International Research Journal on Advanced Science Hub

The obtained images are frequently flawed because of a variety of environmen- tal issues, particularly at night, such as inside illumination, cloudy weather, etc. The dark image has a compressed dynamic range that can be improved in order to see the finer details. This research effort proposes an improved lighting reflection model-based technique for improving extremely dim images. This improved method relies on reconstruction carried out via morphological processing with Top-hat transformation and Contrast Limited Adaptive His- togram Equalization (CLAHE). The HSV colour scheme is used to perceive the image, and the V component is estimated. The inverse of the intensity component (V) is calculated after normalising the intensity component. The negative image is then subjected to the CLAHE algorithm. The final step is  to apply multiscale image enhancement to the obtained image. The brightness component of an image is adaptively adjusted via gamma enhancement. The outcomes of two gamma-enhanced photographs with various gamma values are produced after gamma enhancement. The significant information that can be used for image fusion is extracted from these images using principal com- ponent analysis. The weight value is adaptively determined during the PCA- based image fusion employing morphological Top-hat modification to enhance the image quality and highlight the erratic background pixels. The suggested technique emphasises edge and structural preservation while enhancing detail in extremely dim photos. Results from experimental validations demonstrate that the suggested strategy outperforms the current method in terms of both qualitative and quantitative measures.

Anomaly detection in Internet of Things is a challenging issue and is being addressed in a wide range of domains, including fraudulent detection, mal- ware protection, information security and diagnosis of diseases. Due to the distributed nature of wireless transmission and the insufficient resources of end nodes, traditional anomaly detection techniques cannot be used in IoT directly. To extract uncommon behaviors or patterns from complex data, nevertheless, is a difficult task. As a result, this paper offers a thorough analysis of ML based methods to identify anomaly in the IoT healthcare data. Further, a detailed comparison of their performance is provided with reference to their benefits and disadvantages.

This study describes in detail step-by-step implementation of quality improve- ment activities in mechanical processing plants. Describe how to imple-  ment quality 4.0 technologies in DMAIC (Define-Measure-Analysis-Improve- Control) phases. As well as using statistical formulas, experimental design in data analysis at each phase of DMAIC. This study proposes to use Quality 4.0 Technology in product quality improvement activities in mechanical product processing factories with the aim of becoming zero defect manufacturing. The results of the research found are repair rate reduced from 600 PPM monthly to 0 PPM, processing capacity increased from Cpk1.02 to Cpk2.56, reduce time for inspection product from 702 hours per year (calculated to save USD 2106 per year), reduce the amount of repair products by 196 products per year (calculated in terms of money is reduced by 917 USD per year) and reduce 1 roughing stage (calculated in terms of cost reduction about 171288 USD per year). The roughness dimension has reduced measurement time by about 364 hours per year (save 1092 USD per year). Processing digital signals from sen- sors in an oily environment is a big challenge for researchers. Improving the security of digital data is also a limitation of this study.  This study proposes a model to apply statistical hypothesis testing methods to analyze real data collected from each machining stage and perform each job according to each corresponding DMAIC phase of the model. In addition, digital processing tech- niques and computer vision techniques are also deployed in the improve phase to complete the goal of improving the semiautomatic production stage to the automatic production stage.