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1
Ampuła, Dariusz. "Random Forest in the Tests of Small Caliber Ammunition." Journal of KONBiN 52, no. 1 (March 1, 2022): 73–85. http://dx.doi.org/10.2478/jok-2022-0006.
Анотація:
Abstract In the introduction of this article the method of building a random forest model is presented, which can be used for both classification and regression tasks. The process of designing the random forest module was characterized, paying attention to the classification tasks module, which was used to build the author’s model. Based on the test results, a random forest model was designed for 7,62 mm ammunition with T-45 tracer projectile. Predictors were specified and values of stop parameters and process stop formulas were determined, on the basis of which a random forest module was built. An analysis of the resulting random forest model was made in terms of assessing its prediction and risk assessment. Finally, the designed random forest model has been refined by adding another 50 trees to the model. The enlarged random forest model occurred to be slightly stronger and it should be implemented.
2
K, Srinivasa Reddy. "Texture Filtration Module Under Stabilization Via Random Forest Optimization Methodology." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 3 (June 25, 2019): 458–69. http://dx.doi.org/10.30534/ijatcse/2019/20832019.
3
Ortiz-Reyes, Alma Delia, Efraín Velasco-Bautista, Arian Correa-Díaz, and Gregorio Ángeles-Pérez. "Predicción de variables dasométricas mediante modelos lineales mixtos y datos de LiDAR aerotransportado." E-CUCBA 9, no. 17 (December 29, 2021): 88–95. http://dx.doi.org/10.32870/ecucba.vi17.213.
Анотація:
Adequate estimation of dasometric parameters such as basal area (AB), above-ground biomass (B), and timber volume (VOL) inmanaged forests is a primary requirement to quantify the role of forests in mitigation climate change mitigation. In this context,forest inventories represent the general technique to estimate dasometric parameters, however, they represent a greater consumptionof time and resources. Using data derived from remote sensors in the dasometric modeling offers huge possibilities as an auxiliarytool in forestry activities. The objective of this work was to obtain a statistical model for each forest variable of interest: basal area,above-ground biomass and timber volume in a temperate forest under management in Zacualtipán, Hidalgo, Mexico, using linearmixed models and LiDAR (Light Detection And Ranging) data as predictor variables. For this, we consider that the cluster samplingunits have spatial correlation with respect to them distributed independently in the field. Metrics derived from LiDAR data wereused to fit the models. The metrics related to height and density of the vegetation presented the highest Pearson correlations (r = 0.52- 0.86) with the different dasometric variables and these were used as predictors in the adjusted models. The results indicated thatthe random effect of the cluster and the use of variance function significantly improved the heteroscedasticity, since the spatialcorrelation of the sites was included. This work showed the potential of using linear mixed models to take advantage of thedependency between sites in the same cluster and improve traditional estimates that do not model this hierarchical relationship.
4
Mitra, Mainak, and Soumit Roy. "Comparative Analysis of Predictive Models for Carbon Emission in Major Countries: A Focus on Linear Regression and Random Forest." International Journal of Science and Research (IJSR) 6, no. 8 (August 5, 2017): 2295–302. http://dx.doi.org/10.21275/sr231205142350.
5
Alimbayeva, Zhadyra, Chingiz Alimbayev, Kassymbek Ozhikenov, Nurlan Bayanbay, and Aiman Ozhikenova. "Wearable ECG Device and Machine Learning for Heart Monitoring." Sensors 24, no. 13 (June 28, 2024): 4201. http://dx.doi.org/10.3390/s24134201.
Анотація:
With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.
6
Gao, Quansheng. "Design and Implementation of 3D Animation Data Processing Development Platform Based on Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (May 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/1518331.
Анотація:
Based on the whole process of computer-aided technology, a 3D animation data processing development platform based on artificial intelligence is designed and implemented. A random forest model for animation data processing and development is designed to mine the experience that can guide animation generation from the accumulated animation data. Based on the design goal and implementation principle of animation data processing and development platform, the attributes and categories of random forest model are abstracted. After standardizing a large number of historical data, the training sample set is obtained, and the random forest model is obtained after training. The parameters of the random forest model are continuously optimized by experiments, so that the learning model can better guide the dynamic animation data processing and development platform to generate animation to the satisfaction of users. The designed three-dimensional animation data processing and development platform interacts with the animation generation module, users, and system administrators. It can continuously receive the sample data of the animation generation module, automatically expand the number of training samples, analyze the status of the sample database, and put forward suggestions to the system administrator to update the learning model, so as to realize the initiative of learning. The experimental results show that the designed 3D animation data processing and development platform is effective and feasible.
7
Togatorop, Parmonangan R., Megawati Sianturi, David Simamora, and Desriyani Silaen. "Optimizing Random Forest using Genetic Algorithm for Heart Disease Classification." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 13, no. 1 (August 10, 2022): 60. http://dx.doi.org/10.24843/lkjiti.2022.v13.i01.p06.
Анотація:
Heart disease is a leading cause of death worldwide, and the need for effective predictive systems is a major source of the need to treat affected patients. This study aimed to determine how to improve the accuracy of Random Forest in predicting and classifying heart disease. The experiments performed in this study were designed to select the most optimal parameters using an RF optimization technique using GA. The Genetic Algorithm (GA) is used to optimize RF parameters to predict and classify heart disease. Optimization of the Random Forest parameter using a genetic algorithm is carried out by using the Random Forest parameter as input for the initial population in the Genetic Algorithm. The Random Forest parameter undergoes a series of processes from the Genetic Algorithm: Selection, Crossover Rate, and Mutation Rate. The chromosome that has survived the evolution of the Genetic Algorithm is the best population or best parameter Random Forest. The best parameters are stored in the hall of fame module in the DEAP library and used for the classification process in Random Forest. The optimized RF parameters are max_depth, max_features, n_estimator, min_sample_leaf, and min_sample_leaf. The experimental process performed in RF uses the default parameters, random search, and grid search. Overall, the accuracy obtained for each experiment is the default parameter 82.5%, random search 82%, and grid search 83%. The RF+GA performance is 85.83%; this result is affected by the GA parameters are generations, population, crossover, and mutation. This shows that the Genetic Algorithm can be used to optimize the parameters of Random Forest.
8
Zhao, Lefa, Yafei Zhu, and Tianyu Zhao. "Deep Learning-Based Remaining Useful Life Prediction Method with Transformer Module and Random Forest." Mathematics 10, no. 16 (August 13, 2022): 2921. http://dx.doi.org/10.3390/math10162921.
Анотація:
This paper focuses on the prognosis problem in manufacturing of the electronic chips for devices. Electronic devices are of great importance at present, which are popularly applied in daily life. The basis of supporting the electronic device is the powerful electronic chip and its manufacturing technology. Chip manufacturing has been one of the most important technologies in recent years. The etching machine is the key equipment in the etching process of the wafers in chip manufacturing. Due to the high demands for precise manufacturing, monitoring the health state and predicting the remaining useful life (RUL) of the etching system is quite important. However, the task is very hard because of the lack of knowledge of exact onset of failure or degradation and the multiple operating conditions, etc. This paper proposes a novel deep learning-based RUL prediction method for the etching system. The transformer module and random forest are integrated in the methodology to identify the health state of the machine and predict its RUL, through training with the complex data of the etching machine’s sensors and exploring its underlying features. The experiments are based on the subject of the 2018 PHM Data Challenge—for estimating time-to-failure or RUL of Ion Mill Etching Systems in an online fashion using data from multiple sensors. The results indicate the proposed method is promising for the real applications of the prognosis of the etching system for electronic devices.
9
Ludot-Vlasak, Ronan. "Romulus en Amérique : recyclage et récupération des modèles antiques par John Howard Payne." Recherches anglaises et nord-américaines 45, no. 1 (2012): 65–82. http://dx.doi.org/10.3406/ranam.2012.1424.
Анотація:
Cet article propose une lecture politique et idéologique de Romulus, the Shepherd King, pièce écrite par John Howard Payne à la fin des années 1830 pour Edwin Forrest, mais jamais produite, et explore les modalités selon lesquelles le dramaturge récupère et réinvente l’Antiquité classique. Il s’agit de montrer comment Payne transforme le mythe romain pour l’intégrer à un modèle idéologique démocratique - ce qui n’est pas sans incidence sur le traitement du temps historique dans la pièce - mais également que l’ambivalence du positionnement politique de l’œuvre légitime et subvertit dans le même mouvement l’héritage politique jeffersonien et jacksonien, notamment lorsque les questions de la légitimité du pouvoir politique ou de la propriété individuelle sont soulevées.
10
Zhou, Bo, and Omer Saeed. "Comparative Analysis of Volleyball Serve Action Based on Human Posture Estimation." Mobile Information Systems 2022 (September 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/4817463.
Анотація:
Serving is one of the most crucial techniques in volleyball. Serving is a method that does not require team interaction and is difficult for the opponent to immediately interfere with. The feature migration module with a fixed offset is suggested in this work. This module can be thought of as a cross-channel dilated convolution approximation of dilated convolution. The reason cross-channel dilated convolution is not worse than standard dilated convolution with few parameters is discussed in this article. An improved random forest model is put forth to address the issue of the human pose estimation system’s high memory consumption when utilizing random forest as the classifier. This model presents the Poisson process and incorporates it with the depth data to create a filter before using Bootstrap sampling. In order to optimize and reconstruct the training dataset, a portion of the feature sample points that do not contribute positively to subsequent classification is removed from the original training dataset. This allows the training dataset to better account for the repeated sampling of the random forest during the sampling process. Resampling has some drawbacks, but they are not very representative. The effectiveness of the optimization model, which significantly lowers the system’s time and space complexity and increases the system’s applicability, is demonstrated by experiments.
11
Cai, Jiaowu, Peng Liu, and Liangyu Li. "Pipeline gas leakage early warning system based on wireless sensor network." Frontiers in Computing and Intelligent Systems 2, no. 2 (December 29, 2022): 53–57. http://dx.doi.org/10.54097/fcis.v2i2.4085.
Анотація:
Expounds a community pipeline gas leakage warning system based on wireless sensor network, fuzzy control algorithm and random forest algorithm. System using the wireless sensor network acquisition household pipeline gas data, through the intelligent gateway will collect data reported to the cloud platform, the system through the fuzzy control algorithm to reduce the importance of low interference, make the input random forest model data optimization, visualization module using B/S architecture, responsible for the early warning data display in the Web page. According to the historical data of household gas pipeline in a community in Ganzhou city, the simulation was carried out under laboratory conditions. The results show that the model can effectively improve the function of online monitoring and dynamic early warning of gas leakage. Compared with other algorithms, the fuzzy-random forest algorithm has a better performance in finding small leakage in the early stage.
12
Radivojević, Dušan, Nikola Mirkov, and Slobodan Maletić. "Human activity recognition based on machine learning classification of smartwatch accelerometer dataset." FME Transactions 49, no. 1 (2021): 225–32. http://dx.doi.org/10.5937/fme2101225r.
Анотація:
This paper presents two Machine Learning models that classify time series data given from smartwatch accelerometer of observed subjects. For the purpose of classification we use Deep Neural Network and Random Forest classifier algorithms. The comparison of both models shows that they have similar performance with regard to recognition of subject's activities that are used in the test group of the dataset. Training accuracy reaches approximately 95% and 100% for Deep Learning and Random Forest model respectively. Since the validation and recognition, reached about 81% and 75% respectively, a tendency for improving accuracy as a function of number of participants is considered. The influence of data sample precision to the accuracy of the models is examined since the input data could be given from various wearable devices.
13
Massoud, Rana, Riccardo Berta, Stefan Poslad, Alessandro De Gloria, and Francesco Bellotti. "IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case." Sensors 21, no. 10 (May 20, 2021): 3559. http://dx.doi.org/10.3390/s21103559.
Анотація:
Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users’ privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused.
14
Fu, Mingliang, Yuquan Leng, Haitao Luo, and Weijia Zhou. "An Occlusion-Aware Framework for Real-Time 3D Pose Tracking." Sensors 18, no. 8 (August 20, 2018): 2734. http://dx.doi.org/10.3390/s18082734.
Анотація:
Random forest-based methods for 3D temporal tracking over an image sequence have gained increasing prominence in recent years. They do not require object’s texture and only use the raw depth images and previous pose as input, which makes them especially suitable for textureless objects. These methods learn a built-in occlusion handling from predetermined occlusion patterns, which are not always able to model the real case. Besides, the input of random forest is mixed with more and more outliers as the occlusion deepens. In this paper, we propose an occlusion-aware framework capable of real-time and robust 3D pose tracking from RGB-D images. To this end, the proposed framework is anchored in the random forest-based learning strategy, referred to as RFtracker. We aim to enhance its performance from two aspects: integrated local refinement of random forest on one side, and online rendering based occlusion handling on the other. In order to eliminate the inconsistency between learning and prediction of RFtracker, a local refinement step is embedded to guide random forest towards the optimal regression. Furthermore, we present an online rendering-based occlusion handling to improve the robustness against dynamic occlusion. Meanwhile, a lightweight convolutional neural network-based motion-compensated (CMC) module is designed to cope with fast motion and inevitable physical delay caused by imaging frequency and data transmission. Finally, experiments show that our proposed framework can cope better with heavily-occluded scenes than RFtracker and preserve the real-time performance.
15
Wang, Chao, Yunxiao Sun, Wenting Wang, Hongri Liu, and Bailing Wang. "Hybrid Intrusion Detection System Based on Combination of Random Forest and Autoencoder." Symmetry 15, no. 3 (February 21, 2023): 568. http://dx.doi.org/10.3390/sym15030568.
Анотація:
To cope with the rising threats posed by network attacks, machine learning-based intrusion detection systems (IDSs) have been intensively researched. However, there are several issues that need to be addressed. It is difficult to deal with unknown attacks that do not appear in the training set, and as a result, poor detection rates are produced for these unknown attacks. Furthermore, IDSs suffer from high false positive rate. As different models learn data characteristics from different perspectives, in this work we propose a hybrid IDS which leverages both random forest (RF) and autoencoder (AE). The hybrid model operates in two steps. In particular, in the first step, we utilize the probability output of the RF classifier to determine whether a sample belongs to attack. The unknown attacks can be identified with the assistance of the probability output. In the second step, an additional AE is coupled to reduce the false positive rate. To simulate an unknown attack in experiments, we explicitly remove some samples belonging to one attack class from the training set. Compared with various baselines, our suggested technique demonstrates a high detection rate. Furthermore, the additional AE detection module decreases the false positive rate.
16
Muruganantham, Kavitha, and Subbaiah Shanmugasundaram. "Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (May 4, 2023): 261–68. http://dx.doi.org/10.17762/ijritcc.v11i4.6448.
Анотація:
If online businesses possess valuable interest for suggesting their items by scoring them, then digital advertising gains their profits depending on their promotions or marketing task. Web users cannot be certain that the products handled via big-data recommendation are either advanced or interesting to their needs. In recent decades, recommender system models have been widely used to analyses large quantities of information. Amongst, a Distributed Improved Prediction with Matrix Factorization (MF) and Random Forest (RF) called DIPMF model exploits individual’s desires, choices and social context together for predicting the ratings of a particular item. But, the RF scheme needs high computation power and time for learning process. Also, its outcome was influenced by the training parameters. Hence this article proposes a Distributed Improved Deep Prediction with MF and ensemble learning (DIDPMF) model is proposed to decrease the computational difficulty of RF learning and increasing the efficiency of rating prediction. In this DIDPMF, a forest attribute extractor is ensemble with the Deep Neural Network (fDNN) for extracting the sparse attribute correlations from an extremely large attribute space. So, incorporating RF over DNN has the ability to provide prediction outcomes from all its base trainers instead of a single estimated possibility rate. This fDNN encompasses forest module and DNN module. The forest module is employed as an attribute extractor to extract the sparse representations from the given raw input data with the supervision of learning outcomes. First, independent decision trees are constructed and then ensemble those trees to obtain the forest. After, this forest is fed to the DNN module which acts as a learner to predict the individual’s ratings with the aid of novel attribute representations. Finally, the experimental results reveal that the DIDPMF outperforms than the other conventional recommender systems.
17
Kulkarni, Prasad, Tushar Patil, Aditya Pandey, Vishwesh Vyawahare, Dhiraj Magare, and Gajanan Birajdar. "Performance Assessment of Hetero-Junction Intrinsic Thin Film HIT Photovoltaic Module Using Machine Learning Methods." ITM Web of Conferences 44 (2022): 01009. http://dx.doi.org/10.1051/itmconf/20224401009.
Анотація:
A solar cell built of ultra-thin amorphous silicon and high-quality mono-crystalline silicon is known as a hetero-junction intrinsic thin film. It has a pyramid surface on the front that increases sunlight absorption. The operating environment has a significant impact on the performance of hetero-junction intrinsic thin-film photovoltaic modules with real I–V (current-voltage) characteristics. Changes in the environment have a significant impact on solar irradiation. Clouds also have a significant impact on the solar irradiation that a PV cell receives. In this project, we will use the Random Forest Regression machine learning algorithm to investigate the effects of sudden changes in environmental conditions on power output and module temperature of an HIT (Heterojunction with Intrinsic Thin Layer) module, where irradiance, temperature, and module efficiency parameters are taken into account when designing modules. The algorithm’s output will be studied to gain a better understanding of performance variations as well as the behavior of the power output and module temperature when subjected to random influences induced by various environmental variables. The suggested algorithm is not restricted to a certain module technology or geographic location.
18
Ren, Keyu, Heqing Peng, Junwei Wu, Shengtao Yao, Jinfeng Li, and Pingyu Li. "PT module -A Traffic Signal Classification Model Based on Convolutional Neural Networks and Random Forests." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 374–81. http://dx.doi.org/10.54254/2755-2721/2/20220531.
Анотація:
Autonomous driving and image recognition are the hot directions of Internet development nowadays. In self-driving cars it is necessary to capture traffic signs in front of the vehicle by cameras. To ensure that the information of image recognition is correct, a set of image classification models with high accuracy should be used to classify the recognized objects in order to determine the next instruction of vehicle operation. There were many achievements in the research work of traffic sign recognition, but there are still some shortcomings. By combining CNN and random forests with PT module (a module that can improve the accuracy of feature extraction), we finally came up with a classification model that can efficiently place the received traffic sign images into a specified category, which we obtained 97% accuracy on the GTSRB dataset, which is much more accurate than traditional neural network methods or regression methods. We have also evaluated it on other datasets and the results obtained are more promising.
19
Ji, Yin, Jiandong Fang, and Yudong Zhao. "Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest." Applied Sciences 13, no. 21 (October 26, 2023): 11742. http://dx.doi.org/10.3390/app132111742.
Анотація:
As a key animal feed source, the dry matter content of clover is widely regarded as an important indicator of its nutritional value and quality. The primary aim of this study is to introduce a methodology for forecasting clover dry matter content utilizing a semantic segmentation network. This approach involves constructing a predictive model based on visual image information to analyze the dry matter content within clover. Given the complex features embedded in clover images and the difficulty of obtaining labeled data, it becomes challenging to analyze the dry matter content directly from the images. In order to address this issue, a method for predicting dry matter in clover based on semantic segmentation network is proposed. The method uses the improved DeepLabv3+ network as the backbone of feature extraction, and integrates the SE (Squeeze-and-Excitation) attention mechanism into the ASPP (Atrous Spatial Pyramid Pooling) module to enhance the semantic segmentation performance, in order to realize the efficient extraction of the features of clover images; on this basis, a regression model based on the Random Forest (RF) method is constructed to realize the prediction of dry matter in clover. Extensive experiments conducted by applying the trained model to the dry matter prediction dataset evaluated the good predictor performance and showed that the number of each pixel level after semantic segmentation improved the performance of semantic segmentation by 18.5% compared to the baseline, and there was a great improvement in the collinearity of dry matter prediction.
20
Eu, Song, Chang-Woo Lee, Junpyo Seo, and Choongshik Woo. "Analyzing the Effect of Check Dam in Debris Flow Hazard Map Using Random Walk Model." Crisis and Emergency Management: Theory and Praxis 17, no. 9 (September 30, 2021): 91–103. http://dx.doi.org/10.14251/crisisonomy.2021.17.9.91.
Анотація:
Debris flow transports lots of sediments downstream that cause damage to human lives and properties. The accurate estimation of the debris flow hazard zone is a critical factor of the successful early-warning and evacuation for reducing the debris flow risk. Korea Forest Service has developed a debris flow hazard map using Random Walk Model (RWM). Meanwhile, because check dams in forest watersheds capture discharged debris, the effect of check dams should be considered for mapping the debris flow hazard zone. This study analyzed the effect of check dams on debris flow discharge using RWM with the check dam module. As a result, RWM seems to simulate the sediment capture by check dams. However, the total deposit areas were not significantly different despite the effect of check dams because of input parameters and the flow direction calculation algorithm of RWM. Further studies on appropriate spatial resolution and initial sediment volume should be conducted to improve the debris flow hazard map.
21
Chen, Zheng, and Weixiong Zhang. "Integrative Analysis Using Module-Guided Random Forests Reveals Correlated Genetic Factors Related to Mouse Weight." PLoS Computational Biology 9, no. 3 (March 7, 2013): e1002956. http://dx.doi.org/10.1371/journal.pcbi.1002956.
22
Paunović-Pantić, Jovana, Danijela Vučević, Igor Pantić, Svetlana Valjarević, and Tatjana Radosavljević. "Development of random forest machine learning model for the detection of changes in liver tissue after exposure to iron oxide nanoparticles." Medicinska istrazivanja 57, no. 1 (2024): 21–26. http://dx.doi.org/10.5937/medi57-46969.
Анотація:
Introduction/Aim: The aim of our study was to create a machine learning model, specifically a random forest model, which uses textural data from liver micrographs to differentiate between normal hepatic tissue and damaged tissue exposed to iron oxide nanoparticles. Material and Methods: Regions of interest in micrographs of hepatic tissue, obtained from mice treated with iron oxide nanoparticles and controls, were analyzed using the gray-level co-occurrence matrix (GLCM) method. The resulting GLCM features were employed as input data for the training and testing of the random forest model using the "Scikit-learn" library in the Python programming language. Additionally, a conventional decision tree model was developed, based on the classification and regression tree (CART) algorithm. Results: The random forest model outperformed the alternative CART decision tree approach in terms of classification accuracy, correctly predicting the class for 73.67% of the instances in the validation ROI dataset. The area under the receiver operating characteristic curve was 0.81, indicating relatively good discriminatory power. The F1 score for the model was 0.74, showcasing fairly good precision and recall, though not perfect. Conclusion: The data obtained from this study may be utilized for further development of artificial intelligence computation systems to identify physiological and pathophysiological changes in hepatic tissue. The results also serve as a starting point for additional research on the automation of histopathological analysis of liver tissue exposed to external toxic agents.
23
Elsayed, Khaled, Azrul A. Mutalib, Mohamed Elsayed, and Mohd Reza Azmi. "Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Random Forest Approach." Buildings 14, no. 5 (April 29, 2024): 1254. http://dx.doi.org/10.3390/buildings14051254.
Анотація:
Interlocking Inter-Module Connections (IMCs) in Modular Steel Buildings (MSBs) have garnered significant interest from researchers. Despite this, the optimisation of plate thicknesses in such structures has yet to be extensively explored in the existing literature. Therefore, this paper focuses on optimising the thickness of interlocking IMCs in MSBs by leveraging established experimental and numerical simulation methodologies. The study developed various numerical models for IMCs with plate thicknesses of 4 mm, 6 mm, 10 mm, and 12 mm, all subjected to compression loading conditions. The novelty of this study lies in its comprehensive parametric analysis, which evaluates the slip prediction model. A random forest regression model, trained using the ‘TreeBagger’ function, was also implemented to predict slip values based on applied force. Sensitivity analysis and comparisons with alternative methods underscored the reliability and applicability of the findings. The results indicate that a plate thickness of 11.03 mm is optimal for interlocking IMCs in MSBs, achieving up to 8.08% in material cost reductions while increasing deformation resistance by up to 50.75%. The ‘TreeBagger’ random forest regression significantly enhanced slip prediction accuracy by up to 7% at higher force levels.
24
Afiantara, Agus, Bagus Mahawan, and Eka Budiarto. "Predicting of Banking Stability Using Machine Learning Technique of Random Forests." ACMIT Proceedings 6, no. 1 (July 5, 2021): 1–8. http://dx.doi.org/10.33555/acmit.v6i1.89.
Анотація:
The purpose of this research is to create a predicting model of banking stability in Indonesia. Authors use a small set of explanatory indicators primarily related to the banking industry and some relevant economic variables. Among the indicators candidate to be used in this study are the indicator of banking industry, the money markets, capital markets and creditors, and the macro-economic indicator. The source of data in this research is obtained from CEIC and SEKI (Indonesian Economic and Financial Statistics) that published by Central Bank of Indonesia from 2004 and 2011. Principal Component Analysis is used to avoid the multi-collinearity problem when construct the model. Authors train the model using Random Forest Regression with data over the 2004-2007 period, and made predictions of global financial crisis that happened in 2008/9. Python 2.7.10 and scikit-learn version 0.20.0 module has been exploited for simulations and evaluation of the model. Numerical illustration is provided to demonstrate the efficiency of proposed model. As the result nine most components analysis obtained as input for the machine learning model with explained variance ratio around 97%, accuracy around 89%, and precision 91% and mean absolute error around 11%.
25
Christian, Robby, Balza Achmad, and Hyun Gook Kang. "Prognostic Methods on Accelerator’s Anode Voltage Regulator." E3S Web of Conferences 43 (2018): 01020. http://dx.doi.org/10.1051/e3sconf/20184301020.
Анотація:
This study investigated an adaptive control, fault diagnostics and prognostics of the anode voltage regulator system at an ion implantation accelerator. The system was modeled as a 4th order AutoRegressive with eXogenous (ARX) model, controlled by a Fuzzy Logic Controller (FLC). This model was then used as a basis for constructing and updating a fault diagnosis module and a failure prognostics module. To maintain the system’s performance, the controller’s response was continuously re-adjusted through an optimization scheme. A Failure Mode and Effect Analysis (FMEA) was conducted resulting on five failure modes of the regulator system. Fault data were generated in MATLAB simulation to train a random forest fault classification engine. The optimal random forest classifier was 20 decision trees with a fault diagnostics accuracy of 98.06%. A Hidden Markov Model (HMM) was constructed as the system’s fault progression model based on the interaction between environmental conditions and controller actions. The particle filter and Bayesian inference methods were then employed to continuously update the HMM and predict the system’s Remaining Useful Lifetime (RUL). The proposed methodology was able to integrate an adaptive fuzzy logic control, prognosis and failure diagnosis altogether allowing a continual satisfactory performance of the voltage regulator system throughout its lifetime.
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Yan, Guobing, Qiang Sun, Jianying Huang, and Yonghong Chen. "Helmet Detection Based on Deep Learning and Random Forest on UAV for Power Construction Safety." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 1 (January 20, 2021): 40–49. http://dx.doi.org/10.20965/jaciii.2021.p0040.
Анотація:
Image recognition is one of the key technologies for worker’s helmet detection using an unmanned aerial vehicle (UAV). By analyzing the image feature extraction method for workers’ helmet detection based on convolutional neural network (CNN), a double-channel convolutional neural network (DCNN) model is proposed to improve the traditional image processing methods. On the basis of AlexNet model, the image features of the worker can be extracted using two independent CNNs, and the essential image features can be better reflected considering the abstraction degree of the features. Combining a traditional machine learning method and random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for workers’ helmet detection. The experimental results show that deep learning (DL) is closely related to the traditional machine learning methods. Moreover, adding a DL module to the traditional machine learning framework can improve the recognition accuracy.
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Umutoni, C., and I. Ngaruye. "Prediction of Tea Production in Rwanda Using Data Mining Techniques." Agricultural and Food Science Journal of Ghana 15, no. 1 (March 22, 2023): 1631–40. http://dx.doi.org/10.4314/afsjg.v15i1.10.
Анотація:
Rwanda's main economic activity is agriculture, and tea is the country's most important cash crop. There has been extensive research on prediction of tea production in Rwanda but most of the methods applied were the traditional statistical analyzes with limited prediction capability. Data mining algorithm models, linear regression, K-Nearest Neighbor (KNN), Random Forest Regression, and Extremely Randomized Trees are discussed in this study to identify critical features in different domains to facilitate accurate prediction of tea production in Rwanda. In this study also, an identification of different factors which are strongly associated with tea production and developed data mining models for predicting tea production using training and test data from National Agricultural Export Development Board (NAEB) 2010-2019 is performed and PYTHON, R, and SPSS Version 25 softwares used in this study. The findings reveal that extra tree and random forest are the best model among the others to predict tea production in Rwanda.
French title: Prévision de la production de thé au Rwanda à l'aide de techniques d'exploration de données
La principale activité économique du Rwanda est l'agriculture, et le thé est la culture de rente la plus importante du pays. De nombreuses recherches ont été menées sur la prédiction de la production de thé au Rwanda, mais la plupart des méthodes appliquées étaient des analyses statistiques traditionnelles avec une capacité de prédiction limitée. Les modèles d'algorithmes d'exploration de données, la régression linéaire, le K-Nearest Neighbor (KNN), la régression Random Forest et les arbres extrêmement randomisés sont discutés dans cette étude pour identifier les caractéristiques critiques dans différents domaines afin de faciliter la prédiction précise de la production de thé au Rwanda. Dans cette étude également, une identification des différents facteurs qui sont fortement associés à la production de thé et des modèles d'exploration de données développés pour prédire la production de thé en utilisant des données d'entraînement et de test du National Agricultural Export Development Board (NAEB) 2010-2019 est effectuée et les logiciels PYTHON, R, et SPSS Version 25 sont utilisés dans cette étude. Les résultats révèlent que l'arbre supplémentaire et la forêt aléatoire sont les meilleurs modèles parmi les autres pour prédire la production de thé au Rwanda.
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Ilbeigipour, Sadegh, Amir Albadvi, and Elham Akhondzadeh Noughabi. "Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming." Journal of Healthcare Engineering 2021 (April 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/6624829.
Анотація:
One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient’s electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient’s heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient’s life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias detection and evaluate the impact of using this new module on classification performance metrics and the rate of delay in arrhythmia detection. The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias. We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification than the other two classifiers. The results show previous results in performance metrics of the classification model and a significant decrease in pipeline runtime by using more class labels compared to previous studies.
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Wang, Shun-Yuan, Wen-Bin Lin, and Yu-Chieh Shu. "Design of Machine Learning Prediction System Based on the Internet of Things Framework for Monitoring Fine PM Concentrations." Environments 8, no. 10 (September 24, 2021): 99. http://dx.doi.org/10.3390/environments8100099.
Анотація:
In this study, a mobile air pollution sensing unit based on the Internet of Things framework was designed for monitoring the concentration of fine particulate matter in three urban areas. This unit was developed using the NodeMCU-32S microcontroller, PMS5003-G5 (particulate matter sensing module), and Ublox NEO-6M V2 (GPS positioning module). The sensing unit transmits data of the particulate matter concentration and coordinates of a polluted location to the backend server through 3G and 4G telecommunication networks for data collection. This system will complement the government’s PM2.5 data acquisition system. Mobile monitoring stations meet the air pollution monitoring needs of some areas that require special observation. For example, an AIoT development system will be installed. At intersections with intensive traffic, it can be used as a reference for government transportation departments or environmental inspection departments for environmental quality monitoring or evacuation of traffic flow. Furthermore, the particulate matter distributions in three areas, namely Xinzhuang, Sanchong, and Luzhou Districts, which are all in New Taipei City of Taiwan, were estimated using machine learning models, the data of stationary monitoring stations, and the measurements of the mobile sensing system proposed in this study. Four types of learning models were trained, namely the decision tree, random forest, multilayer perceptron, and radial basis function neural network, and their prediction results were evaluated. The root mean square error was used as the performance indicator, and the learning results indicate that the random forest model outperforms the other models for both the training and testing sets. To examine the generalizability of the learning models, the models were verified in relation to data measured on three days: 15 February, 28 February, and 1 March 2019. A comparison between the model predicted and the measured data indicates that the random forest model provides the most stable and accurate prediction values and could clearly present the distribution of highly polluted areas. The results of these models are visualized in the form of maps by using a web application. The maps allow users to understand the distribution of polluted areas intuitively.
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Heras, Diego, and Carlos Matovelle. "Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 16, no. 3 (May 27, 2021): 1. http://dx.doi.org/10.4136/ambi-agua.2708.
Анотація:
Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data. Within this problem, there are data series that are missing, and that should not necessarily be discarded; this is achieved by means of the imputation of the same ones, obtaining complete sets. For this reason, this research proposes a comparison of computer-learning techniques to identify those best suited for hydrographic systems of the Pacific of Ecuador. For the elaboration of this investigation, the hydro-meteorological records of the monitoring stations located in the watersheds of the Esmeraldas, Cañar and Jubones Rivers were used for 22 years, between 1990 and 2012. The variables that were imputed were precipitation and flow. Automatic learning machines of the Python Scikit_Learn module were used; these modules integrate a wide range of automated learning algorithms, such as Linear Regression and Random Forest. Finally, results were obtained that led to a minimum useful mean square error for Random Forest as an automatic machine-learning imputation method that best fits the systems and data analyzed.
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Kim, Sunhae, Hye-Kyung Lee, and Kounseok Lee. "Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches." International Journal of Environmental Research and Public Health 18, no. 7 (March 24, 2021): 3339. http://dx.doi.org/10.3390/ijerph18073339.
Анотація:
(1) Background: The Patient Health Questionnaire-9 (PHQ-9) is a tool that screens patients for depression in primary care settings. In this study, we evaluated the efficacy of PHQ-9 in evaluating suicidal ideation (2) Methods: A total of 8760 completed questionnaires collected from college students were analyzed. The PHQ-9 was scored in combination with and evaluated against four categories (PHQ-2, PHQ-8, PHQ-9, and PHQ-10). Suicidal ideations were evaluated using the Mini-International Neuropsychiatric Interview suicidality module. Analyses used suicide ideation as the dependent variable, and machine learning (ML) algorithms, k-nearest neighbors, linear discriminant analysis (LDA), and random forest. (3) Results: Random forest application using the nine items of the PHQ-9 revealed an excellent area under the curve with a value of 0.841, with 94.3% accuracy. The positive and negative predictive values were 84.95% (95% CI = 76.03–91.52) and 95.54% (95% CI = 94.42–96.48), respectively. (4) Conclusion: This study confirmed that ML algorithms using PHQ-9 in the primary care field are reliably accurate in screening individuals with suicidal ideation.
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SRISANKAR, M., and Dr K. P. LOCHANAMBAL. "THE SENTIMENTAL ANALYSIS USING DEEP LEARNING MODELS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27151.
Анотація:
ABSTRACT:The tweets are brief and come in a steady stream. Emotions have a significant impact on feelings. People can express their ideas about anything and anything on social media. Public perception is divided into three categories: positive, negative, and neutral. In this study, Twitter hotel reviews are gathered and pre-processed before being analyzed using Python's Tweepy package. Re-tweets, tags, URLs, hash tag symbols, and duplicate entries are all eliminated as part of a screening procedure to remove any discrepancies in the data. Using Python's scikit-learn module, tweets are up-sampled and divided. Python turns textual data into vectors using the keras Tokenizer. Bi-sense Emoji Embedding (BSEE) is used to perform a sentimental analysis. Sentiment is categorized using Support Vector Machines (SVM) and Random Forest (RF),and LSTM (Long Short Term Memory) where compared based on accuracy, recall, F-measure, precision, time duration, and performance. It is clear that the proposed classifier produces better results. Keywords: Bi-Sense Emoji Embedding (BSEE), Long Short Term Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF),
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Lemenkova, Polina. "Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique." Coasts 4, no. 1 (February 26, 2024): 127–49. http://dx.doi.org/10.3390/coasts4010008.
Анотація:
Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa.
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Kurade, Chinmay, Maninder Meenu, Sahil Kalra, Ankur Miglani, Bala Chakravarthy Neelapu, Yong Yu, and Hosahalli S. Ramaswamy. "An Automated Image Processing Module for Quality Evaluation of Milled Rice." Foods 12, no. 6 (March 16, 2023): 1273. http://dx.doi.org/10.3390/foods12061273.
Анотація:
The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model’s performance. It is concluded that the random forest classifier presents an accuracy of 77 percent and is the best-performing model for the classification of rice varieties. Furthermore, the same algorithm is efficiently employed to determine the price of adulterated rice samples based upon the market price of individual rice.
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Zhang, Huacong, Huaiqing Zhang, Keqin Xu, Yueqiao Li, Linlong Wang, Ren Liu, Hanqing Qiu, and Longhua Yu. "A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm." Remote Sensing 15, no. 14 (July 10, 2023): 3480. http://dx.doi.org/10.3390/rs15143480.
Анотація:
Diameter at breast height (DBH) is a critical metric for quantifying forest resources, and obtaining accurate, efficient measurements of DBH is crucial for effective forest management and inventory. A backpack LiDAR system (BLS) can provide high-resolution representations of forest trunk structures, making it a promising tool for DBH measurement. However, in practical applications, deep learning-based tree trunk detection and DBH estimation using BLS still faces numerous challenges, such as complex forest BLS data, low proportions of target point clouds leading to imbalanced class segmentation accuracy in deep learning models, and low fitting accuracy and robustness of trunk point cloud DBH methods. To address these issues, this study proposed a novel framework for BLS stratified-coupled tree trunk detection and DBH estimation in forests (BSTDF). This framework employed a stratified coupling approach to create a tree trunk detection deep learning dataset, introduced a weighted cross-entropy focal-loss function module (WCF) and a cosine annealing cyclic learning strategy (CACL) to enhance the WCF-CACL-RandLA-Net model for extracting trunk point clouds, and applied a (least squares adaptive random sample consensus) LSA-RANSAC cylindrical fitting method for DBH estimation. The findings reveal that the dataset based on the stratified-coupled approach effectively reduces the amount of data for deep learning tree trunk detection. To compare the accuracy of BSTDF, synchronous control experiments were conducted using the RandLA-Net model and the RANSAC algorithm. To benchmark the accuracy of BSTDF, we conducted synchronized control experiments utilizing a variety of mainstream tree trunk detection models and DBH fitting methodologies. Especially when juxtaposed with the RandLA-Net model, the WCF-CACL-RandLA-Net model employed by BSTDF demonstrated a 6% increase in trunk segmentation accuracy and a 3% improvement in the F1 score with the same training sample volume. This effectively mitigated class imbalance issues encountered during the segmentation process. Simultaneously, when compared to RANSAC, the LSA-RANCAC method adopted by BSTDF reduced the RMSE by 1.08 cm and boosted R2 by 14%, effectively tackling the inadequacies of RANSAC’s filling. The optimal acquisition distance for BLS data is 20 m, at which BSTDF’s overall tree trunk detection rate (ER) reaches 90.03%, with DBH estimation precision indicating an RMSE of 4.41 cm and R2 of 0.87. This study demonstrated the effectiveness of BSTDF in forest DBH estimation, offering a more efficient solution for forest resource monitoring and quantification, and possessing immense potential to replace field forest measurements.
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Fried, J. S., and J. K. Gilless. "Stochastic Representation of Fire Occurrence in a Wildland Fire Protection Planning Model for California." Forest Science 34, no. 4 (December 1, 1988): 948–59. http://dx.doi.org/10.1093/forestscience/34.4.948.
Анотація:
Abstract A fire occurrence module was developed for CFES-IAM Version 2, a stochastic simulation model of initial attack on wildfires in California. The module is designed to generate annual sequences of fire start times that are consistent with local fire history. A three-stage approach was employed: (1) For each day of a simulated fire season, a random draw from a Bernoulli distribution is used to determine if any fires occur; (2) if any do occur, a random draw from a geometric multiplicity distribution determines theft number; (3) ignition times for each fire are then randomly drawn from a time of day (beta or Poisson) distribution. This approach and specific distributional forms were selected after analysis of historical fire records from California's Sierra foothills and Central Valley. Fire sequences generated with the module appear to capture historical patterns with respect to diurnal distribution, interfire times, and total number of fires per year. For. Sci. 34(4):948-959.
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Liu, Jin, Shanshan Qiu, and Zhenbo Wei. "Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies." Chemosensors 10, no. 10 (September 20, 2022): 376. http://dx.doi.org/10.3390/chemosensors10100376.
Анотація:
Moisture content is extremely imoprtant to the processes of storage, packaging, and transportation of grains. In this study, a portable moisture measuring device was developed based on microwave microstrip sensors. The device is composed of three parts: a microwave circuit module, a real-time measurement module, and software to display the results. This work proposes an improvement measure by optimizing the thickness of paddy rice samples (8–13 cm) and adding the ambient temperatures and the moisture contents (13.66–27.02% w.b.) at a 3.00 GHz frequency. A random forest, decision tree, k-nearest neighbor, and support vector machine were applied to predict the moisture content in the paddy rice. Microwave characteristics, phase shift, and temperature compensation were selected as the input variables to the prediction models, which have achieved high accuracy. Among those prediction models, the random forest model yielded the best performance with highest accuracy and stability (R2 = 0.99, RMSE = 0.28, MAE = 0.26). The device showed a relatively stable performance (the maximum average absolute error was 0.55%, the minimum absolute error was 0.17%, the mean standard deviation was 0.18%, the maximum standard deviation was 0.41%, and the minimum standard deviation was 0.08%) within the moisture content range of 13–30%. The instrument has the advantages of real-time, simple structure, convenient operation, low cost, and portability. This work is expected to provide an important reference for the real-time in situ measurement of agricultural products, and to be of great significance for the development of intelligent agricultural equipment.
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Rodríguez-Azar, Paula Ivone, Jose Manuel Mejía-Muñoz, Oliverio Cruz-Mejía, Rafael Torres-Escobar, and Lucero Verónica Ruelas López. "Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI." Sensors 24, no. 1 (December 27, 2023): 149. http://dx.doi.org/10.3390/s24010149.
Анотація:
Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.
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Mizuno, Osamu, Naoki Kawashima, and Kimiaki Kawamoto. "Fault-Prone Module Prediction Approaches Using Identifiers in Source Code." International Journal of Software Innovation 3, no. 1 (January 2015): 36–49. http://dx.doi.org/10.4018/ijsi.2015010103.
Анотація:
Prediction of fault-prone modules is an important area of software engineering. The authors assumed that the occurrence of faults is related to the semantics in the source code modules. Semantics in a software module can be extracted from identifiers in the module. Identifiers such as variable names and function names in source code are thus essential information to understand code. The naming for identifiers affects on code understandability; thus, the authors expect that they affect software quality. In this study, the authors examine the relationship between the length of identifiers and existence of software faults in a software module. Furthermore, the authors analyze the relationship between occurrence of “words” in identifiers and the existence of faults. From the experiments using the data from open source software, the authors modeled the relationship between the fault occurrence and the length of identifiers, and the relationship between the fault occurrence and the word in identifiers by the random forest technique. The result of the experiment showed that the length of identifiers can predict the fault-proneness of the software modules. Also, the result showed that the word occurrence model is as good a measure as traditional CK and LOC metrics models.
40
R, Virupaksha Gouda, Anoop R, Joshi Sameerna, Arif Basha, and Sahana Gali. "Forest Fire Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 792–97. http://dx.doi.org/10.22214/ijraset.2023.51496.
Анотація:
Abstract: Forest fire vaticination refers to the process of using colorful ways and tools to read the liability and implicit inflexibility of a fire outbreak in a forested area. Forest fires are caused by a combination of factors similar as dry rainfall conditions, high temperatures, and mortal conditioning similar as conflagrations, cigarettes, and fireworks. There are several styles used in forest fire vaticination, including statistical analysis, machine literacy algorithms, and remote seeing ways. These styles help to gather and dissect data on rainfall conditions, energy humidity content, geomorphology, and other factors that contribute to the liability of a fire outbreak. Forest fire vaticination models can be used to give early warning systems to warn authorities and residers of implicit fire peril. These models also help to identify areas that are at high threat of backfires and enable authorities to take necessary preventives, similar as enforcing fire bans and evacuation orders, to help or minimize the impact of forest fires. Overall, forest fire vaticination plays a critical part in precluding and mollifying the damage caused by backfires. By furnishing accurate and timely information, it allows authorities to take visionary measures to reduce the threat of fire outbreaks and cover both mortal and natural coffers. In future predicting forest fire is expected to reduce the impact of fire. In this paper we are implementing the forest fire prediction system which predicts the probability of catching fire using meteorological parameters like position (latitude and longitude), temperature and more. we used Random Forest regression algorithm to implement this module.
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Jeong, YiNa, SuRak Son, and ByungKwan Lee. "The Lightweight Autonomous Vehicle Self-Diagnosis (LAVS) Using Machine Learning Based on Sensors and Multi-Protocol IoT Gateway." Sensors 19, no. 11 (June 3, 2019): 2534. http://dx.doi.org/10.3390/s19112534.
Анотація:
This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messages as it passes through protocol buses. The changed messages are divided into header information, sensor messages, and payloads and they are stored in an address table, a message queue, and a data collection table separately. In sequence, the sensor messages are converted to the message type of the other protocol and the payloads are transferred to an in-vehicle diagnosis module (In-VDM). The LAVS informs the diagnosis result of Cloud or road side unit(RSU) by the internet of vehicles (IoV) and of drivers by Bluetooth. To design the LAVS, the following two modules are needed. First, a multi-protocol integrated gateway module (MIGM) converts sensor messages for communication between two different protocols, transfers the extracted payloads to the In-VDM, and performs IoV to transfer the diagnosis result and payloads to the Cloud through wireless access in vehicular environment(WAVE). Second, the In-VDM uses random forest to diagnose parts of the vehicle, and delivers the results of the random forest as an input to the neural network to diagnose the total condition of the vehicle. Since the In-VDM uses them for self-diagnosis, it can diagnose a vehicle with efficiency. In addition, because the LAVS converts payloads to a WAVE message and uses IoV to transfer the WAVE messages to RSU or the Cloud, it prevents accidents in advance by informing the vehicle condition of drivers rapidly.
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Pei, Huiqing, Toshiaki Owari, Satoshi Tsuyuki, and Yunfang Zhong. "Application of a Novel Multiscale Global Graph Convolutional Neural Network to Improve the Accuracy of Forest Type Classification Using Aerial Photographs." Remote Sensing 15, no. 4 (February 11, 2023): 1001. http://dx.doi.org/10.3390/rs15041001.
Анотація:
The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and conifer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan.
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Gao, Meizhen, Li Li, and Yetong Gao. "Statistics and Analysis of Targeted Poverty Alleviation Information Integrated with Big Data Mining Algorithm." Security and Communication Networks 2022 (April 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/1496170.
Анотація:
To gain a more comprehensive and systematic understanding of the impact of government assistance to poor households on poverty reduction targets, a targeted poverty alleviation information statistics and analysis integrated with big data mining algorithm is proposed. Combined with the big data knowledge of the new era, according to the machine learning (ML) pipeline module in spark, a big data computing framework, combined with known data mining algorithms, massive sample data are used to replace random stratified sampling data for modeling and analysis, and random forest model, logistic model, and newly proposed waterfall model are constructed for poor households. Finally, through the comparative evaluation of several poor household identification models, the results show that when 100 real data test the accuracy of the three poor household models, the random forest model and logistic model are slightly reduced, which are 82% and 72%, respectively, but the waterfall model is basically unchanged, which is 83%, and the three models have little change. The new waterfall design proposed in this article has the advantage of a high percentage of sample reuse and can effectively prevent overfitting, and there is no need for massive data. It is a stable and reliable new model. The combination of targeted poverty reduction algorithms and big information technology and mining data can get the most common causes more accurate and convincing results. The right rib trunk and rib are often separated from the common cause because of the population.
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Lee, Sang J., Dahee Chung, Akiko Asano, Daisuke Sasaki, Masahiko Maeno, Yoshiki Ishida, Takuya Kobayashi, Yukinori Kuwajima, John D. Da Silva, and Shigemi Nagai. "Diagnosis of Tooth Prognosis Using Artificial Intelligence." Diagnostics 12, no. 6 (June 9, 2022): 1422. http://dx.doi.org/10.3390/diagnostics12061422.
Анотація:
The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.
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Alalayah, Khaled M., Khadija M. Alaidarous, Samah M. Alzanin, Mohammed A. Mahdi, Mohamed A. G. Hazber, Ibrahim M. Alwayle, and Khaled M. G. Noaman. "Design an Internet of Things Standard Machine Learning Based Intrusion Detection for Wireless Sensing Networks." Journal of Nanoelectronics and Optoelectronics 18, no. 2 (February 1, 2023): 217–26. http://dx.doi.org/10.1166/jno.2023.3383.
Анотація:
At the beginning stage, the wireless module Intrusion Detection System (IDS) is used to address the networking and misuse attacks on computers. Furthermore, the attempt of IDS monitors the network traffic or user activity is malicious. The detection of intrusion contains some challenging tasks such as detection accuracy, execution time, quality of data, and error. This research designed a novel Bear Smell-based Random Forest (BSbRF) for accurate detection of intrusion by monitoring the behavior and threshold value of each user. Thus the developed electronic-based sensing processor model was implemented in the python tool and the normal and attack user dataset are collected and trained in the system. Henceforth, pre-processing is employed to remove the errors present in the dataset. Moreover, feature extraction was utilized to extract the relevant features from the dataset. Then, update bear smell fitness in the random forest classification layer which monitors the behavior and detects the intrusion accurately in the output layer. Furthermore, enhance the performance of intrusion detection accuracy by bear smell fitness. Finally developed model experimental outcomes shows better performance to detect intrusion and the attained results are validated with prevailing models in terms of accuracy, precision, recall, execution time, and F1 score for wireless sensing mechanism.
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Xue, Hongxiang, Mingxia Shen, Yuwen Sun, Haonan Tian, Zihao Liu, Jinxin Chen, and Peiquan Xu. "Instance Segmentation and Ensemble Learning for Automatic Temperature Detection in Multiparous Sows." Sensors 23, no. 22 (November 12, 2023): 9128. http://dx.doi.org/10.3390/s23229128.
Анотація:
The core body temperature serves as a pivotal physiological metric indicative of sow health, with rectal thermometry prevailing as a prevalent method for estimating core body temperature within sow farms. Nonetheless, employing contact thermometers for rectal temperature measurement proves to be time-intensive, labor-demanding, and hygienically suboptimal. Addressing the issues of minimal automation and temperature measurement accuracy in sow temperature monitoring, this study introduces an automatic temperature monitoring method for sows, utilizing a segmentation network amalgamating YOLOv5s and DeepLabv3+, complemented by an adaptive genetic algorithm-random forest (AGA-RF) regression algorithm. In developing the sow vulva segmenter, YOLOv5s was synergized with DeepLabv3+, and the CBAM attention mechanism and MobileNetv2 network were incorporated to ensure precise localization and expedited segmentation of the vulva region. Within the temperature prediction module, an optimized regression algorithm derived from the random forest algorithm facilitated the construction of a temperature inversion model, predicated upon environmental parameters and vulva temperature, for the rectal temperature prediction in sows. Testing revealed that vulvar segmentation IoU was 91.50%, while the predicted MSE, MAE, and R2 for rectal temperature were 0.114 °C, 0.191 °C, and 0.845, respectively. The automatic sow temperature monitoring method proposed herein demonstrates substantial reliability and practicality, facilitating an autonomous sow temperature monitoring.
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Yao, Jiaqi, Ying Zhang, and Chen Xin. "Network-on-Chip hardware Trojan detection platform based on machine learning." Journal of Physics: Conference Series 2189, no. 1 (February 1, 2022): 012004. http://dx.doi.org/10.1088/1742-6596/2189/1/012004.
Анотація:
Abstract The extensive use of the Network-on-Chip (NoC) architecture makes it vulnerable to malicious attacks by hardware Trojans, especially Denial of Service (DoS) attack. To address this issue, this paper proposes a general NoC hardware Trojan detection platform based on machine learning. The platform establishes a security detection module including traffic feature tracking unit, feature registration unit, change point detection unit, and random forest detection unit, to accomplish the traffic-related hardware Trojan detection. The live-lock and fault routing Trojans are inserted in the proposed platform, then the simulation results verify the effectiveness of platform function and show its superiority to other existing detection schemes.
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Jiang, Tingyao, and Shuo Chen. "A Lightweight Forest Pest Image Recognition Model Based on Improved YOLOv8." Applied Sciences 14, no. 5 (February 27, 2024): 1941. http://dx.doi.org/10.3390/app14051941.
Анотація:
In response to the shortcomings of traditional pest detection methods, such as inadequate accuracy and slow detection speeds, a lightweight forestry pest image recognition model based on an improved YOLOv8 architecture is proposed. Initially, given the limited availability of real deep forest pest image data in the wild, data augmentation techniques, including random rotation, translation, and Mosaic, are employed to expand and enhance the dataset. Subsequently, the traditional Conv (convolution) layers in the neck module of YOLOv8 are replaced with lightweight GSConv, and the Slim Neck design paradigm is utilized for reconstruction to reduce computational costs while preserving model accuracy. Furthermore, the CBAM attention mechanism is introduced into the backbone network of YOLOv8 to enhance the feature extraction of crucial information, thereby improving detection accuracy. Finally, WIoU is employed as a replacement for the traditional CIOU to enhance the overall performance of the detector. The experimental results demonstrate that the improved model exhibits a significant advantage in the field of forestry pest detection, achieving precision and recall rates of 98.9% and 97.6%, respectively. This surpasses the performance of the current mainstream network models.
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Belova, Ye P. "Using Formant Characteristics of Russian Vowels and Consonants for User Authentication." Herald of the Siberian State University of Telecommunications and Information Science 18, no. 1 (December 17, 2023): 59–69. http://dx.doi.org/10.55648/1998-6920-2024-18-1-59-69.
Анотація:
The article proposes a method for user authentication based on formant characteristics. A biometric image has been developed consisting of the formant characteristics of 46 sounds of the Russian language (6 vowels, 4 diphthongs and 36 consonants). An authentication system architecture based on formant characteristics and frequency of the leading formant of vowels and consonants is proposed. A Module for extracting the formant characteristics of vowels and consonants has been developed. A block diagram of the Module for extracting the formant characteristics of vowels and consonants and block diagrams of its submodules are presented. The Random Forest algorithm, k-nearest neighbors, logistic regression, multilayer perceptron and Support Vector Machine are combined into a cluster of algorithms. A block diagram of training and testing models based on a cluster of algorithms is given. Models were trained on the basis of algorithms clusters. The results of the experiment were obtained and their analysis was carried out.
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Nastić, Filip. "Predlog modela za predviđanje koncentracije suspendovanih (PM2.5) čestica u vazduhu." Energija, ekonomija, ekologija XXV, no. 3 (2023): 39–44. http://dx.doi.org/10.46793/eee23-3.39n.
Анотація:
Increasing number of studies indicate the negative influence of Particulate Matter on human health. One of the ways to avoid their negative consequences is a timely prediction of airborne PM2.5 concentrations. Knowing hourly PM2.5 concentrations, people could organize their daily activities to reduce exposure to intensive pollution. With the goal to train an optimal predictive model, the predictive performances of three machine learning algorithms were analysed: „Random forest“, „XGBoost“, and „Light gradient boosting machine“. Using mentioned regression algorithms in combination with meteorological and chronological data, the models were trained to predict hourly airborne PM2.5 concentrations with relatively high accuracy. The data about airborne PM2.5 concentrations were collected using the laser sensor in the city of Kragujevac, Serbia. The trained models were evaluated using the coefficient of determination (R2), mean absolute error (MAE), and rootmean-square error (RMSE).