourable results,several research issues need to be *** is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-ﬁ*** is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classiﬁer(SKPD-PSC)*** novelty of this method relies on the appropriate non-linear classiﬁcation of exclusive image processing models in har-mony with the Platt Scale Classiﬁer(PSC)to improve the accuracy of DR ***,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background ***,a Platt Scale Classiﬁer(PSC)is applied to the localized features for robust classiﬁ*** the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classiﬁ*** results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to

关键词： Diabetic retinopathy retinal images machine learning image localization Platt Scale classiﬁer accuracy

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Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

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Computers, Materials & Continua 2024年第1期78卷 1189-1205页

作者： K.Akilandeswari Nithya Rekha Sivakumar Hend Khalid Alkahtani shakila basheer Sara Abdelwahab Ghorashi Department of Computer Sciences Government Arts College(Autonomous)Salem636007India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and *** exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and *** many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during ***,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare *** first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction ***,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable *** attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced *** that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and *** evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several *** quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

关键词： Internet of Things smart health care monitoring human activity recognition intelligent agent learning statistical partial regression support vector

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Image Enhancement Using Adaptive Fractional Order Filter

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Computer Systems Science & Engineering 2023年第5期45卷 1409-1422页

作者： Ayesha Heena Nagashettappa Biradar Najmuddin M.Maroof Surbhi Bhatia Arwa Mashat shakila basheer Department of Electronics and Communication Engineering Bheemanna Khandre Institute of TechnologyBhalkiVisveswaraya Technological UniversityBelagaviIndia Department of Electronics and Communication Engineering Khaja Banda Nawaz College of EngineeringVisveswaraya Technological UniversityBelagaviIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-HofufAl-AhsaSaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Information Systems College of Computer and Information Science Princess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should *** research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order *** this fractional-order,differentiation is well focused on the enhancement of echocardiographic *** provoked for developing a non-linear filter mask for image *** designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth *** novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and ***,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the *** is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative *** wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been *** proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous *** values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the *** results of the simulation validate the effectiveness of the proposed algorithm.

关键词： Adaptive filter differential filter enhancement mask fractional differential mask fractional-order calculus hessian matrix

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Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

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Computers, Materials & Continua 2024年第4期79卷 1157-1185页

作者： Sapiah Sakri shakila basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak Ismail Dua’Abdellatef Nassar Manal Abdullah Alohali Mais Ayman Alharaki Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications *** detection is crucial as sepsis can rapidly escalate if left *** in deep learning(DL)offer powerful tools to address this ***:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis *** the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier ***:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of *** time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis *** models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical *** confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate ***:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis *** results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one ***:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

关键词： Temporal derivatives hybrid deep learning predicting sepsis onset MIMIC III machine learning(ML) deep learning

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