Network-Computation in Neural Systems最新文献_第8页

Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet. 使用基于区块链的轻量级深度实例检测网络，在生物识别多模态多融合中提高云用户身份验证的安全性和隐私性。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-31 DOI: 10.1080/0954898X.2024.2304707

Selvarani Poomalai, Keerthika Venkatesan, Surendran Subbaraj, Sundar Radha

{"title":"Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet.","authors":"Selvarani Poomalai, Keerthika Venkatesan, Surendran Subbaraj, Sundar Radha","doi":"10.1080/0954898X.2024.2304707","DOIUrl":"10.1080/0954898X.2024.2304707","url":null,"abstract":"This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based learning-based DetectNet-(IL-DN), privacy-preserving techniques, and blockchain technology. Motivated by the escalating need for robust authentication methods in the face of evolving cyber threats, the research aims to overcome the struggle between accuracy and user privacy inherent in current authentication methods. The proposed system swiftly and accurately identifies users using multimodal biometric data through IL-DN. To address privacy concerns, advanced techniques are employed to encode biometric data, ensuring user privacy. Additionally, the system utilizes blockchain technology to establish a decentralized, tamper-proof, and transparent authentication system. This is reinforced by smart contracts and an enhanced Proof of Work (PoW) mechanism. The research rigorously evaluates performance metrics, encompassing authentication accuracy, privacy preservation, security, and resource utilization, offering a comprehensive solution for secure and privacy-enhanced user authentication in cloud-based environments. This work significantly contributes to filling the existing research gap in this critical domain.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"300-318"},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643429","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

Retraction. 撤回。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-07-31 DOI: 10.1080/0954898X.2024.2385532

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 1.1 3区计算机科学

Network-Computation in Neural Systems Pub 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":"https://doi.org/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":"1-33"},"PeriodicalIF":1.1,"publicationDate":"2024-07-31","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

A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation. 基于四重目标的云隐私保护模型，建议关联规则隐藏和深度学习辅助最优密钥生成。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-07-26 DOI: 10.1080/0954898X.2024.2378836

Smita Sharma, Sanjay Tyagi

{"title":"A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation.","authors":"Smita Sharma, Sanjay Tyagi","doi":"10.1080/0954898X.2024.2378836","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2378836","url":null,"abstract":"Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a \"privacy preservation model in the cloud environment\". The four stages of recommended security preservation methodology are \"identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration\". Initially, owner's data enters the Sensitive data identification process. The sensitive information in the input (owner's data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-36"},"PeriodicalIF":1.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762714","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

Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN). 利用神经模糊逻辑和深度学习神经网络（NFDLNN）的独特组合，加强全球软件开发中的工作量估算。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-07-21 DOI: 10.1080/0954898X.2024.2376703

Manoj Ray Devadas, Philip Samuel

{"title":"Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN).","authors":"Manoj Ray Devadas, Philip Samuel","doi":"10.1080/0954898X.2024.2376703","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2376703","url":null,"abstract":"Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-21"},"PeriodicalIF":1.1,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735684","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

Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow. 基于蚁群优化的人工神经自适应张量流增强物联网网络安全

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-07-15 DOI: 10.1080/0954898X.2024.2336058

Vijaya Bhaskar Sadu, Kumar Abhishek, Omaia Mohammed Al-Omari, Sandhya Rani Nallola, Rajeev Kumar Sharma, Mohammad Shadab Khan

{"title":"Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow.","authors":"Vijaya Bhaskar Sadu, Kumar Abhishek, Omaia Mohammed Al-Omari, Sandhya Rani Nallola, Rajeev Kumar Sharma, Mohammad Shadab Khan","doi":"10.1080/0954898X.2024.2336058","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2336058","url":null,"abstract":"The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-17"},"PeriodicalIF":1.1,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617627","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

MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks. MLNAS：基于元学习的神经架构搜索，用于自动生成植物病害检测任务的深度神经网络。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-07-12 DOI: 10.1080/0954898X.2024.2374852

Sahil Verma, Prabhat Kumar, Jyoti Prakash Singh

{"title":"MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks.","authors":"Sahil Verma, Prabhat Kumar, Jyoti Prakash Singh","doi":"10.1080/0954898X.2024.2374852","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2374852","url":null,"abstract":"Plant diseases pose a significant threat to agricultural productivity worldwide. Convolutional neural networks (CNNs) have achieved state-of-the-art performances on several plant disease detection tasks. However, the manual development of CNN models using an exhaustive approach is a resource-intensive task. Neural Architecture Search (NAS) has emerged as an innovative paradigm that seeks to automate model generation procedures without human intervention. However, the application of NAS in plant disease detection has received limited attention. In this work, we propose a two-stage meta-learning-based neural architecture search system (ML NAS) to automate the generation of CNN models for unseen plant disease detection tasks. The first stage recommends the most suitable benchmark models for unseen plant disease detection tasks based on the prior evaluations of benchmark models on existing plant disease datasets. In the second stage, the proposed NAS operators are employed to optimize the recommended model for the target task. The experimental results showed that the MLNAS system's model outperformed state-of-the-art models on the fruit disease dataset, achieving an accuracy of 99.61%. Furthermore, the MLNAS-generated model outperformed the Progressive NAS model on the 8-class plant disease dataset, achieving an accuracy of 99.8%. Hence, the proposed MLNAS system facilitates faster model development with reduced computational costs.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-24"},"PeriodicalIF":1.1,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141592081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising. 基于深度去马赛克卷积神经网络和量子小波变换的图像去噪。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-07-11 DOI: 10.1080/0954898X.2024.2358950

Anitha Mary Chinnaiyan, Boyed Wesley Alfred Sylam

{"title":"Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising.","authors":"Anitha Mary Chinnaiyan, Boyed Wesley Alfred Sylam","doi":"10.1080/0954898X.2024.2358950","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2358950","url":null,"abstract":"Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor coupled with a Colour Filter Array (CFA). Furthermore, the demosaicking procedure is required to obtain a full-colour image. Image denoising and image demosaicking are the two important image restoration techniques, which have increased popularity in recent years. Finding a suitable strategy for multiple image restoration is critical for researchers. Hence, a deep learning (DL) based image denoising and image demosaicking is developed in this research. Moreover, the Autoregressive Circle Wave Optimization (ACWO) based Demosaicking Convolutional Neural Network (DMCNN) is designed for image demosaicking. The Quantum Wavelet Transform (QWT) is used in the image denoising process. Similarly, Quantum Wavelet Transform (QWT) is used to analyse the abrupt changes in the input image with noise. The transformed image is then subjected to a thresholding technique, which determines an appropriate threshold range. Once the threshold range has been determined, soft thresholding is applied to the resulting wavelet coefficients. After that, the extraction and reconstruction of the original image is carried out using the Inverse Quantum Wavelet Transform (IQWT). Finally, the fused image is created by combining the results of both processes using a weighted average. The denoised and demosaicked images are combined using the weighted average technique. Furthermore, the proposed QWT+DMCNN-ACWO model provided the ideal values of Peak signal-to-noise ratio (PSNR), Second derivative like measure of enhancement (SDME), Structural Similarity Index (SSIM), Figure of Merit (FOM) of 0.890, and computational time of 49.549 dB, 59.53 dB, 0.963, 0.890, and 0.571, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-25"},"PeriodicalIF":1.1,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581539","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

An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images. 改进的阿基米德优化辅助多尺度深度学习分割与扩张集合 CNN 分类法，用于利用 CT 图像检测肺癌。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-07-08 DOI: 10.1080/0954898X.2024.2373127

Shalini Chowdary, Shyamala Bharathi Purushotaman

{"title":"An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images.","authors":"Shalini Chowdary, Shyamala Bharathi Purushotaman","doi":"10.1080/0954898X.2024.2373127","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2373127","url":null,"abstract":"Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system's efficiency of detecting cancer and help the patient to get the appropriate treatment.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-39"},"PeriodicalIF":1.1,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555958","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 1.1 3区计算机科学

Network-Computation in Neural Systems Pub 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":"https://doi.org/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":"1-21"},"PeriodicalIF":1.1,"publicationDate":"2024-07-08","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