In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

With the fast-growing of neural machine translation (NMT), there is still a lack of insight into the performance of these models on semantically and culturally rich texts, especially between linguistically distant languages like Arabic and English. In this paper, we investigate the performance of two state-of-the-art AI translation systems (ChatGPT, DeepSeek) when translating Arabic texts to English in three different genres: journalistic, literary, and technical. The study utilizes a mixed-method evaluation methodology based on a balanced corpus of 60 Arabic source texts from the three genres. Objective measures, including BLEU and TER, and subjective evaluations from human translators were employed to determine the semantic, contextual an

يعد هذا النص أحد النصوص المسمارية المصادرة التي بحوزة المتحف العراقي، ويحمل الرقم المتحفي (235869)، قياساته )12،7x 6x 2،5سم). يتضمن مدخولات كميات من الشعير،أرخ النص الى عصر أور الثالثة (2012-2004 ق.م) و يعود الى السنة الثالثة من حكم الملك أبي-سين (2028-2004 ق.م)،أن الشخصية الرئيسة في هذا النص هو)با-اَ-كا مسمن الماشية( من مدينة أري-ساكرك، ومقارنته مع النصوص المسمارية المنشورة التي تعود الى أرشيفه يبلغ عددها (196) نصاً تضمنت نشاطاته م

This study aims to apply the theory of "Text from Text and the Plus Dimension" in the analysis of the Prophetic discourse found in the section on the virtues of knowledge and scholars from Imam Sahih al-Bukhari's book. This section covers several topics, including the virtue of gathering for the sake of learning, the superiority of a scholar over a worshipper, the excellence of jurisprudence in the religion of Allah, the acquisition of knowledge through the passing away of scholars, the merit of inviting people to Allah, the continuing benefit of beneficial knowledge after a scholar's demise, the warning against seeking knowledge for purposes other than Allah, and the Prophet seeking refuge from knowledge tha

ST Alawi, NA Mustafa, Al-Mustansiriyah Journal of Science, 2013

Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the