The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine l...The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.展开更多
Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optima...This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.展开更多
Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-...Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.展开更多
This review aims to summarize the currently viable vaccine strategies including the approved vaccines and the those in trials for next-generation malaria vaccines.Data on malaria vaccine development was collected thro...This review aims to summarize the currently viable vaccine strategies including the approved vaccines and the those in trials for next-generation malaria vaccines.Data on malaria vaccine development was collected through a comprehensive review.The literature search was performed using databases including Google Scholar,PubMed,NIH,and Web of Science.Various novel approaches of vaccination are being developed,including those based on radiation-attenuated strategies,monoclonal antibodies,targeted immunogenic peptides,RNA and DNA vaccines,nanoparticle-based vaccines,protein-based vaccination protocols,and whole organism-based vaccination strategies.Trials on RTS,S have entered phase Ⅲtesting,and those based on blood-stage vaccines and vaccines to interrupt malarial transmission have advanced to higher stages of trials.Mathematical modeling,combined drug and vaccine strategies,mass drug administration,polyvalent vaccine formulations,and targeted vaccination campaigns is playing an important role in malarial prevention.Furthermore,assessing coverage,accessibility,acceptability,deployment,compilation,and adherence to specific vaccination strategies in endemic regions is essential for vaccination drives against malaria.展开更多
Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Te...Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks.The study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron algorithms.The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching 0.99.This study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.展开更多
Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications...Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.展开更多
Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vu...Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality images.Additionally,these methods often require significant time and expertise,making them less accessible in resource-limited settings.Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare costs.This study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant features.Classification is performed using Optimized TCGN.This approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health monitoring.The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality issues.These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.展开更多
Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,onlin...Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,online forums,or gaming platforms.Cyberbullying involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and cyberstalking.Unfortunately,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this issue.Therefore,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this purpose.Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not.Additionally,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by metaheuristics.For this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost model.Additionally,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was performed.Finally,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.展开更多
As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage p...As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.展开更多
The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using ...The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware classification.This technique optimizes the model’s performance and reduces computational requirements.The proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature vector.Through the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate classification.The evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced features.This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time.The method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and prediction.The new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and efficiency.The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained devices.Novelty and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.展开更多
Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning thes...Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.展开更多
Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other...Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial search.Also,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence.Therefore,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time.Thus,in this review paper,several research publications using ChOA have been overviewed and summarized.Initially,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework.The main operations of ChOA are procedurally discussed,and the theoretical foundation is described.Furthermore,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled versions.The main applications of ChOA are also thoroughly described.The applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,etc.Evaluation of ChOA is also provided.The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and clustering.As well,it is wealthy in research on health,environment,and public safety.Also,it will aid those who are interested by providing them with potential future research.展开更多
Erratum to:J Bionic Eng https://doi.org/10.1007/s42235-023-00414-1.In this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.
Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being purs...Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.展开更多
Copper is one of the most important minerals that has extensive use in environment-friendly technologies and renewable energy generation.The global urgency for environmental and ecological conservation through renewab...Copper is one of the most important minerals that has extensive use in environment-friendly technologies and renewable energy generation.The global urgency for environmental and ecological conservation through renewable energy transition has considerably enhanced the importance of copper and articles thereof.Chile is a major producer of copper.It contributes more than one-third to global supply.Therefore,this study explores the export flow of Chilean copper in response to increasing demand side conditions in major 24 trading partners from 2002 to 2020.This objective is realized by constructing an augmented model for import demand that incorporates bilateral real exchange rate along with real GDP,environmental innovation,and renewable energy transition in major import markets.The estimated results of panel quantiles via moments techniques reveal a significant positive impact with increasing coefficients at higher quantiles,while environmental innovation and renewable energy transition in trading partners show significant positive impact with decreasing values of coefficients at higher quantiles.The findings urge Chile to enhance production capacity of copper and other critical mineral and improve participation in global value chain to meet sharply increasing copper demand from environmental innovation and renewable energy transition.展开更多
The Middle East and North Africa(MENA)region has an active gaming community,with Arab gamers being reliant on games produced in Europe,America,and Japan due to the lack of significant game production companies in the ...The Middle East and North Africa(MENA)region has an active gaming community,with Arab gamers being reliant on games produced in Europe,America,and Japan due to the lack of significant game production companies in the MENA region.This study explores the gamers’reactions to the localization process of two video games,namely PUBG and Free Fire.For data collection purposes,a five-point Likert scale questionnaire that consisted of 18 items and six constructs,namely need for subtitled games,technical aspects,language issues,language preference,attitudes to game localization,and future actions and recommendations,was designed to elicit the reactions of 112 participants.Upon analyzing the responses,the findings showed that the better the technical aspects and language issues of the games’performance,the more positive participants’attitudes to game localization.The study recommends that further research could be conducted on the localization of video games with different themes into Arabic.展开更多
Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapti...Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation.To achieve this,data on fifty geo-blast design parameters were collected and used to train machine learning algorithms.The objective was to develop predictive models for estimating the blast oversize percentage,incorporating seven controlled components and one uncontrollable index.The study employed a combination of hybrid long-short-term memory(LSTM),support vector regression,and random forest algorithms.Among these,the LSTM model enhanced with the tree seed algorithm(LSTM-TSA)demonstrated the highest prediction accuracy when handling large datasets.The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden,spacing,stemming length,drill hole length,charge length,powder factor,and joint set number.The estimated percentage oversize values for these parameters were determined as 0.7 m,0.9 m,0.65 m,1.4 m,0.7 m,1.03 kg/m^(3),35%,and 2,respectively.Application of the LSTM-TSA model resulted in a significant 28.1%increase in the crusher's production rate,showcasing its effectiveness in improving blasting operations.展开更多
文摘The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
基金King Saud University for funding this research through Researchers Supporting Program Number(RSPD2023R704),King Saud University,Riyadh,Saudi Arabia.
文摘This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
文摘Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.
文摘This review aims to summarize the currently viable vaccine strategies including the approved vaccines and the those in trials for next-generation malaria vaccines.Data on malaria vaccine development was collected through a comprehensive review.The literature search was performed using databases including Google Scholar,PubMed,NIH,and Web of Science.Various novel approaches of vaccination are being developed,including those based on radiation-attenuated strategies,monoclonal antibodies,targeted immunogenic peptides,RNA and DNA vaccines,nanoparticle-based vaccines,protein-based vaccination protocols,and whole organism-based vaccination strategies.Trials on RTS,S have entered phase Ⅲtesting,and those based on blood-stage vaccines and vaccines to interrupt malarial transmission have advanced to higher stages of trials.Mathematical modeling,combined drug and vaccine strategies,mass drug administration,polyvalent vaccine formulations,and targeted vaccination campaigns is playing an important role in malarial prevention.Furthermore,assessing coverage,accessibility,acceptability,deployment,compilation,and adherence to specific vaccination strategies in endemic regions is essential for vaccination drives against malaria.
文摘Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks.The study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron algorithms.The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching 0.99.This study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22 MCIN/AEI/10.13039/501100011033 and by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality images.Additionally,these methods often require significant time and expertise,making them less accessible in resource-limited settings.Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare costs.This study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant features.Classification is performed using Optimized TCGN.This approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health monitoring.The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality issues.These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.
基金supported by the Science Fund of the Republic of Serbia,Grant No.7373Characterizing Crises-Caused Air Pollution Alternations Using an Artificial Intelligence-Based Framework-crAIRsis and Grant No.7502Intelligent Multi-Agent Control and Optimization applied to Green Buildings and Environmental Monitoring Drone Swarms-ECOSwarm.
文摘Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,online forums,or gaming platforms.Cyberbullying involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and cyberstalking.Unfortunately,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this issue.Therefore,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this purpose.Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not.Additionally,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by metaheuristics.For this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost model.Additionally,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was performed.Finally,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22,MCIN/AEI/10.13039/501100011033by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware classification.This technique optimizes the model’s performance and reduces computational requirements.The proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature vector.Through the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate classification.The evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced features.This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time.The method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and prediction.The new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and efficiency.The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained devices.Novelty and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.
文摘Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(23UQU4361183DSR03).
文摘Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial search.Also,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence.Therefore,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time.Thus,in this review paper,several research publications using ChOA have been overviewed and summarized.Initially,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework.The main operations of ChOA are procedurally discussed,and the theoretical foundation is described.Furthermore,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled versions.The main applications of ChOA are also thoroughly described.The applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,etc.Evaluation of ChOA is also provided.The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and clustering.As well,it is wealthy in research on health,environment,and public safety.Also,it will aid those who are interested by providing them with potential future research.
文摘Erratum to:J Bionic Eng https://doi.org/10.1007/s42235-023-00414-1.In this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.
文摘Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.
基金support from the German Federal Government,the Federal Ministry of Education and Research and the State of Brandenburg within the framework of the joint project EIZ:Energy Innovation Center(project numbers 85056897 and 03SF0693A)the Structural Devel-opment Act(Strukturstärkungsgesetz)for coal-mining regions.
文摘Copper is one of the most important minerals that has extensive use in environment-friendly technologies and renewable energy generation.The global urgency for environmental and ecological conservation through renewable energy transition has considerably enhanced the importance of copper and articles thereof.Chile is a major producer of copper.It contributes more than one-third to global supply.Therefore,this study explores the export flow of Chilean copper in response to increasing demand side conditions in major 24 trading partners from 2002 to 2020.This objective is realized by constructing an augmented model for import demand that incorporates bilateral real exchange rate along with real GDP,environmental innovation,and renewable energy transition in major import markets.The estimated results of panel quantiles via moments techniques reveal a significant positive impact with increasing coefficients at higher quantiles,while environmental innovation and renewable energy transition in trading partners show significant positive impact with decreasing values of coefficients at higher quantiles.The findings urge Chile to enhance production capacity of copper and other critical mineral and improve participation in global value chain to meet sharply increasing copper demand from environmental innovation and renewable energy transition.
基金This work was supported by the Literature,Publishing and Translation Commission,Ministry of Culture,Kingdom of Saudi Arabia(No.135/2022)as part of the Arabic Observatory of Translation.
文摘The Middle East and North Africa(MENA)region has an active gaming community,with Arab gamers being reliant on games produced in Europe,America,and Japan due to the lack of significant game production companies in the MENA region.This study explores the gamers’reactions to the localization process of two video games,namely PUBG and Free Fire.For data collection purposes,a five-point Likert scale questionnaire that consisted of 18 items and six constructs,namely need for subtitled games,technical aspects,language issues,language preference,attitudes to game localization,and future actions and recommendations,was designed to elicit the reactions of 112 participants.Upon analyzing the responses,the findings showed that the better the technical aspects and language issues of the games’performance,the more positive participants’attitudes to game localization.The study recommends that further research could be conducted on the localization of video games with different themes into Arabic.
基金funded by China Scholarship Council (No.202006370006).
文摘Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation.To achieve this,data on fifty geo-blast design parameters were collected and used to train machine learning algorithms.The objective was to develop predictive models for estimating the blast oversize percentage,incorporating seven controlled components and one uncontrollable index.The study employed a combination of hybrid long-short-term memory(LSTM),support vector regression,and random forest algorithms.Among these,the LSTM model enhanced with the tree seed algorithm(LSTM-TSA)demonstrated the highest prediction accuracy when handling large datasets.The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden,spacing,stemming length,drill hole length,charge length,powder factor,and joint set number.The estimated percentage oversize values for these parameters were determined as 0.7 m,0.9 m,0.65 m,1.4 m,0.7 m,1.03 kg/m^(3),35%,and 2,respectively.Application of the LSTM-TSA model resulted in a significant 28.1%increase in the crusher's production rate,showcasing its effectiveness in improving blasting operations.
