Network-Computation in Neural Systems最新文献_第7页

MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid meta-heuristic optimization algorithm. MCN 投资组合：使用混合元启发式优化算法的多串级联网络的高效投资组合预测和选择模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-08 DOI: 10.1080/0954898X.2024.2346115

Meeta Sharma, Pankaj Kumar Sharma, Hemant Kumar Vijayvergia, Amit Garg, Shyam Sundar Agarwal, Varun Prakash Saxena

{"title":"MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid meta-heuristic optimization algorithm.","authors":"Meeta Sharma, Pankaj Kumar Sharma, Hemant Kumar Vijayvergia, Amit Garg, Shyam Sundar Agarwal, Varun Prakash Saxena","doi":"10.1080/0954898X.2024.2346115","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2346115","url":null,"abstract":"Generally, financial investments are necessary for portfolio management. However, the prediction of a portfolio becomes complicated in several processing techniques which may cause certain issues while predicting the portfolio. Moreover, the error analysis needs to be validated with efficient performance measures. To solve the problems of portfolio optimization, a new portfolio prediction framework is developed. Initially, a dataset is collected from the standard database which is accumulated with various companies' portfolios. For forecasting the benefits of companies, a Multi-serial Cascaded Network (MCNet) is employed which constitutes of Autoencoder, 1D Convolutional Neural Network (1DCNN), and Recurrent Neural Network (RNN) is utilized. The prediction output for the different companies is stored using the developed MCNet model for further use. After predicting the benefits, the best company with the highest profit is selected by Integration of Artificial Rabbit and Hummingbird Algorithm (IARHA). The major contribution of our work is to increase the accuracy of prediction and to choose the optimal portfolio. The implementation is conducted in Python platform. The result analysis shows that the developed model achieves 0.89% and 0.56% regarding RMSE and MAE measures. Throughout the analysis, the experimentation of the developed model shows enriched performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877957","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

Neuromorphic computing spiking neural network edge detection model for content based image retrieval. 基于内容的图像检索的神经形态计算尖峰神经网络边缘检测模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-06 DOI: 10.1080/0954898X.2024.2348018

Ambuj, Rajendra Machavaram

{"title":"Neuromorphic computing spiking neural network edge detection model for content based image retrieval.","authors":"Ambuj, Rajendra Machavaram","doi":"10.1080/0954898X.2024.2348018","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2348018","url":null,"abstract":"In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140866097","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

Neural decoding of inferior colliculus multiunit activity for sound category identification with temporal correlation and transfer learning. 下丘多单元活动对声音类别识别的神经解码与时间相关和迁移学习。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-11-20 DOI: 10.1080/0954898X.2023.2282576

Fatma Özcan, Ahmet Alkan

{"title":"Neural decoding of inferior colliculus multiunit activity for sound category identification with temporal correlation and transfer learning.","authors":"Fatma Özcan, Ahmet Alkan","doi":"10.1080/0954898X.2023.2282576","DOIUrl":"10.1080/0954898X.2023.2282576","url":null,"abstract":"Natural sounds are easily perceived and identified by humans and animals. Despite this, the neural transformations that enable sound perception remain largely unknown. It is thought that the temporal characteristics of sounds may be reflected in auditory assembly responses at the inferior colliculus (IC) and which may play an important role in identification of natural sounds. In our study, natural sounds will be predicted from multi-unit activity (MUA) signals collected in the IC. Data is obtained from an international platform publicly accessible. The temporal correlation values of the MUA signals are converted into images. We used two different segment sizes and with a denoising method, we generated four subsets for the classification. Using pre-trained convolutional neural networks (CNNs), features of the images were extracted and the type of heard sound was classified. For this, we applied transfer learning from Alexnet, Googlenet and Squeezenet CNNs. The classifiers support vector machines (SVM), k-nearest neighbour (KNN), Naive Bayes and Ensemble were used. The accuracy, sensitivity, specificity, precision and F1 score were measured as evaluation parameters. By using all the tests and removing the noise, the accuracy improved significantly. These results will allow neuroscientists to make interesting conclusions.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048830","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

Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques. 基于金鹰的改进型 Att-BiLSTM 模型，采用混合特征提取和特征选择技术进行大数据分类。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-12-28 DOI: 10.1080/0954898X.2023.2293895

Gnanendra Kotikam, Lokesh Selvaraj

{"title":"Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques.","authors":"Gnanendra Kotikam, Lokesh Selvaraj","doi":"10.1080/0954898X.2023.2293895","DOIUrl":"10.1080/0954898X.2023.2293895","url":null,"abstract":"The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139059143","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

Optimization-enabled deep learning model for disease detection in IoT platform. 用于物联网平台疾病检测的优化深度学习模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-12-28 DOI: 10.1080/0954898X.2023.2296568

Amol Dattatray Dhaygude

{"title":"Optimization-enabled deep learning model for disease detection in IoT platform.","authors":"Amol Dattatray Dhaygude","doi":"10.1080/0954898X.2023.2296568","DOIUrl":"10.1080/0954898X.2023.2296568","url":null,"abstract":"Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algorithm, named artificial flora optimization-based chameleon swarm algorithm (AFO-based CSA), is developed for optimal path finding. Here, data are collected by the sensors and transmitted to the base station (BS) using the proposed AFO-based CSA, which is derived by integrating artificial flora optimization (AFO) in chameleon swarm algorithm (CSA). This integration refers to the AFO-based CSA model enhancing the strengths and features of both AFO and CSA for optimal routing of medical data in IoT. Moreover, the proposed AFO-based CSA algorithm considers factors such as energy, delay, and distance for the effectual routing of data. At BS, prediction is conducted, followed by stages, like pre-processing, feature dimension reduction, adopting Pearson's correlation, and disease detection, done by recurrent neural network, which is trained by the proposed AFO-based CSA. Experimental result exhibited that the performance of the proposed AFO-based CSA is superior to competitive approaches based on the energy consumption (0.538 J), accuracy (0.950), sensitivity (0.965), and specificity (0.937).","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139059144","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

CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation. CS-UNet：用于视网膜血管分割的具有语义位置依赖性的跨尺度 U-Net

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-12-05 DOI: 10.1080/0954898X.2023.2288858

Ying Yang, Shengbin Yue, Haiyan Quan

{"title":"CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation.","authors":"Ying Yang, Shengbin Yue, Haiyan Quan","doi":"10.1080/0954898X.2023.2288858","DOIUrl":"10.1080/0954898X.2023.2288858","url":null,"abstract":"Accurate retinal vessel segmentation is the prerequisite for early recognition and treatment of retina-related diseases. However, segmenting retinal vessels is still challenging due to the intricate vessel tree in fundus images, which has a significant number of tiny vessels, low contrast, and lesion interference. For this task, the u-shaped architecture (U-Net) has become the de-facto standard and has achieved considerable success. However, U-Net is a pure convolutional network, which usually shows limitations in global modelling. In this paper, we propose a novel Cross-scale U-Net with Semantic-position Dependencies (CS-UNet) for retinal vessel segmentation. In particular, we first designed a Semantic-position Dependencies Aggregator (SPDA) and incorporate it into each layer of the encoder to better focus on global contextual information by integrating the relationship of semantic and position. To endow the model with the capability of cross-scale interaction, the Cross-scale Relation Refine Module (CSRR) is designed to dynamically select the information associated with the vessels, which helps guide the up-sampling operation. Finally, we have evaluated CS-UNet on three public datasets: DRIVE, CHASE_DB1, and STARE. Compared to most existing state-of-the-art methods, CS-UNet demonstrated better performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138489124","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

A robust genetic algorithm-based optimal feature predictor model for brain tumour classification from MRI data 基于遗传算法的鲁棒性最优特征预测模型，用于磁共振成像数据的脑肿瘤分类

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI: 10.1080/0954898x.2024.2343340

Meenal Thayumanavan, Asokan Ramasamy

引用次数: 0

An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism 利用具有关注机制的多尺度扩张登森网的创新型乳腺癌检测框架

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI: 10.1080/0954898x.2024.2343348

Subhashini Ramachandran, Rajasekar Velusamy, Namakkal Venkataraman Srinivasan Sree Rathna Lakshmi, Chakaravarthi Sivanandam

引用次数: 0

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks 基于拓扑信息嵌入卷积神经网络的莲花效应优化，用于无线传感器网络中移动锚点的路径改进

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI: 10.1080/0954898x.2024.2339477

Bala Subramanian Chokkalingam, Balakannan Sirumulasi Paramasivan, Maragatharajan Muthusamy

引用次数: 0

Enhanced Cardiovascular Disease Prediction Modelling using Machine Learning Techniques: A Focus on CardioVitalnet 利用机器学习技术增强心血管疾病预测建模：聚焦心血管网络

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-04-16 DOI: 10.1080/0954898x.2024.2343341

Chukwuebuka Joseph Ejiyi, Zhen Qin, Grace Ugochi Nneji, Happy Nkanta Monday, Victor K. Agbesi, Makuachukwu Bennedith Ejiyi, Thomas Ugochukwu Ejiyi, Olusola O. Bamisile

引用次数: 0