Network-Computation in Neural Systems最新文献

User behaviour based insider threat detection model using an LSTM integrated RF model.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-03 DOI: 10.1080/0954898X.2025.2483342

S K Uma Maheswaran, L Rajasekar, Ziaul Haque Choudhury, Makarand Shahade

{"title":"User behaviour based insider threat detection model using an LSTM integrated RF model.","authors":"S K Uma Maheswaran, L Rajasekar, Ziaul Haque Choudhury, Makarand Shahade","doi":"10.1080/0954898X.2025.2483342","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2483342","url":null,"abstract":"Insider threat is one of the most serious and frequent security risks facing various industries like governmental organizations, businesses, and institutions. Insider threat identification has a special combination of difficulties, including vastly unbalanced data, insufficient ground truth, and drifting and shifting behaviour. A user behaviour-based insider threat detection model utilizing a hybrid deep long short-term memory-random forest (LSTM-RF) model is developed to address these challenges. In this proposed insider threat detection model, the user log data is preprocessed to replace the missing value and to normalize the data to certain range. Then, these preprocessed data are provided as the input of the attribute selection process that mainly applies for selecting the essential attribute using Spearman's rank correlation coefficient. Then the deep hybrid LSTM-RF classifier to detect whether a system is affected by inside threat or not such as malware, authentication, phishing are fed to the selected features. Hybrid LSTM-RF method is implemented in python and achieved 96% accuracy, 90% precision, 90% specificity, 97% sensitivity, and 94% F1-score. During an attack, it can be easily detected inside the system attack.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-38"},"PeriodicalIF":1.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774981","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

Security-aware user authentication based on multimodal biometric data using dilated adaptive RNN with optimal weighted feature fusion.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-01 DOI: 10.1080/0954898X.2025.2480304

Udhayakumar Selvaraj, Janakiraman Nithiyanantham

{"title":"Security-aware user authentication based on multimodal biometric data using dilated adaptive RNN with optimal weighted feature fusion.","authors":"Udhayakumar Selvaraj, Janakiraman Nithiyanantham","doi":"10.1080/0954898X.2025.2480304","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2480304","url":null,"abstract":"This work plans to develop a biometric authentication model by the combination of multi-modal inputs like voice, fingerprint, and iris to provide high security. At first, the spectrogram images, the collected fingerprint, and the collected iris input were given to a Multi-scale Residual Attention Network (RAN) with Atrous Spatial Pyramid Pooling (ASPP) to extract the best values. These three features are then fed to optimal weighted feature fusion, where weight optimization from the features is done via the Enhanced Lichtenberg Algorithm (ELA). These features are fed into the decision-making stage, where the Dilated Adaptive Recurrent Neural Network is utilized to identify the individuals, where the parameters are optimized from RNN using ELA to improve the recognition performance. The simulation findings achieved from the developed multimodal authentication systems are validated using diverse algorithms over several efficacy metrics like accuracy, precision, sensitivity, F1-score, etc. From the result analysis, the ELA-DARNN-based user authentication system showed a higher accuracy of 96.01, and other models such as 90% than SVM, CNN, CNN-AlexNet, and Dil-ARNN given the accuracy to be 87.94, 89.88, 93.25, and 91.94. Therefore, the outcomes explored that the offered approach has attained elevated results and also effectively supports to reduction of data theft.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755727","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 fruit bee optimization algorithm-based deep convolution neural network for brain tumour classification using MRI images.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-28 DOI: 10.1080/0954898X.2025.2476079

Aynun Jarria S P, Boyed Wesley A

{"title":"Hybrid fruit bee optimization algorithm-based deep convolution neural network for brain tumour classification using MRI images.","authors":"Aynun Jarria S P, Boyed Wesley A","doi":"10.1080/0954898X.2025.2476079","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2476079","url":null,"abstract":"An accurate classification of brain tumour disease is an important function in diagnosing cancer disease. Several deep learning (DL) methods have been used to identify and categorize the tumour illness. Nevertheless, the better categorized result was not consistently obtained by the traditional DL procedures. Therefore, a superior answer to this problem is offered by the optimized DL approaches. Here, the brain tumour categorization (BTC) is done using the devised Hybrid Fruit Bee Optimization based Deep Convolution Neural Network (HFBO-based DCNN). Here, the noise in the image is removed through pre-processing using a Gaussian filter. Next, the feature extraction process is done using the SegNet and this helps to extract the relevant data from the input image. Then, the feature selection is done with the help of the HFBO algorithm. Additionally, the brain tumour classification is done by the Deep CNN, and the established HFBO algorithm is used to train the weight. The devised model is analysed using the testing accuracy, sensitivity, and specificity and produced the values of 0.926, 0.926, and 0.931, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-23"},"PeriodicalIF":1.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733223","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.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub 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":"https://doi.org/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":"1-55"},"PeriodicalIF":1.1,"publicationDate":"2025-03-27","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

New delay-dependent uniform stability criteria for fractional-order BAM neural networks with discrete and distributed delays.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-26 DOI: 10.1080/0954898X.2024.2448534

Shafiya Muthu

引用次数: 0

Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-24 DOI: 10.1080/0954898X.2025.2472626

Sameera Kuppam, Swarnalatha Purushotham

{"title":"Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards.","authors":"Sameera Kuppam, Swarnalatha Purushotham","doi":"10.1080/0954898X.2025.2472626","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2472626","url":null,"abstract":"Plant diseases significantly threaten food security by reducing the quantity and quality of agricultural products. This paper presents a deep learning approach for classifying foliar diseases in apple plants using the Tunicate Swarm Sine Cosine Algorithm-based Deep Residual Network (TSSCA-based DRN). Cluster heads in simulated Internet of Things (IoT) networks are selected by Fractional Lion Optimization (FLION), and images are pre-processed with a Gaussian filter and segmented using the DeepJoint model. The TSSCA, combining the Tunicate Swarm Algorithm (TSA) and Sine Cosine Algorithm (SCA), enhances the classifier's effectiveness. Moreover, Plant Pathology 2020 - FGVC7 dataset is used in this work. This dataset is designed for the classification of foliar diseases in apple trees. The TSSCA-based DRN outperforms other methods, achieving 97% accuracy, 94.666% specificity, 96.888% sensitivity, and 0.0442J maximal energy, with significant improvements over existing approaches. Additionally, the proposed model demonstrates superior accuracy, outperforming other methods by 8.97%, 6.58%, 2.07%, 1.71%, 1.14%, 1.07%, 0.93%, and 0.64% over Multidimensional Feature Compensation Residual neural network (MDFC - ResNet), Convolutional Neural Network (CNN), Multi-Context Fusion Network (MCFN), Advanced Segmented Dimension Extraction (ASDE), and DRN, fuzzy deep convolutional neural network (FCDCNN), ResNet9-SE, Capsule Neural Network (CapsNet), IoT-based scrutinizing model, and Multi-Model Fusion Network (MMF-Net).","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694661","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

Parallel convolutional SpinalNet: A hybrid deep learning approach for breast cancer detection using mammogram images.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-24 DOI: 10.1080/0954898X.2025.2480299

Vinay Gautam, Anu Saini, Alok Misra, Naresh Kumar Trivedi, Shikha Maheshwari, Raj Gaurang Tiwari

{"title":"Parallel convolutional SpinalNet: A hybrid deep learning approach for breast cancer detection using mammogram images.","authors":"Vinay Gautam, Anu Saini, Alok Misra, Naresh Kumar Trivedi, Shikha Maheshwari, Raj Gaurang Tiwari","doi":"10.1080/0954898X.2025.2480299","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2480299","url":null,"abstract":"Breast cancer is the foremost cause of mortality among females. Early diagnosis of a disease is necessary to avoid breast cancer by reducing the death rate and offering a better life to the individuals. Therefore, this work proposes a Parallel Convolutional SpinalNet (PConv-SpinalNet) for the efficient detection of breast cancer using mammogram images. At first, the input image is pre-processed using the Gabor filter. The tumour segmentation is conducted using LadderNet. Then, the segmented tumour samples are augmented using Image manipulation, Image erasing, and Image mix techniques. After that, the essential features, like CNN features, Texton, Local Gabor binary patterns (LGBP), scale-invariant feature transform (SIFT), and Local Monotonic Pattern (LMP) with discrete cosine transform (DCT) are extracted in the feature extraction phase. Finally, the detection of breast cancer is performed using PConv-SpinalNet. PConv-SpinalNet is developed by an integration of Parallel Convolutional Neural Networks (PCNN) and SpinalNet. The evaluation results show that PConv-SpinalNet accomplished a superior range of accuracy as 88.5%, True Positive Rate (TPR) as 89.7%, True Negative Rate (TNR) as 90.7%, Positive Predictive Value (PPV) as 91.3%, and Negative Predictive Value (NPV) as 92.5%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694662","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

HUNHODRL: Energy efficient resource distribution in a cloud environment using hybrid optimized deep reinforcement model with HunterPlus scheduler.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-24 DOI: 10.1080/0954898X.2025.2480294

Senthilkumar Chellamuthu, Kalaivani Ramanathan, Rajesh Arivanandhan

{"title":"HUNHODRL: Energy efficient resource distribution in a cloud environment using hybrid optimized deep reinforcement model with HunterPlus scheduler.","authors":"Senthilkumar Chellamuthu, Kalaivani Ramanathan, Rajesh Arivanandhan","doi":"10.1080/0954898X.2025.2480294","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2480294","url":null,"abstract":"This study aims to enhance the educational security and legitimacy by overcoming the problem of real-time student signature verification. The issue is raised from the growing issue about identity theft and academic fraud in schools, which compromises the validity of tests and other academic evaluations. To overcome these problems, the paper presents a deep learning-based method for signature verification made possible by employing the cutting-edge Convolutional Neural Networks (CNNs). The proposed method utilizes a VGG19 architecture trained and adjusted to handle the unique characteristics of student signatures. Initially, the procedure is pre-processing the image, after the key signature features are extracted. After passing these characteristics across VGG19 network, the signature's authenticity is classified as either unreliable or malicious nodes. The proposed method offers a flexibility and scalability for various educational settings with its capacity to manage both batch and individual processing. The model's efficacy is demonstrated by experiment with accuracy, precision, and recall values, which surpasses the existing techniques. The method ensures dependable performance under circumstances by illustrating resilience to several kinds of noise and distortion. The proposed deep learning model results pay a way for addressing the issue of student signature verification, enhancing the academic institutions' security and legitimacy.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-26"},"PeriodicalIF":1.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694660","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 multi-objective function for deep learning-based automatic energy efficiency power allocation in multicarrier noma system using hybrid heuristic improvement.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-13 DOI: 10.1080/0954898X.2025.2461046

Chiranjeevi Thokala, Pradnya H Ghare

{"title":"A multi-objective function for deep learning-based automatic energy efficiency power allocation in multicarrier noma system using hybrid heuristic improvement.","authors":"Chiranjeevi Thokala, Pradnya H Ghare","doi":"10.1080/0954898X.2025.2461046","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2461046","url":null,"abstract":"Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance. This research suggested EE for the Multi-Carrier NOMA (MC-NOMA) models by optimization algorithm. The main scope of this research tends to improve the EE by Hybrid of Sewing Training and Lemur Optimization for optimizing the system parameters. The improvement made in this developed HSTLO algorithm can provide significant impact on MC-NOMA system, which it renders better user capacity while effectively optimizing the system parameters. Moreover, the Dilated Dense Recurrent Neural Network (DDRNN) model is developed. Employing the improvement in the deep learning model for the MC-NOMA system could effectively manage and enhance the system performance. Considering the DDRNN model can leverage to provide better generalization outcomes in different network scenarios that ensures to provide fast and reliable solutions compared to existing methods. Addressing the energy consumption problems in this research study will be analysed to show the advancement in MC-NOMA system that help to enhance the system performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-32"},"PeriodicalIF":1.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617226","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

Improved bounding box segmentation technique for crowd anomaly detection with optimal trained convolutional neural network.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-03-12 DOI: 10.1080/0954898X.2025.2475070

Rohini P S, Sowmy I

{"title":"Improved bounding box segmentation technique for crowd anomaly detection with optimal trained convolutional neural network.","authors":"Rohini P S, Sowmy I","doi":"10.1080/0954898X.2025.2475070","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2475070","url":null,"abstract":"A crucial role in many security and surveillance applications is crowd anomaly detection, where seeing unusual activity helps avert possible threats or interruptions. For precise anomaly identification, current models might not successfully incorporate spatial and temporal features. To overcome these drawbacks, a novel Crowd Anomaly Detection based on Opposition Behavior Learning updated Chimp Optimization Algorithm (CAD-OBLChoA) is proposed in this research to enhance the detection of abnormal crowd behaviours in dynamic environments. In this research, bilateral filtering is used for smoothening the image and reducing noise for preprocessing phase. For object detection, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based bounding box approach is used. Then, features like Colour features, Shape features, and Improved Texture features are extracted. Finally, the anomalies get detected based on the trained extracted feature set in the system. For this, an optimized CNN is used, where training is done by the OBLChoA scheme via tuning the optimal weights. The proposed CAD-OBLChoA scheme achieved a higher specificity of about 0.924 and 0.931 in the 90% training data for datasets 1 and 2. This approach could significantly improve crowd monitoring and security, enabling faster identification of potential threats or emergencies.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-54"},"PeriodicalIF":1.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607155","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