Network-Computation in Neural Systems最新文献_第3页

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction. 用于量子密钥分发和降低错误率的优化多头自注意和门控稀释卷积神经网络。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-01 Epub Date: 2024-07-16 DOI: 10.1080/0954898X.2024.2375391

R J Kavitha, D Ilakkiaselvan

{"title":"Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction.","authors":"R J Kavitha, D Ilakkiaselvan","doi":"10.1080/0954898X.2024.2375391","DOIUrl":"10.1080/0954898X.2024.2375391","url":null,"abstract":"Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are several approaches that are utilized in practice to calculate the secure key rate. In this manuscript, QKD and error rate optimization based on optimized multi-head self-attention and gated-dilated convolutional neural network (QKD-ERO-MSGCNN) is proposed. Initially, the input signals are gathered from 6G wireless networks which face obstacles to channel. For extending maximum transmission distances and improving secret key rates, the signals are fed to the variable velocity strategy particle swarm optimization algorithm, then the signals are fed to MSGCNN for analysing the quantum bit error rate reduction. The MSGCNN is optimized by intensified sand cat swarm optimization. The performance of the QKD-ERO-MSGCNN approach attains 15.57%, 23.89%, and 31.75% higher accuracy when analysed with existing techniques, like device-independent QKD utilizing random quantum states, practical continuous-variable QKD and feasible optimization parameters, entanglement and teleportation in QKD for secure wireless systems, and QKD for large scale networks methods, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"379-402"},"PeriodicalIF":1.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629294","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

Optimized memory augmented graph neural network-based DoS attacks detection in wireless sensor network. 基于优化内存增强图神经网络的无线传感器网络 DoS 攻击检测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-28 DOI: 10.1080/0954898X.2024.2392786

Ayyasamy Pushpalatha, Sunkari Pradeep, Matta Venkata Pullarao, Shanmuganathan Sankar

{"title":"Optimized memory augmented graph neural network-based DoS attacks detection in wireless sensor network.","authors":"Ayyasamy Pushpalatha, Sunkari Pradeep, Matta Venkata Pullarao, Shanmuganathan Sankar","doi":"10.1080/0954898X.2024.2392786","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2392786","url":null,"abstract":"Wireless Sensor Networks (WSNs) are mainly used for data monitoring and collection purposes. Usually, they are made up of numerous sensor nodes that are utilized to gather data remotely. Each sensor node is small and inexpensive. Due to the increasing intelligence, frequency, and complexity of these malicious attacks, traditional attack detection is less effective. In this manuscript, Optimized Memory Augmented Graph Neural Network-based DoS Attacks Detection in Wireless Sensor Network (DoS-AD-MAGNN-WSN) is proposed. Here, the input data is amassed from WSN-DS dataset. The input data is pre-processing by secure adaptive event-triggered filter for handling negation and stemming. Then, the output is fed to nested patch-based feature extraction to extract the optimal features. The extracted features are given to MAGNN for the effective classification of blackhole, flooding, grayhole, scheduling, and normal. The weight parameter of MAGNN is optimized by gradient-based optimizers for better accuracy. The proposed method is activated in Python, and it attains 31.20%, 23.30%, and 26.43% higher accuracy analyzed with existing techniques, such as CNN-LSTM-based method for Denial of Service attacks detection in WSNs (CNN-DoS-AD-WSN), Trust-based DoS attack detection in WSNs for reliable data transmission (TB-DoS-AD-WSN-RDT), and FBDR-Fuzzy-based DoS attack detection with recovery mechanism for WSNs (FBDR-DoS-AD-RM-WSN), respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-27"},"PeriodicalIF":1.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513157","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

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 1.1 3区计算机科学

Network-Computation in Neural Systems Pub 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":"1-25"},"PeriodicalIF":1.1,"publicationDate":"2024-10-21","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

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 1.1 3区计算机科学

Network-Computation in Neural Systems Pub 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":"https://doi.org/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":"1-23"},"PeriodicalIF":1.1,"publicationDate":"2024-10-14","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

Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set. 深度自组织图神经网络改进了对不足的足底压力成像数据集的分割。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-14 DOI: 10.1080/0954898X.2024.2413849

Dan Wang, Zairan Li, Nilanjan Dey, Adam Slowik, R Simon Sherratt, Fuqian Shi

{"title":"Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set.","authors":"Dan Wang, Zairan Li, Nilanjan Dey, Adam Slowik, R Simon Sherratt, Fuqian Shi","doi":"10.1080/0954898X.2024.2413849","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2413849","url":null,"abstract":"This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-21"},"PeriodicalIF":1.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481197","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

Neuro connect: Integrating data-driven and BiGRU classification for enhanced autism prediction from fMRI data. 神经连接：整合数据驱动和 BiGRU 分类，从 fMRI 数据中增强自闭症预测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-13 DOI: 10.1080/0954898X.2024.2412679

Pavithra Rajaram, Mohanapriya Marimuthu

{"title":"Neuro connect: Integrating data-driven and BiGRU classification for enhanced autism prediction from fMRI data.","authors":"Pavithra Rajaram, Mohanapriya Marimuthu","doi":"10.1080/0954898X.2024.2412679","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2412679","url":null,"abstract":"Autism Spectrum Disorder (ASD) poses a significant challenge in early diagnosis and intervention due to its multifaceted clinical presentation and lack of objective biomarkers. This research presents a novel approach, termed Neuro Connect, which integrates data-driven techniques with Bidirectional Gated Recurrent Unit (BiGRU) classification to enhance the prediction of ASD using functional Magnetic Resonance Imaging (fMRI) data. This study uses both structural and functional neuroimaging data to investigate the complex brain underpinnings of autism spectrum disorder (ASD). They use an Auto-Encoder (AE) to efficiently reduce dimensionality while retaining critical information by learning and compressing important characteristics from high-dimensional data. We treat the feature-extracted data using a BiGRU model for the classification task of predicting ASD. They provide a new optimization strategy, the Horse Herd Algorithm (HHA), and show that it outperforms other established optimizers, such SGD and Adam, in order to improve classification accuracy. The model's performance is greatly enhanced by the HHA's novel optimization technique, which more precisely refines weight modifications made during training. The proposed ASD and EEG dataset accuracy value is 99.5%, and 99.3 compared to the existing method the proposed has a high accuracy value.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-32"},"PeriodicalIF":1.1,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481199","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

Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System. 基于物联网和云计算的疾病诊断，在智能医疗系统中使用优化改进的生成对抗网络。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-13 DOI: 10.1080/0954898X.2024.2392770

Thimmakkondu Babuji Sivakumar, Shahul Hameed Hasan Hussain, R Balamanigandan

{"title":"Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System.","authors":"Thimmakkondu Babuji Sivakumar, Shahul Hameed Hasan Hussain, R Balamanigandan","doi":"10.1080/0954898X.2024.2392770","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2392770","url":null,"abstract":"The integration of IoT and cloud services enhances communication and quality of life, while predictive analytics powered by AI and deep learning enables proactive healthcare. Deep learning, a subset of machine learning, efficiently analyzes vast datasets, offering rapid disease prediction. Leveraging recurrent neural networks on electronic health records improves accuracy for timely intervention and preventative care. In this manuscript, Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System (IOT-CC-DD-OICAN-SHS) is proposed. Initially, an Internet of Things (IoT) device collects diabetes, chronic kidney disease, and heart disease data from patients via wearable devices and intelligent sensors and then saves the patient's large data in the cloud. These cloud data are pre-processed to turn them into a suitable format. The pre-processed dataset is sent into the Improved Generative Adversarial Network (IGAN), which reliably classifies the data as disease-free or diseased. Then, IGAN was optimized using the Flamingo Search optimization algorithm (FSOA). The proposed technique is implemented in Java using Cloud Sim and examined utilizing several performance metrics. The proposed method attains greater accuracy and specificity with lower execution time compared to existing methodologies, IoT-C-SHMS-HDP-DL, PPEDL-MDTC and CSO-CLSTM-DD-SHS respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-24"},"PeriodicalIF":1.1,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481198","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

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm. 使用混合 Lemurs 和 Gannet 优化算法，在多目标约束条件下设计云环境中的最佳任务调度和虚拟机放置。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-09 DOI: 10.1080/0954898X.2024.2412678

Kapil Vhatkar, Atul Baliram Kathole, Savita Lonare, Jayashree Katti, Vinod Vijaykumar Kimbahune

{"title":"Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm.","authors":"Kapil Vhatkar, Atul Baliram Kathole, Savita Lonare, Jayashree Katti, Vinod Vijaykumar Kimbahune","doi":"10.1080/0954898X.2024.2412678","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2412678","url":null,"abstract":"An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-31"},"PeriodicalIF":1.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395378","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 comparative study of early stage Alzheimer's disease classification using various transfer learning CNN frameworks. 使用各种迁移学习 CNN 框架对早期阿尔茨海默病分类的比较研究。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-10-05 DOI: 10.1080/0954898X.2024.2406946

Yajuvendra Pratap Singh, Daya Krishan Lobiyal

{"title":"A comparative study of early stage Alzheimer's disease classification using various transfer learning CNN frameworks.","authors":"Yajuvendra Pratap Singh, Daya Krishan Lobiyal","doi":"10.1080/0954898X.2024.2406946","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2406946","url":null,"abstract":"The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer's disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers. These enhancements improve the model's effectiveness and precision in addressing the specified medical issue. The proposed model is rigorously validated and evaluated using publicly available Kaggle MRI Alzheimer's data consisting of 1280 testing images and 5120 patient training images. For comprehensive performance evaluation, precision, recall, F1-score, and accuracy metrics are utilized. The findings indicate that the Xception method is the most promising of those considered. Without employing five K-fold techniques, this model obtains a 99% accuracy and 0.135 loss score. In addition, integrating five K-fold methods enhances the accuracy to 99.68% while decreasing the loss score to 0.120. The research further included the evaluation of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) for various classes and models. As a result, our model may detect and diagnose Alzheimer's disease quickly and accurately.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-29"},"PeriodicalIF":1.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378624","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 novel approach for heart disease prediction using hybridized AITH²O algorithm and SANFIS classifier. 使用混合 AITH2O 算法和 SANFIS 分类器预测心脏病的新方法。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-09-25 DOI: 10.1080/0954898X.2024.2404915

Jayachitra Sekar, Prasanth Aruchamy

{"title":"A novel approach for heart disease prediction using hybridized AITH2O algorithm and SANFIS classifier.","authors":"Jayachitra Sekar, Prasanth Aruchamy","doi":"10.1080/0954898X.2024.2404915","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2404915","url":null,"abstract":"In today's world, heart disease threatens human life owing to higher mortality and morbidity across the globe. The earlier prediction of heart disease engenders interoperability for the treatment of patients and offers better diagnostic recommendations from medical professionals. However, the existing machine learning classifiers suffer from computational complexity and overfitting problems, which reduces the classification accuracy of the diagnostic system. To address these constraints, this work proposes a new hybrid optimization algorithm to improve the classification accuracy and optimize computation time in smart healthcare applications. Primarily, the optimal features are selected through the hybrid Arithmetic Optimization and Inter-Twinned Mutation-Based Harris Hawk Optimization (AITH2O) algorithm. The proposed hybrid AITH2O algorithm entails advantages of both exploration and exploitation abilities and acquires faster convergence. It is further employed to tune the parameters of the Stabilized Adaptive Neuro-Fuzzy Inference System (SANFIS) classifier for predicting heart disease accurately. The Cleveland heart disease dataset is utilized to validate the efficacy of the proposed algorithm. The simulation is carried out using MATLAB 2020a environment. The simulation results show that the proposed hybrid SANFIS classifier attains a superior accuracy of 99.28% and true positive rate of 99.46% compared to existing state-of-the-art techniques.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-39"},"PeriodicalIF":1.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332540","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