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In the big data era, image compression is of significant importance in today's world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods. [ABSTRACT FROM AUTHOR]

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region. [ABSTRACT FROM AUTHOR]

The release of phenolic-contaminated treated palm oil mill effluent (TPOME) poses a severe threat to human and environmental health. In this work, manganese-modified black TiO2 (Mn-B-TiO2) was produced for the photodegradation of high concentrations of total phenolic compounds from TPOME. A modified glycerol-assisted technique was used to synthesize visible-light-sensitive black TiO2 nanoparticles (NPs), which were then calcined at 300 °C for 60 min for conversion to anatase crystalline phase. The black TiO2 was further modified with manganese by utilizing a wet impregnation technique. Visible light absorption, charge carrier separation, and electron–hole pair recombination suppression were all improved when the band structure of TiO2 was tuned by producing Ti3+ defect states. As a result of the enhanced optical and electrical characteristics of black TiO2 NPs, phenolic compounds were removed from TPOME at a rate of 48.17%, which is 2.6 times higher than P25 (18%). When Mn was added to black TiO2 NPs, the Ti ion in the TiO2 lattice was replaced by Mn, causing a large redshift of the optical absorption edges and enhanced photodegradation of phenolic compounds from TPOME. The photodegradation efficiency of phenolic compounds by Mn-B-TiO2 improved to 60.12% from 48.17% at 0.3 wt% Mn doping concentration. The removal efficiency of phenolic compounds from TPOME diminished when Mn doping exceeded the optimum threshold (0.3 wt%). According to the findings, Mn-modified black TiO2 NPs are the most effective, as they combine the advantages of both black TiO2 and Mn doping. [ABSTRACT FROM AUTHOR]