scholarly journals Lightweight Modeling Attack-Resistant Multiplexer-Based Multi-PUF (MMPUF) Design on FPGA

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 815 ◽  
Author(s):  
Yijun Cui ◽  
Chongyan Gu ◽  
Qingqing Ma ◽  
Yue Fang ◽  
Chenghua Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Sample Size ◽  
High Resistance ◽  
Hardware Implementation ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Large Sample Size ◽  
Prediction Rate ◽  
Arbiter Puf

Physical unclonable function (PUF) is a primary hardware security primitive that is suitable for lightweight applications. However, it is found to be vulnerable to modeling attacks using machine learning algorithms. In this paper, multiplexer (MUX)-based Multi-PUF (MMPUF) design is proposed to thwart modeling attacks. The proposed design uses a weak PUF to obfuscate the challenge of a strong PUF. A mathematical model of the proposed design is presented and analyzed. The three most widely used modeling attack techniques are used to evaluate the resistance of the proposed design. Experimental results show that the proposed MMPUF design is more resistant to the machine learning attack than the previously proposed XOR-based Multi-PUF (XMPUF) design. For a large sample size, the prediction rate of the proposed MMPUF is less than the conventional Arbiter PUF (APUF). Compared with existing attack-resistant PUF designs, the proposed MMPUF design demonstrates high resistance. To verify the proposed design, a hardware implementation on Xilinx 7 Series FPGAs is presented. The hardware experimental results show that the proposed MMPUF designs present good results of uniqueness and reliability.

scholarly journals Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3777 ◽  
Author(s):  
Ataollah Shirzadi ◽  
Karim Soliamani ◽  
Mahmood Habibnejhad ◽  
Ataollah Kavian ◽  
Kamran Chapi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Sample Sizes ◽  
Promising Alternative ◽  
Statistical Measures

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

scholarly journals An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-30 ◽  
Author(s):  
Dongdong Lv ◽  
Shuhan Yuan ◽  
Meizi Li ◽  
Yang Xiang
Keyword(s):  
Machine Learning ◽  
Transaction Cost ◽  
Stock Price ◽  
Large Scale ◽  
Statistical Significance ◽  
Significance Test ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Price Trends ◽  
The Impact

According to the forecast of stock price trends, investors trade stocks. In recent years, many researchers focus on adopting machine learning (ML) algorithms to predict stock price trends. However, their studies were carried out on small stock datasets with limited features, short backtesting period, and no consideration of transaction cost. And their experimental results lack statistical significance test. In this paper, on large-scale stock datasets, we synthetically evaluate various ML algorithms and observe the daily trading performance of stocks under transaction cost and no transaction cost. Particularly, we use two large datasets of 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) from 2010 to 2017 and compare six traditional ML algorithms and six advanced deep neural network (DNN) models on these two datasets, respectively. The experimental results demonstrate that traditional ML algorithms have a better performance in most of the directional evaluation indicators. Unexpectedly, the performance of some traditional ML algorithms is not much worse than that of the best DNN models without considering the transaction cost. Moreover, the trading performance of all ML algorithms is sensitive to the changes of transaction cost. Compared with the traditional ML algorithms, DNN models have better performance considering transaction cost. Meanwhile, the impact of transparent transaction cost and implicit transaction cost on trading performance are different. Our conclusions are significant to choose the best algorithm for stock trading in different markets.

An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model

2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Vishal G. Salunkhe ◽  
R. G. Desavale
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Electrical Discharge Machining ◽  
Experimental Results ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Bearing Failure ◽  
Vibration Characteristic ◽  
Model Based ◽  
Dimension Analysis

Abstract Bearing failure in the heavy rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems acts as a preventive and corrective measure as it avoids breakdown and saves maintenance time and cost. This research paper proposes advanced strategies for early detection and analysis of taper rolling bearings. In view of this, mathematical model-based fault diagnosis and support vector machining (SVM) are proposed in this work. A mathematical model using dimension analysis by the matrix method (Dimension Analysis Method (DAMM)) and SVM is developed that can be used to predict the vibration characteristic of the rotor-bearing system. Types of defects are created using electrical discharge machining (EDM) and analyzed, and correlation is established between dependent and independent parameters. Experiments were performed to evaluate the rotor dynamic characteristic of healthy and unhealthy bearings. Experimental results are used to validate the model obtained by the DAMM and SVM. Experimental results showed that the vibration characteristic could be evaluated by using a theoretical model and SVM. Efforts have been made to extend the service life of the machines and the assembly lines and to improve their efficiency, so as to reduce bearing failure; what provides novelty to these efforts is the use of four machine learning techniques. Thus, an automatic online diagnosis of bearing faults has been made possible with the developed model based on DAMM and SVM.

scholarly journals Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Shafiq ◽  
Xiangzhan Yu
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Network Traffic ◽  
Early Stage ◽  
Statistical Tests ◽  
Internet Traffic ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Traffic Classification ◽  
Network Traffic Classification

Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet onnflow type. Thereafter, we execute 10 well-known machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.

scholarly journals Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1281
Author(s):  
Na Li ◽  
Chenxi Yang ◽  
Sicheng Zhou ◽  
Siyu Song ◽  
Yuyao Jin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Malignant Mesothelioma ◽  
Molecular Mechanisms ◽  
Taurocholic Acid ◽  
Machine Learning Algorithms ◽  
Plasma Metabolites ◽  
Large Sample Size ◽  
Diagnostic Strategy ◽  
Test Set ◽  
Tauroursodeoxycholic Acid

Background: Malignant mesothelioma (MM) is an aggressive and incurable carcinoma that is primarily caused by asbestos exposure. However, the current diagnostic tool for MM is still under-developed. Therefore, the aim of this study is to explore the diagnostic significance of a strategy that combined plasma-based metabolomics with machine learning algorithms for MM. Methods: Plasma samples collected from 25 MM patients and 32 healthy controls (HCs) were randomly divided into train set and test set, after which analyzation was performed by liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were screened out from the samples of the train set. Subsequently, metabolite-based diagnostic models, including receiver operating characteristic (ROC) curves and Random Forest model (RF), were established, and their prediction accuracies were calculated for the test set samples. Results: Twenty differential plasma metabolites were annotated in the train set; 10 of these metabolites were validated in the test set. The seven most prevalent diagnostic metabolites were taurocholic acid), 0.7142 (uracil), 0.7142 (biliverdin), 0.8571 (histidine), 0.5000 (tauroursodeoxycholic acid), 0.8571 (pyrroline hydroxycarboxylic acid), and 0.7857 (phenylalanine). Furthermore, RF based on 20 annotated metabolites showed a prediction accuracy of 0.9286, and its optimized version achieved 1.0000 in the test set. Moreover, the comparison between the samples of peritoneal MM (n = 8) and pleural MM (n = 17) illustrated a significant increase in levels of taurocholic acid and tauroursodeoxycholic acid, as well as an evident decrease in biliverdin. Conclusions: Our results revealed the potential diagnostic value of plasma-based metabolomics combined with machine learning for MM. Further research with large sample size is worthy conducting. Moreover, our data demonstrated dysregulated metabolism pathways in MM, which aids in better understanding of molecular mechanisms related to the initiation and development of MM.

scholarly journals Configurations of memristor-based APUF for improved performance

2019 ◽  
Vol 8 (1) ◽  
pp. 74-82
Author(s):  
Julius Han Loong Teo ◽  
Noor Alia Noor Hashim ◽  
Azrul Ghazali ◽  
Fazrena Azlee Hamid
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Arbiter Puf ◽  
Improved Performance ◽  
The Ideal

The memristor-based arbiter PUF (APUF) has great potential to be used for hardware security purposes. Its advantage is in its challenge-dependent delays, which cannot be modeled by machine learning algorithms. In this paper, further improvement is proposed, which are circuit configurations to the memristor-based APUF. Two configuration aspects were introduced namely varying the number of memristor per transistor, and the number of challenge and response bits. The purpose of the configurations is to introduce additional variation to the PUF, thereby improve PUF performance in terms of uniqueness, uniformity, and bit-aliasing; as well as resistance against support vector machine (SVM). Monte Carlo simulations were carried out on 180 nm and 130 nm, where both CMOS technologies have produced uniqueness, uniformity, and bit-aliasing values close to the ideal 50%; as well as SVM prediction accuracies no higher than 52.3%, therefore indicating excellent PUF performance.

scholarly journals AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN

2019 ◽  
Vol IV-2/W5 ◽  
pp. 503-511 ◽  
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
M. B. A. Gibril ◽  
U. S. Lay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Total Curvature ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Inventory Data ◽  
Prediction Rate

<p><strong>Abstract.</strong> Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, RF could be the best algorithm for producing landslide susceptibility map, and such results could be adopted for the decision-making process to support land use planner for improving landslide risk assessment in similar environmental settings.</p>

scholarly journals BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm

2021 ◽  
Vol 7 ◽  
pp. e390
Author(s):  
Shafaq Abbas ◽  
Zunera Jalil ◽  
Abdul Rehman Javed ◽  
Iqra Batool ◽  
Mohammad Zubair Khan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Support Vector ◽  
Novel Approach ◽  
Whale Optimization

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

scholarly journals Weibo Information Propagation Dissemination Based on User Behavior Using ELM

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Huilin Liu ◽  
Yao Li
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Traditional Method ◽  
Information Dissemination ◽  
User Behavior ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Information Propagation ◽  
New Feature ◽  
Geographical Locations

Information dissemination prediction based on Weibo has been a hot topic in recent years. In order to study this, people always extract features and use machine learning algorithms to do the prediction. But there are some disadvantages. Aiming at these deficiencies, we proposed a new feature, the dependency between the Weibos involved in geographical locations and location of the user. We use ELM to predict behaviors of users. An information dissemination prediction model has also been proposed in this paper. Experimental results show that our proposed new feature is real and effective, and the model we proposed can accurately predict the scale of information dissemination. It also can be seen in the experimental results that the use of ELM significantly reduces the time, and it has a better performance than the traditional method based on SVM.

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