Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced ...Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced datasets and redundant features persist.This study proposes a novel framework that customizes two deep learning models,NasNetMobile and ResNet50,by incorporating bottleneck architectures,named as NasNeck and ResNeck,to enhance feature extraction.The feature vectors are fused into a combined vector,which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power.The optimized feature vector is then classified using artificial neural network classifiers,effectively addressing the limitations of traditional methods.Data augmentation techniques are employed to tackle class imbalance,improving model learning and generalization.The proposed framework was evaluated on two publicly available datasets:Hyper-Kvasir and Kvasir v2.The Hyper-Kvasir dataset,comprising 23 gastrointestinal disease classes,yielded an impressive 96.0%accuracy.On the Kvasir v2 dataset,which contains 8 distinct classes,the framework achieved a remarkable 98.9%accuracy,further demonstrating its robustness and superior classification performance across different gastrointestinal datasets.The results demonstrate the effectiveness of customizing deep models with bottleneck architectures,feature fusion,and optimization techniques in enhancing classification accuracy while reducing computational complexity.展开更多
In the realm of microgrid(MG),the distributed load frequency control(LFC)system has proven to be highly susceptible to the negative effects of false data injection attacks(FDIAs).Considering the significant responsibi...In the realm of microgrid(MG),the distributed load frequency control(LFC)system has proven to be highly susceptible to the negative effects of false data injection attacks(FDIAs).Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG,this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system.Firstly,the method integrates a bi-directional long short-term memory(Bi LSTM)neural network and an improved whale optimization algorithm(IWOA)into the LFC controller to detect and counteract FDIAs.Secondly,to enable the Bi LSTM neural network to proficiently detect multiple types of FDIAs with utmost precision,the model employs a historical MG dataset comprising the frequency and power variances.Finally,the IWOA is utilized to optimize the proportional-integral-derivative(PID)controller parameters to counteract the negative impacts of FDIAs.The proposed detection and defense method is validated by building the distributed LFC system in Simulink.展开更多
In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extr...In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extreme learning machine(ELM)is proposed for precise forecasting of coal and gas outburst disasters in mines.Firstly,based on the influencing factors of coal and gas outburst disasters,nine coupling indexes are selected,including gas pressure,geological structure,initial velocity of gas emission,and coal structure type.The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27,followed by extraction of the principal components of the original data through Kernel Principal Component Analysis(KPCA).The Whale Optimization Algorithm(WOA)was enhanced by incorporating adaptive weight,variable helix position update,and optimal neighborhood disturbance to augment its performance.The improved Whale Optimization Algorithm(IWOA)is subsequently employed to optimize the weight Φ of the Extreme Learning Machine(ELM)input layer and the threshold g of the hidden layer,thereby enhancing its predictive accuracy and mitigating the issue of"over-fitting"associated with ELM to some extent.The principal components extracted by KPCA were utilized as input,while the outburst risk grade served as output.Subsequently,a comparative analysis was conducted between these results and those obtained from WOA-SVC,PSO-BPNN,and SSA-RF models.The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters,with results consistent with actual situations.Compared to other models tested,the model's performance showed an increase in Ac by 0.2,0.3,and 0.2 respectively;P increased by 0.15,0.2167,and 0.1333 respectively;R increased by 0.25,0.3,and 0.2333 respectively;F1-Score increased by 0.2031,0.2607,and 0.1864 respectively;Kappa coefficient k increased by 0.3226,0.4762 and 0.3175,respectively.The practicality and stability of the IWOAELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values.This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.展开更多
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
基金supported by Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia through the Researchers Supporting Project PNURSP2025R333.
文摘Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced datasets and redundant features persist.This study proposes a novel framework that customizes two deep learning models,NasNetMobile and ResNet50,by incorporating bottleneck architectures,named as NasNeck and ResNeck,to enhance feature extraction.The feature vectors are fused into a combined vector,which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power.The optimized feature vector is then classified using artificial neural network classifiers,effectively addressing the limitations of traditional methods.Data augmentation techniques are employed to tackle class imbalance,improving model learning and generalization.The proposed framework was evaluated on two publicly available datasets:Hyper-Kvasir and Kvasir v2.The Hyper-Kvasir dataset,comprising 23 gastrointestinal disease classes,yielded an impressive 96.0%accuracy.On the Kvasir v2 dataset,which contains 8 distinct classes,the framework achieved a remarkable 98.9%accuracy,further demonstrating its robustness and superior classification performance across different gastrointestinal datasets.The results demonstrate the effectiveness of customizing deep models with bottleneck architectures,feature fusion,and optimization techniques in enhancing classification accuracy while reducing computational complexity.
基金supported in part by the National Natural Science Foundation of China(No.61973078)in part by the Natural Science Foundation of Jiangsu Province of China(No.BK20231416)in part by the Zhishan Youth Scholar Program from Southeast University(No.2242022R40042)。
文摘In the realm of microgrid(MG),the distributed load frequency control(LFC)system has proven to be highly susceptible to the negative effects of false data injection attacks(FDIAs).Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG,this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system.Firstly,the method integrates a bi-directional long short-term memory(Bi LSTM)neural network and an improved whale optimization algorithm(IWOA)into the LFC controller to detect and counteract FDIAs.Secondly,to enable the Bi LSTM neural network to proficiently detect multiple types of FDIAs with utmost precision,the model employs a historical MG dataset comprising the frequency and power variances.Finally,the IWOA is utilized to optimize the proportional-integral-derivative(PID)controller parameters to counteract the negative impacts of FDIAs.The proposed detection and defense method is validated by building the distributed LFC system in Simulink.
基金supported by the National Natural Science Foun-dation of China (No.52174188)the Graduate Education Innovation Project of Shanxi Datong University of China (No.23CX49).
文摘In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extreme learning machine(ELM)is proposed for precise forecasting of coal and gas outburst disasters in mines.Firstly,based on the influencing factors of coal and gas outburst disasters,nine coupling indexes are selected,including gas pressure,geological structure,initial velocity of gas emission,and coal structure type.The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27,followed by extraction of the principal components of the original data through Kernel Principal Component Analysis(KPCA).The Whale Optimization Algorithm(WOA)was enhanced by incorporating adaptive weight,variable helix position update,and optimal neighborhood disturbance to augment its performance.The improved Whale Optimization Algorithm(IWOA)is subsequently employed to optimize the weight Φ of the Extreme Learning Machine(ELM)input layer and the threshold g of the hidden layer,thereby enhancing its predictive accuracy and mitigating the issue of"over-fitting"associated with ELM to some extent.The principal components extracted by KPCA were utilized as input,while the outburst risk grade served as output.Subsequently,a comparative analysis was conducted between these results and those obtained from WOA-SVC,PSO-BPNN,and SSA-RF models.The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters,with results consistent with actual situations.Compared to other models tested,the model's performance showed an increase in Ac by 0.2,0.3,and 0.2 respectively;P increased by 0.15,0.2167,and 0.1333 respectively;R increased by 0.25,0.3,and 0.2333 respectively;F1-Score increased by 0.2031,0.2607,and 0.1864 respectively;Kappa coefficient k increased by 0.3226,0.4762 and 0.3175,respectively.The practicality and stability of the IWOAELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values.This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.
