Search results for: predictions

Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: Débora V. Busman, Venerando E. Amaro, Mattheus da C. Prudêncio

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Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.

Keywords: coastal erosion, prognostic model, DSAS, environmental safety

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Authors: Jamal Choudhry, Roland Larsson, Andreas Almqvist

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A multi-scale flash temperature model has been developed and validated against existing work. The core strength of the proposed model is that it can be adapted to predict flash contact temperatures occurring in various types of sliding systems. In this paper, it is used to investigate how different surface roughness parameters affect the flash temperatures. The results show that for decreasing Hurst exponents as well as increasing values of the high-frequency cut-off, the maximum flash temperature increases. It was also shown that the effect of surface roughness does not influence the average interface temperature. The model predictions were validated against data from an experiment conducted in a pin-on-disc machine. This also showed the importance of including a wear model when simulating flash temperature development in a sliding system.

Keywords: multiscale, pin-on-disc, finite element method, flash temperature, surface roughness

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Authors: Chiung-Ju Huang

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This study utilizes a frequency domain approach over the period of 1996 to 2013 to examine the causal relationship between governance and economic growth in ten Asian countries, which have different levels of democracy; classified as “Free”, “Partly Free”, and “Not Free” countries. The empirical results show that there is no Granger causality running from governance to economic growth in “Not Free” countries and “Partly Free” countries with the exception of Singapore. As for “Free” countries such as South Korea and Taiwan, there is a one-way causality running from governance to economic growth. The findings of this study indicate that policy makers in South Korea, Taiwan, and Singapore could use governance index to improve their predictions of the future economic growth.

Keywords: economic growth, frequency domain, governance, granger causality

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

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Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: M. Fathi, Z. Farhaninejad, M. Shahedi, M. Sadeghi

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Drying is one of the oldest preservation methods for food and agriculture products. Appropriate control of operation can be obtained by modeling. Limitation of continues models for complex boundary condition and non-regular geometries leading to appearance of discrete novel methods such as cellular automata, which provides a platform for obtaining fast predictions by rule-based mathematics. In this research a one D dimensional CA was used for simulating thin layer drying of banana. Banana slices were dried with a convectional air dryer and experimental data were recorded for validating of final model. The model was programmed by MATLAB, run for 70000 iterations and von-Neumann neighborhood. The validation results showed a good accordance between experimental and predicted data (R=0.99). Cellular automata are capable to reproduce the expected pattern of drying and have a powerful potential for solving physical problems with reasonable accuracy and low calculating resources.

Keywords: banana, cellular automata, drying, modeling

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Authors: Jaime E. Fernandez, Pablo J. Valverde

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This paper presents a methodological approach that aims to contrast/validate theoretical results from a corruption network game through probabilistic analysis of simulated microdata using Bayesian Networks (BNs). The research develops a public corruption model in a game theory framework. Theoretical results suggest a series of 'optimal settings' of model's exogenous parameters that boost the emergence of corruption. The paper contrasts these outcomes with probabilistic inference results based on BNs adjusted over simulated microdata. Principal findings indicate that probabilistic reasoning based on BNs significantly improves parameter specification and causal analysis in a public corruption game.

Keywords: Bayesian networks, probabilistic reasoning, public corruption, theoretical games

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Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu

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The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Keywords: artificial neural network (ANN), finite element method (FEM), perforated sections, thin-walled Steel, ultimate load

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Authors: Quinn Au, Amedeo Carmine, Tauheed Khan Mohd

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The Internet of Things (IoT) is emerging as a new development in information technology in recent years, with the potential to improve convenience and efficiency in life. Following the rise of IoT, the Social Internet of Things (SIoT) is another new development in which the benefits of connectivity and user-friendliness from social network services (SNS) are its main features. With the introduction of IoT, the world will be much more modernized, convenient, and industrialized. This paper will discuss the applications of IoT in different sectors such as healthcare services, education, and lifestyle. The privacy challenges that IoT still poses to user data will also be discussed. Finally, an empirical study to evaluate the number of active installed IoT connections in recent years demonstrates the increase in usage of IoT regardless of the privacy challenges. The study also examines some types of IoT devices that are being preferred in the market and predictions from researchers about IoT in the upcoming years.

Keywords: IoT, health care, robotics, social Internet of Things

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Authors: Ewedafe Simon Uzezi

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In this paper we presented a method based on Domain Decomposition (DD) for parallelization of 1-D Sugarcane Equation on parallel platform with parallel paradigms on Master-Slave platform using Message Passing Interface (MPI). The 1-D Sugarcane Equation was discretized using explicit method of discretization requiring evaluation nof temporal and spatial distribution of temperature. This platform gives better predictions of the effects of temperature distribution of the sugarcane problem. This work presented parallel overheads with overlapping communication and communication across parallel computers with numerical results across different block sizes with scalability. However, performance improvement strategies from the DD on various mesh sizes were compared experimentally and parallel results show speedup and efficiency for the parallel algorithms design.

Keywords: sugarcane, parallelization, explicit method, domain decomposition, MPI

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Authors: Zied Guidara, Alexander Morgenstern, Aref Maalej

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In this paper, a new desiccant solar unit for air conditioning and desalination is presented first. Secondly, a dynamic modelling study of the desiccant wheel is developed. After that, a simulation study and an experimental investigation of the behaviour of the desiccant wheel are developed. The experimental investigation is done in the chamber of commerce in Freiburg-Germany. Indeed, the variations of calculated and measured temperatures and specific humidity of dehumidified and rejected air are presented where a good agreement is found when comparing the model predictions with experimental data under the considered range of operating conditions. Finally, the study of the compartments of desalination and water condensation shows that the unit can produce an acceptable quantity of water at the same time of the air conditioning operation.

Keywords: air conditioning, desalination, condensation, design, desiccant wheel, modelling, experimental investigation

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Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: FSASEC, academic environment model, decision trees, k-nearest neighbor, machine learning, popularity index, support vector machine

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Authors: Constantinos Vayenas, Athanasios Fokas, Dimitrios Grigoriou

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We use special relativity to compute the inertial and thus gravitational mass of relativistic electron and muon neutrinos, and we find that, for neutrino kinetic energies above 150 MeV/c2, these masses are in the Planck mass range. Consequently, we develop a simple Bohr-type model using gravitational rather than electrostatic forces between the rotating neutrinos as the centripetal force in order to examine the bound rotational states formed by two or three such relativistic neutrinos. We find that the masses of the composite rotational structures formed, are in the meson and baryon mass ranges, respectively. These models contain no adjustable parameters and by comparing their predictions with the experimental values of the masses of protons and pions, we compute a mass of 0.0437 eV/c2 for the heaviest electron neutrino mass and of 1.1 x10-3 eV/c2 for the heaviest muon neutrino mass.

Keywords: geons, gravitational confinement, neutrino masses, special relativity

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Authors: Megan E. Davies

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The effects of climate change impact the environment and our physical health but also demonstrate a growing risk factor for Canadians’ individual and collective mental health. Past research and expert predictions are discussed while exploring the connection between mental health concerns and climate change consequences, resulting in a call to action for psychological sciences to be integrated into solution planning. With the direct and indirect effects of climate change steadily increasing, political and legal aspects of sustainability, as well as the repercussions for mental health being seen in Canada regarding climate change, are investigated. An interdisciplinary perspective for reviewing the challenges of climate change is applied in order to propose a realistic plan for how policymakers and mental health professionals can work together moving forward in applying interventions that mediate against the effects of climate change on Canadians’ mental health.

Keywords: climate change, mental health, policy change, solution planning, sustainability

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Authors: Sima Najafzadehkhoei, George Vega Yon

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This paper presents an approach for calibrating Agent-Based Models (ABMs) efficiently, utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. These machine learning techniques are applied to Susceptible-Infected-Recovered (SIR) models, which are a core framework in the study of epidemiology. Our method replicates parameter values from observed trajectory curves, enhancing the accuracy of predictions when compared to traditional calibration techniques. Through the use of simulated data, we train the models to predict epidemiological parameters more accurately. Two primary approaches were explored: one where the number of susceptible, infected, and recovered individuals is fully known, and another using only the number of infected individuals. Our method shows promise for application in other ABMs where calibration is computationally intensive and expensive.

Keywords: ABM, calibration, CNN, LSTM, epidemiology

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Huei Chu Weng, Chien-Hung Liu

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This paper presents a study on the effect of second-order slip and jump on forced convection through a long isothermally heated or cooled planar microchannel. The fully developed solutions of thermal flow fields are analytically obtained on the basis of the second-order Maxwell-Burnett slip and Smoluchowski jump boundary conditions. Results reveal that the second-order term in the Karniadakis slip boundary condition is found to contribute a negative velocity slip and then to lead to a higher pressure drop as well as a higher fluid temperature for the heated-wall case or to a lower fluid temperature for the cooled-wall case. These findings are contrary to predictions made by the Deissler model. In addition, the role of second-order slip becomes more significant when the Knudsen number increases.

Keywords: microfluidics, forced convection, gas rarefaction, second-order boundary conditions

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Authors: Iqbal Abd. Muhbir Hadi Anam

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The purpose of the study is to discuss the relationship between organizational culture, teacher psychological empowerment, and organizational citizenship behavior at the University of Bangkalan District. The data was obtained using a survey of 100 respondents tested for validity and reliability. The analytical technique used is a hierarchical regression test. The results showed that the organizational culture of the university had a strong influence on the psychological empowerment of teachers and the psychological empowerment of teachers and that the organizational culture and psychological empowerment of teachers provided effective predictions of the psychological empowerment of the university. In addition, organizational culture directly or indirectly influences teachers' organizational citizenship behavior through psychological empowerment. Given these results, universities need to build an organizational culture that reflects the nature of the university.

Keywords: organizational behavior, teacher psychological empowerment, organizational citizenship behavior, universities

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Authors: Elham Sharifineyestani, Mohammad Farshchin

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Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

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Authors: Aminu J. A. Koguna, Aliyu M. Aliyu, Olawale T. Fajemidupe, Yahaya D. Baba

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Production fluids are transported from the platform to tankers or process facilities through transfer pipelines. Water being one of the heavier phases tends to settle at the bottom of pipelines especially at low flow velocities and this has adverse consequences for pipeline integrity. On restart after a shutdown this could result in corrosion and issues for process equipment, thus the need to have the heavier liquid dispersed into the flowing lighter fluid. This study looked at the flow regime of low water cut and low flow velocity oil and water flow using conductive film thickness probes in a large diameter 4-inch pipe to obtain oil and water interface height and the interface structural velocity. A wide range of 0.1–1.0 m/s oil and water mixture velocities was investigated for 0.5–5% water cut. Two fluid model predictions were used to compare with the experimental results.

Keywords: interface height, liquid, velocity, flow regime, dispersed, water cut

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Authors: S. Channgam, A. Sae-Tang, T. Termsaithong

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In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.

Keywords: Bak-Tang-Wiesenfeld sandpile model, cross-correlation, avalanches, prediction method

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Authors: Sung Ham, Chanho Song, Jiabin Wu

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Employees are commonly incentivized on both quantity and quality performance and much of the extant literature focuses on demonstrating that multitasking incentives lead to tradeoffs. Alternatively, we consider potential solutions to the tradeoff problem from both a theoretical and an experimental perspective. Across two field experiments from a call center, we find that tradeoffs can be mitigated when incentives are jointly enhanced across tasks, where previous research has suggested that incentives be reduced instead of enhanced. In addition, we also propose and test, in a laboratory setting, the implications of revising the metric used to assess quality. Our results indicate that metrics can be adjusted to align quality and quantity more efficiently. Thus, this alignment has the potential to thwart the classic tradeoff problem. Finally, we validate our findings with an economic experiment that verifies that effort is largely consistent with our theoretical predictions.

Keywords: incentives, multitasking, field experiment, experimental economics

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: J. Suneetha, Vijayalaxmi

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Sequential Pattern Mining involves applying data mining methods to large data repositories to extract usage patterns. Sequential pattern mining methodologies used to analyze the data and identify patterns. The patterns have been used to implement efficient systems can recommend on previously observed patterns, in making predictions, improve usability of systems, detecting events, and in general help in making strategic product decisions. In this paper, identified performance of approximate sequential pattern mining defines as identifying patterns approximately shared with many sequences. Approximate sequential patterns can effectively summarize and represent the databases by identifying the underlying trends in the data. Conducting an extensive and systematic performance over synthetic and real data. The results demonstrate that ApproxMAP effective and scalable in mining large sequences databases with long patterns.

Keywords: multiple data, performance analysis, sequential pattern, sequence database scalability

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Authors: Damian Islas

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Debates on realism in science concern two different questions: (I) whether the unobservable entities posited by theories can be known; and (II) whether any knowledge we have of them is objective or not. Question (I) arises from the doubt that since observation is the basis of all our factual knowledge, unobservable entities cannot be known. Question (II) arises from the doubt that since scientific representations are inextricably laden with the subjective, idiosyncratic, and a priori features of human cognition and scientific practice, they cannot convey any reliable information on how their objects are in themselves. A way of understanding scientific realism (SR) is through three lines of inquiry: ontological, semantic, and epistemological. Ontologically, scientific realism asserts the existence of a world independent of human mind. Semantically, scientific realism assumes that theoretical claims about reality show truth values and, thus, should be construed literally. Epistemologically, scientific realism believes that theoretical claims offer us knowledge of the world. Nowadays, the literature on scientific realism has proceeded rather far beyond the realism versus antirealism debate. This stance represents a middle-ground position between the two according to which science can attain justified true beliefs concerning relational facts about the unobservable realm but cannot attain justified true beliefs concerning the intrinsic nature of any objects occupying that realm. That is, the structural content of scientific theories about the unobservable can be known, but facts about the intrinsic nature of the entities that figure as place-holders in those structures cannot be known. There are two possible versions of SR: Epistemological Structural Realism (ESR) and Ontic Structural Realism (OSR). On ESR, an agnostic stance is preserved with respect to the natures of unobservable entities, but the possibility of knowing the relations obtaining between those entities is affirmed. OSR includes the rather striking claim that when it comes to the unobservables theorized about within fundamental physics, relations exist, but objects do not. Focusing on ESR, questions arise concerning its ability to explain the empirical success of a theory. Empirical success certainly involves predictive success, and predictive success implies a theory’s power to make accurate predictions. But a theory’s power to make any predictions at all seems to derive precisely from its core axioms or laws concerning unobservable entities and mechanisms, and not simply the sort of structural relations often expressed in equations. The specific challenge to ESR concerns its ability to explain the explanatory and predictive power of successful theories without appealing to their substantive ontologies, which are often not preserved by their successors. The response to this challenge will depend on the various and subtle different versions of ESR and OSR stances, which show a sort of progression through eliminativist OSR to moderate OSR of gradual increase in the ontological status accorded to objects. Knowing the relations between unobserved entities is methodologically identical to assert that these relations between unobserved entities exist.

Keywords: eliminativist ontic structural realism, epistemological structuralism, moderate ontic structural realism, ontic structuralism

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

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Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

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Authors: Khalil Bachiri, Ali Yahyaouy, Nicoleta Rogovschi

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Improved user experience is a goal of contemporary recommender systems. Recommender systems are starting to incorporate reinforcement learning since it easily satisfies this goal of increasing a user’s reward every session. In this paper, we examine the most effective Reinforcement Learning agent tactics on the Movielens (1M) dataset, balancing precision and a variety of recommendations. The absence of variability in final predictions makes simplistic techniques, although able to optimize ranking quality criteria, worthless for consumers of the recommendation system. Utilizing the stochasticity of Leonard-Ornstein processes, our suggested strategy encourages the agent to investigate its surroundings. Research demonstrates that raising the NDCG (Discounted Cumulative Gain) and HR (HitRate) criterion without lowering the Ornstein-Uhlenbeck process drift coefficient enhances the diversity of suggestions.

Keywords: recommender systems, reinforcement learning, deep learning, DDPG, Leonard-Ornstein process

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