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Journal articles on the topic 'RF robustness'
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Elyousseph, Hilal, and Majid Altamimi. "Robustness of Deep-Learning-Based RF UAV Detectors." Sensors 24, no. 22 (November 17, 2024): 7339. http://dx.doi.org/10.3390/s24227339.
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The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV control signals. This approach is enhanced when integrated with machine-learning (ML) and deep-learning (DL) methods. Currently, this field is actively researched, with various studies proposing different ML/DL architectures competing for optimal accuracy. However, there is a notable gap regarding robustness, which refers to a UAV detector’s ability to maintain high accuracy across diverse scenarios, rather than excelling in just one specific test scenario and failing in others. This aspect is critical, as inaccuracies in UAV detection could lead to severe consequences. In this work, we introduce a new dataset specifically designed to test for robustness. Instead of the existing approach of extracting the test data from the same pool as the training data, we allowed for multiple categories of test data based on channel conditions. Utilizing existing UAV detectors, we found that although coefficient classifiers have outperformed CNNs in previous works, our findings indicate that image classifiers exhibit approximately 40% greater robustness than coefficient classifiers under low signal-to-noise ratio (SNR) conditions. Specifically, the CNN classifier demonstrated sustained accuracy in various RF channel conditions not included in the training set, whereas the coefficient classifier exhibited partial or complete failure depending on channel characteristics.
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Bollmeyer, Christian, Mathias Pelka, Hartmut Gehring, and Horst Hellbrück. "Wireless medical sensors – context, robustness and safety." Current Directions in Biomedical Engineering 1, no. 1 (September 1, 2015): 349–52. http://dx.doi.org/10.1515/cdbme-2015-0086.
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AbstractWireless medical sensors are an emerging technology. Wireless sensors form networks and are placed in an unknown environment. For indoor scenarios context detection of medical sensors, e.g. removal of sensors from a specific room, is important. Current algorithms for context detection of wireless sensors are based on RF signals, but RF signal propagation and room location show only a weak correlation. Recent approaches with RSSI-measurements are based on prior fingerprinting and therefore costly. In our approach, we equip wireless sensor nodes with a barometric sensor to measure pressure disturbances that occur, when doors of rooms are opened or closed. By signal processing of these disturbances our proposed algorithm detects rooms and estimates distances without prior knowledge in an unknown environment. Based on these measurement we automatically build a topology graph representing the room context and distances for indoor environment in a model for buildings. We evaluate our algorithm within a wireless sensor network and show the performance of our solution.
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Palego, C., Jie Deng, Zhen Peng, S. Halder, J. C. M. Hwang, D. I. Forehand, D. Scarbrough, et al. "Robustness of RF MEMS Capacitive Switches With Molybdenum Membranes." IEEE Transactions on Microwave Theory and Techniques 57, no. 12 (December 2009): 3262–69. http://dx.doi.org/10.1109/tmtt.2009.2033885.
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Nguyen, Ngoc-Kim-Khanh, Quang Nguyen, Hai-Ha Pham, Thi-Trang Le, Tuan-Minh Nguyen, Davide Cassi, Francesco Scotognella, Roberto Alfierif, and Michele Bellingeri. "Predicting the Robustness of Large Real-World Social Networks Using a Machine Learning Model." Complexity 2022 (November 9, 2022): 1–16. http://dx.doi.org/10.1155/2022/3616163.
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Computing the robustness of a network, i.e., the capacity of a network holding its main functionality when a proportion of its nodes/edges are damaged, is useful in many real applications. The Monte Carlo numerical simulation is the commonly used method to compute network robustness. However, it has a very high computational cost, especially for large networks. Here, we propose a methodology such that the robustness of large real-world social networks can be predicted using machine learning models, which are pretrained using existing datasets. We demonstrate this approach by simulating two effective node attack strategies, i.e., the recalculated degree (RD) and initial betweenness (IB) node attack strategies, and predicting network robustness by using two machine learning models, multiple linear regression (MLR) and the random forest (RF) algorithm. We use the classic network robustness metric R as a model response and 8 network structural indicators (NSI) as predictor variables and trained over a large dataset of 48 real-world social networks, whose maximum number of nodes is 265,000. We found that the RF model can predict network robustness with a mean squared error (RMSE) of 0.03 and is 30% better than the MLR model. Among the results, we found that the RD strategy has more efficacy than IB for attacking real-world social networks. Furthermore, MLR indicates that the most important factors to predict network robustness are the scale-free exponent α and the average node degree <k>. On the contrary, the RF indicates that degree assortativity a, the global closeness, and the average node degree <k> are the most important factors. This study shows that machine learning models can be a promising way to infer social network robustness.
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Ayaz Atalan, Yasemin, and Abdulkadir Atalan. "Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance." Applied Sciences 15, no. 1 (December 30, 2024): 241. https://doi.org/10.3390/app15010241.
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This study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R2 value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (p < 0.001) and rotor speed (p < 0.001) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R2 values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods.
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Kheir, Mohamed, Heinz Kreft, Iris Hölken, and Reinhard Knöchel. "On the physical robustness of RF on-chip nanostructured security." Journal of Information Security and Applications 19, no. 4-5 (November 2014): 301–7. http://dx.doi.org/10.1016/j.jisa.2014.09.007.
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Liu, Alan, Yu-Tai Lin, and Karthikeyan Sundaresan. "View-agnostic Human Exercise Cataloging with Single MmWave Radar." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 3 (August 22, 2024): 1–23. http://dx.doi.org/10.1145/3678512.
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Advances in mmWave-based sensing have enabled a privacy-friendly approach to pose and gesture recognition. Yet, providing robustness with the sparsity of reflected signals has been a long-standing challenge towards its practical deployment, constraining subjects to often face the radar. We present RF-HAC- a first-of-its-kind system that brings robust, automated and real-time human activity cataloging to practice by not only classifying exercises performed by subjects in their natural environments and poses, but also tracking the corresponding number of exercise repetitions. RF-HAC's unique approach (i) brings the diversity of multiple radars to scalably train a novel, self-supervised, pose-agnostic transformer-based exercise classifier directly on 3D RF point clouds with minimal manual effort and be deployed on a single radar; and (ii) leverages the underlying doppler behavior of exercises to design a robust self-similarity based segmentation algorithm for counting the repetitions in unstructured RF point clouds. Evaluations on a comprehensive set of challenging exercises in both seen and unseen environments/subjects highlight RF-HAC's robustness with high accuracy (over 90%) and readiness for real-time, practical deployments over prior art.
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Sanogo, Lamoussa, Eric Alata, Alexandru Takacs, and Daniela Dragomirescu. "Intrusion Detection System for IoT: Analysis of PSD Robustness." Sensors 23, no. 4 (February 20, 2023): 2353. http://dx.doi.org/10.3390/s23042353.
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The security of internet of things (IoT) devices remains a major concern. These devices are very vulnerable because of some of their particularities (limited in both their memory and computing power, and available energy) that make it impossible to implement traditional security mechanisms. Consequently, researchers are looking for new security mechanisms adapted to these devices and the networks of which they are part. One of the most promising new approaches is fingerprinting, which aims to identify a given device by associating it with a unique signature built from its unique intrinsic characteristics, i.e., inherent imperfections, introduced by the manufacturing processes of its hardware. However, according to state-of-the-art studies, the main challenge that fingerprinting faces is the nonrelevance of the fingerprinting features extracted from hardware imperfections. Since these hardware imperfections can reflect on the RF signal for a wireless communicating device, in this study, we aim to investigate whether or not the power spectral density (PSD) of a device’s RF signal could be a relevant feature for its fingerprinting, knowing that a relevant fingerprinting feature should remain stable regardless of the environmental conditions, over time and under influence of any other parameters. Through experiments, we were able to identify limits and possibilities of power spectral density (PSD) as a fingerprinting feature.
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Saha, Sunil, Anik Saha, Tusar Kanti Hembram, Biswajeet Pradhan, and Abdullah M. Alamri. "Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya." Applied Sciences 10, no. 11 (May 29, 2020): 3772. http://dx.doi.org/10.3390/app10113772.
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Landslides are known as the world’s most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling.
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Ouyang, Hui, Weibo Li, Feng Gao, Kangzheng Huang, and Peng Xiao. "Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF." Energies 17, no. 22 (November 20, 2024): 5799. http://dx.doi.org/10.3390/en17225799.
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Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. Then, the data were preprocessed and time-domain features were extracted. Finally, the IVY-optimized random forest model was used to identify, diagnose, and classify faults. The simulation results show that the IVY-RF method could identify faulty and normal states with 100% accuracy and distinguish 12 types with 100% accuracy. Compared to seven different algorithms, the IVY-RF improved accuracy by at least 0.17% and up to 67.45% on the original dataset and by at least 1.19% and up to 49.40% in a dataset with 5% noise added. The IVY-RF-based fault diagnosis method shows excellent accuracy and robustness in complex marine environments, providing a reliable fault identification solution for ship power systems.
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Song, Bingkun, Wenxuan Fan, and Shuo Zhang. "Research on an Integrated Intelligent Classification Algorithm Based on K-Means PCA-RF Machine Learning." Highlights in Science, Engineering and Technology 49 (May 21, 2023): 20–29. http://dx.doi.org/10.54097/hset.v49i.8398.
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With the rapid development of machine learning and artificial intelligence, research on classification models is gradually becoming popular. This article aims to propose a general classification model and classify indicator features. First, this paper constructs the data preprocessing based on K-Means, and data dimensionality reduction based on PCA algorithm. Finally, random forest algorithm (RF) is used for feature classification, and 325 groups of data are used for training. The results show that: (1) The K-Means PCA-RF algorithm constructed in this paper has good robustness and classification performance. (2) K-Means PCA-RF can effectively classify features and perform sensitivity analysis.
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SIMIN, G., J. WANG, B. KHAN, J. YANG, A. SATTU, R. GASKA, and M. SHUR. "NOVEL APPROACHES TO MICROWAVE SWITCHING DEVICES USING NITRIDE TECHNOLOGY." International Journal of High Speed Electronics and Systems 20, no. 01 (March 2011): 219–27. http://dx.doi.org/10.1142/s0129156411006556.
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III-Nitride heterostructure field-effect transistors (HFETs) demonstrated a new paradigm in microwave switching and control applications due to unique combination of extremely low channel resistance (leading to low loss), very high RF power, low off-state capacitance, broad range of operating temperatures, chemical inertness and robustness. The paper reviews novel approaches and recent advances in III-Nitride technology for RF switching devices leading to higher operating frequencies and even lower insertion loss.
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Mo, Haifeng, Yaohui Zhang, and Helun Song. "Improving Linearity and Robustness of RF LDMOS by Mitigating Quasi-Saturation Effect." Active and Passive Electronic Components 2019 (July 14, 2019): 1–7. http://dx.doi.org/10.1155/2019/8425198.
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This paper discusses linearity and robustness together for the first time, disclosing a way to improve them. It reveals that the nonlinear transconductance with device working at quasi-saturation region is significant factor of device linearity. The peak electric field is the root cause of electron velocity saturation. The high electric field at the drift region near the drain will cause more electron-hole pairs generated to trigger the parasitic NPN transistor turn-on, which may cause failure of device. Devices with different drift region doping are simulated with TCAD and measured. With LDD4 doping, the peak electric field in the drift region is reduced; the linear region of the transconductance is broadened. The adjacent channel power ratio is decreased by 2 dBc; 12% more power can be discharged before the NPN transistor turn-on, indicating a better linearity and robustness.
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Liu, Yuanyuan, Jingying Chen, Cunjie Shan, Zhiming Su, and Pei Cai. "A Hierarchical Regression Approach for Unconstrained Face Analysis." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 08 (November 22, 2015): 1556011. http://dx.doi.org/10.1142/s021800141556011x.
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Head pose and facial feature detection are important for face analysis. However, many studies reported good results in constrained environment, the performance could be decreased due to the high variations in facial appearance, poses, illumination, occlusion, expression and make-up. In this paper, we propose a hierarchical regression approach, Dirichlet-tree enhanced random forests (D-RF) for face analysis in unconstrained environment. D-RF introduces Dirichlet-tree probabilistic model into regression RF framework in the hierarchical way to achieve the efficiency and robustness. To eliminate noise influence of unconstrained environment, facial patches extracted from face area are classified as positive or negative facial patches, only positive facial patches are used for face analysis. The proposed hierarchical D-RF works in two iterative procedures. First, coarse head pose is estimated to constrain the facial features detection, then the head pose is updated based on the estimated facial features. Second, the facial feature localization is refined based on the updated head pose. In order to further improve the efficiency and robustness, multiple probabilitic models are learned in leaves of the D-RF, i.e. the patch’s classification, the head pose probabilities, the locations of facial points and face deformation models (FDM). Moreover, our algorithm takes a composite weight voting method, where each patch extracted from the image can directly cast a vote for the head pose or each of the facial features. Extensive experiments have been done with different publicly available databases. The experimental results demonstrate that the proposed approach is robust and efficient for head pose and facial feature detection.
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Hashemi, Seyed Mohammad, Ruxandra Mihaela Botez, and Georges Ghazi. "Robust Trajectory Prediction Using Random Forest Methodology Application to UAS-S4 Ehécatl." Aerospace 11, no. 1 (January 2, 2024): 49. http://dx.doi.org/10.3390/aerospace11010049.
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Accurate aircraft trajectory prediction is fundamental for enhancing air traffic control systems, ensuring a safe and efficient aviation transportation environment. This research presents a detailed study on the efficacy of the Random Forest (RF) methodology for predicting aircraft trajectories. The study compares the RF approach with two established data-driven models, specifically Long Short-Term Memory (LSTM) and Logistic Regression (LR). The investigation utilizes a significant dataset comprising aircraft trajectory time history data, obtained from a UAS-S4 simulator. Experimental results indicate that within a short-term prediction horizon, the RF methodology surpasses both LSTM and LR in trajectory prediction accuracy and also its robustness to overfitting. The research further fine-tunes the performance of the RF methodology by optimizing various hyperparameters, including the number of estimators, features, depth, split, and leaf. Consequently, these results underscore the viability of the RF methodology as a proven alternative to LSTM and LR models for short-term aircraft trajectory prediction.
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Mesadri, Conrado K., Aziz Doukkali, Philippe Descamps, and Christophe Kelma. "A new methodology for optimal RF DFT sensor design." International Journal of Microwave and Wireless Technologies 4, no. 5 (July 3, 2012): 515–21. http://dx.doi.org/10.1017/s1759078712000499.
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In this paper, a new methodology to compare the robustness of sensor structures employed in radiofrequency design for test (RF DFT) architectures for RF integrated circuits (ICs) is proposed. First, the yield loss and defect level of the test technique is evaluated using a statistical model of the Circuit under Test (obtained through non-parametric statistics and copula theory). Then, by carrying out the dispersion analysis of the sensor architecture, a figure of merit is established. This methodology reduces the number of iterations in the design flow of RF DFT sensors and makes it possible to evaluate process dispersion. The case study is a SiGe:C BiCMOS LNA tested by a single-probe measurement.
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Mori, Takeshi, Yuta Ogawa, Izuki Endo, Keiichiro Matsushima, and Jun Noda. "Growth Suppression of a Robust Bacterium Methylobacterium extorquens by Porous Materials with Oxygen Functional Groups." Life 13, no. 11 (November 9, 2023): 2185. http://dx.doi.org/10.3390/life13112185.
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Suppressing the growth of Methylobacterium species without the use of toxic chemicals has been a challenging task owing to their robustness against previous antimicrobial techniques. In this work, we prepared porous materials with various numbers and types of oxygen functional groups and investigated their ability to suppress the growth of Methylobacterium extorquens. It turned out that the number and type of oxygen functional groups in the porous materials greatly affected the growth of the bacterium. Three porous materials (resorcinol–formaldehyde gel (RF), hydrothermally treated RF (RFH), and Wakkanai siliceous shale (WS)) were tested, and RF exhibited the best performance in suppressing the growth of the bacterium. This performance is possibly due to abundant phenolic groups in the porous material.
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Xu, Zihan, Fei Zhao, Pingping Lu, Yao Gao, Tingyu Meng, Yanan Dang, Mofei Li, and Robert Wang. "A Robust Digital Elevation Model-Based Registration Method for Mini-RF/Mini-SAR Images." Remote Sensing 17, no. 4 (February 11, 2025): 613. https://doi.org/10.3390/rs17040613.
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SAR data from the lunar spaceborne Reconnaissance Orbiter’s (LRO) Mini-RF and Chandrayaan-1’s Mini-SAR provide valuable insights into the properties of the lunar surface. However, public lunar SAR data products are not properly registered and are limited by localization issues. Existing registration methods for Earth SAR have proven to be inadequate in their robustness for lunar data registration. And current research on methods for lunar SAR has not yet focused on producing globally registered datasets. To solve these problems, this article introduces a robust automatic registration method tailored for S-band Level-1 Mini-RF and Mini-SAR data with the assistance of lunar DEM. A simulated SAR image based on real lunar DEM data is first generated to assist the registration work, and then an offset calculation approach based on normalized cross-correlation (NCC) and specific processing, including background removal, is proposed to achieve the registration between the simulated image, and the real image. When applying Mini-RF images and Mini-SAR images, high robustness and good accuracy are exhibited, which produces fully registered datasets. After processing using the proposed method, the average error between Mini-RF images and DEM references was reduced from approximately 3000 m to about 100 m. To further explore the additional improvement of the proposed method, the registered lunar SAR datasets are used for further analysis, including a review of the circular polarization ratio (CPR) characteristics of anomalous craters.
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Sewpaul, Ronel, Olushina Olawale Awe, Dennis Makafui Dogbey, Machoene Derrick Sekgala, and Natisha Dukhi. "Classification of Obesity among South African Female Adolescents: Comparative Analysis of Logistic Regression and Random Forest Algorithms." International Journal of Environmental Research and Public Health 21, no. 1 (December 19, 2023): 2. http://dx.doi.org/10.3390/ijerph21010002.
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Background: This study evaluates the performance of logistic regression (LR) and random forest (RF) algorithms to model obesity among female adolescents in South Africa. Methods: Data was analysed on 375 females aged 15–17 from the South African National Health and Nutrition Examination Survey 2011/2012. The primary outcome was obesity, defined as body mass index (BMI) ≥ 30 kg/m2. A total of 31 explanatory variables were included, ranging from socio-economic, demographic, family history, dietary and health behaviour. RF and LR models were run using imbalanced data as well as after oversampling, undersampling, and hybrid sampling of the data. Results: Using the imbalanced data, the RF model performed better with higher precision, recall, F1 score, and balanced accuracy. Balanced accuracy was highest with the hybrid data (0.618 for RF and 0.668 for LR). Using the hybrid balanced data, the RF model performed better (F1-score = 0.940 for RF vs. 0.798 for LR). Conclusion: The model with the highest overall performance metrics was the RF model both before balancing the data and after applying hybrid balancing. Future work would benefit from using larger datasets on adolescent female obesity to assess the robustness of the models.
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Rosker, Eva S., Rajinder Sandhu, Jimmy Hester, Mark S. Goorsky, and Jesse Tice. "Printable Materials for the Realization of High Performance RF Components: Challenges and Opportunities." International Journal of Antennas and Propagation 2018 (2018): 1–19. http://dx.doi.org/10.1155/2018/9359528.
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Printing methods such as additive manufacturing (AM) and direct writing (DW) for radio frequency (RF) components including antennas, filters, transmission lines, and interconnects have recently garnered much attention due to the ease of use, efficiency, and low-cost benefits of the AM/DW tools readily available. The quality and performance of these printed components often do not align with their simulated counterparts due to losses associated with the base materials, surface roughness, and print resolution. These drawbacks preclude the community from realizing printed low loss RF components comparable to those fabricated with traditional subtractive manufacturing techniques. This review discusses the challenges facing low loss RF components, which has mostly been material limited by the robustness of the metal and the availability of AM-compatible dielectrics. We summarize the effective printing methods, review ink formulation, and the postprint processing steps necessary for targeted RF properties. We then detail the structure-property relationships critical to obtaining enhanced conductivities necessary for printed RF passive components. Finally, we give examples of demonstrations for various types of printed RF components and provide an outlook on future areas of research that will require multidisciplinary teams from chemists to RF system designers to fully realize the potential for printed RF components.
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Deng, Rui, Yanning Guan, Danlu Cai, Tao Yang, Klaus Fraedrich, Chunyan Zhang, Jiakui Tang, Zhouwei Liao, Zhishou Wei, and Shan Guo. "Supervised versus Semi-Supervised Urban Functional Area Prediction: Uncertainty, Robustness and Sensitivity." Remote Sensing 15, no. 2 (January 6, 2023): 341. http://dx.doi.org/10.3390/rs15020341.
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To characterize a community-scale urban functional area using geo-tagged data and available land-use information, several supervised and semi-supervised models are presented and evaluated in Hong Kong for comparing their uncertainty, robustness and sensitivity. The following results are noted: (i) As the training set size grows, models’ accuracies are improved, particularly for multi-layer perceptron (MLP) or random forest (RF). The graph convolutional network (GCN) (MLP or RF) model reveals top accuracy when the proportion of training samples is less (greater) than 10% of the total number of functional areas; (ii) With a large amount of training samples, MLP shows the highest prediction accuracy and good performances in cross-validation, but less stability on same training sets; (iii) With a small amount of training samples, GCN provides viable results, by incorporating the auxiliary information provided by the proposed semantic linkages, which is meaningful in real-world predictions; (iv) When the training samples are less than 10%, one should be cautious using MLP to test the optimal epoch for obtaining the best accuracy, due to its model overfitting problem. The above insights could support efficient and scalable urban functional area mapping, even with insufficient land-use information (e.g., covering only ~20% of Beijing in the case study).
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Balakrishnan, Charumathi, and Mangaiyarkarasi Thiagarajan. "CREDIT RISK MODELLING FOR INDIAN DEBT SECURITIES USING MACHINE LEARNING." Buletin Ekonomi Moneter dan Perbankan 24 (March 8, 2021): 107–28. http://dx.doi.org/10.21098/bemp.v24i0.1401.
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We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.
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Utami, Annisaa, Dimas Fanny Hebrasianto Permadi, Yesy Diah Rosita, and Jumanto Unjung. "Performance Comparison of Random Forest (RF) and Classification and Regression Trees (CART) for Hotel Star Rating Prediction." Scientific Journal of Informatics 11, no. 3 (October 22, 2024): 733–48. http://dx.doi.org/10.15294/sji.v11i3.11068.
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Purpose: This study proposes to evaluate the effectiveness of Random Forest (RF) compared to Classification and Regression Trees (CART) in prediction of hotel star ratings. The objective is to identify the algorithm that provides the most reliable and accurate classification outcomes based on diverse hotel attributes in accordance with the standard categorization of star hotel categories. This is necessary due to the important role of accurate star ratings in guiding consumer choices and enhancing competitive positioning in the hospitality industry. Method: This study conducted a comprehensive dataset about Hotel in Banyumas Regency, including location, facilities, the size of rooms, type of rooms, price of rooms, and customer reviews, subjected to training through both RF and CART algorithms. Both algorithms are evaluated using accuracy, precision, recall, and F1 score. Additionally, both algorithms due to in the same preprocessing while performing hyperparameter tuning improve the efficacy of each model. Result: The results showed that RF achieved the best overall accuracy and robustness than CART across all tests conducted. Furthermore, RF also outperformed CART in classification effectiveness among classes, including enhanced precision and recall scores across multiple stars rating categories, signifying increased generalization and consistency in classification tasks. RF classifier consistently surpassed the CART classifier in terms of both accuracy and F1-score throughout all random states and test sizes, with a highest score of 0.9932 at a random state of 100 and a test size of 0.4. The most reliable results were obtained using RF with 42 random states and a test size of 0.2, resulting in an accuracy of 0.9909, precision of 1.0, recall of 1.0, and F1 score of 1.0. Simultaneously, CART shows values of 0.9818, 1.0, 1.0, and 1.0, respectively, while maintaining the same variation. This consistent performance, regardless of fluctuations, illustrates the robustness and suitability of RF for classification tasks compared to CART. Novelty: This study offered new insights about the implementation of machine learning about hotel star rating predictions using RF and CART algorithms. Also, the novelty of the collected hotel dataset used in this study. A detailed comparative analysis was also provided, contributing to the existing literature by showing the effectiveness of RF over CART for this specific application. Future studies could explore the integration of additional machine learning methods to further enhance prediction accuracy and operational efficiency in the hospitality industry.
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Xu, Wenjuan, Xin Huang, Zhengjun Yang, Mengmeng Zhou, and Jiandong Huang. "Developing Hybrid Machine Learning Models to Determine the Dynamic Modulus (E*) of Asphalt Mixtures Using Parameters in Witczak 1-40D Model: A Comparative Study." Materials 15, no. 5 (February 27, 2022): 1791. http://dx.doi.org/10.3390/ma15051791.
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To characterize the dynamic modulus (E*) of the asphalt mixtures more accurately, a comparative study was shown in this paper, combining six ML models (BP, SVM, DT, RF, KNN, and LR) with the novelly developed MBAS (modified BAS, beetle antennae search) algorithm to check the potential to replace the empirical model. The hyperparameter tuning process of the six ML models by the proposed MBAS algorithm showed satisfactory results. The calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the five ML models (BP, SVM, DT, RF, and KNN) to determine the E* of the asphalt mixtures. Comparing the performances of the six ML models in the prediction of the E* by the statistical coefficients and Monte Carlo simulation, the RF model showed the highest accuracy, efficiency, and robustness.
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Panigrahy, Parth Sarathi, Deepjyoti Santra, and Paramita Chattopadhyay. "Decent fault classification of VFD fed induction motor using random forest algorithm." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 4 (July 20, 2020): 492–504. http://dx.doi.org/10.1017/s0890060420000311.
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AbstractA data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.
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Liu, You-Jiang, Bang-Hua Zhou, Jie Zhou, and Yi-Nong Liu. "A Two-Step Identification Approach for Twin-Box Models of RF Power Amplifier." International Journal of Microwave Science and Technology 2011 (September 18, 2011): 1–5. http://dx.doi.org/10.1155/2011/468497.
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We propose a two-step identification approach for twin-box model (Wiener or Hammerstein) of RF power amplifier. The linear filter block and the static nonlinearity block are extracted, respectively, based on least-squares method, by iterative calculation. Simulations show that the method can get quite accurate parameters to model different nonlinear models with memory such as Wiener, Hammerstein, Wiener-Hammerstein (W-H), and memory polynomial models, hence, demonstrating its robustness. Furthermore, experimental results show excellent agreement between measured output and modeled output, where one carrier WCDMA signal is used as the excitation for a wideband RF amplifier.
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Dong, Zhengcheng, Yanjun Fang, Meng Tian, and Rong Zhang. "Approaches to improve the robustness on interdependent networks against cascading failures with load-based model." Modern Physics Letters B 29, no. 32 (November 30, 2015): 1550210. http://dx.doi.org/10.1142/s0217984915502103.
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With load-based model, considering the loss of capacity on nodes, we investigate how the coupling strength (many-to-many coupled pattern) and link patterns (one-to-one coupled pattern) can affect the robustness of interdependent networks. In one-to-one coupled pattern, we take into account the properties of degree and betweenness, and adopt four kinds of inter-similarity link patterns and random link pattern. In many-to-many coupled pattern, we propose a novel method to build new networks via adding inter-links (coupled links) on the existing one-to-one coupled networks. For a full investigation on the effects, we conduct two types of attack strategies, i.e. RO-attack (randomly remove only one node) and RF-attack (randomly remove a fraction of nodes). We numerically find that inter-similarity link patterns and bigger coupling strength can effectively improve the robustness under RO-attacks and RF-attacks in some cases. Therefore, the inter-similarity link patterns can be applied during the initial period of network construction. Once the networks are completed, the robustness level can be improved via adding inter-links appropriately without changing the existing inter-links and topologies of networks. We also find that BA–BA topology is a better choice and that it is not useful to infinitely increase the capacity which is defined as the cost of networks.
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Huang, Huang, and Huang. "A Novel Algorithm for Structural Reliability Analysis Based on Finite Step Length and Armijo Line Search." Applied Sciences 9, no. 12 (June 21, 2019): 2546. http://dx.doi.org/10.3390/app9122546.
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This paper presents a novel algorithm for structural reliability analysis based on the finite step length and Armijo line search to remove the drawbacks of the Hasofer–Lind and Rakwitz–Fiessler (HL-RF) algorithm that may be subjected to non-convergence in the first-order reliability method (FORM). Initially, the sensitivity factor with finite step length is introduced for preventing the iterative process of the algorithm from entering a periodic loop. Subsequently, an optimization method based on the sufficient descent condition with the Armijo line search technique is proposed. With that, the initial step length and adjusting coefficient are optimized to enhance the applicability of the algorithm emphatically for highly nonlinear functions. A comparison analysis is carried out between the proposed algorithm and existing FORM-based algorithms to validate the robustness and efficiency of the proposed algorithm. The results of this demonstrate that the proposed algorithm is superior to the HL-RF algorithm in terms of robustness and surpass the other existing FORM-based algorithms in connection to efficiency.
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Alharbi, Abdulmajeed Atiah. "Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable." Discrete Dynamics in Nature and Society 2024 (February 1, 2024): 1–10. http://dx.doi.org/10.1155/2024/6671395.
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Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms.
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Jiang, Zhi, Longhai Tian, Wei Liu, Bo Song, Chao Xue, Tianzong Li, Jin Chen, and Fang Wei. "Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation." PLOS ONE 17, no. 5 (May 23, 2022): e0268757. http://dx.doi.org/10.1371/journal.pone.0268757.
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As the rate of percutaneous coronary intervention increases, in-stent restenosis (ISR) has become a burden. Random forest (RF) could be superior to logistic regression (LR) for predicting ISR due to its robustness. We developed an RF model and compared its performance with the LR one for predicting ISR. We retrospectively included 1501 patients (age: 64.0 ± 10.3; male: 76.7%; ISR events: 279) who underwent coronary angiography at 9 to 18 months after implantation of 2nd generation drug-eluting stents. The data were randomly split into a pair of train and test datasets for model development and validation with 50 repeats. The predictive performance was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). The RF models predicted ISR with larger AUC-ROCs of 0.829 ± 0.025 compared to 0.784 ± 0.027 of the LR models. The difference was statistically significant in 29 of the 50 repeats. The RF and LR models had similar sensitivity using the same cutoff threshold, but the specificity was significantly higher in the RF models, reducing 25% of the false positives. By removing the high leverage outliers, the LR models had comparable AUC-ROC to the RF models. Compared to the LR, the RF was more robust and significantly improved the performance for predicting ISR. It could cost-effectively identify patients with high ISR risk and help the clinical decision of coronary stenting.
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Shang, Qiang, Derong Tan, Song Gao, and Linlin Feng. "A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis." Journal of Advanced Transportation 2019 (January 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/4202735.
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Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident. Traffic incident duration prediction methods often use more input variables to obtain better prediction results. However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent. In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model. Firstly, the NCA is applied to select feature variables for traffic incident duration. Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters. Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods. In addition, the performance is also tested in the absence of some feature variables. The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.
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Ghasemi, Jahan B., and Somayeh Pirhadi. "Docking alignment-3D-QSAR of a new class of potent and non-chiral indole-3-carboxamide-based renin inhibitors." Collection of Czechoslovak Chemical Communications 76, no. 12 (2011): 1447–69. http://dx.doi.org/10.1135/cccc2011070.
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Using generated conformations from docking analysis by CDOCKER algorithm, some 3D-QSAR models; CoMFA region focusing (CoMFA-RF) and CoMSIA have been created on a series of a new class of potent and non-chiral renin inhibitors. The satisfactory predictions were obtained by CoMFA-RF and CoMSIA based on docking alignment in comparison to CoMFA. Robustness and predictability of the models were further verified by using the test set, cross validation (leave one out and leave ten out), bootstrapping, and progressive scrambling. All-orientation search (AOS) strategy was used to acquire the best orientation and minimize the effect of the initial orientation of aligned compounds. The results of 3D-QSAR models are in agreement with docking results. Moreover, the resulting 3D CoMFA-RF/ CoMSIA contour maps and corresponding models were applied to design new and more active inhibitors.
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Sun, Liting, Da Ke, Xiang Wang, Zhitao Huang, and Kaizhu Huang. "Robustness of Deep Learning-Based Specific Emitter Identification under Adversarial Attacks." Remote Sensing 14, no. 19 (October 7, 2022): 4996. http://dx.doi.org/10.3390/rs14194996.
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Deep learning (DL)-based specific emitter identification (SEI) technique can automatically extract radio frequency (RF) fingerprint features in RF signals to distinguish between legal and illegal devices and enhance the security of wireless network. However, deep neural network (DNN) can easily be fooled by adversarial examples or perturbations of the input data. If a malicious device emits signals containing a specially designed adversarial samples, will the DL-based SEI still work stably to correctly identify the malicious device? To the best of our knowledge, this research is still blank, let alone the corresponding defense methods. Therefore, this paper designs two scenarios of attack and defense and proposes the corresponding implementation methods to specializes in the robustness of DL-based SEI under adversarial attacks. On this basis, detailed experiments are carried out based on the real-world data and simulation data. The attack scenario is that the malicious device adds an adversarial perturbation signal specially designed to the original signal, misleading the original system to make a misjudgment. Experiments based on three different attack generation methods show that DL-based SEI is very vulnerability. Even if the intensity is very low, without affecting the probability density distribution of the original signal, the performance can be reduced to about 50%, and at −22 dB it is completely invalid. In the defense scenario, the adversarial training (AT) of DL-based SEI is added, which can significantly improve the system’s performance under adversarial attacks, with ≥60% improvement in the recognition rate compared to the network without AT. Further, AT has a more robust effect on white noise. This study fills the relevant gaps and provides guidance for future research. In the future research, the impact of adversarial attacks must be considered, and it is necessary to add adversarial training in the training process.
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Zheng, Weiling, Yu Zhang, Landu Jiang, Dian Zhang, and Tao Gu. "MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference." Sensors 24, no. 6 (March 20, 2024): 1978. http://dx.doi.org/10.3390/s24061978.
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Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of 95.17% on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost.
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Xia, Yunya. "Predicting China's Crude Oil Futures Prices: A Strategic Comparison of Random Forest and Time Series Models." Advances in Economics, Management and Political Sciences 136, no. 1 (December 26, 2024): 114–23. https://doi.org/10.54254/2754-1169/2024.18817.
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This article examines the critical roles of Random Forest (RF) and Time Series (TS) models in forecasting China's crude oil futures prices, providing a comprehensive comparison of their predictive capabilities. The study employs ARIMA and SARIMA models, known for their proficiency in capturing data trends and seasonality, to harness the temporal aspects of oil price movements. In contrast, the RF model is recognized for its robustness in handling complex datasets, offering a nuanced approach to non-linear relationships and variable interactions. The analysis reveals that the ARIMA (0,1,4) model outperforms the (1,1,0) model in terms of prediction error and statistical fitting. However, the RF model's strength lies in its precision and flexibility, particularly in responding to market fluctuations. The paper concludes with the insight that ARIMA models are more suitable for long-term strategic planning due to their stability, whereas RF models excel in short-term forecasting and high-accuracy prediction scenarios. These findings are invaluable for market participants, offering them data-driven strategies to optimize their decision-making processes in the volatile oil futures market.
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Ker, Ming-Dou, Bing-Jye Kuo, and Yuan-Wen Hsiao. "Optimization of broadband RF performance and ESD robustness by -model distributed ESD protection scheme." Journal of Electrostatics 64, no. 2 (February 2006): 80–87. http://dx.doi.org/10.1016/j.elstat.2005.03.086.
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Eloudi, Hasna, Mohammed Hssaisoune, Hanane Reddad, Mustapha Namous, Maryem Ismaili, Samira Krimissa, Mustapha Ouayah, and Lhoussaine Bouchaou. "Robustness of Optimized Decision Tree-Based Machine Learning Models to Map Gully Erosion Vulnerability." Soil Systems 7, no. 2 (May 16, 2023): 50. http://dx.doi.org/10.3390/soilsystems7020050.
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Gully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random Forest (RF), C5.0, XGBoost, treebag, Gradient Boosting Machines (GBMs) and Adaboost, in order to map and predict gully erosion-prone areas in a semi-arid mountain context. The first step was to prepare the inventory data, which consisted of 217 gully points. This database was then randomly subdivided into five percentages of Train/Test (50/50, 60/40, 70/30, 80/20, and 90/10) to assess the stability and robustness of the models. Furthermore, 17 geo-environmental variables were used as potential controlling factors, and several metrics were examined to evaluate the performance of the six models. The results revealed that all of the models used performed well in terms of predicting vulnerability to gully erosion. The C5.0 and RF models had the best prediction performance (AUC = 90.8 and AUC = 90.1, respectively). However, according to the random subdivisions of the database, these models exhibit small but noticeable instability, with high performance for the 80/20% and 70/30% subdivisions. This demonstrates the significance of database refining and the need to test various splitting data in order to ensure efficient and reliable output results.
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Zhang, Yajun, Yan Yang, Zijian Li, Zhixiong Yang, Xu Liu, and Bo Yuan. "RF-Alphabet: Cross Domain Alphabet Recognition System Based on RFID Differential Threshold Similarity Calculation Model." Sensors 23, no. 2 (January 13, 2023): 920. http://dx.doi.org/10.3390/s23020920.
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Gesture recognition can help people with a speech impairment to communicate and promote the development of Human-Computer Interaction (HCI) technology. With the development of wireless technology, passive gesture recognition based on RFID has become a research hotspot. In this paper, we propose a low-cost, non-invasive and scalable gesture recognition technology, and successfully implement the RF-alphabet, a gesture recognition system for complex, fine-grained, domain-independent 26 English letters; the RF-alphabet has three major advantages: first, this paper achieves complete capture of complex, fine-grained gesture data by designing a dual-tag, dual-antenna layout. Secondly, to overcome the disadvantages of the large training sets and long training times of traditional deep learning. We design and combine the Difference threshold similarity calculation prediction model to extract digital signal features to achieve real-time feature analysis of gesture signals. Finally, the RF alphabet solves the problem of confusing the signal characteristics of letters. Confused letters are distinguished by comparing the phase values of feature points. The RF-alphabet ends up with an average accuracy of 90.28% and 89.7% in different domains for new users and new environments, respectively, by performing feature analysis on similar signals. The real-time, robustness, and scalability of the RF-alphabet are proven.
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Zhang, Lei. "The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model." Computational Intelligence and Neuroscience 2022 (September 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/6826573.
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Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model.
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Wang, Zhiming, Yafei Zhang, and Yalong Song. "An Adaptive First-Order Reliability Analysis Method for Nonlinear Problems." Mathematical Problems in Engineering 2020 (April 14, 2020): 1–11. http://dx.doi.org/10.1155/2020/3925689.
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The HL-RF algorithm of the first-order reliability method (FORM) is a widely useful tool in structural reliability analysis. However, the iteration results of HL-RF algorithm may not converge due to periodic cycles for some highly nonlinear reliability problems. In this paper, an adaptive first-order reliability method (AFORM) is proposed to improve solution efficiency for some highly nonlinear reliability problems by introducing an adaptive factor. In AFORM, based on the two-parameter approximate first-order reliability method, the new iteration point and the previous iteration point are used to obtain the corresponding angle, and the result of convergence is judged by angle condition. According to the convergence degree of the results, two iteration parameters of the approximate reliability method are adjusted continuously by adaptive factor. Moreover, iteration step size is adjusted by changing the parameters to improve the efficiency and robustness of FORM. Finally, four numerical examples and one mechanical reliability analysis example are used to verify the proposed method. Compared with the different algorithms, the results show that AFORM has better efficiency and robustness for some highly nonlinear reliability problems.
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Mendoza Paz, Santiago, Mauricio F. Villazón Gómez, and Patrick Willems. "Adapting to Climate Change with Machine Learning: The Robustness of Downscaled Precipitation in Local Impact Analysis." Water 16, no. 21 (October 26, 2024): 3070. http://dx.doi.org/10.3390/w16213070.
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The skill, assumptions, and uncertainty of machine learning techniques (MLTs) for downscaling global climate model’s precipitation to the local level in Bolivia were assessed. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machine (SVM) techniques, was used on four zones (highlands, Andean slopes, Amazon lowlands, and Chaco lowlands). The downscaled series’ skill was evaluated in terms of relative errors. The uncertainty was analyzed through variance decomposition. In most cases, MLTs’ skill was adequate, with relative errors less than 50%. Moreover, RF tended to outperform SVM. Robust (weak) stationary (perfect prognosis) assumptions were found in the highlands and Andean slopes. The weakness was attributed to topographical complexity. The downscaling methods were shown to be the dominant source of uncertainties. This analysis allowed the derivation of robust future projections, showing higher annual rainfall, shorter dry spell duration, and more frequent but less intense high rainfall events in the highlands. Apart from the dry spell’s duration, a similar pattern was found for the Andean slopes. A decrease in annual rainfall was projected in the Amazon lowlands and an increase in the Chaco lowlands.
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You, Weizhen, Saidi Alexandre, Mohamed Ichchou, Zine Abdel, and Xiaopin Zhong. "Reliability modeling and prediction of passive controlled structures through Random Forest." MATEC Web of Conferences 241 (2018): 01023. http://dx.doi.org/10.1051/matecconf/201824101023.
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Reliability prediction plays a significant role in risk assessment of engineering structures. Mathematically, the prediction task can be seen as a classification (regression) procedure. In this aspect, machine learning methods have recently shown their superior performance over others in various research domains. Random forest (RF) is distinguished for its robustness and high accuracy in modeling and prediction work. However, its application in the area of structural reliability has not been widely explored. This study aims to explore the feasibility of RF as well as examine its performance in modeling and prediction of structure reliability in passive control mode. A numerical example is introduced in the simulation part to evaluate performance of the proposed method in different perspectives.
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Lai and Tsai. "Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning." Sensors 19, no. 17 (August 27, 2019): 3717. http://dx.doi.org/10.3390/s19173717.
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This study developed a systematic approach with machine learning (ML) to apply the satellite remote sensing images, geographic information system (GIS) datasets, and spatial analysis for multi-temporal and event-based landslide susceptibility assessments at a regional scale. Random forests (RF) algorithm, one of the ML-based methods, was selected to construct the landslide susceptibility models. Different ratios of landslide and non-landslide samples were considered in the experiments. This study also employed a cost-sensitive analysis to adjust the decision boundary of the developed RF models with unbalanced sample ratios to improve the prediction results. Two strategies were investigated for model verification, namely space- and time-robustness. The space-robustness verification was designed for separating samples into training and examining data based on a single event or the same dataset. The time-robustness verification was designed for predicting subsequent landslide events by constructing a landslide susceptibility model based on a specific event or period. A total of 14 GIS-based landslide-related factors were used and derived from the spatial analyses. The developed landslide susceptibility models were tested in a watershed region in northern Taiwan with a landslide inventory of changes detected through multi-temporal satellite images and verified through field investigation. To further examine the developed models, the landslide susceptibility distributions of true occurrence samples and the generated landslide susceptibility maps were compared. The experiments demonstrated that the proposed method can provide more reasonable results, and the accuracies were found to be higher than 93% and 75% in most cases for space- and time-robustness verifications, respectively. In addition, the mapping results revealed that the multi-temporal models did not seem to be affected by the sample ratios included in the analyses.
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Dong, Zhengcheng, Yanjun Fang, Meng Tian, and Zhengmin Kong. "The influence of the depth of k-core layers on the robustness of interdependent networks against cascading failures." International Journal of Modern Physics C 28, no. 02 (February 2017): 1750020. http://dx.doi.org/10.1142/s0129183117500206.
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The hierarchical structure, [Formula: see text]-core, is common in various complex networks, and the actual network always has successive layers from 1-core layer (the peripheral layer) to [Formula: see text]-core layer (the core layer). The nodes within the core layer have been proved to be the most influential spreaders, but there is few work about how the depth of [Formula: see text]-core layers (the value of [Formula: see text]) can affect the robustness against cascading failures, rather than the interdependent networks. First, following the preferential attachment, a novel method is proposed to generate the scale-free network with successive [Formula: see text]-core layers (KCBA network), and the KCBA network is validated more realistic than the traditional BA network. Then, with KCBA interdependent networks, the effect of the depth of [Formula: see text]-core layers is investigated. Considering the load-based model, the loss of capacity on nodes is adopted to quantify the robustness instead of the number of functional nodes in the end. We conduct two attacking strategies, i.e. the RO-attack (Randomly remove only one node) and the RF-attack (Randomly remove a fraction of nodes). Results show that the robustness of KCBA networks not only depends on the depth of [Formula: see text]-core layers, but also is slightly influenced by the initial load. With RO-attack, the networks with less [Formula: see text]-core layers are more robust when the initial load is small. With RF-attack, the robustness improves with small [Formula: see text], but the improvement is getting weaker with the increment of the initial load. In a word, the lower the depth is, the more robust the networks will be.
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Sakhare, R. S., S. S. Pekamwar, and T. V. Gitte. "STABILITY INDICATING HIGH PERFORMANCE THIN-LAYER CHROMATOGRAPHY METHOD FOR SIMULTANEOUS ESTIMATION OF AMBROXOL HYDROCHLORIDE AND LORATADINE IN PHARMACEUTICAL DOSAGE FORM." INDIAN DRUGS 55, no. 08 (August 28, 2018): 44–51. http://dx.doi.org/10.53879/id.55.08.10968.
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A simple, sensitive, accurate, precise and rapid stability indicating HPTLC method for simultaneous determination of ambroxol hydrochloride and Loratadine in pharmaceutical dosage form has been developed. The study was performed on TLC aluminum plates precoated with silica gel 60F254 using chloroform: methanol (9:1v/v) as the mobile phase. This system gives compact and dense spots for both ambroxol hydrochloride (Rf value of 0.36±0.003) and loratadine (Rf value of 0.68±0.002). Densitometric analysis of both drugs was carried out in the reflectance absorbance mode at 216 nm. The coefficient of correlation data for the calibration plots showed a good linear relationship with R2 = 0.997 ± 1.1224 in the range of 600-3600 ng for ambroxol hydrochloride and R2 = 0.998 ± 0.0935 in the range of 50-300 ng for loratadine. The method was validated according to ICH guidelines for specificity, precision, robustness and recovery. Stability study shows that the chromatograms of samples from its degradation products were well resolved with significant Rf value.
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Schoenlinner, Bernhard, Armin Stehle, Christian Siegel, William Gautier, Benedikt Schulte, Sascha Figur, Ulrich Prechtel, and Volker Ziegler. "The low-complexity RF MEMS switch at EADS: an overview." International Journal of Microwave and Wireless Technologies 3, no. 5 (August 3, 2011): 499–508. http://dx.doi.org/10.1017/s1759078711000729.
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This paper gives an overview of the low-complexity radio frequency microelectromechanical systems (RF MEMS) switch concept and technology of EADS Innovation Works in Germany. Starting in 2003, a capacitive switch concept, which is unique in several aspects, was developed to address specific needs in the aeronautic and space. Thermally grown silicon oxide as dielectric layer, the silicon substrate as actuation electrode, and a conductive zone realized by ion implantation make the EADS RF MEMS switch a very simple, low-cost, and reliable approach. In this document, data on experimental investigations are presented, which demonstrate outstanding performance figures in terms of insertion loss, isolation, frequency range, bandwidth, RF-power handling, and robustness with respect to thermal load. Based on this concept, numerous different circuits in particular single-pole single-throws (SPSTs), single-pole multi-throws (SPMTs), tunable filters, phase shifters, and electronically steerable antennas between 6 and 100 GHz have been designed, fabricated, and characterized.
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MD Tanvir Islam, Eftekhar Hossain Ayon, Bishnu Padh Ghosh, MD, Salim Chowdhury, Rumana Shahid, Aisharyja Roy puja, Sanjida Rahman, Aslima Akter, Mamunur Rahman, and Mohammad Shafiquzzaman Bhuiyan. "Revolutionizing Retail: A Hybrid Machine Learning Approach for Precision Demand Forecasting and Strategic Decision-Making in Global Commerce." Journal of Computer Science and Technology Studies 6, no. 1 (January 2, 2024): 33–39. http://dx.doi.org/10.32996/jcsts.2024.6.1.4.
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A thorough comparison of several machine learning methods is provided in this paper, including gradient boosting, AdaBoost, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and a unique hybrid framework (RF-XGBoost-LR). The assessment investigates their efficacy in real-time sales data analysis using key performance metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2 score. The study introduces the hybrid model RF-XGBoost-LR, leveraging both bagging and boosting methodologies to address the limitations of individual models. Notably, Random Forest and XGBoost are scrutinized for their strengths and weaknesses, with the hybrid model strategically combining their merits. Results demonstrate the superior performance of the proposed hybrid model in terms of accuracy and robustness, showcasing potential applications in supply chain studies and demand forecasting. The findings highlight the significance of industry-specific customization and emphasize the potential for improved decision-making, marketing strategies, inventory management, and customer satisfaction through precise demand forecasting.
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Nong, Kaifei, Hua Zhang, and Zhenzhen Liu. "Comparative Study of Different Machine Learning Models for Heat Transfer Performance Prediction of Evaporators in Modular Refrigerated Display Cabinets." Energies 17, no. 23 (December 8, 2024): 6189. https://doi.org/10.3390/en17236189.
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This study explores the potential of machine learning models to predict evaporator heat transfer performance in Modular Refrigerated Display Cases (MRDCs). Six experimental datasets from MRDC systems were analyzed to compare the efficacy of six machine learning models: Linear Regression, Decision Tree Regression, Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM). The findings indicate that the ensemble tree-based models, LightGBM and RF, are particularly effective in predicting evaporator heat transfer performance. These models demonstrate high accuracy and robustness, effectively capturing the nonlinear relationship between the evaporator temperature and heat transfer coefficient. Moreover, LightGBM and RF exhibit notable stability and adaptability in scenarios of limited data availability and elevated noise levels. Their consistent predictive accuracy across different experimental conditions highlights their suitability for complex refrigeration systems. This research provides essential insights for optimizing MRDC evaporator performance, establishing a theoretical and data-driven foundation for energy-efficient enhancements and intelligent management within cold chain systems.
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Song, Minseok, Hyeyoom Jung, Seungyong Lee, Donghyeon Kim, and Minkyu Ahn. "Diagnostic Classification and Biomarker Identification of Alzheimer’s Disease with Random Forest Algorithm." Brain Sciences 11, no. 4 (April 2, 2021): 453. http://dx.doi.org/10.3390/brainsci11040453.
Full textAbstract:
Random Forest (RF) is a bagging ensemble model and has many important advantages, such as robustness to noise, an effective structure for complex multimodal data and parallel computing, and also provides important features that help investigate biomarkers. Despite these benefits, RF is not used actively to predict Alzheimer’s disease (AD) with brain MRIs. Recent studies have reported RF’s effectiveness in predicting AD, but the test sample sizes were too small to draw any solid conclusions. Thus, it is timely to compare RF with other learning model methods, including deep learning, particularly with large amounts of data. In this study, we tested RF and various machine learning models with regional volumes from 2250 brain MRIs: 687 normal controls (NC), 1094 mild cognitive impairment (MCI), and 469 AD that ADNI (Alzheimer’s Disease Neuroimaging Initiative database) provided. Three types of features sets (63, 29, and 22 features) were selected, and classification accuracies were computed with RF, Support vector machine (SVM), Multi-layer perceptron (MLP), and Convolutional neural network (CNN). As a result, RF, MLP, and CNN showed high performances of 90.2%, 89.6%, and 90.5% with 63 features. Interestingly, when 22 features were used, RF showed the smallest decrease in accuracy, −3.8%, and the standard deviation did not change significantly, while MLP and CNN yielded decreases in accuracy of −6.8% and −4.5% with changes in the standard deviation from 3.3% to 4.0% for MLP and 2.1% to 7.0% for CNN, indicating that RF predicts AD more reliably with fewer features. In addition, we investigated the importance of the features that RF provides, and identified the hippocampus, amygdala, and inferior lateral ventricle as the major contributors in classifying NC, MCI, and AD. On average, AD showed smaller hippocampus and amygdala volumes and a larger volume of inferior lateral ventricle than those of MCI and NC.
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Raju, Matiur Rahman, Mahfuzur Rahman, Md Monirul Islam, Noor Md Sadiqul Hasan, Md Mehedi Hasan, Tarin Sharmily, and Mohammed Sajib Hosen. "A Comparative Analysis of Machine Learning Approaches for Evaluating the Compressive Strength of Pozzolanic Concrete." IUBAT Review 7, no. 1 (June 30, 2024): 90–122. http://dx.doi.org/10.3329/iubatr.v7i1.74329.
Full textAbstract:
This study leverages machine learning techniques to predict pozzolanic concrete's compressive strength accurately. Using artificial neural networks (ANN), random forest (RF), and gradient boosting regressor (GBR) models trained on a dataset of 482 samples, the study divides the data into 70% training and 30% testing subsets with seven input parameters. Model performance is assessed through metrics like coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The RF model excels, achieving R2 values of 0.976 in training and 0.964 in testing, along with the lowest RMSE (2.84 MPa) and MAE (2.05 MPa) during training and RMSE values of 7.81 MPa and MAE values of 5.89 MPa during testing, demonstrating superior predictive accuracy. Sensitivity analysis highlights the pivotal role of cement as an input parameter, contributing significantly to the model's accuracy. Employing K-fold cross-validation confirms the RF model's robustness with an average R2 value of 0.959. This research underscores the RF model's reliability and effectiveness in forecasting pozzolanic concrete compressive strength, with practical applications for concrete optimization and construction practices, establishing it as the preferred choice compared to other machine learning models. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 90-122