Hybrid Arabic text steganography

An improved method for Arabic text steganography is introduced in this paper. This method hides an Arabic text inside another based on a hybrid approach. Both Kashida and Arabic Diacritics are used to hide the Arabic text inside another text. In this improved method, the secret message is divided into two parts, the first part is to be hidden by the Kashida method, and the second is to be hidden by the Diacritics or Harakat method. For security purposes, we benefitted from the natural existence of Diacritics as a characteristic of Arabic written language, as used to represent vowel sounds. The paper exploits the possibility of hiding data in Fathah diacritic and Kashida punctuation marks, adjusting previously presented schemes that are based on a single method only. Here, the secret message is divided into two parts, the cover text is prepared, and then we apply the Harakat method on the first part. The Kashida method is applied on the second part, and then the two parts are combined. When the hidden ‘StegoText’ is received, a split mechanism is used to recover the original message. The described hybrid Arabic StegoText showed higher capacity and security with promising results compared to other methods

Quantum-Inspired Moth Flame Optimizer Enhanced Deep Learning for Automated Rice Variety Classification

Automated rice variety detection and classification is a task that includes automatically categorizing and identifying varieties or different types of rice based on different characteristics namely grain texture, shape, color, and size. This process is essential for quality assessment, agricultural management, and research purposes. Deep learning (DL) is a subfield of machine leaching (ML) that focuses on training an artificial neural network (ANN) with multiple layers to learn hierarchical representations of data. Convolutional Neural Network (CNN) was widely applied in image-based tasks such as rice variety detection, as they could efficiently capture visual features and patterns. In this study, we propose an Automated Rice Variety Detection and Classification using Quantum Inspired Moth Flame Optimizer with Deep Learning (ARVDC-QIMFODL) technique. The presented ARVDC-QIMFODL technique focuses on the automated identification and classification of distinct kinds of rice varieties. To accomplish this, the ARVDC-QIMFODL technique uses the Median modified wiener filter (MMWF) technique for the noise removal process. Followed by, the feature extraction process takes place by an improved ShuffleNet model. For rice variety detection and classification, the long short-term memory (LSTM) approach was applied. At last, the QIMFO algorithm-based hyperparameter selection process is performed to optimize the detection results of the LSTM system. The simulation outcome of the ARVDC-QIMFODL method is tested on a rice image dataset. An extensive set of experiments showed the remarkable efficiency of the ARVDC-QIMFODL system over other models