Статті в журналах: "Dynamic machine learning"

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Tashev, Sarvar Norboboyevich. "DYNAMIC PACKET FILTERING USING MACHINE LEARNING METHODS." American Journal of Applied Science and Technology 4, no. 10 (October 1, 2024): 69–79. http://dx.doi.org/10.37547/ajast/volume04issue10-11.

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With the emergence of the Internet, cyber-attacks and threats have become significant issues. Traditional manual network monitoring and rule-based packet filtering methods have become labor-intensive and less effective in combating attacks. Filtering packets based solely on payload and pattern matching is also inefficient. There is a need for a dynamic model capable of learning packet filtering rules. This article proposes a packet filtering model using Neural Networks. After developing the model classified with training and validation data, it can be utilized to support dynamic packet filtering. The proposed model allows filtering packets not only based on static rules but also considering IP packet attributes and rules learned by the model in advance. The model takes into account payloads and other IP packet attributes for filtering. It can automatically update firewall rules to enhance security.

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Lee, Peiyuan, Zhigang Huang, and Yong Tang. "Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning." Security and Communication Networks 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/5972698.

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With the rapid development of the global economy and stock market, stock investment has become a common investment method. People’s research on stock forecasting has never stopped. Accurately predicting the dynamic fluctuation of stocks can bring rich investment returns to investors while avoiding investment risks. Machine learning is a relatively important research field in artificial intelligence today, which is mainly used to study how to use machines to simulate human activities. In recent years, with the continuous development of the economy, machine learning under artificial intelligence has developed comprehensively in different fields, and it has been widely used in the field of the financial economy. Machine learning under artificial intelligence is currently widely used in stock market volatility dynamics and related research. This paper applied machine learning to the prediction of the dynamic relationship of Asian stock market volatility and established a model for predicting the dynamic relationship of stock market volatility under machine learning. By using statistical theory, linear support vector machines, generalizable bounds, and other algorithms, it provides the theoretical basis and feasibility analysis for the model. Through investigation and research, this paper found that compared with ordinary forecasting model methods, the stock volatility dynamic trend forecasting model based on machine learning has a relatively complete forecasting effect, and the accuracy of the machine learning forecasting model was up to 52%. The lowest was 39%, the average prediction accuracy was 46.5%, and the accuracy was improved by 16.8%. This showed that the introduction of machine learning prediction models in the dynamic prediction model of Asian stock volatility is relatively successful.

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Chen, Hao, Tianlei Wang, Jiuwen Cao, Pierre-Paul Vidal, and Yimin Yang. "Dynamic Quaternion Extreme Learning Machine." IEEE Transactions on Circuits and Systems II: Express Briefs 68, no. 8 (August 2021): 3012–16. http://dx.doi.org/10.1109/tcsii.2021.3067014.

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Zheng, Li-E., Shrishti Barethiya, Erik Nordquist, and Jianhan Chen. "Machine Learning Generation of Dynamic Protein Conformational Ensembles." Molecules 28, no. 10 (May 12, 2023): 4047. http://dx.doi.org/10.3390/molecules28104047.

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Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

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Kumar, K. Bindu, K. R. Remesh Babu, Ramesh Unnikrishnan, and U. Sangeetha. "Dynamic Behaviour Modelling of Magneto-Rheological Fluid Damper Using Machine Learning." Indian Journal Of Science And Technology 16, no. 45 (December 13, 2023): 4233–43. http://dx.doi.org/10.17485/ijst/v16i45.1669.

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Lennie, Matthew, Johannes Steenbuck, Bernd R. Noack, and Christian Oliver Paschereit. "Cartographing dynamic stall with machine learning." Wind Energy Science 5, no. 2 (June 29, 2020): 819–38. http://dx.doi.org/10.5194/wes-5-819-2020.

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Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

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Starzyk, J. A., and F. Wang. "Dynamic Probability Estimator for Machine Learning." IEEE Transactions on Neural Networks 15, no. 2 (March 2004): 298–308. http://dx.doi.org/10.1109/tnn.2004.824254.

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Dubach, Christophe, Timothy M. Jones, and Edwin V. Bonilla. "Dynamic microarchitectural adaptation using machine learning." ACM Transactions on Architecture and Code Optimization 10, no. 4 (December 2013): 1–28. http://dx.doi.org/10.1145/2541228.2541238.

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Yadav, Ram Ashish. "Dynamic Playlist Generation using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 10, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32579.

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This study investigates the application of machine learning algorithms for dynamic playlist generation in the context of music recommendation systems. The traditional method of creating playlists frequently depends on immutable standards, like artist or genre, which might not adequately represent the unique and dynamic character of personal preferences. On the other hand, our suggested system makes use of machine learning methods to examine user behavior, preferences, and contextual elements in order to create playlists that are dynamically created and customized to each user’s individual preferences. In order to train machine learning models, the study collects user interaction data, such as listening history, skip patterns, and user feedback. To extract patterns and relationships from the data, a variety of algorithms are used, including hybrid models, content-based filtering, and collaborative filtering. After that, the models are incorporated into a dynamic playlist creation system that can eventually adjust to changing user preferences.Our test findings show how well the suggested strategy works to improve user experience by offering more interesting and customized playlists. With its ability to adjust to changing user preferences and contextual cues, the dynamic playlist generation system provides a smooth and pleasurable music discovery experience. We also talk about possible enhancements, implementation difficulties, and deployment considerations in the real world.This work adds to the ongoing efforts to improve music recommendation systems by demonstrating how machine learning can be used to develop more responsive and intelligent playlist generation systems. The results highlight how crucial customized experiences are in the constantly changing world of digital music consumption. Index Terms—hybrid mode, content-based filtering, intelligent playlist, music

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WANG Peng and MAIMAITINIYAZI Maimaitiabudula. "Quantum Dynamics of Machine Learning." Acta Physica Sinica 74, no. 6 (2025): 0. https://doi.org/10.7498/aps.74.20240999.

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To address the current lack of rigorous theoretical models in the machine learning process, this paper adopts the quantum dynamic method to model the iterative motion process of machine learning based on the principles of first-principles thinking. This approach treats the iterative evolution of algorithms as a physical motion process, defines a generalized objective function in the parameter space of machine learning algorithms, and views the iterative process of machine learning as the process of seeking the optimal value for this generalized objective function. In physical terms, this process corresponds to the system reaching its ground energy state. Since the dynamic equation of a quantum system is the Schrödinger equation, by treating the generalized objective function as the potential energy term in the Schrödinger equation, we can obtain the quantum dynamic equation that describes the iterative process of machine learning. The process of machine learning is thus the process of seeking the ground energy state of the quantum system constrained by a generalized objective function. The quantum dynamic equation for machine learning transforms the iterative process into a time-dependent partial differential equation for precise mathematical representation, allowing for the study of the iterative process of machine learning using physical and mathematical theories. This provides theoretical support for implementing the iterative process of machine learning using quantum computers. To further apply the quantum dynamic equation to explain the iterative process of machine learning on classical computers, the Wick rotation is used to convert the quantum dynamic equation into a thermodynamic equation, demonstrating the convergence of the time evolution process in machine learning. As time approaches infinity, the system will converge to the ground energy state. Since an analytical expression cannot be given for the generalized objective function in the parameter space, Taylor expansion is used to approximate the generalized objective function. Under the zero-order Taylor approximation of the generalized objective function, the quantum dynamic equation and thermodynamic equation for machine learning degrade into the free-particle equation and diffusion equation, respectively. This result indicates that the most basic dynamic processes during the iteration of machine learning on quantum and classical computers are wave packet dispersion and diffusion, respectively. This result explains, from a dynamic perspective, the basic principles of diffusion models that have been successfully applied in the field of generative neural networks in recent years. Diffusion models indirectly realize the thermal diffusion process in the parameter space by adding and removing Gaussian noise to images, thereby optimizing the generalized objective function in the parameter space. The diffusion process is the dynamic process under the zero-order approximation of the generalized objective function. Meanwhile, using the thermodynamic equation of machine learning, we also derived the Softmax and Sigmoid functions commonly used in artificial intelligence. These results show that the quantum dynamic method is an effective theoretical approach for studying the iterative process of machine learning, providing rigorous mathematical and physical models for studying the iterative process of machine learning on both quantum and classical computers.

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Kamoun-Abid, Ferdaous, Hounaida Frikha, Amel Meddeb-Makhoulf, and Faouzi Zarai. "Automating cloud virtual machines allocation via machine learning." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (July 1, 2024): 191. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp191-202.

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In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.

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Kabaldin, Yu G., D. A. Shatagin, M. S. Anosov, and A. M. Kuzmishina. "Development of digital twin of CNC unit based on machine learning methods." Vestnik of Don State Technical University 19, no. 1 (April 1, 2019): 45–55. http://dx.doi.org/10.23947/1992-5980-2019-19-1-45-55.

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Introduction. It is shown that the digital twin (electronic passport) of a CNC machine is developed as a cyber-physical system. The work objective is to create neural network models to determine the operation of a CNC machine, its performance and dynamic stability under cutting.Materials and Methods. The development of mathematical models of machining processes using a sensor system and the Industrial Internet of Things is considered. Machine learning methods valid for the implementation of the above tasks are evaluated. A neural network model of dynamic stability of the cutting process is proposed, which enables to optimize the machining process at the stage of work preparation. On the basis of nonlinear dynamics approaches, the attractors of the dynamic cutting system are reconstructed, and their fractal dimensions are determined. Optimal characteristics of the equipment are selected by input parameters and debugging of the planned process based on digital twins.Research Results. Using machine learning methods allowed us to create and explore neural network models of technological systems for cutting, and the software for their implementation. The possibility of applying decision trees for the problem of diagnosing and classifying malfunctions of CNC machines is shown.Discussion and Conclusions. In real production, the technology of digital twins enables to optimize processing conditions considering the technical and dynamic state of CNC machines. This provides a highly accurate assessment of the production capacity of the enterprise under the development of the production program. In addition, equipment failures can be identified in real time on the basis of the intelligent analysis of the distributed sensor system data.

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Das, Pritom, Tamanna Pervin, Biswanath Bhattacharjee, Md Razaul Karim, Nasrin Sultana, Md Sayham Khan, Md Afjal Hosien, and FNU Kamruzzaman. "OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS." American Journal of Engineering and Technology 06, no. 12 (December 25, 2024): 163–77. https://doi.org/10.37547/tajet/volume06issue12-15.

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This study investigates the application of machine learning models for real-time dynamic pricing strategies in the retail and e-commerce sectors. We employed three prominent supervised machine learning models—Linear Regression, Random Forest, and Gradient Boosting Machines (GBM)—to predict optimal prices using a dataset sourced from Kaggle. The models were trained and evaluated with a 70:30 train-test split, while hyperparameter tuning was performed using grid search and cross-validation. The results indicate that the Gradient Boosting Machines (GBM) model consistently outperformed the other models, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and demonstrating a higher R-squared (R²) value. The comparative analysis highlights GBM's ability to capture complex interactions in dynamic pricing data, making it a robust choice for accurate price forecasting. The Random Forest model also delivered satisfactory results, balancing accuracy and computational efficiency, whereas the Linear Regression model showed higher prediction errors due to its limitations in modeling non-linear relationships. Real-time testing in a simulated environment confirmed the models' adaptability and responsiveness in a dynamic marketplace. These findings provide actionable insights for retail and e-commerce businesses, emphasizing the importance of model selection, hyperparameter optimization, and system integration to implement efficient dynamic pricing strategies. Future work should explore more extensive datasets and real-world applications to address seasonal variations, regional preferences, and consumer behavior, ensuring a more comprehensive and practical deployment of machine learning-driven dynamic pricing models.

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Arif, Md, Md Parvez Ahmed, Abdullah Al Mamun, Md Kafil Uddin, Fuad Mahmud, Tauhedur Rahman, Md Jamil Ahmmed, et al. "DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY." International Interdisciplinary Business Economics Advancement Journal 05, no. 10 (October 28, 2024): 13–27. http://dx.doi.org/10.55640/business/volume05issue10-03.

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The rapid advancement of technology has transformed the financial services sector, leading to the rise of fintech companies that leverage cutting-edge tools such as artificial intelligence (AI) and machine learning (ML) to offer innovative solutions. One area where fintech is particularly impactful is dynamic pricing, which involves adjusting prices in real-time based on market conditions, user behavior, and external factors. The ability to optimize pricing in response to fluctuating conditions is critical for maximizing profitability, improving customer satisfaction, and maintaining competitiveness. In this context, machine learning algorithms provide a powerful framework for making data-driven pricing decisions by learning from historical data and predicting future trends.

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C, Siva, Maheshwari K.G, Nalinipriya G, and Priscilla Mary J. "Dynamic Analytics and Forecasting Model for Covid-19 Using Machine Learning Algorithms." Webology 18, no. 05 (October 29, 2021): 1212–25. http://dx.doi.org/10.14704/web/v18si05/web18302.

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In our day to day life, the availability of correctly labelled data as well as handling of categorical data are mostly acknowledged as two main challenges in dynamic analysis. Therefore, clustering techniques are applied on unlabelled data to group them in accordance with the homogeneity. There are many prediction methods that are being popularly used in handling forecasting problems in real time environment. The outbreak of coronavirus disease (COVID19)-2019 creates the need for a medical emergency of worldwide concern with a rapidly high danger of open out and strike the entire world. Recently, the ML prediction models were used in many real time applications which necessitate the identification and categorization for real time environment. In medical field Prediction models are vital role to obtain observations of spread and significances of infectious diseases. Machine learning related forecasting mechanisms have showed their importance to develop the decision making on the upcoming course of actions. The K-means algorithm and hierarchy were applied directly on the renewed dataset using R programming language to create the covid patient cluster. Confirmed Covid patients count are passed to Prophet package, then the prophet model has been created. This forecasts model predicts the future covid count, which is essential for the clinical and healthcare leaders to make the appropriate measures in advance. The results of the experiments indicate that the quality of Hierarchical clustering outperforms than the K-Means clustering algorithm in the structured structured dataset. Thus, the prediction model also used to support model predictions help for the officials to take timely actions and make decisions to contain the COVID-19 dilemma. This work concludes Hierarchical clustering algorithm is the best model for clustering the covid data set obtained from world health organization (WHO).

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Bangoria, Bhoomi Mansukhlal, Sweta S. Panchal, Sandipkumar R. Panchal, Janvi M. Maheta, and Sweety R. Dhabaliya. "Multidimensional Dynamic Destination Recommender Search System Employing Clustering: A Machine Learning Approach." Indian Journal Of Science And Technology 17, no. 40 (October 31, 2024): 4187–97. http://dx.doi.org/10.17485/ijst/v17i40.2266.

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Objectives: Recommender Systems (RS) powered by algorithms of machine learning is a popular tool for planning and implementing custom-made travel proficiencies. The persistence of this study is to recommend destinations according to a selection of various dimensions by the user. Methods: This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm. For this study dataset was taken from Kaggle. Data considers different cities of India with different dimensions like city, name, type, and significance. According to the city first find latitude and longitude for precise clustering. Future work will incorporate optimization techniques to improve cluster formation recommendation accuracy. Findings: Clustering (unsupervised learning) is a separation technique that involves assigning locations to corresponding subsets of related clusters. The weighted K-means clustering algorithm is used with the elbow method which is used for discovering the optimum number of clusters. In weighted K-means algorithm for clustering uses scaling factor wi which transforms the impression of individual features to the whole distance calculation. It signifies the meaning of the ith feature in the perspective of the grouping task. Offering a scaling factor permits additional tractability in modifying the outcome of specific features on the distance calculation. It enables customization of the distance metric constructed on the specific requirements and characteristics of the records and clustering task. In this study, user can select multiple dimensions of their choice and get recommendations according to their choice. The proposed weighted K-means algorithm shows a significant improvement in accuracy which considers the proportion of correct recommendations out of all recommendations. A comparison with traditional K-means was conducted, where the weighted algorithm achieved a 17% higher accuracy due to its ability to give importance to specific features. The future version of the proposed system will incorporate optimization techniques for enhanced performance. Novelty: The suggested solution in this paper demonstrates that the user can enter the city of their choice. The recommended method indicates the city and nearby predilections once the user has selected their parameters, such as consuming formations or name or type. The ratio of relevant destinations that have been successfully recommended is 18% more compared to the K-means clustering algorithm. Keywords: Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted K-means clustering Algorithm

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Mantri, Arjun. "Predictive Analytics for Dynamic Pricing in Travel Bookings Using Machine Learning Pipelines." International Journal of Science and Research (IJSR) 8, no. 9 (September 5, 2019): 1864–67. http://dx.doi.org/10.21275/sr24724145934.

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Wei-Min Liu, Wei-Min Liu, Jian-Fang Xue Wei-Min Liu, Qing-Chuan Liu Jian-Fang Xue, Xiao-Yang Zhang Qing-Chuan Liu, and Rui Fan Xiao-Yang Zhang. "Dynamic Control Method of Improving Extreme Learning Machine Algorithm in Wood Spinning Process." 電腦學刊 35, no. 2 (April 2024): 105–19. http://dx.doi.org/10.53106/199115992024043502007.

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<p>The precise control of the feed system of the rotary cutting machine is the key to achieving uniform thickness rotary cutting of wood on the log-core veneer lathe. The application of Improving the Extreme Learning Machine Model in the feed system can effectively solve the problem of unstable feed rate matching in traditional control methods. This study analyzed the working mechanism of log-core veneer lathe and established a kinematic model of its feed system. Using the thickness and thickness variation of the wooden board as the control results of Improving the Extreme Learning Machine Model, in order to solve the optimal weight and threshold of Improving the Extreme Learning Machine Model, this paper uses an improved particle swarm optimization algorithm to solve. Finally, in the Matlab software environment, log-core veneer lathe motion model and control model are written, and simulation experiments are conducted to verify. The results show that Improving the Extreme Learning Machine Model effectively improves the control performance of the system, with stable feed rate changes, good real-time performance, fast convergence, high control accuracy, and strong adaptability.</p> <p> </p>

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Giovannozzi, Massimo, Ewen Maclean, Carlo Emilio Montanari, Gianluca Valentino, and Frederik F. Van der Veken. "Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade." Information 12, no. 2 (January 25, 2021): 53. http://dx.doi.org/10.3390/info12020053.

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A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.

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Gong, Yulu, Jiaxin Huang, Bo Liu, Jingyu Xu, Binbin Wu, and Yifan Zhang. "Dynamic resource allocation for virtual machine migration optimization using machine learning." Applied and Computational Engineering 57, no. 1 (April 17, 2024): 1–8. http://dx.doi.org/10.54254/2755-2721/57/20241348.

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This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.

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Xu, Chuan, Giovanni Neglia, and Nicola Sebastianelli. "Dynamic backup workers for parallel machine learning." Computer Networks 188 (April 2021): 107846. http://dx.doi.org/10.1016/j.comnet.2021.107846.

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Ruia, Kushal Kumar, Utkarsh Daga, Aditya Tripathi, Maruf Nissar Rahman, and Saurabh Bilgaiyan. "Airline dynamic price prediction using machine learning." International Journal of Productivity and Quality Management 36, no. 2 (2022): 187. http://dx.doi.org/10.1504/ijpqm.2022.124712.

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Kim, Changhae Andrew, Nathan D. Ricke, and Troy Van Voorhis. "Machine learning dynamic correlation in chemical kinetics." Journal of Chemical Physics 155, no. 14 (October 14, 2021): 144107. http://dx.doi.org/10.1063/5.0065874.

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BILGAIYAN, SAURABH, Maruf Nissar Rahman, Aditya Tripathi, Utkarsh Daga, and Kushal Kumar Ruia. "AIRLINE DYNAMIC PRICE PREDICTION USING MACHINE LEARNING." International Journal of Productivity and Quality Management 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijpqm.2020.10037903.

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Perez, R. A., J. T. Lilkendey, and S. W. Koh. "Machine learning for a dynamic manufacturing environment." ACM SIGICE Bulletin 19, no. 3 (February 1994): 5–9. http://dx.doi.org/10.1145/182063.182067.

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Kalra, Ms Preeti. "Dynamic Ride Pricing Model Using Machine Learning." International Journal of Scientific Research and Engineering Trends 10, no. 6 (November 15, 2024): 2508–13. https://doi.org/10.61137/ijsret.vol.10.issue6.358.

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Nowak, Marcin, and Marta Pawłowska-Nowak. "Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning." Applied Sciences 14, no. 24 (December 13, 2024): 11668. https://doi.org/10.3390/app142411668.

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One of the key areas of contemporary marketing is the formulation of a pricing strategy, which is one of the four pillars of the traditional marketing mix. One way to implement this strategy is through dynamic pricing. It is currently gaining popularity in many industries for two reasons. Firstly, it is possible, easy, and cheap to collect information about transactions and customers. Secondly, machine learning mechanisms, for which these data are essential, are becoming widely available. The aim of this article is to propose a dynamic pricing method for the e-commerce industry. To achieve this goal, machine learning methods such as the Naive Bayes classifier, support vector machines (linear and nonlinear), decision trees, and the k-nearest neighbor algorithm were used. The empirical results indicate that the linear support vector machine achieved the highest accuracy (86.92%), demonstrating the model’s effectiveness in classifying pricing decisions. This article aligns with two leading research trends in dynamic pricing: personalized dynamic pricing (the target model considers customer-related criteria) and the development of systems to assist managers in optimizing pricing strategies to increase revenues (using machine learning methods). This article presents a literature review on dynamic pricing and then discusses the machine learning methods applied. In the final part of this article, verification of the developed dynamic pricing method using real-world conditions is presented.

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Werth, Bernhard, Johannes Karder, Michael Heckmann, Stefan Wagner, and Michael Affenzeller. "Applying Learning and Self-Adaptation to Dynamic Scheduling." Applied Sciences 14, no. 1 (December 20, 2023): 49. http://dx.doi.org/10.3390/app14010049.

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Real-world production scheduling scenarios are often not discrete, separable, iterative tasks but rather dynamic processes where both external (e.g., new orders, delivery shortages) and internal (e.g., machine breakdown, timing uncertainties, human interaction) influencing factors gradually or abruptly impact the production system. Solutions to these problems are often very specific to the application case or rely on simple problem formulations with known and stable parameters. This work presents a dynamic scheduling scenario for a production setup where little information about the system is known a priori. Instead of fully specifying all relevant problem data, the timing and batching behavior of machines are learned by a machine learning ensemble during operation. We demonstrate how a meta-heuristic optimization algorithm can utilize these models to tackle this dynamic optimization problem, compare the dynamic performance of a set of established construction heuristics and meta-heuristics and showcase how models and optimizers interact. The results obtained through an empirical study indicate that the interaction between optimization algorithm and machine learning models, as well as the real-time performance of the overall optimization system, can impact the performance of the production system. Especially in high-load situations, the dynamic algorithms that utilize solutions from previous problem epochs outperform the restarting construction heuristics by up to ~24%.

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29

M, Shah,. "Demystifying Machine Learning." Saudi Journal of Engineering and Technology 9, no. 07 (July 9, 2024): 299–303. http://dx.doi.org/10.36348/sjet.2024.v09i07.004.

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This paper delves into the rapidly evolving domain of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML), a dynamic and influential subset of AI. It explores how ML empowers computers to learn from data, identify patterns, and make decisions with minimal human intervention. The manuscript examines the broad utility of ML across various real-world scenarios, emphasizing its critical role in enabling organizations to evolve and maintain a competitive edge in the fast-paced technological landscape. It discusses the necessity for organizations to adopt new ways of working and embrace the opportunities presented by AI to remain viable in the global, online marketplace. The paper reviews the evolution of ML, evaluates its advantages and disadvantages, and contemplates the future directions ML could lead organizations willing to integrate this powerful technology. The overarching theme is the transformative potential of ML in reshaping organizational strategies and operations for a more interconnected and intelligent future.

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30

Moritsugu, Kei. "Multiscale Enhanced Sampling Using Machine Learning." Life 11, no. 10 (October 12, 2021): 1076. http://dx.doi.org/10.3390/life11101076.

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Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace generated by a machine learning approach, the variational autoencoder (VAE). The molecular dynamic (MD) trajectories of the ribose-binding protein (RBP) in both the closed and open forms were used as the input by extracting the inter-residue distances as the structural features in order to train the VAE model, allowing the encoded latent layer to characterize the difference in the structural dynamics of the closed and open forms. The interpolated data characterizing the RBP structural change in between the closed and open forms were thus efficiently generated in the low-dimensional latent space of the VAE, which was then decoded into the time-series data of the inter-residue distances and was useful for driving the structural sampling at an atomistic resolution via the MSES scheme. The free energy surfaces on the latent space demonstrated the refinement of the generated data that had a single basin into the simulated data containing two closed and open basins, thus illustrating the usefulness of the MD simulation together with the molecular mechanics force field in recovering the correct structural ensemble.

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31

N, Visva, and Prabhakaran Mathialagan. "Machine Learning-Enhanced Dynamic Inventory Control: A Data-Driven Approach to Retail Optimization." International Journal of Research Publication and Reviews 5, no. 5 (May 26, 2024): 13303–9. http://dx.doi.org/10.55248/gengpi.5.0524.1473.

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Tiwari, Sanjana. "Suspicious URL Detection using Dynamic Learning Model with Machine Learning." International Journal for Research in Applied Science and Engineering Technology 7, no. 7 (July 31, 2019): 1315–18. http://dx.doi.org/10.22214/ijraset.2019.7214.

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33

Demirovi?, Emir, Peter J. Stuckey, Tias Guns, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, and Jeffrey Chan. "Dynamic Programming for Predict+Optimise." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1444–51. http://dx.doi.org/10.1609/aaai.v34i02.5502.

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We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. We provide a novel learning technique for predict+optimise to directly reason about the underlying combinatorial optimisation problem, offering a meaningful integration of machine learning and optimisation. This is done by representing the combinatorial problem as a piecewise linear function parameterised by the coefficients of the learning model and then iteratively performing coordinate descent on the learning coefficients. Our approach is applicable to linear learning functions and any optimisation problem solvable by dynamic programming. We illustrate the effectiveness of our approach on benchmarks from the literature.

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Mishra, Chandrahas, and D. L. Gupta. "Deep Machine Learning and Neural Networks: An Overview." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (June 1, 2017): 66. http://dx.doi.org/10.11591/ijai.v6.i2.pp66-73.

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Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.

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Kandekar, Nikhil. "Ecommerce Assisted by Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 351–53. http://dx.doi.org/10.22214/ijraset.2022.40643.

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Abstract: E-commerce applications are growing and becoming complex day by day. Every new feature is taking the time of the user and reducing the speed or the process of purchasing anything. Consumers demand comfortability, usability, authenticity on E-commerce websites, and hence to provide with these needs Machine learning should be introduced in the E-commerce website. In this paper, we will learn various methods to help boost E-commerce using Machine Learning. Product Recommendation will severely help reduce the time of users on purchasing a product as it recommends products based on history. Cloth size estimation can be used for taking measurements for helping users purchase clothes with accurate measurements. Dynamic Pricing can be used to generate discounts and prices based on user history for maintaining users. Fake Review Prediction can be used to filter the reviews for better business. A chatbot can be used to help users during any stage of purchase or after the purchase as support. All of this can contribute towards the personalized experience of consumers and will be discussed in this paper. Keywords: Ecommerce, Machine Learning, Dynamic pricing, Chatbot, Product Recommendation, Cloth Size Estimation, Fake Review Prediction, Ecommerce

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Cavalaglio Camargo Molano, Jacopo, Riccardo Rubini, and Marco Cocconcelli. "Experimental Evidence of the Speed Variation Effect on SVM Accuracy for Diagnostics of Ball Bearings." Machines 6, no. 4 (October 18, 2018): 48. http://dx.doi.org/10.3390/machines6040048.

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In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific community in machine diagnostics has moved to the condition monitoring of machinery in non-stationary conditions (i.e., machines working with variable speed profiles or variable loads). Non-stationarity implies more complex signal processing techniques, and a natural consequence is the use of machine learning techniques for data analysis in non-stationary applications. Several papers have studied the machine learning system, but they focus on specific machine learning systems and the selection of the best input array. No paper has considered the dynamics of the system, that is, the influence of how much the speed profile changes during the training and testing steps of a machine learning technique. The aim of this paper is to show the importance of considering the dynamic conditions, taking the condition monitoring of ball bearings in variable speed applications as an example. A commercial support vector machine tool is used, tuning it in constant speed applications and testing it in variable speed conditions. The results show critical issues of machine learning techniques in non-stationary conditions.

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37

Liu, Zhong Hui, Zhen Shu Wang, and Mei Hua Su. "Dynamic Load Modeling Based on Extreme Learning Machine." Applied Mechanics and Materials 195-196 (August 2012): 1043–48. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1043.

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The dynamic load characteristics have significant impact on the power flow, transient stability computation, voltage stability calculation of the power system, and so on. Noticing that traditional mechanism loads model has difficulty in precisely describing the dynamic characteristics of synthetic load, this paper presents a non-mechanism dynamic load model based on Extreme Learning Machine (ELM). The Power Fault Recorder and Measurement System (PFRMS) is used to obtain data for load modeling. Take voltage and real/reactive power with different time delay as inputs, and take real/reactive power as output, train the ELM using the samples formed by fault data, the real power model and reactive power model are established respectively. The number of hidden layer nodes which has impact on the ELM model is also discussed. Dynamic simulation experiment is conducted at power system dynamic simulation laboratory. The simulation result shows that the ELM load model is simple and flexible, its parameters are easy to be identified. The ELM load model can describe the dynamic load characteristics accurately.

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Mahmoud, Amr, and Mohamed Zohdy. "Dynamic Lyapunov Machine Learning Control of Nonlinear Magnetic Levitation System." Energies 15, no. 5 (March 3, 2022): 1866. http://dx.doi.org/10.3390/en15051866.

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This paper presents a novel dynamic deep learning architecture integrated with Lyapunov control to address the timing latency and constraints of deep learning. The dynamic component permits the network depth to increase or decrease depending on the system complexity/nonlinearity evaluated through the parameterized complexity method. A correlation study between the parameter tuning effect on the error is also made thus causing a reduction in the deep learning time requirement and computational cost during the network training and retraining process. The control Lyapunov function is utilized as an input cost function to the DNN in order to determine the system stability. A relearning process is triggered to account for the introduction of disturbances or unknown model dynamics, therefore, eliminating the need for an observer-based approach. The introduction of the relearning process also allows the algorithm to be applicable to a wider array of cyber–physical systems (CPS). The intelligent controller autonomy is evaluated under different circumstances such as high frequency nonlinear reference, reference changes, or disturbance introduction. The dynamic deep learning algorithm is shown to be successful in adapting to such changes and reaching a safe solution to stabilize the system autonomously.

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39

Bushmakova, Maria А., and Elena V. Kustova. "Modeling vibrational relaxation rate using machine learning methods." Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 9, no. 1 (2022): 113–25. http://dx.doi.org/10.21638/spbu01.2022.111.

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The aim of the present study is to develop an efficient algorithm for simulating nonequilibrium gas-dynamic problems using the detailed state-to-state approach for vibrationalchemical kinetics. Optimization of the vibrational relaxation rate computation using machine learning algorithms is discussed. Since traditional calculation methods require a large number of operations, time and memory, it is proposed to predict the relaxation rates instead of explicit calculations. K-nearest neighbour and histogram based gradient boosting algorithms are applied. The algorithms were trained on datasets obtained using two classical models for the rate coefficients: the forced harmonic oscillator model and that of Schwartz-Slawsky-Herzfeld. Trained algorithms were used to solve the problem of spatially homogeneous relaxation of the O2-O mixture. Comparison of accuracy and calculation time by different methods is carried out. It is shown that the proposed algorithms allow one to predict the relaxation rates with good accuracy and to solve approximately the set of governing equations for the fluid-dynamic variables. Thus, we can recommend the use of machine learning methods in nonequilibrium gas dynamics coupled with detailed vibrational-chemical kinetics. The ways of further optimization of the considered methods are discussed.

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40

Shatnawi, Ahmed S., Aya Jaradat, Tuqa Bani Yaseen, Eyad Taqieddin, Mahmoud Al-Ayyoub, and Dheya Mustafa. "An Android Malware Detection Leveraging Machine Learning." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/1830201.

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Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic analysis, and others are based on static analysis, while some are completely dependent on both. In this paper, we studied static, dynamic, and hybrid analyses to identify malicious applications. We leverage machine learning classifiers to detect malware activities as we explain the effectiveness of these classifiers in the classification process. Our results prove the efficiency of permissions and the action repetition feature set and their influential roles in detecting malware in Android applications. Our results show empirically very close accuracy results when using static, dynamic, and hybrid analyses. Thus, we use static analyses due to their lower cost compared to dynamic and hybrid analyses. In other words, we found the best results in terms of accuracy and cost (the trade-off) make us select static analysis over other techniques.

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41

Enache, Maria Cristina. "Machine Learning for Dynamic Pricing in e-Commerce." Annals of Dunarea de Jos University of Galati. Fascicle I. Economics and Applied Informatics 27, no. 3 (December 24, 2021): 114–19. http://dx.doi.org/10.35219/eai15840409230.

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42

PRIORE, PAOLO, DAVID DE LA FUENTE, ALBERTO GOMEZ, and JAVIER PUENTE. "DYNAMIC SCHEDULING OF MANUFACTURING SYSTEMS WITH MACHINE LEARNING." International Journal of Foundations of Computer Science 12, no. 06 (December 2001): 751–62. http://dx.doi.org/10.1142/s0129054101000849.

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A common way of scheduling jobs dynamically in a manufacturing system is by means of dispatching rules. The drawback of this method is that the performance of these rules depends on the state the system is in at each moment, and no one rule exists that overrules the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. The methodology proposed in this paper may be divided into five basic steps. Firstly, definition of the appropriate control attributes for identifying the relevant manufacturing patterns. In second place, creation of a set of training examples using different values of the control attributes. Subsequently, acquiring of heuristic rules by means of a machine learning program. Then, using of the previously calculated heuristic rules to select the most appropriate dispatching rules, and finally testing of the performance of the approach. The approach that we propose is applied to a flow shop system and to a classic job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules.

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43

Wang, Liuyang, Huaping Liu, and Fuchun Sun. "Dynamic texture video classification using extreme learning machine." Neurocomputing 174 (January 2016): 278–85. http://dx.doi.org/10.1016/j.neucom.2015.03.114.

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44

Scheidegger, Simon, and Ilias Bilionis. "Machine learning for high-dimensional dynamic stochastic economies." Journal of Computational Science 33 (April 2019): 68–82. http://dx.doi.org/10.1016/j.jocs.2019.03.004.

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45

Chandra, Subhash. "Verification of dynamic signature using machine learning approach." Neural Computing and Applications 32, no. 15 (January 25, 2020): 11875–95. http://dx.doi.org/10.1007/s00521-019-04669-w.

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46

Xu, Shuliang, and Junhong Wang. "Dynamic extreme learning machine for data stream classification." Neurocomputing 238 (May 2017): 433–49. http://dx.doi.org/10.1016/j.neucom.2016.12.078.

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47

Zhang, Rui, Yuan Lan, Guang-Bin Huang, Zong-Ben Xu, and Yeng Chai Soh. "Dynamic Extreme Learning Machine and Its Approximation Capability." IEEE Transactions on Cybernetics 43, no. 6 (December 2013): 2054–65. http://dx.doi.org/10.1109/tcyb.2013.2239987.

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48

Martinez, Jose F., and Engin Ipek. "Dynamic Multicore Resource Management: A Machine Learning Approach." IEEE Micro 29, no. 5 (September 2009): 8–17. http://dx.doi.org/10.1109/mm.2009.77.

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49

Gondia, Ahmed, Ahmed Moussa, Mohamed Ezzeldin, and Wael El-Dakhakhni. "Machine learning-based construction site dynamic risk models." Technological Forecasting and Social Change 189 (April 2023): 122347. http://dx.doi.org/10.1016/j.techfore.2023.122347.

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50

Kaushik, Dr Priyanka. "Dynamic Data Scaling Techniques for Streaming Machine Learning." International Journal for Global Academic & Scientific Research 3, no. 1 (April 4, 2024): 1–12. http://dx.doi.org/10.55938/ijgasr.v3i1.68.

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This research delves into innovative dynamic data scaling techniques designed for streaming machine learning environments. In the realm of real-time data streams, conventional static scaling methods may encounter challenges in adapting to evolving data distributions. To overcome this hurdle, our study explores dynamic scaling approaches capable of adjusting and optimizing scaling parameters dynamically as the characteristics of incoming data shift over time. The objective is to augment the performance and adaptability of machine learning models in streaming scenarios by ensuring that the scaling process remains responsive to changing patterns in the data. Through empirical evaluations and comparative analyses, the study aims to showcase the efficacy of the proposed dynamic data scaling techniques in enhancing predictive accuracy and sustaining model relevance in dynamic and fast-paced streaming environments. This research contributes to the advancement of scalable and adaptive machine learning methodologies, particularly in applications where timely and accurate insights from streaming data are crucial.

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