No abstract

No abstract

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

No abstract

The simultaneous production of Bacillus thuringiensis (Bt) based biopesticide and proteases was studied using synthetic medium and wastewater sludge as a raw material. The studies were conducted in shake flask and computer controlled 15-L capacity fermentors. Measuring viable cell and spore counts, entomotoxicity and protease activity monitored the progress of the biopesticide production process. A higher viable cell count and spore count was observed in synthetic Soya medium, however, higher entomotoxicity and protease activity were observed in wastewater sludge medium. Thus, the wastewater sludge is a better raw material than commercial Soya medium for the biopesticides and enzyme production. The maximum entomotoxicity and protease activity observed in the fermentor was 9,332 IU/microL and 4.58 IU/mL, respectively. The proteases produced by Bt were also characterised. Two types of proteases were detected; neutral proteases with pH optimum 7.0 and alkaline proteases with pH optimum 10-11. Further, two types of alkaline proteases were detected; one having a pH and temperature optimum at 10 and 50 degrees C while the other at 11 and 70 degrees C. The protease thermal stability was found to increase in the presence of CaCl2, indicating the proteases were metalloproteases.