Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Complete optical absorption and fluorescence spectra were collected for a diverse suite of 0.2-μm-filtered marine, riverine, and estuarine waters, as well as for colored dissolved organic matter (CDOM) isolated from several of these waters by solid-phase C 18 extraction. Absorption and fluorescence parameters for these samples are reported. For surface waters, variations in the fluorescence quantum yields obtained with 355- and 337-nm excitation fell within a narrow window (< 2.5-fold variation about the mean values), demonstrating that fluorescence measurements can be used to determine absorption coefficients of CDOM in the ultraviolet region with reasonably good accuracy. Methods for predicting absorption coefficients and line shapes from the fluorescence data are introduced and tested. The absorption and fluorescence spectra of CDOM extracted from some seawaters differed significantly from those of the original waters, demonstrating that material isolated by hydrophobic adsorption is not necessarily representative of the suite of colored organic matter present in aquatic systems. These results clearly illustrate that great care must be taken when extracted material is used to infer the optical properties of natural waters

Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).

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A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.

Polyphenol oxidase (PPO)-catalyzed browning reactions are of significant importance in the fruit and vegetable industry. These reactions proceeding in many foods of plant origin cause deterioration and loss of food quality. A better knowledge of the factors that influence the action of PPO is imperative in order to control and manipulate its detrimental activity in plant products. This paper presents an overview of the current understanding of the reaction properties, biochemical characteristics and potential physiological roles of PPO in plants. Reaction properties will include general PPO reactions, specificities and molecular mechanisms of these reactions, and methods available to assess PPO activity. Physicochemical properties will evaluate substrate specificity, environmental influences such as pH and temperature, multiplicity, latency, and activators and inhibitors of PPO. The discussion will conclude with potential physiological roles of PPO in plants.

Physicochemical properties and function of plant polyphenol oxidase: a review'

Glycerol is a valuable byproduct in biodiesel production by transesterification, soap manufacturing by saponification as well as hydrolysis reaction. The purity of glycerol obtained is low due to the presence of impurities such as remaining catalyst, water, soaps, salts and esters formed during the reaction. Purification of glycerol as well as the conversion of glycerol into valuable products has attained growing interest in recent years due to the dramatic growth of the biodiesel industry. This paper reviews different methods of producing crude glycerol as the major by-product. Purification of glycerol was reported as well as value-added products produced from glycerol.

Glycerol production and its applications as a raw material: A review

The use of remote sensing and GIS in water monitoring and management has been long recognized. This paper however discusses the application of remote sensing and GIS specifically in monitoring water quality parameter such as suspended matter, phytoplankton, turbidity, and dissolved organic matter. In fact the capability of this technology offers great tools of how the water quality monitoring and managing can be operationalised in this country. Potential application and management is identified in promoting concept of sustainable water resource management. In conclusion remote sensing and GIS technologies coupled with computer modelling are useful tools in providing a solution for future water resources planning and management to government especially in formulating policy related to water quality.

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Use of Remote Sensing and GIS in Monitoring Water Quality

Integrating GIS and expert judgment in a multi-criteria analysis to map and develop a habitat suitability index: A case study of large mammals on the Malayan Peninsula

In order to develop forest management strategies in
tropical forest in Malaysia, surveying the forest resources and
monitoring the forest area affected by logging activities is essential.
There are tremendous effort has been done in classification of land
cover related to forest resource management in this country as it is a
priority in all aspects of forest mapping using remote sensing and
related technology such as GIS. In fact classification process is a
compulsory step in any remote sensing research. Therefore, the main
objective of this paper is to assess classification accuracy of
classified forest map on Landsat TM data from difference number of
reference data (200 and 388 reference data). This comparison was
made through observation (200 reference data), and interpretation
and observation approaches (388 reference data). Five land cover
classes namely primary forest, logged over forest, water bodies, bare
land and agricultural crop/mixed horticultural can be identified by
the differences in spectral wavelength. Result showed that an overall
accuracy from 200 reference data was 83.5 % (kappa value
0.7502459; kappa variance 0.002871), which was considered
acceptable or good for optical data. However, when 200 reference
data was increased to 388 in the confusion matrix, the accuracy
slightly improved from 83.5% to 89.17%, with Kappa statistic
increased from 0.7502459 to 0.8026135, respectively. The accuracy
in this classification suggested that this strategy for the selection of
training area, interpretation approaches and number of reference data
used were importance to perform better classification result.

Satellite Data Classification Accuracy Assessment Based from Reference Dataset

Purification of Crude Glycerol from Transesterification RBD Palm Oil over Homogeneous and Heterogeneous Catalysts for the Biolubricant Preparation

Ecotourism is widely accepted as the most effective type of sustainable development. For monitoring ecotourism sustainability in the Northern forest of Iran, a study was designed to help identifying criteria and indicators (C&I) with the principle goal to ensure the objectives of forest management, and at the same time – maintain processes in a sustainable manner. Indicators act as an instrument which can be used for monitoring sustainability. Hence indicators should be multidisciplinary, and cover all aspect of social, ecological, cultural, economic and institutional factors affecting sustainability of ecotourism. In this study, C&I were identified by using the Delphi approach through multidisciplinary panel team. Three rounds of meetings were held for discussions and dissemination of research to a panel of local experts. At the end of the second round we reached a consensus on 9 criteria and 61 indicators, which include 21 indicators related to ecological aspects, 8 to economic aspects, 21 to social aspects, 6 to cultural aspects and 5 to institutional aspects. The selected indicators would be applied by the Iranian Cultural, Heritage, Handicrafts and Tourism Organization for monitoring ecotourism sustainability in the Northern forest of Iran.

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Delphi technique for generating criteria and indicators in monitoring ecotourism sustainability in Northern forests of Iran: Case study on Dohezar and Sehezar Watersheds

Mohd Hasmadi Ismail

Papers

Use of Remote Sensing and GIS in Monitoring Water Quality

Integrating GIS and expert judgment in a multi-criteria analysis to map and develop a habitat suitability index: A case study of large mammals on the Malayan Peninsula

Satellite Data Classification Accuracy Assessment Based from Reference Dataset

Purification of Crude Glycerol from Transesterification RBD Palm Oil over Homogeneous and Heterogeneous Catalysts for the Biolubricant Preparation

Delphi technique for generating criteria and indicators in monitoring ecotourism sustainability in Northern forests of Iran: Case study on Dohezar and Sehezar Watersheds