Search results for: efficiency classification

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

Abstract:

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

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

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Authors: Sania Ashraf

Abstract:

The paper focuses on the prevalence of informational efficiency and integration of GCC Shariah Equity market for the period of 01st January 2010 to 31st June 2015 with daily equity returns of Kuwait, Oman, Qatar, Bahrain, Saudi Arabia and United Arab Emirates. The study employs traditional as well as the modern approach of tracing out the efficiency and integration in the return series. From the results of efficiency it was observed that the market lacked efficiency in terms of its past information. The results of integration test clearly indicates that there was a long memory in the returns of GCC Shariah during the study period. Hence it was concluded and proved that the returns of all GCC Equity Shariah were not informationally efficient but fractionally integrated during the study period.

Keywords: efficiency, Fama, GCC shariah, hurst exponent, integration, serial correlation

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Authors: Zhaoxin Luo, Michael Zhu

Abstract:

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

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

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

Abstract:

During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving the army, moving convoys etc. The radar operator selects one of the promising targets into single target tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper, we present a technique using mathematical and statistical methods like fast fourier transformation (FFT) and principal component analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, FFT, principal component analysis, eigenvector, octave-notes, DSP

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Authors: Ali Farzan

Abstract:

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

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

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Authors: Khitem Amiri, Mohamed Farah

Abstract:

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

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

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Authors: Khomkrit Mongkhuntod, Chatchawal Chaichana, Atipoang Nuntaphan

Abstract:

Feed Water Heater is the important equipment for thermal power plant. The heating temperature from feed heating process is an impact to power plant efficiency or heat rate. Normally, the degradation of feed water heater that operated for a long time is effect to decrease plant efficiency or increase plant heat rate. For Mae Moh power plant, each unit operated more than 20 years. The degradation of the main equipment is effect of planting efficiency or heat rate. From the efficiency and heat rate analysis, Mae Moh power plant operated in high heat rate more than the commissioning period. Some of the equipment were replaced for improving plant efficiency and plant heat rates such as HP turbine and LP turbine that the result is increased plant efficiency by 5% and decrease plant heat rate by 1%. For the target of power generation plan that Mae Moh power plant must be operated more than 10 years. These work is focus on thermal efficiency analysis of feed water heater to compare with the commissioning data for find the way to improve the feed water heater efficiency that may effect to increase plant efficiency or decrease plant heat rate by use heat balance model simulation and economic value add (EVA) method to study the investment for replacing the new feed water heater and analyze how this project can stay above the break-even point to make the project decision.

Keywords: feed water heater, power plant efficiency, plant heat rate, thermal efficiency analysis

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Authors: Zhankun Wang, Timothy Makubuya

Abstract:

The efficiency of public sports service of prefecture-level cities in Zhejiang from 2008 to 2012 was evaluated by applying the DEA method, then its influencing factors were also analyzed through Tobit model. Upon analysis, the results revealed the following; (i) the change in average efficiency of public sports service in Zhejiang present a smooth uptrend and at a relatively high level from 2008 to 2012 (ii) generally, the productivity of public sports service in Zhejiang improved from 2008 to 2012, the productivity efficiency varied greatly in different years, and the regional difference of production efficiency increased. (iii) The correlations for urbanization rate, aging rate, per capita GDP and the population density were significantly positive with the public sports service efficiency in Zhejiang, of which the most significant was the aging rate. However, the population density and per capita GDP had less impact on the efficiency of public sports service in Zhejiang. In addition, whether the efficiency of public sports services in different areas in Zhejiang reciprocates to overall benefits in public wellbeing in both rural and urban settings is still arguable.

Keywords: DEA model, public sports service, efficiency, Tobit model, Malmquist productivity index, Zhejiang

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

Abstract:

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

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

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Authors: Michal Krecichwost, Zauzanna Miodonska, Pawel Badura

Abstract:

The diagnosis of sigmatism is mostly based on the observation of articulatory organs. It is, however, not always possible to precisely observe the vocal apparatus, in particular in the oral cavity of the patient. Speech processing can allow to objectify the therapy and simplify the verification of its progress. In the described study the methodology for classification of incorrectly pronounced phoneme [s] is proposed. The recordings come from adults. They were registered with the speech recorder at the sampling rate of 44.1 kHz and the resolution of 16 bit. The database of pathological and normative speech has been collected for the study including reference assessments provided by the speech therapy experts. Ten adult subjects were asked to simulate a certain type of stigmatism under the speech therapy expert supervision. In the recordings, the analyzed phone [s] was surrounded by vowels, viz: ASA, ESE, ISI, SPA, USU, YSY. Thirteen MFCC (mel-frequency cepstral coefficients) and RMS (root mean square) values are calculated within each frame being a part of the analyzed phoneme. Additionally, 3 fricative formants along with corresponding amplitudes are determined for the entire segment. In order to aggregate the information within the segment, the average value of each MFCC coefficient is calculated. All features of other types are aggregated by means of their 75th percentile. The proposed method of features aggregation reduces the size of the feature vector used in the classification. Binary SVM (support vector machine) classifier is employed at the phoneme recognition stage. The first group consists of pathological phones, while the other of the normative ones. The proposed feature vector yields classification sensitivity and specificity measures above 90% level in case of individual logo phones. The employment of a fricative formants-based information improves the sole-MFCC classification results average of 5 percentage points. The study shows that the employment of specific parameters for the selected phones improves the efficiency of pathology detection referred to the traditional methods of speech signal parameterization.

Keywords: computer-aided pronunciation evaluation, sibilants, sigmatism diagnosis, speech processing

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

Abstract:

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

Abstract:

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

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

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Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

Abstract:

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

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

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Authors: Hasan Vural

Abstract:

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

Keywords: agricultural region, factorial analysis, cluster analysis,

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Authors: Bahram Fathi, Seyedhossein Sajadifar, Naser Khiabani

Abstract:

Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as byproducts of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 12 OPEC countries and the results obtained are presented.

Keywords: energy efficiency, undesirable outputs, data envelopment analysis

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

Abstract:

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

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

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Authors: Aharouay Soumaya

Abstract:

Efficiency is a relative concept. It is measured by comparing the productivity obtained in what is intended as standard or objective criteria. The quantity and quality of output achieved and the level of service are also compared to targets or standards, to determine to what extent they could cause changes in efficiency. Efficiency improves when more outputs of a specified quality are produced with the same resource inputs or less, or when the same amount of output is produced with fewer resources. This article proposes a review of the literature on strategies adopted by firms in the age of the financial crisis to overcome these negative effects, and tracks progress chosen by the organization to remain successful despite the plight of firms.

Keywords: effectiveness, efficiency, organizational capacity, strategy, management tool, progress, performance

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Authors: Lei Zhang

Abstract:

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: Aydin Azizi, Abdurrahman Tanira

Abstract:

With the onset of climate change and limited fuel resources, improving fuel efficiency has become an important part of the motor industry. To maximize fuel efficiency, development of technologies must come hand-in-hand with awareness of efficient driving strategies. This study aims to explore the various driving habits that can impact fuel efficiency by reviewing available literature. Such habits include sudden and unnecessary acceleration or deceleration, improper hardware maintenance, driving above or below optimum speed and idling. By studying such habits and ultimately applying it to driving techniques, in combination with improved mechanics of the car, will optimize the use of fuel.

Keywords: fuel efficiency, driving techniques, optimum speed, optimizing fuel consumption

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Authors: Hiba Mezaache

Abstract:

The study aims to assess the financial and social efficiency of microfinance institutions in india for the period 2015-2019 by using two models of economies of scale and choosing the output direction of the data envelope analysis (DEA) method and using the MIX MARKET database. The study concluded that microfinance institutions focus on achieving financial efficiency beyond their focus on achieving social efficiency to ensure their continuity in the market. Convergence in the efficiency ratios that have been achieved, but the optimum ratios have been achieved under the changing economies of scale; Efficiency is affected by the depth of reaching low-income groups, as serving this group raises costs and risks. The importance of lending to women in rural areas and raising their awareness to ensure their financial and social empowerment; Make improvements in operating expenses, asset management, and loan personnel control in order to maximize output.

Keywords: microfinance, financial efficiency, social efficiency, mix market, microfinance institutions

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Authors: Dongfang Qiu, Yilin Lu

Abstract:

The measurement of enterprise-university-research cooperative efficiency has important meanings in improving the cooperative efficiency, strengthening the effective integration of regional resource, enhancing the ability of regional innovation and promoting the development of regional economy. The paper constructs the DEA method and DEA-Malmquist productivity index method to research the cooperation efficiency of Hubei by making comparisons with other provinces in China. The study found out the index of technology efficiency is 0.52 and the enterprise-university- research cooperative efficiency is Non-DEA efficient. To realize the DEA efficiency of Hubei province, the amount of 1652.596 R&D employees and 638.368 R&D employees’ full time equivalence should be reduced or 137.89 billion yuan of new products’ sales income be increased. Finally, it puts forward policy recommendations on existing problems to strengthen the standings of the cooperation, realize the effective application of the research results, and improve the level of management of enterprise-university-research cooperation efficiency.

Keywords: cooperation ability, DEA method, enterprise-university-research cooperation, Malmquist efficiency index

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

Abstract:

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

Abstract:

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: Jeewan Rai, Kinzang, Yeshi Jigme Choden

Abstract:

Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy they produce. While various techniques exist for detecting dust to schedule cleaning, many of these methods use MATLAB image processing tools and other licensed software, which can be financially burdensome. This study will investigate the efficiency of a free open-source computer vision library using the YOLO algorithm. The proposed approach has been tested on images of solar panels with varying dust levels through an experiment setup. The experimental findings illustrated the effectiveness of using the YOLO-based image classification method and the overall dust detection approach with an accuracy of 90% in distinguishing between clean and dusty panels. This open-source solution provides a cost effective and accessible alternative to commercial image processing tools, offering solutions for optimizing solar panel maintenance and enhancing energy production.

Keywords: YOLO, openCV, dust detection, solar panels, computer vision, image processing

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Authors: G. Hafner

Abstract:

A method developed at the University of Stuttgart will be presented: ‘Analysis, Evaluation and Optimization of Food Management’. A major focus is represented by quantification of food losses and food waste as well as their classification and evaluation regarding a system optimization through waste prevention. For quantification and accounting of food, food losses and food waste along the food chain, a clear definition of core terms is required at the beginning. This includes their methodological classification and demarcation within sectors of the food value chain. The food chain is divided into agriculture, industry and crafts, trade and consumption (at home and out of home). For adjustment of core terms, the authors have cooperated with relevant stakeholders in Germany for achieving the goal of holistic and agreed definitions for the whole food chain. This includes modeling of sub systems within the food value chain, definition of terms, differentiation between food losses and food wastage as well as methodological approaches. ‘Food Losses’ and ‘Food Wastes’ are assigned to individual sectors of the food chain including a description of the respective methods. The method for analyzing, evaluation and optimization of food management systems consist of the following parts: Part I: Terms and Definitions. Part II: System Modeling. Part III: Procedure for Data Collection and Accounting Part. IV: Methodological Approaches for Classification and Evaluation of Results. Part V: Evaluation Parameters and Benchmarks. Part VI: Measures for Optimization. Part VII: Monitoring of Success The method will be demonstrated at the example of an invesigation of food losses and food wastage in the Federal State of Bavaria including an extrapolation of respective results to quantify food wastage in Germany.

Keywords: food losses, food waste, resource management, waste management, system analysis, waste minimization, resource efficiency

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

Abstract:

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

Abstract:

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

Procedia PDF Downloads 107

Authors: G. C. Tikkiwal, Mukesh Upadhyay

Abstract:

During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving army, moving convoys etc. The Radar operator selects one of the promising targets into Single Target Tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper we present a technique using mathematical and statistical methods like Fast Fourier Transformation (FFT) and Principal Component Analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, fft, principal component analysis, eigenvector, octave-notes, dsp

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Authors: Refilwe Moeletsi

Abstract:

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

Abstract:

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

Procedia PDF Downloads 341