Search results for: feature extraction method for tremor classification

Authors: Amirali Mahouti, Hooshang Katebi

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Enlargement of Tabriz metropolis to the east and north-east caused urban construction to be built on Marl layers and because of increase in excavations depth, further information of this layer is inescapable. Looking at geotechnical investigation shows there is not enough information about Tabriz Marl and this soil has been classified only by color. Tabriz Marl is lacustrine carbonate sediment outcrops, surrounds eastern, northern and southern region of city in the East Azerbaijan Province of Iran and is known as bed rock of city under alluvium sediments. This investigation aims to characterize geotechnical parameters of this soil to identify and set it in classification system of carbonated soils. For this purpose, specimens obtained from 80 locations over the city and subjected to physical and mechanical tests, such as Atterberg limits, density, moisture content, unconfined compression, direct shear and consolidation. CaCO3 content, organic content, PH, XRD, XRF, TGA and geophysical downhole tests also have been done on some of them.

Keywords: carbonated soils, classification of soils, mineralogy, physical and mechanical tests for Marls, Tabriz Marl

Procedia PDF Downloads 317

Authors: Vaibhav V. Dixit, Shubham Ajay Karwa

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Diabetic foot ulcers speak to a critical medical problem. Right now, clinicians and medical caretakers primarily construct their injury evaluation in light of visual examination of wound size and mending status, while the patients themselves rarely have a chance to play a dynamic part. Henceforth, love quantitative and practical examination technique that empowers the patients and their parental figures to take a more dynamic part in every day wound care possibly can quicken wound recuperating, spare travel cost and diminish human services costs. Considering the commonness of cell phones with a high-determination computerized camera, evaluating wounds by breaking down pictures of ceaseless foot ulcers is an alluring choice. In this paper, we propose a novel injury picture examination framework actualized using feature extraction and color segmentation. Here we are using the Normalized minimum distance classifier for classifying the output.

Keywords: diabetic, Gabor wavelet, normalized minimum distance classifier, quantiable parameters

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Authors: Mukesh Singh Boori, Vít Voženílek

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Olomouc is a unique and complex landmark with widespread forestation and land use. This research work was conducted to assess important and complex land use change trajectories in Olomouc region. Multi-temporal satellite data from 1991, 2001 and 2013 were used to extract land use/cover types by object oriented classification method. To achieve the objectives, three different aspects were used: (1) Calculate the quantity of each transition; (2) Allocate location based landscape pattern (3) Compare land use/cover evaluation procedure. Land cover change trajectories shows that 16.69% agriculture, 54.33% forest and 21.98% other areas (settlement, pasture and water-body) were stable in all three decade. Approximately 30% of the study area maintained as a same land cove type from 1991 to 2013. Here broad scale of political and socio-economic factors was also affect the rate and direction of landscape changes. Distance from the settlements was the most important predictor of land cover change trajectories. This showed that most of landscape trajectories were caused by socio-economic activities and mainly led to virtuous change on the ecological environment.

Keywords: remote sensing, land use/cover, change trajectories, image classification

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

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Authors: Mohammed Mustapha Alhaji, Sadiku Salawu

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A clay soil which classified under A-7-6 soil according to AASHTO soil classification system and CH according to the unified soil classification system was stabilized using A-3 soil (AASHTO soil classification system). The clay soil was replaced with 0%, 10%, 20% to 100% A-3 soil, compacted at both the BSL and BSH compaction energy level and using unconfined compressive strength as evaluation criteria. The MDD of the compactions at both the BSL and BSH compaction energy levels showed increase in MDD from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values reduced to 100% A-3 soil replacement. The trend of the OMC with varied A-3 soil replacement is similar to that of MDD but in a reversed order. The OMC reduced from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values increased to 100% A-3 soil replacement. This trend was attributed to the observed reduction in the void ratio from 0% A-3 soil replacement to 40% A-3 soil replacement after which the void ratio increased to 100% A-3 soil replacement. The maximum UCS for clay at varied A-3 soil replacement increased from 272 and 770kN/m2 for BSL and BSH compaction energy level at 0% A-3 soil replacement to 295 and 795kN/m2 for BSL and BSH compaction energy level respectively at 10% A-3 soil replacement after which the values reduced to 22 and 60kN/m2 for BSL and BSH compaction energy level respectively at 70% A-3 soil replacement. Beyond 70% A-3 soil replacement, the mixture cannot be moulded for UCS test.

Keywords: A-3 soil, clay minerals, pozzolanic action, stabilization

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Authors: Hanifa Taher, Sulaiman Al-Zuhair, Ali H. Al-Marzouqi, Yousef Haik, Mohammed Farid

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Biodiesel, as an alternative renewable fuel, has been receiving increasing attention due to the limited supply of fossil fuels and the increasing need for energy. Microalgae is a promising source for lipids, which can be converted to biodiesel. The biodiesel production from microalgae lipids using lipase catalyzed reaction in supercritical CO2 medium has several advantages over conventional production processes. However, identifying the optimum microalgae lipid extraction and transesterification conditions is still a challenge. In this study, the lipids extracted from Scenedesmus sp. and their enzymatic transesterification using supercritical carbon dioxide have been investigated. The effect of extraction variables (temperature, pressure and solvent flow rate) and reaction variables (enzyme loading, incubation time, methanol to lipids molar ratio and temperature) were considered. Process parameters and their effects were studied using a full factorial analysis of both. Response Surface Methodology (RSM) and was used to determine the optimum conditions for the extraction and reaction steps. For extraction, the optimum conditions were 53 °C and 500 bar, whereas for the reaction the optimum conditions were 35% enzyme loading, 4 h reaction, 9:1 molar ratio and 50 oC. At these optimum conditions, the highest biodiesel production yield was found to be 82 %. The fuel properties of the produced biodiesel, at optimum reaction condition, were determined and compared to ASTM standards. The properties were found to comply with the limits, and showed a low glycerol content, without any separation step.

Keywords: biodiesel, lipase, supercritical CO2, standards

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Authors: Fernanda A. P. Peres, Thiago N. Peres, Flavio S. Fogliatto, Michel J. Anzanello

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Batch processes are widely used in food industry and have an important role in the production of high added value products, such as chocolate. Process performance is usually described by variables that are monitored as the batch progresses. Data arising from these processes are likely to display a strong correlation-autocorrelation structure, and are usually monitored using control charts based on multiway principal components analysis (MPCA). Process control of a new batch is carried out comparing the trajectories of its relevant process variables with those in a reference set of batches that yielded products within specifications; it is clear that proper determination of the reference set is key for the success of a correct signalization of non-conforming batches in such quality control schemes. In chocolate manufacturing, misclassifications of non-conforming batches in the conching phase may lead to significant financial losses. In such context, the accuracy of process control grows in relevance. In addition to that, the main assumption in MPCA-based monitoring strategies is that all batches are synchronized in duration, both the new batch being monitored and those in the reference set. Such assumption is often not satisfied in chocolate manufacturing process. As a consequence, traditional techniques as MPCA-based charts are not suitable for process control and monitoring. To address that issue, the objective of this work is to compare the performance of three dynamic time warping (DTW) methods in the alignment and synchronization of chocolate conching process variables’ trajectories, aimed at properly determining the reference distribution for multivariate statistical process control. The power of classification of batches in two categories (conforming and non-conforming) was evaluated using the k-nearest neighbor (KNN) algorithm. Real data from a milk chocolate conching process was collected and the following variables were monitored over time: frequency of soybean lecithin dosage, rotation speed of the shovels, current of the main motor of the conche, and chocolate temperature. A set of 62 batches with durations between 495 and 1,170 minutes was considered; 53% of the batches were known to be conforming based on lab test results and experts’ evaluations. Results showed that all three DTW methods tested were able to align and synchronize the conching dataset. However, synchronized datasets obtained from these methods performed differently when inputted in the KNN classification algorithm. Kassidas, MacGregor and Taylor’s (named KMT) method was deemed the best DTW method for aligning and synchronizing a milk chocolate conching dataset, presenting 93.7% accuracy, 97.2% sensitivity and 90.3% specificity in batch classification, being considered the best option to determine the reference set for the milk chocolate dataset. Such method was recommended due to the lowest number of iterations required to achieve convergence and highest average accuracy in the testing portion using the KNN classification technique.

Keywords: batch process monitoring, chocolate conching, dynamic time warping, reference set distribution, variable duration

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Authors: Sivananth Murugesan, I. Regupathi, B. Vishwas Prabhu, Ankit Devatwal, Vishnu Sivan Pillai

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Pectinase is an important enzyme which has a wide range of applications including textile processing and bioscouring of cotton fibers, coffee and tea fermentation, purification of plant viruses, oil extraction etc. Selective separation and purification of pectinase from fermentation broth and recover the enzyme form process stream for reuse are cost consuming process in most of the enzyme based industries. It is difficult to identify a suitable medium to enhance enzyme activity and retain its enzyme characteristics during such processes. The cost effective, selective separation of enzymes through the modified Liquid-liquid extraction is of current research interest worldwide. Reverse micellar extraction, globally acclaimed Liquid-liquid extraction technique is well known for its separation and purification of solutes from the feed which offers higher solute specificity and partitioning, ease of operation and recycling of extractants used. Surfactant concentrations above critical micelle concentration to an apolar solvent form micelles and addition of micellar phase to water in turn forms reverse micelles or water-in-oil emulsions. Since, electrostatic interaction plays a major role in the separation/purification of solutes using reverse micelles. These interaction parameters can be altered with the change in pH, addition of cosolvent, surfactant and electrolyte and non-electrolyte. Even though many chemical based commercial surfactant had been utilized for this purpose, the biosurfactants are more suitable for the purification of enzymes which are used in food application. The present work focused on the partitioning of pectinase from the synthetic aqueous solution within the reverse micelle phase formed by a biosurfactant, Soybean Lecithin dissolved in chloroform. The critical micelle concentration of soybean lecithin/chloroform solution was identified through refractive index and density measurements. Effect of surfactant concentrations above and below the critical micelle concentration was considered to study its effect on enzyme activity, enzyme partitioning within the reverse micelle phase. The effect of pH and electrolyte salts on the partitioning behavior was studied by varying the system pH and concentration of different salts during forward and back extraction steps. It was observed that lower concentrations of soybean lecithin enhanced the enzyme activity within the water core of the reverse micelle with maximizing extraction efficiency. The maximum yield of pectinase of 85% with a partitioning coefficient of 5.7 was achieved at 4.8 pH during forward extraction and 88% yield with a partitioning coefficient of 7.1 was observed during backward extraction at a pH value of 5.0. However, addition of salt decreased the enzyme activity and especially at higher salt concentrations enzyme activity declined drastically during both forward and back extraction steps. The results proved that reverse micelles formed by Soybean Lecithin and chloroform may be used for the extraction of pectinase from aqueous solution. Further, the reverse micelles can be considered as nanoreactors to enhance enzyme activity and maximum utilization of substrate at optimized conditions, which are paving a way to process intensification and scale-down.

Keywords: pectinase, reverse micelles, soybean lecithin, selective partitioning

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Authors: Khadri Sihem, Abbaci Nafissa, Zerari Labiba

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In the context of the search for new bioactive natural products, we were interested in evaluating some antibacterial properties of two plant extracts: total phenols and flavonoids of Algerian medicinal plant. Our study occurs in two axes: The first concerns the extraction of phenolic compounds and flavonoids with methanol by liquid-liquid extraction, followed by quantification of the levels of these compounds in the end the analysis of the chemical composition of extracts. In the second axis, we studied the antibacterial power of the studied plant extracts.

Keywords: antibacterial activity, flavonoids, medicinal plants, polyphenols

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Authors: Pajaree Donkhampa, Fuangfa Unob

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Ceftazidime is an antibiotic drug commonly used to treat several human and veterinary infections. However, the presence of ceftazidime residues in the environment may induce microbial resistance and cause side effects to humans. Therefore, monitoring the level of ceftazidime in environmental resources is important. In this work, a melamine foam platform was proposed for simultaneous extraction and colorimetric detection of ceftazidime based on the azo dye formation on the surface. The melamine foam was chemically modified with polyethyleneimine (PEI) and characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Ceftazidime is a sample that was extracted on the PEI-modified melamine foam and further reacted with nitrite in an acidic medium to form an intermediate diazonium ion. The diazotized molecule underwent an azo coupling reaction with chromotropic acid to generate a red-colored compound. The material color changed from pale yellow to pink depending on the ceftazidime concentration. The photo of the obtained material was taken by a smartphone camera and the color intensity was determined by Image J software. The material fabrication and ceftazidime extraction and detection procedures were optimized. The detection of a sub-ppm level of ceftazidime was achieved without using a complex analytical instrument.

Keywords: colorimetric detection, ceftazidime, melamine foam, extraction, azo dye

Procedia PDF Downloads 169

Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

Procedia PDF Downloads 292

Authors: Yesim Özogul, Fethiye Takadaş, Mustafa Durmus, Yılmaz Ucar, Ali Rıza Köşker, Gulsun Özyurt, Fatih Özogul

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It has been well documented that polyunsaturated fatty acids (PUFAs), especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have beneficial effects on health, regarding prevention of cardiovascular diseases, cancer and autoimmune disorders, development the brain and retina and treatment of major depressive disorder etc. Thus, an adequate intake of omega PUFA is essential and generally marine fish are the richest sources of PUFA in human diet. Thus, this study was conducted to evaluate the efficiency of different extraction methods (Bligh and Dyer, soxhlet, microwave and ultrasonics) on the fat content and fatty acid profiles of marine fish species (Mullus babatus, Upeneus moluccensis, Mullus surmuletus, Anguilla anguilla, Pagellus erythrinus and Saurida undosquamis). Fish species were caught by trawl in Mediterranean Sea and immediately iced. After that, fish were transported to laboratory in ice and stored at -18oC in a freezer until the day of analyses. After extracting lipid from fish by different methods, lipid samples were converted to their constituent fatty acid methyl esters. The fatty acid composition was analysed by a GC Clarus 500 with an autosampler (Perkin Elmer, Shelton, CT, USA) equipped with a flame ionization detector and a fused silica capillary SGE column (30 m x 0.32 mm ID x 0.25 mm BP20 0.25 UM, USA). The results showed that there were significant differences (P < 0.05) in fatty acids of all species and also extraction methods affected fat contents and fatty acid profiles of fish species.

Keywords: extraction methods, fatty acids, marine fish, PUFA

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Authors: Chimey Lhamo, Ngawang Tsering

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Traditional Tibetan medicine, known as Sowa Rigpa (Science of healing), originated more than 2500 years ago with an insightful background, and it has been growing significant attention in many Asian countries like China, India, Bhutan, and Nepal. Particularly, the Indian government has targeted Traditional Tibetan medicine as its major Indian medical system, including Ayurveda. Although Traditional Tibetan medicine has been growing interest and has a long history, it is not easily recognized worldwide because it exists only in the Tibetan language and it is neither accessible nor understood by patent examiners at the international patent office, data about Traditional Tibetan medicine is not yet broadly exist in the Internet. There has also been the exploitation of traditional Tibetan medicine increasing. The Traditional Knowledge Digital Library is a database aiming to prevent the patenting and misappropriation of India’s traditional medicine knowledge by using India’s Traditional knowledge Digital Library on Sowa Rigpa in order to prevent its exploitation at international patent with the help of information technology tools and an innovative classification systems-traditional knowledge resource classification (TKRC). As of date, more than 3000 Sowa Rigpa formulations have been transcribed into a Traditional Knowledge Digital Library database. In this paper, we are presenting India's Traditional Knowledge Digital Library for Traditional Tibetan medicine, and this database system helps to preserve and prevent the exploitation of Sowa Rigpa. Gradually it will be approved and accepted globally.

Keywords: traditional Tibetan medicine, India's traditional knowledge digital library, traditional knowledge resources classification, international patent classification

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Authors: S. Nagheli, N. Samani, D. A. Barry

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In this paper, the velocity potential and stream function of capture zone for a well field in an aquifer bounded by two parallel streams with or without a uniform regional flow of any directions are presented. The well field includes any number of extraction or injection wells or a combination of both types with any pumping rates. To delineate the capture envelope, the potential and streamlines equations are derived by conformal mapping method. This method can help us to release constrains of other methods. The equations can be applied as useful tools to design in-situ groundwater remediation systems, to evaluate the surface–subsurface water interaction and to manage the water resources.

Keywords: complex potential, conformal mapping, image well theory, Laplace’s equation, superposition principle

Procedia PDF Downloads 432

Authors: A. Karunasena, P. Bandara, J. A. T. P. Jayasuriya, P. D. Gallage, J. M. S. D. Jayasundara, L. A. P. Y. P. Nuwanjaya

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Usage of smart learning concepts has increased rapidly all over the world recently as better teaching and learning methods. Most educational institutes such as universities are experimenting those concepts with their students. Smart learning concepts are especially useful for students to learn better in large classes. In large classes, the lecture method is the most popular method of teaching. In the lecture method, the lecturer presents the content mostly using lecture slides, and the students make their own notes based on the content presented. However, some students may find difficulties with the above method due to various issues such as speed in delivery. The purpose of this research is to assist students in large classes in the following content. The research proposes a solution with four components, namely note-taker, slide matcher, reference finder, and question presenter, which are helpful for the students to obtain a summarized version of the lecture note, easily navigate to the content and find resources, and revise content using questions.

Keywords: automatic summarization, extractive text summarization, speech recognition library, sentence extraction, automatic web search, automatic question generator, sentence scoring, the term weight

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Authors: Zelalem Fantahun

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Part-of-speech tagging is the process of assigning a part-of-speech or other lexical class marker to each word into naturally occurring text. Part-of-speech tagging is the most fundamental and basic task almost in all natural language processing. In natural language processing, the problem of providing large amount of manually annotated data is a knowledge acquisition bottleneck. Since, Amharic is one of under-resourced language, the availability of tagged corpus is the bottleneck problem for natural language processing especially for POS tagging. A promising direction to tackle this problem is to provide a system that does not require manually tagged data. In unsupervised learning, the learner is not provided with classifications. Unsupervised algorithms seek out similarity between pieces of data in order to determine whether they can be characterized as forming a group. This paper explicates the development of unsupervised part-of-speech tagger using K-Means clustering for Amharic language since large amount of data is produced in day-to-day activities. In the development of the tagger, the following procedures are followed. First, the unlabeled data (raw text) is divided into 10 folds and tokenization phase takes place; at this level, the raw text is chunked at sentence level and then into words. The second phase is feature extraction which includes word frequency, syntactic and morphological features of a word. The third phase is clustering. Among different clustering algorithms, K-means is selected and implemented in this study that brings group of similar words together. The fourth phase is mapping, which deals with looking at each cluster carefully and the most common tag is assigned to a group. This study finds out two features that are capable of distinguishing one part-of-speech from others these are morphological feature and positional information and show that it is possible to use unsupervised learning for Amharic POS tagging. In order to increase performance of the unsupervised part-of-speech tagger, there is a need to incorporate other features that are not included in this study, such as semantic related information. Finally, based on experimental result, the performance of the system achieves a maximum of 81% accuracy.

Keywords: POS tagging, Amharic, unsupervised learning, k-means

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Helen L. Hein, Joachim Schwarte

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As a pillar of sustainable development, ecology has become an important milestone in research community, especially due to global challenges like climate change. The ecological performance of products can be scientifically conducted with life cycle assessments. In the construction sector, significant amounts of CO₂ emissions are assigned to the energy used for building heating purposes. Therefore, sustainable construction materials for insulating purposes are substantial, whereby aerogels have been explored intensively in the last years due to their low thermal conductivity. Therefore, the WALL-ACE project aims to develop an aerogel-based thermal insulating plaster that would achieve minor thermal conductivities. But as in the early stage of development phases, a lot of information is still missing or not yet accessible, the ecological performance of innovative products bases increasingly on uncertain data that can lead to significant deviations in the results. To be able to predict realistically how meaningful the results are and how viable the developed products may be with regard to their corresponding respective market, these deviations however have to be considered. Therefore, a classification method is presented in this study, which may allow comparing the ecological performance of modern products with already established and competitive materials. In order to achieve this, an alternative calculation method was used that allows computing with lower and upper bounds to consider all possible values without precise data. The life cycle analysis of the considered products was conducted with an interval arithmetic based calculation method. The results lead to the conclusion that the interval solutions describing the possible environmental impacts are so wide that the result usability is limited. Nevertheless, a further optimization in reducing environmental impacts of aerogels seems to be needed to become more competitive in the future.

Keywords: aerogel-based, insulating material, early development phase, interval arithmetic

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Authors: J. K. Ebigwai, E. Asuquo

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Systematic affinities among Cucurbitaceae members are highly debatable as exemplified by diverging views on their phylogenies. Worst still is the overriding reliance on morphometric marker in the delimitation of cucurbitoideae members. Considerable symplesiomorphic and synapmorphic character states have been observed among some members of same genera than do with some members of other genera. The broad study aims at establishing phylogenies among species of Cucumis (Melothrieae), Momordica, Telfairia (Jolliffieae), Trichosanthes (Trichosantheae), Citrullus, Lagenaria, Luffa (Benincaseae) and Cucurbita (Cucurbita) using anatomical, cytological, Palynological, serological, and phytolith markers. However, this paper shall present preliminary findings on the phytolith character states for Cucumis melo, Momordica charantia, Telfairia occidentales, Trichosanthes dioica, Citrullus vulgaris, Lagenaria siceraria, Luffa cylindrical, Cucurbita pepo and Cucurbita maxima. Heavy liquid floatation method was employed in the extraction of the phytolith matter from the leaf tissues of these species. The result revealed that a bilobate short cell and a trapeziform sinuate form were absent in all the species except in Cucumis melo, Citrullus vulgaris and Lagenaria siceraria. Also a globular granulate form was observed exclusively in Telfairia occidentales, Cucurbita maxima, Momordica charantia and Luffa cylindrical. Other forms of phytolith observed were not diagnostic as they were not species specific. The results tentatively suggests a closer examination of the existing classification system.

Keywords: bilobate short cell, cucums, phytolith, telfairia, trapeziform sinuate

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Authors: Rajpal Kaur, Pooja Choudhary

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Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods.

Keywords: offline signature verification, offline signature recognition, signatures, SURF features, HMM

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Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

Abstract:

Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Jinlu Ruan, Liang Chen, Bo Liu, Xiaoping Ouyang, Zhichao Zhu, Zhongbing Zhang, Shiyi He, Mengxuan Xu

Abstract:

Plastic scintillators play an important role in the measurement of a mixed neutron/gamma pulsed radiation, neutron radiography and pulse shape discrimination technology. In some research, these luminescent properties are necessary that photons produced by the interactions between a plastic scintillator and radiations can be detected as much as possible by the photoelectric detectors and more photons can be emitted from the scintillators along a specific direction where detectors are located. Unfortunately, a majority of these photons produced are trapped in the plastic scintillators due to the total internal reflection (TIR), because there is a significant light-trapping effect when the incident angle of internal scintillation light is larger than the critical angle. Some of these photons trapped in the scintillator may be absorbed by the scintillator itself and the others are emitted from the edges of the scintillator. This makes the light extraction of plastic scintillators very low. Moreover, only a small portion of the photons emitted from the scintillator easily can be detected by detectors effectively, because the distribution of the emission directions of this portion of photons exhibits approximate Lambertian angular profile following a cosine emission law. Therefore, enhancing the light extraction efficiency and adjusting the emission angular profile become the keys for improving the number of photons detected by the detectors. In recent years, photonic crystal structures have been covered on inorganic scintillators to enhance the light extraction efficiency and adjust the angular profile of scintillation light successfully. However, that, preparation methods of photonic crystals will deteriorate performance of plastic scintillators and even destroy the plastic scintillators, makes the investigation on preparation methods of photonic crystals for plastic scintillators and luminescent properties of plastic scintillators with photonic crystal structures inadequate. Although we have successfully made photonic crystal structures covered on the surface of plastic scintillators by a modified self-assembly technique and achieved a great enhance of light extraction efficiency without evident angular-dependence for the angular profile of scintillation light, the preparation of photonic crystal structures with large area (the diameter is larger than 6cm) and perfect periodic structure is still difficult. In this paper, large area photonic crystals on the surface of scintillators were prepared by nanoimprint lithography firstly, and then a conformal layer with material of high refractive index on the surface of photonic crystal by atomic layer deposition technique in order to enhance the stability of photonic crystal structures and increase the number of leaky modes for improving the light extraction efficiency. The luminescent properties of the plastic scintillator with photonic crystals prepared by the mentioned method are compared with those of the plastic scintillator without photonic crystal. The results indicate that the number of photons detected by detectors is increased by the enhanced light extraction efficiency and the angular profile of scintillation light exhibits evident angular-dependence for the scintillator with photonic crystals. The mentioned preparation of photonic crystals is beneficial to scintillation detection applications and lays an important technique foundation for the plastic scintillators to meet special requirements under different application backgrounds.

Keywords: angular profile, atomic layer deposition, light extraction efficiency, plastic scintillator, photonic crystal

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Authors: Ines Gasmi, Salima Smiti, Makram Soui, Khaled Ghedira

Abstract:

Credit risk is considered as one of the important issues for financial institutions. It provokes great losses for banks. To this objective, numerous methods for credit risk evaluation have been proposed. Many evaluation methods are black box models that cannot adequately reveal information hidden in the data. However, several works have focused on building transparent rules-based models. For credit risk assessment, generated rules must be not only highly accurate, but also highly interpretable. In this paper, we aim to build both, an accurate and transparent credit risk evaluation model which proposes a set of classification rules. In fact, we consider the credit risk evaluation as an optimization problem which uses a genetic programming (GP) algorithm, where the goal is to maximize the accuracy of generated rules. We evaluate our proposed approach on the base of German and Australian credit datasets. We compared our finding with some existing works; the result shows that the proposed GP outperforms the other models.

Keywords: credit risk assessment, rule generation, genetic programming, feature selection

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Authors: Ho-Hsin Lee, Yue-Min Jiang, Shu-Hui Tsai, Jian-Ren Chen, Mei-Yu XU, Wen-Tien Wu

Abstract:

This project presents an AI-based method and system for wound status analysis. The system uses a three-in-one sensor device to analyze wound status, including color, temperature, and a 3D sensor to provide wound information up to 2mm below the surface, such as redness, heat, and blood circulation information. The system has a 90% accuracy rate, requiring only one manual correction in 70% of cases, with a one-second delay. The system also provides an offline application that allows for manual correction of the wound bed range using color-based guidance to estimate wound bed size with 96% accuracy and a maximum of one manual correction in 96% of cases, with a one-second delay. Additionally, AI-assisted wound bed range selection achieves 100% of cases without manual intervention, with an accuracy rate of 76%, while AI-based wound tissue type classification achieves an 85.3% accuracy rate for five categories. The AI system also includes similar case search and expert recommendation capabilities. For AI-assisted wound range selection, the system uses WIFI6 technology, increasing data transmission speeds by 22 times. The project aims to save up to 64% of the time required for human wound record keeping and reduce the estimated time to assess wound status by 96%, with an 80% accuracy rate. Overall, the proposed AI method and system integrate multiple sensors to provide accurate wound information and offer offline and online AI-assisted wound bed size estimation and wound tissue type classification. The system decreases delay time to one second, reduces the number of manual corrections required, saves time on wound record keeping, and increases data transmission speed, all of which have the potential to significantly improve wound care and management efficiency and accuracy.

Keywords: wound status analysis, AI-based system, multi-sensor integration, color-based guidance

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Authors: Yuliya Leonidovna Kiva-Khamzina

Abstract:

The legal nature of the contract for educational services causes a lot of controversies. In particular, it raises the question about industry sector relationships, which require making a contract for educational services. The article describes the different types of contracts classifications for services provision from the perspective of civil law, deals with the specifics of the contract on rendering educational services; the author makes the conclusion that the contract for the provision of educational services is a complex institution that includes elements of the civil and administrative law. The following methods were used to conduct the study: dialectical method of cognition, the historical method, systemic analysis, classification.

Keywords: administrative aspect, civil aspect, educational service, industry, legal nature, services provision

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

Abstract:

This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Ihsan Burak Cam, Ferhan Balci-Torun, Ayhan Topuz, Esin Ari, Ismail Gokhan Deniz, Ilker Genc

Abstract:

The seeds of Vaccaria hispanica have been used in food and pharmaceutical industry. It is an important product due to its superior starch granules, triterpenic saponins, and cyclopeptides suitable for drug delivery. V. hispanica naturally grows in different climatic regions and has genotypes that differ in terms of seed content and composition. Sixty-six V. hispanica seed specimens were collected based on the representation of the distribution in all regions of Turkey and the determination of possible genotypic differences between regions. The seeds, collected from each of the 66 locations, were grown in greenhouse conditions in Akdeniz University, Antalya. Saponin and cyclopeptide contents of the V. hispanica seeds were determined after harvest. Accelerated solvent extraction (ASE) was applied for the extraction of saponins and cyclopeptides. Cyclopeptide (segetalin A) and saponin content of V. hispanica seeds were found in the range of 0.165-0.654 g/100 g and 0.15-1.14 g/100 g, respectively. The results were found to be promising for the seeds from Turkey in terms of saponin content and quality. Acknowledgment: This study was supported by the Scientific and Research Council of Turkey (TUBITAK) (project no 112 O 136).

Keywords: Vaccaria hispanica, saponin, cyclopeptid, cow cockle seeds

Procedia PDF Downloads 295