با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
A Remote Security Computational Ghost Imaging Method Based on Quantum Key Distribution Technology
یک روش تصویربرداری شبح محاسباتی امنیت از راه دور بر اساس فناوری توزیع کلید کوانتومی-2022 Computational ghost imaging (CGI) is a method of acquiring object information by measuring
light field intensity, which would be used to achieve imaging in a complicated environment. The main issue
to be addressed in CGI technology is how to achieve rapid and high-quality imaging while assuring the secure
transmission of detection data in practical distant imaging applications. In order to address the mentioned
issues, this paper proposes a remote secure CGI method based on quantum communication technology.
Using the quantum key distribution (QKD) network, the CGI system can be reconstructed while solving the
problem of information security transmission between the detector and the reconstructed computing device.
By exploring the influence of different random measurement matrices on the quality of image reconstruction,
it is found that the randomness of the numerical sequence constituting the matrix is positively correlated
with the imaging quality. Based on this discovery, a new type of quantum cryptography measurement
matrix is constructed using quantum cryptography with good randomness. In addition, through further
orthogonalization and normalization of the matrix, the matrix has both good randomness and orthogonality,
and high-quality imaging results can be obtained at a low sampling rate. The feasibility and effectiveness of
the method are verified by simulation imaging experiments. Compared with the traditional GI system, the
method proposed in this paper has higher transmission security and high-quality imaging under this premise,
which provides a new idea for the practical development of CGI technology.
INDEX TERMS: Computational ghost imaging | quantum key distribution | quantum cryptography | measurement matrix | randomness | schimidt orthogonalization. |
مقاله انگلیسی |
2 |
A Tokenless Cancellable Scheme for Multimodal Biometric Systems
طرح غیر قابل لغو برای سیستم های بیومتریک چند حالته-2021 Biometric template protection (BTP) is an open problem for biometric identity management systems. Cancellable biometrics is commonly designed to protect biometric templates with two input factors i.e., biometrics and a token used in template replacement. However, the token is often required to be kept secretly; otherwise, the protected template could be vulnerable to several security attacks and breaches of privacy. In this paper, we propose a tokenless cancellable biometrics scheme called Multimodal Extended Feature Vector (M•EFV) Hashing that employs an improved XOR encryption/decryption notion to operate on the transformation key. We stress on multimodal biometrics where the real-valued face and fingerprint vectors are fused and embedded into a binarized cancellable template. Specifically, M•EFV hashing consists of three stages of transformation: 1) normalization and bio- metric fusion; 2) randomization and binarization; and 3) cancellable template generation. To evaluate the proposed scheme, several benchmarking datasets, i.e., FVC2002, FVC2004 for fingerprint and LFW for face are used in experiments. The verification performance is vali- dated by employing the FVC matching protocol. Various attacks are simulated and analysed in the worst-case scenario. Lastly, unlinkability and revocability properties are examined experimentally.© 2021 Elsevier Ltd. All rights reserved. Keywords: Feature-level Fusion | Multimodal Biometrics | Tokenless Cancellable Biometrics | Privacy and security | XOR encryption/decryption |
مقاله انگلیسی |
3 |
Automatic Identification of Abaca Bunchy Top Disease using Deep Learning Models
121-S1877050921000132-2021 Usage of computer vision and artificial intelligence in the detection and identification of plant diseases has been explored and utilized in agricultural crops and had proven to perform efficiently. However, this disease detection and identification technology has not yet being explored and examined for some economically valuable crops like abaca and banana. This study intended to develop an automatic identification system for Abaca Bunchy Top Disease (ABTD) using different deep learning models. The study utilized a total of 3,840 petioles and petioles with leaves images taken using DSLR and mobile camera. Selected and pre- processed images were then subjected to augmentation techniques, normalization techniques, and morphometric and geometric analyses. Images were then trained using AlexNet, ZFNet, VGG16, and VGG19 architectures and the results were evaluated using Confusion Matrix in terms of accuracy, error rate, and precision. DSLR captured images on leaves and petioles with leav es showed an accuracy greater than 90% in all architectures except VGG16 with only 83% accuracy, while on mobile captured images, leaves showed above 90% accuracy compared to other groups. As to precision, DSLR captured images on petioles showed that out of four architectures, two models showed above 90% precision except for AlexNet and VGG16. However, for mobile captured images, three models showed above 90% precision using petioles image except VGG16. Furthermore, the models can be used for development of software application for detection, monitoring, and evaluation of ABTD.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the 5th International Conference on Computer Science and Computational Intelligence 2020 Keywords: Abaca Bunchy Top Disease | Computer Vision | Neural Network | Deep Learning |
مقاله انگلیسی |
4 |
ResTS: Residual Deep interpretable architecture for plant disease detection
ResTS: Residual Deep interpretable architecture for plant disease detection-2021 Recently many methods have been induced for plant disease detection by the influence of Deep Neural Networks in Computer Vision. However, the dearth of transparency in these types of research makes their acquisition in the real-world scenario less approving. We pro- pose an architecture named ResTS (Residual Teacher/Student) that can be used as visualization and a classification technique for diagnosis of the plant disease. ResTS is a tertiary adaptation of formerly suggested Teacher/Student architecture. ResTS is grounded on a Convolutional Neural Network (CNN) structure that comprises two classifiers (ResTeacher and ResStudent) and a decoder. This architecture trains both the classifiers in a reciprocal mode and the conveyed representation between ResTeacher and ResStudent is used as a proxy to envision the dominant areas in the image for categorization. The experiments have shown that the proposed structure ResTS (F1 score: 0.991) has surpassed the Tea- cher/Student architecture (F1 score: 0.972) and can yield finer visualizations of symptoms of the disease. Novel ResTS architecture incorporates the residual connections in all the constituents and it executes batch normalization after each convolution operation which is dissimilar to the formerly proposed Teacher/Student architecture for plant disease diag- nosis. Residual connections in ResTS help in preserving the gradients and circumvent the problem of vanishing or exploding gradients. In addition, batch normalization after each convolution operation aids in swift convergence and increased reliability. All test results are attained on the PlantVillage dataset comprising 54 306 images of 14 crop species.© 2021 China Agricultural University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Autoencoders | Xception | Deep Learning | Computer Vision | Agriculture |
مقاله انگلیسی |
5 |
A full-disk image standardization of the chromosphere observation at Huairou Solar Observing Station
استاندارد سازی دیسک کامل تصویر از مشاهده کروموسفر در ایستگاه مشاهده خورشیدی Huairou-2020 Observations of local features in the solar chromosphere began in 1992 at Huairou Solar Observing Station, while the full-disk chromosphere
observations were carried out since 2000. In order to facilitate researchers to use full-disk chromosphere observation, algorithms
have been developed to standardize the full-disk images. The algorithms include the determination of the center of the image
and size standardization, geometric correction and intensity normalization. The solar limb of each image is determined from a histogram
analysis of its intensity distribution. The center and radius are then calculated and the image is corrected for geometric distortions.
Images are re-scaled to have a fixed radius of 500 pixels and centered within the 1024 1024 frame. Finally, large-scale variations in
intensity, such as limb-darkening, are removed using a median filter. This paper provides a detailed description of these algorithms,
and a summary of the properties of these chromospheric full-disk observations to be used for further scientific investigations. Keywords: Chromosphere | Data standardization | Physical parameters | Big data |
مقاله انگلیسی |
6 |
Application of neural network in abnormal AIS data identification
کاربرد شبکه عصبی در شناسایی داده های غیر عادی AIS-2020 Due to human tampering, equipment failure,
channel congestion and other reasons, AIS data received by
base station may have errors. These abnormal AIS data are not
conducive to the identification and supervision of ship
navigation intention, which greatly reduces the application
value. Based on the analysis of the characteristics of the
abnormal AIS data, through preprocessing and normalization
of several adjacent AIS data, a model of the abnormal AIS
data screening based on neural network is constructed, and the
model is verified by the AIS data of the sea area near the Bohai
Bay, Chengshantou Water Area, with an accuracy of 95.16%.
At the same time, the influence of AIS data length and number
of hidden layer nodes selected in the screening model on the
accuracy rate is analyzed through experiments. The
experimental results show that unreasonable data length and
number of hidden layer nodes will reduce the accuracy rate of
the screening model. When the data length is 4 and the number
of hidden layer nodes is 6, the accuracy rate of the screening
model reaches the highest. Keywords: AIS data | abnormal data | data screening | neural network |
مقاله انگلیسی |
7 |
Application of AI for Frequency Normalization of Solar PV-Thermal Electrical Power System
کاربرد هوش مصنوعی برای عادی سازی فرکانس سیستم برق الکتریکی حرارتی خورشیدی PV-2020 Grid-connected solar-PV schemes have become
a significant part of the energy balance in the power system to
satisfy the growing request for clean, affordable energy. This
study attempts to link solar-PV generation with conventional
thermal power plants and to integrate the control zone
resulting in a hybrid solar PV-thermal electric power system
using an AC tie line. An analysis of the frequency dynamics for
varying load conditions of the interconnected system is studied.
Diverse approaches of proportional, integral, and
proportional-integral fuzzy logic built controllers are design
and tested in order to match the electric power with variable
loads of the system and hence to normalize the frequency ofthe
system in shortest possible time. A comparative analysis of the
design topologies is conducted out for the PV-Thermal scheme.
Results obtain from the implementation are shown to justify
the performance of proposed control efforts, using MATLAB
software tool. Keywords: Solar PV-Thermal electrical power system | frequency dynamics | Proportional| Integral | FLPI control |
مقاله انگلیسی |
8 |
Mental and reproductive health in multisystem youth: An in-depth qualitative approach
سلامت روان و تولید مثل در جوانان چند سیستم: یک رویکرد کیفی عمیق-2020 Background: Multisystem youth involved in both the child welfare and juvenile justice systems report elevated
rates of risky sexual behaviors, associated health problems as well as having a disproportionally higher risk for
concurrent mental health disorders.
Objective: The current study aimed to provide in-depth nuanced insights about mental and reproductive needs
and challenges of multisystem youth using a multi-informant approach (youth, parole/probation officers).
Participants and setting: Qualitative in-depth interviews were conducted with multisystem youth (N = 15;
14–17-years-old; 40% females) and parole/probation officers (N = 20; 35–67-years-old; 33% females) working
with foster care and juvenile justice systems.
Methods: Informed by Socio-Ecological Theory, analyses were conducted using Thematic Network Analysis
(TNA) and qualitative software Atlas.ti.7.
Results: Youth and parole/probation officers identified key challenges related to mental health (e.g., criminalization
of behaviors, unstandardized screening procedures), reproductive health (e.g., abnormalization of
sexual behaviors, lack of parenting and comprehensive reproductive services), and shared challenges across
systems (e.g., lack of data sharing across systems, overuse of “crisis” procedures, lack of trauma-informed
providers and services).
Conclusions: Multisystem youth have significant mental and reproductive health needs that are not adequately
met mostly due to system and institutional challenges, differences in protocols, and discontinuance of services
and treatment across systems among other problems. Mental and reproductive health disparities are key factors
in stigmatization, criminalization and further victimization of youth across systems. Multidisciplinary and multisectoral
partnerships are key to advance service, protocols and procedural gaps that address the mental and
reproductive health needs of multisystem youth. Keywords: Multisystem youth | Mental health | Reproductive health | Qualitative research | Multi-informant |
مقاله انگلیسی |
9 |
Detection of flood disaster system based on IoT, big data and convolutional deep neural network
تشخیص سیستم بحرانی سیل بر اساس اینترنت اشیا، داده های بزرگ و شبکه عصبی عمیق پیچشی-2020 Natural disasters could be defined as a blend of natural risks and vulnerabilities. Each year, natural as well as
human-instigated disasters, bring about infrastructural damages, distresses, revenue losses, injuries in addition
to huge death roll. Researchers around the globe are trying to find a unique solution to gather, store and
analyse Big Data (BD) in order to predict results related to flood based prediction system. This paper has
proposed the ideas and methods for the detection of flood disaster based on IoT, BD, and convolutional deep
neural network (CDNN) to overcome such difficulties. First, the input data is taken from the flood BD. Next,
the repeated data are reduced by using HDFS map-reduce (). After removal of repeated data, the data are
pre-processed using missing value imputation and normalization function. Then, centred on the pre-processed
data, the rule is generated by using a combination of attributes method. At the last stage, the generated rules
are provided as the input to the CDNN classifier which classifies them as a) chances for the occurrence of flood
and b) no chances for the occurrence of a flood. The outcomes obtained from the proposed CDNN method is
compared parameters like Sensitivity, Specificity, Accuracy, Precision, Recall and F-score. Moreover, when the
outcomes is compared other existing algorithms like Artificial Neural Network (ANN) & Deep Learning Neural
Network (DNN), the proposed system gives is very accurate result than other methods. Keywords: Hadoop distributed file system (HDFS) | Convolutional deep neural network (CDNN) | Normalization | Rule generation | Missing value imputation |
مقاله انگلیسی |
10 |
Classification of mechanism of reinforcement in the fiber-matrix interface: Application of Machine Learning on nanoindentation data
طبقه بندی مکانیسم تقویتی در رابط فیبر ماتریس: کاربرد یادگیری ماشین بر روی داده های nanoindentation-2020 Carbon fiber reinforced polymer manufacturing is emerging, with multiple studies to focus on the design of interfacial
reinforcement to ensure the maximum of composite properties, but also respectively to be able to
align with zero defect manufacturing. The controversy on the engineering approach is a data-driven task that
can be efficiently tackled by involving Artificial Intelligence in order to establish unbiased structure-property relations.
In the present study, nanoindentation mapping datawere processedwithMachine Learning classification
models to identify the interfacial reinforcement. The data preparation included normalization and sorting out of
highly similar datawith k-means clustering, since nanoindentation on epoxy matrix does not enhance insight on
the mechanism of reinforcement. The trained models included neural networks, classification trees, and support
vector machines. Realization of models performance was evaluated on the test dataset as screening to obtain
best fitted models for each algorithm. Transfer learning potential was demonstrated by extrapolating the prediction
of best trained models to a validation dataset at different indentation depth with support vector machines
outperforming the othermodels. Overall accuracywas 67% on the test dataset, F1 Score was 65% in the prediction
of reinforcement mechanism classes and 72% in case of pristine specimen, while accuracy on validation dataset
was 72.7%. Prediction metrics were comparable to other case studies of real-world classification problems. Computational
time-cost for tuning and training was sustainable and equal to 2.3 min. Keywords: Artificial intelligence | Machine Learning | Nanoindentation | Interface | Carbon fiber reinforced composites | Multiclass classification |
مقاله انگلیسی |