HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens

The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.     

Optimizing drug regimens in cancer chemotherapy by an efficacy-toxicity mathematical model.

In cancer chemotherapy, it is important to design treatment strategies that ensure a desired rate of tumor cell kill without unacceptable toxicity. To optimize treatment, we used a mathematical model describing the pharmacokinetics of anticancer drugs, antitumor efficacy, and drug toxicity. This model was associated with constraints on the allowed plasma concentrations, drug exposure, and leukopenia. Given a schedule of drug administrations, the mathematical model optimized the drug doses that can minimize the tumor burden while limiting toxicity at the level of the white blood cells. The main result is that the optimal drug administration is an initial high-dose chemotherapy up to saturation of constraints associated with normal cell toxicity and a maintenance continuous infusion at a moderate rate. Data related to etoposide investigations were used in a feasibility study. Simulations with the optimized protocol showed better performances than usual clinical protocols. Model-based optimal drug doses provide for greater cytoreduction, while limiting the risk of unacceptable toxicity.     

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.     

Diffusion-weighted echo planar MR imaging of the neck at 3 T using integrated shimming: comparison of MR sequence techniques for reducing artifacts caused by magnetic-field inhomogeneities

Objective

Our objective was to compare available techniques reducing artifacts in echo planar imaging (EPI)-based diffusion-weighed magnetic resonance imaging MRI (DWI) of the neck at 3 Tesla caused by B0-field inhomogeneities.

Materials and methods

A cylindrical fat–water phantom was equipped with a Maxwell coil allowing for additional linear B0-field variations in z-direction. The effect of increasing strength of this superimposed gradient on image quality was observed using a standard single-shot EPI-based DWI sequence (sEPI), a zoomed single-shot EPI sequence (zEPI), a readout-segmented EPI sequence (rsEPI), and an sEPI sequence with integrated dynamic shimming (intEPI) on a 3-Tesla system. Additionally, ten volunteers were examined over the neck region using these techniques. Image quality was assessed by two radiologists. Scan durations were recorded.

Results

With increasing strength of the external gradient, marked distortions, signal loss, and failure of fat suppression were observed using sEPI, zEPI, and rsEPI. These artifacts were markedly reduced using intEPI. Significantly better in vivo image quality was also observed using intEPI compared with the other techniques. Scan time of intEPI was similar to sEPI and zEPI and shorter than rsEPI.

Conclusion

The use of integrated 2D shim and frequency adjustment for EPI-based DWI results in a significant improvement in image quality of the head/neck region at 3 Tesla. Combining integrated shimming with rsEPI or zEPI can be expected to provide additional improvements.

     

Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus

The need to protect the environment and biodiversity and to safeguard public health require the development of timely and reliable methods for the identification of particularly dangerous invasive species, before they become regulators of ecosystems. These species appear to be morphologically similar, despite their strong biological differences, something that complicates their identification process. Additionally, the localization of the broader space of dispersion and the development of invasive species are considered to be of critical importance in the effort to take proper management measures. The aim of this research is to create an advanced computational intelligence system for the automatic recognition, of invasive or another unknown species. The identification is performed based on the analysis of environmental DNA by employing machine learning methods. More specifically, this research effort proposes a hybrid bio-inspired computational intelligence detection approach. It employs extreme learning machines combined with an evolving Izhikevich spiking neuron model for the automated identification of the invasive fish species “Lagocephalus sceleratus” extremely dangerous for human health.

     

APIS: a software for model identification, simulation and dosage regimen calculations in clinical and experimental pharmacokinetics.

APIS is a software package based on mathematical modelling which provides a reliable approach in optimizing drug therapy. It was designed to assist clinicians in interpreting blood drug levels so that drug therapy may be better and more cost-effective. It is a methodological approach to describe, predict and control the kinetic behaviour of a drug. This software incorporates the principle of Bayesian procedures, i.e. one can use all available patient information (population) to determine patient-specific parameter estimates. These estimates can then be used to design an optimal and individualized drug regimen. APIS is an attractive and useful tool for clinical and experimental pharmacokinetics. APIS may be used on any IBM compatible computer using the Microsoft-Windows environment. The software is menu driven to provide a very user-friendly tool for analysing pharmacokinetic data and for designing dosage regimens.     

Pricing Differentiated Services in the GPRS Environment

The General Packet Radio Service extends the existing GSM mobile communications technology by providing packet switching and higher data rates in order to efficiently access IP-based services in the Internet. We adapt the Differentiated Services Quality-of-Service support framework and apply it over the GPRS air interface in order to provide various levels of service differentiation. We also focus on applying a charging technique so as to publish a unit price for each service class. These prices are designed to lead to the maximization of social welfare and the users' net benefit.     

Head of the line arbitration of packet switches with combined input and output queueing

A single-stage non-blocking N × N packet switch is considered. Data units may be stored before switching at the inputs as well as after switching at the outputs. Some output buffering capacity is intended to achieve high throughput, whereas an additional input buffering capacity keeps losses due to input-buffer overflow reasonably low. The paper studies the impact on performance of the head of the line arbitration policy, i.e. the sequence which is used to transfer data units from the heads of input queues to each output queue. The investigation is based on two performance measures: the average delay and the maximum throughput of the switch. Closed-form expressions for the FCFS, LCFS and the ROS policies are obtained. The result of the average delay with the FCFS policy leads to a lower bound, and that with the LCFS policy to an upper bound for the average delay, corresponding to an arbitrary symmetric policy which does not use information related to the state of the input queues. It is shown that the maximum throughput does not depend on the head of the line arbitration policy. It depends only on the output-buffer size and the packet-size distribution. The cases of fixed and exponentially distributed packet sizes are studied. The effects of asymmetric policies which result in different behaviours of some of the input queues is also considered.