Deforestation is the purpose of converting forest into land and reforestation compared to deforestation is very low.That’s why closely and accurately deforestation monitoring using Sentinel-1 and Sentinel-2 satellite...Deforestation is the purpose of converting forest into land and reforestation compared to deforestation is very low.That’s why closely and accurately deforestation monitoring using Sentinel-1 and Sentinel-2 satellite images for better vision is required.This paper proposes an effective image fusion technique that combines S-1/2 data to improve the deforested areas.Based on review,Optical and SAR image fusion produces high-resolution images for better de-forestation monitoring.To enhance the S-1/2 images,preprocessing is needed as per requirements and then,collocation between the two different types of images to mitigate the image registration problem,and after that,apply an image fu-sion machine learning approach,PCA-Wavelet.As per analysis,PCA helps to maintain spatial resolution,and Wavelet helps to preserve spectral resolution,gives better-fused images compared to other techniques.As per results,2019 S-2 pre-22 processed collocated image enhances 42.2508 km deforested area,S-1 preprocessed collocated image enhances 23.7918 km^(2) deforested area,and after fusion of the 2019 S-1/2 images,it enhances 16.5335 km deforested area.Similarly,the 20232 S-2 preprocessed collocated image enhances 49.2216 km deforested area,S-1 preprocessed collocated image enhances 2223.8459 km deforested area after fusion of the 2023 S-1/2 images,enhancing 35.9185 km deforested area.These im-provements show that combining data sources gives a clearer and more reliable picture of forest loss over time.The overall paper objective is to apply effective techniques for image fusion of Brazil’s Amazon Forest and analyze the difference between collocated image pixels and fused image pixels for accurate analysis of deforested area.展开更多
Background:The study examines the ecological impact of nano-micronutrient composites on the growth and maturation of late-planted wheat within an agroecological framework.Methods:Experiments conducted using a Randomiz...Background:The study examines the ecological impact of nano-micronutrient composites on the growth and maturation of late-planted wheat within an agroecological framework.Methods:Experiments conducted using a Randomized Block Design(RBD)with three replications and eight treatment combinations,ensured uniform plant populations prior to treatment applications.Significant variations were observed across multiple growth parameters,including tiller density per square meter and dry matter accumulation at 30,60,90,and 120 days after sowing(DAS).Results:Notably,the treatment involving RDF+20 ppm rGO-Fe+rGO-Zn with two foliar sprays at 45 and 60 DAS(T6)exhibited markedly superior growth performance compared to the control and conventional zinc and iron applications.Maximum grain yield(29.2 q/ha)was achieved in T8(RDF+20ppm rGO-Fe+rGO-Zn with two sprays at 45 and 60 DAS)whereas straw yield(50.5 q/ha),biological yield(77.1 q/ha),Harvest Index(38.7%)and Grain Straw ratio(0.6)were found maximum in RDF+20ppm rGO-Fe+rGO(Reduced Graphene oxide)−Zn with two sprays at 45 and 60 DAS(T6).Conclusion:The application of reduced graphene oxide(rGO)-based iron and zinc nanoparticles significantly improved nutrient uptake and utilization efficiency,leading to enhanced crop vigor and yield.The study underscores the ecological importance of integrating nanotechnology with nutrient management to sustain a healthy and balanced agroecosystem.This research focuses on sustainable agriculture,nanofertilizers,nutrient use efficiency,and ecological impact,which follows the Q16,Q57,and O13 JEL(Journal of Economic Literature)classification.展开更多
The physico-chemical analysis of agricultural soil revealed a textured sandy loam at the surface(0–15 cm),with low organic carbon content(0.42%)and moderate levels of nitrogen(157 kg/ha),phosphorus(15.5 kg/ha),and po...The physico-chemical analysis of agricultural soil revealed a textured sandy loam at the surface(0–15 cm),with low organic carbon content(0.42%)and moderate levels of nitrogen(157 kg/ha),phosphorus(15.5 kg/ha),and potassium(112.6 kg/ha),under neutral pH conditions(pH 7.4).The chickpea variety PG-186 was used to evaluate the impact of nutrient treatments on plant performance and agroecological outcomes.Experimental findings demonstrated a significant influence of various treatments on the growth,yield,and economic returns of chickpea cultivation.The treatment comprising 100%Recommended Dose of Fertilizers(RDF)along with foliar application of 0.6%ZnSO_(4) and 0.9%FeSO_(4) at pre-flowering and pod development stages(T8)resulted in the maximum plant height(15.5 cm,33.7 cm,45.0 cm),dry matter accumulation(27.5 g,245.2 g,1006.7 g/m^(2)),and number of branches per plant(3.47,5.00,and 8.63)at 45,75,and 105 Days After Sowing(DAS),respectively.This treatment also resulted in the highest grain yield(21.00 q/ha)and stover yield(38.67 q/ha),along with a maximum net return of₹95,392/ha and a benefit-to-cost ratio of 2.32.From an ecological standpoint,this study highlights the vital role of balanced and targeted nutrient management in enhancing agroecosystem productivity while maintaining ecological balance.The integration of micronutrient foliar sprays not only boosts nutrient uptake efficiency and plant health but also reduces dependency on excessive chemical fertilizers,thereby mitigating potential negative impacts on soil ecology.Overall,the findings underscore the ecological importance of optimizing nutrient inputs in legume-based cropping systems to foster sustainable agricultural practices that align with ecological resilience,soil health preservation,and environmental stewardship.展开更多
This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf o...This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.展开更多
Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of ...Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.展开更多
As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data ...As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human faces.The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach.In this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face data.An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering.The close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral pattern.To identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same subject.The behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit measurements.The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,etc.The complexity measures have also been calculated under worst-case and best-case situations.展开更多
The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fi...The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.展开更多
A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart buildings.The city administration has prioritized the use of cuttin...A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart buildings.The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality,with energy resources taking precedence.To achieve optimal energy management in themultidimensional system of a city tribe,it is necessary not only to identify and study the vast majority of energy elements,but also to define their implicit interdependencies.This is because optimal energy management is required to reach this objective.The lighting index is an essential consideration when evaluating the comfort indicators.In order to realize the concept of a smart city,the primary objective of this research is to create a system for managing and monitoring the lighting index.It is possible to identify two distinct phaseswithin the intelligent system.Once data collection concludes,the monitoring system will be activated.In the second step,the operation of the control system is analyzed and its effect on the performance of the numerical model is determined.This evaluation is based on the proposed methodology.The optimized resultswere deemed satisfactory because they maintained the brightness index value(79%)while consuming less energy.The intelligent implementation system generated satisfactory outcomes,which were observed 1.75 times on average.展开更多
Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped....Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However,these algorithms could not use the concept of categorical data for clustering.Following that,suggestions for expanding traditional categorical data processing methods were made.In addition to expansions,several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.展开更多
Security systems are the need of the hour to protect data from unauthorized access.The dissemination of confidential information over the public network requires a high level of security.The security approach such as ...Security systems are the need of the hour to protect data from unauthorized access.The dissemination of confidential information over the public network requires a high level of security.The security approach such as steganography ensures confidentiality,authentication,integrity,and non-repudiation.Steganography helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and receiver.Therefore,we present an efficient hybrid security model that provides multifold security assurance.To this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high computations.The modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret data.The enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)algorithm.The experiments are conducted on the images of the USC-SIPI standard image database.The experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.展开更多
The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches...The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep learning.We used a dataset with 13 primary characteristics to carry out the research.Support vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary disease.Python programming is used to process the datasets.Multiple research initiatives have used machine learning to speed up the healthcare sector.We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks.Using the accuracy and confusion matrix has resulted in some favorable outcomes.To get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.展开更多
Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting re...Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting requires knowledge of these machine learning models and their distinguishing features.The datasets we use as input for each of these different types of ML models,yielding different results.The choice of an ML model for a dataset is critical.A loan risk model is used to show how ML models for a dataset can be linked together.The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk.This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields.If credit risk is considered,a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried.Various approaches to credit risk calculation will be examined,ranging from the most basic to the most complex.In addition,we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches,logistic regression,decision tree,and gradient boost to see which one produced the most commercially useful insights.展开更多
文摘Deforestation is the purpose of converting forest into land and reforestation compared to deforestation is very low.That’s why closely and accurately deforestation monitoring using Sentinel-1 and Sentinel-2 satellite images for better vision is required.This paper proposes an effective image fusion technique that combines S-1/2 data to improve the deforested areas.Based on review,Optical and SAR image fusion produces high-resolution images for better de-forestation monitoring.To enhance the S-1/2 images,preprocessing is needed as per requirements and then,collocation between the two different types of images to mitigate the image registration problem,and after that,apply an image fu-sion machine learning approach,PCA-Wavelet.As per analysis,PCA helps to maintain spatial resolution,and Wavelet helps to preserve spectral resolution,gives better-fused images compared to other techniques.As per results,2019 S-2 pre-22 processed collocated image enhances 42.2508 km deforested area,S-1 preprocessed collocated image enhances 23.7918 km^(2) deforested area,and after fusion of the 2019 S-1/2 images,it enhances 16.5335 km deforested area.Similarly,the 20232 S-2 preprocessed collocated image enhances 49.2216 km deforested area,S-1 preprocessed collocated image enhances 2223.8459 km deforested area after fusion of the 2023 S-1/2 images,enhancing 35.9185 km deforested area.These im-provements show that combining data sources gives a clearer and more reliable picture of forest loss over time.The overall paper objective is to apply effective techniques for image fusion of Brazil’s Amazon Forest and analyze the difference between collocated image pixels and fused image pixels for accurate analysis of deforested area.
文摘Background:The study examines the ecological impact of nano-micronutrient composites on the growth and maturation of late-planted wheat within an agroecological framework.Methods:Experiments conducted using a Randomized Block Design(RBD)with three replications and eight treatment combinations,ensured uniform plant populations prior to treatment applications.Significant variations were observed across multiple growth parameters,including tiller density per square meter and dry matter accumulation at 30,60,90,and 120 days after sowing(DAS).Results:Notably,the treatment involving RDF+20 ppm rGO-Fe+rGO-Zn with two foliar sprays at 45 and 60 DAS(T6)exhibited markedly superior growth performance compared to the control and conventional zinc and iron applications.Maximum grain yield(29.2 q/ha)was achieved in T8(RDF+20ppm rGO-Fe+rGO-Zn with two sprays at 45 and 60 DAS)whereas straw yield(50.5 q/ha),biological yield(77.1 q/ha),Harvest Index(38.7%)and Grain Straw ratio(0.6)were found maximum in RDF+20ppm rGO-Fe+rGO(Reduced Graphene oxide)−Zn with two sprays at 45 and 60 DAS(T6).Conclusion:The application of reduced graphene oxide(rGO)-based iron and zinc nanoparticles significantly improved nutrient uptake and utilization efficiency,leading to enhanced crop vigor and yield.The study underscores the ecological importance of integrating nanotechnology with nutrient management to sustain a healthy and balanced agroecosystem.This research focuses on sustainable agriculture,nanofertilizers,nutrient use efficiency,and ecological impact,which follows the Q16,Q57,and O13 JEL(Journal of Economic Literature)classification.
文摘The physico-chemical analysis of agricultural soil revealed a textured sandy loam at the surface(0–15 cm),with low organic carbon content(0.42%)and moderate levels of nitrogen(157 kg/ha),phosphorus(15.5 kg/ha),and potassium(112.6 kg/ha),under neutral pH conditions(pH 7.4).The chickpea variety PG-186 was used to evaluate the impact of nutrient treatments on plant performance and agroecological outcomes.Experimental findings demonstrated a significant influence of various treatments on the growth,yield,and economic returns of chickpea cultivation.The treatment comprising 100%Recommended Dose of Fertilizers(RDF)along with foliar application of 0.6%ZnSO_(4) and 0.9%FeSO_(4) at pre-flowering and pod development stages(T8)resulted in the maximum plant height(15.5 cm,33.7 cm,45.0 cm),dry matter accumulation(27.5 g,245.2 g,1006.7 g/m^(2)),and number of branches per plant(3.47,5.00,and 8.63)at 45,75,and 105 Days After Sowing(DAS),respectively.This treatment also resulted in the highest grain yield(21.00 q/ha)and stover yield(38.67 q/ha),along with a maximum net return of₹95,392/ha and a benefit-to-cost ratio of 2.32.From an ecological standpoint,this study highlights the vital role of balanced and targeted nutrient management in enhancing agroecosystem productivity while maintaining ecological balance.The integration of micronutrient foliar sprays not only boosts nutrient uptake efficiency and plant health but also reduces dependency on excessive chemical fertilizers,thereby mitigating potential negative impacts on soil ecology.Overall,the findings underscore the ecological importance of optimizing nutrient inputs in legume-based cropping systems to foster sustainable agricultural practices that align with ecological resilience,soil health preservation,and environmental stewardship.
基金supported via funding from Prince Sattam Bin Abdulaziz University Project Number(PSAU/2023/R/1445).
文摘This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.
文摘Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.
文摘As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human faces.The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach.In this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face data.An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering.The close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral pattern.To identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same subject.The behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit measurements.The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,etc.The complexity measures have also been calculated under worst-case and best-case situations.
基金funded by King Saud University,Riyadh,Saudi Arabia.Researchers Supporting Proiect Number(RSP2023R167)King Saud University,Riyadh,Saudi Arabia.
文摘The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.
文摘A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart buildings.The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality,with energy resources taking precedence.To achieve optimal energy management in themultidimensional system of a city tribe,it is necessary not only to identify and study the vast majority of energy elements,but also to define their implicit interdependencies.This is because optimal energy management is required to reach this objective.The lighting index is an essential consideration when evaluating the comfort indicators.In order to realize the concept of a smart city,the primary objective of this research is to create a system for managing and monitoring the lighting index.It is possible to identify two distinct phaseswithin the intelligent system.Once data collection concludes,the monitoring system will be activated.In the second step,the operation of the control system is analyzed and its effect on the performance of the numerical model is determined.This evaluation is based on the proposed methodology.The optimized resultswere deemed satisfactory because they maintained the brightness index value(79%)while consuming less energy.The intelligent implementation system generated satisfactory outcomes,which were observed 1.75 times on average.
基金supporting project number(RSP2022R498),King Saud University,Riyadh,Saudi Arabia.
文摘Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However,these algorithms could not use the concept of categorical data for clustering.Following that,suggestions for expanding traditional categorical data processing methods were made.In addition to expansions,several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.
基金Deanship of Scientific Research at King Khalid University for funding this work through Large Group Research Project under Grant Number RGP2/162/44.
文摘Security systems are the need of the hour to protect data from unauthorized access.The dissemination of confidential information over the public network requires a high level of security.The security approach such as steganography ensures confidentiality,authentication,integrity,and non-repudiation.Steganography helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and receiver.Therefore,we present an efficient hybrid security model that provides multifold security assurance.To this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high computations.The modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret data.The enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)algorithm.The experiments are conducted on the images of the USC-SIPI standard image database.The experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.
文摘The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep learning.We used a dataset with 13 primary characteristics to carry out the research.Support vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary disease.Python programming is used to process the datasets.Multiple research initiatives have used machine learning to speed up the healthcare sector.We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks.Using the accuracy and confusion matrix has resulted in some favorable outcomes.To get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.
文摘Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting requires knowledge of these machine learning models and their distinguishing features.The datasets we use as input for each of these different types of ML models,yielding different results.The choice of an ML model for a dataset is critical.A loan risk model is used to show how ML models for a dataset can be linked together.The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk.This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields.If credit risk is considered,a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried.Various approaches to credit risk calculation will be examined,ranging from the most basic to the most complex.In addition,we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches,logistic regression,decision tree,and gradient boost to see which one produced the most commercially useful insights.
