Search results for: water quality classification

Authors: E. A. Kiridi, A. O. Ogunlela

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Conventional wastewater treatment plants of activated carbon, electrodialysis, ion exchange, reverse osmosis etc. are expensive to install, operate and maintain especially in developing countries; therefore, the use of aquatic macrophytes for wastewater purification is a viable alternative. On the first day of experimentation, approximately 100g of water hyacinth was introduced into the hydroponic units in four replicates. The water quality parameters measured were total suspended solids (TSS), pH and electrical conductivity (EC). Others were concentration of ammonium–nitrogen (NH₄⁺-N), nitrite-nitrogen (NO₂^--N), nitrate-nitrogen (NO₃^--N), phosphate–phosphorus (PO₄^3--P), and biomass value. At phytoremediation intervals of 7, 14, 21 and 28 days, the biomass recorded were 438.2 g, 600.7 g, 688.2 g and 725.7 g. Water hyacinth was able to reduce the pollutant concentration of all the selected parameter. The percentage reduction of pH ranged from 1.9% to 14.7%, EC from 49.8% to 97.0%, TDS from 50.4% to 97.6%, TSS from 34.0% to 78.3%, NH₄⁺-N from 38.9% to 85.2%, NO₂^--N from 0% to 84.6%, NO₃^--N from 63.2% to 98.8% and PO₄^3--P from 10% to 88.0%. Paired sample t-test shows that at 95% confidence level, it can be concluded statistically that the inequality between the pre-treatment and post-treatment values are significant. This suggests that the use of water hyacinth is valuable in the design and operation of aquaculture effluent treatment and should therefore be adopted by environmental and wastewater managers.

Keywords: Aquaculture effluent, phytoremediation, pollutant, water hyacinth.

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Authors: Amal M. Alrayes

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Data and system quality play a central role in organizational success, and the quality of any existing information system has a major influence on the effectiveness of overall system performance. Given the importance of system and data quality to an organization, it is relevant to highlight their importance on organizational performance in the Kingdom of Bahrain. This research aims to discover whether system quality and data quality are related, and to study the impact of system and data quality on organizational success. A theoretical model based on previous research is used to show the relationship between data and system quality, and organizational impact. We hypothesize, first, that system quality is positively associated with organizational impact, secondly that system quality is positively associated with data quality, and finally that data quality is positively associated with organizational impact. A questionnaire was conducted among public and private organizations in the Kingdom of Bahrain. The results show that there is a strong association between data and system quality, that affects organizational success.

Keywords: Data quality, performance, system quality.

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Authors: Iliycho P. Iliev, Desislava S. Voynikova, Snezhana G. Gocheva-Ilieva

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This paper examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting at two wavelengths - 510.6 and 578.2nm. Laser output power is estimated based on 10 independent input physical parameters. A classification and regression tree (CART) model is obtained which describes 97% of data. The resulting binary CART tree specifies which input parameters influence considerably each of the classification groups. This allows for a technical assessment that indicates which of these are the most significant for the manufacture and operation of the type of laser under consideration. The predicted values of the laser output power are also obtained depending on classification. This aids the design and development processes considerably.

Keywords: Classification and regression trees (CART), Copper Bromide laser (CuBr laser), laser generation, nonparametric statistical model.

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Authors: M. Safa, A. Sabet, S. MacGillivray, M. Davidson, K. Kaczmarczyk, C. T. Haas, G. E. Gibson, D. Rayside

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In order to address construction project requirements and specifications, scholars and practitioners need to establish taxonomy according to a scheme that best fits their need. While existing characterization methods are continuously being improved, new ones are devised to cover project properties which have not been previously addressed. One such method, the Project Definition Rating Index (PDRI), has received limited consideration strictly as a classification scheme. Developed by the Construction Industry Institute (CII) in 1996, the PDRI has been refined over the last two decades as a method for evaluating a project's scope definition completeness during front-end planning (FEP). The main contribution of this study is a review of practical project classification methods, and a discussion of how PDRI can be used to classify projects based on their readiness in the FEP phase. The proposed model has been applied to 59 construction projects in Ontario, and the results are discussed.

Keywords: Project classification, project definition rating index (PDRI), project goals alignment, risk.

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Authors: Mohammad Hosein Hojati , Fardin Boustani

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Groundwater resources in Arsanjan plain provide water for agriculture, industry, and human consumption. Continued agricultural development in this area needs to additional groundwater resources for, particularly during of drought periods, and effects on the quantity and quality of ground water available. The purpose of this study is to evaluate water level changes in the aquifer of Arsanjan plain in the Fars province in order to determine the areas of greatest depletion and the causes of depletion. In this plain, farmers and other users are pumping groundwater faster than its natural replenishment rate, causing a continuous drop in groundwater tables and depletion of this resource. In this research variation of groundwater level, their effects and ways to help control groundwater levels in aquifer of the Arsanjan plains were evaluated .Excessive exploitation of groundwater in this aquifer caused the groundwater levels fall too fast or to unacceptable levels. The average drawdown of the groundwater level in this plain were 19.66 meters during 1996 to 2003.

Keywords: Aquifer , ground water depletion, water table

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Authors: P. Prema, B. Ramadoss

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Computer game industry has experienced exponential growth in recent years. A game is a recreational activity involving one or more players. Game input is information such as data, commands, etc., which is passed to the game system at run time from an external source. Conversely, game outputs are information which are generated by the game system and passed to an external target, but which is not used internally by the game. This paper identifies a new classification scheme for game input and output, which is based on player-s input and output. Using this, relationship table for game input classifier and output classifier is developed.

Keywords: Game Classification, Game Input, Game Output, Game Testing.

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Authors: Fardin Boustani , Mohammah Hosein Hojati

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The Beshar River is one aquatic ecosystem,which is affected by pollutants. This study was conducted to evaluate the effects of human activities on the water quality of the Beshar river. This river is approximately 190 km in length and situated at the geographical positions of 51° 20' to 51° 48' E and 30° 18' to 30° 52' N it is one of the most important aquatic ecosystems of Kohkiloye and Boyerahmad province next to the city of Yasuj in southern Iran. The Beshar river has been contaminated by industrial, agricultural and other activities in this region such as factories, hospitals, agricultural farms, urban surface runoff and effluent of wastewater treatment plants. In order to evaluate the effects of these pollutants on the quality of the Beshar river, five monitoring stations were selected along its course. The first station is located upstream of Yasuj near the Dehnow village; stations 2 to 4 are located east, south and west of city; and the 5th station is located downstream of Yasuj. Several water quality parameters were sampled. These include pH, dissolved oxygen, biological oxygen demand (BOD), temperature, conductivity, turbidity, total dissolved solids and discharge or flow measurements. Water samples from the five stations were collected and analysed to determine the following physicochemical parameters: EC, pH, T.D.S, T.H, No2, DO, BOD5, COD during 2008 to 2009. The study shows that the BOD5 value of station 1 is at a minimum (1.5 ppm) and increases downstream from stations 2 to 4 to a maximum (7.2 ppm), and then decreases at station 5. The DO values of station 1 is a maximum (9.55 ppm), decreases downstream to stations 2 - 4 which are at a minimum (3.4 ppm), before increasing at station 5. The amount of BOD and TDS are highest at the 4th station and the amount of DO is lowest at this station, marking the 4th station as more highly polluted than the other stations. The physicochemical parameters improve at the 5th station due to pollutant degradation and dilution. Finally the point and nonpoint pollutant sources of Beshar river were determined and compared to the monitoring results.

Keywords: Beshar river, physicochemical parameter, waterpollution, Yasuj

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Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Classification, Bayesian network; structure learning, K2 algorithm, expert knowledge, surface water analysis.

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Authors: Nor Fazlina Iryani Abdul Hamid, Mohamad Khatim Hasan

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Numbers of software quality measurement system have been implemented over the past few years, but none of them focuses on telecommunication industry. Software quality measurement system for telecommunication industry was a system that could calculate the quality value of the measured software that totally focused in telecommunication industry. Before designing a system, quality factors, quality attributes and quality metrics were identified based on literature review and survey. Then, using the identified quality factors, quality attributes and quality metrics, quality model for telecommunication industry was constructed. Each identified quality metrics had its own formula. Quality value for the system was measured based on the quality metrics and aggregated by referring to the quality model. It would classify the quality level of the software based on Net Satisfaction Index (NSI). The system was designed using object-oriented approach in web-based environment. Thus, existing of software quality measurement system was important to both developers and users in order to produce high quality software product for telecommunication industry.

Keywords: Software Quality, Quality Measurement, Object-oriented Approach, Net satisfaction Index.

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Authors: Hui-Chuan Chu, Min-Ju Liao, Wei-Kai Cheng, William Wei-Jen Tsai, Yuh-Min Chen

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Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.

Keywords: Emotion classification, Physiological and facial Expression measures, Students with autism, Mathematics e-learning.

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Authors: Hasan Kalyoncu, Ayşe Güneş

Abstract:

This study was carried on 12 determined stations, on Alara and Karpuz Streams, between January and November 2014. Seasonal samples were taken from the stations to analyze physicochemical parameters and Plecoptera Fauna in the water. The correlation between identified taxa and physicochemical data were tried to determine. As the result of the study, 2088 individuals from Plecoptera fauna were examined, 3 genera and 13 species were identified. The taxa of Brachyptera risi, Capnia bifrons, Dinocras cephalotes, Diura bicaudata, Isogenus nebecula, Isogenus sp., Isoperla grammatica, Leuctra hippopus, Leuctra inermis, Leuctra moselyi, Leuctra sp., Nemoura sp., Perla bipunctata, Perla marginata, Protonemura meyeri and Rhabdiopteryx acuminata were determined. In Alara Stream, the dominant species were; Isogenus nebecula at stations I and IV, Leuctra moselyi at station II, Leuctra hippopus at stations III, V and VI. In Karpuz Stream, Brachyptera risi was the dominant species in all stations. While Leuctra hippopus was the dominant taxon in Alara Stream, in Karpuz Stream it was Brachyptera risi. The highest diversity value was at station III and the lowest was at station VI in Alara Stream and the lowest diversity value was at station VI, while the highest was at station I in Karpuz Stream. In Alara Stream, the most similar stations were I and III, while in Karpuz Stream the highest similarity was determined between stations I and II. As for the evaluation result, the water quality of Alara and Karpuz Streams were determined as at oligosaprobic level.

Keywords: Alara Stream, Karpuz Stream, Plecoptera, water quality.

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Authors: A. Szabó Nagy, J. Szabó, I. Vass

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Water and sediment samples from the Danube River and Moson Danube Arm (Hungary) have been collected and analyzed for contamination by 18 polycyclic aromatic hydrocarbons (PAHs) and eight trace metal(loid)s (As, Cu, Pb, Ni, Cr, Cd, Hg and Zn) in the period of 2014-2015. Moreover, the trace metal(loid) concentrations were measured in the Rába and Marcal rivers (parts of the tributary system feeding the Danube). Total PAH contents in water were found to vary from 0.016 to 0.133 µg/L and concentrations in sediments varied in the range of 0.118 mg/kg and 0.283 mg/kg. Source analysis of PAHs using diagnostic concentration ratios indicated that PAHs found in sediments were of pyrolytic origins. The dissolved trace metal and arsenic concentrations were relatively low in the surface waters. However, higher concentrations were detected in the water samples of Rába (Zn, Cu, Ni, Pb) and Marcal (As, Cu, Ni, Pb) compared to the Danube and Moson Danube. The concentrations of trace metals in sediments were higher than those found in water samples.

Keywords: Surface water, sediment, PAH, trace metal.

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Authors: Hossein Sahoolizadeh, Mahdi Rahimi, Hamid Dehghani

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We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network-s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.

Keywords: Face recognition, Neural Networks, Multi-layer Perceptron, masking.

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Authors: Mehran Yazdi, Kazem Gheysari

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In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for classifying fingerprints into four common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.

Keywords: Biometrics, fingerprint classification, gray level cooccurrence matrix, regular texture representation.

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Authors: Hasan Kalyoncu, Burcu Şerbetci

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Diatoms are an important group of aquatic ecosystems and diatom-based indices are increasingly becoming important tools for the assessment of ecological conditions in lotic systems. Although the studies are very limited about Turkish rivers, diatom indices were used for monitoring rivers in different basins. In the present study, we used OMNIDIA program for estimation of stream quality. Some indices have less sensitive (IDP, WAT, LOBO, GENRE, TID, CEE, PT), intermediate sensitivities (IDSE, DESCY, IPS, DI-CH, SLA, IDAP), the others higher sensitivities (SID, IBD, SHE, EPI-D). Among the investigated diatom communities, only a few taxa indicated alfa-mesosaprobity and polysaprobity. Most of the sites were characterized by a great relative contribution of eutraphent and tolerant ones as well as oligosaprobic and betamesosaprobic diatoms. In general, SID and IBD indices gave the best results. This study suggests that the structure of benthic diatom communities and diatom indices, especially SID, can be applied for monitoring rivers in Southern Turkey. 

Keywords: Diatom, Darı stream, OMNIDIA, Turkey, Water quality.

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: Remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction.

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Authors: Yiannis G. Smirlis

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The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: Data envelopment analysis, interval DEA, efficiency classification, efficiency prediction.

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Authors: L. Mukosha, M. S. Onyango, A. Ochieng, H. Kasaini

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The remediation of water resources pollution in developing countries requires the application of alternative sustainable cheaper and efficient end-of-pipe wastewater treatment technologies. The feasibility of use of South African cheap and abundant pine tree (Pinus patula) sawdust for development of lowcost AC of comparable quality to expensive commercial ACs in the abatement of water pollution was investigated. AC was developed at optimized two-stage N2-superheated steam activation conditions in a fixed bed reactor, and characterized for proximate and ultimate properties, N2-BET surface area, pore size distribution, SEM, pHPZC and FTIR. The sawdust pyrolysis activation energy was evaluated by TGA. Results indicated that the chars prepared at 800oC and 2hrs were suitable for development of better quality AC at 800oC and 47% burn-off having BET surface area (1086m2/g), micropore volume (0.26cm3/g), and mesopore volume (0.43cm3/g) comparable to expensive commercial ACs, and suitable for water contaminants removal. The developed AC showed basic surface functionality at pHPZC at 10.3, and a phenol adsorption capacity that was higher than that of commercial Norit (RO 0.8) AC. Thus, it is feasible to develop better quality low-cost AC from (Pinus patula) sawdust using twostage N2-steam activation in fixed-bed reactor.

Keywords: Activated carbon, phenol adsorption, sawdust integrated utilization, economical wastewater treatment.

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Authors: A. Castillo, A. Gutiérrez, J. M. Gutiérrez, J. M. Gómez, E. García-López

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Water has always been a very precious resource. However, many of us do not fully understand or appreciate water-s value until there will be a shortage. We intended to analyze the water consumption into the Spanish households to understand their behavior according to the habitants of the house. In this research was carried out a survey of users, asking for water consumption of their households. The aim of this paper is get a reference value of consumers in Spanish households to help to check their bill and realize if their consumption is excessive, including some tips to decrease it.

Keywords: Households, survey, water consumption.

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Authors: Caixia Kang, Takahiro Kuba, Aimin Hao, Yasushi Iseri, Chunjie Li, Zhenjia Zhang

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To improve the water quality of lakes and control algae blooms, the effects of Vallisneria asiatica which is one of aquatic plants spread over Lake Taihu, with different biomasses on the water quality and algae communities were researched. The results indicated that V. asiatica could control an excess of Microcystis spp. when the V. asiatica biomass was larger than 50g in the tank with 30L solution in the laboratory. Planktonic and epiphytic algae responded differently to V. asiatica. The presence of macrophyte V. asiatica in eutrophic waters has a positive effect on algae compositions because of different sensitivities of algae species to allelopathic substances released by macrophyte V. asiatica. That is, V. asiatica could inhibit the growth of Microcystis spp. effectively and was benefited to the diatom on the condition in the laboratory.

Keywords: Algae bloom, algae community, Microcystis spp., Vallisneria asiatica.

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Authors: Samira Vafaye Eslahi

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BCI (Brain Computer Interface) is a communication machine that translates brain massages to computer commands. These machines with the help of computer programs can recognize the tasks that are imagined. Feature extraction is an important stage of the process in EEG classification that can effect in accuracy and the computation time of processing the signals. In this study we process the signal in three steps of active segment selection, fractal feature extraction, and classification. One of the great challenges in BCI applications is to improve classification accuracy and computation time together. In this paper, we have used student’s 2D sample t-statistics on continuous wavelet transforms for active segment selection to reduce the computation time. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier, FKNN (Fuzzy K-Nearest Neighbors), LDA (Linear Discriminate Analysis), and SVM (Support Vector Machines). We resulted that active segment selection method would reduce the computation time and Fractal dimension features with ANFIS analysis on selected active segments is the best among investigated methods in EEG classification.

Keywords: EEG, Student’s t- statistics, BCI, Fractal Features, ANFIS, FKNN.

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Authors: Bhausaheb L. Pangarkar, Saroj B. Parjane, M.G. Sane

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In India, the quarrel between the budding human populace and the planet-s unchanging supply of freshwater and falling water tables has strained attention the reuse of gray water as an alternative water resource in rural development. This paper present the finest design of laboratory scale gray water treatment plant, which is a combination of natural and physical operations such as primary settling with cascaded water flow, aeration, agitation and filtration, hence called as hybrid treatment process. The economical performance of the plant for treatment of bathrooms, basins and laundries gray water showed in terms of deduction competency of water pollutants such as COD (83%), TDS (70%), TSS (83%), total hardness (50%), oil and grease (97%), anions (46%) and cations (49%). Hence, this technology could be a good alternative to treat gray water in residential rural area.

Keywords: Gray water treatment plant, gray water, naturaltechnology, pollutant.

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Authors: S. Boonpoke, B. Marungsri

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This paper presents the effectiveness of artificial intelligent technique to apply for pattern recognition and classification of Partial Discharge (PD). Characteristics of PD signal for pattern recognition and classification are computed from the relation of the voltage phase angle, the discharge magnitude and the repeated existing of partial discharges by using statistical and fractal methods. The simplified fuzzy ARTMAP (SFAM) is used for pattern recognition and classification as artificial intelligent technique. PDs quantities, 13 parameters from statistical method and fractal method results, are inputted to Simplified Fuzzy ARTMAP to train system for pattern recognition and classification. The results confirm the effectiveness of purpose technique.

Keywords: Partial discharges, PD Pattern recognition, PDClassification, Artificial intelligent, Simplified Fuzzy ARTMAP

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Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

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Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: Fake news detection, types of fake news, machine learning, natural language processing, classification techniques.

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Authors: H. El Ayech, A. Trabelsi

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In this article we are going to discuss the improvement of the multi classes- classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes- subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two classes- models, the elected class will be the strongest one that won-t lose any competition with the other classes. Rates of recognition gotten with the multi class-s approach by two-class-s decomposition are clearly better that those gotten by the simple multi class-s approach.

Keywords: Artificial neural network, letter-recognition, Multi class Classification, Multi Layer Perceptron.

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Authors: Insung Jung, Gi-Nam Wang

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The objective of this paper is to a design of pattern classification model based on the back-propagation (BP) algorithm for decision support system. Standard BP model has done full connection of each node in the layers from input to output layers. Therefore, it takes a lot of computing time and iteration computing for good performance and less accepted error rate when we are doing some pattern generation or training the network. However, this model is using exclusive connection in between hidden layer nodes and output nodes. The advantage of this model is less number of iteration and better performance compare with standard back-propagation model. We simulated some cases of classification data and different setting of network factors (e.g. hidden layer number and nodes, number of classification and iteration). During our simulation, we found that most of simulations cases were satisfied by BP based using exclusive connection network model compared to standard BP. We expect that this algorithm can be available to identification of user face, analysis of data, mapping data in between environment data and information.

Keywords: Neural network, Back-propagation, classification.

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Authors: F. Javier Benitez, Carolina Garcia, Juan Luis Acero, Francisco J. Real

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Four phenylurea herbicides (isoproturon, chlortoluron, diuron and linuron) were dissolved in different water matrices in order to study their chemical degradation by using UV radiation, ozone and some advanced oxidation processes (UV/H2O2, O3/H2O2, Fenton reagent and the photo- Fenton system). The waters used were: ultra-pure water, a commercial mineral water, a groundwater and a surface water taken from a reservoir. Elimination levels were established for each herbicide and for several global quality parameters, and a kinetic study was performed in order to determine basic kinetic parameters of each reaction between the target phenylureas and these oxidizing systems.

Keywords: Phenylurea herbicides, UV radiation; Ozone, Fenton reagent, Hydroxyl radicals, Rate constants, Quantum yields

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Authors: Hesham Muhammad Ibrahim

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This paper introduces and proves new concept of salt dissolving in water as very tiny solid sodium chloride particles of nanovolumes, from this point of view salt water can be desalinated by collision with special surface characterized by smoothness upon nano level, high rigidity, high hardness under appropriate conditions of water launching in the form of thin laminar flow under suitable speed and angle of incidence to get desalinated water.

Keywords: Desalination by collision, nano coating, water desalination, water repellent surface.

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Authors: Abdoullah Fath-Allah, Laila Cheikhi, Rafa E. Al-Qutaish, Ali Idri

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Many quality models have been used to measure egovernment portals quality. However, the absence of an international consensus for e-government portals quality models results in many differences in terms of quality attributes and measures. The aim of this paper is to compare and analyze the existing e-government quality models proposed in literature (those that are based on ISO standards and those that are not) in order to propose guidelines to build a good and useful e-government portals quality model. Our findings show that, there is no e-government portal quality model based on the new international standard ISO 25010. Besides that, the quality models are not based on a best practice model to allow agencies to both; measure e-government portals quality and identify missing best practices for those portals.

Keywords: E-government, portal, best practices, quality model, ISO, standard, ISO 25010, ISO 9126.

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Authors: E. A. Kiridi, A. O. Ogunlela

Abstract:

The control of ammonia build-up and its by-product is a limiting factor for a successful commercial aquaculture in a developing country like Nigeria. The technology for an advanced treatment of fish tank effluent is uneconomical to local fish farmers which have led to indiscriminate disposal of aquaculture wastewater, thereby increasing the concentrations of these nitrogenous compound and other contaminants in surface and groundwater above the permissible level. Phytoremediation using water lettuce could offer cheaper and sustainable alternative. On the first day of experimentation, approximately 100 g of water lettuce were replicated in four hydroponic units containing aquaculture effluents. The water quality parameters measured were concentration of ammonium–nitrogen (NH₄⁺-N), nitrite-nitrogen (NO₂^--N), nitrate-nitrogen (NO₃^--N), and phosphate–phosphorus (PO₄^3--P). Others were total suspended solids (TSS), pH, electrical conductivity (EC), and biomass value. At phytoremediation intervals of 7, 14, 21 and 28 days, the biomass recorded were 361.2 g, 498.7 g, 561.2 g, and 623.7 g. Water lettuce was able to reduce the pollutant concentration of all the selected parameter. The percentage reduction of pH ranged from 3.9% to 14.4%, EC from 49.8% to 96.2%, TDS from 50.4% to 96.2%, TSS from 38.3% to 81.7%, NH₄⁺-N from 38.9% to 90.7%, NO₂^--N from 0% to 74.9%, NO₃^--N from 63.2% to 95.9% and PO₄^3--P from 0% to 76.3%. At 95% confidence level, the analysis of variance shows that F(critical) is less than F(cal) and p < 0.05; therefore, it can be concluded statistically that the inequality between the pre-treatment and post-treatment values are significant. This suggests the potency of water lettuce for remediation of aquaculture effluent.

Keywords: Aquaculture effluent, nitrification, phytoremediation, water lettuce.

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