Learning-based Procedure Research Articles

A Survey of Resource Allocation in V2V Communications

In this paper, a comprehensive analysis of resource allocation in vehicle-to-vehicle (V2V) communication systems is presented, which is crucial for enhancing traffic control and improving road safety and driving enjoyment. A variety of resource allocation strategies are classified and examined in this paper, including game theory-based approaches, spectrum allocation, energy allocation, location-based allocation, and machine learning-based procedures. Open research questions and areas that need further study in the field of V2V communication are highlighted for future benefit. This survey helps readers make informed decisions about system design, implementation, and improvement by providing a comprehensive overview of the latest developments in V2V resource allocation. It also indicates areas that need further research and possible directions to enhance the security, flexibility, and scalability of V2V communication networks.

Machine Learning-Based Integration Develops a Macrophage-Related Index for Predicting Prognosis and Immunotherapy Response in Lung Adenocarcinoma

Macrophages play a critical role in tumor immune microenvironment (TIME) formation and cancer progression in lung adenocarcinoma (LUAD). However, few studies have comprehensively and systematically described the characteristics of macrophages in LUAD. This study identified macrophage-related markers with single-cell RNA sequencing data from the GSE189487 dataset. An integrative machine learning-based procedure based on 10 algorithms was developed to construct a macrophage-related index (MRI) in The Cancer Genome Atlas (TCGA), GSE30219, GSE31210, and GSE72094 datasets. Several algorithms were used to evaluate the associations of MRI with TIME and immunotherapy-related biomarkers. The role of MRI in predicting the immunotherapy response was evaluated with the GSE91061 dataset. The optimal MRI constructed by the combination of the Lasso algorithm and plsRCox was an independent risk factor in LUAD and showed a stable and powerful performance in predicting the overall survival rate of patients with LUAD. Those with low MRI scores had a higher TIME score, a higher level of immune cells, a higher immunophenoscore, and a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a better response to immunotherapy. The IC50 value of common drugs for chemotherapy and target therapy with low MRI scores was higher compared to high MRI scores. Moreover, the survival prediction nomogram, developed from MRI, had good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of LUAD. Our study constructed for the first time a consensus MRI for LUAD with 10 machine learning algorithms. The MRI could be helpful for risk stratification, prognosis, and selection of treatment approach in LUAD.

Exploration-exploitation-based trajectory tracking of mobile robots using Gaussian processes and model predictive control

AbstractMobile robots are a key component for the automation of many tasks that either require high precision or are deemed too hazardous for human personnel. One of the typical duties for mobile robots in the industrial sector is to perform trajectory tracking, which involves pursuing a specific path through both space and time. In this paper, an iterative learning-based procedure for highly accurate tracking is proposed. This contribution shows how data-based techniques, namely Gaussian process regression, can be used to tailor a motion model to a specific reoccurring reference. The procedure is capable of explorative behavior meaning that the robot automatically explores states around the prescribed trajectory, enriching the data set for learning and increasing the robustness and practical training accuracy. The trade-off between highly accurate tracking and exploration is done automatically by an optimization-based reference generator using a suitable cost function minimizing the posterior variance of the underlying Gaussian process model. While this study focuses on omnidirectional mobile robots, the scheme can be applied to a wide range of mobile robots. The effectiveness of this approach is validated in meaningful real-world experiments on a custom-built omnidirectional mobile robot where it is shown that explorative behavior can outperform purely exploitative approaches.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Machine learning-based seismic damage assessment of non-ductile RC beam-column joints using visual damage indices of surface crack patterns

After a significant earthquake, the updated status of the structural elements is usually determined based on a qualitative visual inspection. Although visual inspection provides a prompt assessment of the damaged elements, the output of this subjective method is influenced by the experience and decision of a trained inspector, which may vary from case to case. In this study, an innovative machine learning-based procedure is developed to automate damage state identification of non-ductile reinforced concrete moment frames (RCMFs) utilizing visual indices of crack patterns of the concrete surface. An extensive database including 264 surface crack patterns is constructed corresponding to 61 non-ductile beam-column joint specimens tested under the quasi-static cyclic loads at different drift ratios. Two visual damage indices, including the cumulative length of cracking and the areal density of crushing, are extracted using image processing filtering on the collected images. Supplementary information of the specimen, such as aspect ratio and concrete compressive force, is also considered as input variables. Various machine learning-based predictive models are developed for estimating the maximum drift ratio of a damaged non-ductile beam-column joint based on the available information of the specimen. Two scenarios are considered based on the accessibility of the compressive strength of the concrete as the input data, and a predictive model is obtained for each scenario. The peak experienced drift ratio predicted by the proposed methodology is finally used as an engineering demand parameter attributed to the corresponding fragility curves to determine the damage state of non-ductile RCMFs.

A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks

Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees’ competence levels. Drawing on procedures originating in shopper intent prediction on e-commerce platforms, we introduce and showcase a machine learning-based procedure that leverages early-window clickstream data for systematically investigating early predictability of behavioral outcomes on interactive tasks. We derive features related to the occurrence, frequency, sequentiality, and timing of performed actions from early-window clickstreams and use extreme gradient boosting for classification. Multiple measures are suggested to evaluate the quality and utility of early predictions. The procedure is outlined by investigating early predictability of failure on two PIAAC 2012 Problem Solving in Technology Rich Environments (PSTRE) tasks. We investigated early windows of varying size in terms of time and in terms of actions. We achieved good prediction performance at stages where examinees had, on average, at least two thirds of their solution process ahead of them, and the vast majority of examinees who failed could potentially be detected to be at risk before completing the task. In-depth analyses revealed different features to be indicative of success and failure at different stages of the solution process, thereby highlighting the potential of the applied procedure for gaining a finer-grained understanding of the trajectories of behavioral patterns on interactive tasks.

PENGEMBANGAN MODUL PEMBELAJARAN TEKS PROSEDUR BERBASIS MODEL DISCOVERY LEARNING DI SMP KELAS VII

This research was This research aims to produce, develop, and describe the feasibility of teaching material product in the form of discovery learning-based procedure text learning module in SMP class VII. This research used the Research and Development (R&D) method by Borg and Gall. The product research results in the form of modules which consists of four modules containing learning materials, learning activities, practice questions, summaries, and formative tests. Each module has learning activities by implementing the steps in the discovery learning model, namely providing stimulation, identifying problems, collecting data, processing data, verifying data, and drawing conclusions. The module feasibility test was carried out by material expert and educator of Indonesian subject. The results of the validation test by material expert obtained an average percentage of 81.25% in very feasible category. The results of validation by Indonesian language educator obtained an average percentage of 82.5% in very feasible category.

Monitoring and controlling real-time bus services: a reinforcement learning procedure for eliminating bus bunching

Bus bunching is one of the main issues encountered in bus service operations. The headway (i.e., time between two successive buses) of high-frequency services operating on congested routes can be subject to significant variability with two or more buses arriving at the same bus stop and at the same time (bus bunching). This affects the reliability of bus services and causes user frustration with the travel experience. Although public transport is encouraged as the most environment-friendly mass transit solution, bus bunching is undesirable given that it creates inefficiencies and can push passengers to use private transport.The advances in information communication technologies (ICTs) offer new opportunities for transit operators to limit this effect, ensuring a more reliable service. The paper, taking advantage of the innovations in ICTs, proposes a machine learning-based procedure addressing the issues causing bus bunching. The procedure is applied to a real test case with encouraging results. It opens the possibility that it may be incorporated in a decision support system to assist operators (drivers) in taking corrective actions throughout the day, improving bus service operations.

Fruit Image Classification Using Deep Learning

Fruit classification is found to be one of the rising fields in computer and machine vision. Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues. The performance of the classification scheme depends on the range of captured images, the volume of features, types of characters, choice of features from extracted features, and type of classifiers used. This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) application to classify the fruit images. Classification accuracy depends on the extracted and selected optimal features. Deep learning applications CNN, RNN, and LSTM were collectively involved to classify the fruits. CNN is used to extract the image features. RNN is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and RNN. Empirical study shows the supremacy of proposed over existing Support Vector Machine (SVM), Feed-forward Neural Network (FFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) competitive techniques for fruit images classification. The accuracy rate of the proposed approach is quite better than the SVM, FFNN, and ANFIS schemes. It has been concluded that the proposed technique outperforms existing schemes.

Deep learning-based procedure for structural design of cold-formed steel channel sections with edge-stiffened and un-stiffened holes under axial compression

This paper proposes a framework of deep belief network (DBN) for studying the structural performance of cold-formed steel (CFS) channel sections with edge-stiffened/un-stiffened web holes, under axial compression. A total of 50,000 data points for training the DBN are generated from elasto plastic finite element analysis, which incorporates both initial geometric imperfections and residual stresses. A comparison against 23 experimental results was conducted, and it was found that the DBN predictions were conservative by 3% for columns with un-stiffened web holes, and 8% for columns with edge-stiffened web holes. When compared with Backpropagation Neural Network (a typical shallow artificial neural network) and linear regression model based on PaddlePaddle, it was found that the proposed DBN outperformed better than both the methods, using the same big training data used in this paper. When the same comparison was made for Effective Width Method and Direct Strength Method, the results from them were conservative by 5% and 12% against the experimental results, respectively for columns with un-stiffened web holes. Hole effects on the structural performance of channel sections under axial compression were also investigated. Based on the DBN output data, design recommendations of axial capacity enhancement/reduction factors were given for columns (stub, intermediate and slender) with edge-stiffened/un-stiffened web holes. Based on DBN prediction data, a comprehensive reliability analysis was conducted, which shows the proposed equations can predict the enhanced and reduced axial capacity of CFS channel sections with edge-stiffened/un-stiffened web holes accurately.

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms.

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

PENGEMBANGAN MODUL PEMBELAJARAN TEKS PROSEDUR BERBASIS MODEL DISCOVERY LEARNING DI SMP KELAS VII

This research was This research aims to produce, develop, and describe the feasibility of teaching material product in the form of discovery learning-based procedure text learning module in SMP class VII. This research used the Research and Development (R&D) method by Borg and Gall. The product research results in the form of modules which consists of four modules containing learning materials, learning activities, practice questions, summaries, and formative tests. Each module has learning activities by implementing the steps in the discovery learning model, namely providing stimulation, identifying problems, collecting data, processing data, verifying data, and drawing conclusions. The module feasibility test was carried out by material expert and educator of Indonesian subject. The results of the validation test by material expert obtained an average percentage of 81.25% in very feasible category. The results of validation by Indonesian language educator obtained an average percentage of 82.5% in very feasible category.

Deep learning-based procedure compliance check system for nuclear power plant emergency operation

Operating procedures are strictly followed in nuclear power plant operation. However, under a highly stressful condition such as emergency operation, human error probability can increase, with operators making mistakes in complying with the complex operating procedures. This paper proposes a procedure compliance check (PCC) system to monitor operator action and detect procedural deviation. If an operator action does not match the related procedural instruction, the PCC system notifies the operator in order to help them to recognize the mistake. A procedural logic process is constructed by referring to colored Petri nets. In situations requiring complex decisions, the PCC system employs a deep learning algorithm to predict operator judgement. The system was tested with data from a compact nuclear simulator, and demonstrated its potential to detect procedural noncompliance.

Learning-Based Event-Triggered Control for Synchronization of Passive Multiagent Systems Under Attack

In this article, we study the synchronization of a group of output passive agents that communicate with each other according to an underlying communication graph. A distributed event-triggered control framework that guarantees synchronization and reduces the required communication rate is introduced. A general Byzantine attack on a multiagent system is defined and its negative effects on synchronization are characterized. The Byzantine agents are able to intelligently falsify their data and manipulate the underlying communication graph by altering their control feedback weights. Next, a decentralized decision-making and detection framework is introduced and its steady-state and transient performances are analyzed. Furthermore, a method of identifying Byzantine neighbors and a learning-based procedure for estimating the attack parameters are introduced. Finally, learning-based control frameworks to mitigate the effects of the attack are proposed.

A deep learning-based procedure for estimation of ultimate load carrying of steel trusses using advanced analysis

In the present study, Deep Learning (DL) algorithm or Deep Neural Networks (DNN), one of the most powerful techniques in Machine Learning (ML), is employed for estimation of ultimate load factor of nonlinear inelastic steel truss. Datasets consisting of training and test data are created based on advanced analysis. In datasets, input data are the member cross-sections of the truss members and output data is the ultimate load factor of the whole structure. An example of a planar 39-bar steel truss is studied to demonstrate the efficiency and accuracy of the DL method. Five optimizers such as Adadelta, Adam, Nadam, RMSprop and SGD and five activation functions such as ELU, LeakyReLU, Sigmoid, Softplus, and Tanh are considered. Based on analysis results, it is proven that DL algorithm shows very high accuracy in the regression of the ultimate load factor of the planar 39-bar nonlinear inelastic steel truss. The number of layers can be selected with a small value such as 1, 2 or 3 layers and the number of neurons in each layer can be chosen in the range [Ni, 3Ni] with Ni is the number of input variables of the model. The activation functions ELU and LeakyReLU have better convergence speed of the training process compared to Sigmoid, Softplus and Tanh. The optimizer Adam works well with all activation functions considered and produces better MSE values regarding both training and test data.
 Keywords:
 deep learning; artificial neural networks; nonlinear inelastic analysis; steel truss; machine learning.

Local estimation of the global horizontal irradiance using an all-sky camera

Localised and high-frequency measurements of the global horizontal irradiance (GHI) are a key information to assess the level of power production of distributed photovoltaic generation. The paper presents a supervised machine learning-based procedure to estimate the GHI using images obtained from an all-sky camera installed at ground level. The training phase consists, at first, in extracting a large set of features from historical images and sub-selecting them using principal component analysis (PCA). The set of selected features is used to train an artificial neural network (ANN) considering the clear-sky index as the estimated variable and output of the ANN. Then, the same procedure is augmented by considering features from satellites images (i.e., SEVIRI thermal channels). The output of the proposed estimator is compared against ground truth measurements from a pyranometer located in the proximity of the camera and benchmarked against state-of-the-art Heliosat-2 estimations. The performance assessment is presented for four different periods of the year, and for three different time resolutions (i.e., 1, 5, and 15 min). Results show that the estimator based only on images features outperforms the others, and improves the Heliosat-2 estimations by 20–45% (relative improvement in terms of normalized root mean square error).

Learning-Based Procedural Content Generation

Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game research. While some substantial progress has been made in this area, there are still several challenges ranging from content evaluation to personalized content generation. In this paper, we present a novel PCG framework based on machine learning, named learning-based procedure content generation (LBPCG), to tackle a number of challenging problems. By exploring and exploiting information gained in game development and public player test, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their gameplay experience. As the data-driven methodology is emphasized in our framework, we develop learning-based enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content.

Detecting conscious awareness from involuntary autonomic responses

Can conscious awareness be ascertained from physiological responses alone? We evaluate a novel learning-based procedure permitting detection of conscious awareness without reliance on language comprehension or behavioural responses. The method exploits a situation whereby only consciously detected violations of an expectation alter skin conductance responses (SCRs). Thirty participants listened to sequences of piano notes that, without their being told, predicted a pleasant fanfare or an aversive noise according to an abstract rule. Stimuli were presented without distraction (attended), or while distracted by a visual task to remove awareness of the rule (unattended). A test phase included occasional violations of the rule. Only participants attending the sounds reported awareness of violations and only they showed significantly greater SCR for noise occurring in violation, vs. accordance, with the rule. Our results establish theoretically significant dissociations between conscious and unconscious processing and furnish new opportunities for clinical assessment of residual consciousness in patient populations.

Intensive behavior therapy camp for patients with OCD

The treatment of an 11-yr-old boy's multiple-supported fear of dogs is described. A variety of coordinated, learning-based procedures, each of which was directed toward eliminating either the fear or altering circumstances conducive to that fear, were employed. Recovery was complete and had been maintained at a 2-yr follow-up.

Multiple impact behavior therapy in a child's dog phobia

The treatment of an 11-yr-old boy's multiple-supported fear of dogs is described. A variety of coordinated, learning-based procedures, each of which was directed toward eliminating either the fear or altering circumstances conducive to that fear, were employed. Recovery was complete and had been maintained at a 2-yr follow-up.