scholarly journals Algorithmic-Level Approximate Tensorial SVM Using High-Level Synthesis on FPGA

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 205
Author(s):  
Hamoud Younes ◽  
Ali Ibrahim ◽  
Mostafa Rizk ◽  
Maurizio Valle
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
High Level Synthesis ◽  
Support Vector ◽  
Svm Classifier ◽  
Approximate Computing ◽  
Classification Framework ◽  
Time Latency ◽  
High Level

Approximate Computing Techniques (ACT) are promising solutions towards the achievement of reduced energy, time latency and hardware size for embedded implementations of machine learning algorithms. In this paper, we present the first FPGA implementation of an approximate tensorial Support Vector Machine (SVM) classifier with algorithmic level ACTs using High-Level Synthesis (HLS). A touch modality classification framework was adopted to validate the effectiveness of the proposed implementation. When compared to exact implementation presented in the state-of-the-art, the proposed implementation achieves a reduction in power consumption by up to 49% with a speedup of 3.2×. Moreover, the hardware resources are reduced by 40% while consuming 82% less energy in classifying an input touch with an accuracy loss less than 5%.

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning

2018 ◽  
Vol 28 (02) ◽  
pp. 1750036 ◽  
Author(s):  
Shuqiang Wang ◽  
Yong Hu ◽  
Yanyan Shen ◽  
Hanxiong Li
Keyword(s):  
Machine Learning ◽  
Surgical Planning ◽  
Diffusion Tensor ◽  
Mean Value ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Control Groups ◽  
Diffusion Tensor Imaging Dti

In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

scholarly journals Smart Face Detection and Recognition in Low Resolution Images Using Alexnet CNN Compare Accuracy with SVM

2021 ◽  
Vol 36 (1) ◽  
pp. 721-726
Author(s):  
S. Mahesh ◽  
Dr.G. Ramkumar
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Low Resolution ◽  
Accuracy Ratio ◽  
Low Resolution Images

Aim: Machine learning algorithm plays a vital role in various biometric applications due to its admirable result in detection, recognition and classification. The main objective of this work is to perform comparative analysis on two different machine learning algorithms to recognize the person from low resolution images with high accuracy. Materials & Methods: AlexNet Convolutional Neural Network (ACNN) and Support Vector Machine (SVM) classifiers are implemented to recognize the face in a low resolution image dataset with 20 samples each. Results: Simulation result shows that ACNN achieves a significant recognition rate with 98% accuracy over SVM (89%). Attained significant accuracy ratio (p=0.002) in SPSS statistical analysis as well. Conclusion: For the considered low resolution images ACNN classifier provides better accuracy than SVM Classifier.

scholarly journals High-Level Synthesis of Multiclass SVM Using Code Refactoring to Classify Brain Cancer from Hyperspectral Images

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1494 ◽  
Author(s):  
Abelardo Baez ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Giordana Florimbi ◽  
Emanuele Torti ◽  
...  
Keyword(s):  
Brain Cancer ◽  
Research Field ◽  
Hyperspectral Images ◽  
High Level Synthesis ◽  
Support Vector ◽  
Svm Classifier ◽  
Code Refactoring ◽  
Field Programmable ◽  
And Performance ◽  
High Level

Currently, high-level synthesis (HLS) methods and tools are a highly relevant area in the strategy of several leading companies in the field of system-on-chips (SoCs) and field programmable gate arrays (FPGAs). HLS facilitates the work of system developers, who benefit from integrated and automated design workflows, considerably reducing the design time. Although many advances have been made in this research field, there are still some uncertainties about the quality and performance of the designs generated with the use of HLS methodologies. In this paper, we propose an optimization of the HLS methodology by code refactoring using Xilinx SDSoCTM (Software-Defined System-On-Chip). Several options were analyzed for each alternative through code refactoring of a multiclass support vector machine (SVM) classifier written in C, using two different Zynq®-7000 SoC devices from Xilinx, the ZC7020 (ZedBoard) and the ZC7045 (ZC706). The classifier was evaluated using a brain cancer database of hyperspectral images. The proposed methodology not only reduces the required resources using less than 20% of the FPGA, but also reduces the power consumption −23% compared to the full implementation. The speedup obtained of 2.86× (ZC7045) is the highest found in the literature for SVM hardware implementations.

Application of Bayesian Learning Mechanism in Power System Transient Stability Assessment

2010 ◽  
Vol 108-111 ◽  
pp. 765-770
Author(s):  
Lin Niu ◽  
Jian Guo Zhao ◽  
Ke Jun Li ◽  
Zhen Yu Zhou
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Real Time ◽  
Bayesian Learning ◽  
Transient Stability ◽  
Decision Function ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Stability Assessment

One of the most challenging problems in real-time operation of power system is the prediction of transient stability. Fast and accurate techniques are imperative to achieve on-line transient stability assessment (TSA). This problem has been approached by various machine learning algorithms, however they find a class decision estimate rather than a probabilistic confidence of the class distribution. To counter the shortcoming of common machine learning methods, a novel machine learning technique, i.e. ‘relevance vector machine’ (RVM), for TSA is presented in this paper. RVM is based on a probabilistic Bayesian learning framework, and as a feature it can yield a decision function that depends on only a very fewer number of so-called relevance vectors. The proposed method is tested on New England power system, and compared with a state-of-the-art ‘support vector machine’ (SVM) classifier. The classification performance is evaluated using false discriminate rate (FDR). It is demonstrated that the RVM classifier can yield a decision function that is much sparser than the SVM classifier while providing higher classification accuracy. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time implementation.

scholarly journals Author identification for Under-Resourced language (KadazanDusun)

2020 ◽  
Vol 17 (1) ◽  
pp. 248 ◽  
Author(s):  
Nursyahirah Tarmizi ◽  
Suhaila Saee ◽  
Dayang Hanani Abang Ibrahim
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification Task ◽  
Data Set ◽  
Short Text ◽  
Author Identification

<span>This paper presents the task of Author Identification for KadazanDusun language by using tweets as the source of data to perform Author Identification task of short text on KadazanDusun, which is considered as one the under-resourced language in Malaysia. The aim of this paper is to demonstrate Author Identification of short text on KadazanDusun. Besides, this paper also examines the performance of two machine learning algorithms on the KadazanDusun data set by analyzing the stylometric features. Stylometric features are used to quantify the writing styles of the authors which includes character n-grams and word n-grams. The workflow of Author Identification implements the machine learning approach to solve the single-labelled multi-class problem and predict the author of a given message in KadazanDusun. Two classifiers are used to compare the accuracy including Naïve Bayes and Support Vector Machine (SVM). The results show that the combination of n-grams which is word-level unigram and {1-5}-grams with character 3-grams are the most relevant stylometric features in identifying the author of KadazanDusun message with an accuracy of 80.17%. The results also show that SVM classifier has outperformed Naive Bayes in this Author Identification task with the accuracy of 80.17%.</span>

Classification Framework for Healthy Hairs and Alopecia Areata using Machine Learning (Preprint)

2021 ◽  
Author(s):  
Choudhary Sobhan Shakeel ◽  
Saad Jawaid Khan ◽  
Syeda Fatima Aijaz ◽  
Umer Hassan ◽  
Beenish Chaudhry
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Alopecia Areata ◽  
Nearest Neighbor ◽  
Financial Burden ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Classification Framework ◽  
Learning Techniques

BACKGROUND Alopecia areata is an auto-immune disorder that involves non-scarring hair loss in well-defined patches as well as affecting the entire scalp region and ultimately leads to baldness. The latest worldwide statistics have exhibited that Alopecia areata affects millions of people. Furthermore, the use of conventional methods often leads to poor diagnosis of Alopecia ultimately increasing the medical financial burden on the population. It has been reported that 85% of the individuals suffering from Alopecia areata complain about significant financial burden along with associated costs that are beyond cosmetic concerns. Many individuals adhere to treatment discontinuation owing to enhanced expenses and poor diagnosis. OBJECTIVE The objectives of the study comprise of utilizing datasets of healthy hairs and Alopecia areata, extracting color, texture and shape features from the images and applying machine learning algorithms including support vector machine (SVM) and k-nearest neighbor (KNN). METHODS Two datasets with images of healthy hairs and Alopecia areata have been utilized. A total of 200 healthy hair images were retrieved from Figaro1k dataset. A total of 68 images of Alopecia areata were retrieved from a dataset known as Dermnet. The images initially go through pre-processing steps including enhancement and segmentation. Following image segmentation, three features of color, texture and shape are extracted. Following feature extraction, machine learning algorithms including support vector machine (SVM) and k-nearest neighbor (KNN) are applied that aid in classifying Alopecia areata and healthy hairs. RESULTS A total of 81 images are tested with support vector machine (SVM) and k- nearest neighbor (KNN) yielding an accuracy of 91.4% and 88.9% respectively. The results of the paired sample T-test via SPSS analysis demonstrate a p < 0.001 and exhibits that the accuracies acquired from the two machine learning techniques are significantly different. The accuracies reported will enable a hair expert in recommending a suitable diagnosis and hair treatment regimen to a patient. CONCLUSIONS The application of support vector machine (SVM) presented an accuracy of 91.4% and that of k-nearest neighbor (KNN) presented an accuracy of 88.9%. These accuracies exhibit that the proposed classification framework is found to be successful and robust. However, future work with deep learning techniques such as convolutional neural networks (CNN) can be also be carried out and integrated with the existing system.

Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: a proof of concept

2021 ◽  
pp. 028418512110324
Author(s):  
Stavros Charalambous ◽  
Michail E. Klontzas ◽  
Nikolaos Kontopodis ◽  
Christos V Ioannou ◽  
Kostas Perisinakis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Endovascular Aneurysm Repair ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
One Year ◽  
A Prospective Study ◽  
Aneurysm Repair

Background Persistent type 2 endoleaks (T2EL) require lifelong surveillance to avoid potentially life-threatening complications. Purpose To evaluate the performance of radiomic features (RF) derived from computed tomography angiography (CTA), for differentiating aggressive from benign T2ELs after endovascular aneurysm repair (EVAR). Material and Methods A prospective study was performed on patients who underwent EVAR from January 2018 to January 2020. Analysis was performed in patients who were diagnosed with T2EL based on the CTA of the first postoperative month and were followed at six months and one year. Patients were divided into two groups according to the change of aneurysm sac dimensions. Segmentation of T2ELs was performed and RF were extracted. Feature selection for subsequent machine-learning analysis was evaluated by means of artificial intelligence. Two support vector machines (SVM) classifiers were developed to predict the aneurysm sac dimension changes at one year, utilizing RF from T2EL at one- and six-month CTA scans, respectively. Results Among the 944 initial RF of T2EL, 58 and 51 robust RF from the one- and six-month CTA scans, respectively, were used for the machine-learning model development. The SVM classifier trained on one-month signatures was able to predict sac expansion at one year with an area under curve (AUC) of 89.3%, presenting 78.6% specificity and 100% sensitivity. Similarly, the SVM classifier developed with six-month radiomics data showed an AUC of 95.5%, specificity of 90.9%, and sensitivity of 100%. Conclusion Machine-learning algorithms utilizing CTA-derived RF may predict aggressive T2ELs leading to aneurysm sac expansion after EVAR.

scholarly journals An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+

2021 ◽  
Vol 11 (12) ◽  
pp. 5703
Author(s):  
Yifan Si ◽  
Dawei Gong ◽  
Yang Guo ◽  
Xinhua Zhu ◽  
Qiangsheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Learning Algorithms ◽  
Principal Component ◽  
Semantic Segmentation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Framework

DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.

Gesture Classification of Surface Electromyography Signals Using Machine Learning Algorithms for Hand Prosthetics

2021 ◽  
Vol 11 (12) ◽  
pp. 3141-3152
Author(s):  
N. Subhashini ◽  
A. Kandaswamy
Keyword(s):  
Machine Learning ◽  
Surface Electromyography ◽  
Learning Algorithms ◽  
Ensemble Classifier ◽  
Correlation Factor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Movement Classification

The actions of humans executed by their hands play a remarkable part in controlling and handling variety of objects in their daily life activities. The effect of losing or degradation in the functioning of one hand has a greater influence in bringing down the regular activity. Hence the design of prosthetic hands which assists the individuals to enhance their regular activity seems a better remedy in this new era. This paper puts forward a classification framework using machine learning algorithms for classifying hand gesture signals. The surface electromyography (sEMG) dataset acquired for 9 wrist movements of publicly available database are utilized to identify the potential biomarkers for classification and in evaluating the efficacy of the proposed algorithm. The statistical and time domain features of the sEMG signals from 27 intact subjects and 11 trans-radial amputated subjects are extracted and the optimal features are determined implementing the feature selection approach based on correlation factor. The classifiers performance of machine learning algorithms namely support vector machine (SVM), Naïve bayes (NB) and Ensemble classifier are evaluated. The experimental results highlight that the SVM classifier can yield the maximum accuracy movement classification of 99.6% for intact and 97.56% for trans-amputee subjects. The proposed approach offers better accuracy and sensitivity compared to other approaches that have used the sEMG dataset for movement classification.

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