Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.
School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei.
Sensors (Basel). 2022 Jul 8;22(14):5142. doi: 10.3390/s22145142.
The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world's population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.
在特殊需求人群（占世界人口的 15%）中，软件和物联网服务的使用正在大幅增加。然而，根据残疾用户群体不断变化的需求和环境选择合适的服务来创建组合式辅助服务仍然是一项具有挑战性的研究工作。我们的研究应用基于场景的设计技术，旨在为辅助服务选择提供：(1) 一个包容性的残疾本体论；(2) 半合成生成的残疾服务数据集；以及 (3) 一种机器学习 (ML) 框架，以自适应地选择服务，以满足特殊需求人群的动态需求。基于 ML 的选择框架应用于两个互补的阶段。在第一阶段，评估所有可用的原子任务，以确定它们是否适合用户目标和配置文件；在随后的阶段中，通过将服务提供商的质量服务因素与残疾人士的环境和特征进行匹配，缩小服务提供商的名单。我们的方法围绕着无数的用户特征，包括他们的残疾档案、偏好、环境和可用的 IT 资源。为此，我们使用选定的可访问性功能融合扩展了广泛使用的 QWS V2.0 和 WS-DREAM 网络服务数据集。为了确定我们方法的有效性，我们将其性能与常见的多标准决策制定 (MCDM) 模型（即 AHP、SAW、PROMETHEE 和 TOPSIS）进行了比较。研究结果表明，与其他方法相比，我们的方法在确保满足可访问性要求的同时，具有更高的服务选择准确性。
