Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the ...Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.展开更多
In this paper, the problem of finding exact solutions to the magnetohydrodynamic(MHD) equations in the presence of incompressible mass flows with helical symmetry is considered. For ideal flows, a similarity reduction...In this paper, the problem of finding exact solutions to the magnetohydrodynamic(MHD) equations in the presence of incompressible mass flows with helical symmetry is considered. For ideal flows, a similarity reduction method is used to obtain exact solutions for several MHD flows with nonlinear variable Mach number. For resistive flows parallel to a magnetic field, the governing equilibrium equation is derived. The MHD equilibrium state of a helically symmetric incompressible flow is governed by a second-order elliptic partial differential equation(PDE) for the helical magnetic flux function. Exact solutions for the latter equation are obtained. Also, the equilibrium equations of a gravitating plasma with incompressible flow are derived.展开更多
This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation ...This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation to enhance its capabilities.The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues.The suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique characteristics.This model includes many well-known lifespan distributions as separate sub-models.The suggested model is accompanied with a range of statistical features.The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data.In order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing purposes.The relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.展开更多
Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COV...Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among people.Therefore,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and mortalities.With this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population.The current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)model.The presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter tuning.Besides,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of dimensionality.In addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm.The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures.The outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.展开更多
Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomed...Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical images.In this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)model.The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma.At the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from images.In addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors.Then,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of osteosarcoma.EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results.The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study.In order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.展开更多
System reliability optimization problem of multi-source multi-sink flow network is defined by searching the optimal components that maximize the reliability and minimize the total assignment cost. Therefore, a genetic...System reliability optimization problem of multi-source multi-sink flow network is defined by searching the optimal components that maximize the reliability and minimize the total assignment cost. Therefore, a genetic-based approach is proposed to solve the components assignment problem under budget constraint. The mathematical model of the optimization problem is presented and solved by the proposed genetic-based approach. The proposed approach is based on determining the optimal set of lower boundary points that maximize the system reliability such that the total assignment cost does not exceed the specified budget. Finally, to evaluate our approach, we applied it to various network examples with different numbers of available components;two-source two-sink network and three-source two-sink network.展开更多
Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries a...Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries as a powersource and replacing them is not an easy task. With this restriction, the sensornodes must conserve their energy and extend the network lifetime as long as possible.Also, these limits motivate much of the research to suggest solutions in alllayers of the protocol stack to save energy. So, energy management efficiencybecomes a key requirement in WSN design. The efficiency of these networks ishighly dependent on routing protocols directly affecting the network lifetime.Clustering is one of the most popular techniques preferred in routing operations.In this work we propose a novel energy-efficient protocol for WSN based on a batalgorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithmfor WSN) to prolong the network lifetime. We use an objective function thatgenerates an optimal number of sensor clusters with cluster heads (CH) to minimizeenergy consumption. The performance of the proposed approach is comparedwith Low-Energy Adaptive Clustering Hierarchy (LEACH) and EnergyEfficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interestingin terms of energy-saving and prolongation of the network lifetime.展开更多
In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distanc...In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of G.The cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of G.The problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization(BEHHO)algorithm.This is thefirst attempt to determine the connected resolving set heuristically.BEHHO combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary one.The hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving set.The feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving set.The proposed BEHHO algorithm is compared to binary Harris Hawk Optimi-zation(BHHO),binary opposition-based learning Harris Hawk Optimization(BOHHO),binary chaotic local search Harris Hawk Optimization(BCHHO)algorithms.Computational results confirm the superiority of the BEHHO for determining connected metric dimension.展开更多
be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each i...be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each image by itself.In our proposed technique,a classification process is applied,where the set of the input images are classified into groups based on existing technique like L1 and L2 norms,color histograms.All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook.In the process of extracting the different sub-images,we used the mean squared error for comparison and three blurring methods(simple,middle and majority blurring)to increase the compression ratio.Experiments show that varying blurring values,as well as MSE thresholds,enhanced the compression results in a group of images compared to JPEG and PNG compressors.展开更多
Classical unequal erasure protection schemes split data to be protected into classes which are encoded independently. The unequal protection scheme presented in this paper is based on an erasure code which encodes all...Classical unequal erasure protection schemes split data to be protected into classes which are encoded independently. The unequal protection scheme presented in this paper is based on an erasure code which encodes all the data together according to the existing dependencies. A simple algorithm generates dynamically the generator matrix of the erasure code according to the packets streams structure, i.e., the dependencies between the packets, and the rate of the code. This proposed erasure code was applied to a packetized MPEG4 stream transmitted over a packet erasure channel and compared with other classical protection schemes in terms of PSNR and MOS. It is shown that the proposed code allows keeping a high video quality-level in a larger packet loss rate range than the other protection schemes.展开更多
The generalized product bi-conjugate gradient(GPBiCG(m,l))method has been recently proposed as a hybrid variant of the GPBi CG and the Bi CGSTAB methods to solve the linear system Ax=b with non-symmetric coefficient m...The generalized product bi-conjugate gradient(GPBiCG(m,l))method has been recently proposed as a hybrid variant of the GPBi CG and the Bi CGSTAB methods to solve the linear system Ax=b with non-symmetric coefficient matrix,and its attractive convergence behavior has been authenticated in many numerical experiments.By means of the Kronecker product and the vectorization operator,this paper aims to develop the GPBi CG(m,l)method to solve the general matrix equation■ and the general discrete-time periodic matrix equations■ which include the well-known Lyapunov,Stein,and Sylvester matrix equations that arise in a wide variety of applications in engineering,communications and scientific computations.The accuracy and efficiency of the extended GPBi CG(m,l)method assessed against some existing iterative methods are illustrated by several numerical experiments.展开更多
We apply the （G＇/G）-expansion method to solve two systems of nonlinear differential equations and construct traveling wave solutions expressed in terms of hyperbolic functions, trigonometric functions, and rational...We apply the （G＇/G）-expansion method to solve two systems of nonlinear differential equations and construct traveling wave solutions expressed in terms of hyperbolic functions, trigonometric functions, and rational functions with arbitrary parameters. We highlight the power of the （G＇/G）-expansion method in providing generalized solitary wave solutions of different physical structures. It is shown that the （G＇/G）-expansion method is very effective and provides a powerful mathematical tool to solve nonlinear differential equation systems in mathematical physics.展开更多
In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial earl...In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.展开更多
In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic al...In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image segmentation.The proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)segmentation.Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods.In order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these groups.The proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical images.It is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.展开更多
Proliferation of technology,coupled with networking growth,has catapulted cybersecurity to the forefront of modern security concerns.In this landscape,the precise detection of cyberattacks and anomalies within network...Proliferation of technology,coupled with networking growth,has catapulted cybersecurity to the forefront of modern security concerns.In this landscape,the precise detection of cyberattacks and anomalies within networks is crucial,necessitating the development of efficient intrusion detection systems(IDS).This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines(SVM),named FSVM.The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance.Features with minimal significance are prudently disregarded,a method akin to feature selection.This process not only curtails the computational burden of the classification algorithm but also ensures the preservation of high accuracy levels.To ascertain the efficacy of the FSVM model,we have employed a publicly available dataset from Kaggle,which encompasses two distinct decision labels.Our evaluation methodology involves a comprehensive comparison of the classification accuracy of the processed dataset against four contemporary models in the field.Key performance metrics scores are meticulously calculated for each model.The comparative analysis reveals that the FSVM model demonstrates a marked superiority over its counterparts,enhancing classification accuracy by a minimum of 3%.These findings underscore the FSVM model’s robustness and reliability,positioning it as a highly effective tool in the realm of cybersecurity.展开更多
Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more intelligent.However, the upgrading of 5G network will cause a variety of issues increase,one...Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more intelligent.However, the upgrading of 5G network will cause a variety of issues increase,one of them is the increased cost of coverage. In this paper, we proposea sustainable wireless sensor networks system, which avoids the problemsbrought by 5G network system to some extent. In this system, deployingrelays and selecting routing are for the sake of communication and charging.The main aim is to minimize the total energy-cost of communication underthe precondition, where each terminal with low-power should be charged byat least one relay. Furthermore, from the perspective of graph theory, weextract a combinatorial optimization problem from this system. After that,as to four different cases, there are corresponding different versions of theproblem. We give the proofs of computational complexity for these problems,and two heuristic algorithms for one of them are proposed. Finally, theextensive experiments compare and demonstrate the performances of thesetwo algorithms.展开更多
The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the weather.Therefore,weather constitutes a key role in all regular human activities.Weather forecasting must be accurate ...The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the weather.Therefore,weather constitutes a key role in all regular human activities.Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters.Rainfall,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather prediction.Many research works have been conducted on weather forecasting.The drawbacks of existing approaches are that they are less effective,inaccurate,and time-consuming.To overcome these issues,this paper proposes an enhanced and reliable weather forecasting technique.As well as developing weather forecasting in remote areas.Weather data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather forecasting.The aim of this study is to demonstrate its ability to predict weather forecasts as soon as possible.Experimental evaluation shows our ensemble technique achieves 95%prediction accuracy.Also,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.展开更多
We consider the construction of semi-implicit linear multistep methods that can be applied to time-dependent PDEs where the separation of scales in additive form,typically used in implicit-explicit(IMEX)methods,is not...We consider the construction of semi-implicit linear multistep methods that can be applied to time-dependent PDEs where the separation of scales in additive form,typically used in implicit-explicit(IMEX)methods,is not possible.As shown in Boscarino et al.(J.Sci.Comput.68:975-1001,2016)for Runge-Kutta methods,these semi-implicit techniques give a great flexibility,and allow,in many cases,the construction of simple linearly implicit schemes with no need of iterative solvers.In this work,we develop a general setting for the construction of high order semi-implicit linear multistep methods and analyze their stability properties for a prototype lineal'advection-diffusion equation and in the setting of strong stability preserving(SSP)methods.Our findings are demonstrated on several examples,including nonlinear reaction-diffusion and convection-diffusion problems.展开更多
With the flood of information on the Web, it has become increasingly necessary for users to utilize automated tools in order to find, extract, filter, and evaluate the desired information and knowledge discovery. In t...With the flood of information on the Web, it has become increasingly necessary for users to utilize automated tools in order to find, extract, filter, and evaluate the desired information and knowledge discovery. In this research, we will present a preliminary discussion about using the dominant meaning technique to improve Google Image Web search engine. Google search engine analyzes the text on the page adjacent to the image, the image caption and dozens of other factors to determine the image content. To improve the results, we looked for building a dominant meaning classification model. This paper investigated the influence of using this model to retrieve more efficient images, through sequential procedures to formulate a suitable query. In order to build this model, the specific dataset related to an application domain was collected;K-means algorithm was used to cluster the dataset into K-clusters, and the dominant meaning technique is used to construct a hierarchy model of these clusters. This hierarchy model is used to reformulate a new query. We perform some experiments on Google and validate the effectiveness of the proposed approach. The proposed approach is improved for in precision, recall and F1-measure by 57%, 70%, and 61% respectively.展开更多
Recent studies have revealed that concrete can be used as a media to contain As （arsenic） removed from drinking water. Concrete, which is a composite material, has been effective in solidifying hazardous wastes and ...Recent studies have revealed that concrete can be used as a media to contain As （arsenic） removed from drinking water. Concrete, which is a composite material, has been effective in solidifying hazardous wastes and contaminated soils. A research project was conducted to study the effects of uncontaminated soil and arsenic contaminated soil on the microstructure of concrete to qualitatively define the mechanisms of the encapsulation of soils containing inorganic material such as arsenic by application of solidification/stabilization technique. This research paper focused on studying the surface morphology of RPC （reactive powder concrete） containing soil.展开更多
基金We acknowledge funding from NSFC Grant 62306283.
文摘Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.
文摘In this paper, the problem of finding exact solutions to the magnetohydrodynamic(MHD) equations in the presence of incompressible mass flows with helical symmetry is considered. For ideal flows, a similarity reduction method is used to obtain exact solutions for several MHD flows with nonlinear variable Mach number. For resistive flows parallel to a magnetic field, the governing equilibrium equation is derived. The MHD equilibrium state of a helically symmetric incompressible flow is governed by a second-order elliptic partial differential equation(PDE) for the helical magnetic flux function. Exact solutions for the latter equation are obtained. Also, the equilibrium equations of a gravitating plasma with incompressible flow are derived.
基金This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number IMSIU-RG23142).
文摘This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation to enhance its capabilities.The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues.The suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique characteristics.This model includes many well-known lifespan distributions as separate sub-models.The suggested model is accompanied with a range of statistical features.The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data.In order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing purposes.The relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/25/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among people.Therefore,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and mortalities.With this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population.The current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)model.The presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter tuning.Besides,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of dimensionality.In addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm.The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures.The outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(42/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R151)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR16).
文摘Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical images.In this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)model.The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma.At the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from images.In addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors.Then,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of osteosarcoma.EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results.The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study.In order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.
文摘System reliability optimization problem of multi-source multi-sink flow network is defined by searching the optimal components that maximize the reliability and minimize the total assignment cost. Therefore, a genetic-based approach is proposed to solve the components assignment problem under budget constraint. The mathematical model of the optimization problem is presented and solved by the proposed genetic-based approach. The proposed approach is based on determining the optimal set of lower boundary points that maximize the system reliability such that the total assignment cost does not exceed the specified budget. Finally, to evaluate our approach, we applied it to various network examples with different numbers of available components;two-source two-sink network and three-source two-sink network.
文摘Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries as a powersource and replacing them is not an easy task. With this restriction, the sensornodes must conserve their energy and extend the network lifetime as long as possible.Also, these limits motivate much of the research to suggest solutions in alllayers of the protocol stack to save energy. So, energy management efficiencybecomes a key requirement in WSN design. The efficiency of these networks ishighly dependent on routing protocols directly affecting the network lifetime.Clustering is one of the most popular techniques preferred in routing operations.In this work we propose a novel energy-efficient protocol for WSN based on a batalgorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithmfor WSN) to prolong the network lifetime. We use an objective function thatgenerates an optimal number of sensor clusters with cluster heads (CH) to minimizeenergy consumption. The performance of the proposed approach is comparedwith Low-Energy Adaptive Clustering Hierarchy (LEACH) and EnergyEfficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interestingin terms of energy-saving and prolongation of the network lifetime.
文摘In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of G.The cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of G.The problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization(BEHHO)algorithm.This is thefirst attempt to determine the connected resolving set heuristically.BEHHO combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary one.The hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving set.The feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving set.The proposed BEHHO algorithm is compared to binary Harris Hawk Optimi-zation(BHHO),binary opposition-based learning Harris Hawk Optimization(BOHHO),binary chaotic local search Harris Hawk Optimization(BCHHO)algorithms.Computational results confirm the superiority of the BEHHO for determining connected metric dimension.
文摘be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each image by itself.In our proposed technique,a classification process is applied,where the set of the input images are classified into groups based on existing technique like L1 and L2 norms,color histograms.All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook.In the process of extracting the different sub-images,we used the mean squared error for comparison and three blurring methods(simple,middle and majority blurring)to increase the compression ratio.Experiments show that varying blurring values,as well as MSE thresholds,enhanced the compression results in a group of images compared to JPEG and PNG compressors.
文摘Classical unequal erasure protection schemes split data to be protected into classes which are encoded independently. The unequal protection scheme presented in this paper is based on an erasure code which encodes all the data together according to the existing dependencies. A simple algorithm generates dynamically the generator matrix of the erasure code according to the packets streams structure, i.e., the dependencies between the packets, and the rate of the code. This proposed erasure code was applied to a packetized MPEG4 stream transmitted over a packet erasure channel and compared with other classical protection schemes in terms of PSNR and MOS. It is shown that the proposed code allows keeping a high video quality-level in a larger packet loss rate range than the other protection schemes.
基金Supported by the National Natural Sciences Foundation of China(Grant Nos.11501079 11571061)Part by the Higher Education Commission of Egypt
文摘The generalized product bi-conjugate gradient(GPBiCG(m,l))method has been recently proposed as a hybrid variant of the GPBi CG and the Bi CGSTAB methods to solve the linear system Ax=b with non-symmetric coefficient matrix,and its attractive convergence behavior has been authenticated in many numerical experiments.By means of the Kronecker product and the vectorization operator,this paper aims to develop the GPBi CG(m,l)method to solve the general matrix equation■ and the general discrete-time periodic matrix equations■ which include the well-known Lyapunov,Stein,and Sylvester matrix equations that arise in a wide variety of applications in engineering,communications and scientific computations.The accuracy and efficiency of the extended GPBi CG(m,l)method assessed against some existing iterative methods are illustrated by several numerical experiments.
基金Project supported by the Scientific Research Project of Eskisehir Osmangazi University, Turkey (Grant No. 201019031)
文摘We apply the （G＇/G）-expansion method to solve two systems of nonlinear differential equations and construct traveling wave solutions expressed in terms of hyperbolic functions, trigonometric functions, and rational functions with arbitrary parameters. We highlight the power of the （G＇/G）-expansion method in providing generalized solitary wave solutions of different physical structures. It is shown that the （G＇/G）-expansion method is very effective and provides a powerful mathematical tool to solve nonlinear differential equation systems in mathematical physics.
文摘In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.
基金Taif University Researchers for Supporting Project number(TURSP-2020/214),Taif University,Taif Saudi Arabia.
文摘In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image segmentation.The proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)segmentation.Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods.In order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these groups.The proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical images.It is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.
文摘Proliferation of technology,coupled with networking growth,has catapulted cybersecurity to the forefront of modern security concerns.In this landscape,the precise detection of cyberattacks and anomalies within networks is crucial,necessitating the development of efficient intrusion detection systems(IDS).This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines(SVM),named FSVM.The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance.Features with minimal significance are prudently disregarded,a method akin to feature selection.This process not only curtails the computational burden of the classification algorithm but also ensures the preservation of high accuracy levels.To ascertain the efficacy of the FSVM model,we have employed a publicly available dataset from Kaggle,which encompasses two distinct decision labels.Our evaluation methodology involves a comprehensive comparison of the classification accuracy of the processed dataset against four contemporary models in the field.Key performance metrics scores are meticulously calculated for each model.The comparative analysis reveals that the FSVM model demonstrates a marked superiority over its counterparts,enhancing classification accuracy by a minimum of 3%.These findings underscore the FSVM model’s robustness and reliability,positioning it as a highly effective tool in the realm of cybersecurity.
基金The authors would like to extend their gratitude to King Saud University(Riyadh,Saudi Arabia)for funding this research through Researchers Supporting Project number(RSP-2021/260)And this work was supported by the Natural Science Foundation of Hunan Province,China(Grant No.2020JJ4949)the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20200883).
文摘Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more intelligent.However, the upgrading of 5G network will cause a variety of issues increase,one of them is the increased cost of coverage. In this paper, we proposea sustainable wireless sensor networks system, which avoids the problemsbrought by 5G network system to some extent. In this system, deployingrelays and selecting routing are for the sake of communication and charging.The main aim is to minimize the total energy-cost of communication underthe precondition, where each terminal with low-power should be charged byat least one relay. Furthermore, from the perspective of graph theory, weextract a combinatorial optimization problem from this system. After that,as to four different cases, there are corresponding different versions of theproblem. We give the proofs of computational complexity for these problems,and two heuristic algorithms for one of them are proposed. Finally, theextensive experiments compare and demonstrate the performances of thesetwo algorithms.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/42/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R135),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the weather.Therefore,weather constitutes a key role in all regular human activities.Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters.Rainfall,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather prediction.Many research works have been conducted on weather forecasting.The drawbacks of existing approaches are that they are less effective,inaccurate,and time-consuming.To overcome these issues,this paper proposes an enhanced and reliable weather forecasting technique.As well as developing weather forecasting in remote areas.Weather data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather forecasting.The aim of this study is to demonstrate its ability to predict weather forecasts as soon as possible.Experimental evaluation shows our ensemble technique achieves 95%prediction accuracy.Also,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.
基金Open Access funding provided by Universita degli Studi di Verona.
文摘We consider the construction of semi-implicit linear multistep methods that can be applied to time-dependent PDEs where the separation of scales in additive form,typically used in implicit-explicit(IMEX)methods,is not possible.As shown in Boscarino et al.(J.Sci.Comput.68:975-1001,2016)for Runge-Kutta methods,these semi-implicit techniques give a great flexibility,and allow,in many cases,the construction of simple linearly implicit schemes with no need of iterative solvers.In this work,we develop a general setting for the construction of high order semi-implicit linear multistep methods and analyze their stability properties for a prototype lineal'advection-diffusion equation and in the setting of strong stability preserving(SSP)methods.Our findings are demonstrated on several examples,including nonlinear reaction-diffusion and convection-diffusion problems.
文摘With the flood of information on the Web, it has become increasingly necessary for users to utilize automated tools in order to find, extract, filter, and evaluate the desired information and knowledge discovery. In this research, we will present a preliminary discussion about using the dominant meaning technique to improve Google Image Web search engine. Google search engine analyzes the text on the page adjacent to the image, the image caption and dozens of other factors to determine the image content. To improve the results, we looked for building a dominant meaning classification model. This paper investigated the influence of using this model to retrieve more efficient images, through sequential procedures to formulate a suitable query. In order to build this model, the specific dataset related to an application domain was collected;K-means algorithm was used to cluster the dataset into K-clusters, and the dominant meaning technique is used to construct a hierarchy model of these clusters. This hierarchy model is used to reformulate a new query. We perform some experiments on Google and validate the effectiveness of the proposed approach. The proposed approach is improved for in precision, recall and F1-measure by 57%, 70%, and 61% respectively.
文摘Recent studies have revealed that concrete can be used as a media to contain As （arsenic） removed from drinking water. Concrete, which is a composite material, has been effective in solidifying hazardous wastes and contaminated soils. A research project was conducted to study the effects of uncontaminated soil and arsenic contaminated soil on the microstructure of concrete to qualitatively define the mechanisms of the encapsulation of soils containing inorganic material such as arsenic by application of solidification/stabilization technique. This research paper focused on studying the surface morphology of RPC （reactive powder concrete） containing soil.
