Search results for: allele mining

Authors: Sihem Oujdi, Hafida Belbachir

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Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.

Keywords: C4.5 algorithm, decision trees, S-CART, spatial data mining

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Authors: Benjamin Kuester, Bjoern Eilert, Malte Stonis, Ludger Overmeyer

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Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.

Keywords: 8D report, complaint management, evaluation system, text-mining

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Authors: John Dorrell, Matthew Ambrosia, Abilash

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This paper employs econometric analysis to quantify the potential profit wind farms can receive by allocating excess wind energy to power bitcoin mining machines. Cryptocurrency mining consumes a substantial amount of electricity worldwide, and wind energy produces a significant amount of energy that is lost because of the intermittent nature of the resource. Supply does not always match consumer demand. By combining the weaknesses of these two technologies, we can improve efficiency and a sustainable path to mine cryptocurrencies. This paper uses historical wind energy from the ERCOT network in Texas and cryptocurrency data from 2000-2021, to create 4-year return on investment projections. Our research model incorporates the price of bitcoin, the price of the miner, the hash rate of the miner relative to the network hash rate, the block reward, the bitcoin transaction fees awarded to the miners, the mining pool fees, the cost of the electricity and the percentage of time the miner will be running to demonstrate that wind farms generate enough excess energy to mine bitcoin profitably. Excess wind energy can be used as a financial battery, which can utilize wasted electricity by changing it into economic energy. The findings of our research determine that wind energy producers can earn profit while not taking away much if any, electricity from the grid. According to our results, Bitcoin mining could give as much as 1347% and 805% return on investment with the starting dates of November 1, 2021, and November 1, 2022, respectively, using wind farm curtailment. This paper is helpful to policymakers and investors in determining efficient and sustainable ways to power our economic future. This paper proposes a practical solution for the problem of crypto mining energy consumption and creates a more sustainable energy future for Bitcoin.

Keywords: bitcoin, mining, economics, energy

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Authors: Sangdeok Lee, Sangwon Han, Changtaek Hyun

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Construction defects are major components that result in negative impacts on project performance including schedule delays and cost overruns. Since construction defects generally occur when a few associated causes combine, a thorough understanding of defect causality is required in order to more systematically prevent construction defects. To address this issue, this paper uses association rule mining (ARM) to quantify the causality between defect causes, and social network analysis (SNA) to find indirect causality among them. The suggested approach is validated with 350 defect instances from concrete works in 32 projects in Korea. The results show that the interrelationships revealed by the approach reflect the characteristics of the concrete task and the important causes that should be prevented.

Keywords: causality, defect causes, social network analysis, association rule mining

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Authors: Grace Omowunmi Soyebi

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Data Mining is the extraction of information from a large database which helps in predicting a trend or behavior, thereby helping management make knowledge-driven decisions. One principal problem of most hospitals in rural areas is making use of the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved; this may cause an unexpected to happen to the patient. This Data Mining application is to be designed using a Structured System Analysis and design method, which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the Design and Implementation of a Computerized medical record system. This Computerized system will replace the file management system and help to easily retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: data mining, medical record system, systems programming, computing

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Authors: Elsayeda M. Elgaml, Dina M. Ibrahim, Elsayed A. Sallam

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Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FP-growth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy.

Keywords: association rules, FP-growth, multiple minimum supports, Weka tool

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Authors: Manaphol Kulpraneet, Anirut Limtrakul, Surangrat Srisurapanon, Piyatida Tangteerawatana

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Tuberculosis (TB) remains a global health care disease world-wide. Control of the global TB epidemic has been impaired by the lack of an effective vaccine, by the emergence of drug resistant forms of Mycobacterium tuberculosis and by lack of sensitive and rapid diagnostics. Cytokines play a major role in defense against M. tuberculosis infection. Polymorphisms in the genes encoding various cytokines have been associated with tuberculosis susceptibility. Polymorphisms of the regulatory cytokine gene, the interleukin (IL)-10 is associated with the risk of tuberculosis (TB) in different populations. However, IL-10 gene polymorphism and susceptibility to TB in Thai is still unknown. The purpose of this study was to evaluate whether the common IL-10 promoter gene polymorphisms are associated with TB in Thai population. Forty eight patients with newly diagnosed pulmonary tuberculosis were studied. DNA samples were extracted from leukocytes and used to investigate -1087A/G, -819C/T, -252C/A (rs1800896, rs1800871, rs1800872) in IL-10 gene using restriction fragment length polymorphism (PCR-RFLP) methods. In this study, the genotype and allele frequencies of IL-10-1087A/G, -819C/T, -252C/A polymorphism did not significantly different between TB patients and healthy controls ((genotype: p=0.38, p=0.92, p=1; allele: p=0.57, p=0.77, p=0.89, respectively). The lack of association between common IL-10 promoter polymorphisms and TB susceptibility in this study may provide clue for better understanding of IL-10-1087A/G, -819C/T, -252C/A polymorphism and TB susceptibility in Thai population, which might facilitate the rationale design of vaccines. However, further studies in large scales population are required for confirmation.

Keywords: IL-10, cytokines, single nucleotide polymorphism (SNP), tuberculosis

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

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Authors: Muhammad Usman

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Pattern extraction has been done in past to extract hidden and interesting patterns from large datasets. Recently, advancements are being made in these techniques by providing the ability of multi-level mining, effective dimension reduction, advanced evaluation and visualization support. This paper focuses on reviewing the current techniques in literature on the basis of these parameters. Literature review suggests that most of the techniques which provide multi-level mining and dimension reduction, do not handle mixed-type data during the process. Patterns are not extracted using advanced algorithms for large datasets. Moreover, the evaluation of patterns is not done using advanced measures which are suited for high-dimensional data. Techniques which provide visualization support are unable to handle a large number of rules in a small space. We present a theoretical model to handle these issues. The implementation of the model is beyond the scope of this paper.

Keywords: association rule mining, data mining, data warehouses, visualization of association rules

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: K. S. Vimal, D. Bodhini, K. Ramya, N. Lakshmipriya, R. M. Anjana, V. Sudha, J. A. Lovegrove, V. Mohan, V. Radha

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Gene-lifestyle interaction studies have been carried out in various populations. However, to date there are no studies in an Asian Indian population. Hence, we examined whether lifestyle factors such as diet and physical activity modify the association between fat mass and obesity–associated (FTO) gene variants and obesity and type 2 diabetes (T2D) in an Asian Indian population. We studied 734 unrelated T2D and 884 normal glucose-tolerant (NGT) participants randomly selected from the Chennai Urban Rural Epidemiology Study (CURES) in Southern India. Obesity was defined according to the World Health Organization Asia Pacific Guidelines (non-obese, BMI < 25 kg/m2; obese, BMI ≥ 25 kg/m2). Six single nucleotide polymorphisms (SNPs) in the FTO gene (rs9940128, rs7193144, rs8050136, rs918031, rs1588413 and rs11076023) identified from recent genome-wide association studies for T2D were genotyped by polymerase chain reaction-restriction fragment length polymorphism and direct sequencing. Dietary assessment was carried out using a validated food frequency questionnaire and physical activity was based upon the self-report. Interaction analyses were performed by including the interaction terms in the model. A joint likelihood ratio test of the main SNP effects and the SNP-diet/physical activity interaction effects was used in the linear regression analyses to maximize statistical power. Statistical analyses were performed using STATA version 13. There was a significant interaction between FTO SNP rs8050136 and carbohydrate energy percentage (Pinteraction=0.04) on obesity, where the ‘A’ allele carriers of the SNP rs8050136 had 2.46 times higher risk of obesity than those with ‘CC’ genotype (P=3.0x10-5) among individuals in the highest tertile of carbohydrate energy percentage. Furthermore, among those who had lower levels of physical activity, the ‘A’ allele carriers of the SNP rs8050136 had 1.89 times higher risk of obesity than those with ‘CC’ genotype (P=4.0x10-5). We also found a borderline interaction between SNP rs11076023 and carbohydrate energy percentage (Pinteraction=0.08) on T2D, where the ‘A’ allele carriers in the highest tertile of carbohydrate energy percentage, had 1.57 times higher risk of T2D than those with ‘TT’ genotype (P=0.002). There was also a significant interaction between SNP rs11076023 and physical activity (Pinteraction=0.03) on T2D. No further significant interactions between SNPs and macronutrient intake or physical activity on obesity and T2D were observed. In conclusion, this is the first study to provide evidence for a gene-diet and gene-physical activity interaction on obesity and T2D in an Asian Indian population. These findings suggest that the association between FTO gene variants and obesity and T2D is influenced by carbohydrate intake and physical activity levels. Greater understanding of how FTO gene influences obesity and T2D through dietary and exercise interventions will advance the development of behavioral intervention and personalised lifestyle strategies predicted to reduce the development of metabolic diseases in ‘A’ allele carriers of both SNPs in this Asian Indian population.

Keywords: dietary intake, FTO, obesity, physical activity, type 2 diabetes, Asian Indian.

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Authors: Meng-Hui Chen, Chen-Yu Kao, Chia-Yu Hsu, Pei-Chann Chang

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The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems.

Keywords: combinatorial problems, sequential pattern mining, estimationof distribution algorithms, artificial chromosomes

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Authors: Khaleel Saleh Al-Rababah

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Amyloid fibril forming regions, which are known as protein aggregates, in sequences of some protein families are associated with a number of diseases known as amyloidosis. Mutations play a role in forming fibrils by accelerating the fibril formation process. In this paper we want to extract diseases that caused by those mutations as a result of the impact of the mutations on structural and functional properties of the aggregated protein. We propose a text mining system, to automatically extract mutations, diseases and relations between mutations and diseases. We presented an algorithm based on finite state to cluster mutations found in the same sentence as a sentence could contain different mutation cause different diseases. Also, we presented a co reference algorithm that enables cross-link sentences.

Keywords: amyloid, amyloidosis, co reference, protein, text mining

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Authors: O. D. Eniowo

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“Rewashing” as it is commonly called, involves the process of scooping out and washing chunks of mud from a closed alluvial gold mine site with the purpose of extracting any leftover gold deposits in the site. It is usually carried out by illegal miners who infiltrate closed mine sites with the goal of scavenging for any leftover gold deposits. Expectedly, the practice gives little or no regard for environmental protection. This paper examines the process of “rewashing” in a mining community in Nigeria. It then discusses the looming danger it portends for health, safety, and the environment. The study draws lessons from these occurrences to examine and discuss fit-for-purpose mine closure plans that could be adopted by gold mines in Nigeria and other sub-Saharan African countries.

Keywords: mine planning, mine closure, illegal mining, artisanal mining, environmental sustainability

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Authors: M. Bhattarai, B. B. Rana, M. Kamimukai, I. Takamure, T. Kawano, M. Murai

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The sd1-d allele at the sd1 locus on chromosome 1, originating from Taiwanese variety Dee-geo-woo-gen, has been playing important role for developing short-culm and lodging-resistant indica varieties such as IR36 in rice. The dominant allele SD1 for long culm at the locus is differentiated into SD1-in and SD1-ja which are harbored in indica and japonica subspecies’s, respectively. The sd1-d of an indica variety IR36 was substituted with SD1-in or SD1-ja by recurrent backcrosses of 17 times with IR36, and two isogenic tall lines regarding the respective dominant alleles were developed by using an indica variety IR5867 and a japonica one ‘Koshihikari’ as donors, which were denoted by '5867-36' and 'Koshi-36', respectively. The present study was conducted to examine the effect of sd1-d on yield and related traits as compared with SD1-in and SD1-ja, by using the two isogenic tall lines. Seedlings of IR36 and the two isogenic lines were transplanted on an experimental field of Kochi University, by the planting distance of 30 cm × 15 cm with two seedlings per hill, on May 3, 2017. Chemical fertilizers were supplied by basal application and top-dressing at a rate of 8.00, 6.57 and 7.52 g/m², respectively, for N, P₂O₅ and K₂O in total. Yield, yield components, and other traits were measured. Culm length (cm) was in the order of 5867-36 (101.9) > Koshi-36 (80.1) > IR36 (60.0), where '>' indicates statistically significant difference at the 5% level. Accordingly, sd1-d reduced culm by 41.9 and 20.1 cm, compared with SD1-in and SD1-ja, respectively, and the effect of elongating culm was higher in the former allele than in the latter one. Total brown rice yield (g/m²), including unripened grains, was in the order of IR36 (611) ≧ 5867-36 (586) ≧ Koshi-36 (572), indicating non-significant differences among them. Yield-1.5mm sieve (g/m²) was in the order of IR36 (596) ≧ 5867-36 (575) ≧ Koshi-36 (558). Spikelet number per panicle was in the order of 5867-36 (89.2) ≧ IR36 (84.7) ≧ Koshi-36 (79.8), and 5867-36 > Koshi-36. Panicle number per m² was in the order of IR36 (428) ≧ Koshi-36 (403) ≧ 5867-36 (353), and IR36 > 5867-36, suggesting that sd1-d increased number of panicles compared with SD1-in. Ripened-grain percentage-1.5mm sieve was in the order of Koshi-36 (86.0) ≧ 5867-36 (85.0) ≧ IR36 (82.7), and Koshi-36 > IR36. Thousand brown-rice-grain weight-1.5mm sieve (g) was in the order of 5867-36 (21.5) > Koshi-36 (20.2) ≧ IR36 (19.9). Total dry weight at maturity (g/m²) was in the order of 5867-36 (1404 ) ≧ IR36 (1310) ≧ Kosihi-36 (1290). Harvest index of total brown rice (%) was in the order of IR36 (39.6) > Koshi-36 (37.7) > 5867-36 (35.5). Hence, sd1-d did not exert significant effect on yield in indica genetic background. However, lodging was observed from the late stage of maturity in 5867-36 and Koshi-36, particularly in the former, which was principally due to their long culms. Consequently, sd1-d enables higher yield with higher fertilizer application, by enhancing lodging resistance, particularly in indica subspecies.

Keywords: rice, dwarfing gene, sd1-d, SD1-in, SD1-ja, yield

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Authors: Rachida Malaoui, Leila Arabet , Asma Benbouza

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The use of mining waste rock as a material for construction is one of the biggest concerns grabbing the attention of many mining countries. As these materials are abandoned, more effective solutions have been made to offset some of the building materials, and to avoid environmental pollution. The sands of the Djemi Djema deposit mines of the Djebel Onk mines are sedimentary materials of several varieties of layers with varying thicknesses and are worth far more than 300m deep. The sands from the Djemi Djema business area are medium to coarse and are discharged and accumulated, generating a huge estimated quantity of more than 77424250 tonnes. This state of "resource" is of great importance so as to be oriented towards the fields of public works and civil engineering after having reached the acceptable properties of this resource

Keywords: reuse, sands, shear tests, waste rock

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Authors: Diego Mauricio Murillo Gomez, Enney Leon Gonzalez Ramirez, Hugo Piedrahita, Jairo Yate

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This paper proposes a methodology for the management of noise in open mining industries based on an integral concept, which takes into consideration occupational and environmental noise as a whole. The approach relies on the characterization of sources, the combination of several measurements’ techniques and the use of acoustic prediction software. A discussion about the difference between frequently used acoustic indicators such as Leq and LAV is carried out, aiming to establish common ground for homologation. The results show that the correct integration of this data not only allows for a more robust technical analysis but also for a more strategic route of intervention as several departments of the company are working together. Noise control measurements can be designed to provide a healthy acoustic surrounding in which the exposure workers but also the outdoor community is benefited.

Keywords: environmental noise, noise control, occupational noise, open mining

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Authors: Jiameng Wang, Yunfei Yin, Xiyu Deng

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Parallel optimization is one of the important research topics of data mining at this stage. Taking Classification and Regression Tree (CART) parallelization as an example, this paper proposes a parallel data mining algorithm based on SSP-OGini-PCCP. Aiming at the problem of choosing the best CART segmentation point, this paper designs an S-SP model without data association; and in order to calculate the Gini index efficiently, a parallel OGini calculation method is designed. In addition, in order to improve the efficiency of the pruning algorithm, a synchronous PCCP pruning strategy is proposed in this paper. In this paper, the optimal segmentation calculation, Gini index calculation, and pruning algorithm are studied in depth. These are important components of parallel data mining. By constructing a distributed cluster simulation system based on SPARK, data mining methods based on SSP-OGini-PCCP are tested. Experimental results show that this method can increase the search efficiency of the best segmentation point by an average of 89%, increase the search efficiency of the Gini segmentation index by 3853%, and increase the pruning efficiency by 146% on average; and as the size of the data set increases, the performance of the algorithm remains stable, which meets the requirements of contemporary massive data processing.

Keywords: classification, Gini index, parallel data mining, pruning ahead

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Authors: Rosendo Mendoza, María Carmen Hidalgo, María José Campos-Suñol, Julián Martínez, Javier Rey

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The impact of mining environmental liabilities and mine drainage on surface water quality has been investigated in the hydrographic basin of the La Carolina mining district (southern Spain). This abandoned mining district is characterized by the existence of important mineralizations of sulfoantimonides of Pb - Ag, and sulfides of Cu - Fe. All surface waters reach the main river of this mining area, the Grande River, which ends its course in the Rumblar reservoir. This waterbody is intended to supply 89,000 inhabitants, as well as irrigation and livestock. Therefore, the analysis and control of the metal(loid) concentration that exists in these surface waters is an important issue because of the potential pollution derived from metallic mining. A hydrogeochemical campaign consisting of 20 water sampling points was carried out in the hydrographic network of the Grande River, as well as two sampling points in the Rumbler reservoir and at the main tailings impoundment draining to the river. Although acid mine drainage (pH below 4) is discharged into the Grande river from some mine adits, the pH values in the river water are always neutral or slightly alkaline. This is mainly the result of a dilution process of the small volumes of mine waters by net alkaline waters of the river. However, during the dry season, the surface waters present high mineralization due to a constant discharge from the abandoned flooded mines and a decrease in the contribution of surface runoff. The concentrations of dissolved Cd and Pb in the water reach values of 2 and 81 µg/l, respectively, exceeding the limit established by the Environmental Quality Standard for surface water. In addition, the concentrations of dissolved As, Cu, and Pb in the waters of the Rumblar reservoir reached values of 10, 20, and 11 µg/l, respectively. These values are higher than the maximum allowable concentration for human consumption, a circumstance that is especially alarming.

Keywords: environmental quality, hydrogeochemistry, metal mining, surface water

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Authors: Chiung-Man Tsai, Chia-Jui Weng

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Leptin (LEP) and leptin receptor (LEPR) both play a crucial role in the mediation of physiological reactions and carcinogenesis and may serve as a candidate biomarker of oral cancer. The present case-control study aimed to examine the effects of single nucleotide polymorphisms (SNPs) of LEP -2548 G/A (rs7799039), LEPR K109R (rs1137100), and LEPR Q223R (rs1137101) with or without interacting to environmental carcinogens on the risk for oral squamous cell carcinoma (OSCC). The SNPs of three genetic allele, from 567 patients with oral cancer and 560 healthy controls in Taiwan were analyzed. All of The three genetic polymorphisms exhibited insignificant (P > .05) effects on the risk to have oral cancer. However, the patients with polymorphic allele of LEP -2548 have a significant low risk for the development of clinical stage (A/G, AOR = 0.670, 95% CI = 0.454–0.988, P < .05; A/G+G/G, AOR = 0.676, 95% CI = 0.467–0.978, P < .05) compared to patients with ancestral homozygous A/A genotype. Additionally, an interesting result was found that the impact of LEP -2548 G/A SNP on oral carcinogenesis in subjects without tobacco consumption (A/G, AOR=2.078, 95% CI: 1.161-3.720, p=0.014; A/G+G/G, AOR=2.002, 95% CI: 1.143-3.505, p=0.015) is higher than subjects with tobacco consumption. These results suggest that the genetic polymorphism of LEP -2548 G/A (rs7799039), LEPR K109R (rs1137100), and LEPR Q223R (rs1137101) were not associated with the susceptibility of oral cancer; SNP in LEP -2548 G/A showed a poor clinicopathological development of oral cancer; Population without tobacco consumption and with polymorphic LEP -2548 G/A gene may significantly increase the risk to have oral cancer.

Keywords: carcinogen, leptin, leptin receptor, oral squamous cell carcinoma, single nucleotide polymorphism

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Ketan Mahawer, Abeer Mutto, S. K. Gupta

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The mining wastewater contains inorganic metal salts, which makes it saline and additionally contributes to contaminating the surface and underground freshwater reserves that exist nearby mineral processing industries. Therefore, treatment of wastewater and water recovery is obligatory by any available technology before disposing it into the environment. Currently, reverse osmosis (RO) is the commercially acceptable conventional membrane process for saline wastewater treatment, but consumes an enormous amount of energy and makes the process expensive. To solve this industrial problem with minimum energy consumption, we tested the feasibility of forward osmosis-nanofiltration (FO-NF) hybrid process for the mining wastewater treatment. The FO-NF process experimental results for 0.029M concentration of saline wastewater treated by 0.42 M sodium-sulfate based draw solution shows that specific energy consumption of the FO-NF process compared with standalone NF was slightly above (between 0.5-1 kWh/m3) from conventional process. However, average freshwater recovery was 30% more from standalone NF with same feed and operating conditions. Hence, FO-NF process in place of RO/NF offers a huge possibility for treating mining industry wastewater and concentrates the metals as the by-products without consuming an excessive/large amount of energy and in addition, mitigates the fouling in long periods of treatment, which also decreases the maintenance and replacement cost of the separation process.

Keywords: forward osmosis, nanofiltration, mining, draw solution, divalent solute

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Authors: Taufik Hidayat, Yudha Nurhantari, Bambang U. D. Rianto

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Introduction: The examination of the Short Tandem Repeats (STR) locus on nuclear DNA is very useful in solving the paternity cases. The purpose of this study is to know the description of paternity cases and paternity index/probability of paternity analysis based on Indonesian allele frequency at Sardjito Hospital Yogyakarta. Method: This was an observational study with cross-sectional analytic method. Population and sample were all cases of disputed paternity from January 2011 to June 2015 that fulfill the inclusion and exclusion criteria and were examined at Forensic Medicine Unit of Sardjito Hospital, Medical Faculty of Gadjah Mada University. The paternity index was calculated with EasyDNA Program by Fung (2013). Analysis of the study was conducted by comparing the results through unpaired categorical test using Kolmogorov-Smirnov test. This study was designed with 95% confidence interval (CI) with α = 5% and significance level is p < 0,05. Results: From 42 disputed paternity cases we obtained trio paternity cases were 32 cases (76.2%) and duo without a mother was 10 cases (23.8%). The majority of the fathers' estimated ages were 21-30 years (33.3%) and the mother's age was 31-40 years (38.1%). The majority of the ages of children examined for paternity were under 12 months (47.6%). The majority of ethnic clients are Javanese. Conclusion of inclusion was 57.1%, and exclusion was 42.9%. The Kolmogorov-Smirnov test obtained p-value = 0.673. Conclusion: There is no significant difference between paternity index/probability of paternity based on Indonesian allele frequency between trio and duo of paternity.

Keywords: disputed paternity, paternity index, probability of paternity, short tandem

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Authors: G. A. Gevorgyan, K. A. Ghazaryan, T. H. Derdzyan

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The heavy metal pollution of the soils around the mining area near Shamlugh town and related risks to human health were assessed. The investigations showed that the soils were polluted with heavy metals that can be ranked by anthropogenic pollution degree as follows: Cu>Pb>As>Co>Ni>Zn. The main sources of the anthropogenic metal pollution of the soils were the copper mining area near Shamlugh town, the Chochkan tailings storage facility and the trucks transferring are from the mining area. Copper pollution degree in some observation sites was unallowable for agricultural production. The total non-carcinogenic chronic hazard index (THI) values in some places, including observation sites in Shamlugh town, were above the safe level (THI<1) for children living in this territory. Although the highest heavy metal enrichment degree in the soils was registered in case of copper, the highest health risks to humans especially children were posed by cobalt which is explained by the fact that heavy metals have different toxicity levels and penetration characteristics.

Keywords: Armenia, copper mine, heavy metal pollution of soil, health risks

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

Abstract:

Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

Procedia PDF Downloads 589

Authors: Erika Ruíz, Luis Amaya, Diego Carreño

Abstract:

The main objective of this study is to design a mathematical model for the logistics of mining collection centers in the northern region of the department of Boyacá (Colombia), determining the structure that facilitates the flow of products along the supply chain. In order to achieve this, it is necessary to define a suitable design of the distribution network, taking into account the products, customer’s characteristics and the availability of information. Likewise, some other aspects must be defined, such as number and capacity of collection centers to establish, routes that must be taken to deliver products to the customers, among others. This research will use one of the operation research problems, which is used in the design of distribution networks known as Location Routing Problem (LRP).

Keywords: location routing problem, logistic, mining collection, model

Procedia PDF Downloads 217

Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

Abstract:

Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

Procedia PDF Downloads 308

Authors: Nidhin Dani Abraham, T. K. Sri Shilpa

Abstract:

Asset liability management is an important aspect in banking business. Moreover, the today’s banking is based on BASEL III which strictly regulates on the counterparty default. This paper focuses on prediction and analysis of counter party default risk, which is a type of risk occurs when the customers fail to repay the amount back to the lender (bank or any financial institutions). This paper proposes an approach to reduce the counterparty risk occurring in the financial institutions using an appropriate data mining technique and thus predicts the occurrence of NPA. It also helps in asset building and restructuring quality. Liability management is very important to carry out banking business. To know and analyze the depth of liability of bank, a suitable technique is required. For that a data mining technique is being used to predict the dormant behaviour of various deposit bank customers. Various models are implemented and the results are analyzed of saving bank deposit customers. All these data are cleaned using data cleansing approach from the bank data warehouse.

Keywords: data mining, asset liability management, BASEL III, banking

Procedia PDF Downloads 552

Authors: Felermino D. M. A. Ali, S. C. Ng

Abstract:

E-learning is an evolutionary form of distance learning and has become better over time as new technologies emerged. Today, efforts are still being made to embrace E-learning systems with emerging technologies in order to make them better. Among these advancements, Educational Data Mining (EDM) is one that is gaining a huge and increasing popularity due to its wide application for improving the teaching-learning process in online practices. However, even though EDM promises to bring many benefits to educational industry in general and E-learning environments in particular, its principal drawback is the lack of easy to use tools. The current EDM tools usually require users to have some additional technical expertise to effectively perform EDM tasks. Thus, in response to these limitations, this study intends to design and implement an EDM application framework which aims at automating and simplify the development of EDM in E-learning environment. The application framework introduces a Service-Oriented Architecture (SOA) that hides the complexity of technical details and enables users to perform EDM in an automated fashion. The framework was designed based on abstraction, extensibility, and interoperability principles. The framework implementation was made up of three major modules. The first module provides an abstraction for data gathering, which was done by extending Moodle LMS (Learning Management System) source code. The second module provides data mining methods and techniques as services; it was done by converting Weka API into a set of Web services. The third module acts as an intermediary between the first two modules, it contains a user-friendly interface that allows dynamically locating data provider services, and running knowledge discovery tasks on data mining services. An experiment was conducted to evaluate the overhead of the proposed framework through a combination of simulation and implementation. The experiments have shown that the overhead introduced by the SOA mechanism is relatively small, therefore, it has been concluded that a service-oriented architecture can be effectively used to facilitate educational data mining in E-learning environments.

Keywords: educational data mining, e-learning, distributed data mining, moodle, service-oriented architecture, Weka

Procedia PDF Downloads 236

Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

Abstract:

Process mining research discipline bridges the gap between data mining and business process modeling and analysis, it offers the process-centric and end-to-end methods/techniques for analyzing information of real-world process detailed in operational event-logs. In this paper, we have proposed a notation called trace logo for graphically representing control-flow perspective (order of execution of activities) of process. A trace logo consists of a stack of activity names at each position, sizes of the activity name indicates their frequency in the traces and the total height of the activity depicts the information content of the position. A trace logo created from a set of aligned traces generated using Multiple Trace Alignment technique.

Keywords: consensus trace, process mining, multiple trace alignment, trace logo

Procedia PDF Downloads 349