An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

DNA microarray technologies enable the analysis of the expression of numerous genes in an individual experiment and become an important approach in the field of medicine and biology for investing genetic function, regulation, and interaction. Microarray images can be investigated well for obtaining the contained genetic data. But is it undesirable to retain the genetic data and avoid the microarray images? Due to considerable attention to DNA microarray and several experiments being performed under distinct conditions, a massive quantity of data gets produced over the globe. In order to store and share the microarray images, effective storage and communication models are needed in a natural way. Vector quantization (VQ) is a commonly utilized tool for compressing images, which mainly aims to produce effective codebooks comprising a collection of codewords. Therefore, this paper presents a manta ray foraging optimization (MRFO) with Linde–Buzo–Gray (LBG) based microarray image compression (MRFOLBG-MIC) technique. The LBG model is commonly utilized to design local optimal codebooks to compress images. The construction of codebooks can be defined as a nondeterministic polynomial time (NP) hard problem and can be resolved by the MRFO algorithm. The codebooks produced from LBG-VQ are optimized using the MRFO algorithm to attain optimum optimal codebooks. When the codebooks are produced by the MRFOLBG-MIC algorithm, Deflate model can be applied to compress the index tables. The design of the MRFO algorithm with LBG and Deflate based index table compression demonstrate the novelty of the work. For demonstrating the enhanced compression efficacy of the MRFOLBG-MIC model, a wide-ranging experimental validation process is performed using a benchmark dataset. The experimental outcomes inferred that the MRFOLBG-MIC model accomplished superior outcomes over the other existing models.

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“…Table 1 provides an overall PSNR examination of the MRFOLBG-MIC model under five test images and distinct bit rates (BRs) [ 34 ].…”

Section: Performance Validationmentioning

confidence: 99%

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

Alkhaldi

Alsedais

Halawani

et al. 2022

Computational Intelligence and Neuroscience

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“…The efficiency measures for assessment of the proposed solution are the average curve, sobel edge and spectral entropy. Sowmyalakshmi et al, [4] presented the best background subtraction algorithms are defined by means of genetic algorithms, parameter background subtraction are optimised and the optimum number of pre and post-processing operations are determined. Mehmood et al, [5] Built a smart home automation system based on Cloud of Thing (CoT) model view controller (MVC).…”

Section: Related Workmentioning

confidence: 99%

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Murugan¹,

Sathyabama²

2023

Intelligent Automation &Amp; Soft Computing

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Improved picture quality is critical to the effectiveness of object recognition and tracking. The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions, such as mist, fog, dust etc. The pictures then shift in intensity, colour, polarity and consistency. A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient environments. In recent years, target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities. However, the identification of objects in the night world also poses further problems because of the distorted backdrop and dim light. The Correlation aware LSTM based YOLO (You Look Only Once) classifier method for exact object recognition and determining its properties under night vision was a major inspiration for this work. In order to create virtual target sets similar to daily environments, we employ night images as inputs; and to obtain high enhanced image using histogram based enhancement and iterative wiener filter for removing the noise in the image. The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding (AILE) and uplift linear discriminant analysis (ULDA). The region of interest mask can be segmented using the Recurrent-Phase Level set Segmentation. Finally, we use deep convolution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based (LSTM) with YOLO method for object tracking system. A range of experimental findings demonstrate that our technique achieves high average accuracy with a precision of 99.7% for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism. Our approach may therefore satisfy the true demands of night scene target detection applications. We very much believe that our method will help future research.

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Multi-surveillance detection for night surveillance using modified yolo v5 algorithm based optical communication

Pramanik,

Burade,

Sharma

2024

Journal of Optical Communications

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Night vision significantly impacts our daily visual efficiency and has more impact in communications networks. This work primarily emphasises enhancing the safety and security of individuals through advancements in night vision systems. While research on night vision is crucial for addressing contemporary social challenges, there remains a scarcity of databases tailored for deep-learning investigations in this domain. To address these challenges, this study evaluates the performance of three distinct object detection models: Yolo v3 achieving a mean average precision (mAP) of 87.9 % at 45 frames per second (FPS), Yolo v5 with a mAP of 88.7 % at 20 FPS, and modified Yolo v5 with a remarkable mAP of 95.1 % at 79 FPS. From the results, it is proven that this algorithm successfully detects classes including humans, animals, and vehicles.

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An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

Cited by 4 publications

References 23 publications

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Multi-surveillance detection for night surveillance using modified yolo v5 algorithm based optical communication

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