Data analytics and optimization for smart industry

Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial engineering.First,this study describes a general methodology for the fusion of data analytics and optimization.Then,it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing.Finally,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization.The framework uses data analytics to perceive and analyze industrial production and logistics processes.It also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal control.Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental pollution.The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

Urban Sensing Based on Mobile Phone Data:Approaches,Applications,and Challenges

Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications.The ability to accurately and extensively monitor and analyze these data is necessary.Much concern in cellular data analysis is related to human beings and their behaviours.Due to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding patterns.To that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is crucial.The former can be used to determine the utilization of assets like roads and city attractions.The latter is valuable when planning transport infrastructure.Such insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and improvement.Smartphone data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business marketing.It can also help organizations in decision making,policy implementation,monitoring,and evaluation at all levels.This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

Exploring tourism networks in the Guangxi mountainous area using mobility data from user generated content

Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.

Developments and Applications of Data Deidentification Technology under Big Data

In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive information.For instance,healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate privacy.As such,privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big data.As a strict and verifiable definition of privacy,differential privacy has attracted noteworthy attention and widespread research in recent years.In this study,we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection methods.Furthermore,we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate.The common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)algorithms.This paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas industry.The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level indicator.The accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to zero.The performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and F-measure.Results showed that the sensitivity of all classifiers increases as the imbalance rate increases.SVM with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

Artificial intelligence in dentistry:Harnessing big data to predict oral cancer survival

BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.

Recommendation system with minimized transaction data

This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of purchases.This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service.First,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data.The recommendation methodology was verified with the real transaction data.Based on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.

Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.

Radiative Blood-Based Hybrid Copper-Graphene Nanoliquid Flows along a Source-Heated Leaning Cylinder

Variant graphene,graphene oxides(GO),and graphene nanoplatelets(GNP)dispersed in blood-based copper(Cu)nanoliquids over a leaning permeable cylinder are the focus of this study.These forms of graphene are highly beneficial in the biological and medical fields for cancer therapy,anti-infection measures,and drug delivery.The non-Newtonian Sutterby(blood-based)hybrid nanoliquid flows are generalized within the context of the Tiwari-Das model to simulate the effects of radiation and heating sources.The governing partial differential equations are reformulated into a nonlinear set of ordinary differential equations using similar transformational expressions.These equations are then transformed into boundary value problems through a shooting technique,followed by the implementation of the bvp4c tool in MATLAB.The influences of various parameters on the model’s nondimensional velocity and temperature profiles,reduced skin friction,and reduced Nusselt number are presented for detailed discussions.The results indicated that Cu-GNP/blood and Cu-GO/blood hybrid nanofluids exhibit the lowest and highest velocity distributions,respectively,for increased nanoparticles volume fraction,curvature parameter,Sutterby fluid parameter,Hartmann number,and wall permeability parameter.Conversely,opposite trends are observed for the temperature distribution for all considered parameters,except the mixed convection parameter.Increases in the reduced skin friction magnitude and the reduced Nusselt number with higher values of graphene/GO/GNP nanoparticle volume fraction are also reported.Finally,GNP is identified as the superior heat conductor,with an average increase of approximately 5%and a peak of 7.8%in the reduced Nusselt number compared to graphene and GO nanoparticles in the Cu/blood nanofluids.

Abaca Fiber as a Potential Reinforcer for Acoustic Absorption Material at Middle-High Frequencies

Recently,abacafibers have become the focus of specialized research due to their intriguing characteristics,with their outstanding mechanical properties being a particularly notable.In the conducted study,the abacafibers underwent a preliminary treatment process involving an alkaline solution,which was composed of 0.5%sodium hydroxide(NaOH)and 50%acetic acid(CH3COOH).This process entailed immersing eachfiber in the solution for a period of one hour.This treatment led to a 52.36%reduction in lignin content compared to the levels before treatment,resulting in a dramatic decrease in the full width at half maximum(FWHM)in the XRD spectra from 1.13 to 0.13.This change indicates that thefibers became more crystalline following the treatment.The abacafibers were also characterized using BET(Brunauer Emmett Teller)measurements,which revealed that the aver-age pore length ranged from 33–49 nm and the surface area was between 13–28 m^(2)·g^(-1).The morphology of the abacafiber after alkali an hydrolisis treatment(AFAH)appeared rougher and more uniform.DMA measurements revealed a significant rise in the storage modulus of the singlefiber post-treatment,with dependencies on both frequency and temperature.AFAH exhibited an optimal absorption coefficient ofα=0.9 for frequencies above 2500 Hz.The combined effect of alkalization and hydrolyzation treatments,while resulting in an enhancement in the mechanical properties of thefibers,also reduced high-intensity noise produced by sources such as machin-ery,aircraft takeoffs and landings,etc.,across a broader working frequency range.

Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain management.Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges.However,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes.There is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous networks.Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems.To address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain.The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network.This paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity systems.In addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among organizations.Overall,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network.

Adsorption of Malachite Green Using Activated Carbon from Mangosteen Peel: Optimization Using Box-Behnken Design

In this research,activated carbon from mangosteen peel has been synthesized using sulfuric acid as an activator.The adsorption performance of the activated carbon was optimized using malachite green dye as absorbate.Mala-chite green dye waste is a toxic and non-biodegradable material that damages the environment.Optimization of adsorption processes was carried out using Response Surface Methodology(RSM)with a Box-Behnken Design(BBD).The synthesized activated carbon was characterized using FTIR and SEM instruments.The FTIR spectra confirmed the presence of a sulfonate group(-SO_(3)H)in the activated carbon,indicating that the activation pro-cess using sulfuric acid was successful.SEM characterization shows that activated carbon has porous morphology.Optimization was carried out for three adsorption parameters,namely contact time(20,60,and 120 min),adsor-bent mass(0.005,0.025,and 0.05 g),and initial concentration of malachite green solution(5,50,and 100 mg·L^(-1)).The concentration of the malachite green solution was determined using a UV-Vis spectrophotometer at the max-imum wavelength of malachite green,618 nm.The optimum of malachite green adsorption using mangosteen peel activated carbon was obtained at a contact time of 80 min,an adsorbent mass of 0.032 g,and malachite green initial concentration of 25 mg·L^(-1),with a maximum removal percentage and maximum adsorption capacity of 93.66%and 19.345 mg·g^(-1),respectively.

Overview of novel nanobiosensors for electrochemical and optical diagnosis of leukemia:Challenge and opportunity

Leukemia is one of the ten types of cancer that causes the biggest death in the world.Compared to other types of cancer,leukemia has a low life expectancy,so an early diagnosis of the cancer is necessary.A new strategy has been developed to identify various leukemia biomarkers by making blood cancer biosensors,especially by developing nanomaterial applications so that they can improve the performance of the biosensor.Although many biosensors have been developed,the detection of leukemia by using nanomaterials with electrochemical and optical methods is still less carried out compare to other types of cancer biosensors.Even the acoustic and calorimetric testing methods for the detection of leukemia by utilizing nanomaterials have not yet been carried out.Most of the reviewed works reported the use of gold nanoparticles and electrochemical characterization methods for leukemia detection with the object of study being conventional cancer cells.In order to be used clinically by the community,future research must be carried out with a lot of patient blood objects,develop non-invasive leukemia detection,and be able to detect all types of blood cancer specifically with one biosensor.This can lead to a fast and accurate diagnosis thus allowing for early treatment and easy periodic condition monitoring for various types of leukemia based on its biomarker and future design controlable via internet of things(IoT)so that why would be monitoring real times.

Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

Antigen epitopes of animal coronaviruses:a mini-review

Coronaviruses are widespread in nature and can infect mammals and poultry,making them a public health concern.Globally,prevention and control of emerging and re-emerging animal coronaviruses is a great challenge.The mecha-nisms of virus-mediated immune responses have important implications for research on virus prevention and control.The antigenic epitope is a chemical group capable of stimulating the production of antibodies or sensitized lympho-cytes,playing an important role in antiviral immune responses.Thus,it can shed light on the development of diagnos-tic methods and novel vaccines.Here,we have reviewed advances in animal coronavirus antigenic epitope research,aiming to provide a reference for the prevention and control of animal and human coronaviruses.

The Influence of Tartaric Acid in the Silver Nanoparticle Synthesis Using Response Surface Methodology

Silver nanoparticles(AgNPs)synthesized using tartaric acid as a capping agent have a great impact on the reaction kinetics and contribute significantly to the stability of AgNPs.The protective layer formed by tartaric acid is an important factor that protects the silver surface and reduces potential cytotoxicity problems.These attributes are critical for assessing the compatibility of AgNPs with biological systems and making them suitable for drug delivery applications.The aim of this research is to conduct a comprehensive study of the effect of tartaric acid concentration,sonication time and temperature on the formation of silver nanoparticles.Using Response Surface Methodology(RSM)with Face-Centered Central Composite Design(FCCD),the optimization process identifies the most favorable synthesis conditions.UV-Vis spectrum regression analysis shows that AgNPs stabilized with tartaric acid are more stable than AgNPs without tartaric acid.This highlights the increased stability that tartaric acid provides in AgNP ssssynthesis.Particle size distribution analysis showed a multimodal distribution for AgNPs with tartaric acid and showed the smallest size peak with an average size of 20.53 nm.The second peak with increasing intensity shows a dominant average size of 108.8 nm accompanied by one standard deviation of 4.225 nm and a zeta potential of−11.08 mV.In contrast,AgNPs synthesized with polyvinylpyrrolidone(PVP)showed a unimodal particle distribution with an average particle size of 81.62 nm and a zeta potential of−2.96 mV.The more negative zeta potential of AgNP-tartaric acid indicates its increased stability.Evaluation of antibacterial activity showed that AgNPs stabilized with tartaric acid showed better performance against E.coli and B.subtilis bacteria compared with AgNPs-PVP.In summary,this study highlights the potential of tartaric acid in AgNP synthesis and suggests an avenue for the development of stable AgNPs with versatile applications.

Image Fusion Using Wavelet Transformation and XGboost Algorithm

Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.

Facile in situ synthesis and characterization of Fe@Si/zeolite Na composites with magnetic core–shell structures from natural materials for enhanced curcumin loading capacity

Curcumin is a natural polyphenol that is used in various traditional medicines.However,its inherent properties,such as its rapid degradation and metabolism,low bioavailability,and short half-life,are serious problems that must be resolved.To this end,a drug carrier incorporating natural magnetic cores in a zeolite framework was developed and applied to the loading of curcumin in ethanol solutions.In this system,curcumin is encapsulated in a zeolite Na(ZNA)magnetic core–shell structure(Fe@Si/ZNA),which can be easily synthesized using an in situ method.Synthesis of Fe_(3)O_(4) nanoparticles was carried out from natural materials using a co-precipitation method.Analysis of the prepared magnetic core–shell structures and composites was carried out using vibrating-sample magnetometery,Fourier transform infrared spectroscopy,transmission electron microscopy,and x-ray diffraction.The cumulative loading of curcumin in the ZNA composite with 9%nanoparticles was found to reach 90.70%with a relatively long half-life of 32.49 min.Stability tests of curcumin loading in the composite showed that adding magnetic particles to the zeolite framework also increased the stability of the composite structure.Adsorption kinetics and isotherm studies also found that the system follows the pseudo-second-order and Langmuir isotherm models.

Nitrogen-Doped Amorphous Carbon Homojunction from Palmyra Sugar as a Renewable Solar Cell

An a-C/a-C:N junction,which used palmyra sugar as the carbon source and ammonium hydroxide(NH4OH)as the dopant source,was successfully deposited on the ITO glass substrate using the nano-spraying method.The current-voltage relationship of the junction was found to be a Schottky-like contact,and therefore the junction shows the characteristic rectifiers.This means the a-C and a-C:N are semiconductors with different types of conduction.Moreover,the samples showed an increase in current and voltage value when exposed to visible light(bright state)compared to the dark condition,thereby,indicating the creation of electron-hole pairs during the exposure.It was also discovered that the relationship between current and voltage for the a-C/a-C:N junction sample formed a curve that satisfies the rule of the photovoltaic effect when exposed to visible light from a light bulb.The exposure of this sample to direct sunlight at AM 1.5 conditions produced a curve that meets the rules for the emergence of the photovoltaic effect with higher characteristics for the current-voltage relationship.Thus,the a-C/a-C:N junction sample is a solar cell successfully fabricated using a sample method and has a maximum efficiency of 0.0013%.

Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats.The foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other seabirds.In this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its prey.The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water.The theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and exploitation.During the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable seabirds.In the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its meal.To evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal types.The results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization process.Comparative studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various metrics.In addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.