Search results for: feature similarity

Authors: R. Korchiyne, A. Sbihi, S. M. Farssi, R. Touahni, M. Tahiri Alaoui

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Trabecular bone structure is important texture in the study of osteoporosis. Legendre multifractal spectrum can reflect the complex and self-similarity characteristic of structures. The main objective of this paper is to develop a new technique of medical image classification based on Legendre multifractal spectrum. Novel features have been developed from basic geometrical properties of this spectrum in a supervised image classification. The proposed method has been successfully used to classify medical images of bone trabeculations, and could be a useful supplement to the clinical observations for osteoporosis diagnosis. A comparative study with existing data reveals that the results of this approach are concordant.

Keywords: multifractal analysis, medical image, osteoporosis, fractal dimension, Legendre spectrum, supervised classification

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Authors: Hilda Freimuth

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This study examined the interactional resources of self-mentions and engagement markers in twenty official IELTS reading exam passages to determine the passages’ similarity to academic research papers. Although the findings revealed a variation ranging from zero to 22 instances for any given passage, the study found the average number of markers (5.5) per passage in line with those found on research papers. This finding confirms that the IELTS exam’s reading passages mirror the academic nature of research papers in this regard.

Keywords: IELTS exam, IELTS reading, interpersonal resources, self-mentions, engagement markers

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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

Procedia PDF Downloads 128

Authors: Seyedeh Belin Tavakoly Sany, Rosli Hashim, Majid Rezayi, Aishah Salleh

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The primary objectives of this study were to investigate the distribution and composition of the macrobenthic community and their response to environmental parameters in the North Port, west coastal waters of Malaysia. A total of 25 species were identified, including 13 bivalvia, 4 gastropoda, and 3 crustacea. The other taxa were less diversified. There were no temporal changes in the macrobenthic community composition, but significant effects (p < 0.05) on the benthic community composition were found on a spatial scale. The correlation analyses and similarity tests were in good agreement, confirming the significant response of macrobenthic community composition to variations of environmental parameters.

Keywords: distribution, macrobenthic community, diversity, North Port, Malaysia

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Authors: Raj Nandkeolyar, Precious Sibanda

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The flow of a viscous, incompressible, and electrically conducting fluid under the influence of aligned magnetic field acting along the direction of fluid flow over an exponentially stretching sheet is investigated numerically. The nonlinear partial differential equations governing the flow model is transformed to a set of nonlinear ordinary differential equations using suitable similarity transformation and the solution is obtained using a local linearization method followed by the Chebyshev spectral collocation method. The effects of various parameters affecting the flow and heat transfer as well as the induced magnetic field are discussed using suitable graphs and tables.

Keywords: aligned magnetic field, exponentially stretching sheet, induced magnetic field, magnetohydrodynamic flow

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Authors: Tamás Gyulavári, Erzsébet Malota

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The main purpose of our study was to investigate the effect of congruence between the perceived self and perceived culture personality on the evaluation of the examined countries as ideal business/tourist destinations. A measure of Culture Personality (CP) has been developed and implemented to assess the perception of French and Turkish culture. Results show that very similar personality structure of both cultures can be extracted along the dimensions of Competence, Interpersonal approach, Aura, Life approach and Rectitude. Regarding the congruence theory, we found that instead of the effect of similarity between the perceived culture personality and actual self, the more positively culture personality is perceived relative to the perceived self, the more positive attitude the individual has toward the country as business and tourist destination.

Keywords: culture personality, ideal business/tourist destination, personality, scale development

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Authors: Siraa Ben Ftima, Mourad Talbi, Tahar Ezzedine

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In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations.

Keywords: lifting wavelet transform (LWT), sub-space vectorial decomposition, secure, image watermarking, watermark

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Authors: K. Padmavathi, K. Sri Ramakrishna

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This study describes a procedure for the detection of Left and Right Bundle Branch Block (LBBB and RBBB) ECG patterns using spectral Coherence(SC) technique and LM Neural Network. The Coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. The QT variations of Bundle Blocks are observed in lead V1 of ECG. Spectral Coherence technique uses Welch method for calculating PSD. For the detection of normal and Bundle block beats, SC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 99.5 percent. The data was collected from MIT-BIH Arrhythmia database.

Keywords: bundle block, SC, LMNN classifier, welch method, PSD, MIT-BIH, arrhythmia database

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Manish Jain, Dinesh Gopalani

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Growing dependency of mankind on software technology increases the need for thorough testing of the software applications and automated testing techniques that support testing activities. We have outlined our testing strategy for performing various types of automated testing of Java applications using AspectJ which has become the de-facto standard for Aspect Oriented Programming (AOP). Likewise JUnit, a unit testing framework is the most popular Java testing tool. In this paper, we have evaluated our proposed AOP approach for automated testing and JUnit on various parameters. First we have provided the similarity between the two approaches and then we have done a detailed comparison of the two testing techniques on factors like lines of testing code, learning curve, testing of private members etc. We established that our AOP testing approach using AspectJ has got several advantages and is thus particularly more effective than JUnit.

Keywords: aspect oriented programming, AspectJ, aspects, JU-nit, software testing

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Authors: Dong Tran, Thanh Dac Van, Ly Le

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Hemagglutinin (HA) and Neuraminidase (NA) play an important role in host immune evasion across influenza virus evolution process. The correlation between HA and NA evolution in respect to epitopic evolution and drug interaction has yet to be investigated. In this study, combining of sequence to structure evolution and statistical analysis on epitopic/binding site specificity, we identified potential therapeutic features of HA and NA that show specific antibody binding site of HA and specific binding distribution within NA active site of current inhibitors. Our approach introduces the use of sequence variation and molecular interaction to provide an effective strategy in establishing experimental based distributed representations of protein-protein/ligand complexes. The most important advantage of our method is that it does not require complete dataset of complexes but rather directly inferring feature interaction from sequence variation and molecular interaction. Using correlated sequence analysis, we additionally identified co-evolved mutations associated with maintaining HA/NA structural and functional variability toward immunity and therapeutic treatment. Our investigation on the HA binding specificity revealed unique conserved stalk domain interacts with unique loop domain of universal antibodies (CR9114, CT149, CR8043, CR8020, F16v3, CR6261, F10). On the other hand, NA inhibitors (Oseltamivir, Zaninamivir, Laninamivir) showed specific conserved residue contribution and similar to that of NA substrate (sialic acid) which can be exploited for drug design. Our study provides an important insight into rational design and identification of novel therapeutics targeting universally recognized feature of influenza HA/NA.

Keywords: influenza virus, hemagglutinin (HA), neuraminidase (NA), sequence evolution

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Authors: Jae Moon Lee

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In general, algorithms to find continuous k-nearest neighbors have been researched on the location based services, monitoring periodically the moving objects such as vehicles and mobile phone. Those researches assume the environment that the number of query points is much less than that of moving objects and the query points are not moved but fixed. In gaming environments, this problem is when computing the next movement considering the neighbors such as flocking, crowd and robot simulations. In this case, every moving object becomes a query point so that the number of query point is same to that of moving objects and the query points are also moving. In this paper, we analyze the performance of the existing algorithms focused on location based services how they operate under gaming environments.

Keywords: flocking behavior, heterogeneous agents, similarity, simulation

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: Norezzine Abdelaziz, Rebouh Nazih Yacer, Kezimana Parfait, Parpura D. I., Gadzhikurbanov A., Anastasios Dranidis

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The genetic variation of Shvicezebuvides cattle from three different farms in the Tajikistan Republic was studied using 10 microsatellite markers (SSR). The trials were laid out using a multi- locus analysis system for the analysis of cattle microsatellite locus. An estimated genetic variability of the examined livestock is given in the article. The results of our SSR analysis as well as the numbers and frequencies of common alleles in studied samples, we established a high genetic similarity of studied samples. These results can also be furthermore useful in the decision making for preservation and rational genetic resources usage of the Tajik Shvicezebuvides cattle.

Keywords: genetic characteristic, frequencies of the occurrence alleles, microsatellite markers, Swiss cattle

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Authors: Mohammad Safaeipour, Yousef Amer

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This study directs its focus on developing a conceptual model to offer a systematic and integrated method to weigh the related measures and evaluate a competence of hospitals and rank of the selected hospitals that involve and consider the stakeholders’ key performance indicators (KPI’s). The Analytical Hierarchy Process (AHP) approach will use to weigh the dimensions and related sub- components. The weights and performance scores will combine by using the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and rank the selected hospitals. The results of this study provide interesting insight into the necessity of process improvement implementation in which hospital that received the lowest ranking score.

Keywords: performance measurement system, PMS, hospitals, AHP, TOPSIS

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Authors: Takatoshi Ishii, Koji Kimita, Keiichi Muramatsu, Yoshiki Shimomura

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To increase the quality of learning, teacher and learner need mutual effort for realization of educational value. For this purpose, we need to manage the co-creational education among teacher and learners. In this research, we try to find a feature of co-creational education. To be more precise, we analyzed learners’ reports by natural language processing, and extract some features that describe the state of the co-creational education.

Keywords: co-creational education, e-portfolios, ICT integration, latent dirichlet allocation

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: M. Chukhryaeva, R. Skhalyakho, J. Kagazegeva, E. Pocheshkhova, L. Yepiskopossyan, O. Balanovsky, E. Balanovska

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The Middle East is crossroad of different populations at different times. The Kurds are of particular interest in this region. Historical sources suggested that the origin of the Kurds is associated with Medes. Therefore, it was especially interesting to compare gene pool of Kurds with other supposed descendants of Medes-Tats. Yezidis are ethno confessional group of Kurds. Yezidism as a confessional teaching was formed in the XI-XIII centuries in Iraq. Yezidism has caused reproductively isolation of Yezidis from neighboring populations for centuries. Also, isolation helps to retain Yezidian caste system. It is unknown how the history of Yezidis affected its genу pool because it has never been the object of researching. We have examined the Y-chromosome variation in Yezidis and Kurdish males to understand their gene pool. We collected DNA samples from 90 Yezidi males and 24 Kurdish males together with their pedigrees. We performed Y-STR analysis of 17 loci in the samples collected (Yfiler system from Applied Biosystems) and analysis of 42 Y-SNPs by real-time PCR. We compared our data with published data from other Kurdish groups and from European, Caucasian, and West Asian populations. We found that gene pool of Yezidis contains haplogroups common in the Middle East (J-M172(xM67,M12)- 24%, E-M35(xM78)- 9%) and in South Western Asia (R-M124- 8%) and variant with wide distribution area - R-M198(xM458- 9%). The gene pool of Kurdish has higher genetic diversity than Yezidis. Their dominants haplogroups are R-M198- 20,3 %, E-M35- 9%, J-M172- 9%. Multidimensional scaling also shows that the Kurds and Yezidis are part of the same frontier Asian cluster, which, in addition, included Armenians, Iranians, Turks, and Greeks. At the same time, the peoples of the Caucasus and Europe form isolated clusters that do not overlap with the Asian clusters. It is noteworthy that Kurds from our study gravitate towards Tats, which indicates that most likely these two populations are descendants of ancient Medes population. Multidimensional scaling also reveals similarity between gene pool of Yezidis, Kurds with Armenians and Iranians. The analysis of Yezidis pedigrees and their STR variability did not reveal a reliable connection between genetic diversity and caste system. This indicates that the Yezidis caste system is a social division and not a biological one. Thus, we showed that, despite many years of isolation, the gene pool of Yezidis retained a common layer with the gene pool of Kurds, these populations have common spectrum of haplogroups, but Yezidis have lower genetic diversity than Kurds. This study received primary support from the RSF grant No. 16-36-00122 to MC and grant No. 16-06-00364 to EP.

Keywords: gene pool, haplogroup, Kurds, SNP and STR markers, Yezidis

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Authors: Shady Ahmed Emara

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Introductory statement: The temple is characterized by a unique engineering design. This study aimed to explain the means that were used to reach this design. Background of the Study: Temple of Dandara consists of 24 columns with a height of 18m and a diameter of 2m. This paper is about the engineering method for constructing these huge columns. Two experiments were conducted at the temple. The first experiment used AutoCAD to compare the similarity of the columns in terms of dimensions. The second experiment used a laser rangefinder to measure the extent of the match between the heights between the columns. The Major Findings of the Study: (1) The method of constructing the columns was through several divided layers. It is divided into two halves and built opposite each other to maintain the integrity of the columns. (2) The match between the heights of the columns, which reached the error rate between one column and another, is only 1 mm. Concluding Statement: Both experiences will be explained through 2D and 3D.

Keywords: ancient, construction, architecture, building

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Authors: Hilal Keklicek, Fatih Erbahceci, Elif Kirdi, Ali Yalcin, Semra Topuz, Ozlem Ulger, Gul Sener

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Objectives and Goals: The stride-to-stride fluctuations in gait is a determinant of qualified locomotion as known as gait variability. Gait variability is an important predictive factor of fall risk and useful for monitoring the effects of therapeutic interventions and rehabilitation. Comparison of gait variability in individuals with trans-tibial lower limb loss and trans femoral lower limb loss was the aim of the study. Methods: Ten individuals with traumatic unilateral trans femoral limb loss(TF), 12 individuals with traumatic transtibial lower limb loss(TT) and 12 healthy individuals(HI) were the participants of the study. All participants were evaluated with treadmill. Gait characteristics including mean step length, step length variability, ambulation index, time on each foot of participants were evaluated with treadmill. Participants were walked at their preferred speed for six minutes. Data from 4th minutes to 6th minutes were selected for statistical analyses to eliminate learning effect. Results: There were differences between the groups in intact limb step length variation, time on each foot, ambulation index and mean age (p < .05) according to the Kruskal Wallis Test. Pairwise analyses showed that there were differences between the TT and TF in residual limb variation (p=.041), time on intact foot (p=.024), time on prosthetic foot(p=.024), ambulation index(p = .003) in favor of TT group. There were differences between the TT and HI group in intact limb variation (p = .002), time on intact foot (p<.001), time on prosthetic foot (p < .001), ambulation index result (p < .001) in favor of HI group. There were differences between the TF and HI group in intact limb variation (p = .001), time on intact foot (p=.01) ambulation index result (p < .001) in favor of HI group. There was difference between the groups in mean age result from HI group were younger (p < .05).There were similarity between the groups in step lengths (p>.05) and time of prosthesis using in individuals with lower limb loss (p > .05). Conclusions: The pilot study provided basic data about gait stability in individuals with traumatic lower limb loss. Results of the study showed that to evaluate the gait differences between in different amputation level, long-range gait analyses methods may be useful to get more valuable information. On the other hand, similarity in step length may be resulted from effective prosthetic using or effective gait rehabilitation, in conclusion, all participants with lower limb loss were already trained. The differences between the TT and HI; TF and HI may be resulted from the age related features, therefore, age matched population in HI were recommended future studies. Increasing the number of participants and comparison of age-matched groups also recommended to generalize these result.

Keywords: lower limb loss, amputee, gait variability, gait analyses

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Authors: Sang-Keun Moon, Hong-Rok Lim, Jin-O Kim

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This paper presents a Power Quality (PQ) modeling and filtering processes for the distribution system disturbances using recognition learning methods. Typical PQ waveforms with mathematical applications and gathered field data are applied to the proposed models. The objective of this paper is analyzing PQ data with respect to monitoring, discriminating, and evaluating the waveform of power disturbances to ensure the system preventative system failure protections and complex system problem estimations. Examined signal filtering techniques are used for the field waveform noises and feature extractions. Using extraction and learning classification techniques, the efficiency was verified for the recognition of the PQ disturbances with focusing on interactive modeling methods in this paper. The waveform of selected 8 disturbances is modeled with randomized parameters of IEEE 1159 PQ ranges. The range, parameters, and weights are updated regarding field waveform obtained. Along with voltages, currents have same process to obtain the waveform features as the voltage apart from some of ratings and filters. Changing loads are causing the distortion in the voltage waveform due to the drawing of the different patterns of current variation. In the conclusion, PQ disturbances in the voltage and current waveforms indicate different types of patterns of variations and disturbance, and a modified technique based on the symmetrical components in time domain was proposed in this paper for the PQ disturbances detection and then classification. Our method is based on the fact that obtained waveforms from suggested trigger conditions contain potential information for abnormality detections. The extracted features are sequentially applied to estimation and recognition learning modules for further studies.

Keywords: power quality recognition, PQ modeling, waveform feature extraction, disturbance trigger condition, PQ signal filtering

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Authors: D. B. Gohil

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Closed die forging is a very complex process, and measurement of actual forces for real material is difficult and time consuming. Hence, the modelling technique has taken the advantage of carrying out the experimentation with the proper model material which needs lesser forces and relatively low temperature. The results of experiments on the model material then may be correlated with the actual material by using the theory of similarity. There are several methods available to resolve the complexity involved in the closed die forging process. Finite Element Method (FEM) and Finite Difference Method (FDM) are relatively difficult as compared to the slab method. The slab method is very popular and very widely used by the people working on shop floor because it is relatively easy to apply and reasonably accurate for most of the common forging load requirement computations.

Keywords: experimentation, forging, process modeling, strain distribution

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Authors: Carine Kawakami

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In French, as well as in English, there are two types of psychological predicate constructions depending on where the experiencer argument is realized; the first type is in the subject position (e.g. Je regrette d’être venu ici. ‘I regret coming here'), hereinafter called ES construction, and the second type is in the object position (e.g. Cette nouvelle m’a surpris. ‘This new surprised me.'), referred as EO construction. In the previous studies about psychological predicates, the syntactic position of the experiencer argument has been just a matter of its connection with the syntactic or semantic structure of the predicate. So that few attentions have been paid to how two types of realization of experiencer are related to the conceptualization of psychological event and to the function of the sentence describing the psychological event, in the sense of speech act theory. In this research, focusing on the French phenomena limited to the first personal pronoun and the present tense, the ES constructions and the EO constructions will be analyzed from cognitive and functional approach. It will be revealed that, due to the possibility to be used in soliloquy and the high co-occurrence with ça (‘it’), the EO constructions may have expressive function to betray what speaker feels in hic et nunc, like interjection. And in the expressive case, the experiencer is construed as a locus where a feeling appears spontaneously and is construed subjectively (e.g. Ah, ça m’énerve! ‘Oh, it irritates me!'). On the other hand, the ES constructions describe speaker’s mental state in an assertive manner rather than the expressive and spontaneously way. In other words, they describe what speaker feels to the interlocutor (e.g. Je suis énervé. ‘I am irritated.'). As a consequence, when the experiencer argument is realized in the subject position, it is construed objectively and have a participant feature in the sense of cognitive grammar. Finally, it will be concluded that the choice of construction type, at least in French, is correlated to the conceptualization of the psychological event and the discourse feature of its expression.

Keywords: french psychological verb, conceptualization, expressive function, assertive function, experiencer realization

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Authors: Marian Agbugui

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The West African Lung fishes are fishes rich in protein and serve as an important source of food supply for man. The kind of food ingested by this group of fishes is dependent on the alimentary canal as well as the fish’s digestive processes which provide suitable modifications for maximum utilization of food taken. A study of the alimentary canal of P. annectens will expose the best information on the anatomy and histology of the fish. Samples of P. annectens were dissected to reveal the liver, pancreas and entire gut wall. Digital pictures of the mouth, jaws and the Gastrointestinal Tract (GIT) were taken. The entire gut was identified, sectioned and micro graphed. P. annectens was observed to possess a terminal mouth that opens up to 10% of its total body length, an adaptive feature to enable the fish to swallow the whole of its pry. Its dentition is made up of incisors- scissor-like teeth which also help to firmly grip, seize and tear through the skin of prey before swallowing. A short, straight and longitudinal GIT was observed in P. annectens which is known to be common feature in lungfishes, though it is thought to be a primitive characteristic similar to the lamprey. The oesophagus is short and distensible similar to other predatory and carnivorous species. Food is temporarily stored in the stomach before it is passed down into the intestine. A pyloric aperture is seen at the end of the double folded pyloric valve which leads into an intestine that makes up 75% of the whole GIT. The intestine begins at the posterior end of the pyloric aperture and winds down in six coils through the whole length intestine and ends at the cloaca. From this study it is concluded that P. annectens possess a composite GIT with organs similar to other lung fishes; it is a detritor with carnivorous abilities.

Keywords: gastrointestinal tract, incisors scissor-like teeth, intestine, mucus, Protopterus annectens, serosa

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: artificial neural networks, ANN, discrete wavelet transform, DWT, gray-level co-occurrence matrix, GLCM, k-nearest neighbor, KNN, region of interest, ROI

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Mohamad Sakizadeh, Fatemeh Mehrabi Sharafabadi, Hadi Ghorbani

Abstract:

Concentrations of Cr and Ni in 97 plant samples (belonged to eight different plant species) and the associated soil groups were considered in this study. The amounts of Ni in soil groups fluctuated between 26.8 and 36.8 mgkg⁻¹ whereas the related levels of chromium ranged from 67.7 to 94.3mgkg⁻¹. The index of geoaccumulation indicated that 87 percents of the studied soils for chromium and 98.8 percents for nickel are located in uncontaminated zone. The results of Mann-Whitney U-test proved that agricultural practices have not significantly influenced the values of Ni and Cr. In addition, tillage had also little impact on the Ni and Cr transfer in the surface soil. Ni showed higher accumulation and soil-to-plant transfer factor compared with that of chromium in the studied plants. There was a high similarity between the accumulation pattern of Cr and Fe in most of the plant species.

Keywords: bioconcentration factor, chromium, geoaccumulation index, nickel

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Authors: William Wong Xiu Shun, Yoonjin Hyun, Mingyu Kim, Seongi Choi, Namgyu Kim

Abstract:

Recently, the demand of extracting the R&D keywords from the issues and using them in retrieving R&D information is increasing rapidly. But it is hard to identify the related issues or to distinguish them. Although the similarity between the issues cannot be identified, but with the R&D lexicon, the issues that always shared the same R&D keywords can be determined. In details, the R&D keywords that associated with particular issue is implied the key technology elements that needed to solve the problem of the particular issue. Furthermore, the related issues that sharing the same R&D keywords can be showed in a more systematic way through the issue clustering constructed from the perspective of R&D. Thus, sharing of the R&D result and reusable of the R&D technology can be facilitated. Indirectly, the redundancy of investment on the same R&D can be reduce as the R&D information can be shared between those corresponding issues and reusability of the related R&D can be improved. Therefore, a methodology of constructing an issue clustering from the perspective of common R&D keywords is proposed to satisfy the demands mentioned.

Keywords: clustering, social network analysis, text mining, topic analysis

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Authors: Pia I. Hautamäki

Abstract:

Sales has changed. Selling has taken on aspects of relationship marketing and sales force play a critical role in developing long-term relationships between buyers and sellers which is seen to serve the company’s targets and create success for a long run. The purpose of this study was to examine what really matters in buyer-seller encounters and determine what expectations business buyers have. We studied 17 business buyers by a qualitative interview. We found that buyers appreciate encounters where the salesperson face the buyer as a way he or she is as a person, identificate the real needs to improve buyers’ business and build up cooperation for long-term relationship. This study show that personality matters are a key elements when satisfying business buyers’ expectations.

Keywords: business buyer-seller encounters, customer expectations, perceived similarity, personal selling, personality types

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Authors: N. Zarikian

Abstract:

Comparative analysis of the fauna of two families of butterflies (Lepidoptera: Rhopalocera) – Hesperiidae and Nymphalidae were carried out. In general, 122 species of the families are recorded. among these 33 species belong to Hesperiidae and 89 to Nymphalidae. The numbers by countries are as follows: 72 species are found in Syria (including 24 Hesperiidae and 48 Nymphalidae) and 97 in Armenia (26 and 71 species, respectively). Two species of Hesperiidae are reported for Syrian fauna for the first time and one species is newly recorded for Armenia. From the species above mentioned 38 are common both for Syria and Armenia. For estimation of the similarity of faunas studied were used the Jaccard index. By families the index is rather different, consisting for Hesperiidae 0.5151 and for Nymphalidae 0.337.

Keywords: Armenia, fauna, Hesperiidae, Nymphalidae, (Rhopalocera: Lepidoptera), Syria

Procedia PDF Downloads 246