Search results for: CMM (coal mining methane)

Authors: Priyanka Dargode, Suhas Gore, Manju Sharma, Arvind Lali

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Anaerobic digestion has been widely used for production of methane from different biowastes. However, the complexity of microbial communities involved in the process is poorly understood. The performance of biogas production process concerning the process productivity is closely coupled to its microbial community structure and syntrophic interactions amongst the community members. The present study aims at understanding taxonomic variations occurring in any starter inoculum when acclimatised to different lignocellulosic biomass (LBM) feedstocks relating to time of digestion. The work underlines use of high throughput Next Generation Sequencing (NGS) for validating the changes in taxonomic patterns of microbial communities. Biomethane Potential (BMP) batches were set up with different pretreated and non-pretreated LBM residues using the same microbial consortium and samples were withdrawn for studying the changes in microbial community in terms of its structure and predominance with respect to changes in metabolic profile of the process. DNA of samples withdrawn at different time intervals with reference to performance changes of the digestion process, was extracted followed by its 16S rRNA amplicon sequencing analysis using Illumina Platform. Biomethane potential and substrate consumption was monitored using Gas Chromatography(GC) and reduction in COD (Chemical Oxygen Demand) respectively. Taxonomic analysis by QIIME server data revealed that microbial community structure changes with different substrates as well as at different time intervals. It was observed that biomethane potential of each substrate was relatively similar but, the time required for substrate utilization and its conversion to biomethane was different for different substrates. This could be attributed to the nature of substrate and consequently the discrepancy between the dominance of microbial communities with regards to different substrate and at different phases of anaerobic digestion process. Knowledge of microbial communities involved would allow a rational substrate specific consortium design which will help to reduce consortium adaptation period and enhance the substrate utilisation resulting in improved efficacy of biogas process.

Keywords: amplicon sequencing, biomethane potential, community predominance, taxonomic analysis

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Authors: Melake Kuflom

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European power networks involve the use of multiple overhead transmission lines to construct a highly duplicated system that delivers reliable and stable electrical energy to the distribution level. The transmission line protection applied in the existing GB transmission network are normally independent unit differential and time stepped distance protection schemes, referred to as main-1 & main-2 respectively, with overcurrent protection as a backup. The increasing penetration of renewable energy sources, commonly referred as “weak sources,” into the power network resulted in the decline of fault level. Traditionally, the fault level of the GB transmission network has been strong; hence the fault current contribution is more than sufficient to ensure the correct operation of the protection schemes. However, numerous conventional coal and nuclear generators have been or about to shut down due to the societal requirement for CO2 emission reduction, and this has resulted in a reduction in the fault level on some transmission lines, and therefore an adaptive transmission line protection is required. Generally, greater utilization of renewable energy sources generated from wind or direct solar energy results in a reduction of CO2 carbon emission and can increase the system security and reliability but reduces the fault level, which has an adverse effect on protection. Consequently, the effectiveness of conventional protection schemes under low fault levels needs to be reviewed, particularly for future GB transmission network operating scenarios. The proposed paper will evaluate the transmission line challenges under high penetration of renewable energy sources andprovides alternative viable protection solutions based on the problem observed. The paper will consider the assessment ofrenewable energy sources (RES) based on a fully rated converter technology. The DIgSILENT Power Factory software tool will be used to model the network.

Keywords: fault level, protection schemes, relay settings, relay coordination, renewable energy sources

Procedia PDF Downloads 179

Authors: Hugo Lefeuvre, Jerôme Donnard, Michael Descostes, Sophie Billon, Samuel Duval, Tugdual Oger, Herve Toubon, Paul Sardini

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Spectroscopic autoradiography is a method of interest for geological sample analysis. Indeed, researchers may face different issues such as radioelement identification and quantification in the field of environmental studies. Imaging gaseous ionization detectors find their place in geosciences for conducting specific measurements of radioactivity to improve the monitoring of natural processes using naturally-occurring radioactive tracers, but also for the nuclear industry linked to the mining sector. In geological samples, the location and identification of the radioactive-bearing minerals at the thin-section scale remains a major challenge as the detection limit of the usual elementary microprobe techniques is far higher than the concentration of most of the natural radioactive decay products. The spatial distribution of each decay product in the case of uranium in a geomaterial is interesting for relating radionuclides concentration to the mineralogy. The present study aims to provide spectroscopic autoradiography analysis method for measuring the initial energy of alpha particles with a parallel ionization multiplier gaseous detector. The analysis method has been developed thanks to Geant4 modelling of the detector. The track of alpha particles recorded in the gas detector allow the simultaneous measurement of the initial point of emission and the reconstruction of the initial particle energy by a selection based on the linear energy distribution. This spectroscopic autoradiography method was successfully used to reproduce the alpha spectra from a 238U decay chain on a geological sample at the thin-section scale. The characteristics of this measurement are an energy spectrum resolution of 17.2% (FWHM) at 4647 keV and a spatial resolution of at least 50 µm. Even if the efficiency of energy spectrum reconstruction is low (4.4%) compared to the efficiency of a simple autoradiograph (50%), this novel measurement approach offers the opportunity to select areas on an autoradiograph to perform an energy spectrum analysis within that area. This opens up possibilities for the detailed analysis of heterogeneous geological samples containing natural alpha emitters such as uranium-238 and radium-226. This measurement will allow the study of the spatial distribution of uranium and its descendants in geo-materials by coupling scanning electron microscope characterizations. The direct application of this dual modality (energy-position) of analysis will be the subject of future developments. The measurement of the radioactive equilibrium state of heterogeneous geological structures, and the quantitative mapping of 226Ra radioactivity are now being actively studied.

Keywords: alpha spectroscopy, digital autoradiography, mining activities, natural decay products

Procedia PDF Downloads 133

Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

Procedia PDF Downloads 465

Authors: Hillary Onyeka Abugu, Valentine Chinakwugwo Ezea, Janefrances Ngozi Ihedioha, Nwachukwu Romanus Ekere

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The study assessed the spring water quality in Igbo-Etiti, Nigeria, for drinking and irrigation application using Physico-chemical parameters, water quality index, mineral and trace elements, pollution indices and risk assessment. Standard methods were used to determine the physicochemical properties of the spring water in rainy and dry seasons. Trace metals such as Pb, Cd, Zn and Cu were determined with atomic absorption spectrophotometer. The results showed that most of the physicochemical properties studied were within the guideline values set by Nigeria Standard for Drinking Water Quality (NSDWQ), WHO and US EPA for drinking water purposes. However, pH of all the spring water (4.27- 4.73; and 4.95- 5.73), lead (Pb) (0.01-1.08 mg/L) and cadmium (Cd) (0.01-0.15 mg/L) concentrations were above the guideline values in both seasons. This could be attributed to the lithography of the study area, which is the Nsukka formation. Leaching of lead and sulphides from the embedded coal deposits could have led to the increased lead levels and made the water acidic. Two-way ANOVA showed significant differences in most of the parameters studied in dry and rainy seasons. Pearson correlation analysis and cluster analysis showed strong significant positive and negative correlations in some of the parameters studied in both seasons. The water quality index showed that none of the spring water had excellent water status. However, one spring (Iyi Ase) had poor water status in dry season and is considered unsafe for drinking. Iyi Ase was also considered not suitable for irrigation application as predicted by most of the pollution indices, while others were generally considered suitable for irrigation application. Probable cancer and non-cancer risk assessment revealed a probable risk associated with the consumption of the spring in the Igbo-Ettiti area, Nigeria.

Keywords: water quality, pollution index, risk assessment, physico-chemical parameters

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

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Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

Procedia PDF Downloads 465

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Amna Javed, Hisashi Masuda, Youji Kohda

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This study examines the value analysis in Islamic and conventional banking services in Pakistan. Many scholars have focused on co-creation of values in services but mainly economic values not non-economic. As Islamic banking is based on Islamic principles that are more concerned with non-economic values (well-being, partnership, fairness, trust worthy, and justice) than economic values as money in terms of interest. This study is important to know the providers point of view about the co-created values, because, it may be more sustainable and appropriate for today’s unpredictable socioeconomic environment. Data were collected from 4 banks (2 Islamic and 2 conventional banks). Text mining technique is applied for data analysis, and values with 100% occurrences in Islamic banking are chosen. The results reflect that Islamic banking is more centric towards non-economic values than economic values and it promotes team work and partnership concept by applying Islamic spirit and trust worthiness concept.

Keywords: economic values, Islamic banking, non-economic values, value system

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Authors: Young Nam Chun, Soo Hyuk Yun, Byeo Ri Jeong

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The recent gradual increase in the energy demand is mostly met by fossil fuel, but the research on and development of new alternative energy sources is drawing much attention due to the limited fossil fuel supply and the greenhouse gas problem. Biomass is an eco-friendly renewable energy that can achieve carbon neutrality. The conversion of the biomass sludge wastes discharged from a wastewater treatment plant to clean energy is an important green energy technology in an eco-friendly way. In this NRF study, a new type of microwave thermal treatment was developed to apply the biomass-CCS technology to sludge wastes. For this, the microwave dielectric heating characteristics were examined to investigate the energy conversion mechanism for the combined drying-pyrolysis/gasification of the dewatered wet sludge. The carbon dioxide gasification was tested using the CO2 captured from the pre-combustion capture process. In addition, the results of the pyrolysis and gasification test with the wet sludge were analyzed to compare the microwave energy conversion results with the results of the use of the conventional heating method. Gas was the largest component of the product of both pyrolysis and gasification, followed by sludge char and tar. In pyrolysis, the main components of the producer gas were hydrogen and carbon monoxide, and there were some methane and hydrocarbons. In gasification, however, the amount of carbon monoxide was greater than that of hydrogen. In microwave gasification, a large amount of heavy tar was produced. The largest amount of benzene among light tar was produced in both pyrolysis and gasification. NH3 and HCN which are the precursors of NOx, generated as well. In microwave heating, the sludge char had a smooth surface, like that of glass, and in the conventional heating method with an electric furnace, deep cracks were observed in the sludge char. This indicates that the gas obtained from the microwave pyrolysis and gasification of wet sewage sludge can be used as fuel, but the heavy tar and NOx precursors in the gas must be treated. Sludge char can be used as solid fuel or as a tar reduction adsorbent in the process if necessary. This work supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1R1A2A2A03003044).

Keywords: microwave heating, pyrolysis gasification, precombustion CCS, sewage sludge, biomass energy

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Authors: D. A. Bruzon, G. Tapang, I. S. Martinez

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Global warming and climate change are significant environmental concerns, which require immediate global action in carbon emission mitigation. The capture, sequestration, and conversion of carbon dioxide to other products such as methane or ethanol are ways to control excessive emissions. Ionic liquids have shown great potential among the materials studied as carbon capture solvents and catalysts in the reduction of CO2. In this study, ionic liquids comprising of a methoxy (-OCH3) and cyano (-CN) functionalized imidazolium cation, [MOBMIM] and [CNBMIM] respectively, paired with tris(pentafluoroethyl)trifluorophosphate [FAP] anion were evaluated as effective capture solvents, and organocatalysts in the reduction of CO2. An in-situ electrochemical set-up, which can measure controlled amounts of CO2 both in the gas and in the ionic liquid phase, was used. Initially, reduction potentials of CO2 in the CO2-saturated ionic liquids containing the internal standard cobaltocene were determined using cyclic voltammetry. Chronoamperometric transients were obtained at potentials slightly less negative than the reduction potentials of CO2 in each ionic liquid. The time-dependent current response was measured under a controlled atmosphere. Reduction potentials of CO2 in methoxy and cyano-functionalized [FAP] ionic liquids were observed to occur at ca. -1.0 V (vs. Cc+/Cc), which was significantly lower compared to the non-functionalized analog [PMIM][FAP], with an observed reduction potential of CO2 at -1.6 V (vs. Cc+/Cc). This decrease in the potential required for CO2 reduction in the functionalized ionic liquids shows that the functional groups methoxy and cyano effectively decreased the free energy of formation of the radical anion CO2●⁻, suggesting that these electrolytes may be used as organocatalysts in the reduction of the greenhouse gas. However, upon analyzing the solubility of the gas in each ionic liquid, [PMIM][FAP] showed the highest absorption capacity, at 4.81 mM under saturated conditions, compared to [MOBMIM][FAP] at 1.86 mM, and [CNBMIM][FAP] at 0.76 mM. Also, calculated Henry’s constant determined from the concentration-pressure graph of each functionalized ionic liquid shows that the groups -OCH3 and -CN attached terminal to a C4 alkyl chain do not significantly improve CO2 solubility.

Keywords: carbon capture, CO2 reduction, electrochemistry, ionic liquids

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Authors: Anam Alam, Rahim Jamaluddin Kanji

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With the rapid growth of event in everywhere, event extraction has now become an important matter to retrieve the information from the unstructured data. One of the challenging problems is to extract the event from it. An event is an observable occurrence of interaction among entities. The paper investigates the effectiveness of event extraction capabilities of three software tools that are Wandora, Nitro and SPSS. We performed standard text mining techniques of these tools on the data sets of (i) Afghan War Diaries (AWD collection), (ii) MUC4 and (iii) WebKB. Information retrieval measures such as precision and recall which are computed under extensive set of experiments for Event Extraction. The experimental study analyzes the difference between events extracted by the software and human. This approach helps to construct an algorithm that will be applied for different machine learning methods.

Keywords: event extraction, Wandora, nitro, SPSS, event analysis, extraction method, AFG, Afghan War Diaries, MUC4, 4 universities, dataset, algorithm, precision, recall, evaluation

Procedia PDF Downloads 571

Authors: Kenny Raharjo, Ramon Lawrence

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Game designers have the challenging task of building games that engage players to spend their time and money on the game. There are an infinite number of game variations and design choices, and it is hard to systematically determine game design choices that will have positive experiences for players. In this work, we demonstrate how multi-arm bandits can be used to automatically explore game design variations to achieve improved player metrics. The advantage of multi-arm bandits is that they allow for continuous experimentation and variation, intrinsically converge to the best solution, and require no special infrastructure to use beyond allowing minor game variations to be deployed to users for evaluation. A user study confirms that applying multi-arm bandits was successful in determining the preferred game variation with highest play time metrics and can be a useful technique in a game designer's toolkit.

Keywords: game design, multi-arm bandit, design exploration and data mining, player metric optimization and analytics

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

Procedia PDF Downloads 511

Authors: Ahmed Ishtiaq, Maeeda Khalid, Umair Sajjad

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In the fast-moving world, information is the key to success. If information is easily available, then it makes work easy. The Internet is the biggest collection and source of information nowadays, and with every single day, the data on internet increases, and it becomes difficult to find required data. Everyone wants to make his/her website at the top of search results. This can be possible when you have applied some techniques of SEO inside your application or outside your application, which are two types of SEO, onsite and offsite SEO. SEO is an abbreviation of Search Engine Optimization, and it is a set of techniques, methods to increase users of a website on World Wide Web or to rank up your website in search engine indexing. In this paper, we have compared different techniques of Onpage and Offpage SEO, and we have suggested many things that should be changed inside webpage, outside web page and mentioned some most powerful and search engine considerable elements and techniques in both types of SEO in order to gain high ranking on Search Engine.

Keywords: auto-suggestion, search engine optimization, SEO, query, web mining, web crawler

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Authors: Iman Atighi, Jalal Soleimannejad, Reza Pourjafarabadi, Saeid Moradpour

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In the past, we could increase profit by increasing product prices. But in the new decade, a competitive market does not let us to increase profit with increased prices. Therefore, the only way to increase profit will be to reduce costs. A significant percentage of production costs are the maintenance costs, and analysis of these costs could achieve more profit. Most maintenance strategies such as RCM (Reliability-Center-Maintenance), TPM (Total Productivity Maintenance), PM (Preventive Maintenance) and etc., are trying to reduce maintenance costs. In this paper, decreasing the maintenance costs of Concentration Plant of Golgohar Iron Ore Mining & Industrial Company (GeG) was examined by using of MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) analyses. These analyses showed that instead of buying new machines and increasing costs in order to promote capacity, the improving of MTBF and MTTR indexes would solve capacity problems in the best way and decrease costs.

Keywords: GeG company, maintainability, maintenance costs, reliability-center-maintenance

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Authors: Lin Meng, Xi Lu, Haibo Wang, Yong Song, Lili Cao, Wenfang Song, Yong Hu

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China has abundant heavy oil resources, and thermal recovery is the main recovery method for heavy oil reservoirs. However, high energy consumption, high carbon emission and high production costs make heavy oil thermal recovery unsustainable. It is urgent to explore a replacement for developing technology. A low Carbon and cost technology of heavy oil recovery, chemical viscosity-reduction in layer (CVRL), is developed by the petroleum exploration and development research institute of Sinopec via investigated mechanisms, synthesized products, and improved oil production technologies, as follows: (1) Proposed a cascade viscous mechanism of heavy oil. Asphaltene and resin grow from free molecules to associative structures further to bulk aggregations by π - π stacking and hydrogen bonding, which causes the high viscosity of heavy oil. (2) Aimed at breaking the π - π stacking and hydrogen bond of heavy oil, the copolymer of N-(3,4-dihydroxyphenethyl) acryl amide and 2-Acrylamido-2-methylpropane sulfonic acid was synthesized as a viscosity reducer. It achieves a viscosity reduction rate of>80% without shearing for heavy oil (viscosity < 50000 mPa‧s), of which fluidity is evidently improved in the layer. (3) Synthesized hydroxymethyl acrylamide-maleic acid-decanol ternary copolymer self-assembly plugging agent. The particle size is 0.1 μm-2 mm adjustable, and the volume is 10-500 times controllable, which can achieve the efficient transportation of viscosity reducer to enriched oil areas. CVRL has applied 400 wells until now, increasing oil production by 470000 tons, saving 81000 tons of standard coal, reducing CO2 emissions by 174000 tons, and reducing production costs by 60%. It promotes the transformation of heavy oil towards low energy consumption, low carbon emissions, and low-cost development.

Keywords: heavy oil, chemical viscosity-reduction, low carbon, viscosity reducer, plugging agent

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Authors: Sukla Saha, Alok Sinha

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Acid mine drainage (AMD) is the generation of acidic water from active as well as abandoned mines. AMD generates due to the oxidation of pyrites present in the rock in mining areas. Sulfur oxidizing bacteria such as Thiobacillus ferrooxidans acts as a catalyst in this oxidation process. The characteristics of AMD is extreme low pH (2-3) with elevated concentration of different heavy metals such as Fe, Al, Zn, Mn, Cu and Co and anions such sulfate and chloride. AMD contaminate the ground water as well as surface water which leads to the degradation of water quality. Moreover, it carries detrimental effect for aquatic organism and degrade the environment. In the present study, AMD is treated with fly ash, modified with alkaline agent (NaOH). This modified fly ash (MFA) was experimentally proven as a very effective neutralizing agent for the treatment of AMD. It was observed that pH of treated AMD raised to 9.22 from 1.51 with 100g/L of MFA dose. Approximately, 99% removal of Fe, Al, Mn, Cu and Co took place with the same MFA dose. The treated water comply with the effluent discharge standard of (IS: 2490-1981).

Keywords: acid mine drainage, heavy metals, modified fly ash, neutralization

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Authors: K. P. Sandesh, M. H. Suman

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Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures.

Keywords: document classification, document clustering, entropy, accuracy, classifiers, clustering algorithms

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Authors: Lien-Fa Lin, Yung-Ming Li, Fu-Shun Hsieh

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With the advancement of information technology and development of group commerce, people have obviously changed in their lifestyle. However, group commerce faces some challenging problems. The products or services provided by vendors do not satisfactorily reflect customers’ opinions, so that the sale and revenue of group commerce gradually become lower. On the other hand, the process for a formed customer group to reach group-purchasing consensus is time-consuming and the final decision is not the best choice for each group members. In this paper, we design a social decision support mechanism, by using group discussion message to recommend suitable options for group members and we consider social influence and personal preference to generate option ranking list. The proposed mechanism can enhance the group purchasing decision making efficiently and effectively and venders can provide group products or services according to the group option ranking list.

Keywords: social network, group decision, text mining, group commerce

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Authors: Nahid Hassanshahi, Ayoub Karimi-Jashni, Nasser Talebbeydokhti

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Due to the economic and environmental benefits of producing less waste, the US Environmental Protection Agency (EPA) introduces source reduction as one of the most important means to deal with the problems caused by increased landfills and pollution. Waste reduction involves all waste management methods, including source reduction, recycling, and composting, which reduce waste flow to landfills or other disposal facilities. Source reduction of waste can be studied from two perspectives: avoiding waste production, or reducing per capita waste production, and waste deviation that indicates the reduction of waste transfer to landfills. The present paper has investigated home composting as a managerial solution for reduction of waste transfer to landfills. Home composting has many benefits. The use of household waste for the production of compost will result in a much smaller amount of waste being sent to landfills, which in turn will reduce the costs of waste collection, transportation and burial. Reducing the volume of waste for disposal and using them for the production of compost and plant fertilizer might help to recycle the material in a shorter time and to use them effectively in order to preserve the environment and reduce contamination. Producing compost in a home-based manner requires very small piece of land for preparation and recycling compared with other methods. The final product of home-made compost is valuable and helps to grow crops and garden plants. It is also used for modifying the soil structure and maintaining its moisture. The food that is transferred to landfills will spoil and produce leachate after a while. It will also release methane and greenhouse gases. But, composting these materials at home is the best way to manage degradable materials, use them efficiently and reduce environmental pollution. Studies have shown that the benefits of the sale of produced compost and the reduced costs of collecting, transporting, and burying waste can well be responsive to the costs of purchasing home compost machine and the cost of related trainings. Moreover, the process of producing home compost may be profitable within 4 to 5 years and as a result, it will have a major role in reducing waste management.

Keywords: compost, home compost, reducing waste, waste management

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

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Authors: S. Chandrasekaran, S. Nandita, M. Shivathmika, Srikrishnan Shivakumar

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The objective of the research work is to analyze the computational chemical-composition of carbohydrates in the context of healthcare informatics. The computation involves the representation of complex chemical molecular structure of carbohydrate using graph theory and in a deployable Chemical Markup Language (CML). The parallel molecular structure of the chemical molecules with or without other adulterants for the sake of business profit can be analyzed in terms of robustness and derivatization measures. The rural healthcare program should create awareness in malnutrition to reduce ill-effect of decomposition and help the consumers to know the level of such energy storage mixtures in a quantitative way. The earlier works were based on the empirical and wet data which can vary from time to time but cannot be made to reuse the results of mining. The work is carried out on the quantitative computational chemistry on carbohydrates to provide a safe and secure right to food act and its regulations.

Keywords: carbohydrates, chemical-composition, chemical markup, robustness, food safety

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Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

Abstract:

Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

Procedia PDF Downloads 55

Authors: Andriana Mkrtchyan, Vahe Khlghatyan

Abstract:

The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology.

Keywords: color psychology, data acquisition/extraction, data augmentation, disambiguation, natural language processing, outlier detection, semantic similarity, text-mining, user evaluation, visual search

Procedia PDF Downloads 50

Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

Abstract:

Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

Procedia PDF Downloads 510

Authors: Salima Behdenna, Fatiha Barigou, Ghalem Belalem

Abstract:

Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic aspect-based sentiment analysis approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity

Procedia PDF Downloads 143

Authors: Melissa Vang, Raheyma Khan, Haihua Chen

Abstract:

The coronavirus disease 2019 (COVID‐19) pandemic has changed people's lives from all over the world. More people have turned to Twitter to engage online and discuss the COVID-19 vaccine. This study aims to present a text mining approach to identify people's attitudes towards the COVID-19 vaccine on Twitter. To achieve this purpose, we collected 54,268 COVID-19 vaccine tweets from September 01, 2020, to November 01, 2020, then the BERT model is used for the sentiment and topic analysis. The results show that people had more negative than positive attitudes about the vaccine, and countries with an increasing number of confirmed cases had a higher percentage of negative attitudes. Additionally, the topics discussed in positive and negative tweets are different. The tweet datasets can be helpful to information professionals to inform the public about vaccine-related informational resources. Our findings may have implications for understanding people's cognitions and feelings about the vaccine.

Keywords: BERT, COVID-19 vaccine, sentiment analysis, topic modeling

Procedia PDF Downloads 128

Authors: Elkington Sibusiso Mnguni

Abstract:

The Mokolo-Crocodile Water Augmentation Project (MCWAP) is located in the Limpopo Province in the northern-western part of South Africa. Its purpose is to increase water supply by 30 million cubic meters per year to meet current and future demand for users, including power stations, mining houses, and the local municipality in the Lephalale area. This paper documents the planning and implementation aspects of the MCWAP infrastructure project. The study will add to the body of knowledge with respect to bulk water infrastructure development in water-scarce regions. The method used to gather and collate relevant data and information was the desktop study. The key finding was that the project was successfully completed in 2015 using conventional project management and construction methods. The project is currently being operated and maintained by the National Department of Water and Sanitation.

Keywords: construction, contract management, infrastructure project, project management

Procedia PDF Downloads 285