An optimized image watermarking technique based on LU factorization and entropy analysis in combination with lifting wavelet transform and discrete cosine transform is presented in this paper. At first, the original image is decomposed by a 2-level lifting wavelet transform for obtaining the coefficients of a high-frequency subband followed by discrete cosine transform. Afterward, non-overlapping blocks are obtained by dividing the coefficients of discrete cosine transform whereas LU factorization is applied to each nonoverlapping blocks based on pseudo-random sequences. Then, the watermark is embedded into the first row, the first column element of the upper triangular matrix of LU factorization. The normalized cross-correlation (NC), and the peak signal-to-noise ratio (PSNR) are used to evaluate the invisibility and robustness of the presented technique. The experimental results have indicated that the presented technique fulfills all watermarking requirements in terms of invisibility, robustness, security, and capacity. The comparison with the existing scheme has shown that the proposed watermarking technique has a superior performance in terms of invisibility than the existing scheme.

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ABSTRACT:- The use of technologies from many fields, such as mass spectrometry, next-generation sequencing, or image processing, is common in experiments in the life sciences. Complex scripts are frequently used to govern data flow, data transformation, and statistical analysis when passing data between such tools. Such scripts not only tend to be platform dependant, but also tend to expand as the experiment goes on and are rarely clearly documented, which makes the experiment harder to reproduce. Workflow systems like KNIME Analytics Platform, which offers a platform for graphically linking tools and ensures the same results across various operating systems, aim to address these issues. systems that are frequently employed in the biological sciences and describe how they compare and contrast with KNIME. KNIME is an open source program that enables programmers and scientists to share their own extensions with the scientific community. The unified data model of KNIME allows for interoperability, and we describe a few additions from the life sciences that make it easier to explore, analyze, and visualize data. In addition, we mention additional workflow. According to the American Cancer Society, skin cancer is the most prevalent form of malignancy in humans. It is typically identified visually, with first clinical screenings, dermoscopic (skin-related) analysis, a biopsy, and histological examinations as potential follow-up steps. Errors (mutations) in the DNA of skin cells are the cause of skin cancer. The cells proliferate out of control and aggregate into a mass of cancer cells as a result of the mutations. In this paper, convolutional neural networks are used to attempt to categorize photos of skin lesions. The deep neural networks demonstrate enormous potential for classifying images while taking into account the extreme environmental heterogeneity. Due to the current state of technology, it is imperative to use machines rather than people to address the widespread problem of skin cancer. One of the best ways to address skin cancer issues is deep learning. Huge data, virtual reality, augmented reality, and miniservices are all used in the new research area of deep learning in contemporary technology. The advent of powerful arithmetic capabilities enabled deep learning applications using Mobile net (CNN) to revolutionize image classification. The various forms of skin cancer can be categorized using deep learning. Transfer Learning was used during the training on many models. The model's best level of accuracy was over 77.333 %. To guarantee the validity and reproducibility of the aforementioned result, the dataset employed is openly accessible.

Keywords: Artificial Intelligence (AI), Convolutional Neural Networks (CNNs), Deep Learning (DL), Skin Cancer, Image Classification, Knime Analytics Platform Software etc.

Abstract

    The petrophysical analysis plays a significant role in determining the physical properties of reservoir rocks such as volume of shale, porosity and water saturation which in turn are  key factors in identifying the hydrocarbon zone possibilities (net pay). The study was conducted on Belhedan field which located in Sirt Basin. The targeted aria was the Gargaf formation, on which five wells were utilized for the investigation. The logging data studied comprises of gamma ray to determine volume of shale, density log, neutron log, whereas for porosity- and water saturation determination sonic log and resistivity were applied respectively. The results and data derived from the different logs of the current research work were analyzed by using Techlog Software2015 application. Based on the results derived from the software mentioned above the volume of shale for the five wells was calculated to be between (6.8-25.4 %), whereas average porosity was found to be between (8.5- 17%). Finally, water saturation and net pay thickness were computed to be  between (9.6- 49.6%)  and (18.5- 183.5 ft) respectively.

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Abstract  

In some engineering applications soil stiffness only could not be a reliable parameter in accordance with specific standardizations especially, when

dealing with dynamic loading. In other words, foundation soil should be altered regarding its ductility in order to prevent sudden damage due to brittleness. Plenty of researchers have studied the effect of multifilament polypropylene fibers on soil reinforcement. Besides, the alkaline activation method has been adapted to alter the strength properties of soft soil. Furthermore, in a cutting edge method, alkaline activation of soft soil, using fly ash and Potassium hydroxide, coupled with soil reinforcement was adapted to change the post-peak behavior of soft soil. The goal of this novel technique is to increase the ultimate strength and to enhance the failure mode. In this research work alkali activated kaolin soil and alkali-activated reinforced kaolin soil were cured for 28 and 90 days respectively. Compressive stress tests were conducted on both mixtures, namely, SF40 ( Soil +40% fly ash) and SFR0.75 ( Soil+ 39.7% fly ash + 0.75% PP fibers) samples. Results drawn from the tests revealed a drastic increase in Compressive strength for SF40 samples cured for 28 and 90 days, namely, 3680 kPa and 18500 kPa, respectively. Whereas, the strength recorded by the control sample was only 190 kPa. Though a sharp drop was seen when approaching failure. The addition of reinforcing Polypropylene fibers brought about a drastic enhancement in failure mode for both of curing regimes.

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