Search results for: canopy characters classification

Authors: Haotian Wu

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This article examines the figure Zhao Tao in Jia Zhangke’s films in light of Carl Jung’s psychoanalytical theory. Zhao is a recurring aesthetic trope in Jia’s films, and the characters she plays often have an intimate relationship with the past. Nevertheless, this relationship has not been systematically investigated, especially its symbolism of the typical relationship between the past and the self in post-social China. To fill this research gap, the article will explore how Zhao’s characters discover, preserve, and adapt the past in I Wish I knew (2010), Mountains May Depart (2015), and Ash Is Purest White (2018). Through a Jungian lens, these three levels of engagement with the past will be demonstrated as corresponding with Jung’s psychoanalytical theory of self-realisation, which entails the confrontation with the shadow, the embodiment of the archetype, and individuation. Thus, by articulating a film-philosophy dialogue between Jia and Jung, this article will develop a new philosophy of self-realisation based on the symbolism of Zhao. Through the reintegration of the past, the individuals can overcome the fragmentation of temporality and selfhood in the postmodern world and achieve self-realisation.

Keywords: Jia Zhangke, Jung, psychoanalysis, self-realisation

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Authors: Mohammad Hassan Ziraksaz

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Fuel atomizers are used in a wide range of IC engines, turbojets and a variety of liquid propellant rocket engines. As the fuel spray fully develops its characters approach their ultimate amounts. Fuel spray characters such as SMD, injection pressure, mass flow rate, droplet velocity and spray cone angle play important roles to atomize the liquid fuel to finely atomized fuel droplets and finally form the fine fuel spray. Well performed, fully developed, fine spray without any defections, brings the idea of finding an experimental relation between the main effective spray characters. Extracting an experimental relation between SMD and other fuel spray physical characters in swirl fuel atomizers is the main scope of this experimental work. Droplet velocity, fuel mass flow rate, SMD and spray cone angle are the parameters which are measured. A set of twelve reverse engineering atomizers without any spray defections and a set of eight original atomizers as referenced well-performed spray are contributed in this work. More than 350 tests, mostly repeated, were performed. This work shows that although spray cone angle plays a very effective role in spray formation, after formation, it smoothly approaches to an almost constant amount while the other characters are changed to create fine droplets. Therefore, the work to find the relation between the characters is focused on SMD, droplet velocity, fuel mass flow rate, and injection pressure. The process of fuel spray formation begins in 5 Psig injection pressures, where a tiny fuel onion attaches to the injector tip and ended in 250 Psig injection pressure, were fully developed fine fuel spray forms. Injection pressure is gradually increased to observe how the spray forms. In each step, all parameters are measured and recorded carefully to provide a data bank. Various diagrams have been drawn to study the behavior of the parameters in more detail. Experiments and graphs show that the power equation can best show changes in parameters. The SMD experimental relation with pressure P, fuel mass flow rate Q ̇ and droplet velocity V extracted individually in pairs. Therefore, the proportional relation of SMD with other parameters is founded. Now it is time to find an experimental relation including all the parameters. Using obtained proportional relation, replacing the parameters with experimentally measured ones and drawing the graphs of experimental SMD versus proportion SMD (〖SMD〗_P), a correctional equation and consequently the final experimental equation is obtained. This experimental equation is specified to use for swirl fuel atomizers and the use of this experimental equation in different conditions shows about 3% error, which is expected to achieve lower error and consequently higher accuracy by increasing the number of experiments and increasing the accuracy of data collection.

Keywords: droplet velocity, experimental relation, mass flow rate, SMD, swirl fuel atomizer

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Authors: Garg Ramneesh, Uppal Sanjeev, Mittal Rajinder, Shah Sheerin, Jain Vikas, Singla Bhupinder

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Introduction: Despite advances in trauma care, a classification system for soft tissue injuries of the face still needs to be objectively defined. Aim: To develop a classification system for soft tissue injuries of the face; that is objective, easy to remember, reproducible, universally applicable, aids in surgical management and helps to develop a structured data that can be used for future use. Material and Methods: This classification system includes those patients that need surgical management of facial injuries. Associated underlying bony fractures have been intentionally excluded. Depending upon the severity of soft tissue injury, these can be graded from 0 to IV (O-Abrasions, I-lacerations, II-Avulsion injuries with no skin loss, III-Avulsion injuries with skin loss that would need graft or flap cover, and IV-complex injuries). Anatomically, the face has been divided into three zones (Zone 1/2/3), as per aesthetic subunits. Zone 1e stands for injury of eyebrows; Zones 2 a/b/c stand for nose, upper eyelid and lower eyelid respectively; Zones 3 a/b/c stand for upper lip, lower lip and cheek respectively. Suffices R and L stand for right or left involved side, B for presence of foreign body like glass or pellets, C for extensive contamination and D for depth which can be graded as D 1/2/3 if depth is still fat, muscle or bone respectively. I is for damage to facial nerve or parotid duct. Results and conclusions: This classification system is easy to remember, clinically applicable and would help in standardization of surgical management of soft tissue injuries of face. Certain inherent limitations of this classification system are inability to classify sutured wounds, hematomas and injuries along or against Langer’s lines.

Keywords: soft tissue injuries, face, avulsion, classification

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Authors: Kwounghee Choi

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Technological convergence between the various sectors is expected to have a very large impact on future industrial and economy. This study attempts to do empirical approach between specific technologies’ classification. For technological convergence classification, it is necessary to set the target technology to be analyzed. This study selected target technology from national research and development plan. At first we found a source technology for analysis. Depending on the weight of source technology, NT-based, BT-based, IT-based, ET-based, CS-based convergence types were classified. This study aims to empirically show the concept of convergence technology and convergence types. If we use the source technology to classify convergence type, it will be useful to make practical strategies of convergence technology.

Keywords: technology convergence, source technology, convergence type, R&D strategy, technology classification

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Authors: Ruepert Jiel Cao

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This study explores the renegotiation of bakla culture in Philippine media in the context of Kalye Serye segment of the popular Filipino noontime variety show Eat Bulaga. Although the term “bakla” is usually translated to “gay” or “homosexual male” in English, they do not mean the same. The western notion of a gay refers to a male person attracted to another male person but still retains the masculine physical attributes. However, the bakla embodies loudness, femininity, and transvestitism. Hence, a bakla is a gay man aspiring to be a woman by assuming feminine actions and appearance, a definition much closer to a transgender. The Philippine media usually employs the bakla culture in comedy programs. The bakla nowadays is usually associated with the people of lower economic strata and carries a pathological connotation. The Filipino television program Eat Bulaga, which has been airing for more than 36 years, is fond of using bakla in comedy. However, the recently launched segment entitled Kalye Serye (literally “Street [Television] Series”), while still employing drag performance to incorporate bakla culture in comedy, renegotiates the bakla culture by deviating from the stereotypical notion of bakla. In this study, this researcher asks: (1) How does Kalye Serye renegotiate the Filipino concept of bakla in terms of economic aspirations and social norms? (2) How does Kalye Serye reappropriate the bakla culture to fit non-comedic performances? The study examines 15 purposively selected Kalye Serye episodes. Seven were selected from the Thursday episodes, seven from Saturday episodes, and the Lenten special episode. These were selected to cover as many characters and different character roles as possible. Data was constructed by identifying and coding the roles, physical appearance and gestures, and key dialogs of the characters. A total of six female characters played by three different male actors were examined. Semiotic analysis using semiotics of Roland Barthes was performed to produce a reading of the characters. Findings show that through physical appearance, the characters associate bakla with the economic affluence through the use of expensive-looking clothes, jewelries, cars, and elaborate gestures. This represents a new economic but old western aspiration of the bakla. In terms of social norms, the characters try to revive the traditional concepts of femininity, courtship, and respect, values which are touted to be lost in the current generation of Filipinos. This is quite ironic because while there is a seemingly tolerant attitude towards all forms of queerness, the bakla is considered immoral and yet, the bakla is used to teach about morality and values. Finally, the characters break the traditional association of the bakla with slapstick comedy and their roles are reappropriated to suit dramatic roles. By refraining from portraying the bakla in ridiculous manner (physically and in terms of roles), the bakla lends itself well in the performance of dramatic roles and their ridiculous and pathological associations removed. Future research may include other Filipino or Asian portrayals of queerness to get a better understanding of how queerness is incorporated in contemporary popular culture.

Keywords: bakla, drag performance, popular culture, queer representation

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

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This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: intelligent transportation system, object detection, vehicle couting, vehicle classification, video processing

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Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Fuad M. Alkoot

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Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Hei Yuen Pak

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Steer Toward Rock, Fae Myenne Ng’s second novel after the National Bestseller Bone, is superficially concluded as a story of pessimism, which underestimates the sophistication of Ng’s portrayal. It is often summarized as a “heartbreaking novel of unrequited love” or “a story of timeless and tragic”; yet, Ng’s novel conveys more than a mere sense of tragedy and heartbreak, but rather an overflowing warmth and optimism. Ng is complimented of “illuminating a part of U.S. history few are aware of”—the false identity established on the paper relationships. Nevertheless, toward the end of the novel, this falsity enlightens the male protagonist, Jack Moon Szeto, of the ultimate realization of the “truthfulness” to himself, with the escort of the female characters. This paper intends to investigate how Ng’s depiction subverts the traditional sex/gender system and also the patriarchal savior stereotype. This paper mainly examines the characterization of and the relations among the four major characters: Jack Moon Szeto, Joice Qwan, Veda Qwan, and Ilin Cheung. By deploying Kate Millett’s, Marilyn French’s, Mary Daly’s feminist theories, the first half of the essay elucidates the power relations between Jack and the three females Joice, Veda, and Ilin in terms of gender and sexuality. After analyzing the relations, Jack, this male caged bird, is set free by the epiphany derived from the three female characters, which is the pivot of the second half. In reference to Jean-Paul Sartre and Simone de Beauvoir’s existentialist perspectives, I argue how Jack is transformed from, in Satre’s term, being-for-others to being-for-itself. Hence, the caged bird is free by the women saviors.

Keywords: Fae Myenne Ng, gender and sexuality, feminism, power relations

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Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Abdulsalam Abiodun Salman, Abdulmumin Isah

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The study investigated the motivational factors that could enhance the teaching and understanding of ICT-driven cataloguing and classification (Cat and Class) practical classes among students of library and information science (LIS) in Kwara State Library Schools, Nigeria. It deployed a positivist research paradigm using a quantitative method by deploying the use of questionnaires for data collection. The population of the study is one thousand, one hundred and twenty-five (1,125) which was obtained from the department of each respective library school (the University of Ilorin, Ilorin (Unilorin); Federal Polytechnic Offa, (Fedpoffa); and Kwara State University (KWASU). The sample size was determined using the research advisor table. Hence, the study sample of one hundred and ten (110) was used. The findings revealed that LIS students were averagely motivated toward ICT-driven Cataloguing and Classification practical classes. The study recommended that modern cataloguing and classification tools for practical classes should be made available in the laboratories as motivational incentives for students. It was also recommended that library schools should motivate the students beyond the provision of these ICT-driven tools but also extend the practical class periods. Availability and access to medical treatment in case of injuries during the practical classes should be made available. Technologists/Tutors of Cat and Class practical classes should also be exposed to further training in modern trends, especially emerging digital knowledge and skills in cataloguing and classification. This will keep both the tutors and students abreast of the new development in the technological arena.

Keywords: cataloguing and classification, motivational factors, ICT-driven practical classes, LIS students, Nigeria

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Maher Aburrous, Adel Khelifi, Manar Abu Talib

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Transactions performed by financial institutions on daily basis require XML encryption on large scale. Encrypting large volume of message fully will result both performance and resource issues. In this paper a novel approach is presented for securing financial XML transactions using classification data mining (DM) algorithms. Our strategy defines the complete process of classifying XML transactions by using set of classification algorithms, classified XML documents processed at later stage using element-wise encryption. Classification algorithms were used to identify the XML transaction rules and factors in order to classify the message content fetching important elements within. We have implemented four classification algorithms to fetch the importance level value within each XML document. Classified content is processed using element-wise encryption for selected parts with "High", "Medium" or “Low” importance level values. Element-wise encryption is performed using AES symmetric encryption algorithm and proposed modified algorithm for AES to overcome the problem of computational overhead, in which substitute byte, shift row will remain as in the original AES while mix column operation is replaced by 128 permutation operation followed by add round key operation. An implementation has been conducted using data set fetched from e-banking service to present system functionality and efficiency. Results from our implementation showed a clear improvement in processing time encrypting XML documents.

Keywords: XML transaction, encryption, Advanced Encryption Standard (AES), XML classification, e-banking security, fuzzy classification, cryptography, intelligent encryption

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Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Ali Farzan

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Classification of data objects or patterns is a major part in most of Decision making systems. One of the popular and commonly used classification methods is Decision Tree (DT). It is a hierarchical decision making system by which a binary tree is constructed and starting from root, at each node some of the classes is rejected until reaching the leaf nods. Each leaf node is a representative of one specific class. Finding the splitting criteria in each node for constructing or training the tree is a major problem. Particle Swarm Optimization (PSO) has been adopted as a metaheuristic searching method for finding the best splitting criteria. Result of evaluating the proposed method over benchmark datasets indicates the higher accuracy of the new PSO based decision tree.

Keywords: decision tree, particle swarm optimization, splitting criteria, metaheuristic

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Authors: Aschalew Cherie Workneh, K. S. Hari Prasad, Chandra Shekhar Prasad Ojha

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Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops.

Keywords: artificial neural networks; crop water stress index; canopy temperature, prediction capability

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Authors: Ercan Ceyhan, Mehmet Ali Avci

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This research was made during the 2015 growing periods in the trial filed of ‘Research Station for Department of Field Crops, Agricultural Faculty, Selcuk University’ according to ‘Randomized Blocks Design’ with 3 replications. Research material was the following pea lines; PS16, PS18, PS21, PS23, PS24, PS25, PS36, PS47, PS49, PS51, PS54, PS58, PS67, PS69, PS71, PS73, PS83, PS84, PS87 and PSKY and three cultivars and other 2 commercial varieties named as Bolero, Rondo and Ultrello. Some agronomical characteristics such as plant height (cm) number of pod per plant number of seed per pod number of seed per plant 100 seed weight (g) and seed yield (kg ha-1) were determined. Results of the research implicated that the new developed lines were superior compared with the control (commercial) varieties by means of most of the characteristics. Nevertheless, similar researches should be continued in different locations and years.

Keywords: agricultural characters, pea, Pisum sativum, seed yield

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: I. A. Farhat, M. Bin Hasan

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A developed approach for the protection of power transmission lines using Support Vector Machines (SVM) technique is presented. In this paper, the SVM technique is utilized for the classification and isolation of faults in power transmission lines. Accurate fault classification and location results are obtained for all possible types of short circuit faults. As in distance protection, the approach utilizes the voltage and current post-fault samples as inputs. The main advantage of the method introduced here is that the method could easily be extended to any power transmission line.

Keywords: fault detection, classification, diagnosis, power transmission line protection, support vector machines (SVM)

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Authors: Azaze-Azizi Abdul Adis, Hyung Jun Kim, Mohamad Rizwan Abdul Majid, Zaiton Osman, Izyanti Awang Razli

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The increase of online advertising, specifically advergame has become a popular method of strengthening consumer brand recognition by inserting attractive characters and enhancing entertainment value. There have been several remarkable studies on spokes-characters in advertising effectiveness. However, few studies have examined the link between character presence and consumers' brand recall and attitude in advergame. Moreover, how the entertainment value of an advergame influences brand recall and brand attitude and the mediating role of brand recall in influencing character presence and entertainment on brand attitude are still lacking in the advergaming literature. An online survey was conducted with 366 Malaysian gamers. Using structural equation modeling, the results showed that character presence had no influence but entertainment value had a positive influence on brand recall and brand attitude. This study confirmed the role of brand recall as a mediator of the effect of between entertainment and brand attitude in advergame.

Keywords: character presence, entertainment, brand recall, brand attitude, advergame

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Tudor Barbu

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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

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The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error.

Keywords: rough sets, gene expression programming, rough neural networks, classification

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Authors: Hasan Vural

Abstract:

Turkey is a favorable country to produce a great variety of agricultural products because of her different geographic and climatic conditions which have been used to divide the country into four main and seven sub regions. This classification into seven regions traditionally has been used in order to data collection and publication especially related with agricultural production. Afterwards, nine agricultural regions were considered. Recently, the governmental body which is responsible of data collection and dissemination (Turkish Institute of Statistics-TIS) has used 12 classes which include 11 sub regions and Istanbul province. This study aims to evaluate these classification efforts based on the acreage of ten main crops in a ten years time period (1996-2005). The panel data grouped in 11 subregions has been evaluated by cluster and multivariate statistical methods. It was concluded that from the agricultural production point of view, it will be rather meaningful to consider three main and eight sub-agricultural regions throughout the country.

Keywords: agricultural region, factorial analysis, cluster analysis,

Procedia PDF Downloads 381

Authors: I. Gusti A. A. Rai Asmiwyati, Robert J. Corner, Ashraf M. Dewan

Abstract:

Change on land use/cover (LULC) dominantly affects spatial structure and function. It can have such impacts by disrupting social culture practice and disturbing physical elements. Thus, it has become essential to understand of the dynamics in time and space of LULC as it can be used as a critical input for developing sustainable LULC. This study was an attempt to map and monitor the LULC change in Bali Indonesia from 2003 to 2013. Using object based classification to improve the accuracy, and change detection, multi temporal land use/cover data were extracted from a set of ASTER satellite image. The overall accuracies of the classification maps of 2003 and 2013 were 86.99% and 80.36%, respectively. Built up area and paddy field were the dominant type of land use/cover in both years. Patch increase dominantly in 2003 illustrated the rapid paddy field fragmentation and the huge occurring transformation. This approach is new for the case of diverse urban features of Bali that has been growing fast and increased the classification accuracy than the manual pixel based classification.

Keywords: land use/cover, urban, Bali, ASTER

Procedia PDF Downloads 511

Authors: Alina Zafar

Abstract:

In this particular research, various morphometric characters such as total length (TL), wet weight (WW), standard length (SL), fork length (FL), head length (HL), head width (HW), body depth (BD), body girth (BG), dorsal fin length (DFL), pelvic fin length (PelFL), pectoral fin length (PecFL), anal fin length (AFL), dorsal fin base (DFB), anal fin base (AFB), caudal fin length (CFL) and caudal fin width (CFW) of wild collected Puntius sophore were studied, to know the types of growth patterns and correlations in reference to length and weight, however, high significant relationships were recorded between total length and wet weight, as the correlation coefficient (r) possessed value of 0.989. The growth pattern was observed to be positively allometric as the value of ‘b’ was 3.22 (slightly higher than the ideal value, 3) with 95% confidence intervals ranging from 3.076 to 3.372. Wet weight and total length parameters showed high significant correlations (p < 0.001) with all other morphometric characters.

Keywords: Puntius sophore, length and weight relation, morphometrics, small indigenous species

Procedia PDF Downloads 74