Search results for: traditional coal mining

Authors: Ntokozo C. Mthembu

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The notion of sustainable chemistry has become significant in the discourse for a global post-colonial era, including South Africa, especially when it comes to access to the general health system and related policies in relation to disease or ease of human life. In view of the stubborn vestiges of coloniality in the daily lives of indigenous African people in general, the fundamentals of present Western medical and traditional medicine systems and related policies in the democratic era were examined in this study. The situation of traditional healers in relation to current policy was also reviewed. The advent of democracy in South Africa brought about a variety of development opportunities and limitations, particularly with respect to indigenous African knowledge systems such as traditional medicine. There were high hopes that the limitations of previous narrow cultural perspectives would be rectified in the democratic era through development interventions, but some sections of society, such as traditional healers, remain marginalised. The Afrocentric perspective was explored in dissecting government interventions related to traditional medicine. This article highlights that multiple medical systems should be adopted and that health policies should be aligned in order to guarantee mutual respect and to address the remnants of colonialism in South Africa, Africa and the broader global community.

Keywords: Traditional healing system, healers, pluralist healthcare system, post-colonial era.

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Authors: Magdalena Pejas

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This article concerns the presentation of an integrated method for detection of steganographic content embedded by new unknown programs. The method is based on data mining and aggregated hypothesis testing. The article contains the theoretical basics used to deploy the proposed detection system and the description of improvement proposed for the basic system idea. Further main results of experiments and implementation details are collected and described. Finally example results of the tests are presented.

Keywords: Steganography, steganalysis, data embedding, data mining, feature extraction, knowledge base, system learning, hypothesis testing, error estimation, black box program, file structure.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: K. Jahani, J. Razavi

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Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.

Keywords: Computer aided engineering, predictive maintenance, fault detection, mining process line, slurry pump, hydrocyclone.

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Authors: S. Sarumathi, P. Ranjetha, C. Saraswathy, M. Vaishnavi, S. Geetha

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Clustering is an unsupervised learning technique for aggregating data objects into meaningful classes so that intra cluster similarity is maximized and inter cluster similarity is minimized in data mining. However, no single clustering algorithm proves to be the most effective in producing the best result. As a result, a new challenging technique known as the cluster ensemble approach has blossomed in order to determine the solution to this problem. For the cluster analysis issue, this new technique is a successful approach. The cluster ensemble's main goal is to combine similar clustering solutions in a way that achieves the precision while also improving the quality of individual data clustering. Because of the massive and rapid creation of new approaches in the field of data mining, the ongoing interest in inventing novel algorithms necessitates a thorough examination of current techniques and future innovation. This paper presents a comparative analysis of various cluster ensemble approaches, including their methodologies, formal working process, and standard accuracy and error rates. As a result, the society of clustering practitioners will benefit from this exploratory and clear research, which will aid in determining the most appropriate solution to the problem at hand.

Keywords: Clustering, cluster ensemble methods, consensus function, data mining, unsupervised learning.

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Authors: Mehmet Hacibeyoglu, Ahmet Arslan, Sirzat Kahramanli

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This study analyzes the effect of discretization on classification of datasets including continuous valued features. Six datasets from UCI which containing continuous valued features are discretized with entropy-based discretization method. The performance improvement between the dataset with original features and the dataset with discretized features is compared with k-nearest neighbors, Naive Bayes, C4.5 and CN2 data mining classification algorithms. As the result the classification accuracies of the six datasets are improved averagely by 1.71% to 12.31%.

Keywords: Data mining classification algorithms, entropy-baseddiscretization method

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. Our work applies modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Safa Adi

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This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm (level wise algorithms). We will compare two previous approaches GTC and PSP, that resumes the general principles of GSP. Furthermore this paper will discuss PG-hybrid algorithm, that using PSP and GTC. The results show that PSP and GTC are more efficient than GSP. On the other hand, the GTC algorithm performs better than PSP. The PG-hybrid algorithm use PSP algorithm for the two first passes on the database, and GTC approach for the following scans. Experiments show that the hybrid approach is very efficient for short, frequent sequences.

Keywords: Database, GTC algorithm, PSP algorithm, sequential patterns, time constraints.

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Authors: V. Salajka, Z. Kaláb, J. Kala, P. Hradil

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In the territories where high-intensity earthquakes are frequent is paid attention to the solving of the seismic problems. In the paper are described two computational model variants based on finite element method of the construction with different subsoil simulation (rigid or elastic subsoil) is used. For simulation and calculations program system based on method final elements ANSYS was used. Seismic responses calculations of residential building structure were effected on loading characterized by accelerogram for comparing with the responses spectra method.

Keywords: Accelerogram, ANSYS, mining induced seismic, residential building structure, spectra, subsoil.

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Authors: M. G. Nasuruddin, S. Ishak

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This paper deals with the traditional Malay healing ritualistic ceremony known as Main Puteri. This non-invasive intervention uses the vehicle of performance to administer the healing process. It employs the performance elements of Makyung, that is, music, movements/dance and dramatic dialogue to heal psychosomatic maladies. There are two perspectives to this therapeutic healing process, one traditional and the other scientific. From the traditional perspective, the psychosomatic illness is attributed to the infestations/possessions by malevolent spirits. To heal such patients, these spirits must be exorcised through placating them by making offerings. From the scientific perspective, the music (sonic orders), movements (kinetic energy) and smell (olfactory) connect with the brain waves to release the chemicals that would activate the internal healing energy. Currently, in Main Puteri, the therapeutic healing ritual is no longer relevant as modern clinical medicine has proven to be more effective. Thus, Main Puteri is an anachronism in today’s technologically advanced Malaysia.

Keywords: Exorcism, Main Puteri, Shamans, Therapeutic Healing.

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Authors: Boonyarat Chaleephay

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The purpose of this research paper was to describe the relationship of acupressure points on the anterior surface of the upper limb in accordance with Applied Thai Traditional Massage (ATTM) and the deep structures located at those acupressure points. There were 2 population groups; normal subjects and cadaver specimens. Eighteen males with age ranging from 20-40 years old and seventeen females with ages ranging from 30-97 years old were studies. This study was able to obtain a fundamental knowledge concerning acupressure point and the deep structures that related to those acupressure points. It might be used as the basic knowledge for clinically applying and planning treatment as well as teaching in ATTM.

Keywords: Acupressure point (AP), Applie Thai Traditional Medicine (ATTM), Paresthesia, Numbness.

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Authors: Iftikhar U. Sikder

Abstract:

This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: Concept approximation, granular computing, reducts, rough set theory, rule induction.

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Authors: Jinn-Yi Yeh, Tai-Hsi Wu

Abstract:

Hemodialysis patients might suffer from unhealthy care behaviors or long-term dialysis treatments. Ultimately they need to be hospitalized. If the hospitalization rate of a hemodialysis center is high, its quality of service would be low. Therefore, how to decrease hospitalization rate is a crucial problem for health care. In this study we combined temporal abstraction with data mining techniques for analyzing the dialysis patients' biochemical data to develop a decision support system. The mined temporal patterns are helpful for clinicians to predict hospitalization of hemodialysis patients and to suggest them some treatments immediately to avoid hospitalization.

Keywords: Hemodialysis, Temporal abstract, Data mining, Healthcare quality.

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: Active Contour, Bayesian, Echocardiographic image, Feature vector.

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Authors: Nuttawan Yoswathana, M. N. Eshiaghi, K. Jaturapornpanich

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The traditional method for essential oil extraction from agarwood (Aquilaria Crassna) is to soak it in water and follow with hydrodistillation. The effect of various agarwood pretreatments: ethanol, acid, alkaline, enzymes, and ultrasound, and the effect of subcritical water extraction(SWE) was studied to compare with the traditional method. The major compositions of agarwood oil from hydrodistillation were aroma compounds as follow: aristol-9-en-8- one (21.53%), selina-3, 7(11)-diene (12.96%), τ-himachalene (9.28%), β-guaiene (5.79%), hexadecanoic acid (4.90%) and guaia- 3,9-diene (4.21%). Whereas agarwood oil from pretreatments with ethanol and ultrasound, and SWE got fatty acid compounds. Extraction of agarwood oil using these pretreatments could improve the agarwood oil yields up to 2 times that of the traditional method. The components of the pretreated sample with diluted acid (H2SO4) at pH 4 gave quite similar results as the traditional method. Therefore, the enhancement of essential oil from agarwood depends on requirement of type of extracted oil that involved extraction methods.

Keywords: Agarwood, aquilaria crassna, hydrodistillation, subcritical water extraction.

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Authors: M. Gómez-Ramírez, A. Rivas-Castillo, I. Rodríguez-Pozos, R. A. Avalos-Zuñiga, N. G. Rojas-Avelizapa

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Among the diverse types of pollutants produced by anthropogenic activities, metals represent a serious threat, due to their accumulation in ecosystems and their elevated toxicity. The mine tailings of abandoned mines contain high levels of metals such as arsenic (As), zinc (Zn), copper (Cu), and lead (Pb), which do not suffer any degradation process, they are accumulated in environment. Abandoned mine tailings potentially could contaminate rivers and aquifers representing a risk for human health due to their high metal content. In an attempt to remove the metals and thereby mitigate the environmental pollution, an environmentally friendly and economical method of bioremediation has been introduced. Bioleaching has been actively studied over the last several years, and it is one of the bioremediation solutions used to treat heavy metals contained in sewage sludge, sediment and contaminated soil. Acidithiobacillus thiooxidans, an extremely acidophilic, chemolithoautotrophic, gram-negative, rod shaped microorganism, which is typically related to Cu mining operations (bioleaching), has been well studied for industrial applications. The sulfuric acid produced plays a major role in bioleaching. Specifically, Acidithiobacillus thiooxidans strain DSM 26636 has been able to leach Al, Ni, V, Fe, Mg, Si, and Ni contained in slags from coal combustion wastes. The present study reports the ability of A. thiooxidans DSM 26636 for the bioleaching of metals contained in two different mine tailing samples (MT1 and MT2). It was observed that Al, Fe, and Mn were removed in 36.3±1.7, 191.2±1.6, and 4.5±0.2 mg/kg for MT1, and in 74.5±0.3, 208.3±0.5, and 20.9±0.1 for MT2. Besides, < 1.5 mg/kg of Au and Ru were also bioleached from MT1; in MT2, bioleaching of Zn was observed at 55.7±1.3 mg/kg, besides removal of < 1.5 mg/kg was observed for As, Ir, Li, and 0.6 for Os in this residue. These results show the potential of strain DSM 26636 for the bioleaching of metals that came from different mine tailings.

Keywords: A. thiooxidans, bioleaching, metals, mine tailings.

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Authors: Pairaya J., Buddhagarn R., Sukree S., Punthumadee K.

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Application of Information Technology (IT) has revolutionized the functioning of business all over the world. Its impact has been felt mostly among the information of dependent industries. Tourism is one of such industry. The conceptual framework in this study represents an innovation of travel information searching system on mobile devices which is used as tools to deliver travel information (such as hotels, restaurants, tourist attractions and souvenir shops) for each user by travelers segmentation based on data mining technique to segment the tourists- behavior patterns then match them with tourism products and services. This system innovation is designed to be a knowledge incremental learning. It is a marketing strategy to support business to respond traveler-s demand effectively.

Keywords: Tourism, Innovation, Information Searching, Data Mining.

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Authors: A. M. H. Elyasir, K. S. M. Anbananthen

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The internet is growing larger and becoming the most popular platform for the people to share their opinion in different interests. We choose the education domain specifically comparing some Malaysian universities against each other. This comparison produces benchmark based on different criteria shared by the online users in various online resources including Twitter, Facebook and web pages. The comparison is accomplished using opinion mining framework to extract, process the unstructured text and classify the result to positive, negative or neutral (polarity). Hence, we divide our framework to three main stages; opinion collection (extraction), unstructured text processing and polarity classification. The extraction stage includes web crawling, HTML parsing, Sentence segmentation for punctuation classification, Part of Speech (POS) tagging, the second stage processes the unstructured text with stemming and stop words removal and finally prepare the raw text for classification using Named Entity Recognition (NER). Last phase is to classify the polarity and present overall result for the comparison among the Malaysian universities. The final result is useful for those who are interested to study in Malaysia, in which our final output declares clear winners based on the public opinions all over the web.

Keywords: Entity Recognition, Education Domain, Opinion Mining, Unstructured Text.

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Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: Homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data.

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Authors: Ravindra N. Singh, Dheerendra P. Singh

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Recent discoveries of Bronze Age artefacts, tin slag, furnaces and crucibles, together with new geological evidence on tin deposits in Tosham area of Bhiwani district in Haryana (India) provide the opportunity to survey the evidence for possible sources of tin and the use of bronze in the Harappan sites of north western India. Earlier, Afghanistan emerged as the most promising eastern source of tin utilized by Indus Civilization copper-smiths. Our excavations conducted at Khanak near Tosham mining area during 2014 and 2016 revealed ample evidence of metallurgical activities as attested by the occurrence of slag, ores and evidences of ashes and fragments of furnaces in addition to the bronze objects. We have conducted petrological, XRD, EDAX, TEM, SEM and metallography on the slag, ores, crucible fragments and bronze objects samples recovered from Khanak excavations. This has given positive indication of mining and metallurgy of poly-mettalic Tin at the site; however, it can only be ascertained after the detailed scientific examination of the materials which is underway. In view of the importance of site, we intend to excavate the site horizontally in future so as to obtain more samples for scientific studies.

Keywords: Archaeometallurgy, problem of tin, metallography, Indus civilization.

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Authors: M. Takhtravanchi, C. Pathirage

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In the construction industry, project members are conveyor of project knowledge which is, often, not managed properly to be used in future projects. As construction projects are temporary and unique, project members are willing to be recruited once a project is completed. Therefore, poor management of knowledge across construction projects will lead to a considerable amount of knowledge loss; the ignoring of which would be detrimental to project performance. This issue is more prominent in projects undertaken through the traditional procurement system, as this system does not incentives project members for integration. Thus, disputes exist between the design and construction phases based on the poor management of knowledge between those two phases. This paper aims to highlight the challenges of the knowledge management that exists within the traditional procurement system. Expert interviews were conducted and challenges were identified and analysed by the Interpretive Structural Modelling (ISM) approach in order to summarise the relationships among them. Two identified key challenges are the Culture of an Organisation and Knowledge Management Policies. A knowledge of the challenges and their relationships will help project manager and stakeholders to have a better understanding of the importance of knowledge management.

Keywords: Challenges, construction industry, knowledge management, traditional procurement system.

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which cover the variety of figure proportions in both height and girth. 3,000 data have been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from the some states of India to produce the sizing system suitable for clothing manufacture and retailing. The data are used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from the large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: Anthropometric data, data mining, decision tree, garments manufacturing, ready-made garments, sizing systems.

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Authors: Hassan Haleh, Amin Ghaffari, Parisa Farahpour

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Need for an appropriate system of evaluating students- educational developments is a key problem to achieve the predefined educational goals. Intensity of the related papers in the last years; that tries to proof or disproof the necessity and adequacy of the students assessment; is the corroborator of this matter. Some of these studies tried to increase the precision of determining question weights in scientific examinations. But in all of them there has been an attempt to adjust the initial question weights while the accuracy and precision of those initial question weights are still under question. Thus In order to increase the precision of the assessment process of students- educational development, the present study tries to propose a new method for determining the initial question weights by considering the factors of questions like: difficulty, importance and complexity; and implementing a combined method of PROMETHEE and fuzzy analytic network process using a data mining approach to improve the model-s inputs. The result of the implemented case study proves the development of performance and precision of the proposed model.

Keywords: Assessing students, Analytic network process, Clustering, Data mining, Fuzzy sets, Multi-criteria decision making, and Preference function.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.

Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.

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Authors: Iman Ali Ahmed Al-Rashed

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This paper directs attention to the limitations of the teacher-centered strategy in teaching. The aim of this study is to draw more educational attention to learner-centered strategy in order to shift the emphasis from the traditional concept of teaching to a new concept in teaching. To begin bridging the traditional concept of teaching and the new concept, the study will explore the new concept of teaching to support teaching in Arab World generally and in Iraq specifically. A qualitative case study orientation was used to collect data in the form of classroom observations, interviews and field notes. The teaching practices used by three university instructors are investigated and according to the findings, some explanations and recommendations are made.

Keywords: Case study, learner-centered strategy, qualitative study, teacher-centered strategy, traditional teaching.

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Authors: Omer Ishag Eldai, Ahmed Hassan M. H. Ali, S. Raviraja

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Web-based systems have become increasingly important due to the fact that the Internet and the World Wide Web have become ubiquitous, surpassing all other technological developments in our history. The Internet and especially companies websites has rapidly evolved in their scope and extent of use, from being a little more than fixed advertising material, i.e. a "web presences", which had no particular influence for the company's business, to being one of the most essential parts of the company's core business. Traditional software engineering approaches with process models such as, for example, CMM and Waterfall models, do not work very well since web system development differs from traditional development. The development differs in several ways, for example, there is a large gap between traditional software engineering designs and concepts and the low-level implementation model, many of the web based system development activities are business oriented (for example web application are sales-oriented, web application and intranets are content-oriented) and not engineering-oriented. This paper aims to introduce Increment Iterative extreme Programming (IIXP) methodology for developing web based systems. In difference to the other existence methodologies, this methodology is combination of different traditional and modern software engineering and web engineering principles.

Keywords: Web based systems, Web engineering.

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Authors: Matthias Dehmer, Frank Emmert Streib, Jürgen Kilian, Andreas Zulauf

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Methods for organizing web data into groups in order to analyze web-based hypertext data and facilitate data availability are very important in terms of the number of documents available online. Thereby, the task of clustering web-based document structures has many applications, e.g., improving information retrieval on the web, better understanding of user navigation behavior, improving web users requests servicing, and increasing web information accessibility. In this paper we investigate a new approach for clustering web-based hypertexts on the basis of their graph structures. The hypertexts will be represented as so called generalized trees which are more general than usual directed rooted trees, e.g., DOM-Trees. As a important preprocessing step we measure the structural similarity between the generalized trees on the basis of a similarity measure d. Then, we apply agglomerative clustering to the obtained similarity matrix in order to create clusters of hypertext graph patterns representing navigation structures. In the present paper we will run our approach on a data set of hypertext structures and obtain good results in Web Structure Mining. Furthermore we outline the application of our approach in Web Usage Mining as future work.

Keywords: Clustering methods, graph-based patterns, graph similarity, hypertext structures, web structure mining

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Authors: Cigdem Safak Saglam

Abstract:

Most thermal power plants use steam as working fluid in their power cycle. Therefore, in addition to fuel, water is the other main input for thermal plants. Water and steam must be highly pure in order to protect the systems from corrosion, scaling and biofouling. Pure process water is produced in water treatment plants having many several treatment methods. Treatment plant design is selected depending on raw water source and required water quality. Although working principle of fossil-fuel fired thermal power plants are same, there is no standard design and equipment arrangement valid for all thermal power plant utility systems. Besides that, there are many other technology evaluation and selection criteria for designing the most optimal water systems meeting the requirements such as local conditions, environmental restrictions, electricity and other consumables availability and transport, process water sources and scarcity, land use constraints etc. Aim of this study is explaining the adopted methodology for technology selection for process water preparation and industrial waste water treatment plant in a thermal power plant project located in Tufanbeyli, Adana Province in Turkey. Thermal power plant is fired with indigenous lignite coal extracted from adjacent lignite reserves. This paper addresses all above-mentioned factors affecting the thermal power plant water treatment facilities (demineralization + waste water treatment) design and describes the ultimate design of Tufanbeyli Thermal Power Plant Water Treatment Plant.

Keywords: Thermal power plant, lignite coal, pre-treatment, demineralization, electrodialysis, recycling, waste water, process water.

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Authors: S. Kotsiantis, A. Kostoulas, S. Lykoudis, A. Argiriou, K. Menagias

Abstract:

Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.

Keywords: regression algorithms, supervised machine learning.

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Authors: A. Gruzdz, A. Ihnatowicz, J. Siddiqi, B. Akhgar

Abstract:

MATCH project [1] entitle the development of an automatic diagnosis system that aims to support treatment of colon cancer diseases by discovering mutations that occurs to tumour suppressor genes (TSGs) and contributes to the development of cancerous tumours. The constitution of the system is based on a) colon cancer clinical data and b) biological information that will be derived by data mining techniques from genomic and proteomic sources The core mining module will consist of the popular, well tested hybrid feature extraction methods, and new combined algorithms, designed especially for the project. Elements of rough sets, evolutionary computing, cluster analysis, self-organization maps and association rules will be used to discover the annotations between genes, and their influence on tumours [2]-[11]. The methods used to process the data have to address their high complexity, potential inconsistency and problems of dealing with the missing values. They must integrate all the useful information necessary to solve the expert's question. For this purpose, the system has to learn from data, or be able to interactively specify by a domain specialist, the part of the knowledge structure it needs to answer a given query. The program should also take into account the importance/rank of the particular parts of data it analyses, and adjusts the used algorithms accordingly.

Keywords: Bioinformatics, gene expression, ontology, selforganizingmaps.

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