Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using M

Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using M

Vehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles and infrastructure to enhance traffic flow, road safety, and passenger experience. However, the open and dynamic nature of VANETs presents significant privacy and security challenges, including data eavesdropping, message manipulation, and unauthorized access. This study addresses these concerns by leveraging advancements in Fog Computing (FC), which offers lowlatency, distributed data processing near-end devices to enhance the resilience and security of VANET communications. The paper comprehensively analyzes the security frameworks for fog-enabled VANETs, introducing a novel taxonomy that c

This work is concerned with the synthesis of two types of composites thin films (Sb2O3:WO3 and (Sb2O3:In2O3) with different concentrations (0.0,0.05,0.1, and 0.15). The prepared thin films are characterized with the use of the X-ray diffraction atomic force microscope AFM and UV-Vis–infrared spectrophotometer. The structural examination shows that both composite's thin films were polycrystalline with nano size with cubic phase as majority and orthorhombic phase as minority phase. The crystal size decreases from 34.6 to 24.7 and 21.7 nm for (Sb2O3:WO3) and (Sb2O3:In2O3) thin films, respectively. The average roughness shows non-regular growth by increasing the concentration of both oxides. The optical energy band gap shows a red shi

In the present work the preparation of Cu2S alloy was done throughout mixing the sulphur and copper elements according to the proper atomic weight and then put in an evacuated quartz ampoule which then sealed and heated at 1273 K for five hours and left to cool. Thin films from Cu2S powder thicknesses of ~300nm were prepared by thermal evaporation technique on a glass substrate and under vacuum of 10 -5 mbar with rate of deposition 25nm/sec. The prepared thin films subjected to heat treatment at different temperatures(300, 373 , 473 and 573 K)for half an hour. The structures of Cu2S powder and films have been studied by X–ray diffraction technique. The result reveals that the preparedalloy has cubic structure a

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog