A New V-Net Convolutional Neural Network Based on Four-Dimensional Hyperchaotic System for Medical Image Encryption

Wang, Xiaowei; Yin, Shoulin; Shafiq, Muhammad; Laghari, Asif Ali; Karim, Shahid; Cheikhrouhou, Omar; Alhakami, Wajdi; Hamam, Habib

doi:10.1155/2022/4260804

Cited by 16 publications

(11 citation statements)

References 29 publications

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“…GBDT performs optimization in function space that results in flexible use of custom loss function. In addition, boosting is computationally efficient as compared to deep learning [46][47][48]. Figure 2 presents the precision results of the classifiers.…”

Section: Resultsmentioning

confidence: 99%

Empirical Analysis of Machine Learning Algorithms for Multiclass Prediction

Ishfaq

Shabbir

Khan

et al. 2022

Wireless Communications and Mobile Computing

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With the emergence of big data and the interest in deriving valuable insights from ever-growing and ever-changing streams of data, machine learning has appeared as an effective data analytic technique as compared to traditional methodologies. Big data has become a source of incredible business value for almost every industry. In this context, machine learning plays an indispensable role of providing smart data analysis capabilities for uncovering hidden patterns. These patterns are later translated into automating certain aspects of the decision-making processes using machine learning classifiers. This paper presents a state-of-the-art comparative analysis of machine learning and deep learning-based classifiers for multiclass prediction. The experimental setup consisted of 11 datasets derived from different domains, publicly available at the repositories of UCI and Kaggle. The classifiers include Naïve Bayes (NB), decision trees (DTs), random forest (RF), gradient boosted decision trees (GBDTs), and deep learning-based convolutional neural networks (CNN). The results prove that the ensemble-based GBDTs outperform other algorithms in terms of accuracy, precision, and recall. RF and CNN show nearly similar performance on most datasets and outperform the traditional NB and DTs. On the other hand, NB shows the lowest performance as compared to other algorithms. It is worth mentioning that DTs show the lowest precision score on the Titanic dataset. One of the main reasons is that DTs suffer from overfitting and use a greedy approach for attribute relationship analysis.

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Section: Resultsmentioning

confidence: 99%

Empirical Analysis of Machine Learning Algorithms for Multiclass Prediction

Ishfaq

Shabbir

Khan

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…In [ 19 ], the authors presented a V-net convolution neural networks (CNNs) model relying on the 4D hyperchaotic method to encode medicinal images. At first, the plaintext medicinal image deal with 4D hyperchaotic sequence image, including pseudorandom sequence generation, image segmentation, and chaotic system processing.…”

Section: Literature Surveymentioning

confidence: 99%

Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

Geetha

Mani

Mayuri

et al. 2022

Computational Intelligence and Neuroscience

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Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an open medium, the design of secure encryption algorithms becomes essential. Encryption can be considered one of the effective solutions for accomplishing security. Although numerous models have existed in the literature, they could not adaptable to the rising number of medicinal images in the health sector. At the same time, the optimal key generation process acts as a vital part in defining the performance of the encryption techniques. Therefore, this article presents a Pigeon Inspired Optimization with Encryption-based Secure Medical Image Management (PIOE-SMIM) technique. The proposed PIOE-SMIM approach majorly concentrates on the development of secret share creation (SSC) and the encryption process. At the initial stage, the medical images are converted into a collection of 12 shares using the SSC approach. In addition, an elliptic curve cryptography (ECC) scheme is employed for the encryption process. In order to optimum key creation procedure in the ECC model, the PIO technique is exploited with the aim of maximizing PSNR. Finally, on the receiver side, the decryption and share reconstruction processes are performed to construct the original images. The PIOE-SMIM model displayed an enhanced PSNR of 59.37 dB in image 1. Improved PSNR of 59.53 dB is given for image 5 using the PIOE-SMIM model. For demonstrating an enhanced performance of the PIOE-SMIM method, a widespread experimental study is made and the results highlighted the supremacy of the PIOE-SMIM model over other techniques.

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“…After preprocessing of physiological signals, features are extracted from cleaned physiological signals for classification of signals into four classes (i.e., HAHV, HALV, LAHV, LALV). It has been observed that different researchers extracted different domains of hand-crafted features from preprocessed physiological signals [ 4 , 8 , 11 , 12 , 19 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Garg et al [ 3 ] extracted two features, i.e., normalized wavelet energy (NWE) and band-power (NBP), and from the decomposed signals of EEG using Fourier and wavelet transform, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

Awan

Usman

Khalid

et al. 2022

Sensors

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Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.

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A New V-Net Convolutional Neural Network Based on Four-Dimensional Hyperchaotic System for Medical Image Encryption

Cited by 16 publications

References 29 publications

Empirical Analysis of Machine Learning Algorithms for Multiclass Prediction

Empirical Analysis of Machine Learning Algorithms for Multiclass Prediction

Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

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