The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

The good and homogenous lighting is important in resolution image
and obtaining high contrast in visual image. In this research, three texts have
written on Wight board and they were picturing by digital camera on different
distances between the text and digital camera depends on fluorescent lighting
using Sobol Operator image to obtain edges for three image text image. The
mean (statistical and diagonal) contrast has been determined to the text image in edges area. The research notes that the mean (statistical and diagonal) contrast of text images decreases with increases the distance
between the camera and the board.

This study included synthesizing silver nanoparticles (AgNPs) in a green method using AgNO3 solution with glucose exposed to microwave radiation. The prepared NPs were also characterized using ultraviolet and visible (UV-vis) spectroscopy and scanning electron microscopy (SEM). The UV/vis spectroscopy confirmed the production of AgNPs, while SEM analysis showed that the typical spherical AgNPs were 30 nm and 50 nm in size for the NPs prepared using black tea (B) and green tea (G) as reducing agent, respectively. The changes in some of the biochemical parameters related to the liver and kidneys have been analyzed to evaluate the probable toxic effects of AgNPs. 40 adult male mice were included in this study. To assess the probable he

This work introduces the synthesis and the characterization of N-doped TiO2 and Co3O4 thin films prepared via DC reactive magnetron sputtering technique. N-doped TiO2 thin films was deposited on indium-tin oxide (ITO) conducting substrate at different nitrogen ratios, then the Co3O4 thin film was deposited onto the N-doped TiO2 layer to synthesize a double-layer TiO2-N/Co3O4 Photoelectrochromic device. Several techniques were used to characterize the produces which are x-ray diffraction (XRD), field emission-scanning electron microscopy (FE-SEM), Fourier-transform infrared (FTIR) spectroscopy and UV–Vis spectroscopy. The Photoelectrochromic device was characterized by UV–Vis spectroscopy and the results show that the double-layer N-dope

     Atenolol was used with ammonium molybdate to prove the efficiency, reliability and repeatability of the long distance chasing photometer (NAG-ADF-300-2) using continuous flow injection analysis. The method is based on reaction between atenolol and ammonium molybdate in an aqueous medium to obtain a dark brown precipitate. Optimum parameters was studied to increase the sensitivity for developed method. A linear range for calibration graph was 0.1-3.5 mmol/L for cell A and 0.3-3.5 mmol/L for cell B, and LOD 133.1680 ng/100 µL and 532.6720 ng/100 µL for cell A and cell B respectively with correlation coefficient (r) 0.9910 for cell A and 0.9901 for cell B, RSD% was lower than 1%, (n=8) for the determination of ate

In today's digital era, the importance of securing information has reached critical levels. Steganography is one of the methods used for this purpose by hiding sensitive data within other files. This study introduces an approach utilizing a chaotic dynamic system as a random key generator, governing both the selection of hiding locations within an image and the amount of data concealed in each location. The security of the steganography approach is considerably improved by using this random procedure. A 3D dynamic system with nine parameters influencing its behavior was carefully chosen. For each parameter, suitable interval values were determined to guarantee the system's chaotic behavior. Analysis of chaotic performance is given using the

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

Optical Mark Recognition (OMR) is the technology of electronically extracting intended data from marked fields, such as squareand bubbles fields, on printed forms. OMR technology is particularly useful for applications in which large numbers of hand-filled forms need to be processed quickly and with a great degree of accuracy. The technique is particularly popular with schools and universities for the reading in of multiple choice exam papers. This paper proposed OMRbased on Modify Multi-Connect Architecture (MMCA) associative memory, its work in two phases: training phase and recognition phase. The proposed method was also able to detect more than one or no selected choice. Among 800 test samples with 8 types of grid answer sheets and tota