&lt;em&gt;FbMapping&lt;/em&gt;: AN AUTOMATED SYSTEM FOR MONITORING FACEBOOK DATA

Kamal, Sarwar; Dey, Nilanjan; Ashour, Amira S.; Ripon, Shamim; Balas, Valentina E.; Kaysar, Mohammad Shibli

doi:10.14311/nnw.2017.27.002

Cited by 28 publications

(3 citation statements)

References 26 publications

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“…The correlation between the original image and the adversarial sample was assessed using 𝐿 𝑝 norms. The generally used p-norm metrics for assessing perturbation magnitude are 𝐿 0 , 𝐿 2 , and 𝑑(𝑥, 𝑥′) is a distance constraint that should be less than some value ε as represented in (4).…”

Section: E Perturbation Measurement Metricsmentioning

confidence: 99%

“…The use of deep learning changed human life in many fields. Computer vision is the field of deep learning that is increasingly used in many applications, from disease prediction [1][2][3] to automated surveillance systems [4]. The advent of many deep learning technologies has given rise to protecting computing systems from digital attacks [5].…”

Section: Introductionmentioning

confidence: 99%

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Associative Discussion Among Generating Adversarial Samples Using Evolutionary Algorithm and Samples Generated Using GAN

Pavate,

Bansode,

Srinivasu

et al. 2023

IEEE Access

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The remarkable accomplishments of deep neural networks (DNN) have led to their widespread adoption in various contexts, including safety-critical applications. Many strategies have been implemented to generate adversarial samples using DNN, raising the question of the security of the model. Adding slight magnitude noise to the input samples during training or testing can misguide DNN to produce different results than the actual one. DNNs are sensitive to indiscernible adversarial samples but readily identifiable by them. Currently, gradient-based approaches are used to generate adversarial samples. Gradient-based methods require internal details of the model, such as several parameters, model type, Etc. Usually, these details are practically unavailable, and calculating the gradient for non-differential models is impossible. In this work, we propose a novel DESapsDE framework based on evolutionary algorithms to generate adversarial samples from the probability of labels. We also incorporated the discussion with the various Generative Adversarial Networks (GANs) models, such as ACGAN, DCGAN, and SAGAN. It has been observed that GANs differ from adversarial sample generation methods and can be applied as defense mechanisms. The proposed method reduced model confidence to 13.09% for the ResNet50 model, 30.34% for the WideResNet model, and 23.1% for the DenseNet model, with an FID score of 16.45. The proposed model varies from the GAN model. It applies to attack-on-network models as a preventive major to make the model robust.

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Section: E Perturbation Measurement Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Associative Discussion Among Generating Adversarial Samples Using Evolutionary Algorithm and Samples Generated Using GAN

Pavate,

Bansode,

Srinivasu

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Literature survey has revealed that ANN could be efficiently used for weather prediction tasks [26]. In spite of immense success of ANN based models in predicting weather parameters, it suffers from a significant problem regarding the training algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Chatterjee¹,

Datta²,

Dey³

2018

NNW

Self Cite

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The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

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Big Data Analytics in Real Time for Enterprise Applications to Produce Useful Intelligence

Vats

Biswas

2023

Data Wrangling

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<em>FbMapping</em>: AN AUTOMATED SYSTEM FOR MONITORING FACEBOOK DATA

Cited by 28 publications

References 26 publications

Associative Discussion Among Generating Adversarial Samples Using Evolutionary Algorithm and Samples Generated Using GAN

Associative Discussion Among Generating Adversarial Samples Using Evolutionary Algorithm and Samples Generated Using GAN

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Big Data Analytics in Real Time for Enterprise Applications to Produce Useful Intelligence

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