Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

The self-awareness interactions reflects with emotions,assess the child in respect to its relations with the social standards; they are not merely simply reactions , but rather they connect with his moral behavior and other 's thinks. When the self- awareness gets developed with emotions within the child , the latter become highly sensitive, causing him prone to be blamed . AS a result , the children test the self –awareness interactions with emotions and in age 3 years , where the self –awareness interactions with emotion clearly link to the self- assessment. The children in stage of kindergarten could not describe the self-awareness interactions accurately ; they test it under different circumstances in comparison to the youngest o

The childhood stage is considered the most important stage of all the stages through
the human being’s life. In this stage the human being will be more affected by the various
factors that surround him/her. The first five years of his/her life leave a great impact not only
on the human being personality, but also on his/her whole life. Therefore, it is worthwhile tobe concerned with and focus at the raising up and the teaching of the child during the
childhood stage.
The mission of raising up children in this era - the era of globalization and information
bursting or news flooding – has become a very difficult or even an impossible mission.
Furthermore, not only in the Arabic world, but also all over the world, t

This work studies the impact of input machining parameters of Electrical Discharge Machining (EDM) on the machining process performance. Tool steel O1 was selected as the workpiece material, copper as the electrode material, and kerosene as the dielectric medium. Experimental runs have been carried out with a Design of Experiment (DOE) technique. Twenty tests are accomplished with the current range of (18 to 24 Ampere), a pulse duration range of (150 to 200 µs), and a pulse-off time range of (25 to 75 µs). Based upon the experimental study's output results, the EDM parameter's effect (voltage of power supply, discharge current, pulse duration, and pulse pause interval) on the responses of the process represented by sur

The Battle of Kadesh is replete with many military arrangements that reflect the tremendous development of war preparations in the thirteenth century BC; where the expressive pictures the Egyptians left on some of the walls of their temples show the tremendous ability to organize and divide the forces and the great development that affected the war machine. Furthermore, the text accompanied these pictures reveal some news about that battle, which is considered one of the most important wars in the ancient world. Thus, the importance of the study lies in the fact that it examines one of the most important battles of the ancient Near East, the results of which had great repercussions on the region. This is because it is the most abundant B

APFS Mohammed, 2014