Search results for: association rule mining.

Authors: Vikas Tewari, Shivendra Tewari, K. R. Pardasani

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Calcium is a vital second messenger used in signal transduction. Calcium controls secretion, cell movement, muscular contraction, cell differentiation, ciliary beating and so on. Two theories have been used to simplify the system of reaction-diffusion equations of calcium into a single equation. One is excess buffer approximation (EBA) which assumes that mobile buffer is present in excess and cannot be saturated. The other is rapid buffer approximation (RBA), which assumes that calcium binding to buffer is rapid compared to calcium diffusion rate. In the present work, attempt has been made to develop a model for calcium diffusion under excess buffer approximation in neuron cells. This model incorporates the effect of [Na+] influx on [Ca2+] diffusion,variable calcium and sodium sources, sodium-calcium exchange protein, Sarcolemmal Calcium ATPase pump, sodium and calcium channels. The proposed mathematical model leads to a system of partial differential equations which have been solved numerically using Forward Time Centered Space (FTCS) approach. The numerical results have been used to study the relationships among different types of parameters such as buffer concentration, association rate, calcium permeability.

Keywords: Excess buffer approximation, Na+ influx, sodium calcium exchange protein, sarcolemmal calcium atpase pump, forward time centred space.

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Authors: R. Suresh, Pavan Kumar Nimmagadda, Ming Zo Tan, Shane Hart, Sharp Ugwuocha

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Polycrystalline diamond compact (PDC) drill bits are extensively used in the oil and gas industry as well as the mining industry. Industry engineers continually improve upon PDC drill bit designs and hydraulic conditions. Optimized injection nozzles play a key role in improving the drilling performance and efficiency of these ever changing PDC drill bits. In the first part of this study, computational fluid dynamics (CFD) modelling is performed to investigate the hydrodynamic characteristics of drilling fluid flow around the PDC drill bit. An Open-source CFD software – OpenFOAM simulates the flow around the drill bit, based on the field input data. A specifically developed console application integrates the entire CFD process including, domain extraction, meshing, and solving governing equations and post-processing. The results from the OpenFOAM solver are then compared with that of the ANSYS Fluent software. The data from both software programs agree. The second part of the paper describes the parametric study of the PDC drill bit nozzle to determine the effect of parameters such as number of nozzles, nozzle velocity, nozzle radial position and orientations on the flow field characteristics and bit washing patterns. After analyzing a series of nozzle configurations, the best configuration is identified and recommendations are made for modifying the PDC bit design.

Keywords: ANSYS Fluent, computational fluid dynamics, nozzle configuration, OpenFOAM, PDC dill bit.

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Authors: Parvinder S. Sandhu, Sandeep Khimta, Kiranpreet Kaur

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The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.

Keywords: Growing Neural Gas, data clustering, fault prediction.

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Authors: Fawad Hussain, Mohammad Basir Bin Saud, Mohd Azwardi Md Isa

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The aim of this study is to empirically investigate the mediating impact of export performance (EP) between technological advancement capabilities and business performance (BP) of Malaysian manufacturing micro, small and medium sized enterprises (MSME’s). Firm’s technological advancement resources are hypothesized as a platform to enhance both exports and BP of manufacturing MSMEs in Malaysia. This study is twofold, primary it has investigated that technological advancement capabilities helps to appreciates main performance measures noted in terms of EP and Secondly, it investigates that how efficiently and effectively technological advancement capabilities can contribute in overall Malaysian MSME’s BP. Smart PLS-3 statistical software is used to know the association between technological advancement capabilities, MSME’s EP and BP. In this study, the data was composed from Malaysian manufacturing MSME’s in east coast industrial zones known as the manufacturing hub of MSMEs. Seven hundred and fifty (750) questionnaires were distributed, but only 148 usable questionnaires are returned. The finding of this study indicated that technological advancement capabilities helps to strengthen the export in term of time and cost efficient and it plays a significant role in appreciating their BP. This study is helpful for small and medium enterprise owners who intend to expand their business overseas and though smart technological advancement resources they can achieve their business competitiveness and excellence both at local and international markets.

Keywords: Technological advancement capabilities, export performance, business performance, small and medium manufacturing enterprises, Malaysia.

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Authors: Md. Emaj Uddin

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This study compares family communication patterns in association with family socio-cultural status, especially marriage and family pattern, and couples- socio-economic status between Muslim and Santal communities in rural Bangladesh. A total of 288 couples, 145 couples from the Muslim and 143 couples from the Santal were randomly selected through cluster sampling procedure from Kalna village situated in Tanore Upazila of Rajshahi district of Bangladesh, where both the communities dwell as neighbors. In order to collect data from the selected samples, interview method with semistructural questionnaire schedule was applied. The responses given by the respondents were analyzed by Pearson-s chi-squire test and bivariate correlation techniques. The results of Pearson-s chi-squire test revealed that family communication patterns (X2= 25. 90, df= 2, p<0.01, p>0.05) were significantly different between the Muslim and Santal communities. In addition, Spearman-s bivariate correlation coefficients suggested that among the exogenous factors, family type (rs=.135, p<0.05) and occupation of both husband (rs= .197, p<0.01) and wife (rs= .265, p<0.01) were significantly positive associations, and marital arrangement (rs= -.177, p<0.01), education of husband (rs= -.108, p<0.05) and wife (rs= -.142, p<0.01 & p<0.05), and family income (rs= -.164, p<0.01) were significantly negative relations with the family communication patterns followed between the two communities, although age difference between husband and wife, family head and residence patterns were not significant relations with ones.

Keywords: Bangladesh, Cross-Cultural Comparison, Family Communication Patterns, Family Socio-Cultural Status, Muslim, Santal.

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Authors: Asina Gülerarslan

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Today-s children, who are born into a more colorful, more creative, more abstract and more accessible communication environment than their ancestors as a result of dizzying advances in technology, have an interesting capacity to perceive and make sense of the world. Millennium children, who live in an environment where all kinds of efforts by marketing communication are more intensive than ever are, from their early childhood on, subject to all kinds of persuasive messages. As regards advertising communication, it outperforms all the other marketing communication efforts in creating little consumer individuals and, as a result of processing of codes and signs, plays a significant part in building a world of seeing, thinking and understanding for children. Children who are raised with metaphorical expressions such as tales and riddles also meet that fast and effective meaning communication in advertisements. Children-s perception of metaphors, which help grasp the “product and its promise" both verbally and visually and facilitate association between them is the subject of this study. Stimulating and activating imagination, metaphors have unique advantages in promoting the product and its promise especially in regard to print advertisements, which have certain limitations. This study deals comparatively with both literal and metaphoric versions of print advertisements belonging to various product groups and attempts to discover to what extent advertisements are liked, recalled, perceived and are persuasive. The sample group of the study, which was conducted in two elementary schools situated in areas that had different socioeconomic features, consisted of children aged 12.

Keywords: Children, metaphor, perception, print advertisements, recall.

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Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

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Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence (AI) is invaluable in identifying crime. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISAs). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The proposed framework development is implemented using the Java Agent Development Framework, Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISAs and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5% of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: Artificial intelligence, computer science, criminal investigation, digital forensics.

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies  the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: Retail stores, Faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition.

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Authors: Reza Meimandi Parizi, Abdul Azim Abdul Ghani

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The ever-growing usage of aspect-oriented development methodology in the field of software engineering requires tool support for both research environments and industry. So far, tool support for many activities in aspect-oriented software development has been proposed, to automate and facilitate their development. For instance, the AJaTS provides a transformation system to support aspect-oriented development and refactoring. In particular, it is well established that the abstract interpretation of programs, in any paradigm, pursued in static analysis is best served by a high-level programs representation, such as Control Flow Graph (CFG). This is why such analysis can more easily locate common programmatic idioms for which helpful transformation are already known as well as, association between the input program and intermediate representation can be more closely maintained. However, although the current researches define the good concepts and foundations, to some extent, for control flow analysis of aspectoriented programs but they do not provide a concrete tool that can solely construct the CFG of these programs. Furthermore, most of these works focus on addressing the other issues regarding Aspect- Oriented Software Development (AOSD) such as testing or data flow analysis rather than CFG itself. Therefore, this study is dedicated to build an aspect-oriented control flow graph construction tool called AJcFgraph Builder. The given tool can be applied in many software engineering tasks in the context of AOSD such as, software testing, software metrics, and so forth.

Keywords: Aspect-Oriented Software Development, AspectJ, Control Flow Graph, Data Flow Analysis

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Authors: P. Ashok, G. M. Kadhar Nawaz

Abstract:

Rough set theory is used to handle uncertainty and incomplete information by applying two accurate sets, Lower approximation and Upper approximation. In this paper, the rough clustering algorithms are improved by adopting the Similarity, Dissimilarity–Similarity and Entropy based initial centroids selection method on three different clustering algorithms namely Entropy based Rough K-Means (ERKM), Similarity based Rough K-Means (SRKM) and Dissimilarity-Similarity based Rough K-Means (DSRKM) were developed and executed by yeast dataset. The rough clustering algorithms are validated by cluster validity indexes namely Rand and Adjusted Rand indexes. An experimental result shows that the ERKM clustering algorithm perform effectively and delivers better results than other clustering methods. Outlier detection is an important task in data mining and very much different from the rest of the objects in the clusters. Entropy based Rough Outlier Factor (EROF) method is seemly to detect outlier effectively for yeast dataset. In rough K-Means method, by tuning the epsilon (ᶓ) value from 0.8 to 1.08 can detect outliers on boundary region and the RKM algorithm delivers better results, when choosing the value of epsilon (ᶓ) in the specified range. An experimental result shows that the EROF method on clustering algorithm performed very well and suitable for detecting outlier effectively for all datasets. Further, experimental readings show that the ERKM clustering method outperformed the other methods.

Keywords: Clustering, Entropy, Outlier, Rough K-Means, validity index.

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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, cancer dataset, classifiers, cervical cancer, F-score, logistic regression, machine learning, precision, recall, support vector machine.

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Authors: Khadiga Kahwa, Aniza Ismail

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Depression, anxiety, and stress are associated with decreased role functioning, productivity, and quality of life. International students are more prone to psychological distress as they face many stressors while studying abroad. The objectives of the study were to determine the prevalence and associated factors of depression, anxiety, and stress among international students, their help-seeking behavior, and their awareness of the available on-campus mental support services. A cross-sectional study with a purposive sampling method was performed on 280 international students at Universiti Kebangsaan Malaysia (UKM) between the age of 18 and 35 years. The Depression Anxiety Stress Scale-21 (DASS-21) questionnaire was used anonymously to assess the mental health of students. Socio-demographic, help-seeking behavior, and awareness data were obtained. Independent sample t-test, one-way ANOVA test, and multiple linear regression were used to explore associated factors. The overall prevalence of depression, anxiety, and stress among international students were 58.9%, 71.8%, and 53.9%, respectively. Age was significantly associated with depression and anxiety. Ethnicity showed a significant association with depression and stress. No other factors were found to be significantly associated with psychological distress. Only 9.6% of the international students had sought help from on-campus mental support services. Students who were aware of the presence of such services were only 21.4% of the participants. In conclusion, this study addressed the gap in the literature on the mental health of international students and provided data that could be used in intervention programs to improve the mental health of the increasing number of international students in Malaysia.

Keywords: Anxiety, depression, stress, help-seeking behavior, students.

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Authors: Carine Cridelich, Pablo Juesas Cano, Emmanuel Ramasso, Noureddine Zerhouni, Bernd Weiler

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Safety is one of the most important considerations when buying a new car. While active safety aims at avoiding accidents, passive safety systems such as airbags and seat belts protect the occupant in case of an accident. In addition to legal regulations, organizations like Euro NCAP provide consumers with an independent assessment of the safety performance of cars and drive the development of safety systems in automobile industry. Those ratings are mainly based on injury assessment reference values derived from physical parameters measured in dummies during a car crash test. The components and sub-systems of a safety system are designed to achieve the required restraint performance. Sled tests and other types of tests are then carried out by car makers and their suppliers to confirm the protection level of the safety system. A Knowledge Discovery in Databases (KDD) process is proposed in order to minimize the number of tests. The KDD process is based on the data emerging from sled tests according to Euro NCAP specifications. About 30 parameters of the passive safety systems from different data sources (crash data, dummy protocol) are first analysed together with experts opinions. A procedure is proposed to manage missing data and validated on real data sets. Finally, a procedure is developed to estimate a set of rough initial parameters of the passive system before testing aiming at reducing the number of tests.

Keywords: KDD process, passive safety systems, sled test, dummy injury assessment reference values, frontal impact

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Authors: Dongseok Cho, Mitchell Driedger, Sera Han, Noman Khan, Mohammed Elmorsy, Mohamad El-Hajj

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Since the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated their populations within their first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. Results of these correlations identify countries with stronger Health indicators such as lower mortality rates, lower age-dependency ratios, and higher rates of immunization to other diseases display higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.

Keywords: Data mining, Pearson Correlation, COVID-19, vaccination rates, hesitancy.

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Authors: Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-Nearest Neighbors, Neural Network, Radial Basis Function, Red blood cells, Support vector machine.

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to prevent deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network. 

Keywords: Accident risks estimation, artificial neural network, deep learning, K-mean, road safety.

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Authors: M. L. Dinis, M. C. Vila, A. Fiúza, A. Futuro, C. Nunes

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The present paper describes the study of paste tailings prepared in laboratory using gold tailings, produced in a Finnish gold mine with the incorporation of coal ash. Natural leaching tests were conducted with the original materials (tailings, fly and bottom ashes) and also with paste mixtures that were prepared with different percentages of tailings and ashes. After leaching, the solid wastes were physically and chemically characterized and the results were compared to those selected as blank – the unleached samples. The tailings and the coal ash, as well as the prepared mixtures, were characterized, in addition to the textural parameters, by the following measurements: grain size distribution, chemical composition and pH. Mixtures were also tested in order to characterize their mechanical behavior by measuring the flexural strength, the compressive strength and the consistency. The original tailing samples presented an alkaline pH because during their processing they were previously submitted to pressure oxidation with destruction of the sulfides. Therefore, it was not possible to ascertain the effect of the coal ashes in the acid mine drainage. However, it was possible to verify that the paste reactivity was affected mostly by the bottom ash and that the tailings blended with bottom ash present lower mechanical strength than when blended with a combination of fly and bottom ash. Surface paste disposal offer an attractive alternative to traditional methods in addition to the environmental benefits of incorporating large-volume wastes (e.g. bottom ash). However, a comprehensive characterization of the paste mixtures is crucial to optimize paste design in order to enhance engineer and environmental properties.

Keywords: Coal ash, gold tailings, paste, surface disposal.

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Authors: R. E. Masto, L. C. Ram, S. K. Verma, V. A. Selvi, J. George, R. C. Tripathi, N. K. Srivastava, D. Mohanty, S. K.Jha, A. K. Sinha, A. Sinha

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There are many sources trough which the soil get enriched and contaminated with REEs. The determination of REEs in environmental samples has been limited because of the lack of sensitive analytical techniques. Soil samples were collected from four sites including open cast coal mine, natural coal burning, coal washery and control in the coal field located in Dhanbad, India. Total concentrations of rare earth elements (REEs) were determined using the inductively coupled plasma atomic absorption spectrometry in order to assess enrichment status in the coal field. Results showed that the mean concentrations of La, Pr, Eu, Tb, Ho, and Tm in open cast mine and natural coal burning sites were elevated compared to the reference concentrations, while Ce, Nd, Sm, and Gd were elevated in coal washery site. When compared to reference soil, heavy REEs (HREEs) were enriched in open cast mines and natural coal burning affected soils, however, the HREEs were depleted in the coal washery sites. But, the Chondrite-normalization diagram showed significant enrichment for light REEs (LREEs) in all the soils. High concentration of Pr, Eu, Tb, Ho, Tm, and Lu in coal mining and coal burning sites may pose human health risks. Factor analysis showed that distribution and relative abundance of REEs of the coal washery site is comparable with the control. Eventually washing or cleaning of coal could significantly decrease the emission of REEs from coal into the environment.

Keywords: Rare earth elements, coal, soil, factor analysis

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Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization.

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Authors: Francesca Radice

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

Keywords: Sentiment Analysis, data mining, predictive policing, virtual manipulation.

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Authors: Brandon Foggo, Nanpeng Yu

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Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: Distribution network, machine learning, network topology, phase identification, smart grid.

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Authors: Amina Abubakar

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English like any other language is rich by means of arbitrary, conventional, symbols which lend it to lot of inconsistencies in spelling, phonology, syntax, and morphology. The research examines the irregularities prevalent in the structure and meaning of some ‘er’ lexical items in English and its implication to vocabulary acquisition. It centers its investigation on the derivational suffix ‘er’, which changes the grammatical category of word. English language poses many challenges to Second Language Learners because of its irregularities, exceptions, and rules. One of the meaning of –er derivational suffix is someone or somebody who does something. This rule often confuses the learners when they meet with the exceptions in normal discourse. The need to investigate instances of such inconsistencies in the formation of –er words and the meanings given to such words by the students motivated this study. For this purpose, some senior secondary two (SS2) students in six randomly selected schools in the metropolis were provided a large number of alphabetically selected ‘er’ suffix ending words, The researcher opts for a test technique, which requires them to provide the meaning of the selected words with- er. The marking of the test was scored on the scale of 1-0, where correct formation of –er word and meaning is scored one while wrong formation and meaning is scored zero. The number of wrong and correct formations of –er words meaning were calculated using percentage. The result of this research shows that a large number of students made wrong generalization of the meaning of the selected -er ending words. This shows how enormous the inconsistencies are in English language and how are affect the learning of English. Findings from the study revealed that though students mastered the basic morphological rules but the errors are generally committed on those vocabulary items that are not frequently in use. The study arrives at this conclusion from the survey of their textbook and their spoken activities. Therefore, the researcher recommends that there should be effective reappraisal of language teaching through implementation of the designed curriculum to reflect on modern strategies of teaching language, identification, and incorporation of the exceptions in rigorous communicative activities in language teaching, language course books and tutorials, training and retraining of teachers on the strategies that conform to the new pedagogy.

Keywords: ESL, derivational morpheme, inflectional morpheme, suffixes.

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Authors: M. Govindarajan

Abstract:

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: Data mining, ensemble, radial basis function, support vector machine, accuracy.

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Authors: Salwa Hagag Abdelaziz, Naglaa Fathy Youssef, Nadia Abdel Latif Hassan, Rasha Wesam Abdel Rahman

Abstract:

Breast cancer is considered as a substantial health concern and practicing mammography screening [MS] is important in minimizing its related morbidity. So it is essential to have a better understanding of breast cancer screening behaviors of women and factors that influence utilization of them. The aim of this study is to identify the factors that are linked to MS behaviors among the Egyptian women. A cross-sectional descriptive design was carried out to provide a snapshot of the factors that are linked to MS behaviors. A convenience sample of 311 women was utilized and all eligible participants admitted to the Women Imaging Unit who are 40 years of age or above, coming for mammography assessment, not pregnant or breast feeding and who accepted to participate in the study were included. A structured questionnaire was developed by the researchers and contains three parts; Socio-demographic data; Motivating factors associated with MS; and association between MS and model of behavior change. The analyzed data indicated that most of the participated women (66.6%) belonged to the age group of 40- 49.A high proportion of participants (58.1%) of group having previous MS influenced by their neighbors to practice MS, whereas 32.7 % in group not having previous MS were influenced by family members which indicated significant differences (P <0.05). Doctors and media shown to be the least influence of others to practice MS. Women with intention to have a future mammogram had higher OR (1.404) for practicing MS compared with women with no intention. Further studies are needed to examine the relation between Transtheoretical Model [TTM] and practicing MS.

Keywords: Breast cancer, mammography, screening behaviors.

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Authors: Kalsoom Sumra

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The increasing concern of public sector reforms brings new challenges to public service ethics in developing countries not only at central level but also at local level. This paper aims to identify perceptions on public service ethics of public officials and examines more generally the understanding of public servants in Pakistan towards public service ethics in local public organizations. The study uses an independently administered structured questionnaire to collect data to know the extent of the recognition of public service ethics in local organizations. A total of 150 completed questionnaires are analyzed received from public servants working at the local level in Pakistan. The analysis explores how traditional, social patterns and cultural ethics can provide us with a rounded picture of the main antecedents, moderators of public service ethics in Pakistan. Moreover, the findings of this study contribute in association of public service ethics which are crucial in ongoing political and administrative culture of Pakistan, the most crucial core for public organizational ethical climate. This study also has numerous implications for local public administration and it highlights the importance of expanding research agenda on public service ethics in developing settings with challenging institutional contexts with imperfect training and operating environments. This study may well be particularly important for practice of public service ethics in developing countries in public administration. To the best of author’s knowledge, this study is the first of its kind to provide an initial step in practical implications to emphasize relevant public service ethics in public administration in developing transparent and accountable organization.

Keywords: Public service ethics, accountability and transparency, public service reforms, public administration, organizational ethical climate.

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Authors: M. Gómez-Ramírez, A. Rivas-Castillo, I. Rodríguez-Pozos, R. A. Avalos-Zuñiga, N. G. Rojas-Avelizapa

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Among the diverse types of pollutants produced by anthropogenic activities, metals represent a serious threat, due to their accumulation in ecosystems and their elevated toxicity. The mine tailings of abandoned mines contain high levels of metals such as arsenic (As), zinc (Zn), copper (Cu), and lead (Pb), which do not suffer any degradation process, they are accumulated in environment. Abandoned mine tailings potentially could contaminate rivers and aquifers representing a risk for human health due to their high metal content. In an attempt to remove the metals and thereby mitigate the environmental pollution, an environmentally friendly and economical method of bioremediation has been introduced. Bioleaching has been actively studied over the last several years, and it is one of the bioremediation solutions used to treat heavy metals contained in sewage sludge, sediment and contaminated soil. Acidithiobacillus thiooxidans, an extremely acidophilic, chemolithoautotrophic, gram-negative, rod shaped microorganism, which is typically related to Cu mining operations (bioleaching), has been well studied for industrial applications. The sulfuric acid produced plays a major role in bioleaching. Specifically, Acidithiobacillus thiooxidans strain DSM 26636 has been able to leach Al, Ni, V, Fe, Mg, Si, and Ni contained in slags from coal combustion wastes. The present study reports the ability of A. thiooxidans DSM 26636 for the bioleaching of metals contained in two different mine tailing samples (MT1 and MT2). It was observed that Al, Fe, and Mn were removed in 36.3±1.7, 191.2±1.6, and 4.5±0.2 mg/kg for MT1, and in 74.5±0.3, 208.3±0.5, and 20.9±0.1 for MT2. Besides, < 1.5 mg/kg of Au and Ru were also bioleached from MT1; in MT2, bioleaching of Zn was observed at 55.7±1.3 mg/kg, besides removal of < 1.5 mg/kg was observed for As, Ir, Li, and 0.6 for Os in this residue. These results show the potential of strain DSM 26636 for the bioleaching of metals that came from different mine tailings.

Keywords: A. thiooxidans, bioleaching, metals, mine tailings.

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Authors: Omowunmi E. Isafiade, Isaac O. Osunmakinde, Antoine B. Bagula

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Robots- visual perception is a field that is gaining increasing attention from researchers. This is partly due to emerging trends in the commercial availability of 3D scanning systems or devices that produce a high information accuracy level for a variety of applications. In the history of mining, the mortality rate of mine workers has been alarming and robots exhibit a great deal of potentials to tackle safety issues in mines. However, an effective vision system is crucial to safe autonomous navigation in underground terrains. This work investigates robots- perception in underground terrains (mines and tunnels) using statistical region merging (SRM) model. SRM reconstructs the main structural components of an imagery by a simple but effective statistical analysis. An investigation is conducted on different regions of the mine, such as the shaft, stope and gallery, using publicly available mine frames, with a stream of locally captured mine images. An investigation is also conducted on a stream of underground tunnel image frames, using the XBOX Kinect 3D sensors. The Kinect sensors produce streams of red, green and blue (RGB) and depth images of 640 x 480 resolution at 30 frames per second. Integrating the depth information to drivability gives a strong cue to the analysis, which detects 3D results augmenting drivable and non-drivable regions in 2D. The results of the 2D and 3D experiment with different terrains, mines and tunnels, together with the qualitative and quantitative evaluation, reveal that a good drivable region can be detected in dynamic underground terrains.

Keywords: Drivable Region Detection, Kinect Sensor, Robots' Perception, SRM, Underground Terrains.

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Authors: A. Kulprasutidilok

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In order to address the social factors of elderly health care, researcher and community members have turned to more inclusive and participatory approaches to research and interventions. One such approach, community-based participatory research (CBPR) in public health, has received increased attention as the academic and public health communities struggle to address the persistent problems of disparities in the use of health care and health outcomes for several over the past decade. As Thailand becomes an ageing society, health services and proper care systems specifically for the elderly group need to be prepared and well established. The purpose of this assignment was to study the health problems and was to explore the process of community participation in elderly health care. Participants in this study were member of elderly group of Paisanee Ramintra 65 community in Bangkok, Thailand. The results indicated two important components of community participation process in elderly health care: 1) a process to develop community participation in elderly health care, and 2) outcomes resulting from such process. The development of community participation consisted of four processes. As for the outcomes of the community participation development process, they consisted of elderly in the community got jointly and formulated a group, which strengthened the project because of collaborative supervision among themselves. Moreover, inactive health care services have changed to being energetic and focus on health promotion rather than medical achievement and elderly association of community can perform health care activities for chronically illness through the achievement of this development; consequently, they increasingly gained access to physical, cognitive, and social activity.

Keywords: Community-based participatory research, elderly health care, Thailand.

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Authors: Haleh Rezaei Zanjirabadi, Fatemeh Saberi, Bahman Rahimzadeh, Fariborz Masoudi, Mohammad Rahgosha

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In this study, apatite minerals from the iron-phosphate deposit of Yazd have been investigated within the microcontinent zone of Iran in the Zagros structural zone. The geological units in the Esfordi area belong to the pre-Cambrian to lower-Cambrian age, consisting of a succession of carbonate rocks (dolomite), shale, tuff, sandstone, and volcanic rocks. In addition to the mentioned sedimentary and volcanic rocks, the granitoid mass of Bahabad, which is the largest intrusive mass in the region, has intruded into the eastern part of this series and has caused its metamorphism and alteration. After collecting the available data, various samples of Esfordi’s apatite were prepared, and their mineralogy and crystallography were investigated using laboratory methods such as petrographic microscopy, Raman spectroscopy, EDS (Energy Dispersive Spectroscopy), and Scanning Electron Microscopy (SEM). In non-destructive Raman spectroscopy, the molecular structure of apatite minerals was revealed in four distinct spectral ranges. Initially, the spectra of phosphate and aluminum bonds with O2HO, OH, were observed, followed by the identification of Cl, OH, Al, Na, Ca and hydroxyl units depending on the type of apatite mineral family. In SEM analysis, based on various shapes and different phases of apatites, their constituent major elements were identified through EDS, indicating that the samples from the Esfordi mining area exhibit a dense and coherent texture with smooth surfaces. Based on the elemental analysis results by EDS, the apatites in the Esfordi area are classified into the calcic apatite group.

Keywords: Petrology, apatite, Esfordi, EDS, SEM, Scanning Electron Microscopy, Raman spectroscopy.

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: Anomaly detection, digital twin, Generalised Additive Model, Power Consumption Model.

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