The intricate sulfur redox chemistry involves multiple electron transfers and complicated phase *** have been previously explored to overcome the kinetic barrier in lithium-sulfur batteries（LSBs）.This work contribut...
详细信息
The intricate sulfur redox chemistry involves multiple electron transfers and complicated phase *** have been previously explored to overcome the kinetic barrier in lithium-sulfur batteries（LSBs）.This work contributes to closing the knowledge gap and examines electrocatalysis for enhancing LSB *** a strong chemical affinity for polysulfides,the electrocatalyst enables efficient adsorption and accelerated electron transfer *** cells with catalyzed cathodes exhibit improved rate capability and excellent stability over 500 cycles with 91.9% capacity retention at C/*** addition,cells were shown to perform at high rates up to 2C and at high sulfur loadings up to 6 mg *** electrochemical,spectroscopic,and microscopic analyses provide insights into the mechanism for retaining high activity,coulombic efficiency,and *** work delves into crucial processes identifying pivotal reaction steps during the cycling process at commercially relevant areal capacities and rates.
Modern cyber threats have evolved to sophisticated levels, necessitating advanced intrusion detection systems (IDS) to protect critical network infrastructure. Traditional signature-based and rule-based IDS face chall...
详细信息
The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific d...
详细信息
As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly ***,the challenge lies in identifying the right parameters and strategies for these *** this...
详细信息
As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly ***,the challenge lies in identifying the right parameters and strategies for these *** this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization *** employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy *** parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational *** offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and *** is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to *** results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and ***,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.
This paper introduces a novel Peak-to-Average Power Ratio (PAPR) reduction technique for Non-Orthogonal Multiple Access (NOMA) waveforms, leveraging an Airy function-based Partial Transmit Sequence (PTS) method. The p...
详细信息
Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO...
详细信息
Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho
Named Entity Recognition(NER)in cybersecurity is crucial for mining information during cybersecurity *** methods rely on pre-trained models for rich semantic text embeddings,but the challenge of anisotropy may affect ...
详细信息
Named Entity Recognition(NER)in cybersecurity is crucial for mining information during cybersecurity *** methods rely on pre-trained models for rich semantic text embeddings,but the challenge of anisotropy may affect subsequent encoding ***,existing models may struggle with noise *** address these issues,we propose JCLB,a novel model that Joins Contrastive Learning and Belief rule base for NER in *** utilizes contrastive learning to enhance similarity in the vector space between token sequence representations of entities in the same category.A Belief Rule Base(BRB)is developed using regexes to ensure accurate entity identification,particularly for fixed-format phrases lacking ***,a Distributed Constraint Covariance Matrix Adaptation Evolution Strategy(D-CMA-ES)algorithm is introduced for BRB parameter *** results demonstrate that JCLB,with the D-CMA-ES algorithm,significantly improves NER accuracy in cybersecurity.
The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received c...
详细信息
The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing
The spread of misinformation and spam on social media has become a critical challenge, undermining information integrity and online security. Addressing this pressing issue, this study introduces an advanced solution ...
详细信息
This research investigates a hybrid approach for predicting movie revenue by integrating machine learning models with sentiment analysis. The growing influence of social media and online discussions offers a valuable ...
详细信息
暂无评论