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

Key point trajectory prediction method of human stochastic posture falls. 人体随机姿势跌倒的关键点轨迹预测方法。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-04 DOI: 10.1080/0954898X.2024.2412673

Yafei Ding, Gaomin Zhang

{"title":"Key point trajectory prediction method of human stochastic posture falls.","authors":"Yafei Ding, Gaomin Zhang","doi":"10.1080/0954898X.2024.2412673","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2412673","url":null,"abstract":"The human body will show very complex and diversified posture changes in the process of falling, including body posture, limb position, angle and movement trajectory, etc. The coordinates of the key points of the model are mapped to the three-dimensional space to form a three-dimensional model and obtain the three-dimensional coordinates of the key points; The construction decomposition method is used to calculate the rotation matrix of each key point, and the rotation matrix is solved to obtain the angular displacement data of the key points on different degrees of freedom. The method of curve fitting combined with the weight distribution kernel function based on self-organizing mapping theory is used to obtain the motion trajectory prediction equation of the human body falling in different degrees of freedom at random positions in three-dimensional space, determine the key point trajectory of human random fall behaviour. The experimental results show that the mapped 3D model is consistent with the real human body structure. This method can accurately determine whether the human body falls or squats randomly, and the prediction results of the key points of the human fall are consistent with the actions of the human body after the fall.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-23"},"PeriodicalIF":1.1,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570364","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

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI. DTDO：利用磁共振成像进行脑肿瘤分类的深度学习方法（Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI）。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-01 Epub Date: 2024-05-27 DOI: 10.1080/0954898X.2024.2351159

Vadamodula Prasad, Issac Diana Jeba Jingle, Gopalsamy Venkadakrishnan Sriramakrishnan

{"title":"DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI.","authors":"Vadamodula Prasad, Issac Diana Jeba Jingle, Gopalsamy Venkadakrishnan Sriramakrishnan","doi":"10.1080/0954898X.2024.2351159","DOIUrl":"10.1080/0954898X.2024.2351159","url":null,"abstract":"A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"520-561"},"PeriodicalIF":1.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141154757","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

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network. 利用基于深度神经网络的快速傅立叶变换和 coot 优化技术识别人类手势。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-01 Epub Date: 2024-08-21 DOI: 10.1080/0954898X.2024.2389231

Arumugam Arulkumar, Palanisamy Babu

{"title":"Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.","authors":"Arumugam Arulkumar, Palanisamy Babu","doi":"10.1080/0954898X.2024.2389231","DOIUrl":"10.1080/0954898X.2024.2389231","url":null,"abstract":"Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.Initially, the raw input EMG signal is captured then the signal is pre-processed using high-pass Butterworth filter and low-pass filter which is utilized to eliminate the noise present in the signal. After that pre-processed EMG signal is segmented using sliding window which is used for solving the issue of overlapping. Then the features are extracted from the segmented signal using Fast Fourier Transform. Then selected the appropriate and optimal number of features from the feature subset using coot optimization algorithm. After that selected features are given as input for deep neural network classifier for recognizing the hand movements of human. The simulation analysis shows that the proposed method obtain 95% accuracy, 0.05% error, precision is 94%, and specificity is 92%.The simulation analysis shows that the developed approach attain better performance compared to other existing approaches. This prediction model helps in controlling the movement of amputee patients suffering from disable hand motion and improve their living standard.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"488-519"},"PeriodicalIF":1.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019538","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 optimization-assisted multi-scale and dilated adaptive hybrid deep learning network with feature fusion for event detection from social media. 新型优化辅助多尺度和扩张自适应混合深度学习网络与特征融合，用于社交媒体事件检测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-01 Epub Date: 2024-07-17 DOI: 10.1080/0954898X.2024.2376705

Ruhi Patankar, Albert Pravin

{"title":"A novel optimization-assisted multi-scale and dilated adaptive hybrid deep learning network with feature fusion for event detection from social media.","authors":"Ruhi Patankar, Albert Pravin","doi":"10.1080/0954898X.2024.2376705","DOIUrl":"10.1080/0954898X.2024.2376705","url":null,"abstract":"Social media networks become an active communication medium for connecting people and delivering new messages. Social media can perform as the primary channel, where the globalized events or instances can be explored. Earlier models are facing the pitfall of noticing the temporal and spatial resolution for enhancing the efficacy. Therefore, in this proposed model, a new event detection approach from social media data is presented. Firstly, the essential data is collected and undergone for pre-processing stage. Further, the Bidirectional Encoder Representations from Transformers (BERT) and Term Frequency Inverse Document Frequency (TF-IDF) are employed for extracting features. Subsequently, the two resultant features are given to the multi-scale and dilated layer present in the detection network of GRU and Res-Bi-LSTM, named as Multi-scale and Dilated Adaptive Hybrid Deep Learning (MDA-HDL) for event detection. Moreover, the MDA-HDL network's parameters are tuned by Improved Gannet Optimization Algorithm (IGOA) to enhance the performance. Finally, the execution of the system is done over the Python platform, where the system is validated and compared with baseline methodologies. The accuracy findings of model acquire as 94.96 for dataset 1 and 96.42 for dataset 2. Hence, the recommended model outperforms with the superior results while detecting the social events.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"429-462"},"PeriodicalIF":1.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629293","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

Spectrum occupancy prediction using LSTM models for cognitive radio applications. 利用 LSTM 模型为认知无线电应用预测频谱占用率。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-01 Epub Date: 2024-09-30 DOI: 10.1080/0954898X.2024.2393245

Tamizhelakkiya Kolangiyappan, Sabitha Gauni, Prabhu Chandhar

引用次数: 0

SJFO: Sail Jelly Fish Optimization enabled VM migration with DRNN-based prediction for load balancing in cloud computing. SJFO：Sail Jelly Fish Optimization enabled VM migration with DRNN-based prediction for load balancing in cloud computing.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-01 Epub Date: 2024-06-03 DOI: 10.1080/0954898X.2024.2359609

Rajesh Rathinam, Premkumar Sivakumar, Sivakumar Sigamani, Ishwarya Kothandaraman

{"title":"SJFO: Sail Jelly Fish Optimization enabled VM migration with DRNN-based prediction for load balancing in cloud computing.","authors":"Rajesh Rathinam, Premkumar Sivakumar, Sivakumar Sigamani, Ishwarya Kothandaraman","doi":"10.1080/0954898X.2024.2359609","DOIUrl":"10.1080/0954898X.2024.2359609","url":null,"abstract":"The dynamic workload is evenly distributed among all nodes using balancing methods like hosts or VMs. Load Balancing as a Service (LBaaS) is another name for load balancing in the cloud. In this research work, the load is balanced by the application of Virtual Machine (VM) migration carried out by proposed Sail Jelly Fish Optimization (SJFO). The SJFO is formed by combining Sail Fish Optimizer (SFO) and Jellyfish Search (JS) optimizer. In the Cloud model, many Physical Machines (PMs) are present, where these PMs are comprised of many VMs. Each VM has many tasks, and these tasks depend on various parameters like Central Processing Unit (CPU), memory, Million Instructions per Second (MIPS), capacity, total number of processing entities, as well as bandwidth. Here, the load is predicted by Deep Recurrent Neural Network (DRNN) and this predicted load is compared with a threshold value, where VM migration is done based on predicted values. Furthermore, the performance of SJFO-VM is analysed using the metrics like capacity, load, and resource utilization. The proposed method shows better performance with a superior capacity of 0.598, an inferior load of 0.089, and an inferior resource utilization of 0.257.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"403-428"},"PeriodicalIF":1.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201479","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 Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System. 优化的 Wasserstein 深度卷积生成对抗网络促进了花生叶病识别系统。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-11-01 Epub Date: 2024-07-02 DOI: 10.1080/0954898X.2024.2351146

Anna Anbumozhi, Shanthini A

{"title":"Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System.","authors":"Anna Anbumozhi, Shanthini A","doi":"10.1080/0954898X.2024.2351146","DOIUrl":"10.1080/0954898X.2024.2351146","url":null,"abstract":"Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss of groundnut plant growth, which will directly diminish the yield and quality. Therefore, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System (GLDI-WDCGAN-AOA) is proposed in this paper. The pre-processed output is fed to Hesitant Fuzzy Linguistic Bi-objective Clustering (HFL-BOC) for segmentation. By using Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN), the input leaf images are classified into Healthy leaf, early leaf spot, late leaf spot, nutrition deficiency, and rust. Finally, the weight parameters of WDCGAN are optimized by Aquila Optimization Algorithm (AOA) to achieve high accuracy. The proposed GLDI-WDCGAN-AOA approach provides 23.51%, 22.01%, and 18.65% higher accuracy and 24.78%, 23.24%, and 28.98% lower error rate analysed with existing methods, such as Real-time automated identification and categorization of groundnut leaf disease utilizing hybrid machine learning methods (GLDI-DNN), Online identification of peanut leaf diseases utilizing the data balancing method along deep transfer learning (GLDI-LWCNN), and deep learning-driven method depending on progressive scaling method for the precise categorization of groundnut leaf infections (GLDI-CNN), respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"463-487"},"PeriodicalIF":1.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494322","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 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