Data visualizations make data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and styles in groups of data. With the development of the data visualization, more and more data which contains the information of disabled population has been collected. However, researchers have pointed out some problems with visualizing data especially using traditional methods. Therefore, this study aimed to develop a web-based system for data visualization of disabled people distribution in Kuala Selangor, Selangor, Malaysia. Prototyping methodology was chosen in order to develop the web-based system. The results show two main interfaces of the web system which are the admin module and user module. Users may see a specific area, type of disabled people categories and the number of disabled people in the located area from the Google Map. This web system is hope to help public in identifying disabled people location using data visualization technique. In addition, in future it could help the authorize institutions/bodies in doing better planning for disabled people education, facilities and other needs based on its location distribution.

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The UNISEL Bot system was developed for helping marketing department in order to help on giving information in interactive mode for marketing purpose. The current problem is the information are not served in interactive ways, manually serving information using portal and paper are complicated and there is no real-time customer support to help on question and answer. In the era of technology, the information should be served in an interactive platform. The interactive information tends to gather more user attention. Therefore, this project aims to develop marketing assistant Chatbot system for a private academic institution which known as UNISEL Bot. The Chatbot system development is expected to assist the marketing department to use smarter marketing and interactive ways, for instance; to receive FAQs from student and provide real-time feedback whilst encourage people to engage with latest technology. Agile methodology was used in the development of this Chatbot system. Qualitative data gathering using interview method with students and University staffs was implemented. Multiple diagram is presented in this paper to describe the process flow of UNISEL Bot system. UNISEL Bot system was made up of seven main modules including the Ask Question, Feedback, Registration, Event, Appointment, Survey and Map. In future, this Chatbot system can work effectively to replace the traditional method of manual customer service and can also helping people in capturing user data for building analytic data.

Over the past decade, significant papers have shown that rehabilitation exercise is efficient in enhancing Parkinson's disease efficiency. However, the previous devices in Parkinson Disease Rehabilitation are not very efficient as they are far too complicated, heavy in size, and difficult to conduct. This paper will focus on developing a smart rehabilitation hand device prototype using an Arduino microcontroller, to control the soft actuator by using pneumatic system and IOT system for the individual with Parkinson's disease. The actuators are designed mechanically to match and support the human finger range as it features a lightweight structure, simplistic design, cost-effective and safer to use compared to other conventional actuators. A soft actuator, accelerometer sensor, pneumatic air valve and Arduino Mega were designed as a control hardware system to operate the smart rehabilitation glove. Therefore, this study will focus on obtaining data results based on the length of the single actuator, the bending angle of the actuator based on the applied pressure, the hand position of the accelerometer sensor based on the x, y, z-axis and the suitable pressure for the SGRD rehabilitation system for future research purposes. This prototype will assist the subject's hand movement by improving the subject quality in helping the patient with Parkinson's disorder recover.

Conventional Statistical Process Control (SPC) schemes fail to monitor nonlinear and finite-state processes that often result from feedback-controlled processes. SPC methods that are designed to monitor autocorrelated processes usually assume a known model (often an ARIMA) that might poorly describe the real process. In this paper, we present a novel SPC methodology based on context modeling of finite-state processes. The method utilizes a series of context-tree models to estimate the conditional distribution of the process output given the context of previous observations. The Kullback-Leibler divergence statistic is derived to indicate significant changes in the trees along the process. The method is implemented in a simulated flexible manufacturing system in order to detect significant changes in its production mix ratio output.