Dyslexia and dysgraphia are two common learning disabilities that affect children's ability to read, write, and spell accurately. These disabilities can significantly impede a child's academic performance, leading to lack of self-confidence, anxiety, and frustration. Traditional approaches to address these disabilities often involve one-on-one sessions with a tutor or special education teacher, which can be time-consuming and expensive. Artificial intelligence (AI) language learning models have shown tremendous potential in assisting children with dyslexia and dysgraphia. These models can provide real-time feedback and personalized instruction to help children overcome learning difficulties. This chapter highlights the importance of addressing these challenges and proposes a solution that leverages AI language learning models to assist children with dyslexia and dysgraphia. By embracing AI language learning models, educators and parents can empower children with dyslexia and dysgraphia, providing them with the necessary tools and support to overcome their learning challenges.

Radio frequency identification (RFID) is a useful technology that has myriad applications in retail, manufacturing, and healthcare settings. RFID can scan devices in their proximity and report the data to information systems. RFID in healthcare settings presents potential security and privacy concerns to the people and processes being tracked by the devices -- particularly healthcare workers including nurses and doctors. This research presents a theoretical model that will assess the effect of six independent variables: cognitive factors, perception of external control, privacy concerns regarding surveillance and RFID devices, subjective norms, existence of security policy, and persistence of data on three dependent variables, medical staff perceived usefulness of RFID, intention to use RFID, and actual use of RFID. The model is validated with an empirical study. The results suggest that cognitive factors, persistence of data captured through RFID, and the awareness of the existence of security policy influence medical staff members' use of RFID in hospitals.

Service quality, being an assessment of services offered to a customer or the extent to which the services offered meets customers’ expectations, plays a significant role in healthcare industry. Patients pay hefty prices for the services they avail from specialty hospitals and they demand quality services. Hospitals have a larger challenge in delivering these services effectively to the patients. The current study helps us understand the role of information systems in service delivery process. Most of the hospitals have adopted healthcare information systems due to the benefit it provides. The study attempts to analyze the impact of information systems on service quality in the hospitals which are located in Tier II cities. The popular SERVQUAL model is adopted for this purpose. Patients who visit the hospitals were part of the respondent group. Gap score is found in order to observe the expected and actual experience of the patients based on five dimensions.

With the proliferation of disruptive technologies driving innovation, there arises an imperative for businesses to equip themselves with forward-looking skill sets. The stewards of higher education institutions (HEIs), who oversee the preparation of candidates with pertinent skills, bear a significant responsibility. The workforce is grappling with an expanding skill gap (SG) brought about by the rapid adoption of technology and the evolving landscape of job roles. Thus, academia finds itself presented with a pressing challenge—to bridge this growing divide. To address this concern, we propose the HEISG (higher education institution skill gap) framework, leveraging machine learning (ML) techniques to assess and quantify this SG. This framework calculates skill scores pertaining to both supply and demand by analyzing exit skill scores from HEIs and entry skill scores required by industries, respectively. By comparing the resulting student profiles from HEIs with job descriptions from industry, we compute a cosine similarity score that highlights the gap between the supply and demand skill vectors. This iterative process is aimed at narrowing the chasm between the skills demanded by the industry and those imparted by HEIs. Our proposal suggests updating curricula to align with industry needs. The application of ML algorithms and natural language processing aids in generating skill index (SI) with customizable threshold values, tailored to specific disciplines and sectors. This study contributes to a better understanding of the demand for various skill sets, ultimately ensuring that students are well-prepared for the industry, thanks to the implementation of this framework. <!----> <!----> <!----> <!---->