In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

This paper introduces an innovative method for image encryption called "Two-Fold Cryptography," which leverages the Henon map in a dual-layer encryption framework. By applying two distinct encryption processes, this approach offers enhanced security for images. Key parameters generated by the Henon map dynamically shape both stages of encryption, creating a sophisticated and robust security system. The findings reveal that Two-Fold Cryptography provides a notable improvement in image protection, outperforming traditional single-layer encryption techniques.

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

In this work, satellite images for Razaza Lake and the surrounding area
district in Karbala province are classified for years 1990,1999 and
2014 using two software programming (MATLAB 7.12 and ERDAS
imagine 2014). Proposed unsupervised and supervised method of
classification using MATLAB software have been used; these are
mean value and Singular Value Decomposition respectively. While
unsupervised (K-Means) and supervised (Maximum likelihood
Classifier) method are utilized using ERDAS imagine, in order to get
most accurate results and then compare these results of each method
and calculate the changes that taken place in years 1999 and 2014;
comparing with 1990. The results from classification indicated that

Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution. This review is concerned with Different

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

The study of manuscripts that related to medicine and pharmacology is of interest to the study of the history of science., it illustrates aspects of the practice of doctors and pharmacists, the status of patients, and the types of drugs

 مما لاشك فيه أنّ اندفاعنا للقيام بهذه الدراسة المتواضعة له أسبابه ، أهمها إن بعض الأبنية الصرفية مازالت مغمورة في ركام الدراسة قابعة تحت غبار الزمن على أساس أنها خارجة عن القواعد الصرفية الأساسية من غير مسوغ لهذا الخروج ، وهذه محاولة جادة للوقوف على بعضها ، لكي ننفض عنها الغبار لتخرج إلى النور ،

             Diese Forschungsarbeit versteht sich als ein Versuch zur syntaktischen und semantischen Beschreibung des BereichesFragesatz im Deutschen und Arabischen. Hinsichtlich der bedeutsamen Rolle des Fragefeldes haben wir den Entschluss gefasst, dieses Thema im Deutschen und Arabischen zu untersuchen. Diese Forschungsarbeit hat darüber hinaus das Ziel, die Satzarten, besonders die Fragesätze im Deutschen und Arabischen genau zu erläutern. Dieser Beitrag unternimmt also den Versuch, den Bereich desFragesatzes mit seinen vielseitigen Aspekten im Deutschen und ihre Entsprechung im Arabischen zu beschreiben, sowie die entsprechenden Formen der Fragesätze zwischen den beiden Sprachen zu bestimmen. E

Введение

Международные контакты обусловили интерес к изучению основной формы общения – диалога. Форма диалога в каждом из контактирующих в процессе общения языков должна быть рассмотрена в разных аспектах – не только лингвистическом, но и социолингвистическом и этическом. Это объясняется тем, что в речевой коммуникации необходима как адекватность выражения мысли, так и соблюдение тради