Today there is an increasing need for assistive technology to help people with disabilities to attain some level of autonomy in terms of communication and movement. People with disabilities, especially total paralysis is often unable to use the biological communication channels such as voice and gestures hence digital communication channels are required. Research on Human Computer Interaction (HCI) is striving to help such individuals to convert human intentions into control signals to operate devices. The technique of measuring cornea retinal potential associated with eye movement is called Electrooculography. Eye movements are behaviors that can be measured and their measurements provide the sensitive means of learning about cognitive and visual stimuli. A human eye conveys a great deal of information with regards to the direction of the eye movements. Further, the direction in which an individual is looking shows where his or her attention is focused. Eye movements are naturally divided into three categories one is the saccades in which eyes quickly change the point of fixation and another is a smooth pursuit movement in which eyes to closely follow a moving object at a steady coordinated velocity, rather than in saccades and the other is a vergence movement in which eye rotates in the opposite direction. By tracking the position of the eye movement useful interfaces can be developed that permit the user to commune and control in a more general way. This paper convey some basic idea about various feature extraction techniques and classification techniques used to categorize the eye movement tasks and also it gives a vision on different issues associated in the field of Electrooculography based Human Computer Interface.
Internet of Things (IoT) can be defined as a thing or device, physical and virtual, connected and communicating together, and integrated to a network for a specific purpose. The IoT uses technologies and devices such as sensors, radio-frequency identification (RFID) and actuators to collect data. IoT is not only about collecting data generated from sensors, but also about analyzing it. IoT applications must, of necessity, keep out all attackers and intruders so as to thwart attacks. IoT must allow for information to be shared, with every assurance of confidentiality, and is about a connected environment where people and things interact to enhance the quality of life. IoT infrastructure must be an open source, without ownership, meaning that anyone can develop, deploy and use it. The objective of this paper is to discuss the various challenges, issues and applications confronting the Internet of Things.
Most prominent challenges in all business is to retain and satisfy their valuable customers for sustain successfully in the market. Numerous Machine learning approaches are emerging to develop various customer retention models to solve this issue in many applications. This swing is more realized in telecom industry due its enormous significance. This article presents an elaborated survey on machine learning based churn prediction in telecom sector from the year 2000 to 2018. We also extracted the problems and challenges in Telecom Churn Prediction and reported suggestion and solutions. We believe this article helps the researches or data analysts in the telecom field to select optimal and appropriate methods and for designing improved novel model for churn prediction in future
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