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Barr, Joseph R., Eden A. Ellis, Antonio Kassab, Christian L. Redfearn, Narayanan Nani Srinivasan, and Kurtis B. Voris. "Home Price Index: A Machine Learning Methodology." International Journal of Semantic Computing 11, no. 01 (March 2017): 111–33. http://dx.doi.org/10.1142/s1793351x17500015.
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Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible — a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called ‘gradient boosted’ model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the ‘median sale’ and ‘repeat sales’ indices.
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Pérez Moreno, F., V. F. Gómez Comendador, R. Delgado-Aguilera Jurado, M. Zamarreño Suárez, D. Janisch, and R. M. Arnaldo Valdés. "Dynamic sector characterisation model with the application of machine learning techniques." IOP Conference Series: Materials Science and Engineering 1226, no. 1 (February 1, 2022): 012018. http://dx.doi.org/10.1088/1757-899x/1226/1/012018.
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Abstract The ATC service has the objective of controlling airspace operations safely and efficiently. This control is becoming more and more difficult due to the increasing complexity of airspace. With the objective of collaborating and facilitating the provision of the control service, FLUJOS project aims to develop a methodology to characterise ATC sectors according to their complexity. This paper shows the first results obtained in this project. A methodology is proposed that first performs a statistical analysis of the data present in the flight plans of individual aircraft. The statistical analysis will be used to estimate the impact of air traffic flows. With this, the complexity of ATC sectors will finally be determined. In addition, a machine learning tool will be added to develop a dynamic methodology. After evaluating the methodology with data from Spanish airspace in 2019, it can be said that the results obtained are logical from an operational point of view, and that they allow a fairly accurate classification of the sectors based on their complexity. However, the proposed methodology is still a preliminary version, so more work will have to be done to add variables to achieve the objective of obtaining an even more accurate and realistic classification.
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Navarro, Osvaldo, Jones Yudi, Javier Hoffmann, Hector Gerardo Muñoz Hernandez, and Michael Hübner. "A Machine Learning Methodology for Cache Memory Design Based on Dynamic Instructions." ACM Transactions on Embedded Computing Systems 19, no. 2 (March 17, 2020): 1–20. http://dx.doi.org/10.1145/3376920.
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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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Iskhakov, Fedor, John Rust, and Bertel Schjerning. "Machine learning and structural econometrics: contrasts and synergies." Econometrics Journal 23, no. 3 (August 29, 2020): S81—S124. http://dx.doi.org/10.1093/ectj/utaa019.
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Summary We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018, ‘Methodology and Applications of Structural Dynamic Models and Machine Learning'. ML offers a promising class of techniques that can significantly extend the set of questions we can analyse in SE. The scope, relevance and impact of empirical work in SE can be improved by following the lead of ML in questioning and relaxing the assumption of unbounded rationality. For the foreseeable future, however, ML is unlikely to replace the essential role of human creativity and knowledge in model building and inference, particularly with respect to the key goal of SE, counterfactual prediction.
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Ko, Jeong Hoon. "Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning." Metals 12, no. 2 (February 9, 2022): 298. http://dx.doi.org/10.3390/met12020298.
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The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model generates the feature lists including multiple frequencies and amplitudes at each process condition. The feature lists for milling stability are analyzed for training the machine learning algorithm. The amplitude and frequency distributions may change according to the dynamic pattern of the machining stability. The vibration patterns are grouped into stable, chatter, and boundary conditions by performing data training using support vector machines and gradient tree boosting. In the high-speed milling of Al6061-T6 with 6000 to 18,000 RPM and variations of axial and radial depths of cuts, 2400 data sets of the time domain data were trained and tested. Actual experimental tests are carried out for new process conditions with the range of 9890 to 28,470 RPM and 989 to 2847 mm/min. The experimental stability outcomes are compared with predictions from the algorithms. Stability is accurately predicted over new conditions with around 0.9 prediction accuracy, which means the methodology can be used to predict, categorize, and monitor stability in end milling processes.
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García Plaza, Eustaquio, Pedro Jose Núñez López, Angel Ramon Martín, and E. Beamud. "Virtual Machining Applied to the Teaching of Manufacturing Technology." Materials Science Forum 692 (July 2011): 120–27. http://dx.doi.org/10.4028/www.scientific.net/msf.692.120.
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Teaching methodology for industrial engineering must adapt and update its pedagogy by adopting innovative and dynamic approaches to training in state-of-the-art manufacturing technology. The development of virtual reality and computer simulation software has significantly improved the quality of education by raising learner motivation, commitment, and participation in the learning process. In university contexts characterised by large numbers of students, a hands-on approach to training in machine-tool operation on lathes and mills is unfeasible. Hence, the teaching methodology proposed involves the use of machine-tool simulators to undertake practical tasks in a virtual learning environment. The learning tasks focus on the main machine-tool components and their movements as well as on the principles and operations of machining in turning and milling processes performed on virtual machine where learners can acquire skills similar to those using traditional methodology, but require fewer resources and learning time spans.
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Hewawasam, Hasitha, Gayan Kahandawa, and Yousef Ibrahim. "Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal." Machines 11, no. 5 (April 28, 2023): 513. http://dx.doi.org/10.3390/machines11050513.
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This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
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Lu, M., L. Groeneveld, D. Karssenberg, S. Ji, R. Jentink, E. Paree, and E. Addink. "GEOMORPHOLOGICAL MAPPING OF INTERTIDAL AREAS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 75–80. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-75-2021.
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Abstract. Spatiotemporal geomorphological mapping of intertidal areas is essential for understanding system dynamics and provides information for ecological conservation and management. Mapping the geomorphology of intertidal areas is very challenging mainly because spectral differences are oftentimes relatively small while transitions between geomorphological units are oftentimes gradual. Also, the intertidal areas are highly dynamic. Considerable challenges are to distinguish between different types of tidal flats, specifically, low and high dynamic shoal flats, sandy and silty low dynamic flats, and mega-ripple areas. In this study, we harness machine learning methods and compare between machine learning methods using features calculated in classical Object-Based Image Analysis (OBIA) vs. end-to-end deep convolutional neural networks that derive features directly from imagery, in automated geomorphological mapping. This study expects to gain us an in-depth understanding of features that contribute to tidal area classification and greatly improve the automation and prediction accuracy. We emphasise model interpretability and knowledge mining. By comparing and combing object-based and deep learning-based models, this study contributes to the development and integration of both methodology domains for semantic segmentation.
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Carputo, Francesco, Danilo D’Andrea, Giacomo Risitano, Aleksandr Sakhnevych, Dario Santonocito, and Flavio Farroni. "A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider." Vehicles 3, no. 3 (July 15, 2021): 377–89. http://dx.doi.org/10.3390/vehicles3030023.
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A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.
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Karpov, Platon I., Chengkun Huang, Iskandar Sitdikov, Chris L. Fryer, Stan Woosley, and Ghanshyam Pilania. "Physics-informed Machine Learning for Modeling Turbulence in Supernovae." Astrophysical Journal 940, no. 1 (November 1, 2022): 26. http://dx.doi.org/10.3847/1538-4357/ac88cc.
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Abstract Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSNe), but current simulations must rely on subgrid models, since direct numerical simulation is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, machine learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network to preserve the realizability condition of the Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately modeled turbulence on the explosion of these stars.
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Liang, S. Y., and S. A. Perry. "In-Process Compensation for Milling Cutter Runout via Chip Load Manipulation." Journal of Engineering for Industry 116, no. 2 (May 1, 1994): 153–60. http://dx.doi.org/10.1115/1.2901925.
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This paper discusses a real-time chip load compensation methodology for the elimination of cutting force oscillation and machined surface scalloping due to cutter runout so as to gain better utilization of machine tools. The concept and implementation of the methodology is illustrated using end milling as a process of example. In this work a force feedback system was discussed in the angle domain based upon a proportional-integral control strategy and a repetitive learning control strategy to actively manipulate the chip load during end milling. Numerical simulations based on experimentally identified machining dynamics were presented to compare the performance of the two control schemes. Experimental investigation under various cutting conditions was performed to assess the viability of the feedback compensation system in the context of cutting force response as well as machined surface finish. It has been shown that a proportional-integral control has limited effectiveness in eliminating the runout-induced cutting force oscillation due to the constraints of system stability and dynamic performance. On the other hand, the learning control system based on the internal model principal successfully yields a cutting force free of oscillatory components at the spindle frequency and significantly improves the quality of machined surfaces by cancelling the nonasymptotically stable dynamics of cutter runout.
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Sujatha, Dr P., Bora Mounika, Dukka Raju, Gokavarapu Rahul, and Mulli Gangaraju. "Ad Demand Forecasting Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4771–94. http://dx.doi.org/10.22214/ijraset.2023.51330.
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Abstract: In the cutting-edge period business knowledge (BI) plays a vital part in articulating a methodology and going to address lengths in view of information. Business knowledge assumes an essential part in an unavoidable choice emotionally supportive network that empowers the endeavour to perform investigation on information and during the course of business. AI predictsfuture requests of undertakings. Request is one of the principal dynamic assignments of a venture. For request first, crude deals information is gathered from the market, then as per information, the future deal/item requests are determined. This forecast is based on gathered information that incorporates throughvarious sources. The AI motor executes information from various modules and decides the week afterweek, month to month, and quarterly requests of merchandise/products. In request, its ideal precision is non-splitting the difference, the more exact framework model is more productive. Besides, we test the effectiveness by contrasting the anticipated information and genuine information and deciding the rate blunder. Recreation results show that subsequent to applying the purposed arrangement continuously association information, we get up to 92.38 % precisionfor the store as far as shrewd interest determining.
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Rizzo, Valentino, Stefano Traverso, and Marco Mellia. "Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning." Proceedings on Privacy Enhancing Technologies 2021, no. 1 (January 1, 2021): 43–63. http://dx.doi.org/10.2478/popets-2021-0004.
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AbstractFueled by advertising companies’ need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users’ privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters.We apply our methodology on a dataset of more than 400, 000 JavaScript files accessed by about 1, 000 volunteers during a one-month long experiment to observe adoption of fingerprinting in a real scenario. We compare approaches based on both static and dynamic code analysis to automatically detect fingerprinters and show they provide different angles complementing each other. This demonstrates that studies based on either static or dynamic code analysis provide partial view on actual fingerprinting usage in the web. To the best of our knowledge we are the first to perform this comparison with respect to fingerprinting.Our approach achieves 94% accuracy in small decision time. With this we spot more than 840 fingerprinting services, of which 695 are unknown to popular tracker blockers. These include new actual trackers as well as services which use fingerprinting for purposes other than tracking, such as anti-fraud and bot recognition.
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Leventides, John, Evangelos Melas, Costas Poulios, and Paraskevi Boufounou. "Analysis of chaotic economic models through Koopman operators, EDMD, Takens' theorem and Machine Learning." Data Science in Finance and Economics 2, no. 4 (2022): 416–36. http://dx.doi.org/10.3934/dsfe.2022021.
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<abstract><p>We consider dynamical systems that have emerged in financial studies and exhibit chaotic behaviour. The main purpose is to develop a data-based method for reconstruction of the trajectories of these systems. This methodology can then be used for prediction and control and it can also be utilized even if the dynamics of the system are unknown. To this end, we combine merits from Koopman operator theory, Extended Dynamic Mode Decomposition and Takens' embedding theorem. The result is a linear autoregressive model whose trajectories approximate the orbits of the original system. Finally, we enrich this method with machine learning techniques that can be used to train the autoregressive model.</p></abstract>
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Koo, Jamin, Kyucheol Choi, Peter Lee, Amanda Polley, Raghavendra Sumanth Pudupakam, Josephine Tsang, Elmer Fernandez, et al. "Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model." Veterinary Sciences 8, no. 12 (December 2, 2021): 301. http://dx.doi.org/10.3390/vetsci8120301.
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First-line treatments of cancer do not always work, and even when they do, they cure the disease at unequal rates mostly owing to biological and clinical heterogeneity across patients. Accurate prediction of clinical outcome and survival following the treatment can support and expedite the process of comparing alternative treatments. We describe the methodology to dynamically determine remission probabilities for individual patients, as well as their prospects of progression-free survival (PFS). The proposed methodology utilizes the ex vivo drug sensitivity of cancer cells, their immunophenotyping results, and patient information, such as age and breed, in training machine learning (ML) models, as well as the Cox hazards model to predict the probability of clinical remission (CR) or relapse across time for a given patient. We applied the methodology using the three types of data obtained from 242 canine lymphoma patients treated by (L)-CHOP chemotherapy. The results demonstrate substantial enhancement in the predictive accuracy of the ML models by utilizing features from all the three types of data. They also highlight superior performance and utility in predicting survival compared to the conventional stratification method. We believe that the proposed methodology can contribute to improving and personalizing the care of cancer patients.
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Rozos, Evangelos. "Machine Learning, Urban Water Resources Management and Operating Policy." Resources 8, no. 4 (November 14, 2019): 173. http://dx.doi.org/10.3390/resources8040173.
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Meticulously analyzing all contemporaneous conditions and available options before taking operations decisions regarding the management of the urban water resources is a necessary step owing to water scarcity. More often than not, this analysis is challenging because of the uncertainty regarding inflows to the system. The most common approach to account for this uncertainty is to combine the Bayesian decision theory with the dynamic programming optimization method. However, dynamic programming is plagued by the curse of dimensionality, that is, the complexity of the method is proportional to the number of discretized possible system states raised to the power of the number of reservoirs. Furthermore, classical statistics does not consistently represent the stochastic structure of the inflows (see persistence). To avoid these problems, this study will employ an appropriate stochastic model to produce synthetic time-series with long-term persistence, optimize the system employing a network flow programming modelling, and use the optimization results for training a feedforward neural network (FFN). This trained FFN alone can serve as a decision support tool that describes not only reservoir releases but also how to operate the entire water supply system. This methodology is applied in a simplified representation of the Athens water supply system, and the results suggest that the FFN is capable of successfully operating the system according to a predefined operating policy.
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Golmohammadi, Amir-Mohammad, Hasan Rasay, Zaynab Akhoundpour Amiri, Maryam Solgi, and Negar Balajeh. "Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment." Mathematical Problems in Engineering 2021 (November 11, 2021): 1–13. http://dx.doi.org/10.1155/2021/3040391.
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Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.
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Elngar, Ahmed, and Adriana Burlea-Schiopoiu. "Feature Selection and Dynamic Network Traffic Congestion Classification based on Machine Learning for Internet of Things." Wasit Journal of Computer and Mathematics Science 2, no. 2 (July 1, 2023): 76–91. http://dx.doi.org/10.31185/wjcms.150.
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The network traffic congestion classifier is essential for network monitoring systems. Network traffic characterization is a methodology to classify traffic into several classes supporting various attributes. In this paper, payload-based classification is suggested for network traffic characterization. It has a broad scope of utilization like network security assessment, intrusion identification, QoS supplier, et cetera; furthermore, it has significance in investigating different suspicious movements in the network. Numerous supervised classification techniques like Support Vector Machines and unsupervised clustering methods like K-Means connected are used in traffic classification. In current network conditions, minimal supervised data and unfamiliar applications influence the usual classification procedure's performance. This paper implements a methodology for network traffic classification using clustering, feature extraction, and variety for the Internet of Things (IoT). Further, K-Means is used for network traffic clustering datasets, and feature extraction is performed on grouped information. KNN, Naïve Bayes, and Decision Tree classification methods classify network traffic because of extracted features, which presents a performance measurement between these classification algorithms. The results discuss the best machine learning algorithm for network congestion classification. According to the outcome, clustering (k-means) with network classification (Decision Tree) generates a higher accuracy, 86.45 %, than other clustering and network classification
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Narang, Akhil, Victor Mor-Avi, Aldo Prado, Valentina Volpato, David Prater, Gloria Tamborini, Laura Fusini, et al. "Machine learning based automated dynamic quantification of left heart chamber volumes." European Heart Journal - Cardiovascular Imaging 20, no. 5 (October 9, 2018): 541–49. http://dx.doi.org/10.1093/ehjci/jey137.
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Abstract Aims Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-systole and end-diastole. Recently, this methodology was expanded using a machine learning (ML) approach to automatically measure chamber volumes throughout the cardiac cycle, resulting in LV and LA volume–time curves. We aimed to validate ejection and filling parameters obtained from these curves by comparing them to independent well-validated reference techniques. Methods and results We studied 20 patients referred for cardiac magnetic resonance (CMR) examinations, who underwent 3DE imaging the same day. Volume–time curves were obtained for both LV and LA chambers using the ML algorithm (Philips HeartModel), and independently conventional 3DE volumetric analysis (TomTec), and CMR images (slice-by-slice, frame-by-frame manual tracing). Automatically derived LV and LA volumes and ejection/filling parameters were compared against both reference techniques. Minor manual correction of the automatically detected LV and LA borders was needed in 4/20 and 5/20 cases, respectively. Time required to generate volume–time curves was 35 ± 17 s using ML algorithm, 3.6 ± 0.9 min using conventional 3DE analysis, and 96 ± 14 min using CMR. Volume–time curves obtained by all three techniques were similar in shape and magnitude. In both comparisons, ejection/filling parameters showed no significant inter-technique differences. Bland–Altman analysis confirmed small biases, despite wide limits of agreement. Conclusion The automated ML algorithm can quickly measure dynamic LV and LA volumes and accurately analyse ejection/filling parameters. Incorporation of this algorithm into the clinical workflow may increase the utilization of 3DE imaging.
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Piltan, Farzin, Alexander E. Prosvirin, Inkyu Jeong, Kichang Im, and Jong-Myon Kim. "Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer." Applied Sciences 9, no. 24 (December 10, 2019): 5404. http://dx.doi.org/10.3390/app9245404.
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Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system’s dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenges, this paper proposes a novel technique for fault diagnosis of a rolling-element bearing (REB), founded on a machine-learning-based advanced fuzzy sliding mode observer. First, an ARX-Laguerre algorithm is presented to model the bearing in the presence of noise and uncertainty. In addition, a fuzzy algorithm is applied to the ARX-Laguerre technique to increase the system’s modeling accuracy. Next, the conventional sliding mode observer is applied to resolve the problems of fault estimation in a complex system with a high degree of uncertainty, such as rotating machinery. To address the problem of chattering that is inherent in the conventional sliding mode observer, the higher-order super-twisting (advanced) technique is introduced in this study. In addition, the fuzzy method is applied to the advanced sliding mode observer to improve the accuracy of fault estimation in uncertain conditions. As a result, the advanced fuzzy sliding mode observer adaptively improves the reliability, robustness, and estimation accuracy of rolling-element bearing fault estimation. Then, the residual signal delivered by the proposed methodology is split in the windows and each window is characterized by a numerical parameter. Finally, a machine learning technique, called a decision tree, adaptively derives the threshold values that are used for problems of fault detection and fault identification in this study. The effectiveness of the proposed algorithm is validated using a publicly available vibration dataset of Case Western Reverse University. The experimental results show that the machine learning-based advanced fuzzy sliding mode observation methodology significantly improves the reliability and accuracy of the fault estimation, detection, and identification of rolling element bearing faults under variable crack sizes and load conditions.
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Silva-Aravena, Fabián, and Jenny Morales. "Dynamic Surgical Waiting List Methodology: A Networking Approach." Mathematics 10, no. 13 (July 1, 2022): 2307. http://dx.doi.org/10.3390/math10132307.
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In Chile and the world, the supply of medical hours to provide care has been reduced due to the health crisis caused by COVID-19. As of December 2021, the outlook has been critical in Chile, both in medical and surgical care, where 1.7 million people wait for care, and the wait for surgery has risen from 348 to 525 days on average. This occurs mainly when the demand for care exceeds the supply available in the public system, which has caused serious problems in patients who will remain on hold and health teams have implemented management measures through prioritization measures so that patients are treated on time. In this paper, we propose a methodology to work in net for predicting the prioritization of patients on surgical waiting lists (SWL) embodied with a machine learning scheme for a high complexity hospital (HCH) in Chile. That is linked to the risk of each waiting patient. The work presents the following contributions; The first contribution is a network method that predicts the priority order of anonymous patients entering the SWL. The second contribution is a dynamic quantification of the risk of waiting patients. The third contribution is a patient selection protocol based on a dynamic update of the SWL based on the components of prioritization, risk, and clinical criteria. The optimization of the process was measured by a simulation of the total times of the system in HCH. The prioritization strategy proposed savings of medical hours allowing 20% additional surgeries to be performed, thus reducing SWL by 10%. The risk of waiting patients could drop by up to 8% annually. We hope to implement this methodology in real health care units.
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Puissant, Agathe, Roy El Hourany, Anastase Alexandre Charantonis, Chris Bowler, and Sylvie Thiria. "Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning." Remote Sensing 13, no. 8 (April 8, 2021): 1445. http://dx.doi.org/10.3390/rs13081445.
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Observing the vertical dynamic of phytoplankton in the water column is essential to understand the evolution of the ocean primary productivity under climate change and the efficiency of the CO2 biological pump. This is usually made through in-situ measurements. In this paper, we propose a machine learning methodology to infer the vertical distribution of phytoplankton pigments from surface satellite observations, allowing their global estimation with a high spatial and temporal resolution. After imputing missing values through iterative completion Self-Organizing Maps, smoothing and reducing the vertical distributions through principal component analysis, we used a Self-Organizing Map to cluster the reduced profiles with satellite observations. These referent vector clusters were then used to invert the vertical profiles of phytoplankton pigments. The methodology was trained and validated on the MAREDAT dataset and tested on the Tara Oceans dataset. The different regression coefficients R2 between observed and estimated vertical profiles of pigment concentration are, on average, greater than 0.7. We could expect to monitor the vertical distribution of phytoplankton types in the global ocean.
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Szostak, Daniel, and Krzysztof Walkowiak. "Application of Machine Learning Algorithms for Traffic Forecasting in Dynamic Optical Networks with Service Function Chains." Foundations of Computing and Decision Sciences 45, no. 3 (September 1, 2020): 217–32. http://dx.doi.org/10.2478/fcds-2020-0012.
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AbstractKnowledge about future optical network traffic can be beneficial for network operators in terms of decreasing an operational cost due to efficient resource management. Machine Learning (ML) algorithms can be employed for forecasting traffic with high accuracy. In this paper we describe a methodology for predicting traffic in a dynamic optical network with service function chains (SFC). We assume that SFC is based on the Network Function Virtualization (NFV) paradigm. Moreover, other type of traffic, i.e. regular traffic, can also occur in the network. As a proof of effectiveness of our methodology we present and discuss numerical results of experiments run on three benchmark networks. We examine six ML classifiers. Our research shows that it is possible to predict a future traffic in an optical network, where SFC can be distinguished. However, there is no one universal classifier that can be used for each network. Choice of an ML algorithm should be done based on a network traffic characteristics analysis.
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Anand, Dr C., N. Vasuki, S. Nirmala, N. Naveen, and S. Prabakaran. "Greedy Dynamic Blocking for Rumour Detection on Live Twitter Using Machine Learning." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 364–73. http://dx.doi.org/10.47059/revistageintec.v11i2.1673.
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We propose a community multi-Trends assessment grouping way to deal with train notion classifiers for numerous tweets at the same time. In our methodology, the assessment data in various tweets is shared to prepare more exact and vigorous estimation classifiers for each Trends when marked information is scant. In particular, we decay the opinion classifier of each Trends into two segments, a worldwide one and a Trends-explicit one. Various customer surveys of subjects are currently accessible on the Internet. Naturally distinguishes the significant parts of themes from online shopper surveys. The significant item angles are recognized dependent on two perceptions. With the point of arranging patterns from the get-go. This would permit to give a separated subset of patterns to end clients. We investigate and explore different avenues regarding a bunch of direct language-autonomous highlights dependent on the social spread of patterns to classify them into the presented typology. Our strategy gives an effective method to precisely arrange moving points without need of outer information, empowering news associations to find breaking news progressively, or to rapidly recognize viral images that may improve promoting choices, among others. The examination of social highlights additionally uncovers designs related with each sort of pattern, for example, tweets about continuous occasions being more limited the same number of were likely sent from cell phones, or images having more retweets starting from a couple of innovators. The worldwide model can catch the overall conclusion information and is shared by different tweets. The Trends-explicit Greedy and Dynamic Blocking Algorithms model can catch the particular assessment articulations in each Trend. Likewise, we remove Trends-explicit feeling information from both marked and unlabeled examples in each Trend and use it to improve the learning of Trends-explicit notion classifiers.
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Presti, Claudia, Federica De Santis, and Francesca Bernini. "Value co-creation via machine learning from a configuration theory perspective." European Journal of Innovation Management 26, no. 7 (August 3, 2023): 449–77. http://dx.doi.org/10.1108/ejim-01-2023-0104.
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PurposeThis paper aims to propose an interpretive framework to understand how machine learning (ML) affects the way companies interact with their ecosystem and how the introduction of digital technologies affects the value co-creation (VCC) process.Design/methodology/approachThis study bases on configuration theory, which entails two main methodological phases. In the first phase the authors define the theoretically-derived interpretive framework through a literature review. In the second phase the authors adopt a case study methodology to inductively analyze the theoretically-derived domains and their relationships within a configuration.FindingsML enables multi-directional knowledge flows among value co-creators and expands the scope of VCC beyond the boundaries of the firm-client relationship. However, it determines a substantive imbalance in knowledge management power among the actors involved in VCC. ML positively impacts value co-creators’ performance but also requires significant organizational changes. To benefit from VCC via ML, value co-creators must be aligned in terms of digital maturity.Originality/valueThe paper answers the call for more theoretical and empirical research on the impact of the introduction of Industry 4.0 technology in companies and their ecosystem. It intends to improve the understanding of how ML technology affects the determinants and the process of VCC by providing both a static and dynamic analysis of the topic.
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Gultekin, Muaz, and Oya Kalipsiz. "Story Point-Based Effort Estimation Model with Machine Learning Techniques." International Journal of Software Engineering and Knowledge Engineering 30, no. 01 (January 2020): 43–66. http://dx.doi.org/10.1142/s0218194020500035.
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Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.
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Liu, Tao, Dongqi Li, Jianjun Chen, Yanbing Chen, Tao Yang, and Jianhua Cao. "Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System." Sensors 19, no. 16 (August 19, 2019): 3601. http://dx.doi.org/10.3390/s19163601.
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Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter the drift effect in more challenging situations in which the category information (labels) of the drifted samples is difficult or expensive to obtain. Thus, only a few of the drifted samples can be used for label querying. To solve this problem, we propose an innovative methodology based on Active Learning (AL) that selectively provides sample labels for drift correction. Moreover, we utilize a dynamic clustering process to balance the sample category for label querying. In the experimental section, we set up two E-nose drift scenarios—a long-term and a short-term scenario—to evaluate the performance of the proposed methodology. The results indicate that the proposed methodology is superior to the other state-of-art methods presented. Furthermore, the increasing tendencies of parameter sensitivity and accuracy are analyzed. In addition, the Label Efficiency Index (LEI) is adopted to measure the efficiency and labelling cost of the AL methods. The LEI values indicate that our proposed methodology exhibited better performance than the other presented AL methods in the online drift correction of E-noses.
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Sun, Jin, Zhengyu Chen, and Fu Wang. "A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages." Remote Sensing 14, no. 23 (November 23, 2022): 5932. http://dx.doi.org/10.3390/rs14235932.
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To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages.
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Novikov, I. S., Y. V. Suleimanov, and A. V. Shapeev. "Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning." Physical Chemistry Chemical Physics 20, no. 46 (2018): 29503–12. http://dx.doi.org/10.1039/c8cp06037a.
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We propose a methodology for the fully automated calculation of thermal rate coefficients of gas phase chemical reactions, which is based on combining ring polymer molecular dynamics (RPMD) and machine-learning interatomic potentials actively learning on-the-fly.
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Sani, Shehu, Hanbing Xia, Jelena Milisavljevic-Syed, and Konstantinos Salonitis. "Supply Chain 4.0: A Machine Learning-Based Bayesian-Optimized LightGBM Model for Predicting Supply Chain Risk." Machines 11, no. 9 (September 4, 2023): 888. http://dx.doi.org/10.3390/machines11090888.
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In today’s intricate and dynamic world, Supply Chain Management (SCM) is encountering escalating difficulties in relation to aspects such as disruptions, globalisation and complexity, and demand volatility. Consequently, companies are turning to data-driven technologies such as machine learning to overcome these challenges. Traditional approaches to SCM lack the ability to predict risks accurately due to their computational complexity. In the present research, a hybrid Bayesian-optimized Light Gradient-Boosting Machine (LightGBM) model, which accurately forecasts backorder risk within SCM, has been developed. The methodology employed encompasses the creation of a mathematical classification model and utilises diverse machine learning algorithms to predict the risks associated with backorders in a supply chain. The proposed LightGBM model outperforms other methods and offers computational efficiency, making it a valuable tool for risk prediction in supply chain management.
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An, Qing, Ruoli Tang, Hongfeng Su, Jun Zhang, and Xin Li. "Robust configuration and intelligent MPPT control for building integrated photovoltaic system based on extreme learning machine." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 12283–300. http://dx.doi.org/10.3233/jifs-210424.
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Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy.
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Patiño, José, Ángel Encalada-Dávila, José Sampietro, Christian Tutivén, Carlos Saldarriaga, and Imin Kao. "Damping Ratio Prediction for Redundant Cartesian Impedance-Controlled Robots Using Machine Learning Techniques." Mathematics 11, no. 4 (February 17, 2023): 1021. http://dx.doi.org/10.3390/math11041021.
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Implementing impedance control in Cartesian task space or directly at the joint level is a popular option for achieving desired compliance behavior for robotic manipulators performing tasks. The damping ratio is an important control criterion for modulating the dynamic response; however, tuning or selecting this parameter is not easy, and can be even more complicated in cases where the system cannot be directly solved at the joint space level. Our study proposes a novel methodology for calculating the local optimal damping ratio value and supports it with results obtained from five different scenarios. We carried out 162 different experiments and obtained the values of the inertia, stiffness, and damping matrices for each experiment. Then, data preprocessing was carried out to select the most significant variables using different criteria, reducing the seventeen initial variables to only three. Finally, the damping ratio values were calculated (predicted) using automatic regression tools. In particular, five-fold cross-validation was used to obtain a more generalized model and to assess the forecasting performance. The results show a promising methodology capable of calculating and predicting control parameters for robotic manipulation tasks.
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Shin, Hyun-Jun, Kyoung-Woo Cho, and Chang-Heon Oh. "SVM-Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0." Wireless Communications and Mobile Computing 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/5795037.
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CPS is potential application in various fields, such as medical, healthcare, energy, transportation, and defense, as well as Industry 4.0 in Germany. Although studies on the equipment aging and prediction of problem have been done by combining CPS with Industry 4.0, such studies were based on small numbers and majority of the papers focused primarily on CPS methodology. Therefore, it is necessary to study active self-protection to enable self-management functions, such as self-healing by applying CPS in shop-floor. In this paper, we have proposed modeling of shop-floor and a dynamic reconfigurable CPS scheme that can predict the occurrence of anomalies and self-protection in the model. For this purpose, SVM was used as a machine learning technology and it was possible to restrain overloading in manufacturing process. In addition, we design CPS framework based on machine learning for Industry 4.0, simulate it, and perform. Simulation results show the simulation model autonomously detects the abnormal situation and it is dynamically reconfigured through self-healing.
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Kim, Hyun Il, and Kun Yeun Han. "Linking Hydraulic Modeling with a Machine Learning Approach for Extreme Flood Prediction and Response." Atmosphere 11, no. 9 (September 15, 2020): 987. http://dx.doi.org/10.3390/atmos11090987.
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An emergency action plan (EAP) for reservoirs and urban areas downstream of dams can alleviate damage caused by extreme flooding. An EAP is a disaster action plan that can designate evacuation paths for vulnerable districts. Generally, calculation of dam-break discharge in accordance with dam inflow conditions, calculation of maximum water surface elevation as per hydraulic channel routing, and flood map generation using topographical data are prepared for the purposes of creating an EAP. However, rainfall and flood patterns exhibited in the context of climate change can be extremely diverse. In order to prepare an efficient flood response, techniques should be considered that are capable of generating flood maps promptly while taking dam inflow conditions into account. Therefore, this study aims to propose methodology that is capable of generating flood maps rapidly for any dam inflow conditions. The proposed methodology was performed by linking a dynamic numerical analysis model (DAMBRK) with a random forest regression technique. The previous standard method of drawing flood maps often requires a significant amount of time depending on accuracy and personnel availability; however, the technique proposed here is capable of generating a flood map within one minute. Through use of this methodology, the time taken to prepare flood maps in large-scale water-disaster situations can be reduced. Moreover, methodology for estimating flood risk via use of flood mapping has been proposed. This study would provide assistance in establishing disaster countermeasures that take various flood scenarios into account by promptly providing flood inundation information to disaster-related agencies.
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Neff, P., D. Steineder, B. Stummer, and T. Clemens. "Estimation of Initial Hydrocarbon Saturation Applying Machine Learning Under Petrophysical Uncertainty." SPE Reservoir Evaluation & Engineering 24, no. 02 (March 4, 2021): 325–40. http://dx.doi.org/10.2118/203384-pa.
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Summary The initial hydrocarbon saturation has a major effect on field-development planning and resource estimation. However, the bases of the initial hydrocarbon saturation are indirect measurements from spatially distributed wells applying saturation-height modeling using uncertain parameters. Because of the multitude of parameters, applying assisted-matching methods requires trade-offs regarding the quality of objective functions used for the various observed data. Applying machine learning (ML) in a Bayesian framework helps overcome these challenges. In the present study, the methodology is used to derive posterior parameter distributions for saturation-height modeling honoring the petrophysical uncertainty in a field. The results are used for dynamic model initialization and will be applied for forecasting under uncertainty. To determine the dynamic numerical model initial hydrocarbon saturation, the saturation-height model (SHM) needs to be conditioned to the petrophysically interpreted logs. There were 2,500 geological realizations generated to cover the interpreted ranges of porosity, permeability, and saturations for 15 wells. For the SHM, 12 parameters and their ranges were introduced. Latin hypercube sampling was used to generate a training set for ML models using the random forest algorithm. The trained ML models were conditioned to the petrophysical log-derived saturation data. To ensure a fieldwide consistency of the dynamic numerical models, only parameter combinations honoring the interpreted saturation range for all wells were selected. The presented method allows for consistent initialization and for rejection of parameters that do not fit the observed data. In our case study, the most-significant observation concerns the posterior parameter-distribution ranges, which are narrowed down dramatically, such as the free-water-level (FWL) range, which is reduced from 645–670 m subsea level (mSS) to 656–668 mSS. Furthermore, the SHM parameters are proved independent; thus, the resulting posterior parameter ranges for the SHM can be used for conditioning production data to models and subsequent hydrocarbon-production forecasting. Additional observations can be made from the ML results, such as the correlation between wells; this allows for interpreting groups of wells that have a similar behavior, favor the same combinations, and potentially belong to the same compartment.
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Khessam, Medjdoub, Abdelkader Lousdad, Abdeldjebar Hazzab, Miloud Rezkallah, and Ambrish Chandra. "A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1290. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1290-1298.
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This paper introduces <span>a non-linear implementation of the speed control technique of permanent magnetic synchronous motors (PMSM) using electronic differential (ED) command. Artificial neural network (ANN) coupled with particles swarm optimization (ANN-PSO) are implemented to control wheel speed and steering angle. The main purpose of the PMSM system and its application is the command of electric vehicles (EV). In the controller design, three-phase currents and rotor speed shall be measurable and eligible for feedback. Our propulsion platform consists of two PMSM in the back. The study with implemented ANN-PSO is performed after collecting the data from the ED to manage the control of speed EV, Left and right of steering angle and steering ahead. Based on this strategy, a new application can be provided in the GPS application to give the information as input (curved path angle) to ANN-PSO. Next, the application of ANN-PSO can estimate the parameters of ED to avoid the slip, as well as improves better performance and dynamic stability of electric vehicle drive systems.</span>
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Kuzmin, A. G., Y. A. Titiov, and A. Y. Zaitceva. "MASS SPECTROMETRIC DIAGNOSIS OF RECOVERY AFTER RESPIRATORY ILLNESS USING MACHINE LEARNING METHODS." BIOTECHNOLOGY: STATE OF THE ART AND PERSPECTIVES 1, no. 2022-20 (2022): 74–77. http://dx.doi.org/10.37747/2312-640x-2022-20-74-77.
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The express methodology of evaluation of mass-spectrometric parameters of gas composition of exhaled air for differential diagnostics of acute respiratory viral infections and tracing of dynamics of recovery after the disease was developed.
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Lokanan, Mark, Vincent Tran, and Nam Hoai Vuong. "Detecting anomalies in financial statements using machine learning algorithm." Asian Journal of Accounting Research 4, no. 2 (October 14, 2019): 181–201. http://dx.doi.org/10.1108/ajar-09-2018-0032.
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Purpose The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method. Design/methodology/approach The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly. Findings The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable. Research limitations/implications The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels. Practical implications The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud. Originality/value This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.
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Alhussan, Amel Ali, Abdelaziz A. Abdelhamid, S. K. Towfek, Abdelhameed Ibrahim, Marwa M. Eid, Doaa Sami Khafaga, and Mohamed S. Saraya. "Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization." Diagnostics 13, no. 12 (June 12, 2023): 2038. http://dx.doi.org/10.3390/diagnostics13122038.
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Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.
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Shaheen, Memoona, Mehreen Arshad, and Owais Iqbal. "Role and Key Applications of Artificial Intelligence & Machine Learning in Transportation." European Journal of Technology 4, no. 1 (December 31, 2020): 47–59. http://dx.doi.org/10.47672/ejt.632.
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Purpose: The main target of this paper was to examine the significance of Artificial Intelligence and Machine Learning and their effect on the transportation business.
Methodology: This hypothesis was a survey of the significant machine learning calculations and their applications in the field of big data. This paper try to attempt to exhibit the need to remove significant data from the huge measure of enormous information as traffic data available in this day and age and recorded diverse machine learning strategies that can be utilized to separate this information needed to encourage better dynamic for transportation applications.
Findings: This paper present an investigation of the different Artificial Intelligence (AI) methods that have been actualized to improve Intelligent Transportation Systems (ITS). Specifically, this paper assembled them into three main territories relying upon the main field where they were applied: Vehicle control, Traffic control and prediction, and Road security and accident prediction. The aftereffects of this examination uncover that the mix of various AI methodologies is by all accounts promising, particularly to oversee and investigate the huge measure of data created in transportation
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Chitturi, Sathya R., Nicolas G. Burdet, Youssef Nashed, Daniel Ratner, Aashwin Mishra, T. J. Lane, Matthew Seaberg, et al. "A machine learning photon detection algorithm for coherent x-ray ultrafast fluctuation analysis." Structural Dynamics 9, no. 5 (September 2022): 054302. http://dx.doi.org/10.1063/4.0000161.
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X-ray free electron laser experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body quantum systems at the level of the ultrafast pulse durations, but this method has been limited to a select number of examples and required complex and advanced analytical tools. By applying a new methodology to this problem, we have made qualitative advances in three separate areas that will likely also find application to new fields. As compared to the “droplet-type” models, which typically are used to estimate the photon distributions on pixelated detectors to obtain the coherent x-ray speckle patterns, our algorithm achieves an order of magnitude speedup on CPU hardware and two orders of magnitude improvement on GPU hardware. We also find that it retains accuracy in low-contrast conditions, which is the typical regime for many experiments in structural dynamics. Finally, it can predict photon distributions in high average-intensity applications, a regime which up until now has not been accessible. Our artificial intelligence-assisted algorithm will enable a wider adoption of x-ray coherence spectroscopies, by both automating previously challenging analyses and enabling new experiments that were not otherwise feasible without the developments described in this work.
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Vaienti, Beatrice, Rémi Petitpierre, Isabella di Lenardo, and Frédéric Kaplan. "Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction." Remote Sensing 15, no. 13 (June 30, 2023): 3352. http://dx.doi.org/10.3390/rs15133352.
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The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city reconstruction. By integrating procedural modeling techniques and machine learning models within a Geographic Information System (GIS) framework, our pipeline allows for effective management of spatial data and the generation of detailed 3D models. We developed an open-source Python module that fills gaps in 2D GIS datasets and directly generates 3D models up to LOD 2.1 from GIS files. The use of the CityJSON format ensures interoperability and accommodates the specific needs of historical models. A practical case study using footprints of the Old City of Jerusalem between 1840 and 1940 demonstrates the creation, completion, and 3D representation of the dataset, highlighting the versatility and effectiveness of our approach. This research contributes to the accessibility and accuracy of historical city models, providing tools for the generation of informative 3D models. By incorporating machine learning models and maintaining the dynamic nature of the models, we ensure the possibility of supporting ongoing updates and refinement based on newly acquired data. Our procedural modeling methodology offers a streamlined and open-source solution for historical city reconstruction, eliminating the need for additional software and increasing the usability and practicality of the process.
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Legendre, Cesar, Vincent Ficat-Andrieu, Athanasios Poulos, Yuya Kitano, Yoshitaka Nakashima, Wataru Kobayashi, and Gaku Minorikawa. "A machine learning-based methodology for computational aeroacoustics predictions of multi-propeller drones." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 3 (August 1, 2021): 3467–78. http://dx.doi.org/10.3397/in-2021-2415.
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The rapid progress in technological developments of small Unmanned Aircraft Systems (sUAS) or simply "drones" has produced a significant proliferation of this technology. From multinational businesses to drone enthusiasts, such a technology can offer a wide range of possibilities, i.e., commercial services, security, and environmental applications, while placing new demands in the already-congested civil airspace. Noise emission is a key factor that is being addressed with high-fidelity computational fluid dynamics (CFD) and aeroacoustics (CAA) techniques. However, due to uncertainties of flow conditions, wide ranges of propellers' speed variations, and different payload requirements, a complete numerical prediction varying such parameters is unfeasible. In this study, a machine learning-based approach is proposed in combination with high-fidelity CFD and CAA techniques to predict drone noise emission given a wide variation of payloads or propellers' speeds. The transient CFD computations are calculated using a time-marching LES simulation with a WALE sub-grid scale. In contrast, the acoustic propagation is predicted using a finite element method in the frequency domain. Finally, the machine learning strategy is presented in the context of fulfilling two goals: (i) real-time noise prediction of drone systems; and (ii) determination of propeller's rotation speeds leading to a noise prediction matching experimental data.
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Gino, Vinícius L. S., Rogério G. Negri, Felipe N. Souza, Erivaldo A. Silva, Adriano Bressane, Tatiana S. G. Mendes, and Wallace Casaca. "Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series." Sustainability 15, no. 6 (March 7, 2023): 4725. http://dx.doi.org/10.3390/su15064725.
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The synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth’s surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping.
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Santos, Marcone Ferreira, Alessandro Corrêa Victorino, and Hugo Pousseur. "Model-based and machine learning-based high-level controller for autonomous vehicle navigation: lane centering and obstacles avoidance." IAES International Journal of Robotics and Automation (IJRA) 12, no. 1 (March 1, 2023): 84. http://dx.doi.org/10.11591/ijra.v12i1.pp84-97.
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<p>Researchers have been attempting to make the car drive autonomously. The environment perception together with safe guidance and control is an important task and are one of the big challenges when developing this kind of system. Geometrical or physical based models, machine learning based models and those based on a mixture of both models, are the three types of navigation methods used to resolve this problem. The last method takes advantage of the learning capability of machine learning models and uses the safeness of geometric models in order to better perform the navigation task. This paper presents a hybrid autonomous navigation methodology, which takes advantage of the learning capability of machine learning and uses the safeness of the dynamic window approach geometric method. Using a single camera and a 2D lidar sensor, this method actuates as a high-level controller, where optimal vehicle velocities are found, then applied by a low-level controller. The final algorithm is validated on CARLA Simulator environment, where the system proved to be capable to guide the vehicle in order to achieve the following tasks: lane keeping and obstacle avoidance.</p>
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Prajapati, Keyur, and Dinesh J Prajapati. "Web Auto Configuration for N-Tier in VM based Dynamic Environment by Reinforcement Learning Approach: A Study." Computer Science & Engineering: An International Journal 12, no. 1 (February 28, 2022): 25–34. http://dx.doi.org/10.5121/cseij.2022.12104.
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In Web system, configuration is the crucial part to achieve performance with service availability. Now in days, because of dynamics web traffic, virtualization is the key factor. How to handle required resources is a challenging task in virtual environment. Apply optimize configurations for different servers as per available resources is a tedious task to achieve high throughput with low latency. In this paper we have described the studied methodology of machine learning, which will guide how optimize all the parameters with the best results in terms of web usability.
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Pomponio, Laura, Marc Le Goc, Alain Anfosso, and Eric Pascual. "Levels of Abstraction for Behavior Modeling in the GerHome Project." International Journal of E-Health and Medical Communications 3, no. 3 (July 2012): 12–28. http://dx.doi.org/10.4018/jehmc.2012070102.
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Defining activity models in order to monitor human behavior in smart environments is one of the major issues at the moment of building systems of activity supervision for diagnosis, prediction and control. For the purpose of addressing this problem, this paper proposes a general theoretical approach based on the use of a Knowledge Engineering methodology and a Machine Learning process, which are funded on a general theory of dynamic process modeling, the Timed Observation Theory.
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Dadras Javan, Farzad, Italo Aldo Campodonico Avendano, Behzad Najafi, Amin Moazami, and Fabio Rinaldi. "Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses." Energies 16, no. 14 (July 16, 2023): 5407. http://dx.doi.org/10.3390/en16145407.
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This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted.
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Habib, Ahed, and Umut Yildirim. "Estimating mechanical and dynamic properties of rubberized concrete using machine learning techniques: a comprehensive study." Engineering Computations 39, no. 8 (August 19, 2022): 3129–78. http://dx.doi.org/10.1108/ec-09-2021-0527.
Full textAbstract:
PurposeCurrently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.Design/methodology/approachIn this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.FindingsIn general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.Originality/valuePrevious studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.