01912nas a2200193   4500008004100000022001400041245010800055210006900163260011100232300001200343490000700355520111900362100001901481700001501500700002301515700002201538700002601560856013201586       2009                        eng d  a1386-207300aFeature selection and classification employing hybrid ant colony optimization/random forest methodology0 aFeature selection and classification employing hybrid ant colony  aEXECUTIVE STE Y26, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJUN  a507-5130 v123 a<p>Accurate classification of instances depends on identification and removal of redundant features. Classification of data having high dimensionality is usually performed in conjunction with an appropriate feature selection method. Feature selection enables identification of the most informative feature subset from the enormously vast search space that can accurately classify the given data. We propose an ant colony optimization (ACO)/random forest based hybrid filter-wrapper search technique, which traverses the search space and selects a feature subset with high classifying ability. We evaluate the performance of our algorithm on four widely studied CoEPrA (Comparative Evaluation of Prediction Algorithms, http://coepra.org) datasets. The performance of the software ants mediated hybrid filter/wrapper approach compares well with the available competition results. Thus, the proposed Ant Colony Optimization based technique can effectively find small feature subsets capable of classifying with a very good accuracy and can be employed for feature subset selection with a high level of confidence.</p>1 aPatil, Diwakar1 aRaj, Rahul1 aShingade, Prashant1 aKulkarni, Bhaskar1 aJayaraman, Valadi, K.  uhttps://library.ncl.res.in/content/feature-selection-and-classification-employing-hybrid-ant-colony-optimizationrandom-forest-001967nas a2200265   4500008004100000022001400041245008900055210006900144260000800213300001200221490000700233520111500240653001901355653003101374653002501405653001601430653002101446100002901467700001801496700001701514700001901531700002201550700001901572856011001591       2017                        eng d  a0046-899100aExploiting phosphate-starved cells of scenedesmus SP for the treatment of raw sewage0 aExploiting phosphatestarved cells of scenedesmus SP for the trea  cJUN  a241-2490 v573 a<p>Phosphate depletion is one of the favorable ways to enhance the sewage water treatment with the algae, however, detailed information is essential with respect to internal phosphate concentration and physiology of the algae. The growth rate of the phosphate-starved Scenedesmus cells was reduced drastically after 48 h. Indicating cells entered in the stationary phase of the growth cycle. Fourier Transform Infrared analysis of phosphate-starved Scenedesmus cells showed the reduction in internal phosphate concentration and an increase in carbohydrate/phosphate and carbohydrate/lipid ratio. The phosphate-starved Scenedesmus cells, with an initial cell density of, 1 x 10(6) cells mL(-1) shows 87% phosphate and 100 % nitrogen removal in 24 h. The normal Scenedesmus cells need approximately 48 h to trim down the nutrients from wastewater up to this extent. Other microalgae, Ankistrodesmus, growth pattern was not affected due to phosphate starvation. The cells of Ankistrodesmus was able to reduce 71% phosphate and 73% nitrogen within 24 h, with an initial cell density of, 1 x 10(6) cells mL(-1).</p>10aAnkistrodesmus10aFourier Transform Infrared10aPhosphate starvation10aScenedesmus10aSewage treatment1 aYewalkar-Kulkarni, Swati1 aGera, Gayatri1 aNene, Sanjay1 aPandare, Kiran1 aKulkarni, Bhaskar1 aKamble, Sanjay  uhttps://library.ncl.res.in/content/exploiting-phosphate-starved-cells-scenedesmus-sp-treatment-raw-sewage