Support Vector Machine For Identification Research Articles

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

Retracted: Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Retracted: Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Evaluation of optimization techniques with support vector machine for identification of dry beans

<span>Automated recognition or categorization of fruits or crops through image processing technology is a highly sought-after technique in agriculture. <br /> This approach has been demonstrated to outperform manual monitoring in humans. It reduces the time and expenses incurred by farmers and ensures that end users receive high-quality products. This study details the process for identifying dry beans. We employed a support vector machine (SVM) model and three optimization techniques: Bayesian optimization, random search optimization, and grid search optimization. This study aimed to determine the optimal optimization technique for identifying dry beans using an SVM model. Various metrics, including the validation accuracy, test accuracy, total validation cost, total test cost, and minimum classification error, were computed and analyzed to compare the performance of each optimization method. In comparison, Bayesian optimization was determined to be the most effective optimization technique for identifying dry beans, with a validation accuracy of 93.06%, test accuracy of 92.29%, and minimum classification error of 0.069384.</span>

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

This article examines the effect of smart grid systems by implementing artificial intelligence (AI) technique with application of renewable energy sources (RES). The current state generation smart grid system follows a high demand on supply of equal energy load to all grid states. However, in conventional techniques, high demand is observed as manual operation is preformed and load problems are not solved within the stipulated time period due to lack of technological advancements. However, applications of AI in smart grid process reduces risk of operation as manual adjustments are converted to highly automated procedures. This type of automatic process identifies the fault location at stage 1 and diagnosis of identified faults will be processed at stage 2. The abovementioned two stage processes will be incorporated with two constant parameters as dummy load is produced to overcome high- to low-power flows. Additionally, a scrap model has been designed to reduce the wastage of power as 100 percent effective progress can be achieved for low- to high-power supplies. To detect the corresponding regression losses in the grid systems, support vector machine (SVM) which completely identifies the previous state loss in the system is integrated. Hence, to analyze the effectiveness of the SVM model, four different scenarios are evaluated and compared with heuristic algorithms, long short-term memory (LSTM), autoregressive indicated moving average (ARIMA), adaptive ARIMA, and linear regression models with distinct performance analysis that includes error in percentage values where a total efficiency of 81% is achieved for projected SVM in all power lines including large-scale systems as compared to existing approaches.

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.

Implementation of Support Vector Machine for Identification of Skin Cancer

Implementation of Support Vector Machine for Identification of Skin Cancer

A discontinuous derivative-free optimization framework for multi-enterprise supply chain

Supply chain simulation models are widely used for assessing supply chain performance and analyzing supply chain decisions. In combination with derivative-free optimization algorithms, simulation models have shown great potential in effective decision-making. Most of the derivative-free optimization algorithms, however, assume continuity of the response, which may not be true in some practical applications. In this work, a supply chain inventory optimization problem is addressed that results in a discontinuous objective function. A derivative-free optimization framework is proposed that addresses the discontinuities in the objective function. The framework employs a sparse grid sampling and support vector machines for identification of discontinuities. Computational comparisons presented show that addressing discontinuity leads to more cost-effective decisions over existing approaches.

Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities

Similar to a fingerprint search system, face recognition technology can assist law enforcement agencies in identifying suspects or finding missing persons. Face recognition technology lets the police detect a suspect’s face and compare it with image databases of known criminals and provides investigators with a match list of the most similar faces. Face recognition is a highly efficient and accurate tool in investigation processes. However, in some sensitive scenarios covert methods are required for the detection of suspects or missing persons without risking the lives of police. With the availability of the nano devices such as Raspberry Pi, law enforcement agencies such as police can be equipped with a concealed and secure face recognition system. In this paper, a Raspberry Pi and cloud assisted face recognition framework is proposed. A small-sized portable wireless camera is mounted on a police officer’s uniform to capture a video stream, which is passed to Raspberry Pi for face detection and recognition. The proposed method uses Bag of Words for extraction of oriented FAST and rotated BRIEF points from the detected face, followed by support vector machine for identification of suspects. Raspberry Pi has limited resources such as storage space, memory, and processing power, and therefore the proposed classifier is stored and trained on the cloud. The proposed method is implemented on Raspberry Pi 3 model B in Python 2.7 and is tested on various standard datasets. Experimental results validate the efficiency of the proposed method in accurate detection of faces compared to state-of-the-art face detection and recognition methods, and verify its effectiveness for enhancing law-enforcement services in smart cities.

Non-asymptotic Analysis of $\ell_1$-norm Support Vector Machines

Support vector machines (SVMs) with the $\ell _{1}$ -penalty became a standard tool in the analysis of high-dimensional classification problems with sparsity constraints in many applications, including bioinformatics and signal processing. We give non-asymptotic results on the performance of $\ell _{1}$ -SVM in identification of sparse classifiers. We show that an $N$ -dimensional $s$ -sparse classification vector can be (with high probability) well approximated from only $O(s\log (N))$ Gaussian trials. We derive similar estimates also in the presence of misclassifications and for the so-called doubly regularized SVM, which combines the $\ell _{1}$ - and the $\ell _{2}$ -penalty. Similar bounds were obtained earlier in the analysis of LASSO and 1-Bit compressed sensing.

Support vector machine for identification of handwritten Gujarati alphabets using hybrid feature space

Gujarati language is used in the western state of Gujarat. Because of its peculiarities, its Optical Character Recognition becomes very difficult. For this language very less work has been done in the area of Optical Character Recognition. In this paper, I have attempted the problem of Optical Character Recognition for handwritten Gujarati alphabets. For this work, forty handwritten alphabets are collected from about one hundred and ninety nine writers. Here; aspect ratio, extent of alphabet, and image subdivision approach has been used as feature space and support vector machine (SVM) has been used for the classification purpose and it gives 86.66 % of performance accuracy. kNN is also used for classification and the result is compared with that of SVM. The paper also describes support vector machine.

Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

A support vector machine for identification of single-nucleotide polymorphisms from next-generation sequencing data

Accurate determination of single-nucleotide polymorphisms (SNPs) from next-generation sequencing data is a significant challenge facing bioinformatics researchers. Most current methods use mechanistic models that assume nucleotides aligning to a given reference position are sampled from a binomial distribution. While such methods are sensitive, they are often unable to discriminate errors resulting from misaligned reads, sequencing errors or platform artifacts from true variants. To enable more accurate SNP calling, we developed an algorithm that uses a trained support vector machine (SVM) to determine variants from .BAM or .SAM formatted alignments of sequence reads. Our SVM-based implementation determines SNPs with significantly greater sensitivity and specificity than alternative platforms, including the UnifiedGenotyper included with the Genome Analysis Toolkit, samtools and FreeBayes. In addition, the quality scores produced by our implementation more accurately reflect the likelihood that a variant is real when compared with those produced by the Genome Analysis Toolkit. While results depend on the model used, the implementation includes tools to easily build new models and refine existing models with additional training data. Source code and executables are available from github.com/brendanofallon/SNPSVM/

Support Vector Machines for Identification and Classification Problems in Control Engineering

Support Vector Machines for Identification and Classification Problems in Control Engineering