Search results for: CMM (coal mining methane)

Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

Abstract:

Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

Procedia PDF Downloads 108

Authors: Yangze Lan, Ruihua Xv, Feng Zhou, Yijia Shan, Longhao Zhang, Qinghui Xv

Abstract:

Receiving and dispatching trains is an important part of railroad organization, and the risky evaluation of operating personnel is still reflected by scores, lacking further excavation of wrong answers and operating accidents. With natural language processing (NLP) technology, this study extracts the keywords and key phrases of 40 relevant risk events about receiving and dispatching trains and reclassifies the risk events into 8 categories, such as train approach and signal risks, dispatching command risks, and so on. Based on the historical risk data of personnel, the K-Means clustering method is used to classify the risk level of personnel. The result indicates that the high-risk operating personnel need to strengthen the training of train receiving and dispatching operations towards essential trains and abnormal situations.

Keywords: receiving and dispatching trains, natural language processing, risk evaluation, K-means clustering

Procedia PDF Downloads 60

Authors: Tadayuki Kyoutani, Shigeyuki Haruyama, Ken Kaminishi, Zefry Darmawan

Abstract:

Japan’s semiconductor industries have developed greatly in recent years. Many were started from a Small and Medium-sized Enterprises (SMEs) that found at a good circumstance and now become the prosperous industries in the world. Sustainable growth factors that support the creation of spirit value inside the Japanese company were strongly embedded through performance. Those factors were not clearly defined among each company. A series of literature research conducted to explore quantitative text mining about the definition of sustainable growth factors. Sustainable criteria were developed from previous research to verify the definition of the factors. A typical frame work was proposed as a systematical approach to develop sustainable growth factor in a specific company. Result of approach was review in certain period shows that factors influenced in sustainable growth was importance for the company to achieve the goal.

Keywords: SME, manufacture, sustainable, growth factor

Procedia PDF Downloads 231

Authors: Adriano Araujo Carvalho, Arturo Alatrista Corrales

Abstract:

This paper intends to show how electrical energy consumption and production data analysis were used to find opportunities to save energy at Taboada wastewater treatment plant in Callao, Peru. In order to access the data, it was used independent data networks for both electrical and process instruments, which were taken to analyze under an ISO 50001 energy audit, which considered, thus, Energy Performance Indexes for each process and a step-by-step guide presented in this text. Due to the use of aforementioned methodology and data mining techniques applied on information gathered through electronic multimeters (conveniently placed on substation switchboards connected to a cloud network), it was possible to identify thoroughly the performance of each process and thus, evidence saving opportunities which were previously hidden before. The data analysis brought both costs and energy reduction, allowing the plant to save significant resources and to be certified under ISO 50001.

Keywords: energy and production data analysis, energy management, ISO 50001, wastewater treatment plant energy analysis

Procedia PDF Downloads 177

Authors: Salima Smiti, Ines Gasmi, Makram Soui

Abstract:

Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

Procedia PDF Downloads 159

Authors: Mohamed Abdalla, Ruixue Cheng, Jianyong Zhang

Abstract:

In pneumatic conveying, the solids are mixed with air or gas. In industries such as coal fired power stations, blast furnaces for iron making, cement and flour processing, the mass flow rate of solids needs to be monitored or controlled. However the current gas-solids two-phase flow measurement techniques are not as accurate as the flow meters available for the single phase flow. One of the problems that the multi-phase flow meters to face is that the flow profiles vary with measurement locations and conditions of pipe routing, bends, elbows and other restriction devices in conveying system as well as conveying velocity and concentration. To measure solids flow rate or concentration with non-even distribution of solids in gas, a uniform spatial sensitivity is required for a multi-phase flow meter. However, there are not many meters inherently have such property. The circular electrostatic meter is a popular choice for gas-solids flow measurement with its high sensitivity to flow, robust construction, low cost for installation and non-intrusive nature. However such meters have the inherent non-uniform spatial sensitivity. This paper first analyses the spatial sensitivity of circular electrostatic meter in general and then by combining the effect of the sensitivity to a single particle and the sensing volume for a given electrode geometry, the paper reveals first time how a circular electrostatic meter responds to a roping flow stream, which is much more complex than what is believed at present. The paper will provide the recent research findings on spatial sensitivity investigation at the University of Tees side based on Finite element analysis using Ansys Fluent software, including time and frequency domain characteristics and the effect of electrode geometry. The simulation results will be compared tothe experimental results obtained on a large scale (14” diameter) rig. The purpose of this research is paving a way to achieve a uniform spatial sensitivity for the circular electrostatic sensor by mean of compensation so as to improve overall accuracy of gas-solids flow measurement.

Keywords: spatial sensitivity, electrostatic sensor, pneumatic conveying, Ansys Fluent software

Procedia PDF Downloads 353

Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

Abstract:

The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

Procedia PDF Downloads 31

Authors: Monica Lia

Abstract:

This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: customer analysis, business intelligence, data warehouse, data mining, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, process modelling, logical data model, data mart, ETL, star schema, OLAP, data universes

Procedia PDF Downloads 413

Authors: Monika Chuchro

Abstract:

This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

Procedia PDF Downloads 448

Authors: Lali Khurtsia, Vano Tsertsvadze

Abstract:

Georgian drug policy is directed to reduce the supply of drugs. Retrospective analysis has shown that law enforcement activities have been followed by the expulsion of particular injecting drugs. The demand remains unchanged and drugs are substituted by the hand-made, even more dangerous homemade drugs entered the market. To find out expected new trends on the Georgian drug market, qualitative study was conducted with Georgian drug users to determine drug supply routes. It turned out that drug suppliers and consumers for safety reasons and to protect their anonymity, use Skype to make deals. IT in illegal drug trade is even more sophisticated in the worldwide. Trading with Bitcoins in the Darknet ensures high confidentiality of money transactions and the safe circulation of drugs. In 2014 largest Bitcoin mining enterprise in the world was built in Georgia. We argue that the use of Bitcoins and Darknet by Georgian drug consumers and suppliers will be an incentive to response adequately to the government's policy of restricting supply in order to satisfy market demand for drugs.

Keywords: bitcoin, darknet, drugs, policy

Procedia PDF Downloads 418

Authors: G. W. Greubel, A. Kalam

Abstract:

South Africa finds itself at a confluence of forces where the national electricity supply system is constrained with under-supply primarily from old and failing coal-fired power stations and congested and inadequate transmission and distribution systems. Simultaneously the country attempts to meet carbon reduction targets driven by both an alignment with international goals and a consumer-driven requirement. The constrained electricity system is an aspect of an economy characterized by very low economic growth, high unemployment, and frequent and significant load shedding. The fiscus does not have the funding to build new generation capacity or strengthen the grid. The under-supply is increasingly alleviated by the penetration of wind and solar generation capacity and embedded roof-top solar. However, this increased penetration results in less inertia, less synchronous generation, and less capability for fast frequency response, with resultant instability. The renewable energy facilities assist in solving the under-supply issues, but merely ‘kick the can down the road’ by not contributing to grid stability or by substituting the lost inertia, thus creating an expanding issue for the grid to manage. By technically balancing its electricity demand and supply a company with facilities located across the country can be spared the effects of load shedding, and thus ensure financial and production performance, protect jobs, and contribute meaningfully to the economy. By treating the company’s load (across the country) and its various distributed generation facilities as a ‘virtual grid’ which by design will provide ancillary services to the grid one is able to create a win-win situation for both the company and the grid. This paper provides a review of the technical problems facing the South African electricity system and discusses a hypothetical ‘virtual grid’ concept that may assist in solving the problems. The proposed solution has potential application across emerging markets with constrained power infrastructure or for companies who wish to be entirely powered by renewable energy.

Keywords: load shedding, renewable energy integration, smart grid, virtual grid

Procedia PDF Downloads 41

Authors: Svetlana Gusarova

Abstract:

The essential role in the recent development of world economy has led to the developing countries, notably to BRICS countries (Brazil, Russia, India, China, South Africa). Over the next 50 years the BRICS countries are expected to be the engines of global trade and economic growth. Trade cooperation of BRICS countries can enhance their economic development. BRICS countries were among Top 10 world exporters of office and telecom equipment, of textiles, of clothing, of iron and steel, of chemicals, of agricultural products, of automotive products, of fuel and mining products. China was one of the main trading partners of all BRICS countries, maintaining close relationship with all BRICS countries in the development of trade. Author analyzed trade complementarity of BRICS countries and revealed the high level of complementarity of their trade flows in connection with availability of specialization in different types of goods. The correlation and regression analysis of communication of Intra-BRICS merchandise turnover and their GDP (PPP) revealed very strong impact on the development of their economies.

Keywords: BRICS countries, trade cooperation, complementarity, regression analysis

Procedia PDF Downloads 270

Authors: Muhammad Shahzad Shifa, Amna Shifa, Muhammad Omar, Aamir Shahzad, Rahmat Ali Khan

Abstract:

Many applications of remote sensing to vegetation and crop response depend on spectral properties of individual leaves and plants. Vegetation indices are usually determined to estimate crop biophysical parameters like crop canopies and crop leaf area indices with the help of remote sensing. Cotton crops assessment is performed with the help of vegetative indices. Remotely sensed images from an optical multispectral radiometer MSR5 are used in this study. The interpretation is based on the fact that different materials reflect and absorb light differently at different wavelengths. Non-normalized and normalized forms of these datasets are analyzed using two complementary data mining algorithms; K-means and K-nearest neighbor (KNN). Our analysis shows that the use of normalized reflectance data and vegetative indices are suitable for an automated assessment and decision making.

Keywords: cotton, condition assessment, KNN algorithm, clustering, MSR5, vegetation indices

Procedia PDF Downloads 315

Authors: Jafar Ahmadi, Nafiseh Noormohammadi, Sedegeh Fabriki Ourang

Abstract:

GRF, Growth regulating factor, genes encode a novel class of plant-specific transcription factors. The GRF proteins play a role in the regulation of cell numbers in young and growing tissues and may act as transcription activations in growth and development of plants. Identification of GRF genes and their expression are important in plants to performance of the growth and development of various organs. In this study, to better understanding the structural and functional differences of GRFs family, 45 GRF proteins sequences in A. thaliana, Z. mays, O. sativa, B. napus, B. rapa, H. vulgare, and S. bicolor, have been collected and analyzed through bioinformatics data mining. As a result, in secondary structure of GRFs, the number of alpha helices was more than beta sheets and in all of them QLQ domains were completely in the biggest alpha helix. In all GRFs, QLQ, and WRC domains were completely protected except in AtGRF9. These proteins have no trans-membrane domain and due to have nuclear localization signals act in nuclear and they are component of unstable proteins in the test tube.

Keywords: domain, gene family, GRF, motif

Procedia PDF Downloads 440

Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

Abstract:

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

Procedia PDF Downloads 254

Authors: Pawel Sowa, Joachim Bargiel, Bogdan Mol, Katarzyna Luszcz

Abstract:

The article raises the issue of the development of local renewable energy sources and the production of distributed energy in context of improving the reliability of the Polish Power System and the beneficial impact on local and national energy security. The paper refers to the current problems of local governments in the process of investment in the area of distributed energy projects, and discusses the issues of the future role and cooperation within the local power plants and distributed energy. Attention is paid to the local communities the chance to raise their own resources and management of energy fuels (biomass, wind, gas mining) and improving the local energy balance. The material presented takes the issue of the development of the energy potential of municipalities and future cooperation with professional energy. As an example, practical solutions used in one of the communes in Silesia.

Keywords: distributed generation, mini centers energy, renewable energy sources, reliability of supply of rural commune

Procedia PDF Downloads 586

Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

Abstract:

Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

Procedia PDF Downloads 532

Authors: Janusz Kwaśniewski, Ireneusz Dominik, Krzysztof Lalik

Abstract:

The knowledge about rock layers thickness, especially above drilled mining pavements are crucial for workers safety. The measuring systems used nowadays are generally imperfect and there is a strong demand for improvement. The application of a new type of a measurement system called Self-Excited Acoustical System is presented in the paper. The system was applied until now to monitor stress changes in metal and concrete constructions. The change in measurement methodology resulted in possibility of measuring the thickness of the rocks above the tunnels as well as thickness of a singular rock layer. The idea is to find two resonance frequencies of the self-exited system, which consists of a vibration exciter and vibration receiver placed at a distance, which are coupled with a proper power amplifier, and which operate in a closed loop with a positive feedback. The resonance with the higher amplitude determines thickness of the whole rock, whereas the lower amplitude resonance indicates thickness of a singular layer. The results of the laboratory tests conducted on a group of different rock materials are also presented.

Keywords: auto-oscillator, non-destructive testing, rock thickness measurement, geotechnic

Procedia PDF Downloads 359

Authors: Belyayev P. E., Makeyeva I. R., Mastyuk D. A., Pigasov E. E.

Abstract:

Previously, the phrase ”hydrogen safety” was often used in terms of NPP safety. Due to the rise of interest to “green” and, particularly, hydrogen power engineering, the problem of hydrogen safety at industrial facilities has become ever more urgent. In Russia, the industrial production of hydrogen is meant to be performed by placing a chemical engineering plant near NPP, which supplies the plant with the necessary energy. In this approach, the production of hydrogen involves a wide range of combustible gases, such as methane, carbon monoxide, and hydrogen itself. Considering probable incidents, sudden combustible gas outburst into open space with further ignition is less dangerous by itself than ignition of the combustible mixture in the presence of many pipelines, reactor vessels, and any kind of fitting frames. Even ignition of 2100 cubic meters of the hydrogen-air mixture in open space gives velocity and pressure that are much lesser than velocity and pressure in Chapman-Jouguet condition and do not exceed 80 m/s and 6 kPa accordingly. However, the space blockage, the significant change of channel diameter on the way of flame propagation, and the presence of gas suspension lead to significant deflagration acceleration and to its transition into detonation or quasi-detonation. At the same time, process parameters acquired from the experiments at specific experimental facilities are not general, and their application to different facilities can only have a conventional and qualitative character. Yet, conducting deflagration and detonation experimental investigation for each specific industrial facility project in order to determine safe infrastructure unit placement does not seem feasible due to its high cost and hazard, while the conduction of numerical experiments is significantly cheaper and safer. Hence, the development of a numerical method that allows the description of reacting flows in domains with complex geometry seems promising. The base for this method is the modification of Kuropatenko method for calculating shock waves recently developed by authors, which allows using it in Eulerian coordinates. The current work contains the results of the development process. In addition, the comparison of numerical simulation results and experimental series with flame propagation in shock tubes with orifice plates is presented.

Keywords: CFD, reacting flow, DDT, gas explosion

Procedia PDF Downloads 70

Authors: Dominika Collett

Abstract:

AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

Procedia PDF Downloads 111

Authors: Reza Bahboodian

Abstract:

One of the most important factors in the use of construction machinery is the fuel consumption cost of this equipment. The use of diesel engines in off-road vehicles is an important source of nitrogen oxides and particulate matter. Emissions of nitrogen oxides and particulate matter 10 in off-road vehicles (construction and mining) may be high. Due to the high cost of fuel, it is necessary to minimize fuel consumption. Factors affecting the fuel consumption of these cars are very diverse. Climate changes such as changes in pressure, temperature, humidity, fuel type selection, type of gearbox used in the car are effective in fuel consumption and pollution, and engine efficiency. In this paper, methods for reducing fuel consumption and pollutants by considering valid European and European standards are examined based on new methods such as hybridization, optimal gear change, adding hydrogen to diesel fuel, determining optimal working fluids, and using oxidation catalysts.

Keywords: improve fuel consumption, construction machinery, pollutant reduction, determining the optimal working cycle

Procedia PDF Downloads 139

Authors: Kinza Qadeer, Muhammad Abdul Qyyum, Moonyong Lee

Abstract:

Natural gas has become an attractive energy source in comparison with other fossil fuels because of its lower CO₂ and other air pollutant emissions. Therefore, compared to the demand for coal and oil, that for natural gas is increasing rapidly world-wide. The transportation of natural gas over long distances as a liquid (LNG) preferable for several reasons, including economic, technical, political, and safety factors. However, LNG production is an energy-intensive process due to the tremendous amount of power requirements for compression of refrigerants, which provide sufficient cold energy to liquefy natural gas. Therefore, one of the major issues in the LNG industry is to improve the energy efficiency of existing LNG processes through a cost-effective approach that is 'optimization'. In this context, a bio-inspired Krill-herd (KH) step-up approach was examined to enhance the energy efficiency of a single mixed refrigerant (SMR) natural gas liquefaction (LNG) process, which is considered as a most promising candidate for offshore LNG production (FPSO). The optimal design of a natural gas liquefaction processes involves multivariable non-linear thermodynamic interactions, which lead to exergy destruction and contribute to process irreversibility. As key decision variables, the optimal values of mixed refrigerant flow rates and process operating pressures were determined based on the herding behavior of krill individuals corresponding to the minimum energy consumption for LNG production. To perform the rigorous process analysis, the SMR process was simulated in Aspen Hysys® software and the resulting model was connected with the Krill-herd approach coded in MATLAB. The optimal operating conditions found by the proposed approach significantly reduced the overall energy consumption of the SMR process by ≤ 22.5% and also improved the coefficient of performance in comparison with the base case. The proposed approach was also compared with other well-proven optimization algorithms, such as genetic and particle swarm optimization algorithms, and was found to exhibit a superior performance over these existing approaches.

Keywords: energy efficiency, Krill-herd, LNG, optimization, single mixed refrigerant

Procedia PDF Downloads 142

Authors: D. He, Y. Pang, G. Lodewijks

Abstract:

Belt conveyors play an important role in continuous dry bulk material transport, especially at the mining industry. Speed control is expected to reduce the energy consumption of belt conveyors. Transient operation is the operation of increasing or decreasing conveyor speed for speed control. According to literature review, current research rarely takes the conveyor dynamics in transient operation into account. However, in belt conveyor speed control, the conveyor dynamic behaviors are significantly important since the poor dynamics might result in risks. In this paper, the potential risks in transient operation will be analyzed. An existing finite element model will be applied to build a conveyor model, and simulations will be carried out to analyze the conveyor dynamics. In order to realize the soft speed regulation, Harrison’s sinusoid acceleration profile will be applied, and Lodewijks estimator will be built to approximate the required acceleration time. A long inclined belt conveyor will be studied with two major simulations. The conveyor dynamics will be given.

Keywords: belt conveyor , speed control, transient operation, dynamics

Procedia PDF Downloads 309

Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

Abstract:

Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

Procedia PDF Downloads 101

Authors: Sadia Ilyas, Hyunjung Kim, Rajiv R. Srivastava

Abstract:

Current work has attempted to establish the linkages between circular bio-economy and recycling of copper and gold from urban mine by applying microbial activities instead of the smelter and chemical technologies. Thereafter, based on the potential of microbial approaches and research hypothesis, the structural model has been tested for a significance level of 99%, which is supported by the corresponding standardization co-efficient values. A prediction model applied to determine the recycling impact on circular bio-economy indicates to re-circulate 51,833 tons of copper and 58 tons of gold by 2030 for the production of virgin metals/raw-materials, while recycling rate of the accumulated e-waste remains to be 20%. This restoration volume of copper and gold through the microbial activities corresponds to mitigate 174 million kg CO₂ emissions and 24 million m³ water consumption if compared with the primary production activities. The study potentially opens a new window for environmentally-friendly biotechnological recycling of e-waste urban mine under the umbrella concept of circular bio-economy.

Keywords: urban mining, biobleaching, circular bio-economy, environmental impact

Procedia PDF Downloads 140

Authors: Ewa Szarek-Gwiazda, Agata Z. Wojtal, Agnieszka Pociecha, Andrzej Kownacki, Dariusz Ciszewski

Abstract:

Mining of metal ores is one of the largest sources of heavy metals, which deteriorate aquatic systems. The response of organisms to environmental changes can be well recorded in sediments of the affected water bodies and may be reconstructed based on analyses of organisms' remains. The present study aimed at the response of diatoms (Bacillariophyta), Cladocera, and Chironomidae communities to the impact of Zn-Pb mine water discharge recorded in sediment cores of small subsidence ponds on the Chechło River floodplain (Silesia–Krakow Region, southern Poland). We hypothesize various responses of the above groups to high metal concentrations (Cd, Pb, Zn, and Cu). The investigated ponds were formed either during the peak of the ore exploitation (DOWN) or after mining cessation (UP). Currently, the concentrations of dissolved metals (in µg g⁻¹) in water reached up to 0.53 for Cd, 7.3 for Pb, and up to 47.1 for Zn. All the sediment cores from subsidence ponds were heavily polluted with Cd 6.7–612 μg g⁻¹, Pb 0.1–10.2 mg g⁻¹, and Zn 0.5–23.1 mg g⁻¹. Core sediments varied also in respect to pH 5.8-7.1 and concentrations of organic matter (5.7-39.8%). The impact of high metal concentrations was expressed by the occurrence of metal-tolerant taxa like diatoms – Nitzschia amphibia, Sellaphora nigri, and Surirella brebisonii var. kuetzingii; Cladocera – Chydorus sphaericus (dominated in cores from all ponds), and Chironomidae – Chironomus and Cricotopus especially in the DOWN ponds. Statistical analysis exhibited a negative impact of metals on some taxa of diatoms and Cladocera but only on Polypedilum sp. from Chironomidae. The abundance of such diatoms like Gomphonema utae, Staurosirella pinnata, Eunotia bilunaris, and Cladocera like Alona, Chydorus, Graptoleberis, and Pleuroxus decreased with increasing Pb concentration. However, the occurrence or dominance of more sensitive species of diatoms and Cladocera indicates their adaptation to higher metal loads, which was facilitated by neutral pH and slightly alkaline waters. Diatom assemblages were generally resistant to Zn, Pb, Cu, and Cd pollution, as indicated by their large similarity to populations from non-contaminated waters. Comparison with reference objects clearly indicates the dominance of Achnanthidium minutissimum, Staurosira venter, and Fragilaria gracilis in very diverse assemblages of unpolluted waters. The distribution of the Cladocera and Chironomidae taxa depended on the habitat type. The DOWN ponds with stagnant water and overgrown with macrophytes were more suitable for cladocerans (14 taxa, higher diversity) than the UP ponds with river water flowing through their centre and with a small share of macrophytes (8 taxa). The Chironominae, mainly Chironomus and Microspectra, were abundant in cores from the UP ponds with muddy bottoms. Inversely, the density of Orthocladiinae, especially genus Cricotopus, was related to the organic matter content and dominated in cores from the DOWN ponds. The presence of diatoms like Nitzschia amphibia, Sellaphora nigri, and Surirella brebisonii var. kuetzingii, cladocerans: Bosmina longirostris, Chydorus sphaericus, Alona affinis, and A. rectangularis as well as Chironomidae Chironomus sp. (UP ponds) and Psecrotanypus varius (DOWN ponds) indicate the influence of the water trophy on their distribution.

Keywords: Chironomidae, Cladocera, diatoms, metals, Zn-Pb mine, sediment cores, subsidence ponds

Procedia PDF Downloads 49

Authors: Ichihan Tai, Bill Olson, Paul Blessner

Abstract:

Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

Procedia PDF Downloads 322

Authors: Yuhong Zhang, Dan Lu, Chenyang Bu, Peipei Li, Kui Yu, Xindong Wu

Abstract:

The Cross-Lingual Entity Alignment (CLEA) task aims to find the aligned entities that refer to the same identity from two knowledge graphs (KGs) in different languages. It is an effective way to enhance the performance of data mining for KGs with scarce resources. In real-world applications, the neighborhood structures of the same entities in different KGs tend to be non-isomorphic, which makes the representation of entities contain diverse semantic information and then poses a great challenge for CLEA. In this paper, we try to address this challenge from two perspectives. On the one hand, the cross-KG relation completion rules are designed with the alignment constraint of entities and relations to improve the topology isomorphism of two KGs. On the other hand, a representation method combining isomorphic weights is designed to include more isomorphic semantics for counterpart entities, which will benefit the CLEA. Experiments show that our model can improve the isomorphism of two KGs and the alignment performance, especially for two non-isomorphic KGs.

Keywords: knowledge graphs, cross-lingual entity alignment, non-isomorphic, relation completion

Procedia PDF Downloads 107

Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

Procedia PDF Downloads 238

Authors: Farrukh Shahzad

Abstract:

The rich Islamic resources related to religious text, Islamic sciences, and history are widely available in print and in electronic format online. However, most of these works are only available in Arabic language. In this research, an attempt is made to utilize these resources to create interactive web applications in Arabic, English and other languages. The system utilizes the Pattern Recognition, Knowledge Management, Data Mining, Information Retrieval and Management, Indexing, storage and data-analysis techniques to parse, store, convert and manage the information from authentic Arabic resources. These interactive web Apps provide smart multi-lingual search, tree based search, on-demand information matching and linking. In this paper, we provide details of application architecture, design, implementation and technologies employed. We also presented the summary of web applications already developed. We have also included some screen shots from the corresponding web sites. These web applications provide an Innovative On-line Learning Systems (eLearning and computer based education).

Keywords: Islamic resources, Muslim scholars, hadith, narrators, history, fiqh

Procedia PDF Downloads 265