Search results for: mining wastewater

Authors: Mohamed F. Awad, M. Kraume

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Thirty six samples from each (aerobic and anoxic) activated sludge were collected from two wastewater treatment plants with MBRs in Berlin, Germany. The samples were prepared for count and definition of fungal isolates; these isolates were purified by conventional techniques and identified by microscopic examination. Sixty tow species belonging to 28 genera were isolated from activated sludge samples under aerobic conditions (28 genera and 58 species) and anoxic conditions (26 genera and 52 species). The obtained data show that, Aspergillus was found at 94.4% followed by Penicillium 61.1 %, Fusarium (61.1 %), Trichoderma (44.4 %) and Geotrichum candidum (41.6 %) species were the most prevalent in all activated sludge samples. The study confirmed that fungi can thrive in activated sludge and sporulation, but isolated in different numbers depending on the effect of aeration system. Some fungal species in our study are saprophytic, and other a pathogenic to plants and animals.

Keywords: Activated sludge, membrane bioreactors, aerobic, anoxic conditions, fungi

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Authors: Chutimon Thitipornvanid, Siripun Sanguansintukul

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Choosing the right metadata is a critical, as good information (metadata) attached to an image will facilitate its visibility from a pile of other images. The image-s value is enhanced not only by the quality of attached metadata but also by the technique of the search. This study proposes a technique that is simple but efficient to predict a single human image from a website using the basic image data and the embedded metadata of the image-s content appearing on web pages. The result is very encouraging with the prediction accuracy of 95%. This technique may become a great assist to librarians, researchers and many others for automatically and efficiently identifying a set of human images out of a greater set of images.

Keywords: Metadata, Prediction, Multi-layer perceptron, Human facial image, Image mining.

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Authors: Seo Young Kim, Jae Won Lee, Jong Sung Bae

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Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene expression. We evaluated the performance of this method by applying it to real sporulation data and simulated data. The patterns obtained using the iterative clustering were found to be superior to those obtained using existing clustering algorithms.

Keywords: Clustering, microarray experiment, temporal pattern of gene expression data.

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Authors: Konstantinos B. Petrotos, Fani K. Karkanta, Paschalis E. Gkoutsidis, Ioannis Giavasis, Konstantinos N. Papatheodorou, Alexandros C. Ntontos

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In the course of the present work, plain (nonencapsulated) and microencapsulated polyphenols were produced using olive mill wastewater (OMW) as raw material, in order to be used for enrichment of yogurt and dairy products. The OMW was first clarified by using membrane technology and subsequently the contained poly-phenols were isolated by adsorption-desorption technique using selective macro-porous resins and finally recovered in dry form after been processed by RO membrane technique followed by freeze drying. Moreover, the polyphenols were encapsulated in modified starch by freeze drying in order to mask the color and bitterness effect and improve their functionality. The two products were used successfully as additives in yogurt preparations and the produced products were acceptable by the consumers and presented with certain advantage to the plain yogurt. For the herein proposed production scheme a patent application was already submitted.

Keywords: OMW, polyphenol-enriched yogurt, encapsulation, bio-active dairy products

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Authors: Fuyang Peng, Bo Deng, Chao Qi, Mou Zhan

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This paper presents the design and implementation of the WebGD, a CORBA-based document classification and retrieval system on Internet. The WebGD makes use of such techniques as Web, CORBA, Java, NLP, fuzzy technique, knowledge-based processing and database technology. Unified classification and retrieval model, classifying and retrieving with one reasoning engine and flexible working mode configuration are some of its main features. The architecture of WebGD, the unified classification and retrieval model, the components of the WebGD server and the fuzzy inference engine are discussed in this paper in detail.

Keywords: Text Mining, document classification, knowledgeprocessing, fuzzy logic, Web, CORBA

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Authors: S. R. Shamili, J. Karthikeyan

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Fast development of the total populace and far and wide urbanization has surprisingly expanded the advancement of the construction industry. As a result of these activities, old structures are being demolished to make new buildings. Due to these large-scale demolitions, a huge amount of debris is generated all over the world, which results in a landfill. The use of construction and demolition waste as landfill causes groundwater contamination, which is hazardous. Using construction and demolition waste as aggregate can reduce the use of natural aggregates and the problem of mining. The objective of this study is to provide a detailed overview on how the construction and demolition waste material has been used as aggregate in structural concrete. In this study, the preparation, classification, and composition of construction and demolition wastes are also discussed.

Keywords: Aggregate, construction and demolition waste, landfill, large scale demolition.

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Authors: Nahla Barakat, Joachim Diederich

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Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule-extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity.

Keywords: Data mining, hybrid rule-extraction algorithms, medical diagnosis, SVMs

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Authors: R. Hegedűs, É. Kerepeczki, D. Gál, F. Pekár, M. Oncsik Bíróné, Gy. Lakatos

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The growth of the aquaculture industry has been associated with negative environmental impacts through the discharge of raw effluents into the adjacent receiving water bodies. Macrophytes from natural saline lakes, which have adaptability to the high salinity, can be suitable for saline effluent treatment. Eight emergent species from natural saline area were planted in an experimental gravel bed hydroponic mesocosm (GBH) which was treated with effluent water from an intensive fish farm using geothermal water. In order to examine the applicability of the halophytes in treatment processes, we tested the relative efficacy of total nitrogen (TN), total phosphorus (TP), potassium (K), sodium (Na), magnesium (Mg) and calcium (Ca) removal for the saline wastewater treatment. Four of the eight species, which were Phragmites australis, Typha angustifolia, Glyceria maxima, Scirpus lacustris spp. tabernaemontani could survive and contribute the experimental treatment.

Keywords: Gravel bed hydroponic system, halophytes, intensive fish farm, salt removal

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Authors: Kapil Kumar, M. G. Dastidar, T. R. Sreekrishnan

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In the present study, an attempt was made to examine the potential of aerobic mixed culture for decolourization of Remazol Black B dye in batch reactors. The effect of pH, temperature, inoculum, initial concentration of dye and initial concentration of glucose was studied with an aim to determine the optimal conditions required for maximum decolourization and degradation. The culture exhibited maximum decolourization ability at pH between 7-8 and at 30°C. A 10% (v/v) inoculum and 1% (w/v) glucose concentration were found to be the optimum for decolourization. A maximum of 98% decolourization was observed at 25 ppm initial concentration of dye after 18 hours of incubation period. At higher dye concentration of 300 ppm, the removal in colour was found to be 75% in 48 hours of incubation period. The results show that the enriched mixed culture from activated sludge has good potential in removal of Remazol Black B dye from wastewater under aerobic conditions.

Keywords: Aerobic conditions, Decolourization, Mixed culture, Remazol Black B.

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Authors: Young-Jin Baik, Minsung Kim, Young-Soo Lee, Ki-Chang Chang, Seong-Ryong Park

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The compression-absorption heat pump (C-A HP), one of the promising heat recovery equipments that make process hot water using low temperature heat of wastewater, was evaluated by computer simulation. A simulation program was developed based on the continuity and the first and second laws of thermodynamics. Both the absorber and desorber were modeled using UA-LMTD method. In order to prevent an unfeasible temperature profile and to reduce calculation errors from the curved temperature profile of a mixture, heat loads were divided into lots of segments. A single-stage compressor was considered. A compressor cooling load was also taken into account. An isentropic efficiency was computed from the map data. Simulation conditions were given based on the system consisting of ordinarily designed components. The simulation results show that most of the total entropy generation occurs during the compression and cooling process, thus suggesting the possibility that system performance can be enhanced if a rectifier is introduced.

Keywords: Waste heat recovery, Heat Pump.

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Authors: Shigeaki Sakurai, Youichi Kitahata, Ryohei Orihara

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This paper proposes a method that discovers sequential patterns corresponding to user-s interests from sequential data. This method expresses the interests as constraint patterns. The constraint patterns can define relationships among attributes of the items composing the data. The method recursively decomposes the constraint patterns into constraint subpatterns. The method evaluates the constraint subpatterns in order to efficiently discover sequential patterns satisfying the constraint patterns. Also, this paper applies the method to the sequential data composed of stock price indexes and verifies its effectiveness through comparing it with a method without using the constraint patterns.

Keywords: Sequential pattern mining, Constraint pattern, Attribute constraint, Stock price indexes

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Authors: William Wong Xiu Shun, Yoonjin Hyun, Mingyu Kim, Seongi Choi, Namgyu Kim

Abstract:

The need to extract R&D keywords from issues and use them to retrieve R&D information is increasing rapidly. However, it is difficult to identify related issues or distinguish them. Although the similarity between issues cannot be identified, with an R&D lexicon, issues that always share the same R&D keywords can be determined. In detail, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Furthermore, the relationship among issues that share the same R&D keywords can be shown in a more systematic way by clustering them according to keywords. Thus, sharing R&D results and reusing R&D technology can be facilitated. Indirectly, redundant investment in R&D can be reduced as the relevant R&D information can be shared among corresponding issues and the reusability of related R&D can be improved. Therefore, a methodology to cluster issues from the perspective of common R&D keywords is proposed to satisfy these demands.

Keywords: Clustering, Social Network Analysis, Text Mining, Topic Analysis.

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Authors: Tsuyoshi Okita

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For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach in order to make a solver quickly. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. It is noted that our formal modeling language is not intend for providing an efficient notation for data mining application, but for facilitating a designer who develops solvers in machine learning.

Keywords: Formal language, statistical inference problem, reduction.

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Authors: M.M. Jafarpour, Ar. Foolad, M.K. Mansouri, Z. Nikbakhsh, H. Saeedizade

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Ammonia nitrogen is one of the most hazardous water pollutants, discharging into water receptors through industrial effluents. Negative environmental impacts of such chemical species in hydrosphere include accelerated eutrophication, water toxicity and harming the aquatics. Natural zeolite clinoptilolite has very high selectivity & capacity for ammonium cation sorption. It occurs in high abundances and rich mines of this zeolite exist in different parts of Iran and thus are available more cheaply and with different sizing. The aim of this study is to investigate ammonia nitrogen removal over this natural sorbent from real samples of high polluted wastewater discharging from a fertilizer producing plant. The experimental results showed that this natural sorbent without even any pre treatment system & with the same particle size available in Iranian markets has still high capability & selectivity in ammonia nitrogen removal both in batch and continuous tests.

Keywords: Ammonia nitrogen removal, Clinoptilolite, Naturalzeolite, Waste water.

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Authors: Sui Xin, Li Chunsheng, Tian Di

Abstract:

Short message integrated distributed monitoring systems (SM-DMS) are growing rapidly in wireless communication applications in various areas, such as electromagnetic field (EMF) management, wastewater monitoring, and air pollution supervision, etc. However, delay in short messages often makes the data embedded in SM-DMS transmit unreliably. Moreover, there are few regulations dealing with this problem in SMS transmission protocols. In this study, based on the analysis of the command and data requirements in the SM-DMS, we developed a processing model for the control center to solve the delay problem in data transmission. Three components of the model: the data transmission protocol, the receiving buffer pool method, and the timer mechanism were described in detail. Discussions on adjusting the threshold parameter in the timer mechanism were presented for the adaptive performance during the runtime of the SM-DMS. This model optimized the data transmission reliability in SM-DMS, and provided a supplement to the data transmission reliability protocols at the application level.

Keywords: Delay, SMS, reliability, distributed monitoringsystem (DMS), wireless communication.

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Authors: Krassimir Genov, Vladimir Georgiev, Todor Batakliev, Dipak K. Sarker

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The Bulgarian natural expanded mineral obtained from Bentonite AD perlite (A deposit of "The Broken Mountain" for perlite mining, near by the village of Vodenicharsko, in the municipality of Djebel), was loaded with silver (as ion form - Ag+ 2 and 5 wt% by the incipient wetness impregnation method), and as atomic silver - Ag0 using Tollen-s reagent (silver mirror reaction). Some physicochemical characterization of the samples are provided via: DC arc-AES, XRD, DR-IR and UV-VIS. The aim of this work was to obtain and test the silver-loaded catalyst for ozone decomposition. So the samples loaded with atomic silver show ca. 80% conversion of ozone 20 minutes after the reaction start. Then conversion decreases to ca. 20 % but stay stable during the prolongation of time.

Keywords: aluminum-silicates, Ag/perlite expanded glass, ozone decomposition

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Authors: Patiparn Punyapalakul, Thitikamon Sitthisorn

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Ciprofloxacin (CIP) and Carbamazepine (CBZ), nonbiodegradable pharmaceutical residues, were become emerging pollutants in several aquatic environments. The objectives of this research were to study the possibility to recover these pharmaceuticals residues from pharmaceutical wastewater by increasing the selective adsorption on synthesized functionalized porous silicate, comparing with powdered activated carbon (PAC). Hexagonal mesoporous silicate (HMS), functionalized HMSs (3- aminopropyltriethoxy, 3- mercaptopropyltrimethoxy and noctyldimethyl) were synthesized and characterized physico-chemical characteristics. Obtained adsorption kinetics and isotherms showed that 3-mercaptopropyltrimethoxy functional groups grafted on HMS provided highest CIP and CBZ adsorption capacities; however, it was still lower than that of PAC. The kinetic results were compatible with pseudo-second order. The hydrophobicity and hydrogen bonding might play a key role on the adsorption. Furthermore, the capacities were affected by varying pH values due to the strength of hydrogen bonding between targeted compounds and adsorbents. Electrostatic interaction might not affect the adsorption capacities.

Keywords: Adsorption, Carbamazepine, Ciprofloxazin, Mesoporous Silicates, Surface functional groups

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Authors: M. Rehor, V. Ondracek

Abstract:

Restoration research has become important on principle recently in Czech Republic. The reason is simple. More than 70 % of mined brown coal comes from the North Bohemian Basin these days. Open cast brown coal mining has lead to large damage on the landscape. Reclamation of phytotoxic areas is one of the serious problems in the North Bohemian Basin. It mainly concerns the areas with the occurrence of overburden rocks from the coal bed enriched with coal. The presented paper includes the characteristics of the important phytotoxic areas and the methodology of their reclamation. The results are documented with the long term monitoring of physical, mineralogical, chemical and pedological parameters of rocks in the testing areas.

Keywords: Brown coal, dump, methodology, restoration.

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Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

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The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system thus the proposed solution has been verified. The paper documents how is possible to apply the new discovery knowledge to use in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: Hierarchical process control, knowledge discovery from databases, neural network.

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Authors: Faruq A. Al-Omari, Nabeel I. Al-Fayoumi

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In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.

Keywords: Data clustering, Data mining, Image-mapping, Pattern discovery, Predictive analysis.

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Authors: Huang, Wei-Hsiu Chang, Pei-Chann, Wang, Lien-Chun

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The problems with high complexity had been the challenge in combinatorial problems. Due to the none-determined and polynomial characteristics, these problems usually face to unreasonable searching budget. Hence combinatorial optimizations attracted numerous researchers to develop better algorithms. In recent academic researches, most focus on developing to enhance the conventional evolutional algorithms and facilitate the local heuristics, such as VNS, 2-opt and 3-opt. Despite the performances of the introduction of the local strategies are significant, however, these improvement cannot improve the performance for solving the different problems. Therefore, this research proposes a meta-heuristic evolutional algorithm which can be applied to solve several types of problems. The performance validates BBEA has the ability to solve the problems even without the design of local strategies.

Keywords: Combinatorial problems, Artificial Chromosomes, Blocks Mining, Block Recombination

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Authors: Irwin Ma, Tony Wong, Thiagas Sankar

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By systematically applying different engineering methods, difficult financial problems become approachable. Using a combination of theory and techniques such as wavelet transform, time series data mining, Markov chain based discrete stochastic optimization, and evolutionary algorithms, this work formulated a strategy to characterize and forecast non-linear time series. It attempted to extract typical features from the volatility data sets of S&P100 and S&P500 indices that include abrupt drops, jumps and other non-linearity. As a result, accuracy of forecasting has reached an average of over 75% surpassing any other publicly available results on the forecast of any financial index.

Keywords: Discrete stochastic optimization, genetic algorithms, genetic programming, volatility forecast

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Authors: Almudena Aguinaco, Olga Gimeno, Fernando J. Beltrán, Juan José P. Sagasti

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A sequential treatment of ozonation followed by a Fenton or photo-Fenton process, using black light lamps (365 nm) in this latter case, has been applied to remove a mixture of pharmaceutical compounds and the generated by-products both in ultrapure and secondary treated wastewater. The scientifictechnological innovation of this study stems from the in situ generation of hydrogen peroxide from the direct ozonation of pharmaceuticals, and can later be used in the application of Fenton and photo-Fenton processes. The compounds selected as models were sulfamethoxazol and acetaminophen. It should be remarked that the use of a second process is necessary as a result of the low mineralization yield reached by the exclusive application of ozone. Therefore, the influence of the water matrix has been studied in terms of hydrogen peroxide concentration, individual compound concentration and total organic carbon removed. Moreover, the concentration of different iron species in solution has been measured.

Keywords: Fenton, photo-Fenton, ozone, pharmaceutical compounds, hydrogen peroxide, water treatment

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Authors: Yayun Hsu, Henry Horng-Shing Lu

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This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate crosstalk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds, since biological information is inherently included inside the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: Brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning.

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Authors: A. R. Abdul Syukor, A. W. Zularisam, Z. Ideris, M. S. Mohd Ismid, H. M. Nakmal, S. Sulaiman, A. H. Hasmanie, M. R. Siti Norsita, M. Nasrullah

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In this study, the effectiveness of an integrated aquatic plants in phytogreen zone was studied and statistical analysis for the promotional integrated phytogreen system approached was discussed. It was found that's the effectiveness of using aquatic plant such as Typha angustifolia sp., Lepironia articulata sp., Limnocharis flava sp., Monochoria vaginalis sp., Pistia stratiotes sp., and Eichhornia crassipes sp., in the conventional oxidation pond process in order to comply the standard A according to Malaysia Environmental Quality Act 1974 (Act 127); Environmental Quality (Sewage) Regulation 2009 for effluent discharge into inland water near the residential area was successfully shown. It was concluded that the integrated phtogreen system developed in this study has great potential for refurbishment wastewater in conventional oxidation pond.

Keywords: Phytoremediation, integrated phytogreen system, sewage treatment plant, oxidation pond, aquatic plants.

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Authors: Freeman Ntuli

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An overview of the important aspects of managing and controlling industrial effluent discharges to public sewers namely sampling, characterization, quantification and legislative controls has been presented. The findings have been validated by means of a case study covering three industrial sectors namely, tanning, textile finishing and food processing industries. Industrial effluents discharges were found to be best monitored by systematic and automatic sampling and quantified using water meter readings corrected for evaporative and consumptive losses. Based on the treatment processes employed in the public owned treatment works and the chemical oxygen demand and biochemical oxygen demand levels obtained, the effluent from all the three industrial sectors studied were found to lie in the toxic zone. Thus, physico-chemical treatment of these effluents is required to bring them into the biodegradable zone. KL values (quoted to base e) were greater than 0.50 day-1 compared to 0.39 day-1 for typical municipality wastewater.

Keywords: biodegradability, industrial effluent, pollution control, public sewers

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Authors: Suraiya Jabin, Kamal K. Bharadwaj

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In the recent past Learning Classifier Systems have been successfully used for data mining. Learning Classifier System (LCS) is basically a machine learning technique which combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. All LCSs models more or less, comprise four main components; a finite population of condition–action rules, called classifiers; the performance component, which governs the interaction with the environment; the credit assignment component, which distributes the reward received from the environment to the classifiers accountable for the rewards obtained; the discovery component, which is responsible for discovering better rules and improving existing ones through a genetic algorithm. The concatenate of the production rules in the LCS form the genotype, and therefore the GA should operate on a population of classifier systems. This approach is known as the 'Pittsburgh' Classifier Systems. Other LCS that perform their GA at the rule level within a population are known as 'Mitchigan' Classifier Systems. The most predominant representation of the discovered knowledge is the standard production rules (PRs) in the form of IF P THEN D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski and Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: IF P THEN D UNLESS C, where Censor C is an exception to the rule. Such rules are employed in situations, in which conditional statement IF P THEN D holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the IF P THEN D part of CPR expresses important information, while the UNLESS C part acts only as a switch and changes the polarity of D to ~D. In this paper Pittsburgh style LCSs approach is used for automated discovery of CPRs. An appropriate encoding scheme is suggested to represent a chromosome consisting of fixed size set of CPRs. Suitable genetic operators are designed for the set of CPRs and individual CPRs and also appropriate fitness function is proposed that incorporates basic constraints on CPR. Experimental results are presented to demonstrate the performance of the proposed learning classifier system.

Keywords: Censored Production Rule, Data Mining, GeneticAlgorithm, Learning Classifier System, Machine Learning, PittsburgApproach, , Reinforcement learning.

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Authors: Binu Thomas, Raju G., Sonam Wangmo

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In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the cmeans algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan-s Gross National Happiness (GNH) program.

Keywords: Adaptive fuzzy clustering, clustering, fuzzy logic, fuzzy clustering, c-means.

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Authors: Krishnamoorthi Makkithaya, N. V. Subba Reddy, U. Dinesh Acharya

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As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS.

Keywords: Data mining, Decision tree, Feature selection, Fuzzyc- means clustering, Intrusion detection.

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Authors: Yu Wang

Abstract:

Nowadays, Internet enables its users to share the information online and to interact with others. Facing with numerous information, these Internet users are confused and begin to rely on the opinion leaders’ recommendations. The online opinion leaders are the individuals who have professional knowledge, who utilize the online channels to spread word-of-mouth information and who can affect the attitudes or even the behavior of their followers to some degree. Because utilizing the online opinion leaders is seen as an important approach to affect the potential consumers, how to identify them has become one of the hottest topics in the related field. Hence, in this article, the concepts and characteristics are introduced, and the researches related to identifying opinion leaders are collected and divided into three categories. Finally, the implications for future studies are provided.

Keywords: Online opinion leaders, user attributes analysis, text mining analysis, network structure analysis.

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