Search results for: drought classification

Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: Anila Hayat

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Pakistan is one of those developing countries that are least involved in emissions but has the most vulnerable environmental conditions. Pakistan is ranked 8th in most affected countries by climate change on the climate risk index 1992-2011. Pakistan is facing severe water shortages and flooding as a result of changes in rainfall patterns, specifically in the least developed areas such as Tharparkar. Nagarparkar, once an attractive tourist spot located in Tharparkar because of its tropical desert climate, is now facing severe drought conditions for the last few decades. This study investigates the present socio-economic situation of local communities, major impacts of droughts and their underlying causes and current mitigation strategies adopted by local communities. The study uses both secondary (quantitative in nature) and primary (qualitative in nature) methods to understand the impacts and explore causes on the socio-economic life of local communities of the study area. The relevant data has been collected through household surveys using structured questionnaires, focus groups and in-depth interviews of key personnel from local and international NGOs to explore the sensitivity of impacts and adaptation to droughts in the study area. This investigation is limited to four rural communities of union council Pilu of Nagarparkar district, including Bheel, BhojaBhoon, Mohd Rahan Ji Dhani and Yaqub Ji Dhani villages. The results indicate that drought has caused significant economic and social hardships for the local communities as more than 60% of the overall population is dependent on rainfall which has been disturbed by irregular rainfall patterns. The decline in Crop yields has forced the local community to migrate to nearby areas in search of livelihood opportunities. Communities have not undertaken any appropriate adaptive actions to counteract the adverse effect of drought; they are completely dependent on support from the government and external aid for survival. Respondents also reported that poverty is a major cause of their vulnerability to drought. An increase in population, limited livelihood opportunities, caste system, lack of interest from the government sector, unawareness shaped their vulnerability to drought and other social issues. Based on the findings of this study, it is recommended that the local authorities shall create awareness about drought hazards and improve the resilience of communities against drought. It is further suggested to develop, introduce and implement water harvesting practices at the community level to promote drought-resistant crops.

Keywords: migration, vulnerability, awareness, Drought

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Authors: M. Hijazi, A. Zeki, A. Ismail

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An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia.

Keywords: Arabic text classification, Arabic WordNet, bag of words, conceptual representation, semantic relations

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Zulkiflee Abd Latif, Sitinor Atikah Nordin, Alawi Sulaiman

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Different tree species possess different and various benefits. Arenga Pinnata tree species own several potential uses that is valuable for the economy and the country. Mapping vegetation using remote sensing technique involves various process, techniques and consideration. Using satellite imagery, this method enables the access of inaccessible area and with the availability of near infra-red band; it is useful in vegetation analysis, especially in identifying tree species. Pixel-based and object-based classification technique is used as a method in this study. Pixel-based classification technique used in this study divided into unsupervised and supervised classification. Object based classification technique becomes more popular another alternative method in classification process. Using spectral, texture, color and other information, to classify the target make object-based classification is a promising technique for classification. Classification of Arenga Pinnata trees is overlaid with elevation, slope and aspect, soil and river data and several other data to give information regarding the tree character and living environment. This paper will present the utilization of remote sensing technique in order to map Arenga Pinnata tree species

Keywords: Arenga Pinnata, pixel-based classification, object-based classification, remote sensing

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Authors: Ghada S. Moussa

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With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management. This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.

Keywords: appearance attributes, geometric attributes, support vector machine, vehicle classification

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Authors: Dorcas Ropo Abejide, Olamide Ahmed Falusi, Oladipupo Abdulazeez Yusuf Daudu, Bolaji Zuluqurineen Salihu, Muhammad Liman Muhammad

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This study evaluated the morpho-agronomic response of twenty-four (24) Nigerian Bambara groundnut landraces to water stress and genetic diversity of some selected accessions using SSR markers. The studies were carried out in the botanical garden of the Department of Plant Biology, Federal University of Technology, Minna, Niger State, Nigeria in a randomized complete block design using three replicates. Molecular analysis using SSR primers was carried out at the International Institute of Tropical Agriculture (IITA) Ibadan in order to characterize ten selected accessions comprising the seven most drought tolerant and three most susceptible accessions from the 24 accessions evaluated. Results revealed that water stress decreased morpho-agronomic traits such as plant height, leaf area, number of leaves per plant, seed yield, etc. A total of 22 alleles were detected by the SSR markers used with a mean number of 4 allelles. SSR markers MBamCO₃₃, Primer 65, and G358B2-D15 each detected 4 allelles, while Primer 3FR and 4FR detected 5 allelles each. The study revealed significantly high polymorphisms in 10 Loci. The mean value of polymorpic information content was 0.6997, implying the usefulness of the primers used in identifying genetic similarities and differences among the Bambara groundnut genotypes. The SSR analysis revealed a comparable pattern between genetic diversity and drought tolerance of the genotypes. The UPGMA dendrogram showed that at a genetic distance of 0.1, the accessions were grouped into three groups according to their level of tolerance to drought. The two most drought-tolerant accessions were grouped together, and the 5th and 6th most drought-tolerant accessions were also grouped together. This suggests that the genotypes grouped together may be genetically close, may possess similar genes, or have a common origin. The degree of genetic variants obtained from this profiling could be useful in Bambara groundnut breeding for drought tolerance. The identified drought tolerant Bambara groundnut landraces are important genetic resources for drought stress tolerance breeding programme of Bambara groundnut. The genotypes are also useful for germplasm conservation and global implications.

Keywords: Bambara groundnut, genetic diversity, germplasm, SSR markers, water stress

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Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

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Authors: Fatemeh Najafi

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The major stress factor limiting crop growth and development of sesame (Sesamum indicum L.) is drought stress in arid and semiarid regions of the world. For this study the effects of water stress on some qualitative and quantitative traits in sesame germplasm was conducted in the Research Farm of Seed and Plant Improvement Institute, Karaj, in the crop year. Genotypes in a randomized complete block design with three replications in two environments (moisture stress and normal) were studied in regard of the seed weight, capsule weight, grain yield, biomass, plant height, number of capsules per plant, etc. The characteristics were evaluated based on the combined analysis. Irrigation was based on first class evaporation basin. After flowering stage drought stress was applied. The water deficit reduced growth period. Days to reach full ripening decreased so that the reduction was significant at the five percent level. Drought stress reduces yield and plant biomass. Genotypes based on combined analysis of these two traits were significant at the one percent level. Genotypes differ in terms of yield stress in terms of density plots, grain yield, days to first flowering and days to the half of the cap on the confidence level of five percent and traits of days to emergence of the first capsule and days to reach full ripening at the one percent level were significant. Other traits were not significant. The correlation of traits in circumstances of stress the number of seeds per capsule has the greatest impact on performance. The sensitivity and stress tolerance index was calculated. Based on the indicators, (Fars variety) and variety Karaj were identified as the most tolerant genotypes among the studied genotypes to drought stress. The highest sensitivity indicator of stress was related to genotype (FARS).

Keywords: sesamum, drought, stress, germplasm, resistance

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Authors: Kazuhisa Koda, Tsutomu Kobayashi

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Laura Island, which is located about 50 km away from downtown, is a source of water supply in Majuro atoll, which is the capital of the Republic of the Marshall Islands. Low and flat Majuro atoll has neither river nor lake. It is very important for Majuro atoll to ensure the conservation of its water resources. However, up-coning, which is the process of partial rising of the freshwater-saltwater boundary near the water-supply well, was caused by the excess pumping from it during the severe drought in 1998. Up-coning will make the water usage of the freshwater lens difficult. Thus, appropriate water usage is required to prevent up-coning in the freshwater lens because there is no other water source during drought. Numerical simulation of water usage applying SEAWAT model was conducted at the central part of Laura Island, including the water-supply well, which was affected by up-coning. The freshwater lens was created as a result of infiltration of consistent average rainfall. The lens shape was almost the same as the one in 1985. 0 of monthly rainfall and variable daily pump discharge were used to calculate the sustainable pump discharge from the water-supply well. Consequently, the total amount of pump discharge was increased as the daily pump discharge was increased, indicating that it needs more time to recover from up-coning. Thus, a pump standard to reduce the pump intensity is being proposed, which is based on numerical simulation concerning the occurrence of the up-coning phenomenon in Laura Island during the drought.

Keywords: freshwater lens, islands, numerical simulation, sustainable water use

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Authors: Filippo Portera

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Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

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Authors: Shang-Hsin Ou, Yang-Chun Lin, Ke-Hao Cheng

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The year 2021 marked a historic drought in Taiwan, posing unprecedented challenges due to record-low rainfall and inadequate reservoir storage. The central region experienced water scarcity, leading to the implementation of "Groundwater Utilization at Construction Sites" for drought-resilient livelihood water supply. This study focuses on the establishment process of temporary groundwater treatment plants at construction sites in Taichung City, serving as a reference for future emergency response and the utilization of construction site groundwater. To identify suitable sites for groundwater reuse projects, site selection operations were carried out based on relevant water quality regulations and assessment principles. Subsequently, the planning and design of temporary water treatment plants were conducted, considering the water quality, quantity, and on-site conditions of groundwater wells associated with construction projects. The study consolidates the major water treatment facilities at each site and addresses encountered challenges during the establishment process. Practical insights gained from operating temporary groundwater treatment plants are presented, including improvements related to stable water quality, water quantity, equipment operation, and hydraulic control. In light of possible future droughts, this study provides an outlook and recommendations to expedite and improve the setup of groundwater treatment plants at construction sites. This includes considering on-site water abstraction, treatment, and distribution conditions. The study's results aim to offer practical guidelines for effectively establishing and managing such treatment plants, while offering experiences and recommendations for other regions facing similar emergencies, water shortages, and drought situations. These endeavors contribute to ensuring sustainable water supply for drought-resilient livelihoods and maintaining societal stability.

Keywords: drought resilience, groundwater treatment, construction site, water supply

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Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

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Authors: Mohammed Alkiyumi

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Educational systems are currently paying special attention to developing learners' higher thinking skills to develop the capabilities of human resources to deal with contemporary challenges. Although psychologists disagree about the concept of higher-order thinking skills and the skills they include, there is unlimited effort in designing them and building strategies for their implementation. The most important factor helping to develop these skills is their classification according to specific criteria, and the most important of these classifications is Bloom's classification, which is dominant in most educational systems at all levels. Previous classifications have many limitations, including the comprehensiveness of the skills they contain, the logical structure of their hierarchy, and classification criteria. Therefore, this article puts another step in this area by providing a new classification of higher-order thinking skills that includes five categories: the first response stage, transformative stage, application, reasoning stage, and the production stage with a logical justification for this classification, with some techniques to developing it among learners.

Keywords: high-order thinking skills, classification, teaching, education

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Authors: Sunita Singh, Rajani Srivastava

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For natural resource management and in other applications about earth observation revolutionary remote sensing technology plays a significant role. One of such application in monitoring and classification of crop types at spatial and temporal scale, as it provides latest, most precise and cost-effective information. Present study emphasizes the use of three different vegetation indices of Rapid Eye imagery on crop type classification. It also analyzed the effect of each indices on classification accuracy. Rapid Eye imagery is highly demanded and preferred for agricultural and forestry sectors as it has red-edge and NIR bands. The three indices used in this study were: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) and all of these incorporated the Red Edge band. The study area is Varanasi district of Uttar Pradesh, India and Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Classification was performed with these three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 85% was obtained using three vegetation indices. The study concluded that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the Rapid Eye imagery can get satisfactory results of classification accuracy without original bands.

Keywords: GNDVI, NDRE, NDVI, rapid eye, vegetation indices

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Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

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Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5

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Authors: Po-Jen Chen, Jian-Jiun Ding, Hung-Wei Hsu, Chien-Yao Wang, Jia-Ching Wang

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A more accurate scene classification algorithm using label relations and the hierarchy neural network was developed in this work. In many classification algorithms, it is assumed that the labels are mutually exclusive. This assumption is true in some specific problems, however, for scene classification, the assumption is not reasonable. Because there are a variety of objects with a photo image, it is more practical to assign multiple labels for an image. In this paper, two label relations, which are exclusive relation and hierarchical relation, were adopted in the classification process to achieve more accurate multiple label classification results. Moreover, the hierarchy neural network (hierarchy NN) is applied to classify the image and the directed acyclic graph structure is used for predicting a more reasonable result which obey exclusive and hierarchical relations. Simulations show that, with these techniques, a much more accurate scene classification result can be achieved.

Keywords: convolutional neural network, label relation, hierarchy neural network, scene classification

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Authors: Sanchali Das, Swapan Debbarma

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Music mood classification is developing in both the areas of music information retrieval (MIR) and natural language processing (NLP). Some of the Indian languages like Hindi English etc. have considerable exposure in MIR. But research in mood classification in regional language is very less. In this paper, powerful audio based feature for Kokborok Christian song is identified and mood classification task has been performed. Kokborok is an Indo-Burman language especially spoken in the northeastern part of India and also some other countries like Bangladesh, Myanmar etc. For performing audio-based classification task, useful audio features are taken out by jMIR software. There are some standard audio parameters are there for the audio-based task but as known to all that every language has its unique characteristics. So here, the most significant features which are the best fit for the database of Kokborok song is analysed. The regression-based model is used to find out the independent parameters that act as a predictor and predicts the dependencies of parameters and shows how it will impact on overall classification result. For classification WEKA 3.5 is used, and selected parameters create a classification model. And another model is developed by using all the standard audio features that are used by most of the researcher. In this experiment, the essential parameters that are responsible for effective audio based mood classification and parameters that do not significantly change for each of the Christian Kokborok songs are analysed, and a comparison is also shown between the two above model.

Keywords: Christian Kokborok song, mood classification, music information retrieval, regression

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Authors: Sujo Meghwar, Zulfqar Ali laghari, Kanji Harijan, Muhib Ali Lagari, G. M. Mastoi, Ali Mohammad Rind

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Thar Desert is the most populous Desert of the world. Climate variation in Thar Desert has induced an increase in the magnitude of drought. The variation in climate variation has caused a decrease in natural vegetations. Some plant species are eliminated forever. We have applied the SPI (standardized precipitation index) climate model to investigate the drought induced by climate change. We have gathered the anthropogenic response through a developed questionnaire. The data was analyzed in SPSS version 18. The met-data of two meteorological station elaborated by the time series has suggested an increase in temperature from 1-2.5 centigrade, the decrease in rain fall rainfall from 5-25% and reduction in humidity from 5-12 mm in the 20th century. The anthropogenic responses indicate high impact of climate change on human life and vegetations. Triangle data, we have collected, gives a new insight into the understanding of an association between climate change, drought and human activities.

Keywords: Thar desert, human impact, vegetations, temperature, rainfall, humidity

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Authors: Hasen Keder Edris, Ryuji Matsunaga, Toshi Yamanaka

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The issue of climate change and its impact is a major contemporary global concern. Ethiopia is one of the countries experiencing adverse climate change impact including frequent extreme weather events that are exacerbating drought and water scarcity. Due to this reason, the government of Ethiopia develops a strategic document which focuses on the climate resilience green economy. One of the major components of the strategic framework is designed to improve community adaptation capacity and mitigation of drought. For effective implementation of the strategy, identification of regions relative vulnerability to drought is vital. There is a growing tendency of applying Geographic Information System (GIS) and Remote Sensing technologies for collecting information on duration and severity of drought by direct measure of the topography as well as an indirect measure of land cover. This study aims to show an application of remote sensing technology and GIS for developing drought vulnerability index by taking lowland of Ethiopia as a case study. In addition, it assesses integrated Information Communication Technology (ICT) potential of Ethiopia lowland and proposes integrated solution. Satellite data is used to detect the beginning of the drought. The severity of drought risk prone areas of livestock keeping pastoral is analyzed through normalized difference vegetation index (NDVI) and ten years rainfall data. The change from the existing and average SPOT NDVI and vegetation condition index is used to identify the onset of drought and potential risks. Secondary data is used to analyze geographical coverage of mobile and internet usage in the region. For decades, the government of Ethiopia introduced some technologies and approach to overcoming climate change related problems. However, lack of access to information and inadequate technical support for the pastoral area remains a major challenge. In conventional business as usual approach, the lowland pastorals continue facing a number of challenges. The result indicated that 80% of the region face frequent drought occurrence and out of this 60% of pastoral area faces high drought risk. On the other hand, the target area mobile phone and internet coverage is rapidly growing. One of identified ICT solution enabler technology is telecom center which covers 98% of the region. It was possible to identify the frequently affected area and potential drought risk using the NDVI remote-sensing data analyses. We also found that ICT can play an important role in mitigating climate change challenge. Hence, there is a need to strengthen implementation efforts of climate change adaptation through integrated Remote Sensing and web based information dissemination and mobile alert of extreme events.

Keywords: climate changes, ICT, pastoral, remote sensing

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: Mohammad Sarchami, Mohsen Zeinalkhani

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Always, financial reporting system faces some problems to win public ear. The increase in the number of fraud and representation, often combined with the bankruptcy of large companies, has raised concerns about the quality of financial statements. So, investors, legislators, managers, and auditors have focused on significant fraud detection or prevention in financial statements. This article aims to investigate the Sugeno fuzzy classification to consider fraud detection in financial reporting of accepted firms by Tehran stock exchange. The hypothesis is: Sugeno fuzzy classification may detect fraud in financial reporting by financial ratio. Hypothesis was tested using Matlab software. Accuracy average was 81/80 in Sugeno fuzzy classification; so the hypothesis was confirmed.

Keywords: fraud, financial reporting, Sugeno fuzzy classification, firm

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Authors: Vesile Evrim, Aliyu Awwal

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Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

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Authors: Arash Azarpanah, Masoud Zadehbagheri, Shorangiz Javanmardi

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Abiotic stresses are the principle threat to plant growth and crop productivity all over the world. In order to improve the germination of corn seeds in drought stress conditions, effect of seed priming by various concentrations of salicylic acid (SA) (0.8 and 0.2 mM) on activities of catalase and peroxidase in Zea mays L. plant (Var-Sc.704) was evaluated at Agriculture Research Center located in Arsenjan city in Iran, during summer 2013. A farm research was done in RCBD as factorial with three replications. We considered four irrigation was carried out once the cumulative evaporation from Pan Class A come to 40, 60, 80 and 100 mm. Results illustrated that drought stress significantly increased activities of catalase and peroxidase and also treatment with salicylic acid significantly increased activities of catalase and peroxidase. In addition, treatment with salicylic acid enhances drought tolerance in Zea mays L. plant (Var-Sc.704) with increasing activities of antioxidant enzymes.

Keywords: catalase, corn, salicylic acid, water deficits stress, cumulative evaporation, Pan Class A

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Authors: Benhammadi Hocine, Fehdi Chemseddine, Chaffai Hicham

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The study site is the plateau Cheria, a city in south eastern Algeria (Tebessa) thanks to its structure perched syncline is the region of Tebessa a real water tower. Special rates provided by some boreholes and wells around the city Cheria have long been led to believe that the reserves were virtually limitless. The investigations carried out in this region have located karstified limestone areas at depth of 100 meters of the carbonate formation. During the last two decades a rainfall deficit has increased the effect of drought has caused an increase in flow from this aquifer. The effect on water resources is a significant and progressive reduction of the static level of the karst aquifer. The qualitative aspect has also been marked by degradation. This climate variability marked by the decade of drought (1990/2000) has had the effect on the local population, a forced change of their activity primarily agricultural. Abandoning agro pastoral mode due to prolonged drought, populations chose agriculture maraichère consumer a lot of water, this increasing the depletion of water resources. This change in activity was accompanied by a rural exodus to urban areas. The result has led to an increase in population in the urban areas, this has resulted in an increase in water demand and an increase in emissions (waste water). Uncontrolled discharges contribute to pollute a little more groundwater. The second consequence is type Geotechnical, it is the appearance of sinkholes, results of the alternating periods of drought and violent floods. Sinkholes are a real concern for the management and urban development. An interdisciplinary contribution (geology, hydrology, climatology and management) is essential to reduce or avoid impacts in different sectors.

Keywords: aquifer, carbonate formation, drought, exodus, resources, chéria, Algéria

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Authors: Nokwethaba Makhanya, Babatunde J. Abiodun, Piotr Wolski

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Climate change possibly intensifies hydrological droughts and reduces water availability in river basins. Despite this, most research on climate change effects in southern Africa has focused exclusively on meteorological droughts. This thesis projects the potential impact of climate change on the future characteristics of hydrological droughts in the Limpopo River Basin (LRB). The study uses regional climate model (RCM) measurements (from the Coordinated Regional Climate Downscaling Experiment, CORDEX) and a combination of hydrological simulations (using the Soil and Water Assessment Tool Plus model, SWAT+) to predict the impacts at four global warming levels (GWLs: 1.5℃, 2.0℃, 2.5℃, and 3.0℃) under the RCP8.5 future climate scenario. The SWAT+ model was calibrated and validated with a streamflow dataset observed over the basin, and the sensitivity of model parameters was investigated. The performance of the SWAT+LRB model was verified using the Nash-Sutcliffe efficiency (NSE), Percent Bias (PBIAS), Root Mean Square Error (RMSE), and coefficient of determination (R²). The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) have been used to detect meteorological droughts. The Soil Water Index (SSI) has been used to define agricultural drought, while the Water Yield Drought Index (WYLDI), the Surface Run-off Index (SRI), and the Streamflow Index (SFI) have been used to characterise hydrological drought. The performance of the SWAT+ model simulations over LRB is sensitive to the parameters CN2 (initial SCS runoff curve number for moisture condition II) and ESCO (soil evaporation compensation factor). The best simulation generally performed better during the calibration period than the validation period. In calibration and validation periods, NSE is ≤ 0.8, while PBIAS is ≥ ﹣80.3%, RMSE ≥ 11.2 m³/s, and R² ≤ 0.9. The simulations project a future increase in temperature and potential evapotranspiration over the basin, but they do not project a significant future trend in precipitation and hydrological variables. However, the spatial distribution of precipitation reveals a projected increase in precipitation in the southern part of the basin and a decline in the northern part of the basin, with the region of reduced precipitation projected to increase with GWLs. A decrease in all hydrological variables is projected over most parts of the basin, especially over the eastern part of the basin. The simulations predict meteorological droughts (i.e., SPEI and SPI), agricultural droughts (i.e., SSI), and hydrological droughts (i.e., WYLDI, SRI) would become more intense and severe across the basin. SPEI-drought has a greater magnitude of increase than SPI-drought, and agricultural and hydrological droughts have a magnitude of increase between the two. As a result, this research suggests that future hydrological droughts over the LRB could be more severe than the SPI-drought projection predicts but less severe than the SPEI-drought projection. This research can be used to mitigate the effects of potential climate change on basin hydrological drought.

Keywords: climate change, CORDEX, drought, hydrological modelling, Limpopo River Basin

Procedia PDF Downloads 127

Authors: M.R Asghari Bejestani, Gh. R. Mohammad Khani, V.R. Nafisi

Abstract:

Imagined speech recognition is one of the most interesting approaches to BCI development and a lot of works have been done in this area. Many different experiments have been designed and hundreds of combinations of feature extraction methods and classifiers have been examined. Reported classification accuracies range from the chance level to more than 90%. Based on non-stationary nature of brain signals, we have introduced 3 classification modes according to time difference in inter and intra-class samples. The modes can explain the diversity of reported results and predict the range of expected classification accuracies from the brain signal accusation procedure. In this paper, a few samples are illustrated by inspecting results of some previous works.

Keywords: brain computer interface, silent talk, imagined speech, classification, signal processing

Procedia PDF Downloads 152

Authors: Ren Moses, Jaqueline Masaki

Abstract:

This paper addresses the need for accurate and updated vehicle classification system through a thorough evaluation of vehicle class categories to identify errors arising from the existing system and proposing modifications. The data collected from two permanent traffic monitoring sites in Florida were used to evaluate the performance of the existing vehicle classification table. The vehicle data were collected and classified by the automatic vehicle classifier (AVC), and a video camera was used to obtain ground truth data. The Federal Highway Administration (FHWA) vehicle classification definitions were used to define vehicle classes from the video and compare them to the data generated by AVC in order to identify the sources of misclassification. Six types of errors were identified. Modifications were made in the classification table to improve the classification accuracy. The results of this study include the development of updated vehicle classification table with a reduction in total error by 5.1%, a step by step procedure to use for evaluation of vehicle classification studies and recommendations to improve FHWA 13-category rule set. The recommendations for the FHWA 13-category rule set indicate the need for the vehicle classification definitions in this scheme to be updated to reflect the distribution of current traffic. The presented results will be of interest to States’ transportation departments and consultants, researchers, engineers, designers, and planners who require accurate vehicle classification information for planning, designing and maintenance of transportation infrastructures.

Keywords: vehicle classification, traffic monitoring, pavement design, highway traffic

Procedia PDF Downloads 178

Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

Abstract:

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

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

Procedia PDF Downloads 314