Articoli di riviste: "Machine learnings"

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Li, Tianshu. "Fintech Application in Banking Operations - Application of Machine Learning in Mitigating Bank Derivatives Counterparty Risks". Asian Business Research 4, n. 3 (8 ottobre 2019): 1. http://dx.doi.org/10.20849/abr.v4i3.652.

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We all know that human has many psychological biases, including overconfidence, gender discrimination and so on. Although some genuine lenders may outperformance others, machine learnings have been utilized to solve this human psychological bias in many areas. By using machine learnings methods, people can make better financial decisions. This proposal tries to examine the effectiveness of several different machine learning models on predicting the ex-pose default risk, including BP neural network, decision tree, KNN, and random forest. I focus on loans on one electronic P2P lending platform, called “Paipaidai” in which lenders select and supply private loans to borrowers with different characteristics. I use machine learnings methods to predict the default risk and thus provides better ways for investors to select high-quality borrower. I will also further test how different machine learnings methods perform when there is soft information contained by using Prosper platform.

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Makarov, Vladimir, Christophe Chabbert, Elina Koletou, Fotis Psomopoulos, Natalja Kurbatova, Samuel Ramirez, Chas Nelson, Prashant Natarajan e Bikalpa Neupane. "Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise". Computers in Biology and Medicine 177 (luglio 2024): 108632. http://dx.doi.org/10.1016/j.compbiomed.2024.108632.

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Kim, Jin Kook. "A Study on the Estimation Model for the Visitors to Let’s Run Park Using Machine Learning". Korean Journal of Sport Science 32, n. 3 (30 settembre 2021): 411–18. http://dx.doi.org/10.24985/kjss.2021.32.3.411.

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PURPOSE The purpose of this study is to find the best model to predict the demand of visitors in Let’s Run Park by using machine learning and to provide effective data for establishing future marketing strategies.METHODS For this purpose, three methods of machine learning were applied: random forest, adaboost, and gradient boosting. The variables for predicting the audience were weather data and the number of visitors per date for four years as training data, and the accuracy was predicted by comparing the actual data for one year.RESULTS First, the performance evaluation using random forest was conducted, RMSE =1856.067, R2= .965, and error was 6.47%. Second, the performance evaluation using Adaboost was conducted, RMSE =1836.227, R2= .965, and error was 5.25%, which was the lowest among the three machine learnings. Third, the performance evaluation using gradient boosting showed that RMSE =1797.400 and R2= .967 were the most accurate among the three machine learnings and error was 6.99%.CONCLUSIONS As a result of this study, each of the three machine learning features existed, but the most efficient model was gradient boosting. In addition, the best way to utilize it in the field is to predict the number of visitors by comprehensively judging the results of the three machine learning, and it is judged that it will help efficient management decision making in the future.

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Malik, Sehrish, e DoHyeun Kim. "Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory". Actuators 10, n. 2 (31 gennaio 2021): 27. http://dx.doi.org/10.3390/act10020027.

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The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

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PREETHAM S, M C CHANDRASHEKHAR e M Z KURIAN. "METHODOLOGY FOR IMPLEMENTATION OF PREDICTION MODEL FOR STUDENTS USING MACHINE LEARNING". international journal of engineering technology and management sciences 7, n. 3 (2023): 764–66. http://dx.doi.org/10.46647/ijetms.2023.v07i03.116.

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In this era, with the continuing growth in electronic devices and internet technologies, there has been a vast rise in data storage. The word data is explaining each detail that has been interpret into a form that is further convenient to move or process. In this project machine learning data have performed. However, machine learning technology brings a vast benefit which provides a computer the potential to learn without programming it. One of the applications of machine learning is E-learning. E-learning makes many things possible especially for learners to learn anytime and anywhere as well as in online on their own. Customization on E-learnings has two steps- first part of the customization is forecasting the elegance and the second is suggesting the counsel of course selection depending upon the performance. Here the challenges in E-learning to tackle and discuss the customization classification which is the grade prediction.

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Kurniawan, Robi, e Shunsuke Managi. "Forecasting annual energy consumption using machine learnings: Case of Indonesia". IOP Conference Series: Earth and Environmental Science 257 (10 maggio 2019): 012032. http://dx.doi.org/10.1088/1755-1315/257/1/012032.

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Singh, Priyanka, Chakshu Garg, Aman Namdeo, Krishna Mohan Agarwal e Rajesh Kumar Rai. "Development of Prediction models for Bond Strength of Steel Fiber Reinforced Concrete by Computational Machine Learning". E3S Web of Conferences 220 (2020): 01097. http://dx.doi.org/10.1051/e3sconf/202022001097.

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Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.

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Das, Aditi. "Automatic Personality Identification using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 9, n. VI (30 giugno 2021): 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.

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Machine Learning has made significant changes in the world making our life more easier and comfortable .One of the most exciting applications is the prediction of Personality automatically using different algorithms. Personality computing and emotive computing, where the popularity of temperament traits is important, have gained increasing interest and a spotlight in several analysis areas recently. These applications can powerfully predict the personality of a Person. The aim of this paper is to use a more rigorous construct Validation system to extend the potential of machine learning approaches to personality assessment. We have reviewed multiple recent applications of Machine Learning to recognize personality, thus providing a broader context of fundamental principles of constructing, validating, and then providing recommendations on how to use Machine Learning to advance the level of our understanding and applying our learnings to develop advanced personality recognition applications. araphrased Text Output text rewrite / rewrite We use deep neural network learning to recognize characteristics independently and, through feature-level fusion of these networks, we obtain final predictions of obvious personalities. We use a previously trained long-term and short-term memory network to integrate time information. We train large-scale models comprised of specific subnetworks- modalities through a two-stage training process. We first train the subnets separately for and then use these trained networks to fit the overall model. We used the ChaLearn First Impressions V2 challenge dataset to evaluate the proposed method. Our method achieves the most effective overall "medium precision" score, with an average score of for 5 personality characteristics, which is compared to the state-of-the-art method.

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Malinda Sari Sembiring, Windi Astuti, Iskandar Muda,. "The Influence of Cloud Computing, Artificial Intelligence, Machine Learnings and Digital Disruption on the Design of Accounting and Finance Functions Mediated by Data Processing". International Journal on Recent and Innovation Trends in Computing and Communication 11, n. 11 (30 novembre 2023): 56–62. http://dx.doi.org/10.17762/ijritcc.v11i11.9087.

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This research aims to determine the influence of cloud computing, artificial intelligence, machine learning and digital disruption on the design of accounting and financial functions mediated by data processing. This type of research is an explanatory survey, the data used is primary data on 202 respondents working in the accounting and finance sector in Indonesia drawn using the purposive random sampling method. Analysis tool using the Structural Equation Modeling approach with the WarpPLS Version 8.0 test tool. The results show that Cloud Computing, Artificial Intelligence, Machine Learnings and Digital Disruption have a significant influence on the Design of Accounting and Financial Functions Mediated by Data Processing.

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Sendak, Mark P., William Ratliff, Dina Sarro, Elizabeth Alderton, Joseph Futoma, Michael Gao, Marshall Nichols et al. "Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study". JMIR Medical Informatics 8, n. 7 (15 luglio 2020): e15182. http://dx.doi.org/10.2196/15182.

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Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

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Udomchaipitak, Tanatpong, Nathaphon Boonnam, Supattra Puttinaovarat e Paramate Horkaew. "Forecast Coral Bleaching by Machine Learnings of Remotely Sensed Geospatial Data". International Journal of Design & Nature and Ecodynamics 17, n. 3 (30 giugno 2022): 423–31. http://dx.doi.org/10.18280/ijdne.170313.

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With the rapid changes in Earth climates, coral bleaching has been spreading worldwide and getting much severe. It is considered an imminent threat to marine animals as well as causing adverse impacts on fisheries and tourisms. Environmental agencies in affected regions have been made aware of the problem and hence starting to contain coral bleaching. Thus far, they often rely on conventional site survey to determine suitable sites to intervene and commence coral reef reviving process. With the recent advances in remote sensing technology, sea surface temperature (SST), acquired by satellites, has become a viable delegate to coral bleaching. Predicting coral bleaching based solely on SST is limited, as it is only one of many determinants. In addition, areas with different SST levels also exhibit different bleaching characteristics. Hence, area specific models are important for appropriately monitoring the events. Thus far, forecasting the bleaching based on SST alone has limited accuracy, because other disregarded factors are found equally influential. These are turbidity, salinity, and wind speed. Taken into account these geospatial factors, this paper evaluates different machine learning (ML) algorithms, on forecasting coral bleaching levels. Compared with official survey data, it was found that random forest (RF) gave the most accurate results, with accuracy and Kappa of 88.24% and 0.83, respectively. To further assist involved agencies in making data driven solutions to this problem, mapping forecasted by RF were visualized on a web application, implemented with Python and the most recent web frameworks and database systems. The proposed scheme could be extended to modelling coral bleaching in other areas, hence greatly reducing delayed in data acquisition and survey costs.

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Qian, Qingwen, Junfeng Wu e Zhe Wang. "Dynamic balance control of two-wheeled self-balancing pendulum robot based on adaptive machine learning". International Journal of Wavelets, Multiresolution and Information Processing 18, n. 01 (29 marzo 2019): 1941002. http://dx.doi.org/10.1142/s0219691319410029.

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To solve the problem of balance control in dynamic movement of two-wheeled self-balancing pendulum robot, a dynamic balance control method based on adaptive machine learning is proposed based on theoretical analysis of the cause of balance. Firstly, a kinematics model of two-wheeled self-balancing pendulum robot is established. Through numerical calculation and analysis, the root cause for dynamic balance of two-wheeled self-balancing pendulum robot is obtained. On this base, adaptive machine learnings are proposed to control the dynamic balance of two-wheeled self-balancing pendulum robot. The possible lateral movement of robot caused by dynamic balance control is analyzed. Finally, balance simulation test is conducted, which shows that the robot can easily lose balance and overturn without adaptive machine learning. The comparison of simulation test has verified that the proposed dynamic balance control method can effectively control the dynamic balance of two-wheeled self-balancing pendulum robot.

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Kokozinski, Andre, Christian Kubik e Peter Groche. "Komplexität mehrstufiger Umformprozesse beherrschen/Mastering the complexity of multi-stage forming processes – The contribution of domain knowledge to a data-driven monitoring of progressive tools". wt Werkstattstechnik online 112, n. 10 (2022): 696–700. http://dx.doi.org/10.37544/1436-4980-2022-10-66.

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Industrielle Überwachungssysteme in Blechumformprozessen basieren weitestgehend auf Hüllkurven und Grenzwerten von Zeitreihen, die damit lediglich eine binäre Fehlerdetektion erlauben. Die modellbasierte Prozessüberwachung mit Methoden des Machine Learnings bietet jedoch deutlich tiefere Einblicke in diese Prozesse und zeigt das Potenzial, Zustände in komplexen mehrstufigen Werkzeugen inline zu klassifizieren ohne dafür aufwendige manuelle oder stichprobenartige Inspektionen durchführen zu müssen. Industrial monitoring systems in sheet metal forming processes are generally based on envelopes and thresholds of time series, which only allow binary fault detection. However, model-based monitoring using machine learning methods offers significantly deeper insights into these processes. In addition, they show the potential to inline classify states in complex multi-stage tools without having to perform time-consuming manual or random inspections.

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Asari, Yusuke. "SM-3 Noise Reduction Method Based on Machine Learnings for Electron Holography". Microscopy 68, Supplement_1 (1 novembre 2019): i7. http://dx.doi.org/10.1093/jmicro/dfz056.

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Zhang, Evan. "Treating COVID-19 with machine learning". Applied and Computational Engineering 30, n. 1 (22 gennaio 2024): 1–11. http://dx.doi.org/10.54254/2755-2721/30/20230202.

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From 2020 to 2023, SARS-CoV-2 destroyed much of our society, while few treatments were available due to the time required for drug discovery. However, with recent advancements in artificial intelligence, it is now ready to fight viruses such as SARS-CoV-2. Chemprop, a machine-learning backbone for molecular properties prediction, can be used to discover novel antiviral drugs by training a classifier model with hundreds of thousands of data points that include molecular information represented by SMILES strings and the observed efficacy in inhibiting SARS-CoV-2 in laboratory tests. The resulting model predicts the effectiveness of untested molecules, which then can be manually tested, minimizing tedious hunting traditionally done by human scientists. With promising performance, the proposed method pushes the boundary of machine learnings involvement in drug research. The trained model achieved a high accuracy in predicting the effectiveness of drugs against SARS-CoV-2 with an AUC score of 0.8455. However, the model loses accuracy when predicting the effectiveness of drugs against SARS-CoV, a different strand of coronavirus, with an AUC of 0.7302. The model was then run on one of the data sets to locate the molecule most likely effective against COVID-19, demonstrating its applicability. The result was a molecule with SMILES string CN1CCN(CC1)C(=O)COC=2C=CC(C)=CC2 also called 1-(4-Methyl-piperazin-1-yl)-2-p-tolyloxy-ethanone. Then the model DrugChat was utilized to determine the properties of the molecule. The models ability to find likely drugs can hasten drug research drastically, potentially saving countless lives during future pandemics.

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Tang, Muran, Lingyue Gao, Yutong Bian, Shang Xiang e Kaijun Zhang. "Brain tumor MRI images classification based on machine learning". Applied and Computational Engineering 29, n. 1 (26 dicembre 2023): 19–29. http://dx.doi.org/10.54254/2755-2721/29/20230765.

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Recent research has shown machine learnings outstanding performance on image classifying tasks, including applications on Magnetic Resonance Images. While the former models are overly complicated, this paper proposes a simplified model, which is proven to be both accurate and much less time-consuming. Our proposed method is learned from former research and combines Bias Field Correction, DenseNet, and SE-Net to form a concise structure. With small datasets of T1-weighted and T2-weighted labeled MR brain tumor images, our model spent a short training time of 2 hours and showed excellent performance on classifying pituitary, meningioma, glioma or no tumor with an accuracy of 91.32%. After evaluation, our model is proven to be accurate in distinguishing between 3 of the tumor types with an f1-score of 0.96.

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Jin, Xiangyu, Luya Wei e Qihua Zhang. "The Stock Price Prediction Based on Time Series Model, Multifactorial Regression, Machine Learnings". BCP Business & Management 23 (4 agosto 2022): 903–9. http://dx.doi.org/10.54691/bcpbm.v23i.1471.

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In general, it is hard to forecast the prices the stock prices due to the stochastic fluctuations. This research aims to describe the process to use time series models, multifactorial regression, and machine learning to predict stock prices. ARIMA and EGARCH models are frequently used time series models to predict stock prices. Least-squares linear regression model, Lasso, and Polynomial Linear Regression model predict well in statistical regression methods. RNN and LSTM have higher prediction accuracy. Overall, time series models, statistical regression, and machine learning all can predict stock prices. Summarizing the different methods or models to forecast stock market trending can help investors to prepare relevant investing strategies. These results shed light on guiding further exploration of

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Zhai, Weiguang, Changchun Li, Qian Cheng, Bohan Mao, Zongpeng Li, Yafeng Li, Fan Ding, Siqing Qin, Shuaipeng Fei e Zhen Chen. "Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications". Remote Sensing 15, n. 14 (21 luglio 2023): 3653. http://dx.doi.org/10.3390/rs15143653.

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Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.

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Hwang, Gyuyeong, Taehun Kim, Juyong Shin, Naechul Shin e Sungwon Hwang. "Machine learnings for CVD graphene analysis: From measurement to simulation of SEM images". Journal of Industrial and Engineering Chemistry 101 (settembre 2021): 430–44. http://dx.doi.org/10.1016/j.jiec.2021.05.031.

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Kim, Gyeung Min. "Analysis for Factors Determining the Price of Multi-family Housing through Machine Learnings". Residential Environment Institute Of Korea 14, n. 3 (30 giugno 2016): 29–40. http://dx.doi.org/10.22313/reik.2016.14.3.29.

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Nikam, Rahul J. "Legality of usage of Artificial Intelligence and Machine Learnings by Share Market Intermediary". Passagens: Revista Internacional de História Política e Cultura Jurídica 15, n. 2 (15 giugno 2023): 319–39. http://dx.doi.org/10.15175/1984-2503-202315207.

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Artificial Intelligence (AI) and Machine Learning (ML) are increasingly utilized in share market services due to significant efficiencies and benefits for companies and investors across the globe. This has resulted in an alteration in the firm’s business models and has a potential impact on the effectiveness of the share market and could harm investors. Indian share market is also witnessing the usage of this technology by market intermediaries. The present regulatory framework of Securities Exchange Board of India (SEBI) on share market intermediaries is not dealing with the Fintech/ technology 2.0-based products and services offered in retail trading and investment advisor platforms in India. The research is primarily based on the normative method presenting a qualitative analysis of the usage of AI & ML in various business models by share market intermediaries. How various share market regulators are addressing and regulating this technology usage and their judicial exposition. The paper concludes that the Indian share market is no exception & SEBI require to look at this new transformation and address the challenges posed by it. SEBI needs to take a proactive step to promote, guide & regulate usages of AI & ML which is gradually seeking the attention of Indian share market intermediaries into their business models and get the maximum benefit out of these technologies.

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Kang, In-Ae, Soualihou Ngnamsie Njimbouom, Kyung-Oh Lee e Jeong-Dong Kim. "DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine". Applied Sciences 12, n. 6 (16 marzo 2022): 3043. http://dx.doi.org/10.3390/app12063043.

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Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. Classified as one of the most prevalent oral health issues, research on dental caries has been carried out for early detection due to pain and cost of treatment. Medical research in oral healthcare has shown limitations such as considerable funds and time required; therefore, artificial intelligence has been used in recent years to develop models that can predict the risk of dental caries. The data used in our study were collected from a children’s oral health survey conducted in 2018 by the Korean Center for Disease Control and Prevention. Several Machine Learning algorithms were applied to this data, and their performances were evaluated using accuracy, F1-score, precision, and recall. Random forest has achieved the highest performance compared to other machine learnings methods, with an accuracy of 92%, F1-score of 90%, precision of 94%, and recall of 87%. The results of the proposed paper show that ML is highly recommended for dental professionals in assisting them in decision making for the early detection and treatment of dental caries.

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Chao, Paul C. P., Chih-Cheng Wu, Duc Huy Nguyen, Ba-Sy Nguyen, Pin-Chia Huang e Van-Hung Le. "The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage". IEEE Sensors Journal 21, n. 11 (1 giugno 2021): 12498–510. http://dx.doi.org/10.1109/jsen.2021.3073850.

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Hasan, Md Mahadi, Saba Binte Murtaz, Muhammad Usama Islam, Muhammad Jafar Sadeq e Jasim Uddin. "Robust and efficient COVID-19 detection techniques: A machine learning approach". PLOS ONE 17, n. 9 (15 settembre 2022): e0274538. http://dx.doi.org/10.1371/journal.pone.0274538.

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The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.

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Ganie, Shahid Mohammad, Pijush Kanti Dutta Pramanik, Saurav Mallik e Zhongming Zhao. "Chronic kidney disease prediction using boosting techniques based on clinical parameters". PLOS ONE 18, n. 12 (1 dicembre 2023): e0295234. http://dx.doi.org/10.1371/journal.pone.0295234.

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Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.

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Kumar, Yogesh. "The Fellow Traveller: A Machine Learning Approach to Travel Management". International Journal for Research in Applied Science and Engineering Technology 10, n. 4 (30 aprile 2022): 1798–802. http://dx.doi.org/10.22214/ijraset.2022.41613.

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Abstract: Even after the presence of multiple services which helps us in travelling like Uber, Ola, Make My Trip, Goibibo etc, the travelling enthusiasts or peoples who are going on vacation don’t have proper platform where they can plan their entire trip at one place. There are so many places where only local rental services is available means no online cab services, no platform to contact a guide, platform to book self-driving vehicles, to check the current and predicted weather conditions of the destination, proper expense splitter if travelling in group and a platform which can predict the proper ways through which you can travel to the destination in a set budget. These problems demands an application which helps to manage all the travelling requirements like rental vehicles, hotels, activities, budget manager, expense splitter if travelling in group etc. Also to provide direct contacts of local drivers, help centres, emergencies etc. An application which can use machine learnings prediction system like Support Vector Regression Model and Adaptive Neuro Fuzzy Inference Systems, Expectation Maximization and Self-Organizing Map for clustering techniques and for dimensionality reduction, Principal Component Analysis.It will be really helpful to all the travel enthusiasts as well as this platform can also provide broader scope to the local vendors and peoples of the entered destination as it will directly connect the travellers to them, and it can recommend best services to ease the travel. It will also provide air pollution report and alternative tour plan. Index Terms: Travel Management, Prediction, Recommendation, Machine Learning, Alternate Planning

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M. Brandao, Iago, e Cesar da Costa. "FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING". Eletrônica de Potência 27, n. 03 (22 settembre 2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.

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Xue, Yang, Mariela Araujo, Jorge Lopez, Kanglin Wang e Gautam Kumar. "Machine learning to reduce cycle time for time-lapse seismic data assimilation into reservoir management". Interpretation 7, n. 3 (1 agosto 2019): SE123—SE130. http://dx.doi.org/10.1190/int-2018-0206.1.

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Time-lapse (4D) seismic is widely deployed in offshore operations to monitor improved oil recovery methods including water flooding, yet its value for enhanced well and reservoir management is not fully realized due to the long cycle times required for quantitative 4D seismic data assimilation into dynamic reservoir models. To shorten the cycle, we have designed a simple inversion workflow to estimate reservoir property changes directly from 4D attribute maps using machine-learning (ML) methods. We generated tens of thousands of training samples by Monte Carlo sampling from the rock-physics model within reasonable ranges of the relevant parameters. Then, we applied ML methods to build the relationship between the reservoir property changes and the 4D attributes, and we used the learnings to estimate the reservoir property changes given the 4D attribute maps. The estimated reservoir property changes (e.g., water saturation changes) can be used to analyze injection efficiency, update dynamic reservoir models, and support reservoir management decisions. We can reduce the turnaround time from months to days, allowing early engagements with reservoir engineers to enhance integration. This accelerated data assimilation removes a deterrent for the acquisition of frequent 4D surveys.

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Bile, Alessandro, Hamed Tari e Eugenio Fazio. "Episodic Memory and Information Recognition Using Solitonic Neural Networks Based on Photorefractive Plasticity". Applied Sciences 12, n. 11 (31 maggio 2022): 5585. http://dx.doi.org/10.3390/app12115585.

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Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software algorithms and electronic architectures. Recently, both supervised and unsupervised learnings were obtained in photonic neurons by means of spatial-soliton-waveguide X-junctions. This paper investigates the behavior of networks based on these solitonic neurons, which are capable of performing complex tasks such as bit-to-bit information memorization and recognition. By exploiting photorefractive nonlinearity as if it were a biological neuroplasticity, the network modifies and adapts to the incoming signals, memorizing and recognizing them (photorefractive plasticity). The information processing and storage result in a plastic modification of the network interconnections. Theoretical description and numerical simulation of solitonic networks are reported and applied to the processing of 4-bit information.

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Zhou, Wangbao, Lijun Xiong, Lizhong Jiang, Lingxu Wu, Ping Xiang e Liqiang Jiang. "Optimal combinations of parameters for seismic response prediction of high-speed railway bridges using machine learnings". Structures 57 (novembre 2023): 105089. http://dx.doi.org/10.1016/j.istruc.2023.105089.

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Latif, Sarmad Dashti, Vivien Lai, Farah Hazwani Hahzaman, Ali Najah Ahmed, Yuk Feng Huang, Ahmed H. Birima e Ahmed El-Shafie. "Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia". Results in Engineering 21 (marzo 2024): 101872. http://dx.doi.org/10.1016/j.rineng.2024.101872.

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Anam, Khairul, Harun Ismail, Faruq Sandi Hanggara, Cries Avian, Safri Nahela e Muchamad Arif Hana Sasono. "Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband". Journal of Engineering and Technological Sciences 55, n. 5 (30 dicembre 2023): 587–99. http://dx.doi.org/10.5614/j.eng.technol.sci.2023.55.5.8.

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The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.

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Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang e Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning". International Journal of Trade, Economics and Finance 11, n. 6 (dicembre 2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.

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Chen, JueYu. "Identification and analysis of real and fake news by XGBoost algorithm of machine learning". Applied and Computational Engineering 40, n. 1 (21 febbraio 2024): 255–62. http://dx.doi.org/10.54254/2755-2721/40/20230661.

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As the internet develops rapidly, fake news has become increasingly easy to propagate. Numerous academics acknowledge the perilous nature of this phenomenon, particularly in the context of the contemporary post-truth era, highlighting its substantial risk to the public. Hence, the detection and halting of fake news dissemination are absolutely vital. This study utilizes machine learnings eXtreme Gradient Boosting (XGBoost) algorithm to construct a model that can differentiate between genuine and fake news. The model is compared with others utilizing different algorithms and is ultimately selected. The study successfully constructs a model with an accuracy rate of approximately 95% in identifying real and fake news. This model provides the public with a convenient way to differentiate between real and fake news and gradually diminishes the threat of fake news. Additionally, this projects implications extend beyond merely identifying real and fake news. The model can be further developed to detect fake information, providing greater societal benefits.

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Aqil, M., M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati et al. "Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning". Applied Computational Intelligence and Soft Computing 2022 (26 aprile 2022): 1–15. http://dx.doi.org/10.1155/2022/6588949.

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Hybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant/off-type plants. Female and contaminant/off-type plants’ tassels should be removed immediately following flowering initiation, while male tassels should be retained to allow cross-pollination between male and female plants. Therefore, development of an intelligent tassel classification system is deemed critical for hybrid purity decision-making. The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. Tenfold cross-validation was used to evaluate the model performance. Cloud computing was also investigated using EfficientNet to compare the predictive performance of the models. The models’ performance was assessed using four metrics: accuracy, AUC, precision, and recall. The results depicted an appropriate developed model that properly distinguished male, female, and contaminant plants. The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. SqueezeNet and EfficientNet provided comparable results for consistent tassel classification with slightly lower performance measures. The model was also subjected to a multidimensional scaling (MDS) analysis to investigate and comprehend misclassification. Male and female plants are clearly distinguished by MDS, but female and off-type/contamination plants are ambiguous. This indicates that the prediction errors were caused by highly similar data features among female and off-type images. The developed modern plant phenotyping model can be used to assist breeders/technicians in maintaining the quality of large-scale hybrid maize seed production activities in Indonesia.

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Aqil, M., M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati et al. "Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning". Applied Computational Intelligence and Soft Computing 2022 (26 aprile 2022): 1–15. http://dx.doi.org/10.1155/2022/6588949.

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Abstract (sommario):

Hybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant/off-type plants. Female and contaminant/off-type plants’ tassels should be removed immediately following flowering initiation, while male tassels should be retained to allow cross-pollination between male and female plants. Therefore, development of an intelligent tassel classification system is deemed critical for hybrid purity decision-making. The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. Tenfold cross-validation was used to evaluate the model performance. Cloud computing was also investigated using EfficientNet to compare the predictive performance of the models. The models’ performance was assessed using four metrics: accuracy, AUC, precision, and recall. The results depicted an appropriate developed model that properly distinguished male, female, and contaminant plants. The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. SqueezeNet and EfficientNet provided comparable results for consistent tassel classification with slightly lower performance measures. The model was also subjected to a multidimensional scaling (MDS) analysis to investigate and comprehend misclassification. Male and female plants are clearly distinguished by MDS, but female and off-type/contamination plants are ambiguous. This indicates that the prediction errors were caused by highly similar data features among female and off-type images. The developed modern plant phenotyping model can be used to assist breeders/technicians in maintaining the quality of large-scale hybrid maize seed production activities in Indonesia.

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Jin, Yu, Zhe Ren, Wenjie Wang, Yulei Zhang, Liang Zhou, Xufeng Yao e Tao Wu. "Classification of Alzheimer's disease using robust TabNet neural networks on genetic data". Mathematical Biosciences and Engineering 20, n. 5 (2023): 8358–74. http://dx.doi.org/10.3934/mbe.2023366.

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<abstract><p>Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications. For performance evaluation, our proposed approach was compared with three novel DLs of multi-layer perceptron (MLP), neural oblivious decision ensembles (NODE), TabNet as well as five classical machine learnings (MLs) including decision tree (DT), random forests (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and support vector machine (SVM) on the public data set of gene expression omnibus (GEO). Moreover, the biological interpretability of global important genetic features implemented for AD classification was revealed by the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The results demonstrated that our proposed DGS-TabNet achieved the best performance with an accuracy of 93.80% for binary classification, and with an accuracy of 88.27% for multi-class classification. Meanwhile, the gene pathway analyses demonstrated that there existed two most important global genetic features of AVIL and NDUFS4 and those obtained 22 feature genes were partially correlated with AD pathogenesis. It was concluded that the proposed DGS-TabNet could be used to detect AD-susceptible genes and the biological interpretability of susceptible genes also revealed the potential possibility of being AD biomarkers.</p></abstract>

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Song, Yiyan, Shaowei Gao, Wulin Tan, Zeting Qiu, Huaqiang Zhou e Yue Zhao. "Multiple Machine Learnings Revealed Similar Predictive Accuracy for Prognosis of PNETs from the Surveillance, Epidemiology, and End Result Database". Journal of Cancer 9, n. 21 (2018): 3971–78. http://dx.doi.org/10.7150/jca.26649.

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Puttinaovarat, Supattra, e Paramate Horkaew. "Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation". Earth Science Informatics 12, n. 4 (25 giugno 2019): 429–46. http://dx.doi.org/10.1007/s12145-019-00387-y.

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Ahmed Taialla, Omer, Umar Mustapha, Abdul Hakam Shafiu Abdullahi, Esraa Kotob, Mohammed Mosaad Awad, Aliyu Musa Alhassan, Ijaz Hussain, Khalid Omer, Saheed A. Ganiyu e Khalid Alhooshani. "Unlocking the potential of ZIF-based electrocatalysts for electrochemical reduction of CO2: Recent advances, current trends, and machine learnings". Coordination Chemistry Reviews 504 (aprile 2024): 215669. http://dx.doi.org/10.1016/j.ccr.2024.215669.

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Bahrawi, Nfn. "Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based". Journal of Information Technology and Its Utilization 2, n. 2 (19 dicembre 2019): 29. http://dx.doi.org/10.30818/jitu.2.2.2695.

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Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings.

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Jain, Vanita, Monu Gupta, Neeraj Joshi, Anubhav Mishra e Vishakha Bansal. "E-College : an aid for E-Learning systems". Fusion: Practice and Applications 3, n. 2 (2021): 66–72. http://dx.doi.org/10.54216/fpa.030202.

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The use of Android apps has significantly increased over the past few years, making android the most accepted and trusted operating system for smart devices. According to a survey, in 2020, over 30.5 billion Android mobile apps were downloaded compared to merely 6 billion in 2016, which is quite noteworthy. People worldwide are also becoming habitual to the use of android apps such that they want everything to be available on their mobile devices. The authors have developed ‘E-College’ - an android-based mobile learning application that helps users grasp various programming languages like C++, Java, and Python and help them with their undergraduate computer engineering course curriculum-related resources. The proposed application allows the learner to access a particular programming language's tutorials and explains it with an example and the required code snippet. Using this application, users can also assess themselves by utilizing the provided quiz section, which has various questions. The proposed mobile application also has a recommendation system section that uses machine learning techniques to intelligently serve the most relevant and suitable content for each user. It considers the user's previous learnings on the application and suggests new and relevant learning content for that particular user. During the ongoing global pandemic situation, E-college application is the most effective way to learn and acquire technical skills without stepping out of their home and fraee of cost.

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Xu, Pufan, Fei Li e Haipeng Wang. "A novel concatenate feature fusion RCNN architecture for sEMG-based hand gesture recognition". PLOS ONE 17, n. 1 (20 gennaio 2022): e0262810. http://dx.doi.org/10.1371/journal.pone.0262810.

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Hand gesture recognition tasks based on surface electromyography (sEMG) are vital in human-computer interaction, speech detection, robot control, and rehabilitation applications. However, existing models, whether traditional machine learnings (ML) or other state-of-the-arts, are limited in the number of movements. Targeting a large number of gesture classes, more data features such as temporal information should be persisted as much as possible. In the field of sEMG-based recognitions, the recurrent convolutional neural network (RCNN) is an advanced method due to the sequential characteristic of sEMG signals. However, the invariance of the pooling layer damages important temporal information. In the all convolutional neural network (ACNN), because of the feature-mixing convolution operation, a same output can be received from completely different inputs. This paper proposes a concatenate feature fusion (CFF) strategy and a novel concatenate feature fusion recurrent convolutional neural network (CFF-RCNN). In CFF-RCNN, a max-pooling layer and a 2-stride convolutional layer are concatenated together to replace the conventional simple dimensionality reduction layer. The featurewise pooling operation serves as a signal amplitude detector without using any parameter. The feature-mixing convolution operation calculates the contextual information. Complete evaluations are made on both the accuracy and convergence speed of the CFF-RCNN. Experiments are conducted using three sEMG benchmark databases named DB1, DB2 and DB4 from the NinaPro database. With more than 50 gestures, the classification accuracies of the CFF-RCNN are 88.87% on DB1, 99.51% on DB2, and 99.29% on DB4. These accuracies are the highest compared with reported accuracies of machine learnings and other state-of-the-art methods. To achieve accuracies of 86%, 99% and 98% for the RCNN, the training time are 2353.686 s, 816.173 s and 731.771 s, respectively. However, for the CFF-RCNN to reach the same accuracies, it needs only 1727.415 s, 542.245 s and 576.734 s, corresponding to a reduction of 26.61%, 33.56% and 21.19% in training time. We concluded that the CFF-RCNN is an improved method when classifying a large number of hand gestures. The CFF strategy significantly improved model performance with higher accuracy and faster convergence as compared to traditional RCNN.

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Naeini, Ehsan Zabihi, e Kenton Prindle. "Machine learning and learning from machines". Leading Edge 37, n. 12 (dicembre 2018): 886–93. http://dx.doi.org/10.1190/tle37120886.1.

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Zhang, Shenghan, Yufeng Gu, Yinshan Gao, Xinxing Wang, Daoyong Zhang e Liming Zhou. "Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs". Geofluids 2022 (5 ottobre 2022): 1–31. http://dx.doi.org/10.1155/2022/9443955.

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From a general review, most petrophysical models applied for the conventional logging interpretation imply that porosity, permeability, or water saturation mathematically have a linear or nonlinear relationship with well logs, and then arguing the prediction of these three parameters actually is accessible under a regression of logging sequences. Based on this knowledge, ensemble learning technique, partially developed for fitting problems, can be regarded as a solution. Light gradient boosting machine (LightGBM) is proved as one representative of the state-of-the-art ensemble learning, thus adopted as a potential solver to predict three target reservoir characters. To guarantee the predicting quality of LightGBM, continuous restricted Boltzmann machine (CRBM) and Bayesian optimization (Bayes) are introduced as assistants to enhance the significance of input logs and the setting of employed hyperparameters. Thereby, a new hybrid predictor, named CRBM-Bayes-LightGBM, is proposed for the prediction task. To validate the working performance of the proposed predictor, the basic data derived from the member of Chang 8, Jiyuan Oilfield, Ordos Basin, Northern China, is collected to launch the corresponding experiments. Additionally, to highlight the validating effect, three sophisticated predictors, including k-nearest neighbors (KNN), support vector regression (SVR), and random forest (RF), are introduced as competitors to implement a contrast. Since ensemble learning models universally will cause an underfitting issue when dealing with a small-volumetric dataset, transfer learning in this circumstance will be employed as an aided technique for the core predictor to achieve a satisfactory prediction. Then, three experiments are purposefully designed for four validated predictors, and given a comprehensive analysis of the gained experimented results, two critical points are concluded: (1) compared to three competitors, LightGBM-cored predictor has capability to produce more reliable predicted results, and the reliability can be further improved under a usage of more learning samples; (2) transfer learning is really functional in completing a satisfactory prediction for a small-volumetric dataset and furthermore has access to perform better when serving for the proposed predictor. Consequently, CRBM-Bayes-LightGBM combined with transfer learning is solidly demonstrated by a stronger capability and an expected robustness on the prediction of porosity, permeability, and water saturation, which then clarify that the proposed predictor can be viewed as a preferential selection when geologists, geophysicists, or petrophysicists need to finalize a characterization of sandy-mud reservoirs.

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Turner, A., J. Fyfe, P. Rickwood e S. Mohr. "Evaluation of implemented Australian efficiency programs: results, techniques and insights". Water Supply 14, n. 6 (10 luglio 2014): 1112–23. http://dx.doi.org/10.2166/ws.2014.065.

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Australia has invested heavily in water efficiency in recent years, mainly due to severe drought, and has implemented some of the largest efficiency programs in the world. Despite this there is little public information on actual savings achieved or the cost effectiveness of programs implemented. This paper draws together the limited publicly available evaluations from Australia, focusing on the residential sector. It describes some of the large-scale residential programs implemented such as home retrofits, showerhead exchanges, washing machine rebates, toilet retrofits and rainwater tank rebates. It identifies key savings evaluation techniques used including participant-control and regression analysis, and summarizes water savings results from evaluation studies conducted. It also discusses key learnings from both the evaluation techniques employed and the programs implemented. The paper will be of significant interest to water service providers looking for evidence of actual savings achieved and/or wanting to understand how to evaluate their own programs.

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S.Sureshkumar, Et al. "Neural Network-Based Multiplicatively Gait Feature Eradication and Detection". International Journal on Recent and Innovation Trends in Computing and Communication 11, n. 4 (30 aprile 2023): 375–79. http://dx.doi.org/10.17762/ijritcc.v11i4.9843.

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This research proposes an innovative method based on machine learnings for extracting and identifying gait features from multiple sources. The method aims to enhance the accuracy of gait identification by minimizing interferences caused by complex backgrounds and shelters, thereby capturing more precise information that reflects the walking characteristics of moving individuals. The technical approach involves the acquisition of gait data using a video recorder and a pyroelectric IR sensor. The image source information obtained from the video recorder is utilized to extract skeleton feature variables and Radon difference peak characteristic variables. In addition, the pyroelectric IR source information is transformed from a voltage signal to frequency domain characteristic variables. These variables are then merged after undergoing dimension reduction and signal processing. Finally, a backpropagation neural network is employed as the classifier to perform classified identification based on the merged characteristics, and the identification accuracy is evaluated. The primary application of this method is in the field of identification.

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Trott, David. "Deceiving Machines: Sabotaging Machine Learning". CHANCE 33, n. 2 (2 aprile 2020): 20–24. http://dx.doi.org/10.1080/09332480.2020.1754067.

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Bonnevie, Erika, Jennifer Sittig e Joe Smyser. "The case for tracking misinformation the way we track disease". Big Data & Society 8, n. 1 (gennaio 2021): 205395172110138. http://dx.doi.org/10.1177/20539517211013867.

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While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

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Silva Pereira, Fernando. "A prova resultante de “software de aprendizagem automática”". Revista Electrónica de Direito 23, n. 3 (ottobre 2020): 79–98. http://dx.doi.org/10.24840/2182-9845_2020-0003_0006.

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Machine learning is a field of artificial intelligence that gives computers the ability to learn without being explicitly programmed, posing the problem of using the outputs of deep learning software as evidence in a judicial process. Focusing on Civil Procedure Law, this article aims to reflect on this problem, from the point of view of the admissibility and weight of such an evidence, giving close attention to the north-American experience, where the problem of the use of scientific and technic evidence has been largely discussed.

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