Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field,the pre-processing of medical data requires ...Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field,the pre-processing of medical data requires careful handling to make quality clinical decisions.Generally,medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features.These factors indirectly upset the disease prediction and classification accuracy of any ML model.To address this issue,various data pre-processing methods called Feature Selection(FS)techniques have been presented in the literature.However,the majority of such techniques frequently suffer from local minima issues due to large solution space.Thus,this study has proposed a novel wrapper-based Sand Cat SwarmOptimization(SCSO)technique as an FS approach to find optimum features from ten benchmark medical datasets.The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables.Moreover,K-Nearest Neighbor(KNN)classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm.The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy,optimum feature size,and computational cost in seconds.The simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96%by selecting 14.2 features within 1.91 s.Additionally,the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than 5.00E-02.The findings revealed that the proposed algorithm produces better outcomes with an average p-value of 1.82E-02.Moreover,potential future directions are also suggested as a result of the study’s promising findings.展开更多
Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),whi...Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.展开更多
The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective ...The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.展开更多
It is important to understand the mechanism and implications of different modifiers on analytical and preparative processes under chromatography with supercritical fluids (SFs) and under extraction with SFs. Supercrit...It is important to understand the mechanism and implications of different modifiers on analytical and preparative processes under chromatography with supercritical fluids (SFs) and under extraction with SFs. Supercritical fluid chromatography (SFC) and supercritical fluid extraction are generally carried out with neat supercritical carbon dioxide (SCCO2) or with SCCO2 containing modifiers (or cosolvents), especially for strongly polar compounds. For example, methanol is added as a cosolvent/modifier to SCCO2 for the extraction/separation of polar compounds. This paper discusses the influence of the modifier on the colligative properties of the principal mobile phase, which may define the situation in the total mobile phase in a chromatography column or in parts of a column under SFC. No colligative behavior of solutions reflects individual properties of the solutes. Their cross-interactions with solvents are discussed.展开更多
k-ary trees are one of the most basic data structures in Computer Science. A new method is presented to determine how many there are with n nodes. This method gives additional insight into their structure and provides...k-ary trees are one of the most basic data structures in Computer Science. A new method is presented to determine how many there are with n nodes. This method gives additional insight into their structure and provides a new algo-rithm to efficiently generate such a tree randomly.展开更多
Object detection has made a significant leap forward in recent years.However,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susce...Object detection has made a significant leap forward in recent years.However,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susceptible to missed detection due to background noise.Additionally,small object information is affected due to the downsampling operations.Deep learning-based detection methods have been utilized to address the challenge posed by small objects.In this work,we propose a novel method,the Multi-Convolutional Block Attention Network(MCBAN),to increase the detection accuracy of minute objects aiming to overcome the challenge of information loss during the downsampling process.The multi-convolutional attention block(MCAB);channel attention and spatial attention module(SAM)that make up MCAB,have been crafted to accomplish small object detection with higher precision.We have carried out the experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute(KITTI)and Pattern Analysis,Statical Modeling and Computational Learning(PASCAL)Visual Object Classes(VOC)datasets and have followed a step-wise process to analyze the results.These experiment results demonstrate that significant gains in performance are achieved,such as 97.75%for KITTI and 88.97%for PASCAL VOC.The findings of this study assert quite unequivocally the fact that MCBAN is much more efficient in the small object detection domain as compared to other existing approaches.展开更多
Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all whil...Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.展开更多
The monomer fraction density based analysis of precise thermophysical data for pure fluids is developed to study the molecular structures in supercritical fluids in general and in CO2 in particular. The series expansi...The monomer fraction density based analysis of precise thermophysical data for pure fluids is developed to study the molecular structures in supercritical fluids in general and in CO2 in particular. The series expansion by powers of the monomer fraction density of the potential energy density is used to discover the cluster structure in supercritical fluids and the clusters’ bond energies in CO2. The method of clusters separation between classes of loose and dense clusters in the CO2 supercritical fluid is developed. The method of the energetically averaged number of dense clusters is developed to study the mechanism of the soft structural transition between the gas-like and liquid-like fluids in the supercritical CO2.展开更多
In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual...In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.展开更多
In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm i...In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost function.In the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step.The convergence is thoroughly studied.Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.展开更多
The thermal analysis of precise thermophysical data for pure fluids from electronic databases is developed to investigate the molecular interaction mechanisms and parameters and the structural features of heterogeneit...The thermal analysis of precise thermophysical data for pure fluids from electronic databases is developed to investigate the molecular interaction mechanisms and parameters and the structural features of heterogeneities in fluids. The method is based on the series expansion of thermophysical values by powers of the monomer fraction density. Unlike the virial expansion by powers of the total density, the series expansion terms in this method directly reflect properties of the corresponding cluster fractions. The internal energy had been selected among thermophysical properties as the most informative for this method. The thermal analysis of its series expansion coefficients permits to estimate the temperature dependence of the pair bond parameters, the clusters’ bond energies and equilibrium constants, the structural transitions between dominating isomers of clusters. The application of method to different pure fluids, including noble and molecular gases with van der Waals and polar molecular interactions, brings unknown clusters’ characteristics for the fluids under investigation. The thermal analysis of the ordinary and heavy Water vapors points on no trivial isotopic effects. The unpredictable growth of the pair bond energy with temperature in Alkanes points on existence in hydrocarbons of some unknown molecular interaction forces in addition to dispersion forces.展开更多
To discover particular features of pure supercritical fluids, important for the supercritical fluid extraction and cleaning technologies, the preprocessed and generalized experimental data from the US National Institu...To discover particular features of pure supercritical fluids, important for the supercritical fluid extraction and cleaning technologies, the preprocessed and generalized experimental data from the US National Institute of Standards and Technology (NIST) online database have been analyzed. The soft transition between gas-like and liquid-like structures in pure supercritical fluids has been considered in comparison with the abrupt vapor-liquid phase transition. A rough, diffused and boiling boundary between these structures in conditions of extra high gravity is opposed to a flat vapor-liquid boundary at a moderate gravity. The model for molecular diffusivity in carbon dioxide at temperatures near the critical temperature discovers its proportionality to the monomer fraction density. The cluster fraction based model for small molecular weight solids’ solubility in supercritical fluids has been suggested and successfully compared with the well-known experimental results for the solubility of silica in water.The model shows that at growing pressure the dissolution process has already startedin a real gas and discovers the cluster fractions’ role in the solubility process.展开更多
Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a prom...Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.展开更多
Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automat...Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.展开更多
Gold metallic nanoparticles are generally used within a lab as a tracer,to uncover on the presence of specific proteins or DNA in a sample,as well as for the recognition of various antibiotics.They are bio companionab...Gold metallic nanoparticles are generally used within a lab as a tracer,to uncover on the presence of specific proteins or DNA in a sample,as well as for the recognition of various antibiotics.They are bio companionable and have properties to carry thermal energy to tumor cells by utilizing different clinical approaches.As the cancer cells are very smaller so for the infiltration,the properly sized nanoparticles have been injected in the blood.For this reason,gold nanoparticles are very effective.Keeping in mind the above applications,in the present work a generalized model of blood flow containing gold nanoparticles is considered in this work.The blood motion is considered in a cylindrical tube under the oscillating pressure gradient and magnetic field.The problem formulation is done using two types of fractional approaches namely CF(Caputo Fabrizio)and AB(Atangana-Baleanue)derivatives,whereas blood is considered as a counter-example of Casson fluid.Exact solutions of the problem are obtained using joint Laplace and Hankel transforms,and a comparative analysis is made between CF and AB.Results are computed in tables and shown in various plots for embedded parameters and discussed.It is found that adding 0.04-unit gold nanoparticles to blood,increase its heat transfer rate by 4 percent compared to regular blood.It is also noted that the heat transfer can be enhanced in the blood with memory.展开更多
Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GAN...Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.展开更多
Cloud computing is expanding widely in the world of IT infrastructure. This is due partly to the cost-saving effect of economies of scale. Fair market conditions can in theory provide a healthy environment to reflect ...Cloud computing is expanding widely in the world of IT infrastructure. This is due partly to the cost-saving effect of economies of scale. Fair market conditions can in theory provide a healthy environment to reflect the most reasonable costs of computations. While fixed cloud pricing provides an attractive low entry barrier for compute-intensive applications, both the consumer and supplier of computing resources can see high efficiency for their investments by participating in auction-based exchanges. There are huge incentives for the cloud provider to offer auctioned resources. However, from the consumer perspective, using these resources is a sparsely discussed challenge. This paper reports a methodology and framework designed to address the challenges of using HPC （High Performance Computing） applications on auction-based cloud clusters. The authors focus on HPC applications and describe a method for determining bid-aware checkpointing intervals. They extend a theoretical model for determining checkpoint intervals using statistical analysis of pricing histories. Also the latest developments in the SpotHPC framework are introduced which aim at facilitating the managed execution of real MPI applications on auction-based cloud environments. The authors use their model to simulate a set of algorithms with different computing and communication densities. The results show the complex interactions between optimal bidding strategies and parallel applications performance.展开更多
The aim of this research is to apply the author’s original computer aided analysis of thermophysical data for pure fluids to noble gases to investigate the unknown aspects in their equilibrium thermal physics. The me...The aim of this research is to apply the author’s original computer aided analysis of thermophysical data for pure fluids to noble gases to investigate the unknown aspects in their equilibrium thermal physics. The methodology of the analysis is based on the potential energy density series expansion by the monomer fraction density. To discover the important details and particular features of pair atomic interactions in noble gases, the preprocessed and generalized experimental data have been taken from the US National Institute of Standards and Technology (NIST) online database. In this work the temperature range for analysis of the dimers’ bonding parameters is extended as compared to previous author’s works due to accounting for the specific temperature dependence of the repulsions’ contribution to the potential energy. The found temperature dependences of the pair interaction bond energies signal about the hindered rotation of atoms in dimers near the triple point due to the lack of rotational symmetry of their electronic outer shells. The discovered mutually correlated anomalous temperature dependences of the pair bond energy and the constant volume heat capacity in gaseous Helium require a special investigation of this remarkable phenomenon.展开更多
Dzyaloshiniskii-Moriya (DM) interaction in three directions (Dx, Dy and Dz) is used to generate entangled network from partially entangled states in the presence of the spin-orbit coupling. The effect of the spin coup...Dzyaloshiniskii-Moriya (DM) interaction in three directions (Dx, Dy and Dz) is used to generate entangled network from partially entangled states in the presence of the spin-orbit coupling. The effect of the spin coupling on the entanglement between any two nodes of the network is investigated. The entanglement is quantified using Woottores concurrence method. It is shown that the entanglement decays as the coupling increases. For larger values of the spin coupling, the entanglement oscillates within finite bounds. For the initially entangled channels, the upper bound does not exceed its initial value, whereas the entanglement reaches its maximum value for the channels generated via indirect interaction.展开更多
With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of collapse.An important and critical steps in fighting against COVID-19 is power...With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of collapse.An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional approach.The accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems.This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs.To attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature vectors.In addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 model.Moreover,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of COVID19.For ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive aspects.The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.展开更多
基金This research was supported by a Researchers Supporting Project Number(RSP2021/309)King Saud University,Riyadh,Saudi Arabia.The authors wish to acknowledge Yayasan Universiti Teknologi Petronas for supporting this work through the research grant(015LC0-308).
文摘Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field,the pre-processing of medical data requires careful handling to make quality clinical decisions.Generally,medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features.These factors indirectly upset the disease prediction and classification accuracy of any ML model.To address this issue,various data pre-processing methods called Feature Selection(FS)techniques have been presented in the literature.However,the majority of such techniques frequently suffer from local minima issues due to large solution space.Thus,this study has proposed a novel wrapper-based Sand Cat SwarmOptimization(SCSO)technique as an FS approach to find optimum features from ten benchmark medical datasets.The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables.Moreover,K-Nearest Neighbor(KNN)classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm.The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy,optimum feature size,and computational cost in seconds.The simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96%by selecting 14.2 features within 1.91 s.Additionally,the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than 5.00E-02.The findings revealed that the proposed algorithm produces better outcomes with an average p-value of 1.82E-02.Moreover,potential future directions are also suggested as a result of the study’s promising findings.
基金The work is partially funded by CGS Universiti Teknologi PETRONAS,Malaysia.
文摘Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.
基金supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS (YUTP)Fundamental Research Grant Scheme (YUTPFRG/015LC0-274)support by Researchers Supporting Project Number (RSP-2023/309),King Saud University,Riyadh,Saudi Arabia.
文摘The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.
文摘It is important to understand the mechanism and implications of different modifiers on analytical and preparative processes under chromatography with supercritical fluids (SFs) and under extraction with SFs. Supercritical fluid chromatography (SFC) and supercritical fluid extraction are generally carried out with neat supercritical carbon dioxide (SCCO2) or with SCCO2 containing modifiers (or cosolvents), especially for strongly polar compounds. For example, methanol is added as a cosolvent/modifier to SCCO2 for the extraction/separation of polar compounds. This paper discusses the influence of the modifier on the colligative properties of the principal mobile phase, which may define the situation in the total mobile phase in a chromatography column or in parts of a column under SFC. No colligative behavior of solutions reflects individual properties of the solutes. Their cross-interactions with solvents are discussed.
文摘k-ary trees are one of the most basic data structures in Computer Science. A new method is presented to determine how many there are with n nodes. This method gives additional insight into their structure and provides a new algo-rithm to efficiently generate such a tree randomly.
基金funded by Yayasan UTP FRG(YUTP-FRG),grant number 015LC0-280 and Computer and Information Science Department of Universiti Teknologi PETRONAS.
文摘Object detection has made a significant leap forward in recent years.However,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susceptible to missed detection due to background noise.Additionally,small object information is affected due to the downsampling operations.Deep learning-based detection methods have been utilized to address the challenge posed by small objects.In this work,we propose a novel method,the Multi-Convolutional Block Attention Network(MCBAN),to increase the detection accuracy of minute objects aiming to overcome the challenge of information loss during the downsampling process.The multi-convolutional attention block(MCAB);channel attention and spatial attention module(SAM)that make up MCAB,have been crafted to accomplish small object detection with higher precision.We have carried out the experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute(KITTI)and Pattern Analysis,Statical Modeling and Computational Learning(PASCAL)Visual Object Classes(VOC)datasets and have followed a step-wise process to analyze the results.These experiment results demonstrate that significant gains in performance are achieved,such as 97.75%for KITTI and 88.97%for PASCAL VOC.The findings of this study assert quite unequivocally the fact that MCBAN is much more efficient in the small object detection domain as compared to other existing approaches.
基金supported by the Yayasan Universiti Teknologi PETRONAS Grants,YUTP-PRG(015PBC-027)YUTP-FRG(015LC0-311),Hilmi Hasan,www.utp.edu.my.
文摘Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.
文摘The monomer fraction density based analysis of precise thermophysical data for pure fluids is developed to study the molecular structures in supercritical fluids in general and in CO2 in particular. The series expansion by powers of the monomer fraction density of the potential energy density is used to discover the cluster structure in supercritical fluids and the clusters’ bond energies in CO2. The method of clusters separation between classes of loose and dense clusters in the CO2 supercritical fluid is developed. The method of the energetically averaged number of dense clusters is developed to study the mechanism of the soft structural transition between the gas-like and liquid-like fluids in the supercritical CO2.
文摘In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.
基金National Natural Science Foundations of China(No.60705012,No.60802025)
文摘In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost function.In the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step.The convergence is thoroughly studied.Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.
文摘The thermal analysis of precise thermophysical data for pure fluids from electronic databases is developed to investigate the molecular interaction mechanisms and parameters and the structural features of heterogeneities in fluids. The method is based on the series expansion of thermophysical values by powers of the monomer fraction density. Unlike the virial expansion by powers of the total density, the series expansion terms in this method directly reflect properties of the corresponding cluster fractions. The internal energy had been selected among thermophysical properties as the most informative for this method. The thermal analysis of its series expansion coefficients permits to estimate the temperature dependence of the pair bond parameters, the clusters’ bond energies and equilibrium constants, the structural transitions between dominating isomers of clusters. The application of method to different pure fluids, including noble and molecular gases with van der Waals and polar molecular interactions, brings unknown clusters’ characteristics for the fluids under investigation. The thermal analysis of the ordinary and heavy Water vapors points on no trivial isotopic effects. The unpredictable growth of the pair bond energy with temperature in Alkanes points on existence in hydrocarbons of some unknown molecular interaction forces in addition to dispersion forces.
文摘To discover particular features of pure supercritical fluids, important for the supercritical fluid extraction and cleaning technologies, the preprocessed and generalized experimental data from the US National Institute of Standards and Technology (NIST) online database have been analyzed. The soft transition between gas-like and liquid-like structures in pure supercritical fluids has been considered in comparison with the abrupt vapor-liquid phase transition. A rough, diffused and boiling boundary between these structures in conditions of extra high gravity is opposed to a flat vapor-liquid boundary at a moderate gravity. The model for molecular diffusivity in carbon dioxide at temperatures near the critical temperature discovers its proportionality to the monomer fraction density. The cluster fraction based model for small molecular weight solids’ solubility in supercritical fluids has been suggested and successfully compared with the well-known experimental results for the solubility of silica in water.The model shows that at growing pressure the dissolution process has already startedin a real gas and discovers the cluster fractions’ role in the solubility process.
基金This research work was funded by Institutional Fund Projects under grant no.(IFPIP:14-611-1443)Therefore,the authors gratefully acknowledge technical and financial support provided by the Ministry of Education and Deanship of Scientific Research(DSR),King Abdulaziz University(KAU),Jeddah,Saudi Arabia.
文摘Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.
基金The publication of this article is funded by the Qatar National Library.
文摘Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.
基金The research is supported by Universiti Teknologi PETRONAS YUTP Grant(Cost Center 015LC0-173).
文摘Gold metallic nanoparticles are generally used within a lab as a tracer,to uncover on the presence of specific proteins or DNA in a sample,as well as for the recognition of various antibiotics.They are bio companionable and have properties to carry thermal energy to tumor cells by utilizing different clinical approaches.As the cancer cells are very smaller so for the infiltration,the properly sized nanoparticles have been injected in the blood.For this reason,gold nanoparticles are very effective.Keeping in mind the above applications,in the present work a generalized model of blood flow containing gold nanoparticles is considered in this work.The blood motion is considered in a cylindrical tube under the oscillating pressure gradient and magnetic field.The problem formulation is done using two types of fractional approaches namely CF(Caputo Fabrizio)and AB(Atangana-Baleanue)derivatives,whereas blood is considered as a counter-example of Casson fluid.Exact solutions of the problem are obtained using joint Laplace and Hankel transforms,and a comparative analysis is made between CF and AB.Results are computed in tables and shown in various plots for embedded parameters and discussed.It is found that adding 0.04-unit gold nanoparticles to blood,increase its heat transfer rate by 4 percent compared to regular blood.It is also noted that the heat transfer can be enhanced in the blood with memory.
基金This research was supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS(YUTP)Fundamental Research Grant Scheme(YUTPFRG/015LC0-308).
文摘Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.
基金＂This paper is an extended version of ＂SpotMPl： a framework for auction-based HPC computing using amazon spot instances＂ published in the International Symposium on Advances of Distributed Computing and Networking （ADCN 2011）.Acknowledgment This research is supported in part by the National Science Foundation grant CNS 0958854 and educational resource grants from Amazon.com.
文摘Cloud computing is expanding widely in the world of IT infrastructure. This is due partly to the cost-saving effect of economies of scale. Fair market conditions can in theory provide a healthy environment to reflect the most reasonable costs of computations. While fixed cloud pricing provides an attractive low entry barrier for compute-intensive applications, both the consumer and supplier of computing resources can see high efficiency for their investments by participating in auction-based exchanges. There are huge incentives for the cloud provider to offer auctioned resources. However, from the consumer perspective, using these resources is a sparsely discussed challenge. This paper reports a methodology and framework designed to address the challenges of using HPC （High Performance Computing） applications on auction-based cloud clusters. The authors focus on HPC applications and describe a method for determining bid-aware checkpointing intervals. They extend a theoretical model for determining checkpoint intervals using statistical analysis of pricing histories. Also the latest developments in the SpotHPC framework are introduced which aim at facilitating the managed execution of real MPI applications on auction-based cloud environments. The authors use their model to simulate a set of algorithms with different computing and communication densities. The results show the complex interactions between optimal bidding strategies and parallel applications performance.
文摘The aim of this research is to apply the author’s original computer aided analysis of thermophysical data for pure fluids to noble gases to investigate the unknown aspects in their equilibrium thermal physics. The methodology of the analysis is based on the potential energy density series expansion by the monomer fraction density. To discover the important details and particular features of pair atomic interactions in noble gases, the preprocessed and generalized experimental data have been taken from the US National Institute of Standards and Technology (NIST) online database. In this work the temperature range for analysis of the dimers’ bonding parameters is extended as compared to previous author’s works due to accounting for the specific temperature dependence of the repulsions’ contribution to the potential energy. The found temperature dependences of the pair interaction bond energies signal about the hindered rotation of atoms in dimers near the triple point due to the lack of rotational symmetry of their electronic outer shells. The discovered mutually correlated anomalous temperature dependences of the pair bond energy and the constant volume heat capacity in gaseous Helium require a special investigation of this remarkable phenomenon.
文摘Dzyaloshiniskii-Moriya (DM) interaction in three directions (Dx, Dy and Dz) is used to generate entangled network from partially entangled states in the presence of the spin-orbit coupling. The effect of the spin coupling on the entanglement between any two nodes of the network is investigated. The entanglement is quantified using Woottores concurrence method. It is shown that the entanglement decays as the coupling increases. For larger values of the spin coupling, the entanglement oscillates within finite bounds. For the initially entangled channels, the upper bound does not exceed its initial value, whereas the entanglement reaches its maximum value for the channels generated via indirect interaction.
基金the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant no.(G:366-140-38).
文摘With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of collapse.An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional approach.The accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems.This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs.To attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature vectors.In addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 model.Moreover,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of COVID19.For ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive aspects.The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.
