Synthesizing Conjunctive & Disjunctive Linear Invariants by K-means++ and SVM

2020 ◽  
Vol 17 (6) ◽  
pp. 847-856
Author(s):  
Shengbing Ren ◽  
Xiang Zhang
Keyword(s):  
Software Verification ◽  
State Of The Art ◽  
Positive Sample ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Hoare Logic ◽  
Excellent Performance ◽  
Automated Software ◽  
Inductive Invariants ◽  
Linear Invariants

The problem of synthesizing adequate inductive invariants lies at the heart of automated software verification. The state-of-the-art machine learning algorithms for synthesizing invariants have gradually shown its excellent performance. However, synthesizing disjunctive invariants is a difficult task. In this paper, we propose a method k++ Support Vector Machine (SVM) integrating k-means++ and SVM to synthesize conjunctive and disjunctive invariants. At first, given a program, we start with executing the program to collect program states. Next, k++SVM adopts k-means++ to cluster the positive samples and then applies SVM to distinguish each positive sample cluster from all negative samples to synthesize the candidate invariants. Finally, a set of theories founded on Hoare logic are adopted to check whether the candidate invariants are true invariants. If the candidate invariants fail the check, we should sample more states and repeat our algorithm. The experimental results show that k++SVM is compatible with the algorithms for Intersection Of Half-space (IOH) and more efficient than the tool of Interproc. Furthermore, it is shown that our method can synthesize conjunctive and disjunctive invariants automatically

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 AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

scholarly journals Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

2020 ◽  
Author(s):  
Alisson Hayasi da Costa ◽  
Renato Augusto C. dos Santos ◽  
Ricardo Cerri
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Feedforward Neural Networks ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Large Application

AbstractPIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of 0.872, outperforming the state-of-the-art method in the literature.

Text Classification Using Compression-Based Dissimilarity Measures

2015 ◽  
Vol 29 (05) ◽  
pp. 1553004 ◽  
Author(s):  
David Pereira Coutinho ◽  
Mário A. T. Figueiredo
Keyword(s):  
Text Classification ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Feature Space ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Feature Engineering ◽  
Dissimilarity Measures ◽  
Previous State ◽  
Art Techniques

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

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%.

Breast cancer analysis using Machine Learning algorithms

2021 ◽  
Vol 2 (1) ◽  
pp. 12-16
Author(s):  
Lina Alkhathlan ◽  
Abdul Khader Jilani Saudagar
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Causes Of Death ◽  
State Of The Art ◽  
Early Stage ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Risk Of Death ◽  
Breast Cells

Breast cancer (BC) is one of the most common types of cancer and one of the leading causes of death for women around the world. Breast cancer occurs when cells in the breast cells mutate and form a malignant tumor. State-of-the-art technologies can detect BC at an early stage, which helps in treatment and reduces the risk of death. Medical doctors commonly use breast tissue biopsy when diagnosing breast cancer, enabling them to take a microscopic look for breast tissue and determine whether the tissue is benign or malignant. To improve biopsy results, many researchers have studied the feasibility of using artificial intelligence (AI) to help doctors detect any harmful changes that may lead to cancer. In this research work detail analysis of Breast Cancer using support vector machine (SVM) and convolutional neural networks (CNN) algorithms is performed and the results show CNN has more superior results in comparison to SVM in the recognition of images affected by Breast Cancer.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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