Neural Computing & Applications最新文献_第4页

MVDroid: an android malicious VPN detector using neural networks. MVDroid：一个使用神经网络的安卓恶意VPN检测器。

IF 4.5 3区计算机科学

Neural Computing & Applications Pub Date : 2023-04-03 DOI: 10.1007/s00521-023-08512-1

Saeed Seraj, Siavash Khodambashi, Michalis Pavlidis, Nikolaos Polatidis

{"title":"MVDroid: an android malicious VPN detector using neural networks.","authors":"Saeed Seraj, Siavash Khodambashi, Michalis Pavlidis, Nikolaos Polatidis","doi":"10.1007/s00521-023-08512-1","DOIUrl":"10.1007/s00521-023-08512-1","url":null,"abstract":"The majority of Virtual Private Networks (VPNs) fail when it comes to protecting our privacy. If we are using a VPN to protect our online privacy, many of the well-known VPNs are not secure to use. When examined closely, VPNs can appear to be perfect on the surface but still be a complete privacy and security disaster. Some VPNs will steal our bandwidth, infect our computers with malware, install secret tracking libraries on our devices, steal our personal data, and leave our data exposed to third parties. Generally, Android users should be cautious when installing any VPN software on their devices. As a result, it is important to identify malicious VPNs before downloading and installing them on our Android devices. This paper provides an optimised deep learning neural network for identifying fake VPNs, and VPNs infected by malware based on the permissions of the apps, as well as a novel dataset of malicious and benign Android VPNs. Experimental results indicate that our proposed classifier identifies malicious VPNs with high accuracy, while it outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-11"},"PeriodicalIF":4.5,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9688573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning based multipurpose medical image watermarking. 基于机器学习的多用途医学图像水印。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-03-24 DOI: 10.1007/s00521-023-08457-5

Rishi Sinhal, Irshad Ahmad Ansari

{"title":"Machine learning based multipurpose medical image watermarking.","authors":"Rishi Sinhal, Irshad Ahmad Ansari","doi":"10.1007/s00521-023-08457-5","DOIUrl":"10.1007/s00521-023-08457-5","url":null,"abstract":"Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-22"},"PeriodicalIF":6.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9717268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Outlier-resistant variance-constrained $mathit{H}_{infty }$ state estimation for time-varying recurrent neural networks with randomly occurring deception attacks 随机欺骗攻击时变递归神经网络的抗离群方差约束$mathit{H}_{infty }$状态估计

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-03-12 DOI: 10.1007/s00521-023-08419-x

Yan Gao, Jun Hu, Huijun Yu, Junhua Du, Chaoqing Jia

引用次数: 0

Comparative study of three quantum-inspired optimization algorithms for robust tuning of power system stabilizers 电力系统稳定器鲁棒调谐的三种量子优化算法的比较研究

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-03-09 DOI: 10.1007/s00521-023-08429-9

R. N. D. C. Filho

引用次数: 1

A new robust Harris Hawk optimization algorithm for large quadratic assignment problems 大型二次分配问题的一种新的鲁棒Harris Hawk优化算法

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-03-02 DOI: 10.1007/s00521-023-08387-2

Tansel Dökeroglu, Y. Özdemir

引用次数: 1

Adaptive dual niching-based differential evolution with resource reallocation for nonlinear equation systems 非线性方程系统资源再分配的自适应双生态位微分进化

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-02-10 DOI: 10.1007/s00521-023-08330-5

Shuijia Li, Wenyin Gong, Qiong Gu, Zuowen Liao

引用次数: 1

Real-time automated detection of older adults' hand gestures in home and clinical settings. 实时自动检测老年人的手势在家庭和临床设置。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-08090-8

Guan Huang, Son N Tran, Quan Bai, Jane Alty

{"title":"Real-time automated detection of older adults' hand gestures in home and clinical settings.","authors":"Guan Huang, Son N Tran, Quan Bai, Jane Alty","doi":"10.1007/s00521-022-08090-8","DOIUrl":"https://doi.org/10.1007/s00521-022-08090-8","url":null,"abstract":"There is an urgent need, accelerated by the COVID-19 pandemic, for methods that allow clinicians and neuroscientists to remotely evaluate hand movements. This would help detect and monitor degenerative brain disorders that are particularly prevalent in older adults. With the wide accessibility of computer cameras, a vision-based real-time hand gesture detection method would facilitate online assessments in home and clinical settings. However, motion blur is one of the most challenging problems in the fast-moving hands data collection. The objective of this study was to develop a computer vision-based method that accurately detects older adults' hand gestures using video data collected in real-life settings. We invited adults over 50 years old to complete validated hand movement tests (fast finger tapping and hand opening-closing) at home or in clinic. Data were collected without researcher supervision via a website programme using standard laptop and desktop cameras. We processed and labelled images, split the data into training, validation and testing, respectively, and then analysed how well different network structures detected hand gestures. We recruited 1,900 adults (age range 50-90 years) as part of the TAS Test project and developed UTAS7k-a new dataset of 7071 hand gesture images, split 4:1 into clear: motion-blurred images. Our new network, RGRNet, achieved 0.782 mean average precision (mAP) on clear images, outperforming the state-of-the-art network structure (YOLOV5-P6, mAP 0.776), and mAP 0.771 on blurred images. A new robust real-time automated network that detects static gestures from a single camera, RGRNet, and a new database comprising the largest range of individual hands, UTAS7k, both show strong potential for medical and research applications.Supplementary information: The online version contains supplementary material available at 10.1007/s00521-022-08090-8.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 11","pages":"8143-8156"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9212557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating deep learning predictions for COVID-19 from X-ray images using leave-one-out predictive densities. 利用留一预测密度评估x射线图像对COVID-19的深度学习预测。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08219-3

Sergio Hernández, Xaviera López-Córtes

{"title":"Evaluating deep learning predictions for COVID-19 from X-ray images using leave-one-out predictive densities.","authors":"Sergio Hernández, Xaviera López-Córtes","doi":"10.1007/s00521-023-08219-3","DOIUrl":"https://doi.org/10.1007/s00521-023-08219-3","url":null,"abstract":"Early detection of the COVID-19 virus is an important task for controlling the spread of the pandemic. Imaging techniques such as chest X-ray are relatively inexpensive and accessible, but its interpretation requires expert knowledge to evaluate the disease severity. Several approaches for automatic COVID-19 detection using deep learning techniques have been proposed. While most approaches show high accuracy on the COVID-19 detection task, there is not enough evidence on external evaluation for this technique. Furthermore, data scarcity and sampling biases make difficult to properly evaluate model predictions. In this paper, we propose stochastic gradient Langevin dynamics (SGLD) to take into account the model uncertainty. Four different deep learning architectures are trained using SGLD and compared to their baselines using stochastic gradient descent. The model uncertainties are also evaluated according to their convergence properties and the leave-one-out predictive densities. The proposed approach is able to reduce overconfidence of the baseline estimators while also retaining predictive accuracy for the best-performing cases.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 13","pages":"9819-9830"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9330144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robust thermal infrared tracking via an adaptively multi-feature fusion model. 通过自适应多特征融合模型实现稳健的热红外跟踪。

IF 4.5 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2022-10-12 DOI: 10.1007/s00521-022-07867-1

Di Yuan, Xiu Shu, Qiao Liu, Xinming Zhang, Zhenyu He

{"title":"Robust thermal infrared tracking via an adaptively multi-feature fusion model.","authors":"Di Yuan, Xiu Shu, Qiao Liu, Xinming Zhang, Zhenyu He","doi":"10.1007/s00521-022-07867-1","DOIUrl":"10.1007/s00521-022-07867-1","url":null,"abstract":"When dealing with complex thermal infrared (TIR) tracking scenarios, the single category feature is not sufficient to portray the appearance of the target, which drastically affects the accuracy of the TIR target tracking method. In order to address these problems, we propose an adaptively multi-feature fusion model (AMFT) for the TIR tracking task. Specifically, our AMFT tracking method adaptively integrates hand-crafted features and deep convolutional neural network (CNN) features. In order to accurately locate the target position, it takes advantage of the complementarity between different features. Additionally, the model is updated using a simple but effective model update strategy to adapt to changes in the target during tracking. In addition, a simple but effective model update strategy is adopted to adapt the model to the changes of the target during the tracking process. We have shown through ablation studies that the adaptively multi-feature fusion model in our AMFT tracking method is very effective. Our AMFT tracker performs favorably on PTB-TIR and LSOTB-TIR benchmarks compared with state-of-the-art trackers.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 4","pages":"3423-3434"},"PeriodicalIF":4.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10630224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. 球形模糊环境下基于SWARA和MARCOS的道路安全评价与风险排序

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07929-4

Saeid Jafarzadeh Ghoushchi, Sina Shaffiee Haghshenas, Ali Memarpour Ghiaci, Giuseppe Guido, Alessandro Vitale

{"title":"Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment.","authors":"Saeid Jafarzadeh Ghoushchi, Sina Shaffiee Haghshenas, Ali Memarpour Ghiaci, Giuseppe Guido, Alessandro Vitale","doi":"10.1007/s00521-022-07929-4","DOIUrl":"https://doi.org/10.1007/s00521-022-07929-4","url":null,"abstract":"There are a lot of elements that make road safety assessment situations unpredictable and hard to understand. This could put people's lives in danger, hurt the mental health of a society, and cause permanent financial and human losses. Due to the ambiguity and uncertainty of the risk assessment process, a multi-criteria decision-making technique for dealing with complex systems that involves choosing one of many options is an important strategy of assessing road safety. In this study, an integrated stepwise weight assessment ratio analysis (SWARA) with measurement of alternatives and ranking according to compromise solution (MARCOS) approach under a spherical fuzzy (SF) set was considered. Then, the proposed methodology was applied to develop the approach of failure mode and effect analysis (FMEA) for rural roads in Cosenza, southern Italy. Also, the results of modified FMEA by SF-SWARA-MARCOS were compared with the results of conventional FMEA. The risk score results demonstrated that the source of risk (human) plays a significant role in crashes compared to other sources of risk. The two risks, including landslides and floods, had the lowest values among the factors affecting rural road safety in Calabria, respectively. The correlation between scenario outcomes and main ranking orders in weight values was also investigated. This study was done in line with the goals of sustainable development and the goal of sustainable mobility, which was to find risks and lower the number of accidents on the road. As a result, it is thus essential to reconsider laws and measures necessary to reduce human risks on the regional road network of Calabria to improve road safety.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 6","pages":"4549-4567"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10690821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27