Nowadays an educational mobile application has been widely accepted and opened new windows of opportunity to explore. With its flexibility and practicality, the mobile application can promote learning through playing with an interactive environment especially to the children. This paper describes the development of mobile learning to help children above 4 years old in learning English and Arabic language in a playful and fun way. The application is developed with a combination of Android Studio and the machine learning technique, TensorFlow object detection API in order to predict the output result. Developed application namely “LearnWithIman” has successfully been implemented and the results show the prediction of application is accurate based on the captured image with the list item. The inclusion of the user database for lesson tracking and new lesson will be added for improvement in the future.

<span>People with visually impaired had difficulties in doing activities related to environment, social and technology. Furthermore, they are having issues with independent and safe in their daily routine. This research propose deep learning based visual object recognition model to help the visually impaired people in their daily basis using the android application platform. This research is mainly focused on the recognition of the money, cloth and other basic things to make their life easier. The convolution neural network (CNN) based visual recognition model by TensorFlow object application programming interface (API) that used single shot detector (SSD) with a pre-trained model from Mobile V2 is developed at Google dataset. Visually impaired persons capture the image and will be compared with the preloaded image dataset for dataset recognition. The verbal message with the name of the image will let the blind used know the captured image. The object recognition achieved high accuracy and can be used without using internet connection. The visually impaired specifically are largely benefited by this research.</span>

This paper presents IoT innovation project of a smart waste bin with real time monitoring system which integrates multiple technologies such as solar system, sensors and wireless communication technologies. The aim of this project is to provide an efficient and cost-effective waste collection management system hence providing clean, healthy and green environment. This study proposed a new framework that enables remote monitoring of solid waste bin in real-time via Wi-Fi connection, to assist the waste management activity. The system framework is based on wireless sensor network [WSN] contains three segments: renewable energy source, WSN and control station. Within this framework there are four developed subsystems: solar power system, smart waste bin, short messaging service [SMS] notification system and real-time monitoring system that are interrelated to each other to perform as an efficient, cost-effective waste management system that yield to a green and healthy living environment.

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UAV (Unmanned Aerial Vehicle) can be described as aircraft that do not need any presence of pilots inside it. Basically, UAV is come out in a small aircraft so that the aircraft can be easily controlled by the people from afar[1]. The UAV’s motor support bar length control systems are the UAV’s control systems that move according to the variable arm length movement and also a constant revolution of the propeller speeds. The purpose of the study is to run the dynamic analysis at the UAV’s motor support bar length control systems and also to enhance the UAV’s mathematical modelling by using the SOLIDWORKS® software which involved in using both CAD and CAE systems[2]. The detailed design is used SOLIDWORKS® software to conduct the static and dynamic analysis of UAV’s motor support bar length control systems. The design is restricted to the arm due to the critical part that has the highest vibration at the UAV’s motor support bar length control systems. The results that obtain from the study from the static and dynamic analysis are the displacement of the motor, Von Misses stress of the arm, and also the resonance frequency that will give the modes shape to the systems.