Thresholding And Quantization Algorithms for Image Compression Techniques: A Review

Lashari, Saima Anwar; Ibrahim, Rosziati; Taujuddin, Nik Shahidah Afifi; Senan, Norhalina; Sari, Suhaila

doi:10.17576/apjitm-2018-0701-07

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Genetic Deep Learning Convolutional Neural Network (GDCNN) [21] , [22] designed an approach to predict COVID-19 with a partial swam intelligent optimization model and huddle particle swarm optimization. Numerous techniques and methodologies were proposed in the literature study for personal data prevention ( [23] ; Mhayuddin et al., 2020; [24] ). Statistical data collaboration and indicating the cause of information breaches are challenging.…”

Section: Literature Studymentioning

confidence: 99%

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

Dhasarathan

Hasan

Islam

et al. 2023

Computer Communications

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Section: Literature Studymentioning

confidence: 99%

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

Dhasarathan

Hasan

Islam

et al. 2023

Computer Communications

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“…This paper evaluates the contribution of different anomaly detection methods using three commonly used evaluation indicators that have been extensively used in earlier anomaly detection methods [18], [69], [70], [71], [72],and [73].…”

Section: B Evaluation Indicatorsmentioning

confidence: 99%

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

et al. 2022

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A significant aspect of today's digital information is attributed networks, which combine multiple node attributes with the basic network topology in order to extract knowledge. Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in a number of high-impact areas. The majority of current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method. These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. However, they suffer from the problem of ignoring the latent codes' embedding distribution, which results in poor representation in many instances. In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, similar to vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. An adversarial mechanism makes the encoder make more accurate estimates of how potential features might be distributed. As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. Lastly, the Gaussian Mixture Model, a distinct estimation network, is used to approximate the latent vector density, resulting in the detection of anomalies from measuring sample energy. They are trained jointly as an end-to-end framework. DVAEGMM helps in the simultaneous optimization of the mixture model, generative adversarial network, and variational autoencoder parameters. The joint optimization balances the reconstruction probability, the latent representation density approximation, and regularization. Extensive experiments on attributed networks prove that DVAEGMM significantly beats the existing methods, proving the efficiency of the presented approach. The AUC scores of our proposed framework for the BlogCatalog, Flickr, Enron, and Amazon datasets are 0.89380, 0.87130, 0.72480, and 0.75102, respectively.

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“…The next largest amount of variation is defined by the succeeding principal component, and this is orthogonal/ independent to the principal component that precedes it. The main advantage of PCA is that it reduces the redundancy of data [33], [40][41][42][43][44][45]. However, the disadvantage of PCA-based change detection is that it cannot provide complete change information but requires the threshold of the image to identify the changes that occurred in the area.…”

Section: ) Principal Component Analysis (Pca)mentioning

confidence: 99%

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

et al. 2022

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Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial changes due to natural phenomena and/or human-induced activities. Mango is a major fruit crop in India, but detection of changes in mango crops remains a challenging task because the reason that many perennial crops have similar reflectance profiles. Therefore, a potent change detection technique is required for different applications such as the rate of deforestation, urban developments, damage evaluation, and resource monitoring. Compared to annual and seasonal crops, relatively few studies have been conducted on change detection in perennial fruit crops. In this study, a novel change detection technique (i.e., LR-PCA) is developed using the fusion of log-ratio (LR) and principal component analysis (PCA) images derived from bi-temporal soil adjusted vegetation index (SAVI) images to extract meaningful information and detect temporal changes in the mango fruit crop areas with high change detection accuracy. The proposed approach comprised two steps: (1) SAVI images from 2015 and 2019 were used to retrieve the log-ratio (LR) and principal component (PC) images, respectively, and both the images were fused by applying the pixel-by-pixel fusion approach. (2) Fused images were classified into three classes: "positive change", "no change", and "negative change" using a derived threshold value. The results show that the LR-PCA method of change detection yields a high change detection accuracy of 92% in comparison with the other change detection methods viz. vegetation image differencing, image ratioing, PCA, and log-ratio. To validate the adaptability of the proposed algorithm, experiments with two sets of bi-temporal SAVI indices images belonging to the Sitapur district of Uttar Pradesh State determine that the proposed change detection method performs well as compared to the existing individual methods for detection of changes in mango fruit crop. The proposed method is expected to be useful for detecting changes in the area of perennial crops. In the future, an accurate and efficient change detection analysis may be helpful for developing a real-time mango fruit crop monitoring system at the national level.INDEX TERMS Landsat 8 OLI, change detection, log ratio, principal component analysis, mango crop.

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Thresholding And Quantization Algorithms for Image Compression Techniques: A Review

Cited by 4 publications

References 22 publications

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

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