No abstract

-Within this Paper, a concept of machine learning strategies suggested. In this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as other operational and non-operational aspects.

No abstract

No abstract

A single arsenic decontamination system has been developed using shrimp shell for tube well water with arsenic concentration of 120 ?g/L which could be lowered to15?g/L. Arsenic was removed by this system from water by adsorption through fine particles of shrimp shell. Various conditions of adsorption/desorption of arsenic were investigated. Adsorption column method showed the complete removal of As(III) under the following conditions: initial As concentration, 100?g/L, amount of shrimp shell 3.0g, particle size <355?m, treatment flow rate 0.5mL/min and pH 6.5. Desorption efficiency was found in the range of 81-83% with 4M of NaOH after the treatment of groundwater. A combination of techniques was used to remove nine metals of groundwater. Other inorganic constituents of health concern (Cu, Cd, Mn and Fe) in treated water were below their respective WHO guideline for drinking water.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11489 Dhaka Univ. J. Sci. 60(2): 175-180, 2012 (July)