This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work
Photoacoustic
imaging combines both excellent spatial resolution
with high contrast and specificity, without the need for patients
to be exposed to ionizing radiation. This makes it ideal for the study
of physiological changes occurring during tumorigenesis and cardiovascular
disease. In order to fully exploit the potential of this technique,
new exogenous contrast agents with strong absorbance in the near-infrared
range, good stability and biocompatibility, are required. In this
paper, we report the formulation and characterization of a novel series
of endogenous contrast agents for photoacoustic imaging in vivo. These
contrast agents are based on a recently reported series of indigoid
π-conjugated organic semiconductors, coformulated with 1,2-dipalmitoyl-sn-glycero-3-phosphocholine, to give semiconducting polymer
nanoparticles of about 150 nm diameter. These nanoparticles exhibited
excellent absorption in the near-infrared region, with good photoacoustic
signal generation efficiencies, high photostability, and extinction
coefficients of up to three times higher than those previously reported.
The absorption maximum is conveniently located in the spectral region
of low absorption of chromophores within human tissue. Using the most
promising semiconducting polymer nanoparticle, we have demonstrated
wavelength-dependent differential contrast between vasculature and
the nanoparticles, which can be used to unambiguously discriminate
the presence of the contrast agent in vivo.
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