Network-Computation in Neural Systems最新文献

Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-17 DOI: 10.1080/0954898X.2024.2438967

R Anandha Praba, L Suganthi

{"title":"Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data.","authors":"R Anandha Praba, L Suganthi","doi":"10.1080/0954898X.2024.2438967","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2438967","url":null,"abstract":"Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance in intelligent home environments. In this manuscript, Human Activity Recognition utilizing optimized Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 from Mobile Health Data (HAR-AMCNN-MNV1) is proposed. The input data is collected through MHEALTH and UCI HAR datasets. Neural Spectrospatial Filtering (NSF) is used for avoiding accurate labelling and reduces errors. Afterwards, Variational Density Peak Clustering Algorithm (VDPCA) is used for segmenting the data. Feature Extraction and Classification is done by Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 (AMCNN-MNV1). AMCNN is used for extracting Hand-crafted features. AMCNN-MNV1 effectively classifies the human activities as Sitting and relaxing (Sit), Climbing stairs (CS), Walking (Walk), Standing still (Std), Waist bends forward (WBF), Frontal elevation of arms (FEA), Jogging (Jog), Knees bending (crouching) (KB), Cycling (Cycl), Lying down (Lay), Jump front & back (JFB) and Running (Run). Siberian Tiger Optimization Algorithm (STOA) is proposed to optimize the weight parameter of AMCNN-MNV1 classifier. The proposed method attains 21.19%, 23.45%, and 21.76% higher accuracy, 31.15%, 24.65% and 22.72% higher precision; 21.15%, 20.18%, and 21.28% higher recall evaluated to the existing methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-28"},"PeriodicalIF":1.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840251","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

Multiagent DDOS attack detection model: Optimal trained hybrid classifier and entropy-based mitigation process.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-17 DOI: 10.1080/0954898X.2024.2412674

Thiruselvan Palusamy, Balasubramanian Chelliah

{"title":"Multiagent DDOS attack detection model: Optimal trained hybrid classifier and entropy-based mitigation process.","authors":"Thiruselvan Palusamy, Balasubramanian Chelliah","doi":"10.1080/0954898X.2024.2412674","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2412674","url":null,"abstract":"This study proposes a novel multi-agent system designed to detect Distributed Denial of Service (DDoS) attacks, addressing the increasing need for robust cybersecurity measures. The hypothesis posits that a structured multi-agent approach can enhance detection accuracy and response efficiency in DDoS attack scenarios. The methodology involves a five-stage detection model: (1) Preprocessing using a modified double sigmoid normalization technique to eliminate duplicate data; (2) Feature Extraction where raw data and improved correlation-based features, mutual information, and statistical features are identified; (3) Dimensionality Reduction conducted by a reducer agent to streamline the feature set; (4) Classification utilizing Deep Belief Networks (DBN), Bi-LSTM, and Deep Maxout models, with their weights optimally tuned using the hybrid optimization algorithm, WUJSO; and (5) Decision Making by the decision agent to ascertain the presence of attacks, followed by mitigation through modified entropy-based techniques. The results demonstrate that the proposed method achieves a detection accuracy of 0.953 at a learning rate of 90%, significantly outperforming other methods, including Bi-GRU (0.857), DEEP-MAXOUT (0.910), Bi-LSTM (0.865), RNN (0.814), NN (0.894), and DBN (0.761). This research underscores the effectiveness of the multi-agent approach in enhancing DDoS attack detection and mitigation.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-33"},"PeriodicalIF":1.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840253","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

Design of a neural transformer for Spanish to Mexican Sign Language automatic translation/interpretation.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-11 DOI: 10.1080/0954898X.2024.2435495

Diana Vania Lara-Ortiz, Rita Q Fuentes Aguilar, Isaac Chairez

引用次数: 0

ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-11 DOI: 10.1080/0954898X.2024.2435491

Revathi Mohan, Rajesh Arunachalam, Neha Verma, Shital Mali

{"title":"ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images.","authors":"Revathi Mohan, Rajesh Arunachalam, Neha Verma, Shital Mali","doi":"10.1080/0954898X.2024.2435491","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2435491","url":null,"abstract":"One of the most familiar types of disease is Alzheimer's disease (AD) and it mainly impacts people over the age limit of 60. AD causes irreversible brain damage in humans. It is difficult to recognize the various stages of AD, hence advanced deep learning methods are suggested for recognizing AD in its initial stages. In this experiment, an effective deep model-based AD detection approach is introduced to provide effective treatment to the patient. Initially, an essential MRI is collected from the benchmark resources. After that, the gathered MRIs are provided as input to the feature extraction phase. Also, the important features in the input image are extracted by Vision Transformer-based Residual DenseNet (ViT-ResDenseNet). Later, the retrieved features are applied to the Alzheimer's detection stage. In this phase, AD is detected using an Adaptive Deep Bayesian Network (Ada-DBN). Additionally, the attributes of Ada-DBN are optimized with the help of Enhanced Golf Optimization Algorithm (EGOA). So, the implemented Alzheimer's detection model accomplishes relatively higher reliability than existing techniques. The numerical results of the suggested framework obtained an accuracy value of 96.35 which is greater than the 91.08, 91.95, and 93.95 attained by the EfficientNet-B2, TF- CNN, and ViT-GRU, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815056","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

Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-09 DOI: 10.1080/0954898X.2024.2435492

Vijayaganth Viswanathan, Krishnamoorthi Murugasamy

{"title":"Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering.","authors":"Vijayaganth Viswanathan, Krishnamoorthi Murugasamy","doi":"10.1080/0954898X.2024.2435492","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2435492","url":null,"abstract":"In the farming sector, the automatic detection of plant leaf disease is considered a vital landmark. Farmers move long distances to consult pathologists to observe the disease, which is expensive and time-consuming. Moreover, detection of disease in a premature period is a difficult process in the existing model. Thus, all these challenges motivate us to develop an inventive plant leaf disease detection model. In the developed model, the data is gathered initially and given as input to the pre-processing step using Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, the leaves are segmented from the pre-processed images, and then abnormality segmentation is done by the K-means clustering system. Here, parameters are optimized using the Opposition-based Bird Swarm Algorithm (O-BSA). Further, features were extracted from abnormality-segmented images in feature extraction. The extracted features are given in the classification step, where leaf disease detection is carried out using Optimized Ensemble Machine Learning (OEML), where, parameter optimization is done by O-BSA. Finally, the developed plant leaf detection approach is evaluated with various performance metrics, and given an accuracy of up to 92.26. These findings show that the developed model is promising over conventional methods and its effectiveness in detecting plant leaf disease.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-45"},"PeriodicalIF":1.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803550","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

Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-12-03 DOI: 10.1080/0954898X.2024.2428705

Visu Pandurangan, Smitha Ponnayyan Sarojam, Pughazendi Narayanan, Murugananthan Velayutham

{"title":"Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation.","authors":"Visu Pandurangan, Smitha Ponnayyan Sarojam, Pughazendi Narayanan, Murugananthan Velayutham","doi":"10.1080/0954898X.2024.2428705","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2428705","url":null,"abstract":"Skin melanin lesions are typically identified as tiny patches on the skin, which are impacted by melanocyte cell overgrowth. The number of people with skin cancer is increasing worldwide. Accurate and timely skin cancer identification is critical to reduce the mortality rates. An incorrect diagnosis can be fatal to the patient. To tackle these issues, this article proposes the Recurrent Prototypical Object Segmentation Network (RPO-SegNet) for the segmentation of skin lesions and a hybrid Deep Learning (DL) - based skin cancer classification. The RPO-SegNet is formed by integrating the Recurrent Prototypical Networks (RP-Net), and Object Segmentation Networks (O-SegNet). At first, the input image is taken from a database and forwarded to image pre-processing. Then, the segmentation of skin lesions is accomplished using the proposed RPO-SegNet. After the segmentation, feature extraction is accomplished. Finally, skin cancer classification and detection are accomplished by employing the Fuzzy-based Shepard Convolutional Maxout Network (FSCMN) by combining the Deep Maxout Network (DMN), and Shepard Convolutional Neural Network (ShCNN). The established RPO-SegNet+FSCMN attained improved accuracy, True Negative Rate (TNR), True Positive Rate (TPR), dice coefficient, Jaccard coefficient, and segmentation analysis of 91.985%, 92.735%, 93.485%, 90.902%, 90.164%, and 91.734%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-28"},"PeriodicalIF":1.1,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774851","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 pilot study of novel multi-filter CNN layer.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-28 DOI: 10.1080/0954898X.2024.2434487

Mohamed Aboukhair, Abdelrahim Koura, Mohammed Kayed

引用次数: 0

A review on real time implementation of soft computing techniques in thermal power plant. 火力发电厂软计算技术实时应用综述。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-27 DOI: 10.1080/0954898X.2024.2429721

Love Kumar Thawait, Mukesh Kumar Singh

{"title":"A review on real time implementation of soft computing techniques in thermal power plant.","authors":"Love Kumar Thawait, Mukesh Kumar Singh","doi":"10.1080/0954898X.2024.2429721","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2429721","url":null,"abstract":"Thermal Power Plant is a common power plant that generates power by fuel-burning to produce electricity. Being a significant component of the energy sector, the Thermal Power Plant faces several issues that lead to reduced productivity. Conventional researchers have tried using different mechanisms for improvising the production of Thermal Power Plants in varied dimensions. Due to the diverse dimensions considered by existing works, the present review endeavours to afford a comprehensive summary of these works. To achieve this, the study reviews articles in the range (2019-2023) that are allied with the utility of SC methodologies (encompassing AI-ML (Machine Learning) and DL (Deep Learning) in enhancing the productivity of Thermal Power Plants by various dimensions. The conventional AI-based approaches are comparatively evaluated for effective contribution in improvising Thermal Power Plant production. Following this, a critical assessment encompasses the year-wise distribution and varied dimensions focussed by traditional studies in this area. This would support future researchers in determining the dimensions that have attained limited and high focus based on which appropriate research works can be performed. Finally, future suggestions and research gaps are included to offer new stimulus for further investigation of AI in Thermal Power Plants.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142734747","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

Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures. 优化智能温室中的番茄检测和计数：结合高频和低频特征变换器结构的轻量级 YOLOv8 模型。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-21 DOI: 10.1080/0954898X.2024.2428713

Zhimin Tian, Huijuan Hao, Guowei Dai, Yajuan Li

{"title":"Optimizing tomato detection and counting in smart greenhouses: A lightweight YOLOv8 model incorporating high- and low-frequency feature transformer structures.","authors":"Zhimin Tian, Huijuan Hao, Guowei Dai, Yajuan Li","doi":"10.1080/0954898X.2024.2428713","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2428713","url":null,"abstract":"Tomato harvesting in intelligent greenhouses is crucial for reducing costs and optimizing management. Agricultural robots, as an automated solution, require advanced visual perception. This study proposes a tomato detection and counting algorithm based on YOLOv8 (TCAttn-YOLOv8). To handle small, occluded tomato targets in images, a new detection layer (NDL) is added to the Neck and Head decoupled structure, improving small object recognition. The ColBlock, a dual-branch structure leveraging Transformer advantages, enhances feature extraction and fusion, focusing on densely targeted regions and minimizing small object feature loss in complex backgrounds. C2fGhost and GhostConv are integrated into the Neck network to reduce model parameters and floating-point operations, improving feature expression. The WIoU (Wise-IoU) loss function is adopted to accelerate convergence and increase regression accuracy. Experimental results show that TCAttn-YOLOv8 achieves an mAP@0.5 of 96.31%, with an FPS of 95 and a parameter size of 2.7 M, outperforming seven lightweight YOLO algorithms. For automated tomato counting, the R2 between predicted and actual counts is 0.9282, indicating the algorithm's suitability for replacing manual counting. This method effectively supports tomato detection and counting in intelligent greenhouses, offering valuable insights for robotic harvesting and yield estimation research.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683524","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

HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation. HCAR-AM 坚果叶网：基于混合卷积的自适应 ResNet，采用注意力机制，通过自适应分割检测坚花叶病。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-17 DOI: 10.1080/0954898X.2024.2424248

Annamalai Thiruvengadam Madhavi, Kamal Basha Rahimunnisa

{"title":"HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.","authors":"Annamalai Thiruvengadam Madhavi, Kamal Basha Rahimunnisa","doi":"10.1080/0954898X.2024.2424248","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2424248","url":null,"abstract":"Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images are collected from traditional databases, and then they are given to the pre-processing stage. Then, relevant features are drawn out from the preprocessed images in two stages. In the first stage, the preprocessed image is segmented using adaptive TransResunet++, where the variables are tuned with the help of designed Hybrid Position of Beluga Whale and Cuttle Fish (HP-BWCF) and finally get the feature set 1 using Kaze Feature Points and Binary Descriptors. In the second stage, the same Kaze feature points and the binary descriptors are extracted from the preprocessed image separately, and then obtain feature set 2. Then, the extracted feature sets 1 and 2 are concatenated and given to the Hybrid Convolution-based Adaptive Resnet with Attention Mechanism (HCAR-AM) to detect the ground nut leaf diseases very effectively. The parameters from this HCAR-AM are tuned via the same HP-BWCF. The experimental outcome is analysed over various recently developed ground nut leaf disease detection approaches in accordance with various performance measures.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649538","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