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Steganography is a data hiding technique used in the data security. while transmission of data through channel, no guarantee that the data is transmitted safely or not. Variety of data security techniques exists such as patch work, low bit rate data hiding, lossy compression etc. This paper aims to increase the security and robustness.

This paper describes, an approach for multiple images steganography that is oriented on the combination of lifting wavelet transform (LWT) and discrete cosine transform (DCT). Here, we have one cover image and two secret images. The cover image is applied with one of the different noises like Gaussian, Salt & Pepper, Poisson, and speckle noises and converted into different color spaces of YCbCr, HSV, and Lab.

Due to the vast development of Internet access and multimedia technology, it becomes very simple to hack and trace secret information. Using this steganography process in reversible data hiding (RDH) helps to prevent secret information.

We can divide the color space converted image into four sub-bands of images by using lifting wavelet transform. By selecting lower bands, the discrete cosine transform is computed for hiding two secret images into the cover image and again one of the transformed secret images is converted by using Arnold transform to get the encrypted/embedded/encoded image. To extract the Stego image, we can apply the revertible operation. For comparing the results, we can calculate PSNR, SSIM, and MSE values by applying the same process for all color spaces of YCbCr, HSV, and Lab. The experimental results give better performance when compared to all other spaces.

The purpose of this article is to provide an insight into the spice industry.

Uses Eastern Spice & Flavorings as a case study and focuses on its international product development group.

Despite the company's origins as a family‐owned small business, it competes in the global marketplace. To compete successfully, it must localize its blends. Until the company can establish new product development (NPD) centers in its major overseas markets, regional teams from Richmond that could travel to foreign markets to aid product development would be a solution to increasing sales.

Discusses Eastern Spice & Flavorings and its new product development.

The objectives of the study are the standardization of ten Nigerian dishes and the determination of their proximate composition. Recipes for the ten dishes were obtained through the use of questionnaires. There were 200 respondents to the questionnaires consisting of housewives living in the major towns of south‐western Nigeria. One recipe for each meal was extracted from a known cookery book. The means for each ingredient of all the dishes were calculated from ten randomly selected recipes and then used in the preparation of the standardized dishes. The standardized dishes are: Burabisko, Jollof rice, Agbono soup, Stewed beans and fried plantain, Bean pudding, Melon seed and vegetable soup, Ikokore, Eba imoyo, Yam and eggs and Yam pottage. Consumer acceptability tests were conducted as well as proximate analyses according to AOAC. All dishes were considered acceptable since they scored more than 3, the benchmark for acceptability. Energy content of the dishes ranged from 281Kcal/100g (Jollof rice) to 510Kcal/100g (Agbono soup). The crude protein content ranged from 7.5g/100g for Burabisko to 27.4g/100g for Jollof rice. The results suggest that these dishes are good sources of energy and protein.

The purpose of this paper is to construct a novel grey filter model for image denoising and to solve the problems which exist in the image denoising filter method, in which the true intensity value of each noisy pixel cannot be predicted better.

Based on the definition of stepwise, the defects of traditional grey prediction models are found. A new grey filter model, named grey stepwise prediction model, is proposed. The new filter model for the image denoising is based on each noisy pixel's neighborhoods stepwise, which is the eight pixels around the noisy pixel, to predict its intensity value and to solve the problems which exist in the image denoising filter method.

The experiment results show that the improved filter model can effectively eliminate image noise, preserve the image's details and edges, increase SNR (signal‐to‐noise ratio) as well as PSNR (peak signal‐to‐noise ratio), reduce MSE (mean square error) and MAE (mean absolute error), and significantly improve the image's visual effect.

The new filter method exposed in the paper can be used to 8‐bit gray‐scale image denoising. The method can also be used to binary image denoising.

The paper succeeds in constructing a novel filter method for image denoding, and it is undoubtedly a new development in image recovery algorithm and grey systems theory.

Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.

This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.

The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.

The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.

The purpose this paper is to offer an understanding of the role of food in the adjustment journey of Nigerian students in the UK.

A qualitative approach was used, involving interviews with ten Nigerians studying in the UK.

Thematic analysis revealed that participants found the food they ate locally to be bland and fattening, and that they quickly adopted a home country diet, using ingredients bought locally or sent and brought from home to recreate Nigerian dishes. Eating Nigerian food had a positive emotional impact, and it was also a vehicle for social interaction.

It is acknowledged that this is a small-scale preliminary study that could be extended across the UK with a more quantitative approach to get a broader picture of the eating habits of Nigerian students at British Universities. There is also an opportunity to widen it to include other African states which are neglected within the present literature. A more longitudinal study picking up migrants could also explore how adjustments have been made in their eating habits. Participants in this research equated fast food with local, English food due to their limited access to authentic local cuisine.

There are practical implications of this study whereby actions can be taken to help avoid the negative impacts experienced causing concerns in around mental well-being and poor health.

This study fills a gap in knowledge on how this important segment of the international student population adapts to a new food culture.

Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.

The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.

The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.

Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.