Search results for: association rule mining

Authors: Şakir Bingöl, Bayram İslamoğlu, Ebubekir Furkan Tepeli, Fatih Mehmet Karakule, Fatih Küçük, Merve Sena Arpacık, Mustafa Taha Kabar, Muhammet Metin Molak, Osman Emre Turan, Ömer Faruk Yesir, Sıla İnanır

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This study introduces an IoT-based real-time safety system for mining, addressing global safety challenges. The wearable device, seamlessly integrated into miners' jackets, employs LoRa technology for communication and offers real-time monitoring of vital health and environmental data. Unique features include an LCD panel for immediate information display and sound-based location tracking for emergency response. The methodology involves sensor integration, data transmission, and ethical testing. Validation confirms the system's effectiveness in diverse mining scenarios. The study calls for ongoing research to adapt the system to different mining contexts, emphasizing its potential to significantly enhance safety standards in the industry.

Keywords: mining safety, internet of things, wearable technology, LoRa, RFID tracking, real-time safety system, safety alerts, safety measures

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Authors: Mariamawit T. Belete

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Cereal production and marketing are the means of livelihood for millions of households in Ethiopia. However, cereal production is constrained by technical and socio-economic factors. Among the technical factors, cereal crop diseases are the major contributing factors to the low yield. The aim of this research is to develop an integration of data mining and knowledge based system for sorghum anthracnose disease diagnosis that assists agriculture experts and development agents to make timely decisions. Anthracnose diagnosing systems gather information from Melkassa agricultural research center and attempt to score anthracnose severity scale. Empirical research is designed for data exploration, modeling, and confirmatory procedures for testing hypothesis and prediction to draw a sound conclusion. WEKA (Waikato Environment for Knowledge Analysis) was employed for the modeling. Knowledge based system has come across a variety of approaches based on the knowledge representation method; case-based reasoning (CBR) is one of the popular approaches used in knowledge-based system. CBR is a problem solving strategy that uses previous cases to solve new problems. The system utilizes hidden knowledge extracted by employing clustering algorithms, specifically K-means clustering from sampled anthracnose dataset. Clustered cases with centroid value are mapped to jCOLIBRI, and then the integrator application is created using NetBeans with JDK 8.0.2. The important part of a case based reasoning model includes case retrieval; the similarity measuring stage, reuse; which allows domain expert to transfer retrieval case solution to suit for the current case, revise; to test the solution, and retain to store the confirmed solution to the case base for future use. Evaluation of the system was done for both system performance and user acceptance. For testing the prototype, seven test cases were used. Experimental result shows that the system achieves an average precision and recall values of 70% and 83%, respectively. User acceptance testing also performed by involving five domain experts, and an average of 83% acceptance is achieved. Although the result of this study is promising, however, further study should be done an investigation on hybrid approach such as rule based reasoning, and pictorial retrieval process are recommended.

Keywords: sorghum anthracnose, data mining, case based reasoning, integration

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Authors: Jason West

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Low profitability in agriculture production along with increasing scrutiny over environmental effects is limiting food production at scale. In contrast, the mining sector offers access to resources including energy, water, transport and chemicals for food production at low marginal cost. Scalable agricultural production can benefit from the nexus of resources (water, energy, transport) offered by mining activity in remote locations. A decision support bioeconomic model for controlled environment vertical farms was used. Four submodels were used: crop structure, nutrient requirements, resource-crop integration, and economic. They escalate to a macro mathematical model. A demonstrable dynamic systems framework is needed to prove productive outcomes are feasible. We demonstrate a generalized bioeconomic macro model for controlled environment production systems in minesites using systems dynamics modeling methodology. Despite the complexity of bioeconomic modelling of resource-agricultural dynamic processes and interactions, the economic potential greater than general economic models would assume. Scalability of production as an input becomes a key success feature.

Keywords: crop production systems, mathematical model, mining, agriculture, dynamic systems

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Authors: Hamza Zafar, Quratulain Rajput

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Salah (نماز ) is one of five pillars of Islam and obligatory for every Muslims. However, due to the lack of Islamic knowledge it might be very difficult for a layperson to perform it correctly. This paper presents an ontology based representation of Islamic rules to perform Salah. The Salah ontology has been built under the guidance of domain expert in light of Quran and Hadith. The ontology consists of basic concepts as well as relationship among concepts and constraints on them. The basic concepts include cleanness, body cover, Salah timing and steps to perform Salah. The SWRL rule language has been used to represent rule to determine whether the Salah performed correctly or it should be repeated. Finally, we evaluate the use of the Salat ontology through user’s example queries using SPARQL queries.

Keywords: prayer, salah, ontology, SPARQL queries, reasoning

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Murniwati, Nurul Khairiyah, Putri Ovieza Maizar

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The association between general and oral health is clearly important, particularly in adults with medical conditions. Many of the medical systemic conditions are either caused or aggravated by poor oral hygiene and vice versa. Hypertension is one of common medical systemic problem which has been a public health concern worldwide due to its known consequences. Those consequences must be related to oral health status as well, whether it may cause or worsen the oral health conditions. The objective of this study was to find out the association between hypertension and oral health status in adults. This study was an analytical observational study by using cross-sectional method. A total of 42 adults both male and female in Padusunan Village, Pariaman, West Sumatra, Indonesia were selected as subjects by using purposive sampling. Manual sphygmomanometer was used to measure blood pressure and dental examination was performed to calculate the decayed, missing, and filled teeth (DMFT) scores in order to represent oral health status. The data obtained was analyzed statistically using One Way ANOVA to determine the association between hypertensive adults and their oral health status. The result showed that majority age of the subjects was ranging from 51-70 years (40.5%). Based on blood pressure examination, 57.1% of subjects were classified to prehypertension. Overall, the mean of DMFT score calculated in normal, prehypertension and hypertension group was not considered statistically significant. There was no significant association (p>0.05) between hypertension and oral health status in adults.

Keywords: blood pressure, hypertension, DMFT, oral health status

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Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: Najeeb Ullah Khan

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Background: The receptor activator NF-κβ ligand (RANKL) and Osteoprotegerin (OPG) polymorphisms have been associated with the progression of breast cancer to bone metastasis. Here, we aimed to investigate the association of RANKL and OPG gene polymorphism with breast to bone metastasis in the Pashtun population, Pakistan. Methods: Genomic DNA was obtained from all the study subjects (106 breast cancer, 58 breast to bone metastasis, and 51 healthy controls). RANKL (rs9533156) and OPG (rs2073618, rs3102735) polymorphisms were genotyped using Tetra-ARMS PCR. Results: Our results indicated that the frequencies of OPG (rs3102735) risk allele and genotypes carrying risk allele in breast cancer vs healthy control (C- p=0.005; CC- p=0.0208; TC- p=0.0181), bone metastasis vs healthy control (C- p=0.0211; CC- p=0.0153; TC- p=0.0775), and breast cancer vs breast to bone metastasis (C- p=0.0001; CC- p=0.0001; TC- p=0.001) were found significantly associated with disease risk. However, there was no significant association observed for OPG (rs2073618) risk allele and risk allele containing genotypes in all study groups. Similarly, RANKL (rs9533156) risk alleles and corresponding genotypes in breast cancer vs healthy control (C- p=0.0001; CC- p=0.0001; TC- p=0.0084), bone metastasis vs healthy control (C- p=0.0001; CC- p=0.0001; TC- p=0.5593), and breast cancer vs breast to bone metastasis (C- p=0.0185; CC- p=0.6077; TC- p=0.1436) showed significant association except for the risk allele carrying genotypes in breast cancer to bone metastasis (TC, p=0.1436; CC, p=0.6077). Conclusion: OPG (rs3102735) and RANKL (rs9533156) showed significant association with breast to bone metastasis, while OPG (rs2073618) didn’t show a significant association with breast to bone metastasis in Pashtun population of Pakistan. However, more investigation will be required to disseminate the results while gene sequencing or whole-exome sequencing.

Keywords: breast cancer, bone metastasis, OPG, RANKL, polymorphism

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Authors: E. Fathallahi Aghdam, V. Hosseini

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Environmental pollution is not limited to a specific region or country; that is why sustainable development, as a necessary process for improvement, pays attention to issues such as destruction of natural resources, degradation of biological system, global pollution, and climate change in the world, especially in the developing countries. According to the World Health Organization, as a developing city, Tehran (capital of Iran) is one of the most polluted cities in the world in terms of air pollution. In this study, three pollutants including particulate matter less than 10 microns, nitrogen oxides, and sulfur dioxide were evaluated in Tehran using data mining techniques and through Crisp approach. The data from 21 air pollution measuring stations in different areas of Tehran were collected from 1999 to 2013. Commercial softwares Clementine was selected for this study. Tehran was divided into distinct clusters in terms of the mentioned pollutants using the software. As a data mining technique, clustering is usually used as a prologue for other analyses, therefore, the similarity of clusters was evaluated in this study through analyzing local conditions, traffic behavior, and industrial activities. In fact, the results of this research can support decision-making system, help managers improve the performance and decision making, and assist in urban studies.

Keywords: data mining, clustering, air pollution, crisp approach

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Benjamen Nathaniel, Eludiora Safiriyu Ijiyemi, Egume Oneme Lucky

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A review on machine translation systems shows that a lot of computational artefacts has been carried out to translate written or spoken texts from a source language to Yorùbá language through Machine Translation systems. However, there are no work on French to Yorùbá language machine translation system; hence, the study investigated the process involved in the translation of French-to-Yorùbá language equivalent with the view to adopting a rule- based MT approach to build a Machine Translation framework from simple sentences administered through questionnaire. Articles and relevant textbooks were reviewed with key speakers of both languages interviewed to find out the processes involved in the translation of French language and their equivalent in Yorùbálanguage simple sentences using home domain terminologies. Achieving this, a model was formulated using phrase grammar structure, re-write rule, parse tree, automata theory- based techniques, designed and implemented respectively with unified modeling language (UML) and python programming language. Analysing the result, it was observed when carrying out the result that, the Machine Translation system performed 18.45% above Experimental Subject Respondent and 2.7% below Linguistics Expert when analysed with word orthography, sentence syntax and semantic correctness of the sentences. And, when compared with Google Machine Translation system, it was noticed that the developed system performed better on lexicons of the target language.

Keywords: machine translation (MT), rule-based, French language, Yoru`ba´ language

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Authors: Sajjad Akbar, Rabia Bashir

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With the growth of the number of users, the Internet usage has evolved. Due to its key design principle, there is an incredible expansion in its size. This tremendous growth of the Internet has brought new applications (mobile video and cloud computing) as well as new user’s requirements i.e. content distribution environment, mobility, ubiquity, security and trust etc. The users are more interested in contents rather than their communicating peer nodes. The current Internet architecture is a host-centric networking approach, which is not suitable for the specific type of applications. With the growing use of multiple interactive applications, the host centric approach is considered to be less efficient as it depends on the physical location, for this, Information Centric Networking (ICN) is considered as the potential future Internet architecture. It is an approach that introduces uniquely named data as a core Internet principle. It uses the receiver oriented approach rather than sender oriented. It introduces the naming base information system at the network layer. Although ICN is considered as future Internet architecture but there are lot of criticism on it which mainly concerns that how ICN will manage the most relevant content. For this Web Content Mining(WCM) approaches can help in appropriate data management of ICN. To address this issue, this paper contributes by (i) discussing multiple ICN approaches (ii) analyzing different Web Content Mining approaches (iii) creating a new Internet architecture by merging ICN and WCM to solve the data management issues of ICN. From ICN, Content-Centric Networking (CCN) is selected for the new architecture, whereas, Agent-based approach from Web Content Mining is selected to find most appropriate data.

Keywords: agent based web content mining, content centric networking, information centric networking

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Authors: Edoba Bright Omoregie

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Nigeria became a federation in 1954 six years before it gained independence away from British colonial rule. The country has remained a federation since then despite the challenging circumstances of military rule and civil strife which have tasked its federal credentials. Since 1961, when it first decided a federalism dispute, cases over vertical and horizontal powers have inundated the country’s Supreme Court. In its current practice of federalism after democratic rule was resumed in 1999, the country has witnessed a spell of intergovernmental disputes over a good number of federalism issues. Such conflicts have eventually found their way to the Supreme Court for resolution, not as a final appellate court (which it is in other non-federal matters) but as a court of first and final instance following the constitutional provision granting the court such power. However, in April 2014 one of such disputes was denied hearing by the court when it declined original jurisdiction to determine the matter. The suit was instituted by one state of the federation against the federal government and the other 35 states challenging the collection of value added tax (a consumption tax)on certain goods and services within the state. The paper appraises the rationale of the court’s decision and reason that its decision to decline jurisdiction is the result of an avoidable misunderstanding of the dual sovereignty instituted by the federal system of Nigeria as well as a misconception of the role which the court is constitutionally assigned to play in resolving intergovernmental schisms in the federal system.

Keywords: dual sovereignty, federalism, intergovernmental conflict, Supreme Court

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Tugce Duygu Koksal

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In recent years, given the fact that the acts of terrorism and the threat of the latter are taking place without any border and distinction, it has led the states to deal with the terrorism as a priority issue. More recently, as seen in different countries during state of emergency, the adoption of anti-terrorism measures motivated by the sole need of the prevention of terrorism targets directly the fundamental rights of individuals. Therefore, a contribution to the understanding of the value of the principle of legality is becoming more and more important nowadays. This paper aims to reflect the probable effects of the adoption of anti-terrorism measures regardless of the principle of legality, on the fundamental rights. In this respect, this paper will first discuss the margin of appreciation of the national authorities by countering terrorism, and then, the importance of the respect of the legality of the anti-terrorism measures will be examined in the light of actual examples. Indeed, one of the major findings of this study is the fact that the anti-terrorism laws and measures were taken in this framework must be subject to close scrutiny in democracies, which adopted the principle of the rule of law and respect human rights. Although the state's margin of appreciation in the field of counter-terrorism is broad, these measures which are based on the legitimate aim of a democracies’ legitimate right to protect itself against the activities of terrorist organizations should have the legal basis and be strictly required by the exigencies of the fight against terrorism. While combating terrorism, the legal basis shall only be achieved if the legal consequences of an individuals’ actions related to terrorism shall be clear and foreseeable by the individuals of a society. On the other hand, particularly during the state of emergency, the ambiguity of the law might be used to include a wide range of actions under acts of terrorism. This is becoming more dangerous where freedom of expression, freedom of the press, freedom of association and the right to information is in the substance of these actions. Disregarding the principle of legality is susceptible to create a chilling effect on the exercise of human rights, and therefore, the fight against terrorism can be transformed into a repressive regime on opponents. As a result, the efforts to counter terrorism of the national authorities irrespective of the principle of legality are susceptible to cause a transformation of the rule of law to a state of law which cannot be appreciated in a democratic society.

Keywords: anti-terrorism measures, chilling effect, predictability, the principle of legality, state of emergency

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Authors: Carla Idely Palencia-Aguilar

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Mining in Colombia represents around 2% of the GDP (USD 8 billion in 2018), with main productions represented by coal, nickel, gold, silver, emeralds, iron, limestone, gypsum, among others. Sand and Gravel had been decreasing its participation of the GDP with a reduction of 33.2 million m3 in 2015, to 27.4 in 2016, 22.7 in 2017 and 15.8 in 2018, with a consumption of approximately 3 tons/inhabitant. However, with the new government policies it is expected to increase in the following years. Mining causes temporary environmental impacts, once restoration and rehabilitation takes place, social, environmental and economic benefits are higher than the initial state. A way to demonstrate how the mining interventions had contributed to improve the characteristics of the region after sand and gravel mining, the NDVI (Normalized Difference Vegetation Index) from MODIS and ASTER were employed. The histograms show not only increments of vegetation in the area (8 times higher), but also topographies similar to the ones before the intervention, according to the application for sustainable development selected: either agriculture, forestry, cattle raising, artificial wetlands or do nothing. The decision was based upon a Multicriteria analysis for optimal land use, with three main variables: geostatistics, evapotranspiration and groundwater characteristics. The use of remote sensing, meteorological stations, piezometers, sunphotometers, geoelectric analysis among others; provide the information required for the multicriteria decision. For cattle raising and agricultural applications (where various crops were implemented), conservation of products were tested by means of nanotechnology. The results showed a duration of 2 years with no chemicals added for preservation and concentration of vitamins of the tested products.

Keywords: ASTER, Geostatistics, MODIS, Multicriteria

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Authors: Abdulsalami M. Deji

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Prolonged military incursion in Nigeria politics which favored the oligarchy brought agitation for democratic rule it exacerbated ethnicity integration of minority for fear of domination. The advent of democracy ushered in new breath of life to Nigerians from the shackle of military oppression to democratic governance. Democratic rule became a mirage as a result of prevalent insecurity in Nigeria; effort to bring lasting peace to all sections of the country had not yielded positive result till date. In the process of struggling for democracy among ethnic groups in Nigeria, they had instituted various militia groups defending the interest of their identity due to unequal distribution of wealth by military junta. When democracy came on board, these various militia groups became demons hunting democratic institutions. Quest by the successful government to find lasting solution has proved abortive. The security of politics which guaranteed stability is not visible in Nigeria, what we have now is politics of security. The unrest in Nigeria today has cripple socio-political and economy of the nation; the growth of economy favored elites without meaningful impact on the common man. This paper focus on the effects of democracy on Nigerians and, how security under democratic rule has hindered dividends of democracy since 1999-till date and way forward. The source is strictly base on secondary source from textbook, newspapers, internet, and journals.

Keywords: democracy, interest, militia, security

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering.

Keywords: clustering, unsupervised learning, algorithms, hierarchical

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Authors: Muhammad Fiaz, Muhammad Saqlain, Bernard M. Y. Cheung, S. M. Saqlan Naqvi, Ghazala Kaukab Raja

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Background: Association of C allele of rs662799 SNP of APOA5 gene with metabolic syndrome (MetS) has been reported in different populations around the world. A case control study was conducted to explore the relationship of rs662799 variants (T/C) with the MetS and the associated risk phenotypes in a population of Pakistani origin. Methods: MetS was defined according to the IDF criteria. Blood samples were collected from the Pakistan Institute of Medical Sciences, Islamabad, Pakistan for biochemical profiling and DNA extraction. Genotyping of rs662799 was performed using mass ARRAY, iPEX Gold technology. A total of 712 unrelated case and control subjects were genotyped. Data were analyzed using Plink software and SPSS 16.0. Results: The risk allele C of rs662799 showed highly significant association with MetS (OR=1.5, Ρ=0.002). Among risk phenotypes, dyslipidemia, and obesity showed strong association with SNP (OR=1.49, p=0.03; OR =1.46, p=0.01) respectively in models adjusted for age and gender. Conclusion: The rs662799C allele is a significant risk marker for MetS in the local Pakistani population studied. The effect of the SNP is more on dyslipidemia than the other components of the MetS.

Keywords: metabolic syndrome, APOA5, rs662799, dyslipidemia, obesity

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Authors: Zhidong Zhang, Zhi-Chao Zhang

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This study focuses on early adolescent externalizing behavioral problems which specifically concentrate on rule breaking behavior and aggressive behavior using the instrument of Achenbach System of Empirically Based Assessment (ASEBA). The purpose was to analyze the relationships between the externalizing behavioral problems and relevant background variables such as sports activities, hobbies, chores and the number of close friends. The stratified sampling method was used to collect data from 1975 participants. The results indicated that several background variables as predictors could significantly predict rule breaking behavior and aggressive behavior. Further, a hierarchical modeling method was used to explore the causal relations among background variables, breaking behavior variables and aggressive behavior variables.

Keywords: aggressive behavior, breaking behavior, early adolescence, externalizing problem

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Authors: Sahar Khedr, Dina Sayed, Ayman Hanafy

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Arabic is one of the most ancient and critical languages in the world. It has over than 250 million Arabic native speakers and more than twenty countries having Arabic as one of its official languages. In the past decade, we have witnessed a rapid evolution in smart devices, social network and technology sector which led to the need to provide tools and libraries that properly tackle the Arabic language in different domains. Stemming is one of the most crucial linguistic fundamentals. It is used in many applications especially in information extraction and text mining fields. The motivation behind this work is to enhance the Arabic light stemmer to serve the data mining industry and leverage it in an open source community. The presented implementation works on enhancing the Arabic light stemmer by utilizing and enhancing an algorithm that provides an extension for a new set of rules and patterns accompanied by adjusted procedure. This study has proven a significant enhancement for better search accuracy with an average 10% improvement in comparison with previous works.

Keywords: Arabic data mining, Arabic Information extraction, Arabic Light stemmer, Arabic stemmer

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Authors: Angelina Anani, Ignacio Ortiz Flores, Haitao Li

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The use of underground mining methods have increased significantly over the past decades. This increase has also been spared on by several mines transitioning from surface to underground mining. However, determining the transition depth can be a challenging task, especially when coupled with production schedule optimization. Several researchers have simplified the problem by excluding operational features relevant to production schedule optimization. Our research objective is to investigate the extent to which operational features of transition mines accounted for affect the optimal production schedule. We also provide a framework for factors to consider in production schedule optimization for transition mines. An integrated mixed-integer linear programming (MILP) model is developed that maximizes the NPV as a function of production schedule and transition depth. A case study is performed to validate the model, with a comparative sensitivity analysis to obtain operational insights.

Keywords: underground mining, transition mines, mixed-integer linear programming, production schedule

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Authors: Sabeen Alam, Mehboob Alam

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The research work entitled “Effect of Bacillus pumilus strains on heavy metal accumulation in lettuce grown on contaminated soil” focused on functional role of Bacillus pumilus strains inoculated with lettuce seed in mitigating heavy metal in chromite mining soil. In this experiment, factor A was three Bacillus pumilus strains (sequence C-2PMW-8, C-1 SSK-8 and C-1 PWK-7) while soil used for this experiment was collected from Prang Ghar mining site and lettuce seeds were grown in three levels of chromite mining soil (2.27, 4.65 and 7.14 %). For mining soil minimum days to germinate noted in lettuce grown on garden soil inoculated with sequence. Maximum germination percentage noted was for C-1 SSK-8 grown on garden soil, maximum lettuce height for sequence C-2 PWM-8, fresh leaf weight for C-1 PWK-7 inoculated lettuce, dry weight of lettuce leaf for lettuce inoculated with C-1 SSK-8 and C-1 PWK-7 strains, number of leaves per plant for lettuce inoculated with C-1 SSK-8, leaf area for C-2 PMW-8 inoculated lettuce, survival percentage for C-1 SSK-8 treated lettuce and chlorophyll content for C-2 PMW-8. Results related to heavy metals accumulation showed that minimum chromium was in lettuce and in soil for all three sequences, cadmium (Cd) in lettuce and in soil for all three sequences, manganese (Mn) in lettuce and in soil for three sequences, lead (Pb) in lettuce and in soil for three sequences. It can be concluded that chromite mining soil significantly reduced the growth and survival of lettuce, but when lettuce was inoculated with Bacillus.pumilus strains, it enhances growth and survival. Similarly, minimum heavy metal accumulation in plant and soil, regardless of type of Bacillus pumilus used, all three sequences has same mitigating effect on heavy metal in both soil and lettuce. All the three Bacillus pumilus strains ensured reduction in heavy metals content (Mn, Cd, Cr) in lettuce, below the maximum permissible limits of WHO 2011.

Keywords: bacillus pumilus, heavy metals, permissible limits, lettuce, chromite mining soil, mitigating effect

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Authors: Thalia Viveros-Uehara

Abstract:

The virtues of Corporate Social Responsibility (CSR) are explored widely in the academic literature. However, few studies address its link to human rights, per se; specifically, the right to a safe, clean and healthy environment. Fewer still are the research works in this area that relate to developing countries, where a number of areas are biodiversity hotspots. In Mexico, despite the rise and evolution of CSR schemes, grave episodes of pollution persist, especially those caused by the mining industry. These cases set up the question of the correspondence between the current CSR practices of mining companies in the country and their responsibility to respect the right to a safe, clean and healthy environment. The present study approaches precisely such a bridge, which until now has not been fully tackled in light of Mexico's 2011 constitutional human rights amendment and the United Nation's Guiding Principles on Business and Human Rights (UN Guiding Principles), adopted by the Human Rights Council in 2011. To that aim, it initially presents a contextual framework; it then explores qualitatively the adoption of human rights’ language in the CSR strategies of the three main mining companies in Mexico, and finally, it examines their standing with respect to the UN Guiding Principles. The results reveal that human rights are included in the RSE strategies of the analysed businesses, at least at the rhetoric level; however, they do not embrace the right to a safe, clean and healthy environment as such. Moreover, we conclude that despite the finding that corporations publicly express their commitment to respect human rights, some operational weaknesses that hamper the exercise of such responsibility persist; for example, the systematic lack of human rights impact assessments per mining unit, the denial of actual and publicly-known negative episodes on the environment linked directly to their operations, and the absence of effective mechanisms to remediate adverse impacts.

Keywords: corporate social responsibility, environmental impacts, human rights, right to a safe, clean and healthy environment, mining industry

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Authors: Li Sheng

Abstract:

Based on an extended post-Keynesian model, we find that the association between the savings rate and income inequality is negative if savers’ funds are borrowed by spending households for consumption but positive if savings are channeled to investing firms for production. A negative association, such as the one that exists in the U.S., hinges on an income illusion created by an asset bubble and cheap credit. Thus, financial globalization leads consumption and income inequality to diverge, and the divergence is more extreme if lower-income groups have higher debt ratios. A positive association, such as the one that exists in China, relates to liquidity constraints faced by consumers such that consumption inequality closely follows income inequality. Our results imply that income inequality must be reduced in both types of countries to increase savings in deficit economies with negative associations and to reduce savings in surplus economies with positive associations.

Keywords: savings rate, income inequality, financial globalization, global imbalances

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Authors: Ahcene Habbi, Yassine Boudouaoui

Abstract:

This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses global optimization to learn fuzzy models, the second one hybridizes ABC and weighted least squares estimate method. The performances of the proposed ABC and ABC-LS fuzzy modeling strategies are evaluated on complex modeling problems and compared to other advanced modeling methods.

Keywords: automatic design, learning, fuzzy rules, hybrid, swarm optimization

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

Abstract:

In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

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Authors: Qiao Fan, Xiaobo Guo, Junxian Zhu, Xiaohu Ding, Ching-Yu Cheng, Tien-Yin Wong, Mingguang He, Heping Zhang, Xueqin Wang

Abstract:

A systematic, simultaneous analysis of multiple phenotypes in genome-wide association studies (GWASs) draws a great attention to integrate the signals from single phenotypes with increased power. However, lacking an interpretable and efficient multivariate GWAS analysis impede the application of such approach. In this study, we propose to decompose the multivariate model into a series of simple univariate models. This transformation illuminates what exactly the individual trait contributes to the significant signals from the multivariate analyses. By employing our approach in the analysis of three myopia-related endophenotypes from the Singapore Malay Eye Study (SIMES), we identify novel candidate loci which were successfully validated in an independent Guangzhou Twin Eye Study (GTES).

Keywords: GWAS multivariate, multiple traits, myopia, association

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