In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ...In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.展开更多
In today’s digital background,sentiment analysis has become an essential factor of Natural Language Processing(NLP),offering valuable insights from vast online data sources.This paper presents a comparative analysis ...In today’s digital background,sentiment analysis has become an essential factor of Natural Language Processing(NLP),offering valuable insights from vast online data sources.This paper presents a comparative analysis of sentiment analysis techniques leveraging machine learning.As digital content continues to expand rapidly,decoding public sentiment has become increasingly important for businesses and researchers.The study examines various approaches,including traditional machine learning methods like Support Vector Machines(SVM)and Na飗e Bayes(NB),as well as deep learning models such as Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks.It also explores hybrid frameworks that combine the strength of both paradigms.By evaluating the advantages and limitations of these models,especially within the context of e-commerce,this review provides a comprehensive understanding of their performance.Additionally,the paper addresses critical challenges such as real-time sentiment detection and multi-label classification.Through a synthesis of existing research,it highlights promising directions for future work and contributes to the development of more accurate and practical sentiment analysis solutions across various applications.展开更多
The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challeng...The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity.Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats.Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural network.This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication.The proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.展开更多
基金The Open Access publication fee for this article was fully covered by Abu Dhabi University.
文摘In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.
文摘In today’s digital background,sentiment analysis has become an essential factor of Natural Language Processing(NLP),offering valuable insights from vast online data sources.This paper presents a comparative analysis of sentiment analysis techniques leveraging machine learning.As digital content continues to expand rapidly,decoding public sentiment has become increasingly important for businesses and researchers.The study examines various approaches,including traditional machine learning methods like Support Vector Machines(SVM)and Na飗e Bayes(NB),as well as deep learning models such as Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks.It also explores hybrid frameworks that combine the strength of both paradigms.By evaluating the advantages and limitations of these models,especially within the context of e-commerce,this review provides a comprehensive understanding of their performance.Additionally,the paper addresses critical challenges such as real-time sentiment detection and multi-label classification.Through a synthesis of existing research,it highlights promising directions for future work and contributes to the development of more accurate and practical sentiment analysis solutions across various applications.
文摘The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity.Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats.Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural network.This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication.The proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.
