Search results for: mineral potential classification

Authors: Jaiden X. Schraut

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Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

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Authors: Kiraz Esmeli, Alper Ozkan

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In this study, firstly, the effect of ultrasonic treatment on the stability of celestite suspension was investigated. In this context, the variations of the suspension stability with ultrasonic power, treatment time, immersion depth of ultrasonic probe, and treatment regime (batch and continuous) were determined. The experimental results showed that the suspension stability and zeta potential of celestite decreased with ultrasonic treatment. Also, the treatment time, immersion depth of probe, and treatment regime affected the stability of celestite suspension. Secondly, the effect of pre-treatment of the suspension with the ultrasonic process on the shear flocculation of celestite using sodium dodecyl sulfate (SDS) was studied and the variations of the flocculation, zeta potential, and contact angle of the mineral with SDS concentration were presented. It was found that the ultrasonic pre-treatment slightly improved the shear flocculation of celestite particles in accordance with the increase in the contact angles. In addition, the ultrasonic process again relatively reduced the magnitude of the negative potential of celestite particles in the presence of SDS.

Keywords: celestite, contact angle, suspension stability, ultrasonic treatment, zeta potential

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: M. Karthigayan, M. Rizon, Sazali Yaacob, R. Nagarajan, M. Muthukumaran, Thinaharan Ramachandran, Sargunam Thirugnanam

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In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate.

Keywords: ellipse fitness function, genetic algorithm, emotion recognition, fuzzy clustering

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: Amdouni Hamida, Gammoudi Mohamed Mohsen

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In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.

Keywords: association rules, rule-based classification, classification quality, validation

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Authors: Jun-Yong Lee, Hyoung-Gook Kim

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In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: Nikhil John Kollannur, Dali Naidu Arnepalli

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Most of the surface related phenomena in the case of fine-grained soil are attributed to their unique surface charge properties and specific surface area. The temporal variations in soil behavior, to some extent, can be credited to the changes in these properties. Among the multitude of factors that affect the charge and surface area of clay minerals, the inherent system chemistry occupies the cardinal position. The impact is more profound when the chemistry change is manifested in terms of the system pH. pH plays a significant role by modifying the edge charges of clay minerals and facilitating mineral dissolution. Hence there is a need to address the variations in physical and charge properties of fine-grained soils treated over a range of acidic as well as alkaline conditions. In the present study, three soils (two soils commercially procured and one natural soil) exhibiting distinct mineralogical compositions are subjected to different pH environment over a range of 2 to 13. The soil-solutions prepared at a definite liquid to solid ratio are adjusted to the required pH value by adding measured quantities of 0.1M HCl/0.1M NaOH. The studies are conducted over a range of interaction time, varying from 1 to 96 hours. The treated soils are then analyzed for their physical properties in terms of specific surface area and particle size characteristics. Further, modifications in surface morphology are evaluated from scanning electron microscope (SEM) imaging. Changes in the surface charge properties are assessed in terms of zeta potential measurements. Studies show significant variations in total surface area, probably because of the dissolution of clay minerals. This observation is further substantiated by the morphological analysis with SEM imaging. The zeta potential measurements on soils indicate noticeable variation upon pH treatment, which is partially ascribed to the modifications in the pH-dependant edge charges and partially due to the clay mineral dissolution. The results provide valuable insight into the role of pH in a clay-electrolyte system upon surface related phenomena such as species adsorption, fabric modification etc.

Keywords: acid and alkali treatment, mineral dissolution , specific surface area, zeta potential

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: Renata Adriana Labanca, Gabriela Rezende Costa, Nilton de Oliveira Couto e Silva, José Marcos Gontijo Mandarino, Rodrigo Santos Leite, Nilson César Castanheira Guimarães, Roberto Gonçalves Junqueira

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The objective of this study was to evaluate the differences in composition between six brands of conventional soybean and six genetically modified cultivars (GM), all of them from Minas Gerais State, Brazil. We focused on the isoflavones profile and mineral content questioning the substantial equivalence between conventional and GM organisms. The statement of compliance label for conventional grains was verified for the presence of genetic modified genes by real time polymerase chain reaction (PCR). We did not detect the presence of the 35S promoter in commercial samples, indicating the absence of transgene insertion. For mineral analysis, we used the method of inductively coupled plasma-optical emission spectrometry (ICP-OES). Isoflavones quantification was performed by high performance liquid chromatography (HPLC). The results showed no statistical difference between the conventional and transgenic soybean groups concerning isoflavone content and mineral composition. The concentration of potassium, the main mineral component of soy, was the highest in conventional soybeans compared to that in GM soy, while GM samples presented the highest concentrations of iron.

Keywords: glycine max, genetically modified organism, bioactive compounds, ICP-OES, HPLC

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Authors: Shackira Am, Jos T. Puthur

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The phytostabilization potential of a halophyte Acanthus ilicifolius L. has been evaluated with special attention to the nutritional as well as anatomical adaptations developed by the plant. Distribution of essential elements influenced by the excess Zn²⁺ ions in the root tissue was studied by FEG-SEM EDX microanalysis. Significant variations were observed in the uptake and allocation of mineral elements like Mg, P, K, S, Na, Si and Al in the root of A. ilicifolius. The increase in S is in correlation with the increased synthesis of glutathione which might be involved in the biosynthesis of phytochelatins. This in turn might be aiding the plant to tolerate the adverse environmental conditions by stabilizing the excess Zn in the root tissue itself. Moreover it is revealed that most of the Zn were accumulated towards the central region near the vascular tissue. Treatment with ZnSO₄ in A. ilicifolius caused significant increase in the number of glandular trichomes on the adaxial leaf surface as compared to the leaves of control plants. In addition to this, A. ilicifolius when treated with ZnSO₄, exhibited a deeply stained layer of cells immediate to the endodermis, forming more or less a ring like structure around the xylem vessels. Phloem cells in these plants were crushed/reduced in numbers. There were no such deeply stained cells forming a ring around the xylem vessels in the control plants. These adaptive responses make the plant a suitable candidate for the phytostabilization of Zn. In addition the nutritional adjustment of the plant equips them for a better survival under increased concentration of Zn²⁺.

Keywords: Acanthus ilicifolius, mineral elements, phytostabilization, zinc

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Authors: M. Rizwan, S. Ahmad, U. Farooq, U. Mahmood, S. U. Rehman, P. Akhtar

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This experiment was conducted to investigate the effect of supplementation of herbal and mineral mixture on growth performance of boilers. One hundred and eighty birds were randomly distributed into 6 experimental units of 3 replicates (10 birds/replicate) as: negative control (basal diet), positive control (Lincomycin at the rate of 5g/bag), commercially available herbal-mineral product FitFat™ at the rate of 150g/bag and 200g/bag, and herbal-mineral mixture at the rate of 150g/bag and herbal-mineral mixture at the rate of 300g/bag. The data regarding weekly feed intake, body weight gain and feed conversion ratio were recorded, and fecal samples were collected at the end of starter and finisher phase for nutrient digestibility trial. The results of body weight gain showed significant (P < 0.05) differences in 3rd week of age (506.90g), also, feed intake showed significant (P < 0.05) results in 1st (297.22g), 3rd (936.7g) and 4th (967.8g) week and feed conversion ratio indicated significant (P < 0.05) variations in 1st (1.14) and 3rd (1.74) week of age. The starter phase indicated significant (P < 0.05) differences among all treatments groups in body weight gain (902.2g), feed intake (1843.9g) and feed conversion ratio (1.78). In case of nutrient digestibility trial, results showed significant (P < 0.05) values of dry matter, crude protein, and crude fat in starter phase as 77.74%, 69.37%, and 61.18% respectively and 77.65%, 68.79% and 61.03% respectively, in finisher phase. Based on overall results, it was concluded that the dietary inclusion of combination of herbs and mineral can increase the production performance of broilers.

Keywords: herbal blend, minerals, crop filling, nutrient digestibility, broiler

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Authors: Khadija Bouzaachane, El Mahdi El Guarmah

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The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.

Keywords: classification, deep learning, reinforcement learning, satellite imagery

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Sylwia Orzechowska, Andrzej Wróbel, Eugeniusz Rokita

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The mineralization is a curious problem of connective tissues. Factors which may play a decisive role in the regulation of the yellow ligaments (YL) mineralization are still open questions. The aim of the studies was a detailed description of the chemical composition and morphology of mineral deposits in the human yellow ligaments. Investigations of the structural features of deposits were used to explain the impact of various factors on mineralization process. The studies were carried out on 24 YL samples, surgically removed from patients suffer from spinal canal stenosis and the patients who sustained a trauma. The micro-computed tomography was used to describe the morphology of mineral deposits. The X-ray fluorescence method and Fourier transform infrared spectroscopy were applied to determine the chemical composition of the samples. In order to eliminate the effect of blur in microtomographic images, the correction method of partial volume effect was used. The mineral deposits appear in 60% of YL samples, both in patients with a stenosis and following injury. The mineral deposits have a heterogeneous structure and they are a mixture of the tissue and mineral grains. The volume of mineral grains amounts to (1.9 ± 3.4)*10-3 mm3 while the density distribution of grains occurs in two distinct ranges (1.75 - 2.15 and 2.15-2.5) g/cm3. Application of the partial volume effect correction allows accurate calculations by eliminating the averaging effect of gray levels in tomographic images. The B-type carbonate-containing hydroxyapatite constitutes the mineral phase of majority YLs. The main phase of two samples was calcium pyrophosphate dihydrate (CPPD). The elemental composition of minerals in all samples is almost identical. This pathology may be independent on the spine diseases and it does not evoke canal stenosis. The two ranges of grains density indicate two stages of grains growth and the degree of maturity. The presence of CPPD crystals may coexist with other pathologies.

Keywords: FTIR, micro-tomography, mineralization, spinal ligaments

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: M. R. Jokanović, V. M. Tomović, M. T. Jović, S. B. Škaljac, B. V. Šojić, P. M. Ikonić, T. A. Tasić

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Proximate (moisture, protein, total fat, total ash) and mineral (K, P, Na, Mg, Ca, Zn, Fe, Cu and Mn) composition of chicken giblets (heart, liver and gizzard) were investigated. Phosphorous content, as well as proximate composition, were determined according to recommended ISO methods. The content of all elements, except phosphorus, of the giblets tissues were determined using inductively coupled plasma-optical emission spectrometry (ICP-OES), after dry ashing mineralization. Regarding proximate composition heart was the highest in total fat content, and the lowest in protein content. Liver was the highest in protein and total ash content, while gizzard was the highest in moisture and the lowest in total fat content. Regarding mineral composition liver was the highest for K, P, Ca, Mg, Fe, Zn, Cu, and Mn, while heart was the highest for Na content. The contents of almost all investigated minerals in analysed giblets tissues of chickens from Vojvodina were similar to values reported in the literature, i.e. in national food composition databases of other countries.

Keywords: chicken giblets, proximate composition, mineral composition, inductively coupled plasma-optical emission spectrometry (ICP-OES)

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Andrey V. Timofeev

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This paper introduces an original method for guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with a probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smaller the cardinality of the discrete set of hypothetical classes is, the higher is the classification accuracy. Experiments have shown that if the cardinality of the classifiers ensemble is increased then the cardinality of this set of hypothetical classes is reduced. The problem of the guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers is relevant in the multichannel classification of target events in C-OTDR monitoring systems. Results of suggested approach practical usage to accuracy control in C-OTDR monitoring systems are present.

Keywords: Lipschitz classifiers, confidence set, C-OTDR monitoring, classifiers accuracy, classifiers ensemble

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Authors: Abriak Yassine, Zri Abdeljalil, Benzerzour Mahfoud., Hadj Sadok Rachid, Abriak Nor-Edine

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In this study, firstly, the study of the capacity reuse of dredged sediments and treated sediments with lime or cement were used in an establishment layer and the base layer of the roadway. Also, the analysis of mineral changes caused by the addition of lime or cement on the way as described in the mechanical results of stabilised sediments. After determining the quantity of lime and cement required to stabilise the sediment, the compaction characteristics were studied using the modified Proctor method. Then the evolution of the three parameters, that is, ideal water content and maximum dry density had been determined. Mechanical exhibitions can be assessed across the resistance to compression, flexibility modulus and the resistance under traction. The resistance of the formulation treated with cement addition (ROLAC®645) increase with the quantity of ROLAC®645. Traction resistances and the elastic modulus were utilized to assess the potential of the formulation as road construction materials utilizing classification diagram. The results show the various formulations with ROLAC® 645may be employed in subgrades and foundation layers for roads.

Keywords: cement, dredged, sediment, foundation layer, resistance

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Authors: V. Sivakumar, R. Neelakantan

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Mineral mapping on the Moon surface provides the clue to understand the origin, evolution, stratigraphy and geological history of the Moon. Recently, reflectance imaging spectroscopy plays a significant role in identifying minerals on the planetary surface in the Visible to NIR region of the electromagnetic spectrum. The Moon Mineralogy Mapper (M3) onboard Chandrayaan-1 provides unprecedented spectral data of lunar surface to study about the Moon surface. Here we used the M3 sensor data (hyperspectral imaging spectroscopy) for analysing mineralogy of Orientale basin region on the Moon surface. Reflectance spectrums were sampled from different locations of the basin and continuum was removed using ENvironment for Visualizing Images (ENVI) software. Reflectance spectra of unknown mineral composition were compared with known Reflectance Experiment Laboratory (RELAB) spectra for discriminating mineralogy. Minerals like olivine, Low-Ca Pyroxene (LCP), High-Ca Pyroxene (HCP) and plagioclase were identified. In addition to these minerals, an unusual type of spectral signature was identified, which indicates the probable Fe-Mg-spinel lithology in the basin region.

Keywords: chandryaan-1, moon mineralogy mapper, mineral, mare orientale, moon

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Authors: Hala Alshamlan, Ghada Badr, Yousef Alohali

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Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile.

Keywords: gene selection, feature selection, cancer classification, microarray, gene expression profile

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Authors: Agung Rizki Perdana, Asrofi Mursalin, Adniwan Shubhi Banuzaki, M. Indra Novian

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The understanding about sediment-derived plastiglomerate has a wide-range of merit in the academic realm. It can cover discussions about the Anthropocene Epoch in the scope of geoscience knowledge to even provide a solution for the environmental problem of plastic waste. Albeit its importance, very few research has been done regarding this issue. This research aims to create a classification as a pioneer for the study of sediment-derived plastiglomerate. This research was done in Bantul Regency, Daerah Istimewa Yogyakarta Province as an analogue of plastic debris sedimentation process. Observation is carried out in five observation points that shows three different depositional environments, which are terrestrial, fluvial, and transitional environment. The resulting classification uses three parameters and forms in a taxonomical manner. These parameters are composition, degree of lithification, and abundance of matrix respectively in advancing order. There is also a compositional ternary diagram which should be followed before entering the plastiglomerate nomenclature classification.

Keywords: plastiglomerate, classification, sedimentary mechanism, microplastic

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Authors: Prasanna Haddela

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Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm

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Authors: Irena Baranowska-Bosiacka, Mateusz Bosiacki, Patrycja Kupnicka, Anna Lubkowska, Dariusz Chlubek

Abstract:

Physical activity and a balanced diet are among the key factors of "healthy ageing". Physical effort, including swimming in cold water (including bathing in natural water reservoirs), is widely recognized as a hardening factor, with a positive effect on the mental and physical health. At the same time, there is little scientific evidence to verify this hypothesis. In the literature to date, it is possible to obtain data on the impact of these factors on selected physiological and biochemical parameters of the blood, at the same time there are no results of research on the effect of immersing in cold water on mineral metabolism, especially bones, hence it seems important to perform such an analysis in relation to the key elements such as calcium (Ca), magnesium (Mg) and phosphorus (P). Taking the above into account, a hypothesis was put forward about the possibility of a positive effect of exercise in cold water on mineral metabolism and bone density in aging rats. The aim of the study was to evaluate the effect of an 8-week swimming training on mineral metabolism and bone density in aging rats in response to exercise in cold water (5oC) in comparison to swimming in thermal comfort (36oC) and sedentary (control) rats of both sexes. The examination of the concentration of the examined elements in the bones was carried out using inductively coupled plasma atomic emission spectrometry (ICP-OES). The mineral density of the femurs of the rats was measured using the Hologic Horizon DEXA System® densitometer. The results of our study showed that swimming in cold water affects bone mineral metabolism in aging rats by changing the Ca, Mg, P concentration and at the same time increasing their bone density. In males, a decrease in Mg concentration and no changes in bone density were observed. In the light of the research results, it seems that swimming in cold water may be a factor that positively modifies the bone aging process by improving the mechanisms affecting their density.

Keywords: swimming in cold water, adaptation to cold water, bone mineral metabolism, aging

Procedia PDF Downloads 40

Authors: W. Deboucha, M. N. Oudjit, A. Bouzid, L. Belagraa, A.Noui

Abstract:

The cement manufacturing is the one of the factors that pollutes the atmosphere in the industrial sector. The common way to reduce this pollution is using mineral additions as partial replacement of Portland cement. Particularly, natural pozzolana (NP) is component in which they can be used to decrease the rate of pollution. The main objective of this experimental work is the study of the effect of mineral addition (natural pozzolana) on the capillary water absorption and compressive-flexural strength of cement mortar. The results obtained in the present research showed that the higher dosages of natural pozzolana added could be the principal parameter of such decrease in strength at early and medium term. Further, this increase of incorporated addition has been believed to reduce the capillary water absorption.

Keywords: Natural pozzolana, mortar, strength, capillary absorption

Procedia PDF Downloads 323