Search results for: actionable pattern mining

Authors: Igor Belykh

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Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when a digital image is resized on a diagnostic monitor. In this paper, we propose an automated grid artifacts detection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.

Keywords: grid, computed radiography, pattern recognition, image processing, filtering

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Authors: Amer Momani, Tarek Al-Hawari, Abdallah Alakayleh

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This study explains how to construct an actionable activity-based costing and management system to accurately track and account the total costs of architectural aluminum projects. Two ABC models were proposed to accomplish this purpose. First, the learning and development model was introduced to examine how to apply an ABC model in an architectural aluminum firm for the first time and to be familiar with ABC concepts. Second, an actual ABC model was built on the basis of the results of the previous model to accurately trace the actual costs incurred on each project in a year, and to be able to provide a quote with the best trade-off between competitiveness and profitability. The validity of the proposed model was verified on a local architectural aluminum company.

Keywords: activity-based costing, activity-based management, construction, architectural aluminum

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Authors: Rahul Magazine, Shobitha Rao

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The aim of this study was to study the prescribing pattern of drugs in patients with ARDS (Acute Respiratory Distress Syndrome) managed at a tertiary care hospital. This observational study was conducted at Kasturba Hospital, Karnataka, India. Data of patients admitted from January 2010 to December 2012 was collected. A total of 150 patients of ARDS were included. Data included patients’ age, gender, clinical disorders precipitating ARDS, and prescribing pattern of drugs. The mean age of the study population was 42.92±13.91 years. 48% of patients were less than 40 years of age. Infection was the cause of ARDS in 81.3% of subjects. Antibiotics were prescribed in all the subjects and beta-lactams were prescribed in 97.3%. 41.3% were prescribed corticosteroids, 39.3% diuretics and 89.3% intravenous fluids. Infection was the commonest etiology for ARDS, and beta-lactams were the commonest antibiotics prescribed. Corticosteroids and diuretics were prescribed in a significant number of patients. Most of the patients received intravenous fluids.

Keywords: acute respiratory syndrome, beta lactams, corticosteroids, Acute Respiratory Distress Syndrome (ARDS)

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Authors: Nhleko Monique Chiloane

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The gold mining industry has predominantly used ammonium nitrate fuel oil (ANFO) explosives for decades, although these are known to be “gassier” and their detonation results in toxic fumes, for example, carbon monoxide (CO), nitrogen oxides (NOx) and ammonia. Re-entry into underground workings too soon after blasting can lead to fatal exposure to toxic fumes. It is, therefore, required that the polluted air be removed from the affected areas within a reasonable period before employees' re-entry into the working area. Post-blast re-entry times have therefore been described as a productivity bottleneck. The known causes of post-blast fumes are water ingress, incorrect fuel to oxygen ratio, confinement, explosive additives etc. To prevent or minimize post-blast fumes, some researchers have used neutralization, re-burning technique and non-explosive products or different oxidizing agents. The use of commercial explosives without nitrate oxidizing agents can also minimize the production of blasting fumes and thereby reduce the time needed for the clearance of these fumes to allow workers to re-enter the underground workings safely. The reduction in non-production time directly contributes to an increase in the available time per shift for productive work, thus leading to continuous mining. However, owing to its low cost and ease of use, ANFO is still widely used in South African underground blasting operations.

Keywords: post-blast fumes, continuous mining, ammonium nitrate explosive, non-explosive blasting, re-entry period

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

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Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

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Authors: Jihyun Song, Kyeongjoo Kim, Minsoo Lee

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Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems.

Keywords: clustering, data mining, DBSCAN, k-means, k-medoids, sensor data

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Authors: Carla Palencia-Aguilar

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Calculations of Net Carbon Balance can be obtained by means of Net Biome Productivity (NBP), Net Ecosystem Productivity (NEP), and Net Primary Production (NPP). The latter is an important component of the biosphere carbon cycle and is easily obtained data from MODIS MOD17A3HGF; however, the results are only available yearly. To overcome data availability, bands 33 to 36 from MODIS MYD021KM (obtained on a daily basis) were analyzed and compared with NPP data from the years 2000 to 2021 in 7 sites where surface mining takes place in the Colombian territory. Coal, Gold, Iron, and Limestone were the minerals of interest. Scales and Units as well as thermal anomalies, were considered for net carbon balance per location. The NPP time series from the satellite images were filtered by using two Matlab filters: First order and Discrete Transfer. After filtering the NPP time series, comparing the graph results from the satellite’s image value, and running a linear regression, the results showed R2 from 0,72 to 0,85. To establish comparable units among NPP and bands 33 to 36, the Greenhouse Gas Equivalencies Calculator by EPA was used. The comparison was established in two ways: one by the sum of all the data per point per year and the other by the average of 46 weeks and finding the percentage that the value represented with respect to NPP. The former underestimated the total CO2 emissions. The results also showed that coal and gold mining in the last 22 years had less CO2 emissions than limestone, with an average per year of 143 kton CO2 eq for gold, 152 kton CO2 eq for coal, and 287 kton CO2 eq for iron. Limestone emissions varied from 206 to 441 kton CO2 eq. The maximum emission values from unfiltered data correspond to 165 kton CO2 eq. for gold, 188 kton CO2 eq. for coal, and 310 kton CO2 eq. for iron and limestone, varying from 231 to 490 kton CO2 eq. If the most pollutant limestone site improves its production technology, limestone could count with a maximum of 318 kton CO2 eq emissions per year, a value very similar respect to iron. The importance of gathering data is to establish benchmarks in order to attain 2050’s zero emissions goal.

Keywords: carbon dioxide, NPP, MODIS, MINING

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Authors: Shaojian Li

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The traditional Chinese patterns, as an integral part of Chinese culture, possess unique values in history, culture, and art. However, with the passage of time and societal changes, many of these traditional patterns are at risk of being lost, damaged, or forgotten. To undertake the digital preservation and protection of these traditional patterns, this paper will collect and organize images of traditional Chinese patterns. It will provide exhaustive and comprehensive semantic annotations, creating a resource library of traditional Chinese pattern images. This will support the digital preservation and application of traditional Chinese patterns.

Keywords: digitization of cultural heritage, traditional Chinese patterns, digital humanities, database construction

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Authors: Mustafa Col, Funda Gul Koc, Merve Yangaz, Eylem Subasi, Can Akbasoglu

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G38NiCrMo8-4-4 steel is widely used in mining industries, machine parts, gears due to its high strength and toughness properties. In this study, microstructure, wear and mechanical properties of G38NiCrMo8-4-4 steel modified with boron used in the mining industry were investigated. For this purpose, cast materials were alloyed by melting in an induction furnace to include boron with the rates of 0 ppm, 15 ppm, and 50 ppm (wt.) and were formed in the dimensions of 150x200x150 mm by casting into the sand mould. Homogenization heat treatment was applied to the specimens at 1150˚C for 7 hours. Then all specimens were austenitized at 930˚C for 1 hour, quenched in the polymer solution and tempered at 650˚C for 1 hour. Microstructures of the specimens were investigated by using light microscope and SEM to determine the effect of boron and heat treatment conditions. Changes in microstructure properties and material hardness were obtained due to increasing boron content and heat treatment conditions after microstructure investigations and hardness tests. Wear tests were carried out using a pin-on-disc tribometer under dry sliding conditions. Charpy V notch impact test was performed to determine the toughness properties of the specimens. Fracture and worn surfaces were investigated with scanning electron microscope (SEM). The results show that boron element has a positive effect on the hardness and wear properties of G38NiCrMo8-4-4 steel.

Keywords: G38NiCrMo8-4-4 steel, boron, heat treatment, microstructure, wear, mechanical properties

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Authors: A. Ali, V. Strezov, P. Davies, I. Wright, T. Kan

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The environmental impacts arising from mining activities affect the air, water, and soil quality. Impacts may result in unexpected and adverse environmental outcomes. This study reports on the impact of coal production on sediment in Sydney region of Australia. The sediment samples upstream and downstream from the discharge points from three mines were taken, and 80 parameters were tested. The results were assessed against sediment quality based on presence of metals. The study revealed the increment of metal content in the sediment downstream of the reference locations. In many cases, the sediment was above the Australia and New Zealand Environment Conservation Council and international sediment quality guidelines value (SQGV). The major outliers to the guidelines were nickel (Ni) and zinc (Zn).

Keywords: coal mine, environmental impact, produced water, sediment quality guidelines value (SQGV)

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian

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Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS.

Keywords: data mining, k-means, road traffic accidents, Waze, Weka

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Authors: Maryam Ghavam Sadri, Kimia Moiniafshari

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Objectives: The prevalence of Autism in the world has taken on a growing trend. Autism is a neuro-developmental disorder that is identified at the age of three. Studies have been shown that nutritional management can control nutritional deficiencies in Autism. This review study aimed to assess the role of nutritional management strategies for Autism in children has been made. Methods: This review study was accomplished by using the keywords related to the topic, 68 articles were found (2000-2015) and finally 15 articles with criteria such as including dietary pattern, nutritional deficiencies and Autism controlling were selected. Results: The studies showed that intake of vitamins D, E, and calcium because of restricted diet (casein and gluten free) in autistic children is less than typically developing children (TYP) (p value ≤ 0.001) and as a result of restrictions on the consumption of fresh fruits and vegetables, vitamin C and magnesium intake is less than TYP children (p value ≤ 0.001). Autistic children also get omega-3 less than TYP children. Studies have shown that food sources rich in omega-3 can improve behavioral indicators, especially in reducing hyperactivity (95% CI = -2.2 - 5.2). Zinc deficiency in these children leads to a high serum level of mercury, lead and cadmium. As a result of the repetitive dietary pattern, Sodium intake in autistic children is more than TYP children (p value < 0.001).Because of low food variety in autistic children, healthy eating index (HEI) is less than TYP children (p value = 0.008).Food selectivity in Autism due to repetitive and restricted dietary pattern and nutritional deficiencies. Conclusion: Because of restricted (casein and gluten free) and repetitive dietary pattern, the intake of some micronutrients are denied in autistic children. The nutritional strategy programs appear to help controlling of Autism.

Keywords: autism, food selectivity, nutrient intake, nutritional strategies

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Authors: M. F. Mamabolo

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Mining activities are identified as the most significant source of heavy metal contamination in river basins, due to inadequate disposal of mining waste thus resulting in acid mine drainage. Waste materials generated from gold mining and processing have severe and widespread impacts on water resources. Therefore, a total of 30 water samples were collected from Fig Tree Creek, Kaapriver, Sheba mine stream & Sauid kaap river to investigate the impact of gold mines on the Kaap River system. Physicochemical parameters (pH, EC and TDS) were taken using a BANTE 900P portable water quality meter. The concentration of Fe, Cu, Co, and SO₄²⁻ in water samples were analysed using Inductively Coupled Plasma-Mass spectrophotometry (ICP-MS) at 0.01 mg/L. The results were compared to the regulatory guideline of the World Health Organization (WHO) and the South Africa National Standards (SANS). It was found that Fe, Cu and Co were below the guideline values while SO₄²⁻ detected in Sheba mine stream exceeded the 250 mg/L limit for both seasons, attributed by mine wastewater. SO₄²⁻ was higher in wet season due to high evaporation rates and greater interaction between rocks and water. The pH of all the streams was within the limit (≥5 to ≤9.7), however EC of the Sheba mine stream, Suid Kaap River & where the tributary connects with the Fig Tree Creek exceeded 1700 uS/m, due to dissolved material. The TDS of Sheba mine stream exceeded 1000 mg/L, attributed by high SO₄²⁻ concentration. While the tributary connecting to the Fig Tree Creek exceed the value due to pollution from household waste, runoff from agriculture etc. In conclusion, the water from all sampled streams were safe for consumption due to low concentrations of physicochemical parameters. However, elevated concentration of SO₄²⁻ should be monitored and managed to avoid water quality deterioration in the Kaap River system.

Keywords: Kaap river system, mines, heavy metals, sulphate

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Authors: Sidra Naeem, Ayesha Naeem, Sahar Rahim, Nadia Nawaz Qadri

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Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease.

Keywords: citrus greening, pattern recognition, feature extraction, classification

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Authors: Suelane Garcia Fontes, Silvio Luiz Stanzani, Pedro L. Pizzigatti Corrła Ronaldo G. Morato

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Environmental changes and major natural disasters are most prevalent in the world due to the damage that humanity has caused to nature and these damages directly affect the lives of animals. Thus, the study of animal behavior and their interactions with the environment can provide knowledge that guides researchers and public agencies in preservation and conservation actions. Exploratory analysis of animal movement can determine the patterns of animal behavior and with technological advances the ability of animals to be tracked and, consequently, behavioral studies have been expanded. There is a lot of research on animal movement and behavior, but we note that a proposal that combines resources and allows for exploratory analysis of animal movement and provide statistical measures on individual animal behavior and its interaction with the environment is missing. The contribution of this paper is to present the framework AniMoveMineR, a unified solution that aggregates trajectory analysis and data mining techniques to explore animal movement data and provide a first step in responding questions about the animal individual behavior and their interactions with other animals over time and space. We evaluated the framework through the use of monitored jaguar data in the city of Miranda Pantanal, Brazil, in order to verify if the use of AniMoveMineR allows to identify the interaction level between these jaguars. The results were positive and provided indications about the individual behavior of jaguars and about which jaguars have the highest or lowest correlation.

Keywords: data mining, data science, trajectory, animal behavior

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Authors: Shilpa Balan

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Radio Frequency Identification, also popularly known as RFID is used to automatically identify and track tags attached to items. This study focuses on the application of RFID in healthcare. The adoption of RFID in healthcare is a crucial technology to patient safety and inventory management. Data from RFID tags are used to identify the locations of patients and inventory in real time. Medical errors are thought to be a prominent cause of loss of life and injury. The major advantage of RFID application in healthcare industry is the reduction of medical errors. The healthcare industry has generated huge amounts of data. By discovering patterns and trends within the data, big data analytics can help improve patient care and lower healthcare costs. The number of increasing research publications leading to innovations in RFID applications shows the importance of this technology. This study explores the current state of research of RFID in healthcare using a text mining approach. No study has been performed yet on examining the current state of RFID research in healthcare using a data mining approach. In this study, related articles were collected on RFID from healthcare journal and news articles. Articles collected were from the year 2000 to 2015. Significant keywords on the topic of focus are identified and analyzed using open source data analytics software such as Rapid Miner. These analytical tools help extract pertinent information from massive volumes of data. It is seen that the main benefits of adopting RFID technology in healthcare include tracking medicines and equipment, upholding patient safety, and security improvement. The real-time tracking features of RFID allows for enhanced supply chain management. By productively using big data, healthcare organizations can gain significant benefits. Big data analytics in healthcare enables improved decisions by extracting insights from large volumes of data.

Keywords: RFID, data mining, data analysis, healthcare

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Authors: Ali Rabiee, Hessam Ghasemnejad

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Extensive experimental investigation on the effect of stitching pattern on tubular composite structures was conducted. The effect of stitching reinforcement through thickness on using glass flux yarn on energy absorption of fiber-reinforced polymer (FRP) was investigated under high speed loading conditions at axial loading. Keeping the mass of the structure at 125 grams and applying different pattern of stitching at various locations in theory enables better energy absorption, and also enables the control over the behaviour of force-crush distance curve. The study consists of simple non-stitch absorber comparison with single and multi-location stitching behaviour and its effect on energy absorption capabilities. The locations of reinforcements are 10 mm, 20 mm, 30 mm, 10-20 mm, 10-30 mm, 20-30 mm, 10-20-30 mm and 10-15-20-25-30-35 mm from the top of the specimen. The effect of through the thickness reinforcements has shown increase in energy absorption capabilities and crushing load. The significance of this is that as the stitching locations are closer, the crushing load increases and consequently energy absorption capabilities are also increased. The implementation of this idea would improve the mean force by applying stitching and controlling the behaviour of force-crush distance curve.

Keywords: through-thickness stitching, 3D enforcement​, energy absorption, tubular composite structures

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Authors: L. Chevez, A. Apaza, J. Rodriguez, R. Puga, H. Loro, Juan Z. Davalos

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Virtual Dimension (VD) procedure is used to analyze Hyperspectral Image (HIS) treatment-data in order to estimate the abundance of mineral components of a mining sample. Hyperspectral images coming from reflectance spectra (NIR region) are pre-treated using Standard Normal Variance (SNV) and Minimum Noise Fraction (MNF) methodologies. The endmember components are identified by the Simplex Growing Algorithm (SVG) and after adjusted to the reflectance spectra of reference-databases using Simulated Annealing (SA) methodology. The obtained abundance of minerals of the sample studied is very near to the ones obtained using XRD with a total relative error of 2%.

Keywords: hyperspectral imaging, minimum noise fraction, MNF, simplex growing algorithm, SGA, standard normal variance, SNV, virtual dimension, XRD

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Authors: Joshua N. Edokpayi, John O. Odiyo, Patience P. Shikwambana

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Water is a very rare natural resource in South Africa. Ga-Selati River is used for both domestic and industrial purposes. This study was carried out in order to assess the quality of Ga-Selati River in a mining area of Limpopo Province-Phalaborwa. The pH, Electrical Conductivity (EC) and Total Dissolved Solids (TDS) were determined using a Crinson multimeter while turbidity was measured using a Labcon Turbidimeter. The concentrations of Al, Ca, Cd, Cr, Fe, K, Mg, Mn, Na and Pb were analysed in triplicate using a Varian 520 flame atomic absorption spectrometer (AAS) supplied by PerkinElmer, after acid digestion with nitric acid in a fume cupboard. The average pH of the river from eight different sampling sites was 8.00 and 9.38 in wet and dry season respectively. Higher EC values were determined in the dry season (138.7 mS/m) than in the wet season (96.93 mS/m). Similarly, TDS values were higher in dry (929.29 mg/L) than in the wet season (640.72 mg/L) season. These values exceeded the recommended guideline of South Africa Department of Water Affairs and Forestry (DWAF) for domestic water use (70 mS/m) and that of the World Health Organization (WHO) (600 mS/m), respectively. Turbidity varied between 1.78-5.20 and 0.95-2.37 NTU in both wet and dry seasons. Total hardness of 312.50 mg/L and 297.75 mg/L as the concentration of CaCO3 was computed for the river in both the wet and the dry seasons and the river water was categorised as very hard. Mean concentration of the metals studied in both the wet and the dry seasons are: Na (94.06 mg/L and 196.3 mg/L), K (11.79 mg/L and 13.62 mg/L), Ca (45.60 mg/L and 41.30 mg/L), Mg (48.41 mg/L and 44.71 mg/L), Al (0.31 mg/L and 0.38 mg/L), Cd (0.01 mg/L and 0.01 mg/L), Cr (0.02 mg/L and 0.09 mg/L), Pb (0.05 mg/L and 0.06 mg/L), Mn (0.31 mg/L and 0.11 mg/L) and Fe (0.76 mg/L and 0.69 mg/L). Results from this study reveal that most of the metals were present in concentrations higher than the recommended guidelines of DWAF and WHO for domestic use and the protection of aquatic life.

Keywords: contamination, mining activities, surface water, trace metals

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Authors: Seyed Mohammad Ali Zahedi

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This field experiment was conducted in Isfahan in 2011 in order to study the effect of plant density and planting pattern on growth, yield and quality of silage corn (SC 704) using a randomized complete block design with split plot layout and four replications. The main plot consisted of three planting patterns (60 and 75 cm single planting row and 75 cm double planting row referred to as 60S, 75S and 75T, respectively). The subplots consisted of four levels of plant densities (65000, 80000, 95000 and 110000 plants per hectare). Each subplot consisted of 7 rows, each with 10m length. Vegetative and reproductive characteristics of plants at silking and hard dough stages (when the plants were harvested for silage) were evaluated. Results of variance analysis showed that the effects of planting pattern and plant density were significant on leaf area per plant, leaf area index (at silking), plant height, stem diameter, dry weights of leaf, stem and ear in silking and harvest stages and on fresh and dry yield, dry matter percentage and crude protein percentage at harvest. There was no planting pattern × plant density interaction for these parameters. As row space increased from 60 cm with single planting to 75 cm with single planting, leaf area index and plant height increased, but leaf area per plant, stem diameter, dry weight of leaf, stem and ear, dry matter percentage, dry matter yield and crude protein percentage decreased. Dry matter yield reduced from 24.9 to 18.5 t/ha and crude protein percentage decreased from 6.11 to 5.60 percent. When the plant density increased from 65000 to 110000 plant per hectare, leaf area index, plant height, dry weight of leaf, stem and ear and dry matter yield increased from 19.2 to 23.3 t/ha, whereas leaf area per plant, stem diameter, dry matter percentage and crude protein percentage decreased from 6.30 to 5.25. The best results were obtained with 60 cm row distance with single planting and 110000 plants per hectare.

Keywords: silage corn, plant density, planting pattern, yield

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Authors: Sangeeta Dhamdhere, G. V. P. Rao

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Adolescent students need more energy, proteins, vitamins, and minerals because they grow to maturity in this age. Balanced diet plays important role in their wellbeing and health. The study conducted showed 48% students are not normal in their height and weight. 26% students found underweight, 18% overweight and 4% students found obese. The annual income group of underweight students was below 7 Lac and more than 90% students were staying at their home. The researcher has analysed the eating pattern of these students and concluded that there is need of awareness among the parents and students about balance diet and nutrition. The present research will help students improve their dietary habits and health, increase the number of attendees, and achieve academic excellence.

Keywords: balanced diet, nutrition, malnutrition, obesity, health education

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Authors: Bo Liu, Chi-Man Pun

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A novel technique for digital forgery detection by signal noise inconsistency is proposed in this paper. The forged area spliced from the other picture contains some features which may be inconsistent with the rest part of the image. Noise pattern and the level is a possible factor to reveal such inconsistency. To detect such noise discrepancies, the test picture is initially segmented into small pieces. The noise pattern and level of each segment are then estimated by using various filters. The noise features constructed in this step are utilized in energy-based graph cut to expose forged area in the final step. Experimental results show that our method provides a good illustration of regions with noise inconsistency in various scenarios.

Keywords: forgery detection, splicing forgery, noise estimation, noise

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Authors: Chayaporn Kaensar

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Recently, Job Recommender Systems have gained much attention in industries since they solve the problem of information overload on the recruiting website. Therefore, we proposed Extended Personalized Job System that has the capability of providing the appropriate jobs for job seeker and recommending some suitable information for them using Data Mining Techniques and Dynamic User Profile. On the other hands, company can also interact to the system for publishing and updating job information. This system have emerged and supported various platforms such as web application and android mobile application. In this paper, User profiles, Implicit User Action, User Feedback, and Clustering Techniques in WEKA libraries have gained attention and implemented for this application. In additions, open source tools like Yii Web Application Framework, Bootstrap Front End Framework and Android Mobile Technology were also applied.

Keywords: recommendation, user profile, data mining, web and mobile technology

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Authors: Janneth González, George Barreto, Ludis Morales, Angélica Sabogal

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Sea anemone neurotoxins are peptides that interact with Na+ and K+ channels, resulting in specific alterations on their functions. Some of these neurotoxins (1ROO, 1BGK, 2K9E, 1BEI) are important for the treatment of nearly eighty autoimmune disorders due to their specificity for Kv1.3 channel. The aim of this study was to identify the common residues among these neurotoxins by computational methods, and establish whether there is a pattern useful for the future generation of a treatment for autoimmune diseases. Our results showed eight new key common residues between the studied neurotoxins interacting with a histidine ring and the selectivity filter of the receptor, thus showing a possible pattern of interaction. This knowledge may serve as an input for the design of more promising drugs for autoimmune treatments.

Keywords: neurotoxins, potassium channel, Kv1.3, computational methods, autoimmune diseases

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Authors: Kyung Bae Park, Sung Ho Ha

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Online user-generated contents (UGC) significantly change the way customers behave (e.g., shop, travel), and a pressing need to handle the overwhelmingly plethora amount of various UGC is one of the paramount issues for management. However, a current approach (e.g., sentiment analysis) is often ineffective for leveraging textual information to detect the problems or issues that a certain management suffers from. In this paper, we employ text mining of Latent Dirichlet Allocation (LDA) on a popular online review site dedicated to complaint from users. We find that the employed LDA efficiently detects customer complaints, and a further inspection with the visualization technique is effective to categorize the problems or issues. As such, management can identify the issues at stake and prioritize them accordingly in a timely manner given the limited amount of resources. The findings provide managerial insights into how analytics on social media can help maintain and improve their reputation management. Our interdisciplinary approach also highlights several insights by applying machine learning techniques in marketing research domain. On a broader technical note, this paper illustrates the details of how to implement LDA in R program from a beginning (data collection in R) to an end (LDA analysis in R) since the instruction is still largely undocumented. In this regard, it will help lower the boundary for interdisciplinary researcher to conduct related research.

Keywords: latent dirichlet allocation, R program, text mining, topic model, user generated contents, visualization

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Authors: Sokkhey Phauk, Takeo Okazaki

Abstract:

The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

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Authors: Hoda Rahmani, Gary Weckman

Abstract:

The body of research examining the integration of drones into various industries is expanding rapidly. Despite progress made in addressing the cybersecurity concerns for commercial drones, knowledge deficits remain in determining potential occupational hazards and risks of drone use to employees’ well-being and health in the workplace. This creates difficulty in identifying key approaches to risk mitigation strategies and thus reflects the need for raising awareness among employers, safety professionals, and policymakers about workplace drone-related accidents. The purpose of this study is to investigate the prevalence of and possible risk factors for drone-related mishaps by comparing the application of drones in construction with manufacturing industries. The chief reason for considering these specific sectors is to ascertain whether there exists any significant difference between indoor and outdoor flights since most construction sites use drones outside and vice versa. Therefore, the current research seeks to examine the causes and patterns of workplace drone-related mishaps and suggest possible ergonomic interventions through data collection. Potential ergonomic practices to mitigate hazards associated with flying drones could include providing operators with professional pieces of training, conducting a risk analysis, and promoting the use of personal protective equipment. For the purpose of data analysis, two data mining techniques, the random forest and association rule mining algorithms, will be performed to find meaningful associations and trends in data as well as influential features that have an impact on the occurrence of drone-related accidents in construction and manufacturing sectors. In addition, Spearman’s correlation and chi-square tests will be used to measure the possible correlation between different variables. Indeed, by recognizing risks and hazards, occupational safety stakeholders will be able to pursue data-driven and evidence-based policy change with the aim of reducing drone mishaps, increasing productivity, creating a safer work environment, and extending human performance in safe and fulfilling ways. This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432.

Keywords: commercial drones, ergonomic interventions, occupational safety, pattern recognition

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Authors: H. Shafaattalab Dehghani, H. R. Zarei

Abstract:

The large karstic Bell spring with a discharge ranging between 250 and 5300 lit/ sec is one of the most important springs of Kermanshah Province. This spring supplies drinking water of Nodsheh City and its surrounding villages. The spring is located in the reservoir of Daryan Dam and its mouth would be submerged after impounding under a water column of about 110 m height. This paper has aimed to render an account of the karstification pattern around the spring under consideration with the intention of preventing Bell Spring from being submerged in Daryan Dam Reservoir. The studies comprise engineering geology and hydrogeology investigations. Some geotechnical activities included in these studies include geophysical studies, drilling, excavation of exploratory gallery and shaft and diving. The results depict that Bell is a single-conduit siphon spring with 4 m diameter and 85 m height that 32 m of the conduit is located below the spring outlet. To survive the spring, it was decided to plug the outlet and convey the water to upper elevations under the natural pressure of the aquifer. After plugging, water was successfully conveyed to elevation 837 meter above sea level (about 120 m from the outlet) under the natural pressure of the aquifer. This signifies the accuracy of the studies done and proper recognition of the karstification pattern of Bell Spring. This is a unique experience in karst problems in Iran.

Keywords: bell spring, Karst, Daryan Dam, submerged

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Authors: Kisik Song, Kyuwoong Kim, Sungjoo Lee

Abstract:

Many global firms and corporations derive new technology and opportunity by identifying vacant technology from patent analysis. However, previous studies failed to focus on technologies that promised continuous growth in industrial fields. Most studies that derive new technology opportunities do not test practical effectiveness. Since previous studies depended on expert judgment, it became costly and time-consuming to evaluate new technologies based on patent analysis. Therefore, research suggests a quantitative and systematic approach to technology evaluation indicators by using patent data to and from customer communities. The first step involves collecting two types of data. The data is used to construct evaluation indicators and apply these indicators to the evaluation of new technologies. This type of data mining allows a new method of technology evaluation and better predictor of how new technologies are adopted.

Keywords: data mining, evaluating new technology, technology opportunity, patent analysis

Procedia PDF Downloads 348