Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregatio...Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.展开更多
One of the most dangerous diseases that affect people worldwide is lung cancer.The survival rate is minimal,because of the complexity in identifying lung cancer at developed stages.Henceforth,earlier detection of lung...One of the most dangerous diseases that affect people worldwide is lung cancer.The survival rate is minimal,because of the complexity in identifying lung cancer at developed stages.Henceforth,earlier detection of lung cancer is significant.Several Machine Learning(ML)approaches have been modeled for lung cancer recognition with the advent of Artificial Intelligence.However,small-scale datasets and deprived generalizability to recognize unknown data are considered challenges in lung cancer detection.This work proposes an advanced deep learning model,named Generative Adversarial Network-Attention Gated Network(GA-AGN),which is the integration of Generative Adversarial Network(GAN)and Attention Gated Network(AGN).Initially,the chest CT scan images are subjected to the pre-processing phase,where image resizing and normalization are used to preprocess the images.Then,the data augmentation is performed using the GAN model that is trained by Elk Herd Optimizer(EHO).Subsequently,lung cancer detection is done by means of GA-AGN model.Ultimately analysis is performed by using three measures,like accuracy,sensitivity as well as specificity with values of 0.938,0.948 and 0.927.The overall analysis states that the proposed model attained better outcomes than the conventional models.展开更多
基金funded by the Deanship of Scientific Research,the Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia under the project(KFU250420).
文摘Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.
文摘One of the most dangerous diseases that affect people worldwide is lung cancer.The survival rate is minimal,because of the complexity in identifying lung cancer at developed stages.Henceforth,earlier detection of lung cancer is significant.Several Machine Learning(ML)approaches have been modeled for lung cancer recognition with the advent of Artificial Intelligence.However,small-scale datasets and deprived generalizability to recognize unknown data are considered challenges in lung cancer detection.This work proposes an advanced deep learning model,named Generative Adversarial Network-Attention Gated Network(GA-AGN),which is the integration of Generative Adversarial Network(GAN)and Attention Gated Network(AGN).Initially,the chest CT scan images are subjected to the pre-processing phase,where image resizing and normalization are used to preprocess the images.Then,the data augmentation is performed using the GAN model that is trained by Elk Herd Optimizer(EHO).Subsequently,lung cancer detection is done by means of GA-AGN model.Ultimately analysis is performed by using three measures,like accuracy,sensitivity as well as specificity with values of 0.938,0.948 and 0.927.The overall analysis states that the proposed model attained better outcomes than the conventional models.
