A critical review on solid-state welding of high entropy alloys-processing,microstructural characteristics and mechanical properties of joints

The high entropy alloys(HEAs)are the newly developed high-performance materials that have gained significant importance in defence,nuclear and aerospace sector due to their superior mechanical properties,heat resistance,high temperature strength and corrosion resistance.These alloys are manufactured by the equal mixing or larger proportions of five or more alloying elements.HEAs exhibit superior mechanical performance compared to traditional engineering alloys because of the extensive alloying composition and higher entropy of mixing.Solid state welding(SSW)techniques such as friction stir welding(FSW),rotary friction welding(RFW),diffusion bonding(DB)and explosive welding(EW)have been efficiently deployed for improving the microstructural integrity and mechanical properties of welded HEA joints.The HEA interlayers revealed greater potential in supressing the formation of deleterious intermetallic phases and maximizing the mechanical properties of HEAs joints.The similar and dissimilar joining of HEAs has been manifested to be viable for HEA systems which further expands their industrial applications.Thus,the main objective of this review paper is to present a critical review of current state of research,challenges and opportunities and main directions in SSW of HEAs mainly CoCrFeNiMn and Al_xCoCrFeNi alloys.The state of the art of problems,progress and future outlook in SSW of HEAs are critically reviewed by considering the formation of phases,microstructural evolution and mechanical properties of HEAs joints.

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.

Milestones to optimize of transjugular intrahepatic portosystemic shunt technique as a method for the treatment of portal hypertension complications

This editorial describes the milestones to optimize of transjugular intrahepatic portosystemic shunt(TIPS)technique,which have made it one of the main methods for the treatment of portal hypertension complications worldwide.Innovative ideas,subsequent experimental studies and preliminary experience of use in cirrhotic patients contributed to the introduction of TIPS into clinical practice.At the moment,the main achievement in optimize of TIPS technique is progress in the qualitative characteristics of stents.The transition from bare metal stents to extended polytetrafluoroethylene–covered stent grafts made it possible to significantly prevent shunt dysfunction.However,the question of its preferred diameter,which contributes to an optimal reduction of portal pressure without the risk of developing post-TIPS hepatic encephalopathy,remains relevant.Currently,hepatic encephalopathy is one of the most common complications of TIPS,significantly affecting its effectiveness and prognosis.Careful selection of patients based on cognitive indicators,nutritional status,assessment of liver function,etc.,will reduce the incidence of post-TIPS hepatic encephalopathy and improve treatment results.Optimize of TIPS technique has significantly expanded the indications for its use and made it one of the main methods for the treatment of portal hypertension complications.At the same time,there are a number of limitations and unresolved issues that require further randomized controlled trials involving a large cohort of patients.

Contemporary concepts of prevention and management of gastroesophageal variceal bleeding in liver cirrhosis patients

This editorial describes the contemporary concepts of prevention and management of gastroesophageal variceal bleeding in liver cirrhosis(LC)patients according to the current guidelines.Gastroesophageal variceal bleeding is the most dangerous complication of portal hypertension in LC patients.Risk stratification and determination of an individual approach to the choice of therapeutic measures aimed at their prevention and management has emerged as one of the top concerns in modern hepatology.According to the current guidelines,in the absence of clinically significant portal hypertension,etiological and nonetiological therapies of LC is advisable for the primary preventing gastroesophageal variceal bleeding,whereas its presence serves as an indication for the administration of non-selectiveβ-blockers,among which carvedilol is the drug of choice.Non-selectiveβ-blockers,as well as endoscopic variceal ligation and transjugular intrahepatic portosystemic shunt can be used to prevent recurrence of gastroesophageal variceal bleeding.Pharmacotherapy with vasoactive drugs(terlipressin,somatostatin,octreotide),endoscopic variceal ligation,endovascular techniques and transjugular intrahepatic portosystemic shunt are recommended for the treatment of acute gastroesophageal variceal bleeding.Objective and accurate risk stratification of gastroesophageal variceal bleeding will allow developing individual strategies for their prevention and management,avoiding the first and further decompensation in LC,which will improve the prognosis and survival of patients suffering from it.

ChatGPT: Application in Chemistry Education and Challenges

The paper discusses the advancements and applications of neural networks, specifically ChatGPT, in various fields, including chemistry education and research. It examines the benefits of AI and ChatGPT, such as their ability to process and analyze large amounts of data, create personalized training systems, and offer problem-solving recommendations. The paper delves into practical applications, showcasing how ChatGPT can be utilised to augment chemistry learning. It provides examples of using ChatGPT for creating tests, generating multiple-choice questions, and studying chemistry in general. Concerns are voiced about the ethical and societal impact of AI development. In conclusion, it explores the exciting potential of AI to tackle challenges that may exceed human capabilities alone, paving the way for further exploration and collaboration between humans and intelligent machines.

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.

Structural Peculiarities and Magnetic Properties of Nanoscale Terbium in Tb/Ti and Tb/Si Multilayers

The structure and the magnetic properties of rf-sputtered nauoscale Tb in [Tb/Ti]n and [Tb/Si] multilayers have been studied experimentally. It is found that the structure of Tb layers changes, transforming from a fine-crystalline state of Tb into an amorphous state as the magnetic layer thickness decreases. The observed displacement of a magnetic ordering temperature to the lower temperature range and the shift of magnetic hysteresis obtained in the ZFC-FC terms may be caused by the size effect and the amorphization. Both the first and the second suppress the exchange interaction and decrease the crystalline magnetic anisotropy. The type of material of nonmagnetic spacers between the Tb layers plays a certain role in these changes.

Impact of In^(3+) cations on structure and electromagnetic state of M-type hexaferrites

The solid solutions of In^(3+) doped M-type strontium hexaferrites were produced using a conventional solid-state reaction method,and Rietveld analysis of the neutron diffraction patterns was conducted.In^(3+) cations occupy octahedral (4f_(Ⅵ)and 12 k) and tetrahedral (4f_(Ⅳ)) positions (SG=P6_(3)/mmc(No.194)).The average particle size is 837–650 nm.Curie tempearature (T_(C)) of the compounds monotonically decreased down to~520 K with increasing x.A frustrated magnetic state was detected from ZFC and FC magnetizations.saturation magnetization (M_(s)) and effective magnetocrystalline anisotropy coefficient (k_(eff)) were determined using the law of approach to saturation.A real permittivity (ε″) maximum of~3.3 at~45.5 GHz and an imaginary permittivity (ε′) of~1.6 at~42.3 GHz were observed for x=0.1.A real permeability (μ′) maximum of~1.5 at~36.2 GHz was observed for x=0.Aμ″imaginary permeability maximum of~0.8 at~38.3 GHz was observed for x=0.1.The interpretation of the results is based on the type of dielectric polarization and the natural ferromagnetic resonance features.

Therapeutic possibilities of gut microbiota modulation in acute decompensation of liver cirrhosis

The formation of liver cirrhosis(LC) is an unfavorable event in the natural history of chronic liver diseases and with the development of portal hypertension and/or impaired liver function can cause a fatal outcome. Decompensation of LC is considered the most important stratification variable for the risk of death. It is currently postulated that decompensation of LC occurs through an acute(including acute-on-chronic liver failure) and non-acute pathway. Acute decompensation of LC is accompanied by the development of life-threatening complications, characterized by an unfavorable prognosis and high mortality.Progress in understanding the underlying molecular mechanisms has led to the search for new interventions, drugs, and biological substances that can affect key links in the pathogenesis of acute decompensation in LC, for example the impaired gut-liver axis and associated systemic inflammation. Given that particular alterations in the composition and function of gut microbiota play a crucial role here, the study of the therapeutic possibilities of its modulation has emerged as one of the top concerns in modern hepatology. This review summarized the investigations that describe the theoretical foundations and therapeutic potential of gut microbiota modulation in acute decompensation of LC. Despite the encouraging preliminary data, the majority of the suggested strategies have only been tested in animal models or in preliminary clinical trials;additional multicenter randomized controlled trials must demonstrate their efficacy in larger patient populations.

Novel Hybrid X GBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests

When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength parameters.This study makes an effort to propose a novel predictive model of shear strength.The study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various soils.To do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model inputs.Themodel is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this study.Thepredictionperformanceof theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear strength.To investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed model.Overall,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.

Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering

Soft computing(SC)refers to the ability of a digital computer or robot to perform functions that are normally associated with intelligent individuals,such as reasoning and problem-solving.An example of this would be a project aimed at creating systems capable of reasoning,discovering meaning,generalising,or learning from past experience.Science and engineering problems that are both non-linear and complex can be solved using these methodologies.It has been proven that these algorithms can be used to solve numerous real-world problems.The techniques outlined can be used to increase the accuracy of existing models/equations,or they can be used to propose a newmodel that can address the problem.

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

Medi-Block Record Secure Data Sharing in Healthcare System:Issues,Solutions and Challenges

With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.

Is a correlation‑based investment strategy beneficial for long‑term international portfolio investors?

Using negative to low-correlated assets to manage short-term portfolio risk is not uncommon among investors,although the long-term benefits of this strategy remain unclear.This study examines the long-term benefits of the correlation strategy for portfolios based on the stock market in Asia,Central and Eastern Europe,the Middle East and North Africa,and Latin America from 2000 to 2016.Our strategy is as follows.We develop five portfolios based on the average unconditional correlation between domestic and foreign assets from 2000 to 2016.This yields five regional portfolios based on low to high correlations.In the presence of selected economic and financial conditions,long-term diversification gains for each regional portfolio are evaluated using a panel cointegration-based testing method.Consistent across all portfolios and regions,our key cointegration results suggest that selecting a low-correlated portfolio to maximize diversification gains does not necessarily result in long-term diversification gains.Our empirical method,which also permits the estimation of cointegrating regressions,provides the opportunity to evaluate the impact of oil prices,U.S.stock market fluctuations,and investor sentiments on regional portfolios,as well as to hedge against these fluctuations.Finally,we extend our data to cover the years 2017–2022 and find that our main findings are robust.

Hydrocephalic cerebrospinal fluid flowing rotationally with pulsatile boundaries:A mathematical simulation of the thermodynamical approach

To study the kinematics of flow rate and ventricular dilatation,an analytical perturbation approach of hydrocephalus has been devised.This research provides a comprehensive investigation of the characteristics of cerebrospinal fluid(CSF)flow and pressure in a hydrocephalic patient.The influence of hydrocephalic CSF,flowing rotationally with realistic dynamical characteristics on pulsatile boundaries of subarachnoid space,was demonstrated using a nonlinear controlling system of CSF.An analytical perturbation method of hydrocephalus has been developed to investigate the biomechanics of fluid flow rate and the ventricular enlargement.In this paper presents a detailed analysis of CSF flow and pressure dynamics in a hydrocephalic patient.It was elaborated with a nonlinear governing model of CSF to show the influence of hydrocephalic CSF,flowing rotationally with realistic dynamical behaviors on pulsatile boundaries of subarachnoid space.In accordance with the suggested model,the elasticity factor changes depending on how much a porous layer,in this case the brain parenchyma,is stretched.It was improved to include the relaxation of internal mechanical stresses for various perturbation orders,modelling the potential plasticity of brain tissue.The initial geometry that was utilised to create the framework of CSF with pathological disease hydrocephalus and indeed the output of simulations using this model were compared to the actual progression of ventricular dimensions and shapes in patients.According to this observation,the non-linear and elastic mechanical phenomena incorporated into the current model are probably true.Further modelling of ventricular dilation at a normal pressure may benefit from the existence of a valid model whose parameters approximate genuine mechanical characteristics of the cerebral cortex.

Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,2019.The world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi Arabia.The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread.Methodology:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)algorithm.Results:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),2022.In addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach.The achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base models.On the other hand,a statistical analysis is performed to measure the significance of the proposed approach.Conclusions:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies used in IoT applications,have led to major security concerns.Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient.Several artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent years.Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features selected.On the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent decades.In this paper,we proposed a hybrid optimization algorithm for feature selection in IDS.The proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as GWDTO.The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance.On the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its superiority.In addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed approach.Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.