Facilitating the communication with deaf people: Building a largest Saudi sign language dataset

Recently, several countries have been trying hard to facilitate the integration of disabled people into their societies by ensuring equal opportunities through ease of access to social services, daily human necessities, and the labor market. Deafness is considered one of the major disabilities separating the deaf from their society. To integrate the deaf fully into society, a two-way mode of communication is required: one from the deaf to the hearing people, and the other from the hearing to the deaf. Communication from the hearing person to the deaf is generally easy and can be done through speech recognition and text-to-sign representations, but communication from the deaf to the hearing is somewhat difficult and requires a sign recognition module that recognizes the sign motions from the deaf and translates it to a text; following this, a speech synthesis module will translate this text to speech. To build the sign recognition module, a sign language dataset is required. This paper contributes to the literature by introducing a comprehensive survey of 17 Arabic sign language datasets and by developing a well-organized framework that is used to build a sign language dataset. This paper also contributes to the literature by creating the largest Saudi Sign Language (SSL) database—the King Saud University Saudi-SSL (KSU-SSL data-base)—with 293 signs, 33 signers, 145,035 samples, and 10 domains (healthcare, common, alpha-bets, verbs, pronouns and adverbs, numbers, days, kings, family, and regions). This paper also contributes to the literature by introducing a convolutional graph neural network (CGCN) architecture for sign language recognition and applying the proposed architecture to the built KSU-SSL database. The architecture is made up of a small number of separable 3DGCN layers, and is augmented with a spatial attention mechanism. This study is a part of the project that aims to develop a two-way communication system for Saudi deaf individuals.publishedVersio

Unayzah, Saudi Arabia, Urban dwelling environments in rapidly growing cities

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1983.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCHIncludes bibliographical references.The study is concerned with two critical issues: a) the housing situation of middle income groups; b) the use of land in new developments. A tentative planning model for urban land development and a survey of existing dwelling environment from Unayzah urban area are provided. The development of the model is based on three case studies from the existing dwelling environment, stressing land utilization, density, and circulation efficiency. A modified layout is designed at Al-Slimaneih to compare with the one being built. Essentially, the redesigned layout is concerned with reducing costs of urban development and public responsibility by optimizing the physical design elements of the settlement. The design aims toward an efficient layout by minimizing public areas, circulation areas and lengths, infrastructure, and by maximizing private and usable areas. The survey of the existing dwelling environment identifies, analyzes, and evaluates three distinct urban areas in Unayzah which are characterized by their location, origins, layouts and socioeconomic characteristics of their inhabitants. The study is intended to: *Provide a reference for understanding and evaluating the existing housing conditions and urban environment. *Develop guidelines for those involved in planning of residential development and as a reference in the formulation of housing policies.Yousef Nasser Alohali.M.S

Optimizing Autonomous Multi-UAV Path Planning for Inspection Missions: A Comparative Study of Genetic and Stochastic Hill Climbing Algorithms

Efficient path planning is vital for multi-UAV inspection missions, yet the comparative effectiveness of different optimization strategies has not received much attention. This paper introduces the first application of the Genetic Algorithm (GA) and Hill Climbing (HC) to multi-UAV inspection of indoor pipelines, providing a unique comparative analysis. GA exemplifies the global search strategy, while HC illustrates an enhanced stochastic local search. This comparison is impactful as it highlights the trade-offs between exploration and exploitation—two key challenges in multi-UAV path optimization. It also addresses practical concerns such as workload balancing and energy efficiency, which are crucial for the successful implementation of UAV missions. To tackle common challenges in multi-UAV operations, we have developed a novel repair mechanism. This mechanism utilizes problem-specific repair heuristics to ensure feasible and valid solutions by resolving redundant or missed inspection points. Additionally, we have introduced a penalty-based approach in HC to balance UAV workloads. Using the Crazyswarm simulation platform, we evaluated GA and HC across key performance metrics: energy consumption, travel distance, running time, and maximum tour length. The results demonstrate that GA achieves a 22% reduction in travel distance and a 23% reduction in energy consumption compared to HC, which often converges to suboptimal solutions. Additionally, GA outperforms HC, Greedy, and Random strategies, delivering at least a 13% improvement in workload balancing and other metrics. These findings establish a novel and impactful benchmark for comparing global and local optimization strategies in multi-UAV tasks, offering researchers and practitioners critical insights for selecting efficient and sustainable approaches to UAV operations in complex inspection environments

The Design and Development of 3D Auditory Environment for Computer-based Aural Rehabilitation Programs

In this paper we describe the design of a zoomable user interfaces (ZUI) that was incorporated in an interactive educational multimedia system , Ranan, which is targeted to native Arab-based speaking children with hearing disabilities, and offers auditory aural rehabilitation training. The ZUI was used to browse & navigate the training materials , and maintain visual effects to tutorials in this system

Rannan: Computer Based Auditory Training For Arabic-speaking Children

This paper describes the design and development of a computer based aural rehabilitation therapy program, called Rannan. This system is targeted to native Arabic-speaking children who received a cochlear implant, and undergo clinical & home based Auditory Training. It covers key therapy stages such as auditory detection and discrimination. Many issues have been addressed in this system such as localization, and user centered design, which involved extensive evaluations to investigate the usability of Rannan

PWCT2: A Self-Hosting Visual Programming Language Based on Ring with Interactive Textual-to-Visual Code Conversion

Visual programming languages (VPLs) play a significant role in simplifying the process of learning to program and reducing development time. Most VPLs are developed for use in education or specific domains. Recently, some projects have aimed to provide general-purpose VPLs. Among these projects is the Programming Without Coding Technology (PWCT) project, which has been used for several years to develop and maintain the compiler and virtual machine for the Ring programming language. However, PWCT faces several issues related to code generation performance and the operating systems it supports. Additionally, its visual editor lacks many features, such as rich comments, auto-run, and the ability to import textual code, which are highly important in the era of using large language models for generating textual code. In this research, we present the PWCT2 visual programming language, which is distributed on the Steam platform. On Steam, 1772 users have launched the software, and the total usage time recorded exceeds 17,000 h. This generation provides approximately 36 times faster code generation and 20 times lower storage requirements for visual source files. It also allows for the conversion of Ring code into visual code, enabling the creation of a self-hosting VPL. It consists of approximately 92,000 lines of Ring code and comes with 394 visual components. Moreover, using Ring in this project demonstrates the feasibility of utilizing the language for projects of this scale. Ring compiles PWCT2 in less than one second, and the generated bytecode consists of approximately 724,000 instructions

Ring: A Lightweight and Versatile Cross-Platform Dynamic Programming Language Developed Using Visual Programming

New programming languages are often designed to keep up with technological advancements and project requirements while also learning from previous attempts and introducing more powerful expression mechanisms. However, most existing dynamic programming languages rely on English keywords and lack features that facilitate easy translation of language syntax. Additionally, maintaining multiple implementations of the same language for different platforms, such as desktops and microcontrollers, can lead to inconsistencies and fragmented features. Furthermore, they usually do not use visual programming to fully implement the compiler and virtual machine. In this research paper, we introduce Ring—a dynamically-typed language with a lightweight implementation. However, it boasts several advantages, including a rich and versatile standard library and direct support for classes and object-oriented programming. The Ring language offers customization features. For instance, it allows easy modification of the language syntax multiple times, enabling programming by writing code using Arabic, English, or other keywords. Additionally, the language permits the creation of domain-specific languages through new features that extend object-oriented programming, allowing for specialized languages resembling CSS or Supernova. In the era of the Internet of Things, instead of creating another language implementation to support microcontrollers, the same Ring implementation allows us to create projects and applications for desktops, the web, WebAssembly, Android, or Raspberry Pi Pico. The Ring Compiler and Virtual Machine are designed using the PWCT Visual Programming language based on ANSI C. The visual implementation is composed of 18,945 components that generate 24,743 lines of code, which increases the abstraction level by approximately 23.5% and hides unnecessary details

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems

A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

The IoT has connected a vast number of devices on a massive internet scale. With the rapid increase in devices and data, offloading tasks from IoT devices to remote Cloud data centers becomes unproductive and costly. Optimizing energy consumption in IoT devices while meeting deadlines and data constraints is challenging. Fog Computing aids efficient IoT task processing with proximity to nodes and lower service delay. Cloud task offloading occurs frequently due to Fog Computing’s limited resources compared to remote Cloud, necessitating improved techniques for accurate categorization and distribution of IoT device task offloading in a hybrid IoT, Fog, and Cloud paradigm. This article explores relevant offloading strategies in Fog Computing and proposes MCEETO, an intelligent energy-aware allocation strategy, utilizing a multi-classifier-based algorithm for efficient task offloading by selecting optimal Fog Devices (FDs) for module placement. MCEETO decision parameters include task attributes, Fog node characteristics, network latency, and bandwidth. The method is evaluated using the iFogSim simulator and compared with edge-ward and Cloud-only strategies. The proposed solution is more energy-efficient, saving around 11.36% compared to Cloud-only and approximately 9.30% compared to the edge-ward strategy. Additionally, the MCEETO algorithm achieved a 67% and 96% reduction in network usage compared to both strategies