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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： machine learning algorithms Knowledge based systems Decision making Asia Games

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Guest Editorial Special Section on Signal Processing and machine learning in Intelligent Instrumentation, IEEE Open Journal of Instrumentation and Measurement

IEEE Open Journal of Instrumentation and Measurement

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IEEE Open Journal of Instrumentation and Measurement 2023年 2卷 1-2页

作者： Mukherjee, Anirban Gupta, Rajarshi Chatterjee, Amitava Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur721302 India

There has been tremendous interest in the development and deployment of Signal Processing and machine learning algorithms for almost all areas of instrumentation and measurement systems, starting from power systems, transportation, biomedical and healthcare, industrial measurements and automation, robotics and mechatronics, smart infrastructure, and facility management to aerospace and navigation. Their combination, signal processing and machine learning, is expected to dominate the next decade industrial developments. In order to embed the “intelligence” into the measurement, signal processing has been one of the ubiquitous techniques for quite some time. machine learning methods make these intelligent methods “experienced.” Because machine learning has been around in recent years, signal processing software–hardware systems equipped with machine learning are relatively mature. In this Special Section, a call for paper included (but were not limited to) the following areas.

关键词： Special issues and sections machine learning algorithms Signal processing algorithms Instrumentation and measurement Industrial engineering

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Exploring attitudes and behavioral patterns in residential energy consumption: Data-driven by a machine learning approach

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ENERGY AND CLIMATE CHANGE 2024年 5卷

作者： Fakhr, Bahereh Vojdani Yeganeh, Mansour Walzberg, Julien Ghayehbashi, Ahad Rezayan Univ Maryland Dept Urban & Reg Planning & Design College Pk MD USA Tarbiat Modares Univ Dept Architecture Digital Architecture & Artificial Intelligence Lab Tehran Iran Tarbiat Modares Univ Dept Architecture Architectural Design Modeling & Fabricat Lab Tehran Iran Polytech Montreal CP 6079Succ Ctr Ville Montreal PQ H3C 3A7 Canada Univ Tehran Fac Governance Futures Studies Dept Tehran Iran

The present study focuses on two main objectives: firstly, to clarify the mechanisms by which attitudes impact behavioral changes related to household energy consumption, and secondly, to offer valuable insights to enhance the understanding of residential energy usage through a novel technique called Support Vector Regression (SVR). This method employs several feature space transformations to convert nNar relationships into linear ones. The results highlight the crucial role of psychological factors in determining energy consumption behaviors, demonstrating that cognitive factors significantly influence attitudes and behavioral patterns. The findings show that psychological variables have a major role in determining how people consume energy, with cognitive variables having a particularly large impact on attitudes and behavior patterns. Our findings demonstrate the superior performance of Support Vector Regression (SVR) with radial basis function kernels over traditional predictive models, with a prediction accuracy of 93.7 % for changes in behavior patterns (CHP) and 94.4 % for changes in attitudes (CHA). These results highlight the value of applying cutting-edge machine-learning approaches to create precise models for comprehending and directing energy-saving actions. The policy implications suggest that reducing cognitive barriers can significantly encourage energy-saving behaviors and contribute to a comprehensive approach for energy-efficiency initiatives

关键词： Theoretical model Sustainable energy consumption Pattern of behavior Households machine learning algorithms Attitudes

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Predictive roles of environment, social, and governance scores on firms' diversity: a machine learning approach

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NANKAI BUSINESS REVIEW INTERNATIONAL 2025年第2期16卷 284-306页

作者： Koseoglu, Mehmet Ali Arici, Hasan Evrim Saydam, Mehmet Bahri Olorunsola, Victor Oluwafemi Metropolitan State Univ Coll Management St Paul MN USA Kastamonu Univ Fac Tourism Kastamonu Turkiye EU Business Sch Digital Campus Barcelona Spain Eastern Mediterranean Univ Famagusta Turkiye West Univ Timisoara East European Ctr Res Econ & Business ECREB Timisoara Romania Eastern Mediterranean Univ Fac Tourism Famagusta Turkiye

PurposeEnvironmental, social and governance (ESG) scores are compelling for firm strategy and performance. Thus, this study aims to explore ESG scores' predictive roles on global firms' diversity ***/methodology/approachA total of 1,114 global firm-year data from the Thomson Reuters Eikon database was analyzed using machine learning algorithms like rpart, support vector machine, partykit and *** results reveal a positive association between diversity, resulting in greater comprehensiveness and relevance. Broadly speaking, the two factors with the most significant values for calculating the overall diversity scores of businesses are ESG scores and social scores. ESG scores and environmental scores are the most effective predictors for the diversity pillar and people development scores. In contrast, community and social scores are the most important predictor factors for the inclusion ***/valueThe research is particularly pertinent to managers and investors considering ESG issues while making decisions. The results indicate that leaders and practitioners should prioritize ESG elements and diversity problems to enhance performance.

关键词： Firm diversity ESG scores machine learning algorithms International firms M14

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Synergizing First-Principles and machine learning: Predicting Steel Flatness in the Era of Digital Twins and Physics-Informed Intelligence 12th

Synergizing First-Principles and Machine Learning: Predictin...

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12th World Conference on Information Systems and Technologies (WorldCIST)

作者： Hallmanns, Nils Dunayvitser, Alexander Krambeer, Hagen Wolff, Andreas Lathe, Roger Goffin, Colin Feldges, Monika Adamyanets, Pavel Evers, Christoph VDEh Betriebsforschungsinst GmbH Sohnstr 69 D-40237 Dusseldorf Germany Thyssenkrupp Steel AG Duisburg Germany Thyssenkrupp Hohenlimburg GmbH Hagen Germany

ISBN: (纸本)9783031602269;9783031602276

Flatness in steel strips and sheets is a paramount quality metric in steel manufacturing, vital for ensuring product integrity and safety during production. Contemporary rolling processes adeptly maintain local flatness, but challenges arise particularly with thinner and high-strength steel grades. These complexities manifest as pronounced flatness deviations during subsequent processing and post-production phases. Merging traditional first-principle models with advanced machine learning algorithms, this new initiative achieves a comprehensive holistic grasp of the origins of flatness discrepancies. Taking into account the physical nature of the forming process. This synergy offers plausible prediction of cross-process flatness deviations while ensuring transparency and interpretability. Key advancements of this method are the development of predictive models, real-time flatness monitoring tools and a state-of-the-art digital twin structure connected to a network of software agents. Through sensitivity analysis, critical process parameters influencing flatness are identified. These outcomes not only elevate flatness control and product quality but also amplify plant efficiency. Ultimately, the research holds the potential for significant cost savings for steel producers, augmented product standards, and a dedicated focus on sustainable production with minimized environmental repercussions.

关键词： Flatness in steel machine learning algorithms Digital twin Industry 4.0 Big data Predictive analytics Smart manufacturing Steel Industry

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Switched Reluctance Motor Design Optimization: A Framework for Effective machine learning Algorithm Selection and Evaluation

Switched Reluctance Motor Design Optimization: A Framework f...

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Transportation Electrification Conference and Expo (ITEC)

作者： Omar, Mohamed Bakr, Mohamed Emadi, Ali McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Elect Res Inst Dept Power Elect & Energy Convers Syst Cairo Egypt

ISBN: (纸本)9798350317671;9798350317664

This study employs various machine learning algorithms (MLAs) to map out the stator and rotor pole arc angles of 6/14 switched reluctance motor (SRM) and their static and dynamic nonlinear characteristics. The MLAs under consideration include a back-propagation neural network, radial basis function neural network, generalized regression neural network, and conventional regression fitting algorithms. This work introduces an extensive analysis of these MLAs, including their structure, fundamentals, and learning process. Additionally, a comprehensive evaluation framework is established, encompassing assessments of training results, generalization capability, and computational time. It also addresses key challenges inherent in learning MLAs, specifically overfitting and underlining issues. These evaluation criteria guide the selection of the optimal machine learning topology tailored for geometry optimization in SRMs. The chosen MLA is then applied to predict the optimal pole arc angles that enhance the average torque and decrease torque ripples of the considered SRM.

关键词： machine learning algorithms Motor design Switched reluctance motors

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Analysis of the characteristics and environmental benefits of rice husk ash as a supplementary cementitious material through experimental and machine learning approaches

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2024年第4期9卷 1-19页

作者： Datta, Shuvo Dip Sarkar, Md. Mamun Rakhe, Arifa Sultana Aditto, Fahim Shahriyar Sobuz, Md. Habibur Rahman Shaurdho, Nur Mohammad Nazmus Nijum, Nusrat Jahan Das, Suman Khulna Univ Engn & Technol Dept Bldg Engn & Construct Management Khulna 9203 Bangladesh Univ Adelaide Sch Architecture & Civil Engn Adelaide 5005 Australia Khulna Univ Engn & Technol Dept Mech Engn Khulna 9203 Bangladesh

Rising cement consumption over the past few decades has become a huge environmental concern since Ordinary Portland Cement emits huge amounts of atmospheric CO2. Thus, governments and environmental organizations have required strict emission reduction measures by switching to greener cement additives. The significance of this experiment was to assess the implication of varying concentrations of rice husk ash (RHA) on mechanical characteristics with machine learning-based prediction, microstructural analysis and environmental impact assessment of concrete. This environmental impact investigation takes into account every stage of the life cycle of RHA concrete, from the extraction of raw materials and RHA manufacture to the final disposal and inclusion of RHA into concrete. A range of RHA weight percentages (0, 5, 10, 15, 20, 25 and 30%) was used to partly replace the cement. The most beneficial compressive and tensile strength results were shown in concrete and mortars that used 15% RHA as an alternative to cement. Additionally, incorporating RHA resulted in the most advantageous in terms of microstructural assessment and led to higher environmental resilience. Finally, the environmental impact assessment includes metrics such as the reduction of embodied CO2 equivalent (CO2-eq, measured in kg CO2) per MPa for RHA concrete, showcasing a considerable positive impact on the lowered global warming potential (GWP). Meanwhile, the mechanical characteristics are explored through the use of two distinct approaches to machine learning, extreme gradient boosting (XGBoost) and k-nearest neighbors (KNN), demonstrating the feasibility of precisely foreseeing the characteristics of mortar without the need for rigorous experimental work. XGBoost is chosen for its adaptability and excellent accuracy predictions for analyzing numerous data types and operations. In contrast, KNN is picked for its ease of detecting specific correlations and trends within the dataset. With a larger R2

关键词： Rice husk ash machine learning algorithms Mechanical properties Environmental impact XGBoost KNN

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Fundamental predictors of price bubbles in precious metals: a machine learning analysis

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MINERAL ECONOMICS 2024年第1期37卷 65-87页

作者： Uyar, Sinem Guler Kangalli Uyar, Umut Balkan, Emrah Pamukkale Univ Dept Econometr Ekonometri Bolumu IIBF Kinikli Kampusu TR-20100 Denizli Turkiye Pamukkale Univ Dept Business Adm Isletme Bolumu IIBF Kinikli Kampusu TR-20100 Denizli Turkiye Southern Aegean Dev Agcy Denizli Turkiye

In this study, we present a two-step method for predicting price bubbles in precious metals, which combines a widely recognized right-tailed unit root test to detect bubbles with various machine learning algorithms to pinpoint the potential predictors of bubble formation and their relative significance. We utilize logistic regression, support vector machines, CART, random forests, extreme gradient boosting, and neural networks algorithms, which are more precise than traditional methods in making predictions and can handle binary classification and regression issues. Our analysis covers monthly prices for gold, silver, palladium, and platinum from 1990M1-2022M10. The study extends the literature by utilizing the Generalized Supremum Augmented Dickey-Fuller test to identify potential price bubbles and analyzing the effect of macroeconomic, financial, and uncertainty factors on the likelihood of bubbles using machine learning algorithms. The findings indicate that macroeconomic factors play a significant role in the formation of price bubbles in precious metals;specifically the consumer confidence index in the USA was a common factor that had a positive impact on the likelihood of bubbles in gold, platinum, and silver. However, the leading factors in the formation of bubbles in palladium were found to be financial variables and uncertainty variables. As predicting bubbles is crucial for regulators and policymakers to take preventive measures against future crises, identifying the key predictors of bubble formation and forecasting them in the early stages is essential.

关键词： Precious metal prices Multiple bubbles Early warning signals machine learning algorithms

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Low-Voltage Alternating Current Series Arc Fault Detection Using Periodic Background Subtraction and Linear Dividing Lines

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IEEE ACCESS 2025年 13卷 47201-47216页

作者： Zhang, Xiaofei Li, Jinjie Han, Bangzheng Wang, Wei Zou, Guofeng Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China Pingyuan Cty Power Supply Co Dezhou 253000 Peoples R China

To address the challenges in fully expressing the fault features of low-voltage series arcs and the limitations of existing detection algorithms, this paper proposes a novel method combining periodic background subtraction and linear dividing lines for detecting arc faults. During feature extraction, a periodic background subtraction method is introduced, which calculates the difference between the periodic current signal and the average of the first four current periods nearest to the signal. This approach effectively suppresses interference from normal current variations and environmental noise, enabling a more robust expression of arc fault characteristics. For fault detection, a method based on linear dividing lines is developed to determine the optimal dividing line between fault and non-fault states for both linear and nonlinear load types via adaptive learning of the logistic regression model, achieving effective arc fault detection.

关键词： Feature extraction Circuit faults Fault detection Noise Time-frequency analysis Time-domain analysis Low voltage Fault diagnosis Reactive power Interference Arc discharges artificial intelligence electrical fault detection electrical safety fault currents feature extraction machine learning machine learning algorithms power system faults power system protection

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Mental Health Prediction using machine learning Models and Large Language Model 2

Mental Health Prediction using Machine Learning Models and L...

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2nd International Conference on Inventive Computing and Informatics (ICICI)

作者： Kamoji, Supriya Rozario, Sanika Almeida, Sania Patil, Shruti Patankar, Sanika Pendhari, Heenakausar Fr Conceicao Rodrigues Coll Engn Dept Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9798350373301;9798350373295

Mental health is integral to overall well-being, impacting human ability to deal with challenges in life. machine learning and AI hold promise in predicting mental illnesses by analysing behavioural patterns, aiding in early detection and intervention. This proactive approach can mitigate symptom escalation, improving mental health outcomes. This research study introduces a novel predictive framework integrating ensemble learning techniques and large language models (LLMs). Initially, ensemble learning, including AdaBoost, voting, and bagging, constructs a robust model with Random Forest emerging as optimal. Subsequently, a Large Language Model (LLM) enhances the pipeline. User input triggers mental health prediction by Random Forest, forwarded to a Google Gemini model via an API key, generating personalised insights, marking a significant advancement in mental health prediction.

关键词： Mental Health Prediction machine learning algorithms Artificial Intelligence(AI) Large Language Model (LLM) Early Detection In Behavioural Patterns

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