Search results for: land use classification

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: Damian Czubak

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The Białowieża Forest is a very valuable natural area, included in the World Natural Heritage at UNESCO, where, due to infestation by the bark beetle (Ips typographus), norway spruce (Picea abies) have deteriorated. This catastrophic scenario led to an increase in fire danger. This was due to the occurrence of large amounts of dead wood and grass cover, as light penetrated to the bottom of the stands. These factors in a dry state are materials that favour the possibility of fire and the rapid spread of fire. One of the objectives of the study was to monitor the quantitative and qualitative changes of combustible material on the permanent decay plots of spruce stands from 2012-2022. In addition, the size of the area with highly flammable vegetation was monitored and a classification of the stands of the Białowieża Forest by flammability classes was made. The key factor that determines the potential fire hazard of a forest is combustible material. Primarily its type, quantity, moisture content, size and spatial structure. Based on the inventory data on the areas of forest districts in the Białowieża Forest, the average fire load and its changes over the years were calculated. The analysis was carried out taking into account the changes in the health status of the stands and sanitary operations. The quantitative and qualitative assessment of fallen timber and fire load of ground cover used the results of the 2019 and 2021 inventories. Approximately 9,000 circular plots were used for the study. An assessment was made of the amount of potential fuel, understood as ground cover vegetation and dead wood debris. In addition, monitoring of areas with vegetation that poses a high fire risk was conducted using data from 2019 and 2021. All sub-areas were inventoried where vegetation posing a specific fire hazard represented at least 10% of the area with species characteristic of that cover. In addition to the size of the area with fire-prone vegetation, a very important element is the size of the fire load on the indicated plots. On representative plots, the biomass of the land cover was measured on an area of 10 m2 and then the amount of biomass of each component was determined. The resulting element of variability of ground covers in stands was their flammability classification. The classification developed made it possible to track changes in the flammability classes of stands over the period covered by the measurements.

Keywords: classification, combustible material, flammable vegetation, Norway spruce

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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: Tayfun Cay, Fazil Nacar

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Energy investments have increased in North Mediterranean Ceyhan and Yumurtalık districts of Turkey in the last years because of the treaties which are signed between Turkey and other countries for petroleum and natural gas transmission. Authority of land use has passed to district and metropolitan municipalities from town municipalities because of changes in coast legislation and local management legislation. Also Ministry of Environment and Urban Planning and Ministry of Industry and Commerce have had a right to comment on planning unofficially. Public investments increase in area and related planning and expropriation services continue. On the other hand, a lot of private sectors invest in organised industrial sites and industrial areas and it causes a rapid change in ownership status. Also Ceyhan-yumurtalık region is the tourism centre of North Mediterranean. Tourism investments continue in this district. Especially construction sector gain speed and a lot of country sites and apartments are built. In these studies, changes in planning activities in management of different administrative organisations and changes in ownership status and changes in private properties will be presented.

Keywords: coast management, land management, land use, property, public interest

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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: Ar.Abhishek Ranka

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Cultural landscapes are a part of a nation’s heritage, which represent the exquisite combination of Natural (Ecological) & Built (Architectural) fabric, consisting of many historic gardens, water management system, sustainable planning, and designed framework. The use of landscape and topography with Tangible &Intangible heritage components (forts, temples, tombs, mosques, etc.) are locally, regionally, and nationally significant. The paper speaks about the contribution of Bahmani Sultanate to military architecture in the Deccan region. It is a study of the series of seven land forts as a cultural landscape, which plays an important role in shaping the knowledge systems in the form of typologies of military architecture, water management system, and the administrative setups, which are presently located in the cultural region, Marathwada of the Deccan. Conservation of Culturall and scapeasan approach offers opportunities to better integrate natural and cultural heritage conservation. Conserving of Seven Land forts could act as an inspirational model for other sites.

Keywords: bahmani sultanate, deccan region, land forts, culture landscape, military architecture, tradational knowledge system, architectural conservation

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Authors: Hasan Basri Jumin

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The swamp land contains high iron, aluminum, and low pH. Calcium and magnesium in the rice husk ash can reduce plant poisoning so that plant growth increases in fertility. The first factor was the doze of rice husk, and the second factor was 0.0 g rhizobium inoculant /kg seed, 4.0 g rhizobium inoculant/kg seed, 8 g rhizobium inoculant /kg seed, and 12 g l rhizobium inoculant /kg seed. The plants were maintained under light conditions with a + 11.45 – 12.15 hour photoperiod. The combination between rhizobium inoculant and rice husk ash has been an interacting effect on the production of long bean pod fresh weight. The mean relative growth rate, net assimilation rate, and pod fresh weight are increased by a combination of husk rice ash and rhizobium inoculant. Rice husk ash affected increases the availability of nitrogen in the land, albeit in poor condition of nutrition. Rhizobium is active in creating a fixation of nitrogen in the atmosphere because rhizobium increases the abilities of intercellular and symbiotic nitrogen in the long beans. The combination of rice husk ash and rhizobium could be effected to create a thriving in the land.

Keywords: aluminium, calcium, fixation, iron, nitrogen

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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: Kamal Donko

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In the frontier zone of Benin Republic, land seems to be a fundamental political resource as it is used as a tool for socio-political mobilization, blackmail, inclusion and exclusion, conquest and political control. This paper seeks to examine the complex and intriguing interlinks between land, identity and politics in central Benin. It aims to investigate what roles territorialisation and land ownership are playing in the electioneering process in central Benin. It employs ethnographic multi-sited approach to data collections including observations, interviews and focused group discussions. Research findings reveal a complex and intriguing relationship between land ownership and politics in central Benin. Land is found to be playing a key role in the electioneering process in the region. The study has also discovered many emerging socio-spatial patterns of controlling and maintaining political power in the zone which are tied to land politics. These include identity reconstruction and integration mechanism through intermarriages, socio-political initiatives and construction of infrastructure of sovereignty. It was also found that ‘Diaspora organizations’ and identity issues; strategic creation of administrative units; alliance building strategy; gerrymandering local political field, etc. These emerging socio-spatial patterns of territorialisation for maintaining political power affect migrant and native communities’ relationships. It was also found that ‘Diaspora organizations’ and identity issues; strategic creation of administrative units; alliance building strategy; gerrymandering local political field, etc. are currently affecting migrant’s and natives’ relationships. The study argues that territorialisation is not only about national boundaries and the demarcation between different nation states, but more importantly, it serves as a powerful tool of domination and political control at the grass root level. Furthermore, this study seems to provide another perspective from which the political situation in Africa can be studied. Investigating how the dynamics of land ownership is influencing politics at the grass root or micro level, this study is fundamental to understanding spatial issues in the frontier zone.

Keywords: land, migration, politics, territorialisation

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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: 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

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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: 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: Marie-Claude Roy, David Locky, Ermias Azeria, Jim Schieck

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It is estimated that up to 70% of the wetlands in the Parkland and Grassland natural regions of Alberta have been lost due to various land-use activities. These losses include ecosystem function and services they once provided. Those wetlands remaining are often embedded in a matrix of human-modified habitats and despite efforts taken to protect them the effects of land-uses on wetland condition and function remain largely unknown. We used biophysical field data and remotely-sensed human footprint data collected at 322 open-water wetlands by the Alberta Biodiversity Monitoring Institute (ABMI) to evaluate the impact of surrounding land use on the physico-chemistry characteristics and plant functional traits of wetlands. Eight physio-chemistry parameters were assessed: wetland water depth, water temperature, pH, salinity, dissolved oxygen, total phosphorus, total nitrogen, and dissolved organic carbon. Three plant functional traits were evaluated: 1) origin (native and non-native), 2) life history (annual, biennial, and perennial), and 3) habitat requirements (obligate-wetland and obligate-upland). Intensity land-use was quantified within a 250-meter buffer around each wetland. Ninety-nine percent of wetlands in the Grassland and Parkland regions of Alberta have land-use activities in their surroundings, with most being agriculture-related. Total phosphorus in wetlands increased with the cover of surrounding agriculture, while salinity, total nitrogen, and dissolved organic carbon were positively associated with the degree of soft-linear (e.g. pipelines, trails) land-uses. The abundance of non-native and annual/biennial plants increased with the amount of agriculture, while urban-industrial land-use lowered abundance of natives, perennials, and obligate wetland plants. Our study suggests that land-use types surrounding wetlands affect the physicochemical and biological conditions of wetlands. This research suggests that reducing human disturbances through reclamation of wetland buffers may enhance the condition and function of wetlands in agricultural landscapes.

Keywords: wetlands, biophysical assessment, land use, grassland and parkland natural regions

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Authors: Anusree Ghosh, Nahid Morshed, Tapas Ranjan Chakraborty, Moniruzzaman Khan, Liakath Ali

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To establish the Bangabandhu Sheikh Mujib Shilpa Nagar in Mirsarai, Chattogram land was reclaimed from the river mouth of the Feni River in the Bay of Bangle. The sandy land has a salinity of 9.5 EC ds/m, and the water of the adjacent Bay was 13.2 EC ds/m during winter, i.e., it has moderate salinity. The selection of plant species for the plantation was following the local practices. Mangrove plantation in a such landscape is not common in the country, and some actions towards the plantation seem ineffective and could be accomplished differently. The aim of this paper is to analyze the trial and develop a strategy for mangrove afforestation in reclaimed land where the tidal effect does not occur year-round. Though the Keora (Sonneratia apetala) is the priority species in a typical mangrove plantation, the success rate is comparatively high for the Baen (Avcennia officinalis) and Sada Baen (Avicennia alba). The natural growth was recorded for Keora, Goran (Ceriops decandra), Lal Jhau (Tamarix dioica) and Baen. Though there was the natural growth of Durba grass (Cynodon dactylon) and Motha Gash (Cyperus rotundus), no growth of climber was reported at the early stage of the natural growth. The transplanted growth of Keora, Gewa (Excoecaria agallocha), and Baen was found not suitable for plantation. The saplings growing from the viviparous germinated bean show no germination failure. Since the plantation site remains dry for 5 months, there was irrigation from the river; though it resulted in an increase in land salinity. To increase fertility, cow dung was used, and green manuring by planting Doincha (Sesbania bispinosa) shows a very insignificant contribution. The plantation of, only one and a half years old, is now a habitat of more than 100 species. The learning from the mangrove plantation from August 2021 to February 2023 assumes that in reclaimed land where there is inundation during monsoon only and salinity is moderate, the plantation from viviparous germinated Baen is better.

Keywords: mangrove plantation, reclaimed land, climate change, habitat restoration

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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: Feng Yin

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Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

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

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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

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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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Authors: Hlabishi Peter Ntloana

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This paper aims to present the effectiveness of the Spatial Planning and Land Use Management Act, 2013, in addressing key spatial challenges in Fetakgomo Tubatse Local Municipality, mainly focusing on Apel nodal point. Spatial Planning and Land Use Management Act, 2013, popularly known as SPLUMA, aimed at addressing emerging and existing spatial planning and land use management challenges in South Africa. There are critical key spatial challenges that are continuously encountered in Apel Nodal Point, which include dispersed rural settlement mainly in a communal settlement. The spatial patterns and rural settlements development patterns are a challenge, and such results in uncoordinated human settlements. The objective of this research paper is to analyze the spatial planning of Apel nodal points and determine the effectiveness of the SPLUMA policy. Key Informant interviews were conducted with 20 participants, and also the municipal Spatial Development Framework was considered to explore more challenges and proposed recommendations. The results divulged that there is a huge gap in addressing spatial planning, mainly in rural areas, and correlation with the findings of the Municipal Spatial Development framework. In conclusion, spatial planning remains a critical dilemma in most rural settlements, and there must be programmes and strategies to balance the effectiveness of spatial planning in urban and rural settlements.

Keywords: land use management, rural settlement, spatial development framework, spatial planning

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: N. L. Letsoalo, H. T. Pule, J. T. Tjelele, N. R. Mkhize, K. R. Mbatha

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Rangelands are major feed resource for livestock farming in South Africa, despite being subjected to different forms of degradation. These forms of degradation are as a result of inappropriate veld and livestock management practices such as excessive stocking rates. While information on judicious veld management is available, adoption of appropriate practices is still unsatisfactory and seems to depend partly on the type of land ownership of farmers. The objectives of this study were to; (I) compare rangeland condition (species richness, basal cover, veld condition score, and herbaceous biomass) among three land ownership types (leased land, communal land and private land), and (II) determine the relationships between veld condition score (%) and herbaceous biomass (kg DM/ha) production. Vegetation was assessed at fifty farms under different land use types using nearest plant technique. Grass species composition and forage value were estimated using PROC FREQ procedure of SAS 9.3. A one-way ANOVA was used to determine significant differences (P < 0.05) in species richness, basal cover, veld condition (%) large stock units, grazing capacity and herbaceous biomass production among the three grazing systems. A total of 28 grass species were identified, of which 95% and 5% were perennials and annuals, respectively. The most commonly distributed and highly palatable grass species, Digitaria eriantha had significantly higher frequency under private owned lands (32.3 %) compared to communal owned lands (12.3%). There were no significant difference on grass species richness and basal cover among land ownership types (P > 0.05). There were significant differences on veld condition score and biomass production (P < 0.05). Private lands had significantly higher (69.63%) veld condition score than leased (56.07%) and communal lands (52.55%). Biomass production was significantly higher (± S.E.) 2990.30 ± 214 kg DM/ha on private owned lands, compared to leased lands 2069.85 ± 196 kg DM/ha and communal lands 1331.04 ± 102 kg DM/ha. Biomass production was positively correlated with rangeland condition (r = 0.895; P < 0.005). These results suggest that rangeland conditions on communal and leased lands are in poor condition than those on private lands. More research efforts are needed to improve management of rangelands in communal and leased land in Gauteng province.

Keywords: grazing, herbaceous biomass, management practices, species richness

Procedia PDF Downloads 140

Authors: Ng Liang Shen, Hau Yuan Wen

Abstract:

Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

Procedia PDF Downloads 344

Authors: Ashkan Asgari, Korous Khoshbakht, Saeid Soufizadeh

Abstract:

Various factors including land lots can affect biodiversity indices in Agricultural systems. Field study conducted to evaluate factors affecting crop diversity in Sarpolzahab in 2012. Required data were collected through direct observation of farms and filling questionnaires. Total numbers of 140 questionnaires were filled, SAS Software was used to analyse data and Ecological Methodology Program was applied to calculate Shannon-Winner index, subsequently. Results of study indicated that average number of land lots for each farmer was 2.78 and various from 2.2 in Rikhak Olia Village to 4.31 in Golam Kaboud Olia Village which shows small size of land lots due to separating larger lots by children of deceased farmers. The correlation between number of land lots and species biodiversity (0.308**) was significant and Shannon-Winner index was (0.262**). Therefore, according to the mentioned results one can assume that increase in number of land lots results in improving of the target index. Multiple land lots allow farmers to cultivate various crops which results in increasing biodiversity of crops in agro ecosystem. Subsequently, this increase will facilitate economic sustainability of the farmers and distribution of work force in the region throughout the year. The correlation of seasonal workers with biodiversity of crop species (0.256**) and Shannon-Winner (0.286**) was statistically significant and increasing number of seasonal work forces had resulted in improving crop biodiversity and decreasing dominant species or single crop farming systems. Vegetable farms which have a significant diversity, require a significant number of work forces which describes correlation between number of workers and diversity of species.

Keywords: agricultural systems, biodiversity indices, Shannon-Winner index, sustainability, rural

Procedia PDF Downloads 513

Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

Abstract:

The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

Procedia PDF Downloads 341

Authors: Mohamed R. Mhereeg

Abstract:

The paper discusses the implementation of the MultiAgent classification System (MACS) and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies, which are the .NET widows service based agents, the Windows Communication Foundation (WCF) services, the Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). Microsoft's .NET windows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW. The Monitoring Agents (MAs) were configured to execute automatically to monitor excel spreadsheets development activities by content. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent (DUA) residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers.

Keywords: MACS, implementation, multi-agent, SOA, autonomous, WCF

Procedia PDF Downloads 258