A novel approach for modeling positive vectors with inverted Dirichlet-based hidden Markov models

Nasfi, Rim; Amayri, Manar; Bouguila, Nizar

doi:10.1016/j.knosys.2019.105335

Cited by 28 publications

(3 citation statements)

References 49 publications

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“…As the stream ciphered image is of size (256×256) pixels then the blocks will be of size (128×128) pixels each as fig. (A & B) [2][3]. This matrix is the first block of the final ciphered image.…”

Section: Secret Imagesmentioning

confidence: 99%

“…This system (hiding image) will be illustrated which embeds four gray-scale secret images of size (128 x 128) pixels into cover image of size (512 x 512) pixels. The techniques will be used in this work is talks about some process that we apply on these four images that I will to hide it to apply it to vector decimal to binary 8-bit [3][4][5]. Another hand, we most select this image that names cover and also apply some process by using wavelet transform until we get on anew vector decimal to binary 23-bit at this stage embedding process will have happened as coming explain this program in the research.…”

Section: Introductionmentioning

confidence: 99%

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Encryption System for Hiding Information Based on Internet of Things

Naman

Hussien

Al-Dabag

et al. 2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">One of the unexpected intelligence tactics known in World War II was to conceal the data in images that were reduced to the size of a point that was used in every text and transported in front of the enemy's eyes. In the new age, and after the expansion of Internet science and the use of the Internet worldwide, we will establish a security feature of the IOT service that will work more reliably and more effectively to deal with the Internet of Things and ensure the work of the services that the customer interacts with. A secret-key stenographic scheme that embeds four gray-scale secret size (128*128) pixel images into a size (512*512) pixel cover image in this work. Wavelet transform is the method used in this project to analyze the cover into its frequency components. In this work, combinations of steganography and cryptography were made to increase the level of safety and make the device more difficult for attackers to beat. The resulting stego-image that will be transmitted did not raise any suspicion by both objective and subjective evaluation, so the primary objective of Steganography is achieved. The proposed system was designed by using (MATLAB R2018b) and running on a Pentium-4 computer. The Internet of Things works with the encryption system for data in a synchronized manner with the technological development, and in order to maintain the stability of any Internet of things service, whether it is information signal services, visual or audio data, a remote control system, or data storage in the Internet cloud, we must focus on data preservation from internet pirates and internet system hackers. The picture Figure<strong> </strong>4 below shows the method of encryption and dealing with the Internet of things system..</p>

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Section: Secret Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Encryption System for Hiding Information Based on Internet of Things

Naman

Hussien

Al-Dabag

et al. 2021

Int. J. Interact. Mob. Technol.

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“…Some powerful machine learning algorithms are hidden Markov models (HMMs), which are commonly used in several machine learning problems [13]. HMMs have been applied successfully to speech recognition [14], face detection [15], bioinformatics [16], finance analysis [17], etc. The use of HMMs for big data applications (i.e., a high number of states and a high number of observations) is growing rapidly, which explains the focus of researchers on new methods of adapting HMMs to the big data context and improving their performance.…”

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confidence: 99%

A graph-based big data optimization approach using hidden Markov model and constraint satisfaction problem

2021

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To address the challenges of big data analytics, several works have focused on big data optimization using metaheuristics. The constraint satisfaction problem (CSP) is a fundamental concept of metaheuristics that has shown great efficiency in several fields. Hidden Markov models (HMMs) are powerful machine learning algorithms that are applied especially frequently in time series analysis. However, one issue in forecasting time series using HMMs is how to reduce the search space (state and observation space). To address this issue, we propose a graph-based big data optimization approach using a CSP to enhance the results of learning and prediction tasks of HMMs. This approach takes full advantage of both HMMs, with the richness of their algorithms, and CSPs, with their many powerful and efficient solver algorithms. To verify the validity of the model, the proposed approach is evaluated on real-world data using the mean absolute percentage error (MAPE) and other metrics as measures of the prediction accuracy. The conducted experiments show that the proposed model outperforms the conventional model. It reduces the MAPE by 0.71% and offers a particularly good trade-off between computational costs and the quality of results for large datasets. It is also competitive with benchmark models in terms of the running time and prediction accuracy. Further comparisons substantiate these experimental findings.

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