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Image filtering is a common technique used in digital image processing that can be used to take a picture appear differently aesthetically. Noise, also known as distracting visual artifacts, can lower the overall quality of a picture, which is why image improvement techniques are required to fix the problem. It can be utilized in a variety of ways, including smoothing, sharpening, reducing noise, and detecting borders, to name a few. In this piece, we will be using convolutional techniques to correct the images that were messed up. The first thing that needs to be done is a point-by-point multiplication of the frequency domain representation of the picture that's being entered through a black image that has a small white rectangle in the mid of it. This is the first step. Only the lowest harmonics are kept after we apply a filter that gets rid of the higher ones. Because the high frequencies in the input picture are filtered out, the special domain of the image that is produced should look like a blurrier variation of the original picture. Therefore, a greater degree of detail preservation is indicated when the white rectangle W is larger because this indicates that more high-frequency components of I have been preserved.

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The information and data exchanged between people over the Internet needs security to maintain the confidentiality of the data and avoid unauthorized access. Therefore, it is necessary to use more powerful encryption techniques and methods in the face of hostile attacks. In this paper, the propose an encryption method that depends primarily on the sensitivity of the key and the randomness of the encryption. The key generation was based on audio files as a basic seed, then Grigorchuk's group was applied as a hash of data and then entered it on the hash function SHA-3 (256), which outputs a series of 256-bit data as a key. Audio files This increases the sensitivity of the key. Then encrypted with two methods XOR and the RC6 algorithm to enhance the security of the encryption. The final stage is the application of Z-Fractal to the encrypted image and this is also applied before encoding. Noted through the security analysis of the algorithm that it has strength in the face of hostile attacks based on the randomness of the encryption and the sensitivity of the key and in view of the measure’s values as the value of the scale NPCR, UACI was 100 and 35, respectively. In addition to its execution time, which not exceed a minute.

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Research on artificial neural networks (ANN) is still being active leading to many new network types as well as hybrid algorithms and hardware for neural information processing. An artificial neural network consists of a pool of simple processing units which communicate by sending signals to each other over a large number of weighted connections. On the other hand, most control systems, today, use digital computers (usually microprocessors) to implement the controllers such as: Machine Tools, Metal Working Processes, and Chemical Processes. Most electronic systems are designed according to the device and then manufactured as an attached electronic device. But if conditions change or the factory is updated, then the control device must be replaced. Due to the complexity of the control system units represented by the program implementation algorithms, the complexity of mathematical analysis, the time delay caused by digital to analog converter DAC or analog to digital converter ADC, and the deterioration of the system’s stability due to the conversion of the system to be digital, this conversion causes the loss of some signal information. In this study, another controller based on Artificial Neural Network control is examined to replace the system controlling the motion of a worktable at a certain location; that is an important positioning system in manufacturing systems. Simulation after training the neural network (supervised learning) has shown that results are acceptable with the advantage of simplicity and adaptability to new updates and applicability in industry processes for reference control applications. The study also indicates that an artificial neural network controller could be less complex and cheaper to implement in industrial control applications compared to some of the other proposed schemes

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Skin conditions are more common than other illnesses. Skin issues can be brought on by viruses, bacteria, allergies, fungi, etc. The detection of skin diseases has been made better by lasers and photonics since it is now quicker and more precise. However, such a diagnosis is pricey. A system for automated dermatology screening is built with the use of computer vision. Using the Gray Level Co-occurrence Matrix (GLCM) and Convolution Neural Network (CNN) provides an improved model for accurately diagnosing skin disorders. In order to classify skin photos, CNN is used by the model to extract features from the images using GLCM. The high-level features utilizing the statistical features were retrieved via GLCM separately because the photos utilized in the research are for skin conditions. Once merged, these features created a high-accuracy categorization. Two distinct classification processes are used to categorize photos into 13 diseases: First, the Deep Neural Network (DNN) classifier obtains 96.69% accuracy, 96.2% recall, 96.2% precision, and 96.2% F1-score in terms of performance evaluation measures. Second, accuracy, recall, precision, and F1-score are the performance evaluation metrics for the Multiple Support Vector Machine (MSVM) classifiers. The model outperforms other cutting-edge models in terms of accuracy and effectiveness when compared to them. This work thus indicates the capability of GLCM and CNN for the classification of skin diseases and their prospective uses in the healthcare sector.