Machine Learning
Nowadays technology is being used worldwide to cure deadly diseases. Hepatitis is rapidly spreading in Asia over time. Every 12th Pakistani is suffering from a specific form of hepatitis. In this study, we have explored design and technology solutions for assisting patients of hepatitis and to create awareness among the general public. We have suggested an android app, LiveDliver and a paper-based diary, HepOrganizer to help the patients manage their disease and the general public to acquire awareness.
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Visual representations are always better than narrations in accordance to children, for better understanding. This is quite advantageous in learning school lessons and it eventually helps in engaging the children and enhancing their imaginative skills. Using natural language processing techniques and along the computer graphics it is possible to bridge the gap between these two individual fields, it will not only eliminate the existing manual labor involved instead it can also give rise to efficient and effective system frameworks that can form a foundation for complex applications.
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A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.
An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function
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The analysis is based on two kinds of measured dataset. In both cases, uplink data are measured (A-UL0) i.e. the transmitters are UBSs and the receiver is UBSC and FDD is used. The first dataset has been collected from August 17, 2018, to August 20, 2018. The experiment has been carried over two separate distances, i.e., 1 km, and 3 km between the transmitter (Tx) and receiver (Rx) in Mohang Port (Taean-gun).
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Supplementary materials of the paper titled
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We obtained 6 million instances to be used as an analysis for modelling CO2 behavior. The Data Logging and sensors nodes acquisition are every 1 second.
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Pedestrian detection has never been an easy task for computer vision and automotive industry. Systems like the advanced driver assistance system (ADAS) highly rely on far infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what is the performance in adverse weather conditions.
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