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| 1 | Vegetation-environment relationships in the forests of Chitral district Hindukush range of Pakistan显示文摘We investigated the composition of plant communities to quantify their relationships with environmental parameters in the Chitral Hindukush range of Pakistan. We sampled tree vegetation using the Point Centered Quarter (PCQ) method while understory vegetation was sampled in 1.5-m circular quadrats. Cedrus deodara is the national symbol of Pakistan and was dominant in the sampled communities. Because environmental variables determine vegetation types, we analyzed and evaluated edaphic and topographic factors. DCA-Ordination showed the major gradient as an amalgam of elevation (p<0.05) and slope (p<0.01) as the topographic factors correlated with species distribution. Soil variables were the factors of environmental significance along DCA axes. However, among these factors, Mg2+ , K + and N2+ contributed not more than 0.054% 0.20% and 0.073%, respectively, to variation along the first ordination axis. We conclude that the principal reason for weak or no correlation with many edaphic variables was the anthropogenic disturbance of vegetation. The understory vegetation was composed of perennial herbs in most communities and was most dense under the tree canopy. The understory vegetation strongly regulates tree seedling growth and regeneration patterns. We recommend further study of the understory vegetation using permanent plots to aid development of forest regeneration strategies. | Nasrullah Khan Syed Shahid Shaukat Moinuddin Ahmed Muhammad Faheem Siddiqui | 2013 | Journal of Forestry Research2013，24，2： | 8 |
| 2 | Multi-material Bio-inspired Soft Octopus Robot for Underwater Synchronous Swimming显示文摘Inspired by the simple yet amazing morphology of the Octopus, we propose the design, fabrication, and characterization of multi-material bio-inspired soft Octopus robot (Octobot). 3D printed molds for tentacles and head were used. The tentacles of the Octobot were casted using Ecoflex-0030 while head was fabricated using relatively flexible material, i.e., OOMOO-25. The head is attached to the functionally responsive tentacles (each tentacle is of 79.12 mm length and 7 void space diameter), whereas Shape Memory Alloy (SMA) muscle wires of 0.5 mm thickness are used in Octobot tentacles for dual thrust generation and actuation of Octobot. The tentacles were separated in two groups and were synchronously actuated. Each tentacle of the developed Octobot contains a pair of SMA muscles (SMA-α and SMA-β). SMA-α muscles being the main actuator, was powered by 9 V, 350 mA power supply, whereas SMA-β was used to provide back thrust and thus helps to increase the actuation frequency. Simulation work of the proposed model was performed in the SolidWorks environment to verify the vertical velocity using the octopus tentacle actuation. The design morphology of Octobot was optimized using simulation and TRACKER software by analyzing the experimental data of angle, displacement, and velocity of real octopus. The as-developed Octobot can swim at variable frequencies (0.5–2 Hz) with the average speed of 25 mm/s (0.5 BLS). Therefore, the proposed soft Octopus robot showed an excellent capability of mimicking the gait pattern of its natural counterpart. | Faheem Ahmed Muhammad Waqas Bushra Shaikh Umair Khan Afaque Manzoor Soomro Suresh Kumar Hina Ashraf Fida Hussain Memon Kyung Hyun Choi | 2022 | Journal of Bionic Engineering2022，19，5： | 1 |
| 3 | Effect of annealing on structural, optical and electrical properties of nanostructured Ge thin films显示文摘 | Abdul Faheem Khan Mazhar Mehmood Anwar M. Rana Taj Muhammad | 2009 | Applied Surface Science2009，，7： | 1 |
| 4 | Vegetation-environment relationship in conifer dominating forests of the mountainous range of Indus Kohistan in northern Pakistan显示文摘Environmental variables play a crucial role in shaping vegetation structure,mainly in mountainous ecosystems.Different studies have attempted to identify the environment-vegetation relationship of Conifer Dominating Forests(CDF)worldwide.However,due to differences in local climate and soil composition,different environmental drivers can be found.By applying multivariate analysis techniques,this study investigated the vegetation-environment relationship of CDF of Indus Kohistan in northern Pakistan.Our results showed that CDF of Indus Kohistan are distributed in five distinct ecological groups,which are dominated by different trees and understory species.A total of 7 trees and 71 understory species were recorded from the sampling sites.Cedrus deodara was the leading species among four groups,having the highest importance value(IV),density and basal area.Group I was dominated by Pinus wallichiana with the second highest importance value,density and basal area.In addition,elevation,slope,maximum water holding capacity(MWHC),soil moisture(SM),total organic matter(TOM),sodium,phosphorus and nickel showed highly significant influence on composition and distribution pattern of Indus Kohistan vegetation.Therefore,this study shows a new evidence of vegetation-environment relationship,pointing out specific drivers of vegetation structure in CDF of Indus Kohistan region in northern Pakistan. | Adam KHAN Moinuddin AHMED Muhammad Faheem SIDDIQI Mohib SHAH Eduardo Soares CALIXTO Afsheen KHAN Paras SHAH Javed IQBAL Muhammad AZEEM | 2020 | Journal of Mountain Science2020，17，8： | 1 |
| 5 | Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning显示文摘Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In 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%accuracy.Finally,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. | Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad Faheem Mushtaq Rizwan Ali Naqvi Dildar Hussain Amir Haider | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 6 | Metal-catalyzed synthesis of ultralong tin dioxide nanobelts: Electrical and optical properties with oxygen vacancy-related orange emission显示文摘 | Faheem K. Butt Chuanbao Cao Tariq Mahmood Faryal Idrees Muhammad Tahir Waheed S. Khan Zulfiqar Ali Muhammad Rizwan M. Tanveer Sajad Hussain Imran Aslam Dapeng Yu | 2014 | Materials Science in Semiconductor Processing2014，，： | 1 |
| 7 | Presentation delay in breast cancer patients and its association with sociodemographic factors in North Pakistan显示文摘Background: There is strong evidence that delayed diagnosis of breast cancer is associated with poor survival. The objectives were to determine the frequency of breast cancer patients with delayed presentation, the reasons of delay and its association with different socio-demographic variables in our settings.Methods: We interviewed 315 histologically confirmed breast cancer patients. Delay was defined as more than 3 months from appearance of symptoms to the consultation from doctor. Questions were asked from each patient which could reflect their understanding about the disease and which could be the likely reasons for their delayed presentation.Results: A total of 39.01%(n=123) of patients presented late and out of those, 40.7% wasted time using alternative medicines; 25.2% were not having enough resources; 17.1% presented late due to painless lump; 10.6% felt shyness and 6.5% presented late due to other reasons. Higher age, negative family history, <8 school years of education and low to middle socio-economic status were significantly associated with delayed presentation(P<0.05). Education and socioeconomic status were two independent variables related to the delayed presentation after adjustment for others [odds ratios(OR) of 2.26, 2.29 and 95% confidence intervals(CI) was 1.25-4.10, 1.06-4.94 respectively].Conclusions: Significant percentage of women with breast cancer in North Pakistan is experiencing presentation delay due to their misconceptions about the disease. Coordinated efforts with public health department are needed to educate the focused groups and mitigating the barriers identified in the study. Long term impact will be reduced overall burden of the disease in the region. | Muhammad Aleem Khan Sheharyar Hanif Sundas Iqbal Muhammad Faheem Shahzad Sehrish Shafique Muhammad Taha Khan | 2015 | Chinese Journal of Cancer Research2015，27，3： | 1 |
| 8 | Fusion-Based Machine Learning Architecture for Heart Disease Prediction显示文摘The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human lives.Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease globally.Heart attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal values.The efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be reduced.It has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and timely.Therefore,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’reports.In recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart diseases.However,the existing techniques have some limitations in terms of their accuracy.In this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is presented.The SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved. | Muhammad Waqas Nadeem Hock Guan Goh Muhammad Adnan Khan Muzammil Hussain Muhammad Faheem Mushtaq Vasaki a/p Ponnusamy | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 9 | Coronavirus: A “Mild” Virus Turned Deadly Infection显示文摘Coronaviruses are a family of viruses that can be transmitted from one person to another.Earlier strains have only been mild viruses,but the current form,known as coronavirus disease 2019(COVID-19),has become a deadly infection.The outbreak originated in Wuhan,China,and has since spread worldwide.The symptoms of COVID-19 include a dry cough,sore throat,fever,and nasal congestion.Antimicrobial drugs,pathogen–host interaction,and 2 weeks of isolation have been recommended for the treatment of the infection.Safe operating procedures,such as the use of face masks,hand sanitizer,handwashing with soap,and social distancing,are also suggested.Moreover,travel bans for cities,states,and countries have been put in place,along with lockdowns to control the outbreak.Travel restrictions,mask use,sanitizer or soap use,and avoidance of touching the face and nose have produced encouraging results,whereas the effectiveness of antibiotics has not been proved.The results of isolation for the recovery of infected people have also been promising.Travel bans and lockdowns have caused a slump in economies,and unemployment has risen sharply,resulting in an increase in mental health cases globally.To date,vaccines have been developed and are in use in certain countries,but following standard operating procedures remain critical.The countries following the guidelines can eradicate this virus.New Zealand was the rst country to eliminate the virus from their territory. | Rizwan Ali Naqvi Muhammad Faheem Mushtaq Natash Ali Mian Muhammad Adnan Khan Atta-ur-Rahman Muhammad Ali Yousaf Muhammad Umair Rizwan Majeed | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 10 | Intelligent Model for Predicting the Quality of Services Violation显示文摘Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs accountability.Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it.Cloud users’major concerns;the factors for service reliability are based on response time,accessibility,availability,and speed.In this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and availability.This paper also compares Simple-PSO and Parallel MutantPSO.In simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique. | Muhammad Adnan Khan Asma Kanwal Sagheer Abbas Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 11 | Two-Stream Deep Learning Architecture-Based Human Action Recognition显示文摘Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture recognition.Many HAR techniques are introduced in the literature.Nonetheless,the challenge of redundant and irrelevant features reduces recognition accuracy.They also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among others.In this work,a deep learning and improved whale optimization algorithm based framework is proposed for HAR.The proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final classification.Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets.The fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best features.The best selected features are finally classified usingmachine learning(ML)classifiers.Four publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%respectively.Comparison with state of the art techniques(SOTA),the proposed method showed the improved accuracy. | Faheem Shehzad Muhammad Attique Khan Muhammad Asfand E.Yar Muhammad Sharif Majed Alhaisoni Usman Tariq Arnab Majumdar Orawit Thinnukool | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 12 | Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine显示文摘The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries,including the transportation sector.The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features.One of these tasks is to ensure that vehicles are autonomous,intelligent and able to grow their repository of information.Machine learning has recently been implemented in wireless networks,as a major artificial intelligence branch,to solve historically challenging problems through a data-driven approach.In this article,we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field.Deep Extreme Learning Machine(DELM)framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments.The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions.It leads to the concept of vehicle controller making self-decisions.The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations.This overcomes inadequacy of the current in-vehicle route-finding technology and its support.All the relevant route-related information for the ride will be provided to the user based on its availability.Using the DELM method,a high degree of precision in smart decision taking with a minimal error rate is obtained.During investigation,it has been observed that proposed framework has the highest accuracy rate with 70%of training(1435 samples)and 30%of validation(612 samples).Simulation results validate the intelligent prediction of the proposed method with 98.88%,98.2%accuracy during training and validation respectively. | Dildar Hussain Muhammad Adnan Khan Sagheer Abbas Rizwan Ali Naqvi Muhammad Faheem Mushtaq Abdur Rehman Afrozah Nadeem | 2021 | Computers, Materials & Continua2021，，1： | 0 |
| 13 | Mobile Devices Interface Adaptivity Using Ontologies显示文摘Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs. | Muhammad Waseem Iqbal Muhammad Raza Naqvi Muhammad Adnan Khan Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 14 | Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic显示文摘Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively.Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment.The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand.Elasticity in cloud computing is one of the fundamental properties,and elastic load balancing automatically distributes incoming load to multiple virtual machines.This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing.In this article,a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources.A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model.The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches. | Atif Ishaq Khan Syed Asad Raza Kazmi Ayesha Atta Muhammad Faheem Mushtaq Muhammad Idrees Ilyas Fakir Muhammad Safyan Muhammad Adnan Khan Awais Qasim | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 15 | D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism显示文摘Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures.The seizures are defined as the unexpected electrical changes in brain neural activity,which leads to unconsciousness.Existing researches made an intense effort for predicting the epileptic seizures using brain signal data.However,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's reliability.In addition,the existing prediction models have severe issues,such as overfitting and false positive rates.To overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain signals.Deep neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict ES.The multi‐network design enhances the trained model's generalisability and stability efficiently.Also,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal classification.The earlier warning of epilepsy with the proposed model obtains the auxiliary diagnosis.The data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing techniques.To be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%respectively.Overall,the numerical results unveil that the proposed work outperforms the existing models. | Arfat Ahmad Khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem | 2023 | CAAI Transactions on Intelligence Technology2023，8，3： | 0 |
| 16 | Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis显示文摘Education sector has witnessed several changes in the recent past.These changes have forced private universities into fierce competition with each other to get more students enrolled.This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands.To 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 competitors.This 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 universities.These 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 Pakistan.Experimental results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local universities.Based 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. | Shahid Mehmood Imran Ahmad Muhammad Adnan Khan Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 17 | Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning显示文摘Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming signals.It is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place,the signal having more strength under the influence of emotions effects the response accordingly.Current research inmultisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or depression.Therefore,there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or depression.In this study,two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or depression.It was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals. | Muhammad Adnan Khan Sagheer Abbas Ali Raza Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 18 | A UNIFIED THREE POINT APPROXIMATING SUBDIVISION SCHEME显示文摘In this paper,we propose a three point approximating subdivision scheme,with three shape parameters,that unifies three different existing three point approximating schemes.Some sufficient conditions for subdivision curve C0 to C3 continuity and convergence of the scheme for generating tensor product surfaces for certain ranges of parameters by using Laurent polynomial method are discussed.The systems of curve and surface design based on our scheme have been developed successfully in garment CAD especially for clothes modelling. | Ghulam Mustafa Faheem Khan Muhammad Sadia Hashmi Muhammad Zeshan Afzal | 2011 | Analysis in Theory and Applications2011，27，1： | 0 |
| 19 | Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning显示文摘Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in humans.It is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune systems.The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates.Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans.It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken.The latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific community.Artificial Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an outbreak.These forecasts may help to monitor and prevent the spread of these outbreaks.This article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of COVID-19.The findings indicate that the suggested system outperforms cutting-edge approaches.The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance.The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity.The proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively. | Rachid Zagrouba Muhammad Adnan Khan Atta-ur-Rahman Muhammad Aamer Saleem Muhammad Faheem Mushtaq Abdur Rehman Muhammad Farhan Khan | 2021 | Computers, Materials & Continua2021，，3： | 0 |
| 20 | ILSM:Incorporated Lightweight Security Model for Improving QOS in WSN显示文摘In the network field,Wireless Sensor Networks(WSN)contain prolonged attention due to afresh augmentations.Industries like health care,traffic,defense,and many more systems espoused the WSN.These networks contain tiny sensor nodes containing embedded processors,TinyOS,memory,and power source.Sensor nodes are responsible for forwarding the data packets.To manage all these components,there is a need to select appropriate parameters which control the quality of service of WSN.Multiple sensor nodes are involved in transmitting vital information,and there is a need for secure and efficient routing to reach the quality of service.But due to the high cost of the network,WSN components have limited resources to manage the network.There is a need to design a lightweight solution that ensures the quality of service in WSN.In this given manner,this study provides the quality of services in a wireless sensor network with a security mechanism.An incorporated hybrid lightweight security model is designed in which random waypoint mobility(RWM)model and grey wolf optimization(GWO)is used to enhance service quality and maintain security with efficient routing.MATLAB version 16 andNetwork Stimulator 2.35(NS2.35)are used in this research to evaluate the results.The overall cost factor is reduced at 60%without the optimization technique and 90.90%reduced by using the optimization technique,which is assessed by calculating the signal-to-noise ratio,overall energy nodes,and communication overhead. | Ansar Munir Shah Mohammed Aljubayri Muhammad Faheem Khan Jarallah Alqahtani Mahmood ul Hassan Adel Sulaiman Asadullah Shaikh | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
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