Search results for: underground mining

Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

Procedia PDF Downloads 100

Authors: Markéta Chmelíková, David Ullrich, Iva Burešová

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The paper maps the possibilities and limits of diagnosing selected personality and performance characteristics of military leadership and psychology students in the context of coping with challenging situations. Individuals vary greatly inter-individually in their ability to effectively manage extreme situations, yet existing diagnostic tools are often criticized mainly for their low predictive power. Nowadays, every modern army focuses primarily on the systematic minimization of potential risks, including the prediction of desirable forms of behavior and the performance of military commanders. The context of military leadership is well known for its life-threatening nature. Therefore, it is crucial to research stress load in the specific context of military leadership for the purpose of possible anticipation of human failure in managing extreme situations of military leadership. The aim of the submitted pilot study, using an experiment of 24 hours duration, is to verify the possibilities of a specific combination of psychodiagnostic to predict people who possess suitable equipment for coping with increased stress load. In our pilot study, we conducted an experiment of 24 hours duration with an experimental group (N=13) in the bomb shelter and a control group (N=11) in a classroom. Both groups were represented by military leadership students (N=11) and psychology students (N=13). Both groups were equalized in terms of study type and gender. Participants were administered the following test battery of personality characteristics: Big Five Inventory 2 (BFI-2), Short Dark Triad (SD-3), Emotion Regulation Questionnaire (ERQ), Fatigue Severity Scale (FSS), and Impulsive Behavior Scale (UPPS-P). This test battery was administered only once at the beginning of the experiment. Along with this, they were administered a test battery consisting of the Test of Attention (d2) and the Bourdon test four times overall with 6 hours ranges. To better simulate an extreme situation – we tried to induce sleep deprivation - participants were required to try not to fall asleep throughout the experiment. Despite the assumption that a stay in an underground bomb shelter will manifest in impaired cognitive performance, this expectation has been significantly confirmed in only one measurement, which can be interpreted as marginal in the context of multiple testing. This finding is a fundamental insight into the issue of stress management in extreme situations, which is crucial for effective military leadership. The results suggest that a 24-hour stay in a shelter, together with sleep deprivation, does not seem to simulate sufficient stress for an individual, which would be reflected in the level of cognitive performance. In the context of these findings, it would be interesting in future to extend the diagnostic battery with physiological indicators of stress, such as: heart rate, stress score, physical stress, mental stress ect.

Keywords: bomb shelter, extreme situation, military leadership, psychodiagnostic

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: C. J. Coetzee, E. Horn

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One of the main challenges in using the Discrete Element Method (DEM) is to specify the correct input parameter values. In general, the models are sensitive to the input parameter values and accurate results can only be achieved if the correct values are specified. For the linear contact model, micro-parameters such as the particle density, stiffness, coefficient of friction, as well as the particle size and shape distributions are required. There is a need for a procedure to accurately calibrate these parameters before any attempt can be made to accurately model a complete bulk materials handling system. Since DEM is often used to model applications in the mining and quarrying industries, a calibration procedure was developed for materials that consist of relatively large (up to 40 mm in size) particles. A coarse crushed aggregate was used as the test material. Using a specially designed large shear box with a diameter of 590 mm, the confined Young’s modulus (bulk stiffness) and internal friction angle of the material were measured by means of the confined compression test and the direct shear test respectively. DEM models of the experimental setup were developed and the input parameter values were varied iteratively until a close correlation between the experimental and numerical results was achieved. The calibration process was validated by modelling the pull-out of an anchor from a bed of material. The model results compared well with experimental measurement.

Keywords: Discrete Element Method (DEM), calibration, shear box, anchor pull-out

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Authors: Carlos H. Cuadra

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Seismic isolation can be used as a retrofit method for historical buildings with the advantage that minimum intervention on super-structure is required. However, selection of isolation devices depends on weight and stiffness of upper structure. In this study, two buildings are considered for analyses to evaluate the applicability of this retrofitting methodology. Both buildings are located at Akita prefecture in the north part of Japan. One building is a wooden structure that corresponds to the old council meeting hall of Noshiro city. The second building is a brick masonry structure that was used as house of a foreign mining engineer and it is located at Ani town. Ambient vibration measurements were performed on both buildings to estimate their dynamic characteristics. Then, target period of vibration of isolated systems is selected as 3 seconds is selected to estimate required stiffness of isolation devices. For wooden structure, which is a light construction, it was found that natural rubber isolators in combination with friction bearings are suitable for seismic isolation. In case of masonry building elastomeric isolator can be used for its seismic isolation. Lumped mass systems are used for seismic response analysis and it is verified in both cases that seismic isolation can be used as retrofitting method of historical construction. However, in the case of the light building, most of the weight corresponds to the reinforced concrete slab that is required to install isolation devices.

Keywords: historical building, finite element method, masonry structure, seismic isolation, wooden structure

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Authors: Mahdi Karazmodeh, Pooyan Jafari

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One of the most interesting topics in finance is the relation between stock prices and exchange rates. During the past decades different stock markets in different countries have been the subject of study for researches. The volatilities of exchange rates and its effect on stock prices during the past 10 years have continued to be an attractive research topic. The subject of this study is one of the most important indices, FTSE 100. 20 firms with the highest market capitalization in 5 different industries are chosen. Firms are included in oil and gas, mining, pharmaceuticals, banking and food related industries. 5 different criteria have been introduced to evaluate the relationship between stock markets and exchange rates. Return of market portfolio, returns on broad index of Sterling are also introduced. The results state that not all firms are sensitive to changes in exchange rates. Furthermore, a Granger Causality test has been run to observe the route of changes between stock prices and foreign exchange rates. The results are consistent, to some level, with the previous studies. However, since the number of firms is not large, it is suggested that a larger number of firms being used to achieve the best results. However results showed that not all firms are affected by foreign exchange rates changes. After testing Granger Causality, this study found out that in some industries (oil and gas, pharmaceuticals), changes in foreign exchange rate will not cause any changes in stock prices (or vice versa), however, in banking sector the situation was different. This industry showed more reaction to these changes. The results are similar to the ones with Richards and Noel, where a variety of firms in different industries were evaluated.

Keywords: stock prices, foreign exchange rate, exchange rate exposure, Granger Causality

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Authors: Loveline Ambo Angwah

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The processing of cassava from tubers or roots into food using crude and rudimentary method (hand peeling, grating, frying and to sun drying) is a very cumbersome and difficult process. The crude methods are time consuming and labour intensive. While on the other hand, modern processing method, that is using machines to perform the various processes as washing, peeling, grinding, oven drying, fermentation and frying is easier, less time consuming, and less labour intensive. Rudimentarily, cassava roots are processed into numerous products and utilized in various ways according to local customs and preferences. For the people of Ngwo village, cassava is transformed locally into flour or powder form called ‘cumcum’. It is also sucked into water to give a kind of food call ‘water fufu’ and fried to give ‘garri’. The leaves are consumed as vegetables. Added to these, its relative high yields; ability to stay underground after maturity for long periods give cassava considerable advantage as a commodity that is being used by poor rural folks in the community, to fight poverty. It plays a major role in efforts to alleviate the food crisis because of its efficient production of food energy, year-round availability, tolerance to extreme stress conditions, and suitability to present farming and food systems in Africa. Improvement of cassava processing and utilization techniques would greatly increase labor efficiency, incomes, and living standards of cassava farmers and the rural poor, as well as enhance the-shelf life of products, facilitate their transportation, increase marketing opportunities, and help improve human and livestock nutrition. This paper presents a general overview of crude ways in cassava processing and utilization methods now used by subsistence and small-scale farmers in Ngwo village of the North West region in Cameroon, and examine the opportunities of improving processing technologies. Cassava needs processing because the roots cannot be stored for long because they rot within 3-4 days of harvest. They are bulky with about 70% moisture content, and therefore transportation of the tubers to markets is difficult and expensive. The roots and leaves contain varying amounts of cyanide which is toxic to humans and animals, while the raw cassava roots and uncooked leaves are not palatable. Therefore, cassava must be processed into various forms in order to increase the shelf life of the products, facilitate transportation and marketing, reduce cyanide content and improve palatability.

Keywords: cassava roots, crude ways, food system, poverty

Procedia PDF Downloads 139

Authors: Ghulam Haider Haidaree, Nsenda Lukumwena

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Today the world of research enjoys abundant data, available in virtually any field, technology, science, and business, politics, etc. This is commonly referred to as big data. This offers a great deal of precision and accuracy, supportive of an in-depth look at any decision-making process. When and if well used, Big Data affords its users with the opportunity to produce substantially well supported and good results. This paper leans extensively on big data to investigate possible smart solutions to urban mobility and related issues, namely road traffic accidents, its casualties, and fatalities based on multiple factors, including age, gender, location occurrences of accidents, etc. Multiple technologies were used in combination to produce an Information Communication Technology (ICT) based solution with embedded technology. Those technologies include principally Geographic Information System (GIS), Orange Data Mining Software, Bayesian Statistics, to name a few. The study uses the Leeds accident 2016 to illustrate the thinking process and extracts thereof a model that can be tested, evaluated, and replicated. The authors optimistically believe that the proposed model will significantly and smartly help to flatten the curve of road traffic accidents in the fast-growing population densities, which increases considerably motor-based mobility.

Keywords: accident factors, geographic information system, information communication technology, mobility

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Authors: Elkington Sibusiso Mnguni

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In South Africa, mining has been a key economic sector since the discovery of gold in 1886 in the Witwatersrand region, where the city of Johannesburg is located. However, some mines have since been decommissioned, and the continuous pumping of acid mine drainage (AMD) also stopped causing the AMD to rise towards the ground surface. This posed a serious environmental risk to the groundwater resources and river systems in the region. This paper documents the development and extent of the environmental damage as well as the measures implemented by the government to alleviate such damage. The study will add to the body of knowledge on the subject of AMD treatment to prevent environmental degradation. The method used to gather and collate relevant data and information was the desktop study. The key findings include the social and environmental impact of the AMD, which include the pollution of water sources for domestic use leading to skin and other health problems and the loss of biodiversity in some areas. It was also found that the technical intervention of constructing a plant to pump and treat the AMD using the high-density sludge technology was the most effective short-term solution available while a long-term solution was being explored. Some successes and challenges experienced during the implementation of the project are also highlighted. The study will be a useful record of the current status of the AMD treatment interventions in the region.

Keywords: acid mine drainage, groundwater resources, pollution, river systems, technical intervention, high density sludge

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Mohammad Talei, Neda Ranjbar Nosheri, Reza Kazemi Gorzadini

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Today municipalities are searching for the new tools for increasing the public participation in different levels of urban planning. This approach of urban planning involves the community in planning process using participatory approaches instead of the long traditional top-down planning methods. These tools can be used to obtain the particular problems of urban furniture form the residents’ point of view. One of the tools that is designed with this goal is public participation GIS (PPGIS) that enables citizen to record and following up their feeling and spatial knowledge regarding main problems of the city, specifically urban furniture, in the form of maps. However, despite the good intentions of PPGIS, its practical implementation in developing countries faces many problems including the lack of basic supporting infrastructure and services and unavailability of sophisticated public participatory models. In this research we develop a PPGIS using of Web 2 to collect voluntary geodataand to perform spatial analysis based on Spatial OnLine Analytical Processing (SOLAP) and Spatial Data Mining (SDM). These tools provide urban planners with proper informationregarding the type, spatial distribution and the clusters of reported problems. This system is implemented in a case study area in Tehran, Iran and the challenges to make it applicable and its potential for real urban planning have been evaluated. It helps decision makers to better understand, plan and allocate scarce resources for providing most requested urban furniture.

Keywords: PPGIS, spatial information, urbanizing futures, urban planning

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Authors: Sinan Yapici, Hayrettin Eroglu

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The discharge of the aqueous radionuclides wastes used for the diagnoses of diseases and treatments of patients in nuclear medicine can cause fatal health problems when the radionuclides and its stable daughter component mix with underground water. Tl-201, which is one of the radionuclides commonly used in the nuclear medicine, is a toxic substance and is converted to its stable daughter component Hg-201, which is also a poisonous heavy metal: Tl201 → Hg201 + Gamma Ray [135-167 Kev (12%)] + X Ray [69-83 Kev (88%)]; t1/2 = 73,1 h. The purpose of the present work was to remove Tl-201 radionuclides from aqueous solution by biosorption on the solid bio wastes of food and cosmetic industry as bio sorbents of prina from an olive oil plant, rose residue from a rose oil plant and tea residue from a tea plant, and to make a comparison of the biosorption efficiencies. The effects of the biosorption temperature, initial pH of the aqueous solution, bio sorbent dose, particle size and stirring speed on the biosorption yield were investigated in a batch process. It was observed that the biosorption is a rapid process with an equilibrium time less than 10 minutes for all the bio sorbents. The efficiencies were found to be close to each other and measured maximum efficiencies were 93,30 percent for rose residue, 94,1 for prina and 98,4 for tea residue. In a temperature range of 283 and 313 K, the adsorption decreased with increasing temperature almost in a similar way. In a pH range of 2-10, increasing pH enhanced biosorption efficiency up to pH=7 and then the efficiency remained constant in a similar path for all the biosorbents. Increasing stirring speed from 360 to 720 rpm enhanced slightly the biosorption efficiency almost at the same ratio for all bio sorbents. Increasing particle size decreased the efficiency for all biosorbent; however the most negatively effected biosorbent was prina with a decrease in biosorption efficiency from about 84 percent to 40 with an increase in the nominal particle size 0,181 mm to 1,05 while the least effected one, tea residue, went down from about 97 percent to 87,5. The biosorption efficiencies of all the bio sorbents increased with increasing biosorbent dose in the range of 1,5 to 15,0 g/L in a similar manner. The fit of the experimental results to the adsorption isotherms proved that the biosorption process for all the bio sorbents can be represented best by Freundlich model. The kinetic analysis showed that all the processes fit very well to pseudo second order rate model. The thermodynamics calculations gave ∆G values between -8636 J mol-1 and -5378 for tea residue, -5313 and -3343 for rose residue, and -5701 and -3642 for prina with a ∆H values of -39516 J mol-1, -23660 and -26190, and ∆S values of -108.8 J mol-1 K-1, -64,0, -72,0 respectively, showing spontaneous and exothermic character of the processes. An empirical biosorption model in the following form was derived for each biosorbent as function of the parameters and time, taking into account the form of kinetic model, with regression coefficients over 0.9990 where At is biosorbtion efficiency at any time and Ae is the equilibrium efficiency, t is adsorption period as s, ko a constant, pH the initial acidity of biosorption medium, w the stirring speed as s-1, S the biosorbent dose as g L-1, D the particle size as m, and a, b, c, and e are the powers of the parameters, respectively, E a constant containing activation energy and T the temperature as K.

Keywords: radiation, diosorption, thallium, empirical modelling

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Authors: Isaac Owusu Asante, Yushi Jiang, Hailin Tao

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Live streaming marketing, the new electronic commerce element, became an optional marketing channel following the COVID-19 pandemic. Many sellers have leveraged the features presented by live streaming to increase sales. Studies on live streaming have focused on gaming and consumers’ loyalty to brands through live streaming, using interview questionnaires. This study, however, was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during live streaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study introduces a new way of measuring interactions in live streaming commerce and proposes a way to manually gather data on consumer behaviors in live streaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

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Authors: Kevin F. Hsu, Alvin Chua, Sarah X. Lin

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As a multilingual society, Singaporean names for different parts of the city have changed over time. Residents included Indigenous Malays, dialect-speakers from China, European settler-colonists, and Tamil-speakers from South India. Each group would name locations in their own languages. Today, as ancestral tongues are increasingly supplanted by English, contemporary Singaporeans’ understanding of once-common place names is disappearing. After demolition or redevelopment, some urban places will only exist in archival records or in human memory. United Nations conferences on the standardization of geographic names have called attention to how place names relate to identity, well-being, and a sense of belonging. The Singapore Place-Naming Project responds to these imperatives by capturing past and present place names through digitizing historical maps, mining archival records, and applying selective crowd-sourcing to trace the evolution of place names throughout the city. The project ensures that both formal and vernacular geographical names remain accessible to historians, city planners, and the public. The project is compiling a gazetteer, a geospatial archive of placenames, with streets, buildings, landmarks, and other points of interest (POI) appearing in the historic maps and planning documents of Singapore, currently held by the National Archives of Singapore, the National Library Board, university departments, and the Urban Redevelopment Authority. To create a spatial layer of information, the project links each place name to either a geo-referenced point, line segment, or polygon, along with the original source material in which the name appears. This record is supplemented by crowd-sourced contributions from civil service officers and heritage specialists, drawing from their collective memory to (1) define geospatial boundaries of historic places that appear in past documents, but maybe unfamiliar to users today, and (2) identify and record vernacular place names not captured in formal planning documents. An intuitive interface allows participants to demarcate feature classes, vernacular phrasings, time periods, and other knowledge related to historical or forgotten spaces. Participants are stratified into age bands and ethnicity to improve representativeness. Future iterations could allow additional public contributions. Names reveal meanings that communities assign to each place. While existing historical maps of Singapore allow users to toggle between present-day and historical raster files, this project goes a step further by adding layers of social understanding and planning documents. Tracking place names illuminates linguistic, cultural, commercial, and demographic shifts in Singapore, in the context of transformations of the urban environment. The project also demonstrates how a moderated, selectively crowd-sourced effort can solicit useful geospatial data at scale, sourced from different generations, and at higher granularity than traditional surveys, while mitigating negative impacts of unmoderated crowd-sourcing. Stakeholder agencies believe the project will achieve several objectives, including Supporting heritage conservation and public education; Safeguarding intangible cultural heritage; Providing historical context for street, place or development-renaming requests; Enhancing place-making with deeper historical knowledge; Facilitating emergency and social services by tagging legal addresses to vernacular place names; Encouraging public engagement with heritage by eliciting multi-stakeholder input.

Keywords: collective memory, crowd-sourced, digital heritage, geospatial, geographical names, linguistic heritage, place-naming, Singapore, Southeast Asia

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Authors: Joanna Szymanska, Paulina Wawulska-Marek, Jaroslaw Mizera

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Nowadays, the rapid growth of global energy consumption and uncontrolled depletion of natural resources become a serious problem. Shale rocks are the largest and potential global basins containing hydrocarbons, trapped in closed pores of the shale matrix. Regardless of the shales origin, mining conditions are extremely unfavourable due to high reservoir pressure, great depths, increased clay minerals content and limited permeability (nanoDarcy) of the rocks. Taking into consideration such geomechanical barriers, effective extraction of natural gas from shales with plastic zones demands effective operations. Actually, hydraulic fracturing is the most developed technique based on the injection of pressurized fluid into a wellbore, to initiate fractures propagation. However, a rapid drop of pressure after fluid suction to the ground induces a fracture closure and conductivity reduction. In order to minimize this risk, proppants should be applied. They are solid granules transported with hydraulic fluids to locate inside the rock. Proppants act as a prop for the closing fracture, thus gas migration to a borehole is effective. Quartz sands are commonly applied proppants only at shallow deposits (USA). Whereas, ceramic proppants are designed to meet rigorous downhole conditions to intensify output. Ceramic granules predominate with higher mechanical strength, stability in strong acidic environment, spherical shape and homogeneity as well. Quality of ceramic proppants is conditioned by raw materials selection. Aim of this study was to obtain the proppants from aluminosilicates (the kaolinite subgroup) and mix of minerals with a high alumina content. These loamy minerals contain a tubular and platy morphology that improves mechanical properties and reduces their specific weight. Moreover, they are distinguished by well-developed surface area, high porosity, fine particle size, superb dispersion and nontoxic properties - very crucial for particles consolidation into spherical and crush-resistant granules in mechanical granulation process. The aluminosilicates were mixed with water and natural organic binder to improve liquid-bridges and pores formation between particles. Afterward, the green proppants were subjected to sintering at high temperatures. Evaluation of the minerals utility was based on their particle size distribution (laser diffraction study) and thermal stability (thermogravimetry). Scanning Electron Microscopy was useful for morphology and shape identification combined with specific surface area measurement (BET). Chemical composition was verified by Energy Dispersive Spectroscopy and X-ray Fluorescence. Moreover, bulk density and specific weight were measured. Such comprehensive characterization of loamy materials confirmed their favourable impact on the proppants granulation. The sintered granules were analyzed by SEM to verify the surface topography and phase transitions after sintering. Pores distribution was identified by X-Ray Tomography. This method enabled also the simulation of proppants settlement in a fracture, while measurement of bulk density was essential to predict their amount to fill a well. Roundness coefficient was also evaluated, whereas impact on mining environment was identified by turbidity and solubility in acid - to indicate risk of the material decay in a well. The obtained outcomes confirmed a positive influence of the loamy minerals on ceramic proppants properties with respect to the strict norms. This research is perspective for higher quality proppants production with costs reduction.

Keywords: aluminosilicates, ceramic proppants, mechanical granulation, shale gas

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: Angelina A. Tzacheva, Jaishree Ranganathan

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Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.

Keywords: actionable pattern discovery, education, emotion, data mining

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Authors: Dilsad Akal

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Aim: Companies communicate with each other and with their costumers via call centers. Call centers are defined as stressful because of their uncertain working hours, inadequate relief time, performance based system and heavy workload. In literature, this sector is defined as risky as mining sector by means of health and safety. The aim of this research is to enlight the relatively dark area. Subject and Methods: The collection of data for this study completed during April-May 2015 for the two selected call centers in different parts of Turkey. The applied question mostly investigated the health conditions of call center workers. Electromagnetic field measurements were completed at the same time with applying the question poll. The ratio of employee accessibility noted as 73% for the first call center and 87% for the second. Results: The results of electromagnetic field measurements were as between 371 V/m-32 V/m for the first location and between 370 V/m-61 V/m for the second. The general complaints of the employees for both workplaces can be counted as; inadequate relief time, inadequate air conditioning, disturbance, poor thermal conditions, inadequate or extreme lighting. Furthermore, musculoskeletal discomfort, stress, ear and eye discomfort are main health problems of employees. Conclusion: The measured values and the responses to the question poll were found parallel with the other similar research results in literature. At the end of this survey, a risk map of workplace was prepared in terms of safety and health at work in general and some suggestions for resolution were provided.

Keywords: call center, health and safety, electromagnetic field, risk map

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Authors: Y. H. Wang, C. C. Hsieh

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Good service design can increase organization revenue and consumer satisfaction while reducing labor and time costs. The problems facing consumers in the original serve model for eyewear and optical industry includes the following issues: 1. Insufficient information on eyewear products 2. Passively dependent on recommendations, insufficient selection 3. Incomplete records on progression of vision conditions 4. Lack of complete customer records. This study investigates the case of Kobayashi Optical, applying the Theory of Inventive Problem Solving (TRIZ) to develop innovative solutions for eyewear and optical industry. Analysis results raise the following conclusions and management implications: In order to provide customers with improved professional information and recommendations, Kobayashi Optical is suggested to establish customer purchasing records. Overall service efficiency can be enhanced by applying data mining techniques to analyze past consumer preferences and purchase histories. Furthermore, Kobayashi Optical should continue to develop a 3D virtual trial service which can allow customers for easy browsing of different frame styles and colors. This 3D virtual trial service will save customer waiting times in during peak service times at stores.

Keywords: theory of inventive problem solving (TRIZ), service design, augmented reality (AR), eyewear and optical industry

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Authors: Kaushiki Sanyal, Rajesh Chakrabarti

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In India, on average, about 60 Bills are passed every year in both Houses of Parliament – Lok Sabha and Rajya Sabha (calculated from information on websites of both Houses). These are debated in both Lok Sabha (House of Commons) and Rajya Sabha (Council of States) before they are passed. However, lawmakers rarely use empirical evidence to make a case for a law. Most of the time, they support a law on the basis of anecdote, intuition, and common sense. While these do play a role in law-making, without the necessary empirical evidence, laws often fail to achieve their desired results. The quality of legislative debates is an indicator of the efficacy of the legislative process through which a Bill is enacted. However, the study of legislative debates has not received much attention either in India or worldwide due to the difficulty of objectively measuring the quality of a debate. Broadly, three approaches have emerged in the study of legislative debates. The rational-choice or formal approach shows that speeches vary based on different institutional arrangements, intra-party politics, and the political culture of a country. The discourse approach focuses on the underlying rules and conventions and how they impact the content of the debates. The deliberative approach posits that legislative speech can be reasoned, respectful, and informed. This paper aims to (a) develop a framework to judge the quality of debates by using the deliberative approach; (b) examine the legislative debates of three Bills passed in different periods as a demonstration of the framework, and (c) examine the broader structural issues that disincentive MPs from scrutinizing Bills. The framework would include qualitative and quantitative indicators to judge a debate. The idea is that the framework would provide useful insights into the legislators’ knowledge of the subject, the depth of their scrutiny of Bills, and their inclination toward evidence-based research. The three Bills that the paper plans to examine are as follows: 1. The Narcotics Drugs and Psychotropic Substances Act, 1985: This act was passed to curb drug trafficking and abuse. However, it mostly failed to fulfill its purpose. Consequently, it was amended thrice but without much impact on the ground. 2. The Criminal Laws (Amendment) Act, 2013: This act amended the Indian Penal Code to add a section on human trafficking. The purpose was to curb trafficking and penalise traffickers, pimps, and middlemen. However, the crime rate remains high while the conviction rate is low. 3. The Surrogacy (Regulation) Act, 2021: This act bans commercial surrogacy allowing only relatives to act as surrogates as long as there is no monetary payment. Experts fear that instead of preventing commercial surrogacy, it would drive the activity underground. The consequences would be borne by the surrogate, who would not be protected by law. The purpose of the paper is to objectively analyse the quality of parliamentary debates, get insights into how MPs understand the evidence and deliberate on steps to incentivise them to use empirical evidence.

Keywords: legislature, debates, empirical, India

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Authors: Ayan Giri, Lukaranjan Phukan, Shantanu Karmakar

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Structure and tectonics play a vital role in ore genesis and deposition. The existence of a swelling structure below the current level of a mine leads to the discovery of ores below some permeant developments of the mine. The discovery and the extraction of the ore body are very critical to sustain the business requirement of the mine. The challenge was to extract the ore without hampering the global stability of the mine. In order to do so, different mining options were considered and analysed by numerical modelling in FLAC3d software. The constitutive model prepared for this simulation is the improved unified constitutive model, which can better and more accurately predict the stress-strain relationships in a continuum model. The IUCM employs the Hoek-Brown criterion to determine the instantaneous Mohr-Coulomb parameters cohesion (c) and friction (ɸ) at each level of confining stress. The extra swelled part can be dimensioned as north-south strike width 50m, east-west strike width 50m. On the north side, already a stope (P1) is excavated of the dimension of 25m NS width. The different options considered were (a) Open stoping of extraction of southern part (P0) of 50m to the full extent, (b) Extraction of the southern part of 25m, then filling of both the primaries and extraction of secondary (S0) 25m in between. (c) Extraction of the southern part (P0) completely, preceded by backfill and modify the design of the secondary (S0) for the overall stability of the permanent excavation above the stoping.

Keywords: extraction, IUCM, FLAC 3D, stoping, tectonics

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Authors: Mace Bentley, Zhuojun Duan, Tobias Gerken, Dudley Bonsal, Henry Way, Endre Szakal, Mia Pham, Hunter Donaldson, Chelsea Lang, Hayden Abbott, Leah Wilcynzski

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Air pollution has the potential to modify the lifespan and intensity of thunderstorms and the properties of lightning. Using data mining and geovisualization, we investigate how background climate and weather conditions shape variability in urban air pollution and how this, in turn, shapes thunderstorms as measured by the intensity, distribution, and frequency of cloud-to-ground lightning. A spatiotemporal analysis was conducted in order to identify thunderstorms using high-resolution lightning detection network data. Over seven million lightning flashes were used to identify more than 196,000 thunderstorms that occurred between 2006 - 2020 in the Washington, DC Metropolitan Region. Each lightning flash in the dataset was grouped into thunderstorm events by means of a temporal and spatial clustering algorithm. Once the thunderstorm event database was constructed, hourly wind direction, wind speed, and atmospheric thermodynamic data were added to the initiation and dissipation times and locations for the 196,000 identified thunderstorms. Hourly aerosol and air quality data for the thunderstorm initiation times and locations were also incorporated into the dataset. Developing thunderstorm climatologies using a lightning tracking algorithm and lightning detection network data was found to be useful for visualizing the spatial and temporal distribution of urban augmented thunderstorms in the region.

Keywords: lightning, urbanization, thunderstorms, climatology

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Ablo Paul Igor Hounzandji, Benoit Lafleur, Annie DesRochers

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Introduction: Populus tremuloides [Michx] regenerates rapidly and abundantly by root suckering after harvest, creating stands with interconnected stems. Pre-commercial thinning can be used to concentrate growth on fewer stems to reach merchantability faster than un-thinned stands. However, conventional thinning methods are typically designed to reach even spacing between residual stems (1,100 stem ha⁻¹, evenly distributed), which can lead to treated stands consisting of weaker/smaller stems compared to the original stands. Considering the nature of P. tremuloides's regeneration, with large underground biomass of interconnected roots, aiming to keep the most vigorous and largest stems, regardless of their spatial distribution, inverted diameter-limit thinning could be more beneficial to post-thinning stand productivity because it would reduce the imbalance between roots and leaf area caused by thinning. Aims: This study aimed to compare stand and stem productivity of P. tremuloides stands thinned with a conventional thinning treatment (CT; 1,100 stem ha⁻¹, evenly distributed), two levels of inverted diameter-limit thinning (DL1 and DL2, keeping the largest 1100 or 2200 stems ha⁻¹, respectively, regardless of their spatial distribution) and a control unthinned treatment. Because DL treatments can create substantial or frequent gaps in the thinned stands, we also aimed to evaluate the potential of this treatment to recreate mixed conifer-broadleaf stands by fill-planting Picea glauca seedlings. Methods: Three replicate 21 year-old sucker-regenerated aspen stands were thinned in 2010 according to four treatments: CT, DL1, DL2, and un-thinned control. Picea glauca seedlings were underplanted in gaps created by the DL1 and DL2 treatments. Stand productivity per hectare, stem quality (diameter and height, volume stem⁻¹) and survival and height growth of fill-planted P. glauca seedlings were measured 8 year post-treatments. Results: Productivity, volume, diameter, and height were better in the treated stands (CT, DL1, and DL2) than in the un-thinned control. Productivity of CT and DL1 stands was similar 4.8 m³ ha⁻¹ year⁻¹. At the tree level, diameter and height of the trees in the DL1 treatment were 5% greater than those in the CT treatment. The average volume of trees in the DL1 treatment was 11% higher than the CT treatment. Survival after 8 years of fill planted P. glauca seedlings was 2% greater in the DL1 than in the DL2 treatment. DL1 treatment also produced taller seedlings (+20 cm). Discussion: Results showed that DL treatments were effective in producing post-thinned stands with larger stems without affecting stand productivity. In addition, we showed that these treatments were suitable to introduce slower growing conifer seedlings such as Picea glauca in order to re-create or maintain mixed stands despite the aggressive nature of P. tremuloides sucker regeneration.

Keywords: Aspen, inverted diameter-limit, mixed forest, populus tremuloides, silviculture, thinning

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Authors: Erik Eklund

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Mary Kathleen was a purpose-built company town located in northwest Queensland in Australia. It was created to work on a rich uranium deposit discovered in the area in July 1954. The town was complete by 1958, possessing curved streets, modern materials, and a progressive urban planning scheme. Formed in the minds of corporate executives and architects and made manifest in arid zone country between Cloncurry and Mount Isa, Mary Kathleen was a modern marvel in the outback, a town that tamed the wild country of northwest Queensland, or so it seemed. The town was also a product of the Cold War. In the context of a nuclear arms race between the Soviet Union and her allies, and the United States of America (USA) and her Allies, a rapid rush to locate, mine, and process uranium after 1944 led to the creation of uranium towns in Czechoslovakia, Canada, the Soviet Union, USA and Australia of which Mary Kathleen was one such example. Mary Kathleen closed in 1981, and most of the town’s infrastructure was removed. Since then, the town’s ghostly remains have attracted travellers and tourists. Never an officially-sanctioned tourist site, the area has nevertheless become a regular stop for campers and day trippers who have engaged with the site often without formal interpretation. This paper explores the status of this vernacular heritage and asks why it has not gained any official status and what visitors might see in the place despite its uncertain status.

Keywords: uranium mining, planned communities, official heritage, vernacular heritage, Australian history

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