Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Smartphones evolve rapidly and become more porful in computing capabilities. More importantly, they are becoming smarter as more sensors such as the accelerometer, gyroscope, compass and the camera have been embedded on the digital board. ,  propose a novel framework to recognize public scenes based on the sensors embedded in mobile phones.  build individual models for audio, light, wifi and bluetooth first, then integrate these sub-models using dynamically-ighted majority voting.  consider two factors when deciding the voting ight. One factor is the recognition rate of each sub-model and the other factor is recognition precision of the sub-model in specific scenes.  build the data-collecting app on the Android phone and implement the recognition algorithm on a Linux server. Evaluation of the data collected in the bar, cafe, elevator, library, subway station and the office shows that the ensemble recognition model is more accurate and robust than each individual sub-models.  achieved 83.33% (13.33% higher than audio sub-model) recognition accuracy when  evaluated the ensemble model with test dataset.
['Shuang Liang', 'Xiaojiang Du', 'Ping Dong']
I am writing a research paper on a idea Public scene recognition using mobile phone sensors
[{"from": "human", "value": "I am writing a research paper on a idea Public scene recognition using mobile phone sensors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Smartphones evolve rapidly and become more porful in computing capabilities. More importantly, they are becoming smarter as more sensors such as the accelerometer, gyroscope, compass and the camera have been embedded on the digital board. ,  propose a novel framework to recognize public scenes based on the sensors embedded in mobile phones.  build individual models for audio, light, wifi and bluetooth first, then integrate these sub-models using dynamically-ighted majority voting.  consider two factors when deciding the voting ight. One factor is the recognition rate of each sub-model and the other factor is recognition precision of the sub-model in specific scenes.  build the data-collecting app on the Android phone and implement the recognition algorithm on a Linux server. Evaluation of the data collected in the bar, cafe, elevator, library, subway station and the office shows that the ensemble recognition model is more accurate and robust than each individual sub-models.  achieved 83.33% (13.33% higher than audio sub-model) recognition accuracy when  evaluated the ensemble model with test dataset."}]