Network-Computation in Neural Systems最新文献_第5页

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 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub 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":"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":"1752-1775"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","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

Multi-level authentication for security in cloud using improved quantum key distribution. 利用改进的量子密钥分配实现云安全的多级认证。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-07-08 DOI: 10.1080/0954898X.2024.2367480

Ashutosh Kumar, Garima Verma

{"title":"Multi-level authentication for security in cloud using improved quantum key distribution.","authors":"Ashutosh Kumar, Garima Verma","doi":"10.1080/0954898X.2024.2367480","DOIUrl":"10.1080/0954898X.2024.2367480","url":null,"abstract":"Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1443-1463"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555959","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 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub 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":"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":"1810-1836"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","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

EGDP based feature extraction and deep convolutional belief network for brain tumor detection using MRI image. 基于 EGDP 特征提取和深度卷积信念网络的磁共振成像脑肿瘤检测。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-09-16 DOI: 10.1080/0954898X.2024.2389248

Loganayagi T, Pooja Panapana, Ganji Ramanjaiah, Smritilekha Das

{"title":"EGDP based feature extraction and deep convolutional belief network for brain tumor detection using MRI image.","authors":"Loganayagi T, Pooja Panapana, Ganji Ramanjaiah, Smritilekha Das","doi":"10.1080/0954898X.2024.2389248","DOIUrl":"10.1080/0954898X.2024.2389248","url":null,"abstract":"This research presents a novel deep learning framework for MRI-based brain tumour (BT) detection. The input brain MRI image is first acquired from the dataset. Once the images have been obtained, they are passed to an image preprocessing step where a median filter is used to eliminate noise and artefacts from the input image. The tumour-tumour region segmentation module receives the denoised image and it uses RP-Net to segment the BT region. Following that, in order to prevent overfitting, image augmentation is carried out utilizing methods including rotating, flipping, shifting, and colour augmentation. Later, the augmented image is forwarded to the feature extraction phase, wherein features like GLCM and proposed EGDP formulated by including entropy with GDP are extracted. Finally, based on the extracted features, BT detection is accomplished based on the proposed deep convolutional belief network (DCvB-Net), which is formulated using the deep convolutional neural network and deep belief network.The devised DCvB-Net for BT detection is investigated for its performance concerning true negative rate, accuracy, and true positive rate is established to have acquired values of 93%, 92.3%, and 93.1% correspondingly.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1721-1751"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301213","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 for smart irrigation control and crop recommendation using interactive guide-deep model in Agriculture 4.0 applications. 在农业 4.0 应用中使用交互式深度引导模型进行智能灌溉控制和作物推荐的物联网。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2024-07-31 DOI: 10.1080/0954898X.2024.2383893

Smita Sandeep Mane, Vaibhav E Narawade

{"title":"Internet-of-Things for smart irrigation control and crop recommendation using interactive guide-deep model in Agriculture 4.0 applications.","authors":"Smita Sandeep Mane, Vaibhav E Narawade","doi":"10.1080/0954898X.2024.2383893","DOIUrl":"10.1080/0954898X.2024.2383893","url":null,"abstract":"The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1663-1695"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857222","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 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub 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":"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":"2075-2105"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","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

Multiagent DDOS attack detection model: Optimal trained hybrid classifier and entropy-based mitigation process. 多代理DDOS攻击检测模型：最优训练混合分类器和基于熵的缓解过程。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub 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":"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":"2042-2074"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","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

Automated glaucoma diagnosis: Optimized hybrid classification model with improved U-net segmentation. 青光眼自动诊断：改进U-net分割的优化混合分类模型。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub Date: 2025-03-27 DOI: 10.1080/0954898X.2025.2481958

Krishnamoorthy Varadharajalu, Logeswari Shanmugam

{"title":"Automated glaucoma diagnosis: Optimized hybrid classification model with improved U-net segmentation.","authors":"Krishnamoorthy Varadharajalu, Logeswari Shanmugam","doi":"10.1080/0954898X.2025.2481958","DOIUrl":"10.1080/0954898X.2025.2481958","url":null,"abstract":"Glaucoma is a leading cause of blindness, requiring early detection for effective management. Traditional diagnostic methods have challenges such as precise segmentation of small structures and accurate classification of disease stages remain. This research addresses these challenges by developing an optimized hybrid classification model for automated glaucoma diagnosis. At first, the preprocessing stage employs the histogram equalization technique known as Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Consequently, an improved U-Net segmentation process implemented with the proposed cross-entropy loss function is utilized. Then, features such as fractal features, cup-to-disc-based features, Inferior-Superior-Nasal-Temporal (ISNT) rule-based features and improved Pyramid Histogram of Orient Gradient (PHOG) based features are extracted. Further, a hybrid classification model, a combination of Improved Convolutional Neural Network (ICNN) and optimized Recurrent Neural Network (RNN) classifiers for diagnosing glaucoma disease. Also, to improve the performance of the diagnosis process, a new Opposition-based Learning-enabled Namib Beetle Optimization (OBL-NBO) approach is proposed to optimize the weights of the RNN classifier. Moreover, the ICNN classifier is employed for classifying the presence of glaucoma and non-glaucoma conditions. The proposed OBL-NBO scheme achieved an accuracy of 0.927 for dataset 1 and 0.945 for dataset 2 at an 80% training data.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"2148-2202"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722665","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

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 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-11-01 Epub 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":"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":"2019-2041"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","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

Statement of Retraction. 撤回。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-08-03 DOI: 10.1080/0954898X.2024.2385540

引用次数: 0