Search results for: portuguese mining

Authors: Chao-Ton Su, Li-Fei Chen

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The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made.

Keywords: quality engineering, parameter design, neural network, genetic algorithm, experimental design

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Richard Adu Antwako

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Coastal wetland destruction and its consequences have recently taken the center stage of global discussions. This phenomenon is no gray area to humanity as coastal wetland-human interaction seems inevitably ingrained in the earliest civilizations, amidst the demanding use of its resources to meet their necessities. The severity of coastal wetland destruction parallels with growing civilizations, and it is against this backdrop that, this paper interrogated the causes of coastal wetland destruction in Harper City in Liberia, compared the degree of coastal wetland stressors to the non-equilibrium thermodynamic scale as well as suggested an integrated coastal zone management to address the problems. Literature complemented the primary data gleaned via global positioning system devices, field observation, questionnaire, and interviews. Multi-sampling techniques were used to generate data from the sand miners, institutional heads, fisherfolk, community-based groups, and other stakeholders. Non-equilibrium thermodynamic theory remains vibrant in discerning the ecological stability, and it would be employed to further understand the coastal wetland destruction in Harper City, Liberia and to measure the coastal wetland stresses-amplitude and elasticity. The non-equilibrium thermodynamics postulates that the coastal wetlands are capable of assimilating resources (inputs), as well as discharging products (outputs). However, the input-output relationship exceedingly stretches beyond the thresholds of the coastal wetlands, leading to coastal wetland disequilibrium. Findings revealed that the sand mining, mangrove removal, and crude dumping have transformed the coastal wetlands, resulting in water pollution, flooding, habitat loss and disfigured beaches in Harper City in Liberia. This paper demonstrates that the coastal wetlands are converted into developmental projects and agricultural fields, thus, endangering the future society against nature.

Keywords: amplitude, crude dumping, elasticity, non-equilibrium thermodynamics, wetland destruction

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Authors: Francesca Radice

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Domestic and sexual violence provokes, on average in Australia, one female death per week due to intimate violence behaviours. 83% of couples meet online, and intercepting domestic and sexual violence at this level would be beneficial. It has been observed that violent or coercive behaviour has been apparent from initial conversations on dating apps like Tinder. Child pornography, stalking, and coercive control are some criminal offences from dating apps, including women murdered after finding partners through Tinder. Police databases and predictive policing are novel approaches taken to prevent crime before harm is done. This research will investigate how police databases can be used in a privacy-preserving way to characterise users in terms of their potential for violent crime. Using the COPS database of NSW Police, we will explore how the past criminal record can be interpreted to yield a category of potential danger for each dating app user. It is up to the judgement of each subscriber on what degree of the potential danger they are prepared to enter into. Sentiment analysis is an area where research into natural language processing has made great progress over the last decade. This research will investigate how sentiment analysis can be used to interpret interchanges between dating app users to detect manipulative or coercive sentiments. These can be used to alert law enforcement if continued for a defined number of communications. One of the potential problems of this approach is the potential prejudice a categorisation can cause. Another drawback is the possibility of misinterpreting communications and involving law enforcement without reason. The approach will be thoroughly tested with cross-checks by human readers who verify both the level of danger predicted by the interpretation of the criminal record and the sentiment detected from personal messages. Even if only a few violent crimes can be prevented, the approach will have a tangible value for real people.

Keywords: sentiment analysis, data mining, predictive policing, virtual manipulation

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Authors: Olga Shuvaeva, Tamara Romanova, Sergey Volynkin, Valentina Podolinnaya

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Green chemistry concept is focused on the prevention of environmental pollution caused by human activity. However, there are a lot of contaminated areas in the world which pose a serious threat to ecosystems in terms of their conservation. Therefore in accordance with the principles of green chemistry, it should not be forgotten about the need to clean these areas. Furthermore, the waste material often contains the valuable components, the extraction of which by traditional wet chemical technologies is inefficient both from the economic and environmental protection standpoint. Wherein, the plants may be successfully used to ‘scavenge’ a range of metals from polluted land sites in an approach allowing to carry out both of these processes – phytoremediation and phytomining in conjunction. The goal of the present work was to study bioaccumulation ability of floating macrophytes such as water hyacinth and pondweed toward Hg, Ba, Cd, Mo and Pb as pollutants in aquatic medium and terrestrial plants (birch, reed, and cane) towards gold and silver as valuable components. The peculiarity of ongoing research was that the plants grew under extreme conditions (pH of drainage and pore waters was about 2.5). The study was conducted at the territory of Ursk tailings (Southwestern Siberia, Russia) formed as a result of primary polymetallic ores cyanidation. The waste material is mainly presented (~80%) by pyrite (FeS₂) and barite (BaSO₄), the raw minerals included FeAsS, HgS, PbS, Ag₂S as minor ones. It has been shown that water hyacinth demonstrates high ability to accumulate different metals, and what is especially important – to remove mercury from polluted waters with BCF value more than 1000. As for the gold, its concentrations in reed and cane growing near the waste material were estimated as 500 and 900 μg∙kg⁻¹ respectively. It was also found that the plants can survive under extreme conditions of acidic environment and hence we can assume that there is a principal opportunity to use them for the valuable substances extraction from an area of the mining waste dumps burial.

Keywords: bioaccumulation, gold, heavy metals, mine tailing

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Authors: Siah Koka, Rony Swennen

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Arumeru district is located in Arusha region in Upper Pangani basin in Tanzania. Economically it is dominated with agricultural activities. Banana, coffee, maize, beans, tomatoes, and cassava are the most important food and cash crops. This paper characterized the banana-based farming system of Arumeru district, evaluates its sustainability as well as research needs. The household questionnaire was performed on-site and on farm observation. Transect walk also involved to identify different agro- ecological zones. Results show that farm holdings (home gardens) are smaller than a hectare (0.7 ha) and continue to fragment as population continues to grow. Banana cultivation is the backbone of the farming systems present both in the upland and plains. In the upper belt banana found their place in the forest, which form the home garden structure typical to East African highland banana production systems. However, in the plains, cultivation is done in monoculture and depends heavily on irrigation. We found slightly less cultivars present and hypothetically more pest and disease pressure. This was mainly seen for Fusarium oxysporum species, which eradicates susceptible cultivars such as Mchare cultivars rapidly given the method of irrigation. The smaller permanent upland home garden plots provide thus a more suitable environment where banana perform better. It should be noted that findings indicated good performance to occur in the less suitable plains too. Good management is believed to be the most influencing factor, although our survey failed in identifying them. Population pressure is currently pushing the sustainable system in the uplands to its boundaries. Nutrient mining, deforestation and changing rain patterns threat production not only on Mt. Meru but on a global scale.

Keywords: Arumeru district, banana-based farming system, Tanzania, Arumeru district

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Authors: Xue Wang, Liwei Fan

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Renewable energy technology innovation is an important way to realize the energy transformation. Our government has issued a series of policies to guide and support the development of renewable energy. The implementation of these policies will affect the further development, utilization and technological innovation of renewable energy. In this context, it is of great significance to systematically sort out and evaluate the renewable energy technology innovation policy for improving the existing policy system. Taking the 190 renewable energy technology innovation policies issued during 2005-2021 as a sample, from the perspectives of policy issuing departments and policy keywords, it uses text mining and content analysis methods to analyze the current situation of the policies and conduct a semantic network analysis to identify the core issuing departments and core policy topic words; A PMC (Policy Modeling Consistency) index model is built to quantitatively evaluate the selected policies, analyze the overall pros and cons of the policy through its PMC index, and reflect the PMC value of the model's secondary index The core departments publish policies and the performance of each dimension of the policies related to the core topic headings. The research results show that Renewable energy technology innovation policies focus on synergy between multiple departments, while the distribution of the issuers is uneven in terms of promulgation time; policies related to different topics have their own emphasis in terms of policy types, fields, functions, and support measures, but It still needs to be improved, such as the lack of policy forecasting and supervision functions, the lack of attention to product promotion, and the relatively single support measures. Finally, this research puts forward policy optimization suggestions in terms of promoting joint policy release, strengthening policy coherence and timeliness, enhancing the comprehensiveness of policy functions, and enriching incentive measures for renewable energy technology innovation.

Keywords: renewable energy technology innovation, content analysis, policy evaluation, PMC index model

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Authors: Mahmoud B. Rokaya

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The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

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Authors: Emiel Caron

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Patent databases contain patent related data, organized in a relational data model, and are used to produce various patent statistics. These databases store raw data about scientific references cited by patents. For example, Patstat holds references to tens of millions of scientific journal publications and conference proceedings. These references might be used to connect patent databases with bibliographic databases, e.g. to study to the relation between science, technology, and innovation in various domains. Problematic in such studies is the low data quality of the references, i.e. they are often ambiguous, unstructured, and incomplete. Moreover, a complete bibliographic reference is stored in only one attribute. Therefore, a computerized cleaning and disambiguation method for large patent databases is developed in this work. The method uses rule-based scoring and clustering. The rules are based on bibliographic metadata, retrieved from the raw data by regular expressions, and are transparent and adaptable. The rules in combination with string similarity measures are used to detect pairs of records that are potential duplicates. Due to the scoring, different rules can be combined, to join scientific references, i.e. the rules reinforce each other. The scores are based on expert knowledge and initial method evaluation. After the scoring, pairs of scientific references that are above a certain threshold, are clustered by means of single-linkage clustering algorithm to form connected components. The method is designed to disambiguate all the scientific references in the Patstat database. The performance evaluation of the clustering method, on a large golden set with highly cited papers, shows on average a 99% precision and a 95% recall. The method is therefore accurate but careful, i.e. it weighs precision over recall. Consequently, separate clusters of high precision are sometimes formed, when there is not enough evidence for connecting scientific references, e.g. in the case of missing year and journal information for a reference. The clusters produced by the method can be used to directly link the Patstat database with bibliographic databases as the Web of Science or Scopus.

Keywords: clustering, data cleaning, data disambiguation, data mining, patent analysis, scientometrics

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Authors: Yulna Yulnafatmawita, Syafrimen Yasin, Lusi Maira

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Irrigation source is one of the factors affecting physical properties of rice field. This research was aimed to determine the impact of polluted irrigation wáter on soil physical properties of rice field. The study site was located in Koto Nan IV, Dharmasraya Regency, West Sumatra, Indonesia. The rice field was irrigated with wáter from Momongan river in which people do gold mining. The soil was sampled vertically from the top to 100 cm depth with 20 cm increment of soil profile from 2 year-fallowed rice field, as well as from the top 20 cm of cultivated rice field from the terrace-1 (the highest terrace) to terrace-5 (the lowest terrace) position. Soil samples were analysed in laboratory. For comparison, rice field receiving irrigation wáter from non-polluted source was also sampled at the top 20 cm and anaysed for the physical properties. The result showed that there was a change in soil physical properties of rice field after 9 years of getting irrigation from the river. Based on laboratory analyses, the total suspended solid (TSS) in the tailing reached 10,736 mg/L. The texture of rice field at polluted rice field (PRF) was dominated (>55%) by sand particles at the top 100 cm soil depth, and it tended to linearly decrease (R2=0.65) from the top 20 cm to 100 cm depth. Likewise, the sand particles also linearly decreased (R2=0.83), but clay particles linearly increased (R2=0.74) horizontally as the distance from the wáter input (terrace-1) was fartherst. Compared to nonpolluted rice field (NPRF), percentage of sand was higher, and clay was lower at PRF. This sandy texture of soil in PRF increased soil hydraulic conductivity (up to 19.1 times), soil bulk density (by 38%), and sharply decreased SOM (by 88.5 %), as well as soil total pore (by 22.1%) compared to the NPRF at the top 20 cm soil. The rice field was suggested to be reclaimed before reusing it. Otherwise the soil characteristics requirement, especially soil wáter retention, for rice field could not be fulfilled.

Keywords: gold mine tailing, polluted irrigation, rice field, soil physical properties

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Authors: Shih-Ting Yang, Chih-Yun Lin, Ming-Yu Li, Jhong-Ting Syue, Wei-Ming Huang

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Global positioning system (GPS) has become increasing precise in recent years, and the location-based service (LBS) has developed rapidly. Take the example of finding a parking lot (such as Parking apps). The location-based service can offer immediate information about a nearby parking lot, including the information about remaining parking spaces. However, it cannot provide expected search results according to the requirement situations of users. For that reason, this paper develops a “Location-based Search Approach according to Users’ Application Scenario” according to the location-based search and demand determination to help users obtain the information consistent with their requirements. The “Location-based Search Approach based on Users’ Application Scenario” of this paper consists of one mechanism and three kernel modules. First, in the Information Pre-processing Mechanism (IPM), this paper uses the cosine theorem to categorize the locations of users. Then, in the Information Category Evaluation Module (ICEM), the kNN (k-Nearest Neighbor) is employed to classify the browsing records of users. After that, in the Information Volume Level Determination Module (IVLDM), this paper makes a comparison between the number of users’ clicking the information at different locations and the average number of users’ clicking the information at a specific location, so as to evaluate the urgency of demand; then, the two-dimensional space is used to estimate the application situations of users. For the last step, in the Location-based Search Module (LBSM), this paper compares all search results and the average number of characters of the search results, categorizes the search results with the Manhattan Distance, and selects the results according to the application scenario of users. Additionally, this paper develops a Web-based system according to the methodology to demonstrate practical application of this paper. The application scenario-based estimate and the location-based search are used to evaluate the type and abundance of the information expected by the public at specific location, so that information demanders can obtain the information consistent with their application situations at specific location.

Keywords: data mining, knowledge management, location-based service, user application scenario

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Authors: Sara Mosallanejad, Bogdan Z. Dlugogorski, Jeff Gore, Mohammednoor Altarawneh

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Ammonium nitrate (NH­₄NO₃, AN) is commonly used as the main component of AN emulsion and fuel oil (ANFO) explosives, that use extensively in civilian and mining operations for underground development and tunneling applications. The emulsion formulation and wettability of AN prills, which affect the physical stability and detonation of ANFO, highly depend on the surface tension, density, viscosity of the used liquid. Therefore, for engineering applications of this material, the determination of density and surface tension of concentrated aqueous solutions of AN is essential. The molecular dynamics (MD) simulation method have been used to investigate the density and the surface tension of high concentrated ammonium nitrate solutions; up to its solubility limit in water. Non-polarisable models for water and ions have carried out the simulations, and the electronic continuum correction model (ECC) uses a scaling of the charges of the ions to apply the polarisation implicitly into the non-polarisable model. The results of calculated density and the surface tension of the solutions have been compared to available experimental values. Our MD simulations show that the non-polarisable model with full-charge ions overestimates the experimental results while the reduce-charge model for the ions fits very well with the experimental data. Ions in the solutions show repulsion from the interface using the non-polarisable force fields. However, when charges of the ions in the original model are scaled in line with the scaling factor of the ECC model, the ions create a double ionic layer near the interface by the migration of anions toward the interface while cations stay in the bulk of the solutions. Similar ions orientations near the interface were observed when polarisable models were used in simulations. In conclusion, applying the ECC model to the non-polarisable force field yields the density and surface tension of the AN solutions with high accuracy in comparison to the experimental measurements.

Keywords: ammonium nitrate, electronic continuum correction, non-polarisable force field, surface tension

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

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Authors: Hui-Chen Tsai, Chien-Jen Ho, Bo-Wei Power Liang, Ying Shen, Yi-Hsin Lai

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Former Taiwan Metal Mining Corporation and its associated 3 wasted flue gas tunnels, hereinafter referred to as 'TMMC', was contaminated with heavy metals, Polychlorinated biphenyls (PCBs) and Total Petroleum Hydrocarbons (TPHs) in soil. Since the contamination had been exposed and unmanaged in the environment for more than 40 years, the extent of the contamination area is estimated to be more than 25 acres. Additionally, TMMC is located in a remote, mountainous area where almost no residents are residing in the 1-km radius area. Thus, it was deemed necessary to conduct an ecological risk assessment in order to evaluate the details of future contaminated site management plan. According to the winter and summer, ecological investigation results, one type of endangered, multiple vulnerable and near threaten plant was discovered, as well as numerous other protected species, such as Crested Serpent Eagle, Crested Goshawk, Black Kite, Brown Shrike, Taiwan Blue Magpie were observed. Ecological soil screening level (Eco-SSLs) developed by USEPA was adopted as a reference to conduct screening assessment. Since all the protected species observed surrounding TMMC site were birds, screening ecological risk assessment was conducted on birds only. The assessment was assessed mainly based on the chemical evaluation, which the contamination in different environmental media was compared directly with the ecological impact levels (EIL) of each evaluation endpoints and the respective hazard quotient (HQ) and hazard index (HI) could be obtained. The preliminary ecological risk assessment results indicated HI is greater than 1. In other words, the biological stressors (birds) were exposed to the contamination, which was already exceeded the dosage that could cause unacceptable impacts to the ecological system. This result was mainly due to the high concentration of arsenic, metal and lead; thus it was suggested the above mention contaminants should be remediated as soon as possible or proper risk management measures should be taken.

Keywords: screening, ecological risk assessment, ecological impact levels, risk management

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Authors: King Kumire

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The Outer Space Treaty and the Moon Treaty have been the backbone of space law. However, scientists, engineers, and policymakers have been silent about how human settlement on celestial bodies would change the legal dimensions of space law. Indeed, these legal space regimes should have a prescription on how galactic courts should deal with the aspect of space property ownership. On this planet earth, one can vindicate his own assets. In extraterrestrial environments, this is not the case because space law is fatigued by terrestrial body sovereignty, which must be upheld. However, the recent commercialization of microgravity environments requires property ownership laws to be enacted. Space activities have mutated to the extent that it is almost possible to build communities in space. The discussions on the moon village concept will be mentioned as well to give clarity on the subject to the audience. It should be stated that launchers can now explore the cosmos with space tourists. The world is also busy doing feasibility studies on how to implement space mining projects. These activities indisputably show that the research is important because it will not only expose how the cosmic world is constrained by existing legal frameworks, but it will provide a remedy for how the inevitable dilemma of property rights can be resolved through the formulation of multilateral and all-inclusive policies. The discussion will model various aspects of terrestrial property rights and the associated remedies against what can be applicable and customized for use in extraterrestrial environments. Transfer of ownership in space is also another area of interest as the researcher shall try to distinguish between envisaged personal and real rights in the new frontier vis-a-vis mainland transfer transactions. The writer imagines the extent to which the concepts of servitudes, accession, prescription and commixes, and other property templates can act as a starting point when cosmic probers move forward with the revision of orbital law. The article seeks to reconcile these ownership constraints by working towards the development of a living space common law which is elastic and embroidered by sustainable recommendations. A balance between transplanting terrestrial laws to the galactic arena and the need to enact new ones which will complement the existing space treaties will be meticulously pivoted.

Keywords: rights, commercialisation, ownership, sovereignty

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Negin Bayat, Nahid HasanZadeh

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Effluents of different industries such as metalworking, battery industry, mining, including heavy metal are considered problematic issues for both humans and the environment. These heavy metals include cadmium, copper, zinc, nickel, chromium, cyanide, lead, etc. Different physicochemical and biological methods are used to remove heavy metals, such as sedimentation, coagulation, flotation, chemical precipitation, filtration, membrane processes (reverse osmosis and nanofiltration), ion exchange, biological methods, adsorption with activated carbon, etc. These methods are generally either expensive or ineffective. In recent years, considerable attention has been given to the removal of heavy metal ions from solution by absorption using discarded and low-cost materials. In this study, nickel removal using an adsorption process by eucalyptus powdered leaves and poplar ash was investigated. This is an applied study. The effect of various parameters on metal removal, such as pH, amount of adsorbent, contact time, and stirring speed, was studied using a discontinuous method. This research was conducted in aqueous solutions on the laboratory scale. Then, optimum absorption conditions were obtained. Then, the study was conducted on real wastewater samples. In addition, the nickel concentration in the wastewater before and after the absorption process was measured. In all experiments, the remaining nickel was measured using an atomic absorption spectrometry device at 382 nm wavelength after an appropriate time and filtration. The results showed that increasing both adsorbent and pH parameters increase the metal removal rate. Nickel removal increased at the first 60 minutes. Then, the absorption rate remained constant and reached equilibrium. A desired removal rate with 40 mg in 100 ml adsorbent solution at pH = 9.5 was observed. According to the obtained results, the best absorption rate was observed at 40 mg dose using a combination of eucalyptus leaves and poplar ash in this study, which was equal to 99.76%. Thus, this combined method can be used as an inexpensive and effective absorbent for the removal of nickel from aqueous solutions.

Keywords: absorption, wastewater, nickel, poplar ash, eucalyptus leaf, treatment

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Authors: Cahya Wimar Wicaksono, Reynara Davin Chen, Alvian Kristianto Santoso

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Grasberg Cu-Au ore deposit as one of the biggest porphyry deposits located in Papua Province, Indonesia produced by several intrusion that restricted by Heavy Sulphide Zone (HSZ) in peripheral. HSZ is the rock that becomes the contact between Grassberg Igneous Complex (GIC) with sedimentary and igneous rock outside, which is rich in sulphide minerals such as pyrite ± pyrrhotite. This research is to obtain the characteristic of HSZ based on geotechnical, geochemical and mineralogy aspect and those implication for daily mining operational activities. Method used in this research are geological and alteration mapping, core logging, FAA (Fire Assay Analysis), AAS (Atomic absorption spectroscopy), RQD (Rock Quality Designation) and rock water content. Data generated from methods among RQD data, mineral composition and grade, lithological and structural geology distribution in research area. The mapping data show that HSZ material characteristics divided into three type based on rocks association, there are near igneous rocks, sedimentary rocks and on HSZ area. And also divided based on its location, north and south part of research area. HSZ material characteristic consist of rock which rich of pyrite ± pyrrhotite, and RQD range valued about 25%-100%. Pyrite ± pyrrhotite which outcropped will react with H₂O and O₂ resulting acid that generates corrosive effect on steel wire and rockbolt. Whereas, pyrite precipitation proses in HSZ forming combustible H₂S gas which is harmful during blasting activities. Furthermore, the impact of H₂S gas in blasting activities is forming poison gas SO₂. Although HSZ high grade Cu-Au, however those high grade Cu-Au rich in sulphide components which is affected in flotation milling process. Pyrite ± pyrrhotite in HSZ will chemically react with Cu-Au that will settle in milling process instead of floating.

Keywords: combustible, corrosive, heavy sulphide zone, pyrite ± pyrrhotite

Procedia PDF Downloads 326

Authors: Kung-Jen Tu, Danny Vernatha

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To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.

Keywords: database, electricity sub-meters, energy anomaly detection, sensor

Procedia PDF Downloads 307

Authors: Inaam Khalaf, Nadin M. Abdel Razeq, Hamza Alduraidi, Suhaila Halasa, Omayyah S. Nassar, Eman Al-Horani, Jumana Shehadeh, Anna Talal

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Background: Nursing researchers are responsible for generating knowledge that corresponds to national and global research priorities in order to promote, restore, and maintain the health of individuals and societies. The objectives of this scoping review of Jordanian literature are to assess the existing research on pediatric nursing in terms of evolution, authorship and collaborations, funding sources, methodologies, topics of research, and pediatric subjects' age groups so as to identify gaps in research. Methodology: A search was conducted using related keywords obtained from national and international databases. The reviewed literature included pediatric health articles published through December 2019 in English and Arabic, authored by nursing researchers. The investigators assessed the retrieved studies and extracted data using a data-mining checklist. Results: The review included 265 articles authored by Jordanian nursing researchers concerning children's health, published between 1987 and 2019; 95% were published between 2009 and 2019. The most commonly applied research methodology was the descriptive non-experimental method (76%). The main generic topics were health promotion and disease prevention (23%), chronic physical conditions (19%), mental health, behavioral disorders, and forensic issues (16%). Conclusion: The review findings identified a grave shortage of evidence concerning nursing care issues for children below five years of age, especially those between ages two and five years. The research priorities identified in this review resonate with those identified in international reports. Implications: Nursing researchers are encouraged to conduct more research targeting topics of national-level importance in collaboration with clinically involved nurses and international scholars.

Keywords: Jordan, scoping review, children health nursing, pediatric, adolescents

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Authors: Semire Yekta

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Many online retailers set up manual review teams to overcome the limitations of automated online fraud detection systems. This study critically examines the strategies they adapt in their decision-making process to set apart fraudulent individuals from non-fraudulent online shoppers. The study uses a mix method research approach. 32 in-depth interviews have been conducted alongside with participant observation and auto-ethnography. The study found out that all steps of the decision-making process are significantly affected by a level of subjectivity, personal understandings of online fraud, preferences and judgments and not necessarily by objectively identifiable facts. Rather clearly knowing who the fraudulent individuals are, the team members have to predict whether they think the customer might be a fraudster. Common strategies used are relying on the classification and fraud scorings in the automated fraud detection systems, weighing up arguments for and against the customer and making a decision, using cancellation to test customers’ reaction and making use of personal experiences and “the sixth sense”. The interaction in the team also plays a significant role given that some decisions turn into a group discussion. While customer data represent the basis for the decision-making, fraud management teams frequently make use of Google search and Google Maps to find out additional information about the customer and verify whether the customer is the person they claim to be. While this, on the one hand, raises ethical concerns, on the other hand, Google Street View on the address and area of the customer puts customers living in less privileged housing and areas at a higher risk of being classified as fraudsters. Phone validation is used as a final measurement to make decisions for or against the customer when previous strategies and Google Search do not suffice. However, phone validation is also characterized by individuals’ subjectivity, personal views and judgment on customer’s reaction on the phone that results in a final classification as genuine or fraudulent.

Keywords: online fraud, data mining, manual review, social construction

Procedia PDF Downloads 343

Authors: B. Mavhuru, N. S. Nethengwe

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Human activities on land degradation have triggered several environmental problems especially in rural areas that are underdeveloped. The main aim of this study is to analyze the contribution of different land uses to gully development and sediment load on the Nzhelele River Valley in the Limpopo Province. Data was collected using different methods such as observation, field data techniques and experiments. Satellite digital images, topographic maps, aerial photographs and the sediment load static model also assisted in determining how land use affects gully development and sediment load. For data analysis, the researcher used the following methods: Analysis of Variance (ANOVA), descriptive statistics, Pearson correlation coefficient and statistical correlation methods. The results of the research illustrate that high land use activities create negative changes especially in areas that are highly fragile and vulnerable. Distinct impact on land use change was observed within settlement area (9.6 %) within a period of 5 years. High correlation between soil organic matter and soil moisture (R=0.96) was observed. Furthermore, a significant variation (p ≤ 0.6) between the soil organic matter and soil moisture was also observed. A very significant variation (p ≤ 0.003) was observed in bulk density and extreme significant variations (p ≤ 0.0001) were observed in organic matter and soil particle size. The sand mining and agricultural activities has contributed significantly to the amount of sediment load in the Nzhelele River. A high significant amount of total suspended sediment (55.3 %) and bed load (53.8 %) was observed within the agricultural area. The connection which associates the development of gullies to various land use activities determines the amount of sediment load. These results are consistent with other previous research and suggest that land use activities are likely to exacerbate the development of gullies and sediment load in the Nzhelele River Valley.

Keywords: drainage basin, geomorphological processes, gully development, land degradation, riparian land use and sediment load

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Authors: Sergio V Davalos, Alison L. Watkins

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User generated content (UGC) such as website post has data associated with it: time of the post, gender, location, type of device, and number of words. The text entered in user generated content (UGC) can provide a valuable dimension for analysis. In this research, each user post is treated as a collection of terms (words). In addition to the number of words per post, the frequency of each term is determined by post and by the sum of occurrences in all posts. This research focuses on one specific aspect of UGC: sentiment. Sentiment analysis (SA) was applied to the content (user posts) of two sets of political forums related to the US presidential elections for 2012 and 2016. Sentiment analysis results in deriving data from the text. This enables the subsequent application of data analytic methods. The SASA (SAIL/SAI Sentiment Analyzer) model was used for sentiment analysis. The application of SASA resulted with a sentiment score for each post. Based on the sentiment scores for the posts there are significant differences between the content and sentiment of the two sets for the 2012 and 2016 presidential election forums. In the 2012 forums, 38% of the forums started with positive sentiment and 16% with negative sentiment. In the 2016 forums, 29% started with positive sentiment and 15% with negative sentiment. There also were changes in sentiment over time. For both elections as the election got closer, the cumulative sentiment score became negative. The candidate who won each election was in the more posts than the losing candidates. In the case of Trump, there were more negative posts than Clinton’s highest number of posts which were positive. KNIME topic modeling was used to derive topics from the posts. There were also changes in topics and keyword emphasis over time. Initially, the political parties were the most referenced and as the election got closer the emphasis changed to the candidates. The performance of the SASA method proved to predict sentiment better than four other methods in Sentibench. The research resulted in deriving sentiment data from text. In combination with other data, the sentiment data provided insight and discovery about user sentiment in the US presidential elections for 2012 and 2016.

Keywords: sentiment analysis, text mining, user generated content, US presidential elections

Procedia PDF Downloads 192

Authors: Ali Mohammed Yimer, Ayalew Assen, Youssef Belmabkhout

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Environmental remediation is a pressing global concern, necessitating innovative strategies to address the challenges posed by industrial waste and pollution. This study aims to advance environmental remediation by developing cutting-edge functional porous materials from phosphorite residue tailings. Phosphorite mining activities generate vast amounts of waste, which pose significant environmental risks due to their contaminants. The proposed approach involved transforming these phosphorite residue tailings into valuable porous materials through a series of physico-chemical processes including milling, acid-base leaching, designing or templating as well as formation processes. The key components of the tailings were extracted and processed to produce porous arrays with high surface area and porosity. These materials were engineered to possess specific properties suitable for environmental remediation applications, such as enhanced adsorption capacity and selectivity for target contaminants. The synthesized porous materials were thoroughly characterized using advanced analytical techniques (XRD, SEM-EDX, N2 sorption, TGA, FTIR) to assess their structural, morphological, and chemical properties. The performance of the materials in removing various pollutants, including heavy metals and organic compounds, were evaluated through batch adsorption experiments. Additionally, the potential for material regeneration and reusability was investigated to enhance the sustainability of the proposed remediation approach. The outdoors of this research holds significant promise for addressing the environmental challenges associated with phosphorite residue tailings. By valorizing these waste materials into porous materials with exceptional remediation capabilities, this study contributes to the development of sustainable and cost-effective solutions for environmental cleanup. Furthermore, the utilization of phosphorite residue tailings in this manner offers a potential avenue for the remediation of other contaminated sites, thereby fostering a circular economy approach to waste management.

Keywords: functional porous materials, phosphorite residue tailings, adsorption, environmental remediation, sustainable solutions

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

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This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

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Authors: A. Asaga, N. I. Basuki

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The research area is located in Cisarua, Bogor Regency, West Java, with 12,8 km2 wide. This area belongs to mining region of PT Aneka Tambang Tbk. The purpose of this research is to study geological condition, alteration type and pattern, and type of mineralization. Geomorphology of the research area is at young to mature stage, which can be divided into Ciparigi’s Parasite Volcanic Cone Unit, Ciparigi Caldera Valley Unit, Ciparigi Caldera Rim Hill Unit, and Pongkor Volcanic Hill. Stratigraphy of the research area consist of five units, they are Laharic Breccia (Pliocene), Pyroclastic Breccia, Lapilli Tuff, Flow Tuff, Fall Tuff, and Andesite Lava (Pleistocene). Based on mineral composition, it is interpreted that there is magma composition changing from rhyolite to andesitic. Geological structures in the research area are caused by NE-SW and N-S stress direction; they are Ciparay Right Strike-Slip Fault (Pliocene), Cisarua Right Strike-Slip Fault, G. Singa Left Strike-Slip Fault, and Cinyuncung Right Strike-Slip Fault (Pleistocene). Weak to strong hydrothermal alteration can be found in the research area.They are Chlorite ± Smectite ± Halloysite Zone, Smectite - Illite - Quartz Zone, Smectite - Kaolinite - Illite - Chlorite Zone, and Smectite - Chlorite - Calcite - Quartz Zone. The distribution and assemblage of alteration minerals is controlled by lithology and geological structures in Pleistocene. Mineralization produce ore minerals, those are pyrite, marcasite, chalcopyrite, sphalerite, galena, and chalcocite. There are calcite and quartz veins that show colloform, comb, and crystalline textures. Hydrothermal alteration assemblages, ore minerals, and cavity filling textures suggest that mineralization type in research area is epithermal low sulphidation.

Keywords: Pongkor, hydrothermal alteration, epithermal, geochemistry

Procedia PDF Downloads 396

Authors: Xiaobin Liang, Wei Liang, Laibin Zhang, Xiaoyan Guo

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Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality.

Keywords: bow-tie model, natural gas pipeline, coal mine gob, cloud inference

Procedia PDF Downloads 250

Authors: Temesgen Oljira, Olugbenga Akindeji Okunlola, Akinade Shadrach Olatunji, Dereje Ayalew, Bekele A. Bedada, Tasin Godlove Bafon

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The Megele area gold-bearing Neoproterozoic rocks in the Western Ethiopian Shield has been under exploration for the last few decades. The geochemical and ore petrological characterization of the gold-bearing granite gneiss and associated quartz vein is crucial in understanding the gold's genesis. The present study concerns the ore petrological, geochemical, and stable O2 and S characterization of the gold-bearing granite gneiss and associated quartz vein. This area is known for its long history of placer gold mining. The presence of quartz veins of different generations and orientations, visible sulfide mineralization, and oxidation suggests that the Megele area is geologically fertile for mineralization. The Au and base metals analysis also indicate that Megele area rocks are characterized by Cu (2-22 ppm av. 7.83 ppm), Zn (2-53 ppm av. 29.33 ppm), Co (1-27 ppm av. 13.33 ppm), Ni (2-16 ppm av. 10 ppm), Pb (5-10 ppm av. 8.33 ppm), Au (1-5 ppb av. 2.11 ppb), Ag (0.5 ppm), As (5-12 ppm av. 7.83 ppm), Cd (0.5ppm), Li (0.5 ppm), Mo (1-4 ppm av. 1.6 ppm), Sc (5-13 ppm av. 9.3 ppm), and Tl (10 ppm). The oxygen isotope (δ18O) values of gold-bearing granite gneiss and associated quartz veins range from +8.6 to +11.5 ‰, suggesting the mixing of metamorphic water with magmatic water within the ore-forming fluid. The Sulfur isotope (δ34S) values of gold-bearing granite gneiss range from -1.92 to -0.45 ‰ (mean value of -1.13 ‰) indicating the narrow range of value. This suggests that the sulfides have been precipitated from the fluid system originating from a single source of the magmatic component under sulfur isotopic fractionation equilibrium condition. The tectonic setting of the host rocks, the occurrence of ore bodies, mineral assemblages of the host rocks and proposed ore-forming fluids of the Megele area gold prospects have similarities with features of orogenic gold deposit. The δ18O and δ34S isotopic values also suggested a metamorphic origin with the magmatic components. Thus, the Megele gold prospect could be related to an orogenic gold deposit related to metamorphism and associated intrusions.

Keywords: fluid source, gold mineralization, oxygen isotope, stable isotope, sulfur isotope

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 80