Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

Detecting causality from online psychiatric texts using inter-sentential language patterns.

BACKGROUND: Online psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors' problems, thus increasing the effectiveness of online psychiatric services. METHODS: Previous studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life) and (boyfriend, meaningless) are two word pairs extracted from the sentence pair: "I broke up with my boyfriend. Life is now meaningless to me". The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>,

An attribute weight assignment and particle swarm optimization algorithm for medical database classifications.

In this research, a hybrid model is developed by integrating a case-based reasoning approach and a particle swarm optimization model for medical data classification. Two data sets from UCI Machine Learning Repository, i.e., Liver Disorders Data Set and Breast Cancer Wisconsin (Diagnosis), are employed for benchmark test. Initially a case-based reasoning method is applied to preprocess the data set thus a weight vector for each feature is derived. A particle swarm optimization model is then applied to construct a decision-making system for diseases identified. The PSO algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions and then reducing the number of clusters into two. The average forecasting accuracy for breast cancer of CBRPSO model is 97.4% and for liver disorders is 76.8%. The proposed case-based particle swarm optimization model is able to produce more accurate and comprehensible results for medical experts in medical diagnosis.

Ambulance 12-lead electrocardiography transmission via cell phone technology to cardiologists.

This study demonstrates transmission of 12-lead electrocardiography (ECG) in an ambulance to the cell phone of the attendant emergency medical technician and then to the hospital and to cell phones of off-site cardiologists. The emergency medical technician cell phone receives Extensible Markup Language files generated by a Phillips Extensible Markup Language ECG instrument via Wi-Fi-based wireless network and then sends them to an ECG-processing server at the hospital over the mobile telephone network. After reducing ECG noises and artifacts, the server converts files to Digital Imaging and Communications in Medicine-based ECG reports stored in Picture Archiving and Communication System. These reports are sent to the cell phones of off-site cardiologists. Consequently, on-site Emergency Department physicians and off-site cardiologists can discuss ECG reports via Picture Archiving and Communication System on their computers or cell phones to prepare for the most appropriate treatment while the patient is on the way to the hospital. In conclusion, this 12-lead ECG transmission e-technology expands the functions of a 12-lead ECG instrument and facilitates more efficient prehospital cardiac care.