Search results for: sequential pattern mining

Authors: Sandra Carrillo Hoyos

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Mining activities in South America have significantly grown during the last decades, given the abundance of natural resources, the implemented governmental policies to incentivize foreign investment as well as the boom in international prices for metals and oil between 2002 and 2008. While this context allowed the region to occupy a leading position between the top producers of minerals around the world, it has also meant an increase in socio-environmental conflicts which have generated costs and negative impacts not only for the companies but especially for the governments and local communities.During the latest decade, the mining sector in Peru has faced with the social resistance of a large number of communities, which began organizing actions against the implementation of high investing projects. The dissatisfaction has derived in the prevalence of socio-environmental conflicts associated with mining activities, some of them never solved into an agreement. In order to prevent those socio-environmental conflicts and obtain the social license from local communities, most of the mining companies have developed diverse initiatives within the framework of policies and practices of corporate social responsibility (CSR). This paper has assessed the mining sector’s contribution toward the local development management along the last decade, as part of CSR strategies as well as the policies promoted by the Peruvian State. This assessment found that, in the beginning, these initiatives have been based on a philanthropic approach and were reacting to pressures from local stakeholders to maintain the consent to operate from the surrounding communities as well as to create, as a result, a harmonious atmosphere for operations. Due to the weak State presence, such practices have increased the expectations of communities related to the participation of mining companies in solving structural development problems, especially those related to primary needs, infrastructure, education, health, among others. In other words, this paper was focused on analyze in what extent these initiatives have promoted local empowerment for development planning and integrated management of natural resources from a territorial approach. From this perspective, the analysis demonstrates that, while the design and planning of social investment initiatives have improved due to the sector´s sustainability approach, many companies have developed actions beyond their competence during this process. In some cases, the referenced actions have generated dependency with communities, even though this relationship has not exempted the companies of conflict situations with unfortunate consequences. Furthermore, the social programs developed have not necessarily generated a significant impact in improving the quality of life of affected populations. In fact, it is possible to identify that those regions with high mining resources and investment are facing with a situation of poverty and high dependency on mining production. In spite of the revenues derived from mining industry, local governments have not been able to translate the royalties into sustainable development opportunities. For this reason, the proposed paper suggests some challenges for the mining sector contribution to local development based on the best practices and lessons learnt from a benchmarking for the leading mining companies.

Keywords: corporate social responsibility, local development, mining, socio-environmental conflict

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Authors: G. Medkour Ishak-Boushaki, A. Taibi, M. Allab

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Quantitative environmental radioactivity measurements are needed to determine the level of exposure of a population to ionizing radiations and for the assessment of the associated risks. Gamma spectrometry remains a very powerful tool for the analysis of radionuclides present in an environmental sample but the basic problem in such measurements is the low rate of detected events. Using large environmental samples could help to get around this difficulty but, unfortunately, new issues are raised by gamma rays attenuation and self-absorption. Recently, a new method has been suggested, to detect and identify without quantification, in a short time, a gamma ray of a low count source. This method does not require, as usually adopted in gamma spectrometry measurements, a pulse height spectrum acquisition. It is based on a chronological record of each detected photon by simultaneous measurements of its energy ε and its arrival time τ on the detector, the pair parameters [ε,τ] defining an event mode sequence (EMS). The EMS serials are analyzed sequentially by a Bayesian approach to detect the presence of a given radioactive source. The main object of the present work is to test the applicability of this sequential approach in radioactive environmental materials detection. Moreover, for an appropriate health oversight of the public and of the concerned workers, the analysis has been extended to get a reliable quantification of the radionuclides present in environmental samples. For illustration, we consider as an example, the problem of detection and quantification of 238U. Monte Carlo simulated experience is carried out consisting in the detection, by a Ge(Hp) semiconductor junction, of gamma rays of 63 keV emitted by 234Th (progeny of 238U). The generated EMS serials are analyzed by a Bayesian inference. The application of the sequential Bayesian approach, in environmental radioactivity analysis, offers the possibility of reducing the measurements time without requiring large environmental samples and consequently avoids the attached inconvenient. The work is still in progress.

Keywords: Bayesian approach, event mode sequence, gamma spectrometry, Monte Carlo method

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Patikineti Sreenivasulu, K. srinivasa Rao, A. Vinaya Babu

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The demand for portability, performance and high functional integration density of digital devices leads to the scaling of complementary metal oxide semiconductor (CMOS) devices inevitable. The increase in power consumption, coupled with the increasing demand for portable/hand-held electronics, has made power consumption a dominant concern in the design of VLSI circuits today. MTCMOS technology provides low leakage and high performance operation by utilizing high speed, low Vt (LVT) transistors for logic cells and low leakage, high Vt (HVT) devices as sleep transistors. Sleep transistors disconnect logic cells from the supply and/or ground to reduce the leakage in the sleep mode. In this technology, energy consumption while doing the mode transition and minimum time required to turn ON the circuit upon receiving the wake up signal are issues to be considered because these can adversely impact the performance of VLSI circuit. In this paper we are introducing an enhancing method of MTCMOS technology to optimize the power in MTCMOS sequential circuits.

Keywords: power consumption, ultra-low power, leakage, sub threshold, MTCMOS

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: Harry Aginta

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Using Phillips curve framework, this paper seeks for new empirical evidence on the relationship between inflation and output in a major emerging economy. By exploiting sub-national data, the contribution of this paper is threefold. First, it resolves the issue of using on-target national inflation rates that potentially causes weakening inflation-output nexus. This is very relevant for Indonesia as its central bank has been adopting inflation targeting framework based on national consumer price index (CPI) inflation. Second, the study tests the relevance of mining sector in output gap estimation. The test for mining sector is important to control for the effects of mining regulation and nominal effects of coal prices on real economic activities. Third, the paper applies panel econometric method by incorporating regional variation that help to improve model estimation. The results from this paper confirm the strong presence of Phillips curve in Indonesia. Positive output gap that reflects excess demand condition gives rise to the inflation rates. In addition, the elasticity of output gap is higher if the mining sector is excluded from output gap estimation. In addition to inflation adaptation, the dynamics of exchange rate and international commodity price are also found to affect inflation significantly. The results are robust to the alternative measurement of output gap

Keywords: Phillips curve, inflation, Indonesia, panel data

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Authors: Faizah Almalki, Simeon Gill, Steve G. Hayes, Lisa Taylor

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A core element of cultural identity is the traditional costumes that provide insight into the heritage that has been acquired over time. This heritage is apparent in the use of colour, the styles and the functions of the clothing and it also reflects the skills of those who created the items and the time taken to produce them. Modern flat pattern drafting methods for making garment patterns are simple in comparison to the relatively laborious traditional approaches that would require personal interaction with the wearer throughout the production process. The current study reflects on the main elements of the pattern cutting system and how this has evolved in Saudi Arabia to affect the design of the Sawan garment. Analysis of the traditional methods for constructing Sawan garments was undertaken through observation of the practice and the garments and consulting documented guidance. This provided a foundation through which to explore how modern technology can be applied to improve the process. In this research, modern methods are proposed for producing traditional Saudi garments more efficiently while retaining elements of the conventional style and design. The current study has documented the vital aspects of Sawan garment style. The result showed that the method had been used to take the body measurements and pattern making was elementary and offered simple geometric shape and the Sawan garment is composed of four pieces. Consequently, this research allows for classical pattern shapes to be embedded in garments now worn in Saudi Arabia and for the continuation of cultural heritage.

Keywords: traditional Sawan garment technique, modern pattern cutting technique, the shape of the garment and software, Lectra Modaris

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Authors: Honggang Qu, Yong Xu, Rongmei Liu, Zhenji Gao, Bin Wang

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Today's mining industry is advancing gradually toward digital and visual direction. The three-dimensional visualization geological modeling of mine is the digital characterization of mineral deposits and is one of the key technology of digital mining. Three-dimensional geological modeling is a technology that combines geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in a three-dimensional environment with computer technology and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between the distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provides scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

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Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

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Authors: Sahil Kumar, Rajaram Ghadge, Ramesh Bhujade

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Historically, lipid extraction from dried algal biomass remained a focus area of the algal research. It has been realized over the past few years that the lipid-centric approach and conversion technologies that require dry algal biomass have several challenges. Algal culture in cultivation systems contains more than 99% water, with algal concentrations of just a few hundred milligrams per liter ( < 0.05 wt%), which makes harvesting and drying energy intensive. Drying the algal biomass followed by extraction also entails the loss of water and nutrients. In view of these challenges, focus has shifted toward developing processes that will enable oil production from wet algal biomass without drying. Hydrothermal liquefaction (HTL), an emerging technology, is a thermo-chemical conversion process that converts wet biomass to oil and gas using water as a solvent at high temperature and high pressure. HTL processes wet algal slurry containing more than 80% water and significantly reduces the adverse cost impact owing to drying the algal biomass. HTL, being inherently feedstock agnostic, i.e., can convert carbohydrates and proteins also to fuels and recovers water and nutrients. It is most effective with low-lipid (10--30%) algal biomass, and bio-crude yield is two to four times higher than the lipid content in the feedstock. In the early 2010s, research remained focused on increasing the oil yield by optimizing the process conditions of HTL. However, various techno-economic studies showed that simply converting algal biomass to only oil does not make economic sense, particularly in view of low crude oil prices. Making the best use of every component of algae is a key for economic viability of algal to oil process. On investigation of HTL reactions at the molecular level, it has been observed that sequential HTL has the potential to recover value-added products along with biocrude and improve the overall economics of the process. This potential of sequential HTL makes it a most promising technology for converting wet waste to wealth. In this presentation, we will share our experience on the techno-economic and engineering aspects of sequential HTL for conversion of algal biomass to algal bio-oil and co-products.

Keywords: algae, biomass, lipid, protein

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Authors: Nabil Elkhatri, Mohamed Louay Metougui, Ngonidzashe Chirinda

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Mining activities have left a legacy of degraded landscapes, prompting urgent efforts for ecological restoration. Reforestation holds promise as a potent tool to rehabilitate these old mining sites, with the potential to sequester carbon and contribute to climate change mitigation. This study focuses on evaluating the carbon stock and sequestration potential of reforestation species in the context of Morocco's mining areas, employing the DeNitrification-DeComposition (DNDC) model. The research is grounded in recognizing the need to connect theoretical models with practical implementation, ensuring that reforestation efforts are informed by accurate and context-specific data. Field data collection encompasses growth patterns, biomass accumulation, and carbon sequestration rates, establishing an empirical foundation for the study's analyses. By integrating the collected data with the DNDC model, the study aims to provide a comprehensive understanding of carbon dynamics within reforested ecosystems on old mining sites. The major findings reveal varying sequestration rates among different reforestation species, indicating the potential for species-specific optimization of reforestation strategies to enhance carbon capture. This research's significance lies in its potential to contribute to sustainable land management practices and climate change mitigation strategies. By quantifying the carbon stock and sequestration potential of reforestation species, the study serves as a valuable resource for policymakers, land managers, and practitioners involved in ecological restoration and carbon management. Ultimately, the study aligns with global objectives to rejuvenate degraded landscapes while addressing pressing climate challenges.

Keywords: carbon stock, carbon sequestration, DNDC model, ecological restoration, mining sites, Morocco, reforestation, sustainable land management.

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: semantic links, data mining, linked data, SKOS

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Authors: Sidi Yang, Haiyi Zhang

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Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields.

Keywords: text mining, Twitter, topic model, sentiment analysis

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Authors: Assem El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Multi-Agent TeleRobotic Security Control System (MTSCS). The event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are the Behavioral Pattern Analysis (BPA) modeling methodology, and the development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, multi-agent, TeleRobotics control, security, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

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Authors: Alok S Chauhan, Sridhar S., Pradyumna R.

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In the process of precision investment casting of turbine hollow blade/vane components, a part of the dimensional deviations observed in the castings can be attributed to the wax pattern. In the process of injection moulding of wax to produce patterns, heated wax shrinks in size during cooling in the die, leading to a reduction in the dimensions of the pattern. Also, flow and thermal induced residual stresses result in shrinkage & warpage of the component after removal from the die, further adding to the deviations. Injection moulding parameters such as wax temperature, flow rate, packing pressure, etc. affect the flow and thermal behavior of the component and hence are directly responsible for the dimensional deviations. There is a need to precisely determine and control these deviations in order to achieve stringent dimensional accuracies imposed on these castings by aerospace standards. Simulation based approaches provide a platform to predict these dimensional deviations without resorting to elaborate experimentation. In the present paper, Moldex3D simulation package has been utilized to analyze the effect of variations in injection temperature, packing pressure and cooling time on the shrinkage behavior of a stepped pattern. Two types of waxes with different rheological properties have been included in the study to gauge the effect of change in wax on the dimensional deviations. A full factorial design of experiments has been configured with these parameters and results of analysis of variance have been presented.

Keywords: wax patterns, investment casting, pattern die/mould, wax injection, Moldex3D simulation

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

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Authors: Anatoly V. Stepanov, Maria Y. Ilyushkina, Alexander S. Burnasov

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Expansion of tourism, especially after WWII, has led to significant improvements in the regional infrastructure. The present study has revealed a lot of progress in the advancement of industrial heritage narrative in the Central Urals. The evidence comes from the general public’s increased fascination with some of Europe’s oldest mining and industrial sites, and the agreement of many stakeholders that the Urals industrial heritage should be preserved. The development of tourist sites in Nizhny Tagil and Nevyansk, gold-digging in Beryosovsky, gemstone search in Murzinka, and the progress with the Urals Gemstone Ring project are the examples showing the immense opportunities of industrial heritage tourism development in the region that are still to be realized. Regardless of the economic future of the Central Urals, whether it will remain an industrial region or experience a deeper deindustrialization, the sprouts of the industrial heritage tourism should be advanced and amplified for the benefit of local communities and the tourist community at large as it is hard to imagine a more suitable site for the discovery of industrial and mining heritage than the Central Urals Region of Russia.

Keywords: industrial heritage, mining heritage, Central Urals, Russia

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Authors: Feras Hanandeh, Majdi Shannag

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This research studies the different factors that could affect the Faculty of Information Technology in Hashemite University students’ accumulative average. The research paper verifies the student information, background, their academic records, and how this information will affect the student to get high grades. The student information used in the study is extracted from the student’s academic records. The data mining tools and techniques are used to decide which attribute(s) will affect the student’s accumulative average. The results show that the most important factor which affects the students’ accumulative average is the student Acceptance Type. And we built a decision tree model and rules to determine how the student can get high grades in their courses. The overall accuracy of the model is 44% which is accepted rate.

Keywords: data mining, classification, extracting rules, decision tree

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Authors: Marwa Hussien Mohamed, Mohamed Helmy Khafagy

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Map-Reduce is a programming model which is widely used to extract valuable information from enormous volumes of data. Map-reduce designed to support heterogeneous datasets. Apache Hadoop map-reduce used extensively to uncover hidden pattern like data mining, SQL, etc. The most important operation for data analysis is joining operation. But, map-reduce framework does not directly support join algorithm. This paper explains and compares two-way and multi-way map-reduce join algorithms for map reduce also we implement MR join Algorithms and show the performance of each phase in MR join algorithms. Our experimental results show that map side join and map merge join in two-way join algorithms has the longest time according to preprocessing step sorting data and reduce side cascade join has the longest time at Multi-Way join algorithms.

Keywords: Hadoop, MapReduce, multi-way join, two-way join, Ubuntu

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Authors: Md. Enamul Haque, Abdullah Al Kaisan, Mahmudur R. Saniat, Aminur Rahman

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In this paper, we have implemented both sequential and parallel version of fractal image compression algorithms using CUDA (Compute Unified Device Architecture) programming model for parallelizing the program in Graphics Processing Unit for medical images, as they are highly similar within the image itself. There is several improvements in the implementation of the algorithm as well. Fractal image compression is based on the self similarity of an image, meaning an image having similarity in majority of the regions. We take this opportunity to implement the compression algorithm and monitor the effect of it using both parallel and sequential implementation. Fractal compression has the property of high compression rate and the dimensionless scheme. Compression scheme for fractal image is of two kinds, one is encoding and another is decoding. Encoding is very much computational expensive. On the other hand decoding is less computational. The application of fractal compression to medical images would allow obtaining much higher compression ratios. While the fractal magnification an inseparable feature of the fractal compression would be very useful in presenting the reconstructed image in a highly readable form. However, like all irreversible methods, the fractal compression is connected with the problem of information loss, which is especially troublesome in the medical imaging. A very time consuming encoding process, which can last even several hours, is another bothersome drawback of the fractal compression.

Keywords: accelerated GPU, CUDA, parallel computing, fractal image compression

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Authors: Daniel Olshansky, Ramiro Rodrıguez Colmeiro

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Relay Mining presents a scalable solution employing probabilistic mechanisms and crypto-economic incentives to estimate RPC volume usage, facilitating decentralized multitenant rate limiting. Network traffic from individual applications can be concurrently serviced by multiple RPC service providers, with costs, rewards, and rate limiting governed by a native cryptocurrency on a distributed ledger. Building upon established research in token bucket algorithms and distributed rate-limiting penalty models, our approach harnesses a feedback loop control mechanism to adjust the difficulty of mining relay rewards, dynamically scaling with network usage growth. By leveraging crypto-economic incentives, we reduce coordination overhead costs and introduce a mechanism for providing RPC services that are both geopolitically and geographically distributed.

Keywords: remote procedure call, crypto-economic, commit-reveal, decentralization, scalability, blockchain, rate limiting, token bucket

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Authors: Xuguang Leng

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The crater pattern on the Moon indicates the Moon was captured by Earth in the more recent years, disproves the theory that the Moon was born as a satellite to the Earth. The Moon was tidal locked since it became the satellite of the Earth. Moon’s near side is shielded by Earth from asteroid/comet collisions, with the center of the near side most protected. Yet the crater pattern on the Moon is fairly random, with no distinguishable empty spot/strip, no distinguishable difference near side vs. far side. Were the Moon born as Earth’s satellite, there would be a clear crater free spot, or strip should the tial lock shifts over time, on the near side; and far more craters on the far side. The nonexistence of even a vague crater free spot on the near side of the Moon indicates the capture was a more recent event. Given Earth’s much larger mass and sphere size over the Moon, Earth should have collided with asteroids and comets in much higher frequency, resulting in significant mass gain over the lifespan. Earth’s larger mass and magnetic field are better at retaining water and gas from solar wind’s stripping effect, thus accelerating the mass gain. A dwarf planet Moon can be pulled closer and closer to the Earth over time as Earth’s gravity grows stronger, eventually being captured as a satellite. Given enough time, it is possible Earth’s mass would be large enough to cause the Moon to collide with Earth.

Keywords: moon, origin, crater, pattern

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Authors: Lubabatu Sada Sodangi

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The rapid growth in exchange and accessibility of information via the internet makes many organisations acquire data on their own operation. The aim of data mining is to analyse the different behaviour of a dataset using observation. Although, the subset of the dataset being analysed may not display all the behaviours and relationships of the entire data and, therefore, may not represent other parts that exist in the dataset. There is a range of techniques used in data mining to determine the hidden or unknown information in datasets. In this paper, the performance of two algorithms Chi-Square Automatic Interaction Detection (CHAID) and multilayer perceptron (MLP) would be matched using an Adult dataset to find out the percentage of an/the adults that earn > 50k and those that earn <= 50k per year. The two algorithms were studied and compared using IBM SPSS statistics software. The result for CHAID shows that the most important predictors are relationship and education. The algorithm shows that those are married (husband) and have qualification: Bachelor, Masters, Doctorate or Prof-school whose their age is > 41<57 earn > 50k. Also, multilayer perceptron displays marital status and capital gain as the most important predictors of the income. It also shows that individuals that their capital gain is less than 6,849 and are single, separated or widow, earn <= 50K, whereas individuals with their capital gain is > 6,849, work > 35 hrs/wk, and > 27yrs their income will be > 50k. By comparing the two algorithms, it is observed that both algorithms are reliable but there is strong reliability in CHAID which clearly shows that relation and education contribute to the prediction as displayed in the data visualisation.

Keywords: data mining, CHAID, multi-layer perceptron, SPSS, Adult dataset

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Authors: Patricia Jiménez, Rafael Corchuelo

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Nowadays the World Wide Web is the most popular source of information that relies on billions of on-line documents. Web mining is used to crawl through these documents, collect the information of interest and process it by applying data mining tools in order to use the gathered information in the best interest of a business, what enables companies to promote theirs. Unfortunately, it is not easy to extract the information a web site provides automatically when it lacks an API that allows to transform the user-friendly data provided in web documents into a structured format that is machine-readable. Rule-based information extractors are the tools intended to extract the information of interest automatically and offer it in a structured format that allow mining tools to process it. However, the performance of an information extractor strongly depends on the search heuristic employed since bad choices regarding how to learn a rule may easily result in loss of effectiveness and/or efficiency. Improving search heuristics regarding efficiency is of uttermost importance in the field of Web Information Extraction since typical datasets are very large. In this paper, we employ an information extractor based on a classical top-down algorithm that uses the so-called Information Gain heuristic introduced by Quinlan and Cameron-Jones. Unfortunately, the Information Gain relies on some well-known problems so we analyse an intuitive alternative, Termini, that is clearly more efficient; we also analyse other proposals in the literature and conclude that none of them outperforms the previous alternative.

Keywords: information extraction, search heuristics, semi-structured documents, web mining.

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Authors: Stella O. Olubodun, George E. Eriyamremu

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The study assessed the effect of crude oil applied at rates, 0, 2, 5, and 10% on the fractional chemical forms and availability of some metals in soils from Usen, Edo State, with no known crude oil contamination and soil from a crude oil spill site in Ubeji, Delta State, Nigeria. Three methods were used to determine the bioavailability of metals in the soils: maize (Zea mays) plant, EDTA and BCR sequential extraction. The sequential extract acid soluble fraction of the BCR extraction (most labile fraction of the soils, normally associated with bioavailability) were compared with total metal concentration in maize seedlings as a means to compare the chemical and biological measures of bioavailability. Total Fe was higher in comparison to other metals for the crude oil contaminated soils. The metal concentrations were below the limits of 4.7% Fe, 190mg/kg Cu and 720mg/kg Zn intervention values and 36mg/kg Cu and 140mg/kg Zn target values for soils provided by the Department of Petroleum Resources (DPR) guidelines. The concentration of the metals in maize seedlings increased with increasing rates of crude oil contamination. Comparison of the metal concentrations in maize seedlings with EDTA extractable concentrations showed that EDTA extracted more metals than maize plant.

Keywords: availability, crude oil contamination, EDTA, maize, metals

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Authors: Victor Radich, Tania Basso, Regina Moraes

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Lead qualification is one of the main procedures in Customer Relationship Management (CRM) projects. Its main goal is to identify potential consumers who have the ideal characteristics to establish a profitable and long-term relationship with a certain organization. Social networks can be an important source of data for identifying and qualifying leads since interest in specific products or services can be identified from the users’ expressed feelings of (dis)satisfaction. In this context, this work proposes the use of machine learning techniques and sentiment analysis as an extra step in the lead qualification process in order to improve it. In addition to machine learning models, sentiment analysis or opinion mining can be used to understand the evaluation that the user makes of a particular service, product, or brand. The results obtained so far have shown that it is possible to extract data from social networks and combine the techniques for a more complete classification.

Keywords: lead qualification, sentiment analysis, opinion mining, machine learning, CRM, lead scoring

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: Katsue Edo, Yu Hiroi

Abstract:

T Increasing attention has been paid to using pictures and photographs in research since the beginning of the 21th century in social sciences. Meanwhile we have been studying the usefulness of Picture mining, which is one of the new ways for a these picture using researches. Picture Mining is an explorative research analysis method that takes useful information from pictures, photographs and static or moving images. It is often compared with the methods of text mining. The Picture Mining concept includes observational research in the broad sense, because it also aims to analyze moving images (Ochihara and Edo 2013). In the recent literature, studies and reports using pictures are increasing due to the environmental changes. These are identified as technological and social changes (Edo et.al. 2013). Low price digital cameras and i-phones, high information transmission speed, low costs for information transferring and high performance and resolution of the cameras of mobile phones have changed the photographing behavior of people. Consequently, there is less resistance in taking and processing photographs for most of the people in the developing countries. In these studies, this method of collecting data from respondents is often called as ‘participant-generated photography’ or ‘respondent-generated visual imagery’, which focuses on the collection of data and its analysis (Pauwels 2011, Snyder 2012). But there are few systematical and conceptual studies that supports it significance of these methods. We have discussed in the recent years to conceptualize these picture using research methods and formalize theoretical findings (Edo et. al. 2014). We have identified the most efficient fields of Picture mining in the following areas inductively and in case studies; 1) Research in Consumer and Customer Lifestyles. 2) New Product Development. 3) Research in Fashion and Design. Though we have found that it will be useful in these fields and areas, we must verify these assumptions. In this study we will focus on the field of fashion and design, to determine whether picture mining methods are really reliable in this area. In order to do so we have conducted an empirical research of the respondents’ attitudes and behavior concerning pictures and photographs. We compared the attitudes and behavior of pictures toward fashion to meals, and found out that taking pictures of fashion is not as easy as taking meals and food. Respondents do not often take pictures of fashion and upload their pictures online, such as Facebook and Instagram, compared to meals and food because of the difficulty of taking them. We concluded that we should be more careful in analyzing pictures in the fashion area for there still might be some kind of bias existing even if the environment of pictures have drastically changed in these years.

Keywords: empirical research, fashion and design, Picture Mining, qualitative research

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Authors: Adesuwa Omorede, Rufai Haruna Kilu

Abstract:

Affective motivation (motivation that is emotionally laden usually related to affect, passion, emotions, moods) in the workplace stimulates individuals to reinforce, persist and commit to their task, which leads to the individual and organizational performance. This leads individuals to reach goals especially in situations where task are highly challenging and hostile. In such situations, individuals are more disposed to be more creative, innovative and see new opportunities from the loopholes in their workplace. However, when individuals feel displaced and less important, an adverse reaction may suffice which may be detrimental to the organization and its performance. One sector where affective motivation is eminently present and relevant, is the mining industry. Due to its intense work environment; mostly dominated by men and masculinity cultures; and deliberate exclusion of women in this environment which, makes the women working in these environments to feel marginalized. In Ghana, the mining industry is mostly seen as a very physical environment especially underground and mostly considerd as 'no place for a woman'. Despite the fact that these women feel less 'needed' or 'appreciated' in such environments, they still have to juggle between intense work shifts; face violence and other health risks with their families, which put a strain on their affective motivational reaction. Beyond these challenges, however, several mining companies in Ghana today are working towards providing a fair and equal working situation for both men and women miners, by recognizing them as key stakeholders, as well as including them in the stages of mining projects from the planning and designing phase to the evaluation and implementation stage. Drawing from the psychology and gender literature, this study takes a narrative approach to identify and understand the shifting gender dynamics within the mine works in Ghana, occasioning a change in background disposition of miners, which leads to more women taking up mine jobs in the country. In doing so, a qualitative study was conducted using semi-structured interviews from Ghana. Several women working within the mining industries in Ghana shared their experiences and how they felt and still feel in their workplace. In addition, archival documents were gathered to support the findings. The results suggest a change in enrolment regimes in a mining and technology university in Ghana, making room for a more gender equal enrolments in the university. A renowned university that train and feed mine work professional into the industry. The results further acknowledge gender equal and diversity recruitment policies and initiatives among the mining companies of Ghana. This study contributes to the psychology and gender literature by highlighting the hindrances women face in the mining industry as well as highlighting several of their affective reactions towards gender inequality. The study also provides several suggestions for decision makers in the mining industry of what can be done in the future to reduce the gender inequality gap within the industry.

Keywords: affective motivation, gender shape shifting, mining industry, women miners

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