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classification</title> <meta name="description" content="Search results for: species classification"> <meta name="keywords" content="species classification"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="species classification"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5258</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: species classification</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5258</span> Mapping of Arenga Pinnata Tree Using Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zulkiflee%20Abd%20Latif">Zulkiflee Abd Latif</a>, <a href="https://publications.waset.org/abstracts/search?q=Sitinor%20Atikah%20Nordin"> Sitinor Atikah Nordin</a>, <a href="https://publications.waset.org/abstracts/search?q=Alawi%20Sulaiman"> Alawi Sulaiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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 <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arenga%20Pinnata" title="Arenga Pinnata">Arenga Pinnata</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel-based%20classification" title=" pixel-based classification"> pixel-based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=object-based%20classification" title=" object-based classification"> object-based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/13681/mapping-of-arenga-pinnata-tree-using-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13681.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">380</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5257</span> Taxonomic Classification for Living Organisms Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saed%20Khawaldeh">Saed Khawaldeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Elsharnouby"> Mohamed Elsharnouby</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaa%20%20Eddin%20Alchalabi"> Alaa Eddin Alchalabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Usama%20Pervaiz"> Usama Pervaiz</a>, <a href="https://publications.waset.org/abstracts/search?q=Tajwar%20Aleef"> Tajwar Aleef</a>, <a href="https://publications.waset.org/abstracts/search?q=Vu%20Hoang%20Minh"> Vu Hoang Minh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20networks" title="deep networks">deep networks</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=taxonomic%20classification" title=" taxonomic classification"> taxonomic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20sequences%20classification" title=" DNA sequences classification "> DNA sequences classification </a> </p> <a href="https://publications.waset.org/abstracts/65170/taxonomic-classification-for-living-organisms-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65170.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5256</span> Improved Rare Species Identification Using Focal Loss Based Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chad%20Goldsworthy">Chad Goldsworthy</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Rajeswari%20Matam"> B. Rajeswari Matam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20imbalance" title=" data imbalance"> data imbalance</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=focal%20loss" title=" focal loss"> focal loss</a>, <a href="https://publications.waset.org/abstracts/search?q=species%20classification" title=" species classification"> species classification</a>, <a href="https://publications.waset.org/abstracts/search?q=wildlife%20conservation" title=" wildlife conservation"> wildlife conservation</a> </p> <a href="https://publications.waset.org/abstracts/132442/improved-rare-species-identification-using-focal-loss-based-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5255</span> High Resolution Satellite Imagery and Lidar Data for Object-Based Tree Species Classification in Quebec, Canada</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bilel%20Chalghaf">Bilel Chalghaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Mathieu%20Varin"> Mathieu Varin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tree%20species" title="tree species">tree species</a>, <a href="https://publications.waset.org/abstracts/search?q=object-based" title=" object-based"> object-based</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral" title=" multispectral"> multispectral</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=WorldView-3" title=" WorldView-3"> WorldView-3</a>, <a href="https://publications.waset.org/abstracts/search?q=LiDAR" title=" LiDAR"> LiDAR</a> </p> <a href="https://publications.waset.org/abstracts/119023/high-resolution-satellite-imagery-and-lidar-data-for-object-based-tree-species-classification-in-quebec-canada" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119023.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5254</span> Unsupervised Classification of DNA Barcodes Species Using Multi-Library Wavelet Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdesselem%20Dakhli">Abdesselem Dakhli</a>, <a href="https://publications.waset.org/abstracts/search?q=Wajdi%20Bellil"> Wajdi Bellil</a>, <a href="https://publications.waset.org/abstracts/search?q=Chokri%20Ben%20Amar"> Chokri Ben Amar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNA Barcode, a short mitochondrial DNA fragment, made up of three subunits; a phosphate group, sugar and nucleic bases (A, T, C, and G). They provide good sources of information needed to classify living species. Such intuition has been confirmed by many experimental results. Species classification with DNA Barcode sequences has been studied by several researchers. The classification problem assigns unknown species to known ones by analyzing their Barcode. This task has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. To make this type of analysis feasible, heuristics, like progressive alignment, have been developed. Another tool for similarity search against a database of sequences is BLAST, which outputs shorter regions of high similarity between a query sequence and matched sequences in the database. However, all these methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. This method permits to avoid the complex problem of form and structure in different classes of organisms. On empirical data and their classification performances are compared with other methods. Our system consists of three phases. The first is called transformation, which is composed of three steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. The second is called approximation, which is empowered by the use of Multi Llibrary Wavelet Neural Networks (MLWNN).The third is called the classification of DNA Barcodes, which is realized by applying the algorithm of hierarchical classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA%20barcode" title="DNA barcode">DNA barcode</a>, <a href="https://publications.waset.org/abstracts/search?q=electron-ion%20interaction%20pseudopotential" title=" electron-ion interaction pseudopotential"> electron-ion interaction pseudopotential</a>, <a href="https://publications.waset.org/abstracts/search?q=Multi%20Library%20Wavelet%20Neural%20Networks%20%28MLWNN%29" title=" Multi Library Wavelet Neural Networks (MLWNN)"> Multi Library Wavelet Neural Networks (MLWNN)</a> </p> <a href="https://publications.waset.org/abstracts/27669/unsupervised-classification-of-dna-barcodes-species-using-multi-library-wavelet-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27669.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5253</span> Palyno-Morphological Characteristics of Gymnosperm Flora of Pakistan and Its Taxonomic Implications with Light Microscope and Scanning Electron Microscopy Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raees%20Khan">Raees Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheikh%20Z.%20Ul%20Abidin"> Sheikh Z. Ul Abidin</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20S.%20Mumtaz"> Abdul S. Mumtaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Liu"> Jie Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study is intended to assess gymnosperms pollen flora of Pakistan using Light Microscope (LM) and Scanning Electron Microscopy (SEM) for its taxonomic significance in identification of gymnosperms. Pollens of 35 gymnosperm species (12 genera and five families) were collected from its various distributional sites of gymnosperms in Pakistan. LM and SEM were used to investigate different palyno-morphological characteristics. Five pollen types (i.e., Inaperturate, Monolete, Monoporate, Vesiculate-bisaccate, and Polyplicate) were observed. In equatorial view seven types of pollens were observed, in which ten species were sub-angular, nine species were triangular, six species were perprolate, three species were rhomboidal, three species were semi-angular, two species were rectangular and two species were prolate. While five types of pollen were observed in polar view, in which ten species were spheroidal, nine species were angular, eight were interlobate, six species were circular, and two species were elliptic. Eighteen species have rugulate and 17 species has faveolate ornamentation. Eighteen species have verrucate and 17 have gemmate type sculpturing. The data was analysed through cluster analysis. The study showed that these palyno-morphological features have significance value in classification and identification of gymnosperms. Based on these different palyno-morphological features, a taxonomic key was proposed for the accurate and fast identifications of gymnosperms from Pakistan. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gymnosperms" title="gymnosperms">gymnosperms</a>, <a href="https://publications.waset.org/abstracts/search?q=palynology" title=" palynology"> palynology</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakistan" title=" Pakistan"> Pakistan</a>, <a href="https://publications.waset.org/abstracts/search?q=taxonomy" title=" taxonomy"> taxonomy</a> </p> <a href="https://publications.waset.org/abstracts/82016/palyno-morphological-characteristics-of-gymnosperm-flora-of-pakistan-and-its-taxonomic-implications-with-light-microscope-and-scanning-electron-microscopy-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82016.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">221</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5252</span> Evaluating Classification with Efficacy Metrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guofan%20Shao">Guofan Shao</a>, <a href="https://publications.waset.org/abstracts/search?q=Lina%20Tang"> Lina Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Zhang"> Hao Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy%20assessment" title="accuracy assessment">accuracy assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=efficacy" title=" efficacy"> efficacy</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/142555/evaluating-classification-with-efficacy-metrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142555.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">211</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5251</span> Urban Land Cover from GF-2 Satellite Images Using Object Based and Neural Network Classifications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lamyaa%20Gamal%20El-Deen%20Taha">Lamyaa Gamal El-Deen Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20Sharawi"> Ashraf Sharawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GF-2%20images" title="GF-2 images">GF-2 images</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction-rectification" title=" feature extraction-rectification"> feature extraction-rectification</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbour%20object%20based%20classification" title=" nearest neighbour object based classification"> nearest neighbour object based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20algorithms" title=" segmentation algorithms"> segmentation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20classification" title=" neural network classification"> neural network classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/84243/urban-land-cover-from-gf-2-satellite-images-using-object-based-and-neural-network-classifications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84243.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5250</span> Arabic Text Representation and Classification Methods: Current State of the Art</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rami%20Ayadi">Rami Ayadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Maraoui"> Mohsen Maraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have presented a brief current state of the art for Arabic text representation and classification methods. We decomposed Arabic Task Classification into four categories. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title="text classification">text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=impact%20of%20preprocessing" title=" impact of preprocessing"> impact of preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title=" classification algorithms"> classification algorithms</a> </p> <a href="https://publications.waset.org/abstracts/10277/arabic-text-representation-and-classification-methods-current-state-of-the-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10277.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">469</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5249</span> Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Virender%20Singh">Virender Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mathew%20Rees"> Mathew Rees</a>, <a href="https://publications.waset.org/abstracts/search?q=Simon%20Hampton"> Simon Hampton</a>, <a href="https://publications.waset.org/abstracts/search?q=Sivaram%20Annadurai"> Sivaram Annadurai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=plant%20identification" title="plant identification">plant identification</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20transformer" title=" vision transformer"> vision transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/162359/plant-identification-using-convolution-neural-network-and-vision-transformer-based-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5248</span> Heuristic of Style Transfer for Real-Time Detection or Classification of Weather Conditions from Camera Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Ouattara">Hamed Ouattara</a>, <a href="https://publications.waset.org/abstracts/search?q=Pierre%20Duthon"> Pierre Duthon</a>, <a href="https://publications.waset.org/abstracts/search?q=Fr%C3%A9d%C3%A9ric%20Bernardin"> Frédéric Bernardin</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20Ait%20Aider"> Omar Ait Aider</a>, <a href="https://publications.waset.org/abstracts/search?q=Pascal%20Salmane"> Pascal Salmane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we present three neural network architectures for real-time classification of weather conditions (sunny, rainy, snowy, foggy) from images. Inspired by recent advances in style transfer, two of these architectures -Truncated ResNet50 and Truncated ResNet50 with Gram Matrix and Attention- surpass the state of the art and demonstrate re-markable generalization capability on several public databases, including Kaggle (2000 images), Kaggle 850 images, MWI (1996 images) [1], and Image2Weather [2]. Although developed for weather detection, these architectures are also suitable for other appearance-based classification tasks, such as animal species recognition, texture classification, disease detection in medical images, and industrial defect identification. We illustrate these applications in the section “Applications of Our Models to Other Tasks” with the “SIIM-ISIC Melanoma Classification Challenge 2020” [3]. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weather%20simulation" title="weather simulation">weather simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20measurement" title=" weather measurement"> weather measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20classification" title=" weather classification"> weather classification</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20detection" title=" weather detection"> weather detection</a>, <a href="https://publications.waset.org/abstracts/search?q=style%20transfer" title=" style transfer"> style transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=Pix2Pix" title=" Pix2Pix"> Pix2Pix</a>, <a href="https://publications.waset.org/abstracts/search?q=CycleGAN" title=" CycleGAN"> CycleGAN</a>, <a href="https://publications.waset.org/abstracts/search?q=CUT" title=" CUT"> CUT</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20style%20transfer" title=" neural style transfer"> neural style transfer</a> </p> <a href="https://publications.waset.org/abstracts/194615/heuristic-of-style-transfer-for-real-time-detection-or-classification-of-weather-conditions-from-camera-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194615.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">5</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5247</span> Microbiological Analysis of Soil from Onu-Ebonyi Contaminated with Inorganic Fertilizer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20Alo">M. N. Alo</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20C.%20C.%20Egbule"> U. C. C. Egbule</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20O.%20Orji"> J. O. Orji</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20J.%20Aneke"> C. J. Aneke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microbiological analysis of soil from Onu-Ebonyi Izzi local government area of Ebonyi State, Nigeria contaminated with inorganic fertilizer was carried out with a view to determine the effect of the fertilizer on the microbial flora of the soil. soil samples were analyzed for microbial burden. the result showed that the following organisms were isolated with their frequency of their occurrence as follows:pseudomonas species (33.3%) and aspergillus species (54.4%) had the highest frequncy of occurence in the whole sample of batches, while streptococcus species had 6.0% and Geotrichum species (5.3%) had the least and other predominant microorganism isolated: bacillus species,staphylococcus species and vibrio species, Escherichia species, rhzizopus species, mucor species and fusaruim species. From the result, it could be concluded that the soil was contaminated and this could affect adversely the fertility of the soil . <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soil" title="soil">soil</a>, <a href="https://publications.waset.org/abstracts/search?q=bacteria" title=" bacteria"> bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=fungi" title=" fungi"> fungi</a>, <a href="https://publications.waset.org/abstracts/search?q=inorganic%20fertilizer" title=" inorganic fertilizer"> inorganic fertilizer</a>, <a href="https://publications.waset.org/abstracts/search?q=Onu-%20Ebonyi" title=" Onu- Ebonyi "> Onu- Ebonyi </a> </p> <a href="https://publications.waset.org/abstracts/15269/microbiological-analysis-of-soil-from-onu-ebonyi-contaminated-with-inorganic-fertilizer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">512</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5246</span> Sensitive Analysis of the ZF Model for ABC Multi Criteria Inventory Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Makram%20Ben%20Jeddou">Makram Ben Jeddou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ABC classification is widely used by managers for inventory control. The classical ABC classification is based on the Pareto principle and according to the criterion of the annual use value only. Single criterion classification is often insufficient for a closely inventory control. Multi-criteria inventory classification models have been proposed by researchers in order to take into account other important criteria. From these models, we will consider the ZF model in order to make a sensitive analysis on the composite score calculated for each item. In fact, this score based on a normalized average between a good and a bad optimized index can affect the ABC items classification. We will then focus on the weights assigned to each index and propose a classification compromise. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABC%20classification" title="ABC classification">ABC classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20criteria%20inventory%20%20classification%20models" title=" multi criteria inventory classification models"> multi criteria inventory classification models</a>, <a href="https://publications.waset.org/abstracts/search?q=ZF-model" title=" ZF-model"> ZF-model</a> </p> <a href="https://publications.waset.org/abstracts/22613/sensitive-analysis-of-the-zf-model-for-abc-multi-criteria-inventory-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">508</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5245</span> Tomato-Weed Classification by RetinaNet One-Step Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dionisio%20Andujar">Dionisio Andujar</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20l%C3%B3pez-Correa"> Juan lópez-Correa</a>, <a href="https://publications.waset.org/abstracts/search?q=Hugo%20Moreno"> Hugo Moreno</a>, <a href="https://publications.waset.org/abstracts/search?q=Angela%20Ri"> Angela Ri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cnn" title=" cnn"> cnn</a>, <a href="https://publications.waset.org/abstracts/search?q=tomato" title=" tomato"> tomato</a>, <a href="https://publications.waset.org/abstracts/search?q=weeds" title=" weeds"> weeds</a> </p> <a href="https://publications.waset.org/abstracts/158383/tomato-weed-classification-by-retinanet-one-step-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5244</span> Design of Bacterial Pathogens Identification System Based on Scattering of Laser Beam Light and Classification of Binned Plots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mubashir%20Hussain">Mubashir Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Mu%20Lv"> Mu Lv</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaohan%20Dong"> Xiaohan Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiyang%20Li"> Zhiyang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liu"> Bin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Nongyue%20He"> Nongyue He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection and classification of microbes have a vast range of applications in biomedical engineering especially in detection, characterization, and quantification of bacterial contaminants. For identification of pathogens, different techniques are emerging in the field of biomedical engineering. Latest technology uses light scattering, capable of identifying different pathogens without any need for biochemical processing. Bacterial Pathogens Identification System (BPIS) which uses a laser beam, passes through the sample and light scatters off. An assembly of photodetectors surrounded by the sample at different angles to detect the scattering of light. The algorithm of the system consists of two parts: (a) Library files, and (b) Comparator. Library files contain data of known species of bacterial microbes in the form of binned plots, while comparator compares data of unknown sample with library files. Using collected data of unknown bacterial species, highest voltage values stored in the form of peaks and arranged in 3D histograms to find the frequency of occurrence. Resulting data compared with library files of known bacterial species. If sample data matching with any library file of known bacterial species, sample identified as a matched microbe. An experiment performed to identify three different bacteria particles: Enterococcus faecalis, Pseudomonas aeruginosa, and Escherichia coli. By applying algorithm using library files of given samples, results were compromising. This system is potentially applicable to several biomedical areas, especially those related to cell morphology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microbial%20identification" title="microbial identification">microbial identification</a>, <a href="https://publications.waset.org/abstracts/search?q=laser%20scattering" title=" laser scattering"> laser scattering</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20identification" title=" peak identification"> peak identification</a>, <a href="https://publications.waset.org/abstracts/search?q=binned%20plots%20classification" title=" binned plots classification"> binned plots classification</a> </p> <a href="https://publications.waset.org/abstracts/95711/design-of-bacterial-pathogens-identification-system-based-on-scattering-of-laser-beam-light-and-classification-of-binned-plots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95711.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5243</span> A New Approach for Improving Accuracy of Multi Label Stream Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kunal%20Shah">Kunal Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Swati%20Patel"> Swati Patel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20relevance" title="binary relevance">binary relevance</a>, <a href="https://publications.waset.org/abstracts/search?q=concept%20drift" title=" concept drift"> concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20stream%20mining" title=" data stream mining"> data stream mining</a>, <a href="https://publications.waset.org/abstracts/search?q=MLSC" title=" MLSC"> MLSC</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20window%20with%20buffer" title=" multiple window with buffer"> multiple window with buffer</a> </p> <a href="https://publications.waset.org/abstracts/33035/a-new-approach-for-improving-accuracy-of-multi-label-stream-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33035.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">584</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5242</span> Comparison of Machine Learning and Deep Learning Algorithms for Automatic Classification of 80 Different Pollen Species</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endrick%20Barnacin">Endrick Barnacin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Luc%20Henry"> Jean-Luc Henry</a>, <a href="https://publications.waset.org/abstracts/search?q=Jimmy%20Nagau"> Jimmy Nagau</a>, <a href="https://publications.waset.org/abstracts/search?q=Jack%20Molinie"> Jack Molinie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pollens%20identification" title="pollens identification">pollens identification</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=pollens%20classification" title=" pollens classification"> pollens classification</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20palynology" title=" automated palynology"> automated palynology</a> </p> <a href="https://publications.waset.org/abstracts/148945/comparison-of-machine-learning-and-deep-learning-algorithms-for-automatic-classification-of-80-different-pollen-species" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5241</span> Classification of Attacks Over Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Abouelmehdi">Karim Abouelmehdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Loubna%20Dali"> Loubna Dali</a>, <a href="https://publications.waset.org/abstracts/search?q=Elmoutaoukkil%20Abdelmajid"> Elmoutaoukkil Abdelmajid</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Elsayed"> Hoda Elsayed</a>, <a href="https://publications.waset.org/abstracts/search?q=Eladnani%20Fatiha"> Eladnani Fatiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Benihssane%20Abderahim"> Benihssane Abderahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The security of cloud services is the concern of cloud service providers. In this paper, we will mention different classifications of cloud attacks referred by specialized organizations. Each agency has its classification of well-defined properties. The purpose is to present a high-level classification of current research in cloud computing security. This classification is organized around attack strategies and corresponding defenses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/31849/classification-of-attacks-over-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">548</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5240</span> Shark Detection and Classification with Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeremy%20Jenrette">Jeremy Jenrette</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Y.%20C.%20Liu"> Z. Y. C. Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Pranav%20Chimote"> Pranav Chimote</a>, <a href="https://publications.waset.org/abstracts/search?q=Edward%20Fox"> Edward Fox</a>, <a href="https://publications.waset.org/abstracts/search?q=Trevor%20Hastie"> Trevor Hastie</a>, <a href="https://publications.waset.org/abstracts/search?q=Francesco%20Ferretti"> Francesco Ferretti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Instagram" title=" Instagram"> Instagram</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20monitoring" title=" remote monitoring"> remote monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=sharks" title=" sharks"> sharks</a> </p> <a href="https://publications.waset.org/abstracts/144505/shark-detection-and-classification-with-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144505.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5239</span> Vegetation Assessment Under the Influence of Environmental Variables; A Case Study from the Yakhtangay Hill of Himalayan Range, Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hameed%20Ullah">Hameed Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Shujaul%20Mulk%20Khan"> Shujaul Mulk Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ullah"> Zahid Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Zeeshan%20Ahmad%20Sadia%20Jahangir"> Zeeshan Ahmad Sadia Jahangir</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah"> Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Ur%20Rahman"> Amin Ur Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Suliman"> Muhammad Suliman</a>, <a href="https://publications.waset.org/abstracts/search?q=Dost%20Muhammad"> Dost Muhammad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interrelationship between vegetation and abiotic variables inside an ecosystem is one of the main jobs of plant scientists. This study was designed to investigate the vegetation structure and species diversity along with the environmental variables in the Yakhtangay hill district Shangla of the Himalayan Mountain series Pakistan by using multivariate statistical analysis. Quadrat’s method was used and a total of 171 Quadrats were laid down 57 for Tree, Shrubs and Herbs, respectively, to analyze the phytosociological attributes of the vegetation. The vegetation of the selected area was classified into different Life and leaf-forms according to Raunkiaer classification, while PCORD software version 5 was used to classify the vegetation into different plants communities by Two-way indicator species Analysis (TWINSPAN). The CANOCCO version 4.5 was used for DCA and CCA analysis to find out variation directories of vegetation with different environmental variables. A total of 114 plants species belonging to 45 different families was investigated inside the area. The Rosaceae (12 species) was the dominant family followed by Poaceae (10 species) and then Asteraceae (7 species). Monocots were more dominant than Dicots and Angiosperms were more dominant than Gymnosperms. Among the life forms the Hemicryptophytes and Nanophanerophytes were dominant, followed by Therophytes, while among the leaf forms Microphylls were dominant, followed by Leptophylls. It is concluded that among the edaphic factors such as soil pH, the concentration of soil organic matter, Calcium Carbonates concentration in soil, soil EC, soil TDS, and physiographic factors such as Altitude and slope are affecting the structure of vegetation, species composition and species diversity at the significant level with p-value ≤0.05. The Vegetation of the selected area was classified into four major plants communities and the indicator species for each community was recorded. Classification of plants into 4 different communities based upon edaphic gradients favors the individualistic hypothesis. Indicator Species Analysis (ISA) shows the indicators of the study area are mostly indicators to the Himalayan or moist temperate ecosystem, furthermore, these indicators could be considered for micro-habitat conservation and respective ecosystem management plans. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=species%20richness" title="species richness">species richness</a>, <a href="https://publications.waset.org/abstracts/search?q=edaphic%20gradients" title=" edaphic gradients"> edaphic gradients</a>, <a href="https://publications.waset.org/abstracts/search?q=canonical%20correspondence%20analysis%20%28CCA%29" title=" canonical correspondence analysis (CCA)"> canonical correspondence analysis (CCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=TWCA" title=" TWCA"> TWCA</a> </p> <a href="https://publications.waset.org/abstracts/149820/vegetation-assessment-under-the-influence-of-environmental-variables-a-case-study-from-the-yakhtangay-hill-of-himalayan-range-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149820.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5238</span> Modelling the Effect of Biomass Appropriation for Human Use on Global Biodiversity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karina%20Reiter">Karina Reiter</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Dullinger"> Stefan Dullinger</a>, <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Plutzar"> Christoph Plutzar</a>, <a href="https://publications.waset.org/abstracts/search?q=Dietmar%20Moser"> Dietmar Moser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to population growth and changing patterns of production and consumption, the demand for natural resources and, as a result, the pressure on Earth’s ecosystems are growing. Biodiversity mapping can be a useful tool for assessing species endangerment or detecting hotspots of extinction risks. This paper explores the benefits of using the change in trophic energy flows as a consequence of the human alteration of the biosphere in biodiversity mapping. To this end, multiple linear regression models were developed to explain species richness in areas where there is no human influence (i.e. wilderness) for three taxonomic groups (birds, mammals, amphibians). The models were then applied to predict (I) potential global species richness using potential natural vegetation (NPPpot) and (II) global ‘actual’ species richness after biomass appropriation using NPP remaining in ecosystems after harvest (NPPeco). By calculating the difference between predicted potential and predicted actual species numbers, maps of estimated species richness loss were generated. Results show that biomass appropriation for human use can indeed be linked to biodiversity loss. Areas for which the models predicted high species loss coincide with areas where species endangerment and extinctions are recorded to be particularly high by the International Union for Conservation of Nature and Natural Resources (IUCN). Furthermore, the analysis revealed that while the species distribution maps of the IUCN Red List of Threatened Species used for this research can determine hotspots of biodiversity loss in large parts of the world, the classification system for threatened and extinct species needs to be revised to better reflect local risks of extinction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biodiversity%20loss" title="biodiversity loss">biodiversity loss</a>, <a href="https://publications.waset.org/abstracts/search?q=biomass%20harvest" title=" biomass harvest"> biomass harvest</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20appropriation%20of%20net%20primary%20production" title=" human appropriation of net primary production"> human appropriation of net primary production</a>, <a href="https://publications.waset.org/abstracts/search?q=species%20richness" title=" species richness"> species richness</a> </p> <a href="https://publications.waset.org/abstracts/106794/modelling-the-effect-of-biomass-appropriation-for-human-use-on-global-biodiversity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106794.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5237</span> Comparison of Support Vector Machines and Artificial Neural Network Classifiers in Characterizing Threatened Tree Species Using Eight Bands of WorldView-2 Imagery in Dukuduku Landscape, South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Galal%20Omer">Galal Omer</a>, <a href="https://publications.waset.org/abstracts/search?q=Onisimo%20Mutanga"> Onisimo Mutanga</a>, <a href="https://publications.waset.org/abstracts/search?q=Elfatih%20M.%20Abdel-Rahman"> Elfatih M. Abdel-Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Elhadi%20Adam"> Elhadi Adam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=threatened%20tree%20species" title=" threatened tree species"> threatened tree species</a>, <a href="https://publications.waset.org/abstracts/search?q=indigenous%20forest" title=" indigenous forest"> indigenous forest</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/33246/comparison-of-support-vector-machines-and-artificial-neural-network-classifiers-in-characterizing-threatened-tree-species-using-eight-bands-of-worldview-2-imagery-in-dukuduku-landscape-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33246.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">515</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5236</span> Identification and Classification of Medicinal Plants of Indian Himalayan Region Using Hyperspectral Remote Sensing and Machine Learning Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kishor%20Chandra%20%20Kandpal">Kishor Chandra Kandpal</a>, <a href="https://publications.waset.org/abstracts/search?q=Amit%20%20Kumar"> Amit Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=himalaya" title="himalaya">himalaya</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20remote%20sensing" title=" hyperspectral remote sensing"> hyperspectral remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%3B%20medicinal%20plants" title=" machine learning; medicinal plants"> machine learning; medicinal plants</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forests" title=" random forests"> random forests</a> </p> <a href="https://publications.waset.org/abstracts/136705/identification-and-classification-of-medicinal-plants-of-indian-himalayan-region-using-hyperspectral-remote-sensing-and-machine-learning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136705.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">204</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5235</span> A Predator-Prey Model with Competitive Interaction amongst the Preys</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Titus%20G.%20Kassem">Titus G. Kassem</a>, <a href="https://publications.waset.org/abstracts/search?q=Izang%20A.%20Nyam"> Izang A. Nyam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A mathematical model is constructed to study the effect of predation on two competing species in which one of the competing species is a prey to the predator whilst the other species are not under predation. Conditions for the existence and stability of equilibrium solutions were determined. Numerical simulation results indicate the possibility of a stable coexistence of the three interacting species in form of stable oscillations under certain parameter values. We also noticed that under some certain parameter values, species under predation go into extinction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=competition" title="competition">competition</a>, <a href="https://publications.waset.org/abstracts/search?q=predator-prey" title=" predator-prey"> predator-prey</a>, <a href="https://publications.waset.org/abstracts/search?q=species" title=" species"> species</a>, <a href="https://publications.waset.org/abstracts/search?q=ecology" title=" ecology"> ecology</a> </p> <a href="https://publications.waset.org/abstracts/5979/a-predator-prey-model-with-competitive-interaction-amongst-the-preys" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5979.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5234</span> Butterfly Diversity along Urban-Rural Gradient in Kolkata, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sushmita%20Chaudhuri">Sushmita Chaudhuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Parthiba%20Basu"> Parthiba Basu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urbanization leads to habitat degradation and is responsible for the fast disappearance of native butterfly species. Random sampling of rural, suburban and urban sites in an around Kolkata metropolis revealed the presence of 28 species of butterfly belonging to 5 different families in winter (February-March). Butterfly diversity, species richness and abundance decreased with increase in urbanization. Psyche (Leptosia nina of family Pieridae) was the most predominant butterfly species found everywhere in Kolkata during the winter period. The most dominant family was Nymphalidae (11species), followed by Pieridae (6 species), Lycaenidae (5 species), Papilionidae (4 species) and Hesperiidae (2 species). The rural and suburban sites had butterfly species that were unique to those sites. Vegetation cover and flowering shrub density were significantly related to butterfly diversity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=butterfly" title="butterfly">butterfly</a>, <a href="https://publications.waset.org/abstracts/search?q=Kolkata%20metropolis" title=" Kolkata metropolis"> Kolkata metropolis</a>, <a href="https://publications.waset.org/abstracts/search?q=Shannon-Weiner%20diversity%20index" title=" Shannon-Weiner diversity index"> Shannon-Weiner diversity index</a>, <a href="https://publications.waset.org/abstracts/search?q=species%20diversity" title=" species diversity"> species diversity</a> </p> <a href="https://publications.waset.org/abstracts/52028/butterfly-diversity-along-urban-rural-gradient-in-kolkata-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52028.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">289</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5233</span> Review and Comparison of Associative Classification Data Mining Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suzan%20Wedyan">Suzan Wedyan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=associative%20classification" title="associative classification">associative classification</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20ranking" title=" rule ranking"> rule ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20pruning" title=" rule pruning"> rule pruning</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/4191/review-and-comparison-of-associative-classification-data-mining-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4191.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">537</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5232</span> Computing the Similarity and the Diversity in the Species Based on Cronobacter Genome</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Al%20Daoud">E. Al Daoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of computing the similarity and the diversity in the species is to trace the process of evolution and to find the relationship between the species and discover the unique, the special, the common and the universal proteins. The proteins of the whole genome of 40 species are compared with the cronobacter genome which is used as reference genome. More than 3 billion pairwise alignments are performed using blastp. Several findings are introduced in this study, for example, we found 172 proteins in cronobacter genome which have insignificant hits in other species, 116 significant proteins in the all tested species with very high score value and 129 common proteins in the plants but have insignificant hits in mammals, birds, fishes, and insects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genome" title="genome">genome</a>, <a href="https://publications.waset.org/abstracts/search?q=species" title=" species"> species</a>, <a href="https://publications.waset.org/abstracts/search?q=blastp" title=" blastp"> blastp</a>, <a href="https://publications.waset.org/abstracts/search?q=conserved%20genes" title=" conserved genes"> conserved genes</a>, <a href="https://publications.waset.org/abstracts/search?q=Cronobacter" title=" Cronobacter"> Cronobacter</a> </p> <a href="https://publications.waset.org/abstracts/82396/computing-the-similarity-and-the-diversity-in-the-species-based-on-cronobacter-genome" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82396.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5231</span> Meta-Learning for Hierarchical Classification and Applications in Bioinformatics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabio%20Fabris">Fabio Fabris</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20A.%20Freitas"> Alex A. Freitas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm%20recommendation" title="algorithm recommendation">algorithm recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-learning" title=" meta-learning"> meta-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20classification" title=" hierarchical classification"> hierarchical classification</a> </p> <a href="https://publications.waset.org/abstracts/81005/meta-learning-for-hierarchical-classification-and-applications-in-bioinformatics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5230</span> Phylogenetic Relationships of Common Reef Fish Species in Vietnam</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dang%20Thuy%20Binh">Dang Thuy Binh</a>, <a href="https://publications.waset.org/abstracts/search?q=Truong%20Thi%20Oanh"> Truong Thi Oanh</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Phan%20Khanh%20Hung"> Le Phan Khanh Hung</a>, <a href="https://publications.waset.org/abstracts/search?q=Luong%20thi%20Tuong%20Vy"> Luong thi Tuong Vy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the greatest environmental challenges facing Asia is the management and conservation of the marine biodiversity threaten by fisheries overexploitation, pollution, habitat destruction, and climate change. To date, a few molecular taxonomical studies has been conducted on marine fauna in Vietnam. The purpose of this study was to clarify the phylogeny of economic and ecological reef fish species in Vietnam Reef fish species covering Labridae, Scaridae, Nemipteridae, Serranidae, Acanthuridae, Lutjanidae, Lethrinidae, Mullidae, Balistidae, Pseudochromidae, Pinguipedidae, Fistulariidae, Holocentridae, Synodontidae, and Pomacentridae representing 28 genera were collected from South and Center, Vietnam. Combine with Genbank sequences, a phylogenetic tree was constructed based on 16S gene of mitochondrial DNA using maximum parsimony, maximum likelihood, and Bayesian inference approaches. The phylogram showed the well-resolved clades at genus and family level. Perciformes is the major order of reef fish species in Vietnam. The monophyly of Perciformes is not strongly supported as it was clustered in the same clade with Tetraodontiformes syngnathiformes and Beryciformes. Continue sampling of commercial fish species and classification based on morphology and genetics to build DNA barcoding of fish species in Vietnam is really necessary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reef%20fish" title="reef fish">reef fish</a>, <a href="https://publications.waset.org/abstracts/search?q=16s%20rDNA" title=" 16s rDNA"> 16s rDNA</a>, <a href="https://publications.waset.org/abstracts/search?q=Vietnam" title=" Vietnam"> Vietnam</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogeny" title=" phylogeny"> phylogeny</a> </p> <a href="https://publications.waset.org/abstracts/33651/phylogenetic-relationships-of-common-reef-fish-species-in-vietnam" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33651.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">438</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5229</span> Subtidal Crabs of Oman Sea: New Collections and Biogeographic Considerations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Negar%20Ghotbeddin">Negar Ghotbeddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Seied%20Mohammad%20Reza%20Fatemi"> Seied Mohammad Reza Fatemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tooraj%20Valinassab"> Tooraj Valinassab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The samplings were carried out at 8 stations (Govatr, Pasabandar, Beriss, Ramin, Chabahar, Pozm, Gordim, and Meidani) in subtidal zones of Oman Sea during the year 2009-2010. The specimens were collected by trawl net and preserved in 70% alcohol. A total of 23 species belonged to 9 families and 15 genera were caught. The results of the present study revealed that families Portunidae had the highest species enriched with 9 species. Most of the species had high distribution in the west Indian Ocean (69.56%) and 8.69% of species were endemic. Almost species were similar to those found in the Persian Gulf. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brachyura" title="Brachyura">Brachyura</a>, <a href="https://publications.waset.org/abstracts/search?q=biogeography" title=" biogeography"> biogeography</a>, <a href="https://publications.waset.org/abstracts/search?q=subtidal" title=" subtidal"> subtidal</a>, <a href="https://publications.waset.org/abstracts/search?q=Oman%20Sea" title=" Oman Sea"> Oman Sea</a> </p> <a href="https://publications.waset.org/abstracts/30543/subtidal-crabs-of-oman-sea-new-collections-and-biogeographic-considerations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30543.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">589</span> </span> </div> </div> <ul class="pagination"> <li 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