Detecting Power Analysis Attacks with Machine Learning Through Voltage Differential Monitoring

The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vector machine (LSVM) and proximal support vector machine (PSVM) is proposed to carry out the wind speed prediction using the available real time wind farm data. In developed PSVM predictor, it is modeled for both linear PSVM predictor and non-linear PSVM predictor. The difference between the developed linear and non-linear PSVM predictor models lies in their applicability of kernel functions to perform effective wind speed prediction. The prediction application is implemented for the set of wind farm data with a wind mill height of 50 m in a manner to minimize the mean square error. The training process of the neural network algorithmic flow is done with the developed LSVM, L-PSVM (Linear PSVM) and N-PSVM (nonlinear PSVM) for predicting the wind speed in renewable energy systems. Results computed are compared with the other types of predictors to prove the effectiveness of the proposed variants of SVM predictors. The simulated results presents the effectiveness of the proposed predictors for the real time wind farm data and the applicability of the predictors for the considered datasets.

Near infrared spectroscopy was used in discrimination of intact bovine teeth in terms of animal sex, diet, tooth type and place of origin. Discriminant Analysis (DA) models were developed and tested using a stratified random 70:30 data split to calibration and test sets. Of the discriminant techniques of Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares – DA, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), SVM and PLS-DA models performed best in most instances, with pretreatment choice impacting technique success. For test set prediction of animal sex, an accuracy of 95% was achieved using a PLS-DA model, and similar performance achieved using a SVM model. SVM, ANN and SIMCA models predicted three categories of tooth type (deciduous, permanent unerupted and erupted) with similar accuracies (89, 79 and 82%, respectively), however, permanent unerupted teeth were conflated with permanent erupted teeth. Prediction accuracy for ANN, SVM and PLS-DA models were also similar for discrimination of teeth from animals on a grain versus grass diet (PLS-DA 82%). There was considerable variation between models in prediction of geographic origin. Of the seven locations, two were discriminated with true positive rate around 70% using SVM and ANN models. Potential applications of this technology are discussed.

The results of directly comparing the prediction accuracy of optimized 3D Quantitative Structure-Activity Relationship (3D-QSAR) models and linear Support Vector Machine (SVM) classifiers to identify small molecule inhibitors of the BRAF-V600E and HIV Integrase targets are reported. Performance comparisons were carried out using 303 compounds (68 active) against BRAF-V600E and 204 compounds (159 active) against HIV Integrase. A SVM prediction accuracy of 95% (BRAF-V600E) and 100% (HIV Integrase) and 3D-QSAR prediction accuracy of 76% (BRAFV600E) and 82% (HIV Integrase) was observed. To help explain the better performance of SVM in the comparison reported here and to help assess the degree to which a SVM or 3D-QSAR model is likely to perform best for other targetligands of interest a new EPP (Expected Predictive Performance) metric is introduced. How EPP can be used to help predict future performance of SVM and 3D-QSAR models by quantifying the degree of similarity between candidate compounds and training data is also demonstrated. Results show that the EPP metric is capable of predicting future prediction accuracy of SVM and 3D-QSAr models within 7% of actual performance.

Support Vector Machine (SVM) is a widely used supervised learning algorithm known for its robustness, efficiency, and theoretical reliability. This study analyzes the performance of linear kernel SVMs on real-world binary classification tasks from healthcare and social behavior domains. Two datasets were used: a heart disease dataset with 14 clinical features, and a social network advertisement dataset with demographic features like age and salary. Both were pre-processed using normalization, label encoding, and train-test splitting, and implemented in Python using scikit-learn. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The heart disease model achieved 83.3% accuracy, while the social network model reached 76.7%, showing linear SVM’s effectiveness on structured, moderately sized datasets. The study also covers core SVM concepts like support vectors, hyperplanes, and margin maximization. Soft margin classification was used to handle noisy data, and hyperparameter tuning (e.g., the regularization parameter C) improved performance. In conclusion, linear SVM is still a strong baseline for binary classification due to its simplicity, speed, and interpretability. Future work may explore multi-class problems, kernel comparisons, and ensemble methods to expand its applicability.

기업의 부도를 예측하는 것은 회계나 재무 분야에서 중요한 연구주제이다. 지금까지 기업 부도예측을 위해 여러 가지 데이터마이닝 기법들이 적용되었으나 주로 단일 모형을 사용함으로서 복잡한 분류 문제에의 적용에 한계를 갖고 있었다. 본 논문에서는 최근에 각광받고 있는 SVM (support vector machine) 모형들을 결합한 앙상블 SVM 모형 (ensemble SVM model)을 부도예측에 사용하고자 한다. 제안된 앙상블 모형은 v-조각 교차 타당성 (v-fold cross-validation)에 의해 얻어진 여러 가지 모형 중에서 성능이 좋은 상위 k개의 단일 모형으로 구성하고 과반수 투표 방식 (majority voting)을 사용하여 미지의 클래스를 분류한다. 본 논문에서 제안된 앙상블 SVM 모형의 성능을 평가하기 위해 실제 기업의 재무비율 자료와 모의실험자료를 가지고 실험하였고, 실험결과 제안된 앙상블 모형이 여러 가지 평가척도 하에서 단일 SVM 모형들보다 좋은 성능을 보임을 알 수 있었다. Corporate bankruptcy prediction has been an important topic in the accounting and finance field for a long time. Several data mining techniques have been used for bankruptcy prediction. However, there are many limits for application to real classification problem with a single model. This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The k top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble. In this paper, we investigate the performance of ensemble SVM classifier in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on financial ratios dataset and simulation dataset. The results confirmed the advantages of our method: It is robust while providing good performance.

Software logging is an essential programming practice that saves important runtime information that can be used later by software developers for troubleshooting, debugging and monitoring the software. Even though software logging has numerous benefits this practice is underutilized because of lack of any formal guiding principles to developers for making strategic and efficient logging decisions. Logging should be optimized because too much logging can cause performance overheads; sparse logging can leave out vital information that might give clues to developers about the real issues. In absence of any formal guidelines developers rely solely on their domain knowledge and experience while making logging decisions. In order to lessen this effort of making decisions we have proposed a machine learning based framework, Logger4u for if-block logging prediction. We extract and use 28 distinctive static features from the source code helpful in making well informed logging decisions. We use Support Vector Machine (two variants, 1 linear and 1 RBF kernel based) models, Multilayer Perceptron with back propagation model and Random forest model in our work. Our approach gives encouraging results for if-block logging task. The accuracy achieved by the Linear SVM, MLP, Random Forest and kernel SVM are 73.05%, 74.62%, 79.84% and 81.22% respectively

Astrocytoma is the most common neuroepithelial neoplasm, and its grading greatly affects treatment and prognosis. According to relevant factors of astrocytoma, this study developed a support vector machine (SVM) model to predict the astrocytoma grades and compared the SVM prediction with the clinician's diagnostic performance. Patients were recruited from a cohort of astrocytoma patients in our hospital between January 2008 and April 2009. Among all astrocytoma patients, nine had grade I, 25 had grade II, 12 had grade III, and 60 had grade IV astrocytoma. An SVM model was constructed using radial basis kernel. The SVM model was trained with nine magnetic resonance (MR) features and one clinical parameter by fivefold cross-validation and differentiated astrocytomas of grades I-IV at two levels, respectively. The clinician also predicted the grade of astrocytoma. According to the two prediction methods above, the areas under receiving operating characteristics (ROC) curves to discriminate low- and high-grade groups, accuracies of high-grade grouping, overall accuracy, and overall kappa values were compared. For SVM, the overall accuracy was 0.821 and the overall kappa value was 0.679; for clinicians, the overall accuracy was 0.651 and the overall kappa value was 0.466. The diagnostic performance of SVM is significantly better than clinician performance, with the exception of the low-grade group. The SVM model can provide useful information to help clinicians improve diagnostic performance when predicting astrocytoma grade based on MR images.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.

Construction accident narrative classification: An evaluation of text mining techniques

With the increasing popularity of Internet of Things Edge (IoTe) devices, RISC-V emerges as the most suitable architecture for various applications. Since attackers have direct physical access to these IoTe devices, securing these RISC-V designs is of major concern. This research article first focuses on designing a low-power high-performance, RISC-V crypto-core for high security applications, which significantly enhances the efficiency of the AES encryption algorithm, but still susceptible to Power Analysis Attacks (PAA). A novel Non-Linear Cyclic Variable Clock Feistel Bridge-Inspired Countermeasure (NCVCFB) providing variation in both amplitude and temporal domains of signal was introduced to enhance AES security in RISC-V based IoTe devices, specially to address the vulnerability against PAA. The NCVCFB countermeasure employs the rolling architecture and will operate in tandem with each round of AES to obscure power consumption patterns. It will execute additional random number of rounds cyclically in the first and last round of AES, based on the randomly generated number. The newly designed, secured RISC-V achieved remarkably low area and power overheads of 1.05% and 1.23%, respectively, without affecting the maximum operating frequency. Nevertheless, the throughput was decreased due to variable clock count for different plaintext. The PAA was performed using the power traces captured from post-layout design on ASIC platform at UMC 65 nm technology node as well as on the experimental hardware setup employing a Side-channel Attack Security Evaluation Board (SASEBO). The resilience of the secured RISC-V architecture against PAA was tested by subjecting it to 2 Million traces, and none of the bytes got recovered. Thus, the NCVCFB secured RISC-V attained a Measurement To Disclose (MTD) >2M, Signal to Noise Ratio (SNR) <0.5, Mutual Information (MI) in the milli range, and Test Vector Leakage Assessment (TVLA) within +/−4.5 limits.

The paper explores the hybrid of SVM and system of Artificial Neural Network as the finest binary classification system for calculating the diabetic nature of people in comparison to Support Vector Machine (SVM). In this research, the sets of all the parameters describing the diabetic nature of a person are taken from the laboratories. This approach is chosen so that a better learning method can be used for various problems. The testing results were found to be in accordance with the accepted results that resemble with the direct diagnosis of a physician. The results of this research shows that this hybrid SVM and Artificial Neural Network (ANN) model is more precise than the SVM model. These results of the hybrid SVM and ANN model suggest that it is very effective for the classification of Diabetic and Non Diabetic nature of a person. This paper highlights the concept of Support Vector Machines and its integration with Artificial Neural Network, the two key characteristics with one being the generalization theory of the SVM model that best describes how to select a hypothesis and functions given by Kernel that introduces the idea of non-linearity without the inclusion of the actual algorithm.

This study compares single and hybrid soft computing models for estimating daily solar radiation flux for two scenarios. Scenario I developed single soft computing models, including multilayer perceptron (MLP), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multivariate adaptive regression splines (MARS), for estimating daily solar radiation flux at two stations from the USA and South Korea. The MLP model was used to evaluate the effect of factors controlling daily solar radiation flux. Using different combinations of controlling factors as input, the MLP and SVM models, based on evaluation measures, were found to be superior to the ANFIS and MARS models at Big Bend station, USA. In addition, the MLP, SVM, and MARS models performed better than did the ANFIS model at Incheon station, South Korea. Scenario II combined the discrete wavelet transform (DWT) and single soft computing models (e.g., MLP and SVM) for improved performance using 4-input combination. The wavelet-based MLP (WMLP) and SVM (WSVM) models were superior to other single soft computing models (MLP, SVM, ANFIS, and MARS) at two stations. Taylor diagrams, violin plots and point density plots were also utilized to examine the similarity between the observed and estimated solar radiation flux values. Results showed that scenarios I and II can be alternatives for estimating daily solar radiation flux based on different meteorological information, such as peninsular and continental conditions.

Interpreting support vector machines applied in laser-induced breakdown spectroscopy

Purpose: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients.Methods and Materials: A group of 106 ICC patients, who were diagnosed between April 2011 and February 2016, was used for prediction model training. Image features were extracted from T1-weighted contrast-enhanced MR images. A support vector machine (SVM) model was built by using the most LN status-related features, which were selected using the maximum relevance minimum redundancy (mRMR) algorithm. The mRMR method ranked each feature according to its relevance to the LN status and redundancy with other features. An SVM score was calculated for each patient to reflect the LN metastasis (LNM) probability from the SVM model. Finally, a combination nomogram was constructed by incorporating the SVM score and clinical features. An independent group of 42 patients who were diagnosed from March 2016 to November 2017 was used to validate the prediction models. The model performances were evaluated on discrimination, calibration, and clinical utility.Results: The SVM model was constructed based on five selected image features. Significant differences were found between patients with LNM and non-LNM in SVM scores in both groups (the training group: 0.5466 (interquartile range (IQR), 0.4059-0.6985) vs. 0.3226 (IQR, 0.0527-0.4659), P<0.0001; the validation group: 0.5831 (IQR, 0.3641-0.8162) vs. 0.3101 (IQR, 0.1029-0.4661), P=0.0015). The combination nomogram based on the SVM score, the CA 19-9 level, and the MR-reported LNM factor showed better discrimination in separating patients with LNM and non-LNM, comparing to the SVM model alone (AUC: the training group: 0.842 vs. 0.788; the validation group: 0.870 vs. 0.787). Favorable clinical utility was observed using the decision curve analysis for the nomogram.Conclusion: The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.