Face Image Forgery Detection by Weight Optimized Neural Network Model

doi:10.46253/j.mr.v2i2.a3

Cited by 5 publications

(2 citation statements)

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“…These techniques have been widely used, however they can only address straight difficulties, and the variables must adhere to a standard appropriation (Rajakumar, 2013). To defeat these impediments, AI models like the "Artificial Neural Networks (ANNs), Bayesian neural networks, and SVR" have been used to foresee the cost of BTC (Cristin et al, 2019). These AI approaches permit the extraction of covered up, novel designs and unprecedented data from huge information sets without requiring any earlier information about the information.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A two level ensemble classification approach to forecast bitcoin prices

Kundra

Sharma²,

Nancy³

et al. 2022

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PurposeBitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.Design/methodology/approachIn this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.FindingsThe proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.Originality/valueIn this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A two level ensemble classification approach to forecast bitcoin prices

Kundra

Sharma²,

Nancy³

et al. 2022

View full text Add to dashboard Cite

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Optimization assisted deep learning based intrusion detection system in wireless sensor network with two‐tier trust evaluation

Kagade

Jayagopalan

2022

Int J Network Mgmt

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Nowadays, owing to the openness of transmission medium, wireless sensor networks (WSNs) suffer from a variety of attacks, together with DoS attacks, tampering attacks, sinkhole attacks, and so on. Therefore, an effectual system is necessary for recognizing the intrusions in WSN. This paper aims to set up a novel intrusion detection system (IDS) via a deep learning model. Initially, optimal cluster head (CH) is selected among the sensor nodes, from which the sensor nodes that have high energy will be prioritized to act as CH. In this proposed work, the CH selection is evaluated optimally by not only considering the energy parameter, further under the constraints like delay and distance. For optimal selection, a novel approach named as self‐improved sea lion optimization (SI‐SLnO) model is introduced in this work. As per the proposed strategy, the trust of CH and nodes is evaluated based on a multidimensional two‐tier hierarchical trust model by considering content trust, honesty trust, and interactive trust. Finally, the deep learning‐based intrusion detection takes place via optimized neural network (NN), where the training is done by the proposed SI‐SLnO algorithm via the optimal weight tuning process. At last, the supremacy of the developed approach is examined via evaluation over numerous extant techniques.

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Fake news detection on social media using Adaptive Optimization based Deep Learning Approach

T S,

Sreeja

2024

Multimed Tools Appl

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Face Image Forgery Detection by Weight Optimized Neural Network Model

Cited by 5 publications

References 11 publications

A two level ensemble classification approach to forecast bitcoin prices

A two level ensemble classification approach to forecast bitcoin prices

Optimization assisted deep learning based intrusion detection system in wireless sensor network with two‐tier trust evaluation

Fake news detection on social media using Adaptive Optimization based Deep Learning Approach

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