Zahraa ِAzhar Muhammad Alramahi

Secure image encryption algorithms received considerable development in cyber securityapplications in the age of digital data. This paper proposes a new hybrid image encryption schemebased on a chaotic system and bit-plane processing. The suggested algorithm uses a key generationmechanism based on plaintext, where the initial condition in a standard Logistic map is influencedby statistical features of the input image in addition to four external secret keys to increase keysensitivity and expand the size of the key space (2186). It is a two-stage encryption processconsisting of a permutation and diffusion. The permutation process separated the input image intoeight bit-planes and each plane is reordered according to a key-dependent scanning algorithm.During the diffusion phase, a feedback-based XOR operation is performed by using a chaotic keystream obtained from Logistic map such that adjacent pixels are strongly dependent. Theexperimental results show the proposed scheme presents favorable security performance of bothcolor and gray image. The correlation coefficients of the encrypted images are low (≈ 0.0004–0.0044), and their entropy values are high (≈ 7.9970). In addition, the cryptographic algorithm is alsovery robust against differential attack such as Number of Pixel Change Rate (NPCR) = 99.63% andUnified Average Changing Intensity (UACI) = 33.39%. The results validate that the developedencryption scheme exhibits excellent confusion and diffusion characteristics, significant keysensitivity, and a high level of resistance to statistical and differential attacks, thus making it idealfor secure image transmission applications. 12:01 ص ،2026/10/2 A Plaintext Dependent Chaos based Image Encryption Scheme using Bit-Plane Scrambling and XOR based Diffusion | Jour…

This paper explores the combined effect of diverse datasets and advanced feature extraction techniques on the performance of email spam detection using unsupervised clustering algorithms Fuzzy C-Means and K-Means. Leveraging the Enron Email Dataset and the SMS Spam Collection Dataset, we extract both traditional features (TF-IDF) and novel attributes (punctuation count, capital letter usage, and body length) that reflect spam characteristics. Clustering is applied to group emails based on these features, and the generated cluster labels are subsequently used to train supervised classifiers, including K-Nearest Neighbours, Naive Bayes, Support Vector Machines, and XGBoost. Comparative analysis reveals that clustering significantly enhances classification accuracy by uncovering latent structures within high-dimensional data. Moreover, our results highlight the strengths and limitations of Fuzzy C-Means versus K-Means in this context. The study demonstrates that integrating unsupervised clustering with rich feature engineering contributes to more robust and efficient spam detection, paving the way for future hybrid spam filtering models.

Image mining is a very important technique that is used to get information directly from images. In the era of big data and the fast growth in digital picture complexity Consequently, image mining technology now faces additional obstacles, such as a fair amount of time cannot be allotted for the analysis of such massive datasets using a personal computer or database process because there are limits to how much data can be stored and how much it can be computed. As a result, distributed computing is required for current image exclude collection mining. The Hadoop platform is an appropriate paradigm; because of its reliance on MapReduce functionalities. This paper gives an overview of the different image mining techniques that have been suggested in the past some of them are implemented using Hadoop map reduce.

The amount of today's image collections has exploded to petabytes of data. A fair amount of time cannot be allotted for the analysis of such massive datasets using a personal computer. As a result, distributed computing is required for current image collection mining. This work used multi-nodes-computers for image mining in order to improve reliability, fall tolerance, and time efficiency. Because of this, the data was divided up across the nodes in the Hadoop multi-node cluster, and the results were then compiled to create an image clustering algorithm. One master node and two slave nodes were used to test our technique on a huge dataset. Using multi-node Hadoop, we found that we could get a speed-up in implementing as high as a single-node Hadoop implementation.

Common mistakes in official communications are among the mostimportant problems that face us in our institutional societies. Thepresent has invaded with its scientific and practical life, but we mustemploy them to serve our Arabic language, which is the language of theHoly Quran, to be a lifeline seeking to preserve that language on the onehand, and to overcome the difficulties experienced by the employee onthe other hand. Thus, this research came to reduce those mistakes thathave spread – and noticeably in our official speeches – by creating aprogram that can diagnose the grammatical, spelling, and linguisticerror in those speeches, and then correct them, whether the correction iswith one word, a sentence or the full text