Search results for: SQL injection attack classification

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

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

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

Procedia PDF Downloads 552

Authors: Asis Kumar Khan, Rajeev Kumar Goel

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Recent infrastructure growth all around the world lead to increase in demand for concrete day by day. Cement being binding material for concrete the usage of cement also gone up significantly. Cement manufacturing utilizes abundant natural resources and causes environment pollution by releasing a huge quantity of CO₂ into the atmosphere. So, it is high time to look for alternates to reduce the cement consumption in concrete. Geopolymer concrete is one such material which utilizes the industrial waste such as fly ash, ground granulated blast furnace slag and low-cost alkaline liquids such as sodium hydroxide and sodium silicate to produce the concrete. On the other side, river sand is becoming very expensive due to its large-scale depletion at source and the high cost of transportation. In this view, river sand is replaced by crushed sand in this study. In this work, an attempt has been made to understand the durability parameters of geopolymer concrete by partially replacing fly ash with cement. Fly ash is replaced by cement at various levels e.g., from 0 to 50%. Concrete cubes of 100x100x100mm were used for investigating different durability parameters. The various parameters studied includes compressive strength, split tensile strength, drying shrinkage, sodium sulphate attack resistance, sulphuric acid attack resistance and chloride permeability. Highest compressive strength & highest split tensile strength is observed in 30% replacement level. Least drying is observed with 30% replacement level. Very good resistance for sulphuric acid & sodium sulphate is found with 30% replacement. However, it was not possible to find out the chloride permeability due to the high conductivity of geopolymer samples of all replacement levels.

Keywords: crushed sand, compressive strength, drying shrinkage, geopolymer concrete, split tensile strength, sodium sulphate attack resistance, sulphuric acid attack resistance

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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

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

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Authors: Tudor Barbu

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

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

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Authors: Saleh Alkarri, Dimple Sharma, Teresa M. Bergholz, Muhammad Rabnawaz

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Aims: Develop a methodology for the fabrication of anti-microbial polypropylene (PP) surfaces with (i) leachable copper, (II) chloride dihydrate (CuCl₂·₂H₂O) and (ii) non-leachable magnesium hydroxide (Mg(OH)₂) biocides. Methods and Results: Two methodologies are used to develop anti-microbial PP surfaces. One method involves melt-blending and subsequent injection molding, where the biocide additives were compounded with PP and subsequently injection-molded. The other method involves the thermal embossing of anti-microbial agents on the surface of a PP substrate. The obtained biocide-bearing PP surfaces were evaluated against E. coli K-12 MG1655 for 0, 4, and 24 h to evaluate their anti-microbial properties. The injection-molded PP bearing 5% CuCl2·₂H₂O showed a 6-log reduction of E. coli K-12 MG1655 after 24 h, while only 1 log reduction was observed for PP bearing 5% Mg(OH)2. The thermally embossed PP surfaces bearing CuCl2·2H2O and Mg(OH)₂ particles (at a concentration of 10 mg/mL) showed 3 log and 4 log reduction, respectively, against E.coli K-12 MG1655 after 24 h. Conclusion: The results clearly demonstrate that CuCl₂·2H₂O conferred anti-microbial properties to PP surfaces that were prepared by both injection molding as well as thermal embossing approaches owing to the presence of leachable copper ions. In contrast, the non-leachable Mg(OH)₂ imparted anti-microbial properties only to the surface prepared via the thermal embossing technique. Significance and Impact of The Study: Plastics with leachable biocides are effective anti-microbial surfaces, but their toxicity is a major concern. This study provides a fabrication methodology for non-leachable PP-based anti-microbial surfaces that are potentially safer. In addition, this strategy can be extended to many other plastics substrates.

Keywords: anti-microbial activity, E. coli K-12 MG1655, copper (II) chloride dihydrate, magnesium hydroxide, leachable, non-leachable, compounding, thermal embossing

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Authors: Saleh Alkarri, Dimple Sharma, Teresa M. Bergholz, Muhammad Rabnawa

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Aims: Develop a methodology for the fabrication of anti-microbial polypropylene (PP) surfaces with (i) leachable copper (II) chloride dihydrate (CuCl2·2H2O) and (ii) non-leachable magnesium hydroxide (Mg(OH)2) biocides. Methods and Results: Two methodologies are used to develop anti-microbial PP surfaces. One method involves melt-blending and subsequent injection molding, where the biocide additives were compounded with PP and subsequently injection-molded. The other method involves the thermal embossing of anti-microbial agents on the surface of a PP substrate. The obtained biocide-bearing PP surfaces were evaluated against E. coli K-12 MG1655 for 0, 4, and 24 h to evaluate their anti-microbial properties. The injection-molded PP bearing 5% CuCl2·2H2O showed a 6-log reduction of E. coli K-12 MG1655 after 24 h, while only 1 log reduction was observed for PP bearing 5% Mg(OH)2. The thermally embossed PP surfaces bearing CuCl2·2H2O and Mg(OH)2 particles (at a concentration of 10 mg/mL) showed 3 log and 4 log reduction, respectively, against E.coli K-12 MG1655 after 24 h. Conclusion: The results clearly demonstrate that CuCl2·2H2O conferred anti-microbial properties to PP surfaces that were prepared by both injection molding as well as thermal embossing approaches owing to the presence of leachable copper ions. In contrast, the non-leachable Mg(OH)2 imparted anti-microbial properties only to the surface prepared via the thermal embossing technique. Significance and Impact of The Study: Plastics with leachable biocides are effective anti-microbial surfaces, but their toxicity is a major concern. This study provides a fabrication methodology for non-leachable PP-based anti-microbial surfaces that are potentially safer. In addition, this strategy can be extended to many other plastics substrates.

Keywords: anti-microbial activity, E. coli K-12 MG1655, copper (II) chloride dihydrate, magnesium hydroxide, leachable, non-leachable, compounding, thermal embossing

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Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

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

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

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Authors: Hasan Vural

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

Keywords: agricultural region, factorial analysis, cluster analysis,

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

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

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

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Authors: Changyong Liu, Junda Tong, Feng Xu, Ninggui Huang

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In the injection molding process, the performance of cooling channels is crucial to the part quality. Through the application of conformal cooling channels fabricated via metal additive manufacturing, part distortion, warpage can be greatly reduced and cycle time can be greatly shortened. However, the properties of additively manufactured conformal cooling channels are quite different from conventional drilling processes such as the poorer dimensional accuracy and larger surface roughness. These features have significant influences on its cooling performance. In this study, test molds with the cooling channel diameters of φ2 mm, φ3 mm and φ4 mm were fabricated via selective laser melting and conventional drilling process respectively. A test system was designed and manufactured to measure the pressure difference between the channel inlet and outlet, the coolant flow rate and the temperature variation during the heating process. It was found that the cooling performance of SLM-fabricated channels was poorer than drilled cooling channels due to the smaller sectional area of cooling channels resulted from the low dimensional accuracy and the unmolten particles adhered to the channel surface. Theoretical models were established to determine the friction factor and heat transfer coefficient of SLM-fabricated cooling channels. These findings may provide guidance to the design of conformal cooling channels.

Keywords: conformal cooling channels, selective laser melting, cooling performance, injection molding

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Authors: Lei Zhang

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The carbon cycle greatly influences global change, and the land cover changes contribute to the status and rate of the carbon budget in ecosystems. This paper proposes a land cover classification system for mapping land cover, the national ecological environment assessment, and estimating carbon storage in ecosystems. The classification system consists of basic land cover classes at levels Ⅰ and Ⅱ and auxiliary features at level III. The basic 38 classes characterizing land cover features are derived from 19 criteria referring to composition, structure, pattern, phenology, etc. The basic classes reflect the status of carbon storage in ecosystems. The auxiliary classes at level III complement the attributes of higher levels by 9 criteria. The 5 environmental criteria of temperature, moisture, landform, aspect and slope mainly reflect the potential and intensity of carbon storage in ecosystems. The disturbance of vegetation succession caused by land use type influences the vegetation carbon budget. The other 3 vegetation cover criteria, growth period, and species characteristics further refine the vegetation types. The hierarchical structure of the land cover map (the classes of levels Ⅰ and Ⅱ) is independent of the products of level III, which is helpful for land cover product management and applications. The classification system has been adopted in the Chinese national land cover database for the carbon budget in ecosystems at a 30 m scale.

Keywords: classification system, land cover, ecosystem, carbon storage, object based

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Authors: Justie Su-Tzu Juan, Ming-Jheng Li, Ching-Fen Lee, Ruei-Yu Wu

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A secret image sharing scheme is a way to protect images. The main idea is dispersing the secret image into numerous shadow images. A secret image sharing scheme can withstand the impersonal attack and achieve the highly practical property of multiuse  is more practical. Therefore, this paper proposes a verifiable and detectable secret image-sharing scheme called VDGMSISS to solve the impersonal attack and to achieve some properties such as encrypting multi-secret images at one time and multi-use. Moreover, our scheme can also be used for any genera access structure.

Keywords: multi-secret image sharing scheme, verifiable, de-tectable, general access structure

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: Sang Chul Lee, Gi-Young Park, Dong Rak Kwon

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Objective: The aim of this study was to investigate regenerative effects of ultrasound (US)-guided injection with human umbilical cord blood-derived mesenchymal stem cells (UCB-MSCs) and/or polydeoxyribonucleotide (PDRN) injection in a chronic traumatic full-thickness rotator cuff tendon tear (FTRCTT) in a rabbit model. Material and Methods: Rabbits (n = 32) were allocated into 4 groups. After a 5-mm sized FTRCTT just proximal to the insertion site on the subscapularis tendon was created by excision, the wound was immediately covered by silicone tube to prevent natural healing. After 6 weeks, 4 injections (0.2 mL normal saline, G1; 0.2 mL PDRN, G2; 0.2 mL UCB-MSCs, G3; and 0.2 mL UCB-MSCs with 0.2ml PDRN, G4) were injected into FTRCTT under US guidance. We evaluated gross morphologic changes on all rabbits after sacrifice. Masson’s trichrome, anti-type 1 collagen antibody, bromodeoxyuridine, proliferating cell nuclear antigen, vascular endothelial growth factor and platelet endothelial cell adhesion molecule stain were performed to evaluate histological changes. Motion analysis was also performed. Results: The gross morphologic mean tendon tear size in G3 and 4 was significantly smaller than that of G1 and 2 (p < .05). However, there were no significant differences in tendon tear size between G3 and 4. In G4, newly regenerated collagen type 1 fibers, proliferating cells activity, angiogenesis, walking distance, fast walking time, and mean walking speed were greater than in the other three groups on histological examination and motion analysis. Conclusion: Co-injection of UCB-MSCs and PDRN was more effective than UCB-MSCs injection alone in histological and motion analysis in a rabbit model of chronic traumatic FTRCTT. However, there was no significant difference in gross morphologic change of tendon tear between UCB-MSCs with/without PDRN injection. The results of this study regarding the combination of UCB-MSCs and PDRN are worth additional investigations.

Keywords: mesenchymal stem cell, umbilical cord, polydeoxyribonucleotides, shoulder, rotator cuff, ultrasonography, injections

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

Procedia PDF Downloads 122

Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Refilwe Moeletsi

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Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

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Authors: Vikram Prabhu, Mohammad Shikh Bahaei

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This paper depicts the implementation of a new infallible technique to protect an Unmanned Aerial Vehicle from cyber-attacks. An Unmanned Aerial Vehicle (UAV) could be vulnerable to cyber-attacks because of jammers or eavesdroppers over the network which pose as a threat to the security of the UAV. In the field of network security, there are quite a few protocols which can be used to establish a secure connection between UAVs and their Operators. In this paper, we discuss how the Interlock Protocol could be implemented to foil the Man-in-the-Middle Attack. In this case, Wireshark has been used as the sniffer (man-in-the-middle). This paper also shows a comparison between the Interlock Protocol and the TCP Protocols using cryptcat and netcat and at the same time highlights why the Interlock Protocol is the most efficient security protocol to prevent eavesdropping over the communication channel.

Keywords: interlock protocol, Diffie-Hellman algorithm, unmanned aerial vehicles, control station, man-in-the-middle attack, Wireshark

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Authors: F. O. Oboite, P. G. Comeau

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Advance regeneration is present in many lodgepole pine stands in Alberta. When the overstory pine canopy is killed by Mountain Pine Beetle (MPB) the growth of this advance is likely to increase. Understanding the growth response of these understory tree species is needed to improve mid-term timber supply projections and management decisions. To quantify the growth (diameter, height, height/diameter ratio) responses of black spruce and white spruce to lodgepole pine mortality, sample trees of black and white spruce advance regeneration were selected from 7 lodgepole pine dominated stands (5 attacked; 2 control) in the Foothills Region of western Alberta. Measurements were collected 7-8 years after MPB attack across a wide range of spruce height and stand densities. Analysis was done using mixed model linear regression. Result indicates that there was an increase in both diameter and height growth after MPB attack; however, this increase in growth was delayed for about four years. Both spruce species had similar height response and their height/diameter ratio decreased after release, partly as a result of increased understory light associated with loss of needles in the pine canopy. In addition, the diameter and height growth responses of both spruce species were strongly related to density, prerelease growth and initial size.

Keywords: mountain pine beetle, forest regeneration, lodgepole pine, growth response

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Authors: Rabia Shabbir, Sheikh Saeed Ahmad, Amna Butt

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Rapid land use changes have taken place in Islamabad, the capital city of Pakistan, over the past decades due to accelerated urbanization and industrialization. In this study, land use changes in the metropolitan area of Islamabad was observed by the combined use of GIS and satellite remote sensing for a time period of 15 years. High-resolution Google Earth images were downloaded from 2000-2015, and object-based classification method was used for accurate classification using eCognition software. The information regarding urban settlements, industrial area, barren land, agricultural area, vegetation, water, and transportation infrastructure was extracted. The results showed that the city experienced a spatial expansion, rapid urban growth, land use change and expanding transportation infrastructure. The study concluded the integration of GIS and remote sensing as an effective approach for analyzing the spatial pattern of urban growth and land use change.

Keywords: land use change, urban growth, Islamabad, object-based classification, Google Earth, remote sensing, GIS

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: Khosro Ghazvinian, Reza Narenji Sani, Touba Khodaiean, Melika Moezifar

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Many previous studies have reported that eCG was effective for completing spermatogenesis. In mice, eCG increased testes weight. In addition, Oxytocin (OT) was important in sperm transition and sperm motility in domestic animals. Peripheral circulation of OT also, was increased during sex incitement and ejaculation The objective of this study was to investigate the effect of IM injection of eCG and OT on semen characteristics in Zel rams in out of breeding season. Eighteen 3-year-old Zel adult rams were randomly divided into five equal groups (control and four treatment groups). 0.9% NaCl (1 ml) was injected IM into each ram in the control group, whereas eCG was administered IM at a single dose of 400 IU and 600 IU to each ram in the two eCG treatment groups and OT was administered IM at a single dose of 5 IU and 10 IU to each ram in the other two OT treatment groups. Semen samples were taken by an electroejaculator from all rams 10 min after the IM injection of 0.9% NaCl, eCG, or OT. eCG did not alter semen volume, and OT did not alter sperm motility or abnormal sperm, in comparison to the control values. Mass activity, sperm motility and total sperm number increased significantly in eCG group compared to the control group; and semen volume, mass activity, total sperm number of the OT treatment groups increased significantly compared to the control group. Exogenous 600 IU eCG and 10 IU OT increase mass activity, total sperm number, lived sperm and sperm concentration in Zel rams.

Keywords: eCG, oxytocine, semen characteristics, Zel Ram, nonbreeding season

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

Abstract:

For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

Abstract:

Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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