This paper addresses the critical challenge of human-induced signal attenuation in millimeter-wave (mmWave) communications, a key concern for fifth-generation (5G) network reliability in indoor environments. Our study introduces a simplified model to quantify the impact of human body blockage on indoor communication links at a frequency of 32.5 GHz., a frequency relevant to 5G systems. The influence of nearby scattering objects is investigated through experimental measurements involving a human body. Key wave propagation phenomena, including diffraction, are considered for each scattering object. The Double Knife-Edge Diffraction (DKED) model is used to estimate the attenuation caused by the human body (to estimate blockage losses). Through controlled experiments with human subjects, we systematically analyze how scattering objects and body positioning influence signal propagation. The model's performance is validated by comparing simulation results with experimental data. The findings show that the proposed model effectively predicts signal attenuation in indoor environments, providing valuable insights for future studies on human presence effects in fifth-generation (5G) communication systems. Keywords: 5G, DKED, diffraction, human shadowing, millimeter-wave, blockage.

-This paper examines the effect of human body blockage on signal propagation (millimeter-wave (mmWave) signal propagation) in indoor environments links at 32.5 GHz (a critical frequency for fifth-generation (5G) network), with a particular focus on the diffraction effects caused by the human body, where diffraction is one of the important wave propagation mechanisms. In this study, measurements were taken to assess the effect of the human body as it moves between the transmitter and the receiver. To predict the signal attenuation, the principles of Fresnel diffraction were utilized, particularly emphasizing complex Fresnel integrals. Our results show that the received power varies significantly based on the person’s position, as diffraction loss highly depends on the body’s location. This study enhances our understanding of how human-induced diffraction, is critical for designing more reliable wireless networks. As the findings demonstrate that the proposed model effectively predicts signal attenuation in indoor environments and emphasizes the importance of accounting for human interference when optimizing communication systems, thus supporting the effective deployment of 5G technology. 

This paper presents a comprehensive study of modelling human body blockage (the most critical challenges in fifth-generation (5G)) effects on indoor millimetre wave (mmWave) communication links at 32.5 GHz, a key frequency for 5G networks. Through controlled experiments in a laboratory environment, we analyse signal attenuation as a human subject obstructs the line-of-sight (LOS) path between transmitter and receiver, recording received power at incremental positions. To model the observed phenomena, we propose a hybrid framework integrating deterministic and statistical components: (1) a modified Double Knife-Edge Diffraction (DKED) model with Gaussian-shaped blockage attenuation (20.8 dB peak at full blockage) and reflection-induced signal enhancement (−15.0 dB peak from nearby objects), and (2) a log-normal shadowing component (σ = 11.8 dB) capturing environmental randomness. Our results reveal strong agreement between simulations and measurements, achieving a mean absolute error of 3.2 dB and a correlation coefficient R² = 0.89. The analysis demonstrates that human-induced diffraction dominates near the LOS centre, while multipath reflections significantly alter signal strength at peripheral positions. We further derive practical guidelines for 5G network design, recommending a 44.4 dB link budget safety margin to account for combined blockage and shadowing effects. This work advances indoor mmWaves channel modelling by unifying physics-based diffraction analysis with empirical reflection characterization, the framework achieves strong experimental validation and offers actionable insights for 5G network design. Keywords— mmWaves, blockage, DKED, attenuation, shadowing  

This paper investigates human-induced signal attenuation in indoor mm-wave communications at 32.5 GHz, a critical concern for 5G systems. Two distinct diffraction-based models are applied to the same indoor scenario to assess human blockage effects: one employs the Double Knife-Edge Diffraction (DKED) approach, and the other uses Fresnel diffraction principles with complex Fresnel integrals. Controlled experiments with a human subject moving between a transmitter (TX) and a receiver (RX) reveal that the DKED model consistently underestimates the received power by 2 6 dB, while the Fresnel diffraction approach underestimates it by 2–5 dB Based on the comparative results, the DKED model demonstrates higher accuracy in predicting signal attenuation, offering valuable insights for improving indoor 5G network performance

Abstract— Arabic language is characterized by its rich morphological structure, presenting unique challenges in Nat- ural Language Processing (NLP). The categorization of Arabic plurals is the subject of this study, which uses a trans- former-based model—more precisely, the pre-trained Arabic BERT architecture—and has never been studied previously. Given the complexities of Arabic language, particularly in pluralization which includes sound masculine, sound feminine, and irregular (broken) plurals, the research aims to enhance NLP capabilities in this area. By utilizing a dataset of 7,400 instances classified into four distinct categories, the study demonstrates the effectiveness of transfer learning in achieving high classification accuracy, with results indicating an accuracy of 97% across both validation and testing sets. Addition- ally, the model achieves high precision, recall, and F1-score metrics. A confusion matrix provides insights into classifica- tion performance, highlighting areas of misclassification. The findings underscore the potential of transformer models in overcoming the linguistic challenges posed by Arabic plural forms.

Abstract— This survey explores different machine translation methods utilized in various systems and platforms for commercial and research purposes. These methods play a vital role in enabling global communication, enhancing accessibility, supporting business and trade, fostering intercultural understanding, facilitating travel and tourism, aiding education, delivering fast and efficient translations, contributing to humanitarian aid efforts, promoting research and collaboration, and preserving language and culture. The survey aims to equip software developers and researchers interested in machine translation with valuable insights into these methods. Its objective is to help them improve translation quality with great accuracy by providing them with the necessary knowledge and understanding of these approaches. The papers utilized in this survey were obtained from Open Access Journals and online databases. All these methods are essential and can differ based on the specific context, available resources, and the quality of the translation required. To achieve optimal translation results, researchers and practitioners commonly employ a combination of various methods and techniques. 

Abstract—Arabic Named Entity Recognition (NER) serves as a crucial facet within Natural Language Processing, given the intricacies of the Arabic language. This survey consolidates the current landscape of Arabic NER, covering methodologies, challenges, and advance- ments. The review encompasses an in-depth analysis of diverse approaches, from rule-based systems to modern deep learning techniques, highlighting their effectiveness and limitations. It also addresses the specific challenges inherent to Arabic NER, such as dialectal variations and limited annotated data, while exploring recent advancements and their applications in sentiment analysis, information retrieval, and other domains. This survey aims to provide a comprehensive overview, catering to researchers, practitioners, and enthusiasts in the field of Arabic NER and NLP.

Abstract— Speech recognition is a valuable tool in various industries; however, achieving high accuracy remains a major challenge, despite the rapid growth of the speech recognition market. Arabic in particular lags behind other languages in the field of speech recognition, requiring further attention and development. To address this issue, this research uses deep neural networks to develop an automatic Arabic speech recognition model based on isolated words technology. A hybrid model, which is originally developed by Radfar et al. [1] for English speech recognition, is adopted and adapted to be used for Arabic speech recognition. This model combines the strengths of recurrent neural networks (RNNs), which are critical in speech recognition tasks, with convolutional neural networks (CNNs) to form a hybrid model known as ConvRNN. A specific model for Arabic speech recognition which is referred to as “Arabic_ConvRNN” model has been developed based on “ConvRNN” model. The adopted model is trained using an Arabic speech publicly available dataset of isolated words, along with a custom-generated dataset specially prepared for this research. The performance of the built model has been evaluated using standard metrics, including word error rate (WER), accuracy, precision, recall, and F-measure (also referred to as f1 score). In addition, K-fold cross-validation method has been employed generalizability. to ensure robustness and The results demonstrated that Arabic_ConvRNN model achieved a high accuracy rate of 95.7% on unseen data, with a minimal WER of about 4.3%. These findings highlight the model's effectiveness in accurately recognizing Arabic speech with minimal errors. Comparisons with similar models from previous studies further Arabic_ConvRNN validated model. the superiority Overall, of the Arabic_ConvRNN model shows great promise for applications requiring accurate and efficient Arabic speech recognition. This research contributes to narrowing the gap in Arabic speech recognition technology, offering a robust solution for accurately converting Arabic speech into text. 

Abstract— The verification of handwritten signatures is a significant area of research in computer vision and machine learning (ML). Handwritten signatures serve as unique biometric identifiers, making it essential to distinguish between genuine and forged signatures. This binary classification task is crucial in legal and financial contexts to prevent fraud and protect customers from potential losses. However, verifying offline handwritten signatures is challenging due to variations in handwriting influenced by factors such as mood, fatigue, writing surface, and writing instrument. This research paper focuses on recognizing offline handwritten Arabic signatures using deep learning (DL), specifically transfer learning (TL) technique which is called “Inception-V3 TL model”. Three distinct datasets are used to build a model for recognizing signatures. The first dataset is referred to as Dataset1. It is an English signature dataset called I. INTRODUCTION A signature is defined as a unique, individual, and personal sign. It is regarded as one of the biometric measurements that can be used for identification and verification. Handwritten signatures have been used in different practical areas of life for many centuries, for example, in contracts, financial operations, documents, identification documents such as passports, driver’s licenses, etc. Additionally, signatures are used in bank cheques and money transfers. However, with the great benefits of using a handwritten signature, came certain challenges for societies such as identity and fraud [1]. "CEDAR" which contains 1,320 genuine and 1,320 forged signatures. Dataset1 is publicly available at: https://www.kaggle.com/datasets/shreelakshmigp/cedard ataset .The second dataset is referred to as Dataset2. It is a new Arabic signature dataset created for this research which contains 1,320 genuine and 1,320 forged signatures. The third dataset is referred to as Dataset3. It is created by merging the English and Arabic signature datasets (Dataset1 and Dataset2). The Inception-V3 TL model is trained on these distinct datasets (Dataset1, Dataset2, and Dataset3). Both normal training and k-fold cross-validation (CV) methods are applied to evaluate the model’s performance, ensuring robustness and reliability. The Inception-V3 model achieved impressive accuracies of 97.48% on the Dataset1, 98.23% on Dataset2, and 97.85% on Dataset3, demonstrating its effectiveness in distinguishing between genuine and forged signatures. 

The purpose of this study is to determine the suitability of system measurements on indoor radio wave propagation at (28–33GHz) which might be used by 5G communication.