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| 1 | Sludge granulation and efficiency of phase separator in UASB reactor treating combined industrial effluent显示文摘污泥使成粒状和 gas-liquid-solid 的效果隔板(图文集) designon upflow 的效率厌氧的污泥毛毯( UASB )和 upflow 厌氧的 sludgefilter ( UASF )反应堆,在从 3~12 h 的 HRT 操作 investigated.VSS/TS ratiogradually 在两个被增加有增加污泥的反应堆变老(从 0.5 到超过 0.7 forUASB 反应堆并且 0.012~0.043 为 UASF 反应堆) UASF sludgeshowed 的.X光线衍射分析扩展揭示它的另外的 abs | Abdullah Yasar Nasir Ahmad Muhammad Nawaz Chaudhry Aamir Amanat Ali Khan | 2007 | Journal of Environmental Sciences2007，19，5： | 6 |
| 2 | Recent Progress,Challenges,and Prospects in Two‑Dimensional Photo‑Catalyst Materials and Environmental Remediation显示文摘The successful photo-catalyst library gives significant information on feature that affects photo-catalytic performance and proposes new materials.Competency is considerably significant to form multi-functional photo-catalysts with flexible characteristics.Since recently,two-dimensional materials(2DMs)gained much attention from researchers,due to their unique thickness-dependent uses,mainly for photo-catalytic,outstanding chemical and physical properties.Photo-catalytic water splitting and hydrogen(H2)evolution by plentiful compounds as electron(e−)donors is estimated to participate in constructing clean method for solar H2-formation.Heterogeneous photocatalysis received much research attention caused by their applications to tackle numerous energy and environmental issues.This broad review explains progress regarding 2DMs,significance in structure,and catalytic results.We will discuss in detail current progresses of approaches for adjusting 2DMs-based photo-catalysts to assess their photo-activity including doping,hetero-structure scheme,and functional formation assembly.Suggested plans,e.g.,doping and sensitization of semiconducting 2DMs,increasing electrical conductance,improving catalytic active sites,strengthening interface coupling in semiconductors(SCs)2DMs,forming nano-structures,building multi-junction nano-composites,increasing photo-stability of SCs,and using combined results of adapted approaches,are summed up.Hence,to further improve 2DMs photo-catalyst properties,hetero-structure design-based 2DMs’photo-catalyst basic mechanism is also reviewed. | Karim Khan Ayesha Khan Tareen Muhammad Aslam Rizwan Ur Rehman Sagar Bin Zhang Weichun Huang Asif Mahmood Nasir Mahmood Kishwar Khan Han Zhang Zhongyi Guo | 2020 | Nano-Micro Letters2020，12，12： | 2 |
| 3 | Regolith thickness modeling using a GIS approach for landslide distribution analysis, NW Himalayas显示文摘Regolith thickness is considered as a contributing factor for the occurrence of landslides.Although, mostly it is ignored because of complex nature and as it requires more time and resources for investigation. This study aimed to appraise the role of regolith thickness on landslide distribution in the Muzaffarabad and surrounding areas, NW Himalayas.For this purpose regolith thickness samples were evenly collected from all the lithological units at representative sites within different slope and elevation classes in the field. Topographic attributes(slope, aspect, drainage, Topographic Wetness Index,elevation and curvature) were derived from the Digital Elevation Model(DEM)(12.5 m resolution).Arc GIS Model Builder was used to develop the regolith thickness model. Stepwise regression technique was used to explore the spatial variation of regolith thickness using topographic attributes and lithological units. The derived model explains about 88% regolith thickness variation. The model was validated and shows good agreement(70%) between observed and predicted values. Subsequently, the derived regolith model was used to understand the relationship between regolith thickness and landslide distribution. The analysis shows that most of the landslides were located within 1-5 m regolith thickness. However, landslide concentration is highest within 5-10 m regolith thickness, which shows that regolith thickness played a significant role for the occurrence of landslide in the studied area. | Muhammad BASHARAT Masood QASIM Muhammad SHAFIQUE Nasir HAMEED Muhammad Tayyib RIAZ Muhammad Rustam KHAN | 2018 | Journal of Mountain Science2018，15，11： | 2 |
| 4 | A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products:An Application for Medical-Tech Classification显示文摘Comic character detection is becoming an exciting and growing research area in the domain of machine learning.In this regard,recently,many methods are proposed to provide adequate performance.However,most of these methods utilized the custom datasets,containing a few hundred images and fewer classes,to evaluate the performances of their models without comparing it,with some standard datasets.This article takes advantage of utilizing a standard publicly dataset taken from a competition,and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN.In addition,to classify the superheroes efficiently,a custom 17-layer deep convolutional neural network is also proposed.The computed results achieved overall classification accuracy of 97.9%which is significantly superior to the accuracy of competition’s winner. | Inzamam Mashood Nasir Muhammad Attique Khan Majed Alhaisoni Tanzila Saba Amjad Rehman Tassawar Iqbal | 2020 | Computer Modeling in Engineering & Sciences2020，，9： | 1 |
| 5 | Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning显示文摘One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives. | Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 6 | CFD applications in various heat exchangers design: A review显示文摘 | Muhammad Mahmood Aslam Bhutta Nasir Hayat Muhammad Hassan Bashir Ahmer Rais Khan Kanwar Naveed Ahmad Sarfaraz Khan | 2011 | Applied Thermal Engineering2011，，： | 1 |
| 7 | Weak stem under shade reveals the lignin reduction behavior显示文摘Shades caused by neighboring tall plants in intercropping systems and weak sunlight are constraints in yield optimization. Shade influences many aspects of plant growth and development, leading to weak stems and susceptibility to lodging. The plant cell wall is composed of certain proteins that allow the walls to stretch out, a process called cell wall loosening. Shade affects anatomical, morphological, and physiological traits of plants, thus reducing the physical strength of the stem in crops by changing the loosening of cell walls. Flexibility of cells facilitates further modifications such as wall loosening. In addition, shade stress causes increased internode length, and reduced xylem synthesis and photosynthesis. In shaded plants, lignin deposition in vascular bundles and sclerenchyma cells of stems is decreased. Lignin is a light sensitive phenolic compound and shading decreases the transcript abundance of several phenolic compound(flavone and lignin) related genes. Shading significantly influences the metabolic activities of phenylalanine ammonia-lyase(PAL), peroxidase(POD), 4-coumarate: CoA ligase(4 CL), and cinnamyl alcohol dehydrogenase(CAD) involved in lignin biosynthesis. Furthermore, suppression of lignin biosynthesis activities by abiotic stresses causes abnormal phenotypes such as collapsed xylem, bent stems, and growth retardation. In this review, the underlying mechanisms illustrate that under shading conditions reduced lignin content results in slender, weak, and unstable stems. The objective of this review is to elaborate lignin biosynthesis and its variability under stressful environmental conditions, especially in shade stress environments. The effects of shade on stem lignin metabolism are discussed on the morphogenetic, physiological, and proteomic levels. | Sajad Hussain Nasir Iqbal PANG Ting Muhammad Naeem Khan LIU Wei-guo YANG Wen-yu | 2019 | Journal of Integrative Agriculture2019，18，3： | 1 |
| 8 | Distribution, toxicity level, and concentration of polycyclic aromatic hydrocarbons (PAHs) in surface soil and groundwater of Rawalpindi, Pakistan显示文摘 | Beenish Saba Imran Hashmi Muhammad Ali Awan Habib Nasir Sher Jamal Khan | 2012 | 2012 (1-3)2012，，1： | 1 |
| 9 | On Hiding Secret Information in Medium Frequency DCT Components Using Least Significant Bits Steganography显示文摘This work presents a new method of data hiding in digital images,in discrete cosine transform domain.The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding the secret information,while the low and high frequency coefficients are kept unaltered.The unaltered low frequency DCT coefficients preserves the quality of the smooth region of the cover image,while no changes in the high DCT coefficient preserve the quality of the edges.As the medium frequency components have less contribution towards energy and image details,so the modification of these coefficients for data hiding results in high quality stego images.The distortion due to the changes in the medium frequency coefficients is insignificant to be detected by the human visual system.The proposed methods demonstrated a hiding capacity of 43:11%with the stego image quality of a peak signal to the noise ration of 36:3 dB,which is significantly higher than the threshold of 30 dB for a stego image quality.The proposed technique is immune to steganalysis and has proved to be highly secured against both spatial and DCT domain steganalysis techniques. | Sahib Khan M A Irfan Arslan Arif Syed Tahir Hussain Rizvi Asma Gul Muhammad Naeem Nasir Ahmad | 2019 | Computer Modeling in Engineering & Sciences2019，，3： | 0 |
| 10 | Properties of Certain Subclasses of Analytic Functions Involving q-Poisson Distribution显示文摘By using the basic(or q)-Calculus many subclasses of analytic and univalent functions have been generalized and studied from different viewpoints and perspectives.In this paper,we aim to define certain new subclasses of an analytic function.We then give necessary and sufficient conditions for each of the defined function classes.We also study necessary and sufficient conditions for a function whose coefficients are probabilities of q-Poisson distribution.To validate our results,some known consequences are also given in the form of Remarks and Corollaries. | Bilal Khan Zhi-Guo Liu Nazar Khan Aftab Hussain Nasir Khan Muhammad Tahir | 2022 | Computer Modeling in Engineering & Sciences2022，，6： | 0 |
| 11 | Telepresence Robots and Controlling Techniques in Healthcare System显示文摘In this era of post-COVID-19,humans are psychologically restricted to interact less with other humans.According to the world health organization(WHO),there are many scenarios where human interactions cause severe multiplication of viruses from human to human and spread worldwide.Most healthcare systems shifted to isolation during the pandemic and a very restricted work environment.Investigations were done to overcome the remedy,and the researcher developed different techniques and recommended solutions.Telepresence robot was the solution achieved by all industries to continue their operations but with almost zero physical interaction with other humans.It played a vital role in this perspective to help humans to perform daily routine tasks.Healthcare workers can use telepresence robots to interact with patients who visit the healthcare center for initial diagnosis for better healthcare system performance without direct interaction.The presented paper aims to compare different telepresence robots and their different controlling techniques to perform the needful in the respective scenario of healthcare environments.This paper comprehensively analyzes and reviews the applications of presented techniques to control different telepresence robots.However,our feature-wise analysis also points to specific technical,appropriate,and ethical challenges that remain to be solved.The proposed investigation summarizes the need for further multifaceted research on the design and impact of a telepresence robot for healthcare centers,building on new perceptions during the COVID-19 pandemic. | Fawad Naseer Muhammad Nasir Khan Zubair Nawaz Qasim Awais | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 12 | Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification显示文摘Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique. | Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 13 | Analysis of LDPC Code in Hybrid Communication Systems显示文摘Free-space optical(FSO)communication is of supreme importance for designing next-generation networks.Over the past decades,the radio frequency(RF)spectrum has been the main topic of interest for wireless technology.The RF spectrum is becoming denser and more employed,making its availability tough for additional channels.Optical communication,exploited for messages or indications in historical times,is now becoming famous and useful in combination with error-correcting codes(ECC)to mitigate the effects of fading caused by atmospheric turbulence.A free-space communication system(FSCS)in which the hybrid technology is based on FSO and RF.FSCS is a capable solution to overcome the downsides of current schemes and enhance the overall link reliability and availability.The proposed FSCS with regular low-density parity-check(LDPC)for coding techniques is deliberated and evaluated in terms of signal-to-noise ratio(SNR)in this paper.The extrinsic information transfer(EXIT)methodology is an incredible technique employed to investigate the sum-product decoding algorithm of LDPC codes and optimize the EXIT chart by applying curve fitting.In this research work,we also analyze the behavior of the EXIT chart of regular/irregular LDPC for the FSCS.We also investigate the error performance of LDPC code for the proposed FSCS. | Hasnain Kashif Muhammad Nasir Khan Zubair Nawaz | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 14 | Design and Implementation of a State-feedback Controller Using LQR Technique显示文摘The main objective of this research is to design a state-feedback controller for the rotary inverted pendulum module utilizing the linear quadratic regulator(LQR)technique.The controller maintains the pendulum in the inverted(upright)position and is robust enough to reject external disturbance to maintain its stability.The research work involves three major contributions:mathematical modeling,simulation,and real-time implementation.To design a controller,mathematical modeling has been done by employing the NewtonEuler,Lagrange method.The resulting model was nonlinear so linearization was required,which has been done around a working point.For the estimation of the controller parameters,MATLAB LQR function has been utilized.Simulation has been performed for the designed controller and it also has been implemented and tested over the real inverted pendulum.From the results,it is vivid that the designed controller keeps the inverted pendulum in an upright position and rejects the disturbances and falling under gravitational force by adjusting the rotation of the horizontal link. | Aamir Shahzad Shadi Munshi Sufyan Azam Muhammad Nasir Khan | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 15 | Forecast the Influenza Pandemic Using Machine Learning显示文摘Forecasting future outbreaks can help in minimizing their spread.Influenza is a disease primarily found in animals but transferred to humans through pigs.In 1918,influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population.Afterwards,that influenza became a pandemic several times on a local and global levels.In 2009,influenza‘A’subtype H1N1 again took many human lives.The disease spread like in a pandemic quickly.This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network(MSDII-FFNN).This model helps us predict the outbreak,and determines which type of influenza becomes a pandemic,as well as which geographical area is infected.Data collection for the model is done by using IoT devices.This model is divided into 2 phases:The training phase and the validation phase,both being connected through the cloud.In the training phase,the model is trained using FFNN and is updated on the cloud.In the validation phase,whenever the input is submitted through the IoT devices,the system model is updated through the cloud and predicts the pandemic alert.In our dataset,the data is divided into an 85%training ratio and a 15%validation ratio.By applying the proposed model to our dataset,the predicted output precision is 90%. | Muhammad Adnan Khan Wajhe Ul Husnain Abidi Mohammed A.Al Ghamdi Sultan H.Almotiri Shazia Saqib Tahir Alyas Khalid Masood Khan Nasir Mahmood | 2021 | Computers, Materials & Continua2021，，1： | 0 |
| 16 | Engineered Hybrid Materials with Smart Surfaces for Effective Mitigation of Petroleum-Originated Pollutants显示文摘The generation and controlled or uncontrolled release of hydrocarbon-contaminated industrial wastewater effluents to water matrices are a major environmental concern.The contaminated water comes to surface in the form of stable emulsions,which sometimes require different techniques to mitigate or separate effectively.Both the crude emulsions and hydrocarbon-contaminated wastewater effluents contain suspended solids,oil/grease,organic matter,toxic elements,salts,and recalcitrant chemicals.Suitable treatment of crude oil emulsions has been one of the most important challenges due to the complex nature and the substantial amount of generated waste.Moreover,the recovery of oil from waste will help meet the increasing demand for oil and its derivatives.In this context,functional nanostructured materials with smart surfaces and switchable wettability properties have gained increasing attention because of their excellent performance in the separation of oil–water emulsions.Recent improvements in the design,composition,morphology,and fine-tuning of polymeric nanostructured materials have resulted in enhanced demulsification functionalities.Herein,we reviewed the environmental impacts of crude oil emulsions and hydrocarbon-contaminated wastewater effluents.Their effective treatments by smart polymeric nanostructured materials with wettability properties have been stated with suitable examples.The fundamental mechanisms underpinning the efficient separation of oil–water emulsions are discussed with suitable examples along with the future perspectives of smart materials. | Nisar Ali Muhammad Bilal Adnan Khan Farman Ali Mohamad Nasir Mohamad Ibrahim Xiaoyan Gao Shizhong Zhang Kun Hong Hafiz MNIqbal | 2021 | Engineering2021，7，10： | 0 |
| 17 | A Blockchain Based Framework for Stomach Abnormalities Recognition显示文摘Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured.The privacy of patients is very important and manual inspection is time consuming and costly.Therefore,an automated system for recognition of stomach infections from WCE frames is always needed.An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding.Initially,images are normalized in fixed dimension and passed in pre-trained deep models.These architectures are modified at each layer,to make them safer and more secure.Each layer contains an extra block,which stores certain information to avoid possible tempering,modification attacks and layer deletions.Information is stored in multiple blocks,i.e.,block attached to each layer,a ledger block attached with the network,and a cloud ledger block stored in the cloud storage.After that,features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function.The Softmax classifier is applied at the end for final classification.Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%.The statistical analysis and individual model comparison show the proposed method’s authenticity. | Muhammad Attique Khan Inzamam Mashood Nasir Muhammad Sharif Majed Alhaisoni Seifedine Kadry Syed Ahmad Chan Bukhari Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 18 | Improved Shark Smell Optimization Algorithm for Human Action Recognition显示文摘Human Action Recognition(HAR)in uncontrolled environments targets to recognition of different actions froma video.An effective HAR model can be employed for an application like human-computer interaction,health care,person tracking,and video surveillance.Machine Learning(ML)approaches,specifically,Convolutional Neural Network(CNN)models had beenwidely used and achieved impressive results through feature fusion.The accuracy and effectiveness of these models continue to be the biggest challenge in this field.In this article,a novel feature optimization algorithm,called improved Shark Smell Optimization(iSSO)is proposed to reduce the redundancy of extracted features.This proposed technique is inspired by the behavior ofwhite sharks,and howthey find the best prey in thewhole search space.The proposed iSSOalgorithmdivides the FeatureVector(FV)into subparts,where a search is conducted to find optimal local features fromeach subpart of FV.Once local optimal features are selected,a global search is conducted to further optimize these features.The proposed iSSO algorithm is employed on nine(9)selected CNN models.These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition.To evaluate the model,two publicly available datasets UCF-Sports and Hollywood2 are selected. | Inzamam Mashood Nasir Mudassar Raza Jamal Hussain Shah Muhammad Attique Khan Yun-Cheol Nam Yunyoung Nam | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 19 | Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography显示文摘Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results. | Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan | 2024 | Computers, Materials & Continua2024，78，3： | 0 |
| 20 | Deep Learning-Based Classification of Fruit Diseases:An Application for Precision Agriculture显示文摘Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artificial intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of infections.Recently,the convolutional neural network(CNN)techniques show a massive success for image classification tasks.CNN extracts more detailed features and can work efficiently with large datasets.In this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were extracted.Next,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based approach.During the fusion process,a few pieces of redundant information were added in the form of features.Then,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final classifications.With the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous techniques.Moreover,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT). | Inzamam Mashood Nasir Asima Bibi Jamal Hussain Shah Muhammad Attique Khan Muhammad Sharif Khalid Iqbal Yunyoung Nam Seifedine Kadry | 2021 | Computers, Materials & Continua2021，，2： | 0 |
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