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Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022 Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom-
mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to
the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind
the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in
plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing
with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the
samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm-
ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to
256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means
clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted
using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis
and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally,
the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM),
Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is
tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples. keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease |
مقاله انگلیسی |
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Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope
پیشبرد خصوصیات با آمار پراش الکترون همبستگی و طیف سنجی اشعه X ، در میکروسکوپ الکترونی روبشی-2020 The routine and unique determination of minor phases in microstructures is critical to
materials science. In metallurgy alone, applications include alloy and process development and
the understanding of degradation in service. We develop a correlative method, exploring
superalloy microstructures which are examined in the scanning electron microscope (SEM)
using simultaneous energy dispersive X-ray spectroscopy (EDS) and electron backscatter
diffraction (EBSD). This is performed at an appropriate length scale for characterisation of
carbide phases’ shape, size, location, and distribution. EDS and EBSD data are generated using
two different physical processes, but each provide a signature of the material interacting with
the incoming electron beam. Recent advances in post-processing, driven by ‘big data’
approaches, include use of principal component analysis (PCA). Components are subsequently
characterised to assign labels to a mapped region. To provide physically meaningful signals,
the principal components may be rotated to control the distribution of variance. In this work,
we develop this method further through a weighted PCA approach. We use the EDS and EBSD
signals concurrently, thereby labelling each region using both EDS (chemistry) and EBSD
(crystal structure) information. This provides a new method of amplifying signal-to-noise for
very small phases in mapped regions, especially where the EDS or EBSD signal is not unique
enough alone for classification. Keywords: Principal component analysis | EBSD | EDS | microstructure | carbides | superalloy |
مقاله انگلیسی |
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An empirical study on stakeholder management in construction projects
یک مطالعه تجربی در مورد مدیریت ذینفعان در پروژه های ساختمانی-2020 The intent of this study is to analyze the various factors governing stakeholder management in construction
projects. In general, the construction companies involving in major infrastructure projects mostly
tend to have a number of stakeholders. In such situations, managing the stakeholders and getting adequate
support from them becomes necessary. Therefore this paper made an empirical study on their
management through questionnaire survey taken among various engineering and managerial personnel
(Project managers). This study identified the major factors influencing stakeholder management in construction
projects and analyzed them using Principal Component Analysis and Mean Score analysis by
frequency distribution method.. Keywords: Stakeholder management | Construction projects | Questionnaire survey | Principle component analysis | Mean Score analysis | Frequency distribution method |
مقاله انگلیسی |
4 |
Combining hierarchical clustering approaches using the PCA method
ترکیب روشهای خوشه بندی سلسله مراتبی با استفاده از روش PCA-2019 In expert systems, data mining methods are algorithms that simulate humans’ problem-solving capabil- ities. Clustering methods as unsupervised machine learning methods are crucial approaches to catego- rize similar samples in the same categories. The use of different clustering algorithms to a given dataset produces clusters with different qualities. Hence, many researchers have applied clustering combination methods to reduce the risk of choosing an inappropriate clustering algorithm. In these methods, the out- puts of several clustering algorithms are combined. In these research works, the input hierarchical clus- terings are transformed to descriptor matrices and their combination is achieved by aggregating their descriptor matrices. In previous works, only element-wise aggregation operators have been used and the relation between the elements of each descriptor matrix has been ignored. However, the value of each element of the descriptor matrix is meaningful in comparison with its other elements. The current study proposes a novel method of combining hierarchical clustering approaches based on principle component analysis (PCA). PCA as an aggregator allows considering all elements of the descriptor matrices. In the proposed approach, basic clusters are made and transformed to descriptor matrices. Then, a final ma- trix is extracted from the descriptor matrices using PCA. Next, a final dendrogram is constructed from the matrix that is used to summarize the results of the diverse clustering. The experimental results on popular available datasets show the superiority of the clustering accuracy of the proposed method over basic clustering methods such as single, average and centroid linkage and previously combined hierar- chical clustering methods. In addition, statistical tests show that the proposed method significantly out- performed hierarchical clustering combination methods with element-wise averaging operators in almost all tested datasets. Several experiments have also been conducted which confirm the robustness of the proposed method for its parameter setting. Keywords: Clustering | Hierarchical clustering | Principle component analysis | PCA |
مقاله انگلیسی |
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Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis
شتاب دهنده سخت افزاری بهینه سازی شده برای برنامه های استخراج داده بر روی چهارچوب های embedded: مطالعه موردی تجزیه و تحلیل مؤلفه اصلی-2019 With the proliferation of mobile, handheld, and embedded devices, many applications such as data min- ing applications have found their way into these devices. However, mobile devices have stringent area and power limitations, high speed-performance, reduced cost, and time-to-market requirements. Furthermore, applications running on mobile devices are becoming more complex requiring high processing power. These design constraints pose serious challenges to the embedded system designers. In order to pro- cess the applications on mobile and embedded systems, effectively and efficiently, optimized hardware architectures are needed. We are investigating the utilization of FPGA-based customized hardware to ac- celerate embedded data mining applications including handwritten analysis and facial recognition. For these biometric applications, Principal Component Analysis (PCA) is applied initially, followed by similar- ity measure. In this research work, we introduce novel and efficient embedded hardware architectures to accelerate the PCA computation. PCA is a classic technique to reduce the dimensionality of data by transforming the original data set into a new set of variables called Principal Components (PCs) that rep- resent the key features of the data. We propose two hardware versions for PCA computation, each with its unique optimization techniques to enhance the performance of our designs, and one specifically with additional techniques to reduce the memory access latency of embedded platforms. To the best of our knowledge, we could not find similar work for PCA, specifically catered to the embedded devices, in the published literature. We perform experiments to evaluate the feasibility and efficiency of our designs us- ing a benchmark dataset for biometrics. Our embedded hardware designs are generic, parameterized, and scalable; and achieve 78 times speedup as compared to its software counterparts Keywords: Data mining | Dimensionality reduction techniques | Embedded and mobile systems | FPGAs | Hardware acceleration | Principal Component Analysis |
مقاله انگلیسی |
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Projection-free kernel principal component analysis for denoising
تجزیه و تحلیل مؤلفه اصلی هسته بدون طرح برای حذف نویز-2019 Kernel principal component analysis (KPCA) forms the basis for a class of methods commonly used for denoising a set of multivariate observations. Most KPCA algorithms involve two steps: projection and preimage approximation. We argue that this two-step procedure can be inefficient and result in poor denoising. We propose an alternative projection-free KPCA denoising approach that does not involve the usual projection and subsequent preimage approximation steps. In order to denoise an observation, our approach performs a single line search along the gradient descent direction of the squared projection er- ror. The rationale is that this moves an observation towards the underlying manifold that represents the noiseless data in the most direct manner possible. We demonstrate that the approach is simple, compu- tationally efficient, robust, and sometimes provides substantially better denoising than the standard KPCA algorithm. Keywords: Image processing | Feature space | Pattern recognition | Preimage problem |
مقاله انگلیسی |
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Detection and determination of harmful gases in confined spaces for the Internet of Things based on cataluminescence sensor
تشخیص و تعیین گازهای مضر در فضاهای محدود برای اینترنت اشیاء بر اساس سنسور کاتالیزنسانس-2019 Gaseous cataluminescence (CTL) sensor instrument was developed for detecting and measuring pernicious gases
such as ethyl ether, acetone, n-hexane, chloroform by using aluminum/iron oxides composites. The materials
synthesis, apparatus and software, optimal conditions (flow rate, wavelength, temperature and concentration),
selectivity and stability were thoroughly studied. The results indicated the CTL sensor instrument could response
to ethyl ether under 180 °C, which was lower than most of the reported CTL reaction temperatures. In addition,
the results showed that the linear detection range was wide, ranging from 10 ppm to 5800 ppm (R=0.9978,
n=7), and the detection limit was also low (4.3 ppm). Moreover, for ethyl ether detection, the CTL sensor
showed the response time of 4 s and recovery time of 8 s, which was relatively short. The pattern recognition
methods included PCA, KPCA were selected for evaluating the recognition performance of this CTL sensor,
results showed the analytes could be classified clearly. The low reaction temperature, excellent sensitivity, selectivity,
stability and recognition performance indicated this CTL sensor was ideal for gas contaminants detection
in confined spaces for the Internet of Things. Keywords: Cataluminescence sensor | Pattern recognition | Principal component analysis | Internet of Things |
مقاله انگلیسی |
8 |
Improving the Performance of Manufacturing Technologies for Advanced Material Processing Using a Big Data and Machine Learning Framework
بهبود عملکرد فن آوری های ساخت برای پردازش مواد پیشرفته با استفاده از یک چارچوب یادگیری ماشین و داده های بزرگ-2019 The paper offers a new approach to improving the performance of the materials knowledge analysis based on Big Data
processing and machine learning. We consider a framework in which thread functioning of five machine learning mechanisms
intended for solving the classification problem is realized. Classifier operation results are exposed to majority voting. The
experimental assessment of performance and accuracy of framework operation is made on the data set containing technological
data of the production line. Assessment showed that the offered framework provides a scoring on productivity of materials
knowledge processing by 7.4 times. Keywords: material processing | big data | machine learning | principal component analysis | classifier |
مقاله انگلیسی |
9 |
Automatic detection of cereal rows by means of pattern recognition techniques
تشخیص خودکار ردیف های غلات با استفاده از تکنیک های تشخیص الگو-2019 Automatic locating of weeds from fields is an active research topic in precision agriculture. A reliable and
practical plant identification technique would enable the reduction of herbicide amounts and lowering of production
costs, along with reducing the damage to the ecosystem. When the seeds have been sown row-wise, most
weeds may be located between the sowing rows. The present work describes a clustering-based method for
recognition of plantlet rows from a set of aerial photographs, taken by a drone flying at approximately ten
meters. The algorithm includes three phases: segmentation of green objects in the view, feature extraction, and
clustering of plants into individual rows. Segmentation separates the plants from the background. The main
feature to be extracted is the center of gravity of each plant segment. A tentative clustering is obtained piecewise
by applying the 2D Fourier transform to image blocks to get information about the direction and the distance
between the rows. The precise sowing line position is finally derived by principal component analysis. The
method was able to find the rows from a set of photographs of size 1452 × 969 pixels approximately in 0.11 s,
with the accuracy of 94 per cent. Keywords: Computer vision | Pattern recognition | Principal component analysis | Fourier transform | Precision agriculture |
مقاله انگلیسی |