Search results for: predictive mining

Authors: Ismene Ithai Bras-Ruiz

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Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Sukla Saha, Alok Sinha

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Acid mine drainage (AMD) refers to the production of acidified water from abandoned mines and active mines as well. The reason behind the generation of this kind of acidified water is the oxidation of pyrites present in the rocks in and around mining areas. Thiobacillus ferrooxidans, which is a sulfur oxidizing bacteria, helps in the oxidation process. AMD is extremely acidic in nature, (pH 2-3) with high concentration of several trace and heavy metals such as Fe, Al, Zn, Mn, Cu and Co and anions such as chloride and sulfate. AMD has several detrimental effect on aquatic organism and environment. It can directly or indirectly contaminate the ground water and surface water as well. The present study considered the treatment of AMD with metallurgical slag, which is a waste material. Slag helped to enhance the pH of AMD to 8.62 from 1.5 with 99% removal of trace metals such as Fe, Al, Mn, Cu and Co. Metallurgical slag was proven as efficient neutralizing material for the treatment of AMD.

Keywords: acid mine drainage, Heavy metals, metallurgical slag, Neutralization

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Purva Mishra, Rajesh Potlia, Kuljeet Singh Sandhu

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The role of glycyl residues in the protein structure has lingered within the research community for the last several decades. Glycyl residue is the only amino acid that is achiral due to the lack of a side chain and can, therefore, exhibit Ramachandran conformations that are disallowed for L-amino acids. The structural and functional significance of glycyl residues with L-disallowed conformation, however, remains obscure. Through statistical analysis of various datasets, we found that the glycyls with L-disallowed conformations are over-represented at disease-associated sites and tend to be evolutionarily conserved. The mutations of L-disallowed glycyls tend to destabilize the native conformation, reduce protein solubility, and promote inter-molecular aggregations. We uncovered a structural motif referred to as “β-crescent” formed around the L-disallowed glycyl, which prevents β-sheet aggregation by disrupting the alternating pattern of β-pleats. The L-disallowed conformation of glycyls also holds predictive power to infer the pathogenic missense variants. Altogether, our observations highlight that the L-disallowed conformation of glycyls is selected to facilitate native folding and prevent inter-molecular aggregations. The findings may also have implications for designing more stable proteins and prioritizing the genetic lesions implicated in diseases.

Keywords: Ramachandran plot, β-sheet, protein stability, protein aggregation

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Authors: Mohammad Javad Zehtab, S. Alireza Mirghasemi, Ali Majlesara, Parvin Tajik, Babak Siavashi

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Objective: There are different modalities proposed for tennis elbow treatment with few randomized trials comparing them. We designed a study to compare the effectiveness of five different modalities and determine the usefulness of recently proposed extensor grip test (EGT) in predicting the response to treatment. Methods: In a randomized controlled clinical trial 92 of 98 tennis elbow patients in Sina hospital of Tehran, Iran between 2006 and 2007 fulfill trial entry criteria, among these patients 56 (60.9%) had positive EGT result. Stratified on EGT result, patients allocated randomly to 5 treatment groups: Brace (B) group, physiotherapy (P), brace + physiotherapy (BP), injection (I) and injection + physiotherapy (IP). Results: Patients who had positive result of EGT had better response to treatments: less SOC (p = 0.06), less PFFQ and patients’ satisfaction scores (p < 0.001). Among the treatment IP was the most successful, then BP, P and B, respectively; injection was the worst treatment modality. Response to treatment was comparable in all groups between EGT positive and negative patients except bracing; in which positive EGT was correlated with a dramatic response to treatment. Conclusion: In all patients IP and then BP is recommended but in EGT negatives, bracing seems to be of no use. Injection alone is not recommended in either group.

Keywords: tennis elbow, extensor grip test, physiotherapy, tennis elbow treatment

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Authors: Karlijn Van Den Broek, Ian Walker, Christian Klöckner

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Campaigns aiming to induce energy-saving behaviour among householders use a wide range of approaches that address many different drivers thought to underpin this behaviour. However, little research has compared the relative importance of the different factors that influence energy behaviour, meaning campaigns are not informed about where best to focus resources. Therefore, this study applies the Comprehensive Action Determination Model (CADM) to compare the role of normative, intentional, habitual, and situational processes on energy-saving behaviour. An online survey on a sample of households (N = 247) measured the CADM variables and the data was analysed using structural equation modelling. Results showed that situational and habitual processes were best able to account for energy saving behaviour while normative and intentional processes had little predictive power. These findings suggest that policymakers should move away from motivating householders to save energy and should instead focus their efforts on changing energy habits and creating environments that facilitate energy saving behaviour. These findings add to the wider development in social and environmental psychology that emphasizes the importance of extra-personal variables such as the physical environment in shaping behaviour.

Keywords: energy consumption, behavioural modelling, environmental psychology theory, habits, values

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Authors: Hugo Lefeuvre, Jerôme Donnard, Michael Descostes, Sophie Billon, Samuel Duval, Tugdual Oger, Herve Toubon, Paul Sardini

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Spectroscopic autoradiography is a method of interest for geological sample analysis. Indeed, researchers may face different issues such as radioelement identification and quantification in the field of environmental studies. Imaging gaseous ionization detectors find their place in geosciences for conducting specific measurements of radioactivity to improve the monitoring of natural processes using naturally-occurring radioactive tracers, but also for the nuclear industry linked to the mining sector. In geological samples, the location and identification of the radioactive-bearing minerals at the thin-section scale remains a major challenge as the detection limit of the usual elementary microprobe techniques is far higher than the concentration of most of the natural radioactive decay products. The spatial distribution of each decay product in the case of uranium in a geomaterial is interesting for relating radionuclides concentration to the mineralogy. The present study aims to provide spectroscopic autoradiography analysis method for measuring the initial energy of alpha particles with a parallel ionization multiplier gaseous detector. The analysis method has been developed thanks to Geant4 modelling of the detector. The track of alpha particles recorded in the gas detector allow the simultaneous measurement of the initial point of emission and the reconstruction of the initial particle energy by a selection based on the linear energy distribution. This spectroscopic autoradiography method was successfully used to reproduce the alpha spectra from a 238U decay chain on a geological sample at the thin-section scale. The characteristics of this measurement are an energy spectrum resolution of 17.2% (FWHM) at 4647 keV and a spatial resolution of at least 50 µm. Even if the efficiency of energy spectrum reconstruction is low (4.4%) compared to the efficiency of a simple autoradiograph (50%), this novel measurement approach offers the opportunity to select areas on an autoradiograph to perform an energy spectrum analysis within that area. This opens up possibilities for the detailed analysis of heterogeneous geological samples containing natural alpha emitters such as uranium-238 and radium-226. This measurement will allow the study of the spatial distribution of uranium and its descendants in geo-materials by coupling scanning electron microscope characterizations. The direct application of this dual modality (energy-position) of analysis will be the subject of future developments. The measurement of the radioactive equilibrium state of heterogeneous geological structures, and the quantitative mapping of 226Ra radioactivity are now being actively studied.

Keywords: alpha spectroscopy, digital autoradiography, mining activities, natural decay products

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Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: Nathan Labiosa, Aryan Kohli

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This study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic physical properties of proteins. Trained on a comprehensive dataset from the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the model demonstrates a classification accuracy of 95%, outperforming existing methods by at least 15%. The high accuracy achieved demonstrates the potential of this approach to innovate protein classification, facilitating advancements in drug discovery and the development of personalized medicine. By enabling precise protein function prediction, the hybrid model allows for specialized strategies in therapeutic targeting and the exploration of protein dynamics in biological systems. Future work will focus on enhancing the model’s generalizability across diverse datasets and exploring the integration of more machine learning techniques to refine predictive capabilities further. The implications of this research offer potential breakthroughs in biomedical research and the broader field of protein engineering.

Keywords: amino acids, deep learning, enzymes, neural networks, protein classification, proteins, transformers

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Levi Corallo

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Typed language made by humans on computers and cell phones has made a significant distinction from previous modes of written language exchanges. While acronyms remain one of the most predominant markings of typed language, another and perhaps more recent revolution in the way humans communicate has been with the use of symbols or glyphs, primarily Emojis—globally introduced on the iPhone keyboard by Apple in 2008. This paper seeks to analyze the use of symbols in typed communication from both a linguistic and machine learning perspective. The Unicode system will be explored and methods of encoding will be juxtaposed with the current machine and human perception. Topics in how typed symbol usage exists in conversation will be explored as well as topics across current research methods dealing with Emojis like sentiment analysis, predictive text models, and so on. This study proposes that sequential analysis is a significant feature for analyzing unicode characters in a corpus with machine learning. Current models that are trying to learn or translate the meaning of Emojis should be starting to learn using bi- and tri-grams of Emoji, as well as observing the relationship between combinations of different Emoji in tandem. The sociolinguistics of an entire new vernacular of language referred to here as ‘typed language’ will also be delineated across my analysis with unicode glyphs from both a semantic and technical perspective.

Keywords: unicode, text symbols, emojis, glyphs, communication

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Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

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Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Amna Javed, Hisashi Masuda, Youji Kohda

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This study examines the value analysis in Islamic and conventional banking services in Pakistan. Many scholars have focused on co-creation of values in services but mainly economic values not non-economic. As Islamic banking is based on Islamic principles that are more concerned with non-economic values (well-being, partnership, fairness, trust worthy, and justice) than economic values as money in terms of interest. This study is important to know the providers point of view about the co-created values, because, it may be more sustainable and appropriate for today’s unpredictable socioeconomic environment. Data were collected from 4 banks (2 Islamic and 2 conventional banks). Text mining technique is applied for data analysis, and values with 100% occurrences in Islamic banking are chosen. The results reflect that Islamic banking is more centric towards non-economic values than economic values and it promotes team work and partnership concept by applying Islamic spirit and trust worthiness concept.

Keywords: economic values, Islamic banking, non-economic values, value system

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Authors: Jun Wang, Ge Zhang

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Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample.

Keywords: machine learning, ETF prediction, dynamic trading, asset allocation

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Authors: Adil Balla Elkrail

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Visual MODFLOW computer codes were selected to simulate head distribution, calculate the groundwater budgets of the area, and evaluate the effect of external stresses on the groundwater head and to demonstrate how the groundwater model can be used as a comparative technique in order to optimize utilization of the groundwater resource. A conceptual model of the study area, aquifer parameters, boundary, and initial conditions were used to simulate the flow model. The trial-and-error technique was used to calibrate the model. The most important criteria used to check the calibrated model were Root Mean Square error (RMS), Mean Absolute error (AM), Normalized Root Mean Square error (NRMS) and mass balance. The maps of the simulated heads elaborated acceptable model calibration compared to observed heads map. A time length of eight years and the observed heads of the year 2004 were used for model prediction. The predictive simulation showed that the continuation of pumping will cause relatively high changes in head distribution and components of groundwater budget whereas, the low deficit computed (7122 m3/d) between inflows and outflows cannot create a significant drawdown of the potentiometric level. Hence, the area under consideration may represent a high permeability and productive zone and strongly recommended for further groundwater development.

Keywords: aquifers, model simulation, groundwater, calibrations, trail-and- error, prediction

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Authors: Anam Alam, Rahim Jamaluddin Kanji

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With the rapid growth of event in everywhere, event extraction has now become an important matter to retrieve the information from the unstructured data. One of the challenging problems is to extract the event from it. An event is an observable occurrence of interaction among entities. The paper investigates the effectiveness of event extraction capabilities of three software tools that are Wandora, Nitro and SPSS. We performed standard text mining techniques of these tools on the data sets of (i) Afghan War Diaries (AWD collection), (ii) MUC4 and (iii) WebKB. Information retrieval measures such as precision and recall which are computed under extensive set of experiments for Event Extraction. The experimental study analyzes the difference between events extracted by the software and human. This approach helps to construct an algorithm that will be applied for different machine learning methods.

Keywords: event extraction, Wandora, nitro, SPSS, event analysis, extraction method, AFG, Afghan War Diaries, MUC4, 4 universities, dataset, algorithm, precision, recall, evaluation

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

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Authors: Florge Francis A. Sy

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Acute pancreatitis (AP) is an inflammatory condition of the pancreas with local and systemic complications. Severe acute pancreatitis (SAP) has a higher mortality rate. Laboratory parameters like the neutrophil-to-lymphocyte ratio (NLR), red cell distribution width (RDW), and mean platelet volume (MPV) have been associated with SAP but with conflicting results. This study aims to determine the predictive value of these parameters on the severity and in-hospital mortality of AP. This retrospective, cross-sectional study was done in a private hospital in Cebu City, Philippines. One-hundred five patients were classified according to severity based on the modified Marshall scoring. The admitting hemogram, including the NLR, RDW, and MPV, was obtained from the complete blood count (CBC). Cut-off values for severity and in-hospital mortality were derived from the ROC. Association between NLR, RDW, and MPV with SAP and mortality were determined with a p-value of < 0.05 considered significant. The mean age for AP was 47.6 years, with 50.5% being male. Most had an unknown cause (49.5%), followed by a biliary cause (37.1%). Of the 105 patients, 23 patients had SAP, and 4 died. Older age, longer in-hospital duration, congestive heart failure, elevated creatinine, urea nitrogen, and white blood cell count were seen in SAP. The NLR was associated with in-hospital mortality using a cut-off of > 10.6 (OR 1.133, 95% CI, p-value 0.003) with 100% sensitivity, 70.3% specificity, 11.76% PPV and 100% NPV (AUC 0.855). The NLR was not associated with SAP. The RDW and MPV were not associated with SAP and mortality. The admitting NLR is, therefore, an easily accessible parameter that can predict in-hospital mortality in acute pancreatitis. Although the present study did not show an association of NLR with SAP nor RDW and MPV with both SAP and mortality, further studies are suggested to establish their clinical value.

Keywords: acute pancreatitis, mean platelet volume, neutrophil-lymphocyte ratio, red cell distribution width

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Authors: Kenny Raharjo, Ramon Lawrence

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Game designers have the challenging task of building games that engage players to spend their time and money on the game. There are an infinite number of game variations and design choices, and it is hard to systematically determine game design choices that will have positive experiences for players. In this work, we demonstrate how multi-arm bandits can be used to automatically explore game design variations to achieve improved player metrics. The advantage of multi-arm bandits is that they allow for continuous experimentation and variation, intrinsically converge to the best solution, and require no special infrastructure to use beyond allowing minor game variations to be deployed to users for evaluation. A user study confirms that applying multi-arm bandits was successful in determining the preferred game variation with highest play time metrics and can be a useful technique in a game designer's toolkit.

Keywords: game design, multi-arm bandit, design exploration and data mining, player metric optimization and analytics

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Authors: Michael Ayodeji Boyede

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Studies have shown that workers’ job performance is very low in Nigeria, especially in the food and beverage industry. This trend had been partially attributed to low workers’ self-efficacy, poor self-knowledge, lack of empathy and poor psychological well-being. The descriptive survey design was adopted. Four factories were purposively selected from three states in Southwestern, Nigeria (Lagos, Ogun and Oyo States). Proportionate random sampling techniques were used in selecting 1,820 junior and supervisory cadre workers in Nestle Plc (369), Coca-Cola Plc (392), Cadbury Plc (443) and Nigeria Breweries (616). The five research instruments used were: Workers’ self-efficacy (r=0.81), Workers’ self-knowledge (r=0.78), Workers’ empathy (r=0.74), Workers’ psychological well-being (r=0.70) and Workers’ performance rating (r=0.72) scales. Quantitative data were analysed using Pearson product moment correlation, Multiple regression at 0.05 level of significance. Findings show that there were significant relationships between Workers’ job performance and self-efficacy (r=.56), self-knowledge (r=.54), Empathy (r=.55) and Psychological Well-being (r=.69) respectively. Self-efficacy, self-knowledge, empathy and psychological well-being jointly predict workers’ job performance (F (4,1815) = 491.05) accounting for 52.0% of its variance. Psychological well-being (B=.52). Self-efficacy (B=.10), self-knowledge (B=.11), empathy (B=. 09) had predictive relative weights on workers’ job performance. Inadequate knowledge and training of the supervisors led to a mismatch of workers thereby reducing workers’ job performance. High self-efficacy, empathy, psychological well-being and good self-knowledge influence workers job performance in the food and beverage industry. Based on the finding employers of labour should provide work environment that would enhance and promote the development of these factors among the workers.

Keywords: self-efficacy, self-knowledge, empathy, psychological well-being, job performance

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Ahmed Ishtiaq, Maeeda Khalid, Umair Sajjad

Abstract:

In the fast-moving world, information is the key to success. If information is easily available, then it makes work easy. The Internet is the biggest collection and source of information nowadays, and with every single day, the data on internet increases, and it becomes difficult to find required data. Everyone wants to make his/her website at the top of search results. This can be possible when you have applied some techniques of SEO inside your application or outside your application, which are two types of SEO, onsite and offsite SEO. SEO is an abbreviation of Search Engine Optimization, and it is a set of techniques, methods to increase users of a website on World Wide Web or to rank up your website in search engine indexing. In this paper, we have compared different techniques of Onpage and Offpage SEO, and we have suggested many things that should be changed inside webpage, outside web page and mentioned some most powerful and search engine considerable elements and techniques in both types of SEO in order to gain high ranking on Search Engine.

Keywords: auto-suggestion, search engine optimization, SEO, query, web mining, web crawler

Procedia PDF Downloads 145

Authors: Mirza Mujtaba Baig

Abstract:

Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

Procedia PDF Downloads 113

Authors: B. Keawaram, P. Dumrongchai

Abstract:

The volume of overburden and coal excavations in open-pit mine is generally determined by conventional survey such as total station. This study aimed to evaluate the accuracy of terrestrial laser scanner (TLS) used to measure overburden and coal excavations, and to compare TLS survey data sets with the data of the total station. Results revealed that, the reference points measured with the total station showed 0.2 mm precision for both horizontal and vertical coordinates. When using TLS on the same points, the standard deviations of 4.93 cm and 0.53 cm for horizontal and vertical coordinates, respectively, were achieved. For volume measurements covering the mining areas of 79,844 m², TLS yielded the mean difference of about 1% and the surface error margin of 6 cm at the 95% confidence level when compared to the volume obtained by total station.

Keywords: mine, survey, terrestrial laser scanner, total station

Procedia PDF Downloads 381

Authors: Iman Atighi, Jalal Soleimannejad, Reza Pourjafarabadi, Saeid Moradpour

Abstract:

In the past, we could increase profit by increasing product prices. But in the new decade, a competitive market does not let us to increase profit with increased prices. Therefore, the only way to increase profit will be to reduce costs. A significant percentage of production costs are the maintenance costs, and analysis of these costs could achieve more profit. Most maintenance strategies such as RCM (Reliability-Center-Maintenance), TPM (Total Productivity Maintenance), PM (Preventive Maintenance) and etc., are trying to reduce maintenance costs. In this paper, decreasing the maintenance costs of Concentration Plant of Golgohar Iron Ore Mining & Industrial Company (GeG) was examined by using of MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) analyses. These analyses showed that instead of buying new machines and increasing costs in order to promote capacity, the improving of MTBF and MTTR indexes would solve capacity problems in the best way and decrease costs.

Keywords: GeG company, maintainability, maintenance costs, reliability-center-maintenance

Procedia PDF Downloads 218

Authors: Okoh Charity N.

Abstract:

This study investigated the predictive power of preschoolers’ involvement in indoor and outdoor learning activities on their social learning skills in Niger state, Nigeria. Two research questions and two null hypotheses guided the study. Correlational research design was employed in the study. The population of the study consisted of 8,568 Nursery III preschoolers across the 549 preschools in the five Local Education Authorities in Niger State. A sample of 390 preschoolers drawn through multistage sampling procedure. Two instruments; Preschoolers’ Learning Activities Rating Scale (PLARS) and Preschoolers’ Social Learning Skills Rating Scale (PSLSRS) developed by the researcher were used for data collection. The reliability coefficients obtained for the PLARS and PSLSRS were 0.83 and 0.82, respectively. Data collected were analyzed using simple linear regression. Results showed that 37% of preschoolers’ social learning skills are predicted by their involvement in indoor learning activities, which is statistically significant (p < 0.05). It also shows that 11% of preschoolers’ social learning skills are predicted by their involvement in outdoor learning activities, which is statistically significant (p < 0.05). Therefore, it was recommended among others, that government and school administrators should employ qualified teachers who will stand as role models for preschoolers’ social skills development and provide indoor and outdoor activities and materials for preschoolers in schools.

Keywords: preschooler, social learning, indoor activities, outdoor activities

Procedia PDF Downloads 122