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 non-standalone architecture (NSA) of 5G networks builds upon existing 4G long-term evolution (LTE) infrastructure, integrating 5G new radio (NR) technology while still relying on the 4G core network. In contrast the standalone architecture (SA) of 5G networks is designed as a fully independent system, with its own 5G core network. It does not rely on the existing 4G LTE infrastructure. The NSA integrates 5G NR technology into existing 4G LTE networks, utilizing the 4G core network for control and signaling. On the other hand, the SA establishes a fully independent 5G network with its own core components, providing more advanced features and greater autonomy. The transition from NSA to SA architecture is expected as network operators deploy more comprehensive 5G networks. This paper investigated in details the major different between both architectures NSA and SA of 5G networks.

The main purpose of cloud cryptography is to protect sensitive data without causing any delay in data transfer, various cryptographic protocols designed to balance data security and performance to secure data through encryption. One such approach is to encrypt the data before uploading it to the cloud.

This study proposes an effective framework for protecting small and medium companies (SMEs) from cybersecurity risks and threats. The framework evaluates the system of private encryption data in a server environment using the advanced encryption standard (AES) 128 algorithm and a virtual private network (VPN) tunnel. The goal is to secure data through encryption and ensure data transfer without causing delays. The framework includes a test case where data is transferred from a storage area network (SAN) storage to the cloud. To assess the system's performance and security, a penetration test using Kali Linux is conducted. The results of this study provide insights into securing SMEs' data and mitigating cybersecurity risks effectively.

The introduction of the fifth generation (5G) networks indeed brings significant advancements in connectivity and has the potential to revolutionize various industries. The technologies that make 5G powerful include features such as faster speeds, reduced latency, increased capacity, and the ability to connect a wide range of devices and objects.

However, implementing 5G networks involves upgrading existing infrastructure and deploying new infrastructure, which can be both costly and time-consuming. This process requires significant investments from telecommunication companies to install new equipment and upgrade existing infrastructure to support 5G technology. Additionally, the deployment of 5G networks requires a substantial amount of radio spectrum, and regulatory frameworks need to be in place to allocate and manage the spectrum effectively. This paper provides an overview of 5G technologies, highlighting their key features and potential benefits. It also delves into the existing challenges that arise with the implementation of 5G networks and discusses some possible solutions to address these challenges.

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.

This paper is the continuation of a research carried out by Alabish et.al, which depicts the scattering objects effects on a blocked indoor wireless 5G link by a human body using a simple approach. Some measurement were made on the scattering effects due to having an object close to the link in this case it is a human body. In this paper, more measurements were conducted at 32 GHz. The double knife-edge diffraction (DKED) model was used in order to foresee the attenuation due to human body. To test the prediction precision of the model, simulation was then compared with measurements. The obtained results indicate that the assumed simple models for the indoor links performed well.

This paper is concerned with studying the effects of human body blockage as well as surrounding objects scattering effects for an indoor 31 and 33 GHz link utilizing the Double Knife Edge Diffraction (DKED) model for diffraction effect on received signal.