Geopolymers are inorganic aluminosilicate polymers that solidify into ceramic-like substances at tempera-tures close to ambient. The elements in silicate oxide (SiO2) and aluminum oxide (Al2O3) are essential for the hardening of geopolymers because they combine with other elements to create N-A-S-H formation, which gives the material its distinctive strength. Geopolymers based on industrial wastes are increasingly being used to stabilize soft soils. Fly ash, GGBS, metakaolin, glass powders, and others are a few of the industrial wastes that aid in synthesizing geopolymers. Several experimental studies were carried out to determine the mechanical strength, durability, and microstructure im-provement of soft soils stabilized with geopolymers. Some of the experiments include X-ray diffraction (XRD), scan-ning electron microscopy (SEM), unconfined compression testing (UCS), and durability testing. The main objective of this review was to assess the different types of binders, binder ratios, alkali activator types, alkali activator concentra-tions, and other parameters used in synthesizing geopolymers. The binder's proportion varies between 5% and 30% of the soil's dry weight. Researchers commonly use sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) solution for the alkali activator. Since the unconfined compression test is one of the quickest and least expensive ways to determine shear strength, most researchers were used to measure stabilized soils' mechanical strength. This paper highlights the most frequently used industrial wastes used to synthesize geopolymers. The review enables researchers to acquire es-sential and complementary inputs for future research.

Image Enhancement is crucial phase of particular application. These enhancement techniques become essential when there is every possibility of image degradation due to uncontrolled variations. These variations are categorized into light, emotion, noise, pose, blur and corruption. The enhanced images provide better images from which feature extraction is performed more effectively. Therefore the two major objectives of the proposed work are aligned in two phases. First phase of this paper discuss about Image Enhancement Techniques (IET) for improving image intensity. Second phase provide detailed elaboration of various Full Reference Based Image Quality Measures (FRBIQM). FRBIQM is further categorized into Pixel Difference Based Image Quality Measures (PDBIQM), Edge Based Image Quality Measures (EBIQM) and Corner Based Image Quality Measures (CBIQM). First image quality measure employs different techniques to evaluate performance between original and distorted image. Second image quality measure deploy edge detection techniques, which are essential for increasing the robustness (in feature extraction) and third image quality measure discuss corner based detection techniques, which are essential for enhancing robustness (in feature extraction). All these techniques are discussed with their examples. This paper provide brief survey of IET and FRBIQM. The significance and the value of the proposed work is to select the best image enhancement techniques and image quality measures among all (described ones) for features extraction. The one which gives the best results will be used for feature extraction.

Surveillance camera is used as a new technology for security. In this research, the combination of OpenCV with image processing will be discussed. Saving the space in the hard drive by recording only video when here would be an image formed in the subtraction of the original image to the next image captured. With the use of Image Processing and Fuzzy logic, the research was enhanced by eliminating the recording of same image captured. After analyzing the background images, it can now determine when to start recording the video or when to stop recording a video by subtracting the images in the backdrop image and comparing the image if there was an object in motion using template matching. With the application of the project, memory storage saved up to forty-six percentage points.

India's Karnataka state is home to a vast treasure trove of artefacts, antiquities, and historic and archaeologically significant monuments. Its culture and tradition are linked. In Karnataka, there are numerous Neolithic and Megalithic structures; these historic buildings from illustrious ruling dynasties have endured for thousands of years. They have miracles of their own in their own style, innate sculpture, architecture, technique, immensity, and enormity. However, modern generation is not ready for mining archaeological knowledge regarding empires or ruling dynasties of these ancient Karnataka temples through the archaeological guidance. Hence, a new approach required to bring this valuable information to the modern generation by a proper platform. In this paper both threshold and regional based segmentation methods are applied in order to segment the structural elements of temple. The analysis of segmented structural elements by applying both methods is done in order to provide comparative study. Comparative study on temple structural element shows that regional segmentation is more accurate than threshold method based on VOE and DSC metrics which are used for evaluating the performance of segmentation methods. Further, more efficient segmentation approaches may be applied to improve the efficiency of segmentation and it may be used for classification of viman styles.

During the past years, it is observed from the literature that, identification of the brain tumor identification in human being is gaining popularity. Diagnosing any disease without manual interaction with great accuracy makes computer science research more demanding, therefore, the present work is related to identify the tumor clots in the affected patients. For this purpose, a well-known Safdarganj Hospital, New Delhi, India is consulted and 2165 Magnetic Resonance Images (MRI) of a single patient are collected through scanning, and interpolation technique of numerical method used to identify the accurate position of the brain tumor. A system model is developed and implemented by the use of Python programming language and MATLAB for the identification of affected areas in the form of a contour of a patient. The desired accuracy and specificity are evaluated using the computed results and also presented in the form of graphs.