Network-Computation in Neural Systems最新文献_第4页

Tree hierarchical deep convolutional neural network optimized with sheep flock optimization algorithm for sentiment classification of Twitter data. 采用羊群优化算法优化的树状分层深度卷积神经网络，用于 Twitter 数据的情感分类。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-10-21 DOI: 10.1080/0954898X.2024.2388109

Lakshmanaprakash Sanmugaraja, Pandiaraj Annamalai

{"title":"Tree hierarchical deep convolutional neural network optimized with sheep flock optimization algorithm for sentiment classification of Twitter data.","authors":"Lakshmanaprakash Sanmugaraja, Pandiaraj Annamalai","doi":"10.1080/0954898X.2024.2388109","DOIUrl":"10.1080/0954898X.2024.2388109","url":null,"abstract":"The increasing volume of online reviews and tweets poses significant challenges for sentiment classification because of the difficulty in obtaining annotated training data. This paper aims to enhance sentiment classification of Twitter data by developing a robust model that improves classification accuracy and computational efficiency. The proposed method named Tree Hierarchical Deep Convolutional Neural Network optimized with Sheep Flock Optimization Algorithm for Sentiment Classification of Twitter Data (SCTD-THDCNN-SFOA) utilizes the Stanford Sentiment Treebank dataset. The process begins with pre-processing steps including Tokenization, Stop words Elimination, Filtering, Hashtag Removal, and Multiword Grouping. The Gray Level Co-occurrence Matrix Window Adaptive Algorithm is employed to extract features, such as emoticon counts, punctuation counts, gazetteer word existence, n-grams, and part of speech tags. These features are selected using Entropy-Kurtosis-based Feature Selection approach. Finally, the Tree Hierarchical Deep Convolutional Neural Network enhanced by the Sheep Flock Optimization Algorithm is used to categorize the Twitter data as positive, negative, and neutral sentiments. The proposed SCTD-THDCNN-SFOA method demonstrates superior performance, achieving higher accuracy and lesser computation time than the existing models, respectively. The SCTD-THDCNN-SFOA framework significantly improves the accuracy and efficiency of sentiment classification for Twitter data.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1696-1720"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance analyses of weighted superposition attraction-repulsion algorithms in solving difficult optimization problems. 加权叠加吸引-排斥算法在解决困难优化问题中的性能分析。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-06-24 DOI: 10.1080/0954898X.2024.2367481

Adil Baykasoğlu

引用次数: 0

Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing. 用于云计算负载平衡和容错的混合深度学习和优化聚类机制。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-06-27 DOI: 10.1080/0954898X.2024.2369137

Vahini Siruvoru, Shivampeta Aparna

{"title":"Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing.","authors":"Vahini Siruvoru, Shivampeta Aparna","doi":"10.1080/0954898X.2024.2369137","DOIUrl":"10.1080/0954898X.2024.2369137","url":null,"abstract":"Cloud services are one of the most quickly developing technologies. Furthermore, load balancing is recognized as a fundamental challenge for achieving energy efficiency. The primary function of load balancing is to deliver optimal services by releasing the load over multiple resources. Fault tolerance is being used to improve the reliability and accessibility of the network. In this paper, a hybrid Deep Learning-based load balancing algorithm is developed. Initially, tasks are allocated to all VMs in a round-robin method. Furthermore, the Deep Embedding Cluster (DEC) utilizes the Central Processing Unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors while determining if a VM is overloaded or underloaded. The task performed on the overloaded VM is valued and the tasks accomplished on the overloaded VM are assigned to the underloaded VM for cloud load balancing. In addition, the Deep Q Recurrent Neural Network (DQRNN) is proposed to balance the load based on numerous factors such as supply, demand, capacity, load, resource utilization, and fault tolerance. Furthermore, the effectiveness of this model is assessed by load, capacity, resource consumption, and success rate, with ideal values of 0.147, 0.726, 0.527, and 0.895 are achieved.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1521-1542"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks. MLNAS：基于元学习的神经架构搜索，用于自动生成植物病害检测任务的深度神经网络。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-07-12 DOI: 10.1080/0954898X.2024.2374852

Sahil Verma, Prabhat Kumar, Jyoti Prakash Singh

{"title":"MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks.","authors":"Sahil Verma, Prabhat Kumar, Jyoti Prakash Singh","doi":"10.1080/0954898X.2024.2374852","DOIUrl":"10.1080/0954898X.2024.2374852","url":null,"abstract":"Plant diseases pose a significant threat to agricultural productivity worldwide. Convolutional neural networks (CNNs) have achieved state-of-the-art performances on several plant disease detection tasks. However, the manual development of CNN models using an exhaustive approach is a resource-intensive task. Neural Architecture Search (NAS) has emerged as an innovative paradigm that seeks to automate model generation procedures without human intervention. However, the application of NAS in plant disease detection has received limited attention. In this work, we propose a two-stage meta-learning-based neural architecture search system (ML NAS) to automate the generation of CNN models for unseen plant disease detection tasks. The first stage recommends the most suitable benchmark models for unseen plant disease detection tasks based on the prior evaluations of benchmark models on existing plant disease datasets. In the second stage, the proposed NAS operators are employed to optimize the recommended model for the target task. The experimental results showed that the MLNAS system's model outperformed state-of-the-art models on the fruit disease dataset, achieving an accuracy of 99.61%. Furthermore, the MLNAS-generated model outperformed the Progressive NAS model on the 8-class plant disease dataset, achieving an accuracy of 99.8%. Hence, the proposed MLNAS system facilitates faster model development with reduced computational costs.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1582-1605"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141592081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines. 基于变压器的深度学习网络，用于输电线路故障检测、分类和位置预测。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-09-03 DOI: 10.1080/0954898X.2024.2393746

Bousaadia Baadji, Soufiane Belagoune, Sif Eddine Boudjellal

{"title":"Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines.","authors":"Bousaadia Baadji, Soufiane Belagoune, Sif Eddine Boudjellal","doi":"10.1080/0954898X.2024.2393746","DOIUrl":"10.1080/0954898X.2024.2393746","url":null,"abstract":"Fault detection, classification, and location prediction are crucial for maintaining the stability and reliability of modern power systems, reducing economic losses, and enhancing system protection sensitivity. This paper presents a novel Hierarchical Deep Learning Approach (HDLA) for accurate and efficient fault diagnosis in transmission lines. HDLA leverages two-stage transformer-based classification and regression models to perform Fault Detection (FD), Fault Type Classification (FTC), and Fault Location Prediction (FLP) directly from synchronized raw three-phase current and voltage samples. By bypassing the need for feature extraction, HDLA significantly reduces computational complexity while achieving superior performance compared to existing deep learning methods. The efficacy of HDLA is validated on a comprehensive dataset encompassing various fault scenarios with diverse types, locations, resistances, inception angles, and noise levels. The results demonstrate significant improvements in accuracy, recall, precision, and F1-score metrics for classification, and Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for prediction, showcasing the effectiveness of HDLA for real-time fault diagnosis in power systems.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1837-1857"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Speaker-based language identification for Ethio-Semitic languages using CRNN and hybrid features. 使用 CRNN 和混合特征，基于扬声器识别 Ethio-Semitic 语言。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-06-04 DOI: 10.1080/0954898X.2024.2359610

Malefia Demilie Melese, Amlakie Aschale Alemu, Ayodeji Olalekan Salau, Ibrahim Gashaw Kasa

{"title":"Speaker-based language identification for Ethio-Semitic languages using CRNN and hybrid features.","authors":"Malefia Demilie Melese, Amlakie Aschale Alemu, Ayodeji Olalekan Salau, Ibrahim Gashaw Kasa","doi":"10.1080/0954898X.2024.2359610","DOIUrl":"10.1080/0954898X.2024.2359610","url":null,"abstract":"Natural language is frequently employed for information exchange between humans and computers in modern digital environments. Natural Language Processing (NLP) is a basic requirement for technological advancement in the field of speech recognition. For additional NLP activities like speech-to-text translation, speech-to-speech translation, speaker recognition, and speech information retrieval, language identification (LID) is a prerequisite. In this paper, we developed a Language Identification (LID) model for Ethio-Semitic languages. We used a hybrid approach (a convolutional recurrent neural network (CRNN)), in addition to a mixed (Mel frequency cepstral coefficient (MFCC) and mel-spectrogram) approach, to build our LID model. The study focused on four Ethio-Semitic languages: Amharic, Ge'ez, Guragigna, and Tigrinya. By using data augmentation for the selected languages, we were able to expand our original dataset of 8 h of audio data to 24 h and 40 min. The proposed selected features, when evaluated, achieved an average performance accuracy of 98.1%, 98.6%, and 99.9% for testing, validation, and training, respectively. The results show that the CRNN model with (Mel-Spectrogram + MFCC) combination feature achieved the best results when compared to other existing models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1313-1335"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment analysis using graph-based Quickprop method for product quality enhancement. 利用基于图的 Quickprop 方法进行情感分析，以提高产品质量。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-10-14 DOI: 10.1080/0954898X.2024.2410777

Raj Kumar Veerasamy Subramani, Thirumoorthy Kumaresan

{"title":"Sentiment analysis using graph-based Quickprop method for product quality enhancement.","authors":"Raj Kumar Veerasamy Subramani, Thirumoorthy Kumaresan","doi":"10.1080/0954898X.2024.2410777","DOIUrl":"10.1080/0954898X.2024.2410777","url":null,"abstract":"The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method's efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1996-2018"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ContrAttNet: Contribution and attention approach to multivariate time-series data imputation. ContrAttNet：多变量时间序列数据估算的贡献和关注方法。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-06-03 DOI: 10.1080/0954898X.2024.2360157

Yunfei Yin, Caihao Huang, Xianjian Bao

{"title":"ContrAttNet: Contribution and attention approach to multivariate time-series data imputation.","authors":"Yunfei Yin, Caihao Huang, Xianjian Bao","doi":"10.1080/0954898X.2024.2360157","DOIUrl":"10.1080/0954898X.2024.2360157","url":null,"abstract":"The imputation of missing values in multivariate time-series data is a basic and popular data processing technology. Recently, some studies have exploited Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs) to impute/fill the missing values in multivariate time-series data. However, when faced with datasets with high missing rates, the imputation error of these methods increases dramatically. To this end, we propose a neural network model based on dynamic contribution and attention, denoted as ContrAttNet. ContrAttNet consists of three novel modules: feature attention module, iLSTM (imputation Long Short-Term Memory) module, and 1D-CNN (1-Dimensional Convolutional Neural Network) module. ContrAttNet exploits temporal information and spatial feature information to predict missing values, where iLSTM attenuates the memory of LSTM according to the characteristics of the missing values, to learn the contributions of different features. Moreover, the feature attention module introduces an attention mechanism based on contributions, to calculate supervised weights. Furthermore, under the influence of these supervised weights, 1D-CNN processes the time-series data by treating them as spatial features. Experimental results show that ContrAttNet outperforms other state-of-the-art models in the missing value imputation of multivariate time-series data, with average 6% MAPE and 9% MAE on the benchmark datasets.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1336-1359"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EfficientNet-deep quantum neural network-based economic denial of sustainability attack detection to enhance network security in cloud. 基于 EfficientNet 深度量子神经网络的经济拒绝可持续性攻击检测，以增强云中的网络安全。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-06-21 DOI: 10.1080/0954898X.2024.2361093

Mariappan Navaneethakrishnan, Maharajan Robinson Joel, Sriram Kalavai Palani, Gandhi Jabakumar Gnanaprakasam

{"title":"EfficientNet-deep quantum neural network-based economic denial of sustainability attack detection to enhance network security in cloud.","authors":"Mariappan Navaneethakrishnan, Maharajan Robinson Joel, Sriram Kalavai Palani, Gandhi Jabakumar Gnanaprakasam","doi":"10.1080/0954898X.2024.2361093","DOIUrl":"10.1080/0954898X.2024.2361093","url":null,"abstract":"Cloud computing (CC) is a future revolution in the Information technology (IT) and Communication field. Security and internet connectivity are the common major factors to slow down the proliferation of CC. Recently, a new kind of denial of service (DDoS) attacks, known as Economic Denial of Sustainability (EDoS) attack, has been emerging. Though EDoS attacks are smaller at a moment, it can be expected to develop in nearer prospective in tandem with progression in the cloud usage. Here, EfficientNet-B3-Attn-2 fused Deep Quantum Neural Network (EfficientNet-DQNN) is presented for EDoS detection. Initially, cloud is simulated and thereafter, considered input log file is fed to perform data pre-processing. Z-Score Normalization ;(ZSN) is employed to carry out pre-processing of data. Afterwards, feature fusion (FF) is accomplished based on Deep Neural Network (DNN) with Kulczynski similarity. Then, data augmentation (DA) is executed by oversampling based upon Synthetic Minority Over-sampling Technique (SMOTE). At last, attack detection is conducted utilizing EfficientNet-DQNN. Furthermore, EfficientNet-DQNN is formed by incorporation of EfficientNet-B3-Attn-2 with DQNN. In addition, EfficientNet-DQNN attained 89.8% of F1-score, 90.4% of accuracy, 91.1% of precision and 91.2% of recall using BOT-IOT dataset at K-Fold is 9.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1360-1384"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can human brain connectivity explain verbal working memory? 人脑连通性能否解释言语工作记忆？

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-11-12 DOI: 10.1080/0954898X.2024.2421196

Maxime Carriere, Rosario Tomasello, Friedemann Pulvermüller

{"title":"Can human brain connectivity explain verbal working memory?","authors":"Maxime Carriere, Rosario Tomasello, Friedemann Pulvermüller","doi":"10.1080/0954898X.2024.2421196","DOIUrl":"10.1080/0954898X.2024.2421196","url":null,"abstract":"The ability of humans to store spoken words in verbal working memory and build extensive vocabularies is believed to stem from evolutionary changes in cortical connectivity across primate species. However, the underlying neurobiological mechanisms remain unclear. Why can humans acquire vast vocabularies, while non-human primates cannot? This study addresses this question using brain-constrained neural networks that realize between-species differences in cortical connectivity. It investigates how these structural differences support the formation of neural representations for spoken words and the emergence of verbal working memory, crucial for human vocabulary building. We develop comparative models of frontotemporal and occipital cortices, reflecting human and non-human primate neuroanatomy. Using meanfield and spiking neural networks, we simulate auditory word recognition and examine verbal working memory function. The \"human models\", characterized by denser inter-area connectivity in core language areas, produced larger cell assemblies than the \"monkey models\", with specific topographies reflecting semantic properties of the represented words. Crucially, longer-lasting reverberant neural activity was observed in human versus monkey architectures, compatible with robust verbal working memory, a necessary condition for vocabulary building. Our findings offer insights into the structural basis of human-specific symbol learning and verbal working memory, shedding light on humans' unique capacity for large vocabulary acquisition.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"2106-2147"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0