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Research on Game Analysis and Prediction Based on ARIMA Time Series Model 3

Research on Game Analysis and Prediction Based on ARIMA Time...

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2023 IEEE 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Pi, Tuo Liu, Chong Wu, Yuchen Li, Shiyuan Hu, Huiying Dalian Polytechnic University School of Mechanical Engineering and Automation Dalian China Hubei University of Art and Science Food Science Technology College•Chemical Engineering College Xiangyang China

ISBN: (纸本)9798350341546

With the improvement of people's living quality and technology level, video games have become the product and trend which formed by the gradual evolution of people's entertainment forms. The New York Time currently offers a popular puzzle called Wordle everyday, where each player gets 6 chances to guess a 5-letter word[1]. A scatter plot has been drawn about the number of reported results varies over time according to the data file, fitting the curve. Therefore this passage finds that the number of reported results had risen sharply over time first in a short time, then it declines slowly over time. Its influence has been declined. Then the ARIMA time series model[2] is used to predict the interval for the number of reported results on March 1, 2023, that is, [10409, 10446]. At the same time, the word attribute is defined, which includes the frequency of word in books, the repetition of letters in a word and the part of speech. For the frequency of word, the data from Goole Books Ngram Viewer[3] has been found, standardized method is considering the number of occurrences per million words as frequency of word. The passage establishes the Pearson model[4] to analyse the correlation between attributes of words and the percentage of scores reported that were played in Hard Mode. The three attributes of the word do affect the percentage of scores reported that were played in Hard Mode and the the frequency of word has the greatest influence. Then, The TOPSIS model[5] was used to classify the words according to their difficulty, which is divided three types:hard, moderate difficulty, easy. Categorize each word by giving it an overall score. The wore'EERIE'belongs to the hard, which conform the result studied before. Hence the accuracy of the model is high. The above can help game developers or decision makers make more accurate decisions, thus increasing the popularity of the game. © 2023 IEEE.

关键词： ARMIMA Pearson TOPSIS

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Adaptive Fuzzy Fault-Tolerant Event-Triggered Consensus for Switched Nonlinear Multi-agent Systems 1

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Chen, Yongsheng Zhang, Ronghao Li, Shi Zou, Wencheng School of Electrical and Automation Engineering Nanjing Normal University Nanjing210023 China School of Automation Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9789819710836

ISBN: (纸本)9789819710829

In this paper, an adaptive fuzzy fault-tolerant event-triggered consensus problem is considered for the switched nonlinear multi-agent systems (SNMASs). In the considered systems, the dynamics of agents are switching and heterogeneous. In the process of protocol design, the unknown functions are approximated by the fuzzy logic systems (FLSs). A distributed controller and an event-triggering mechanism (ETM) are derived to reduce the wastage of communication resources (WCRs). By constructing a common Lyapunov function (CLF), the consensus tracking objective can be achieved and all states of the closed-loop system are bounded. At last, the effectiveness of the scheme developed in this paper is verified by simulation results. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Multi agent systems

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A Multi-Objective Optimization Register Clustering Method 3

A Multi-Objective Optimization Register Clustering Method

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3rd international conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Liu, Ningzhuang Deng, Hanyu College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The clock tree synthesis (CTS) step is critical for the physical design of ultra-large-scale integrated circuits (VLSI). Outstanding CTS results can reduce the skew of clock networks, decrease chip power consumption while improving performance and reliability of the chip. This paper proposes a register clustering method based on the nondominated sorting genetic algorithm II (NSGA-II) algorithm to generate the leaf-level topology of the clock tree, addressing multi-objective optimization issues in integrated circuit physical design. We model register clustering as a multi-objective optimization problem. By analyzing the clock network model and power consumption to design the objective functions, register clustering schemes are encoded. Pareto optimal solutions are obtained through iterative evolution and non-dominated sorting. This method is integrated into the traditional CTS process. Three circuits were selected from the ISCAS89 benchmark circuit for testing and analysis proving its effectiveness. Experimental results show that compared to traditional clock tree synthesis methods, our approach reduces timing skew by more than 5%, maximum clock delay by over 15%, and power consumption by over 10%. Our strategy achieves a balance among multiple physical design objectives, obtaining superior clustering schemes. This paper offers an effective algorithm for multi-objective register clustering. © 2024 SPIE.

关键词： Electric power utilization

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Research on the Key technology Identification of Supply Chain Based on Entropy Weight and TOPSIS Method 3

Research on the Key Technology Identification of Supply Chai...

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3rd international conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2023

作者： Zhang, Ke Chu, Jiayi Chen, Yi School of Management Wuhan University of Technology Hubei Wuhan430070 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan430070 China School of International Education Wuhan University of Technology Hubei Wuhan430070 China

ISBN: (纸本)9781643684581

With the development of global economy and the intensification of market competition, identifying and applying appropriate key technologies has become the key for enterprises to achieve excellent performance in supply chain. This paper proposes an entropy weight and TOPSIS method for identifying key technologies in the supply chain. It addresses the issue of neglecting indicator weights in traditional evaluation methods and improves objectivity and accuracy. The method involves constructing an indicator system through literature research and expert questionnaires, calculating weights using entropy weight and TOPSIS, and evaluating and ranking technologies. © 2023 The Authors.

关键词： Supply chains

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd international Semantic Intelligence conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Harnessing Ridge Regression and SHAP for Predicting Student Grades: An Approach Towards Explainable AI in Education 3rd

Harnessing Ridge Regression and SHAP for Predicting Student ...

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3rd international Semantic Intelligence conference, ISIC 2023

作者： Katkar, Vijay Kadam, Swapnil Mulla, Juber Nadaf, Niyaj Department of Computer Science and Engineering Annasaheb Dange College of Engineering and Technology Maharashtra Sangli India

ISBN: (纸本)9789819773558

This paper presents a comparative analysis of several regression techniques for predicting student academic performance, with a particular focus on Ridge Regression and its interpretability through SHAP, a method of Explainable Artificial Intelligence (XAI). The models evaluated include Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Decision Tree Regression, Random Forest Regression, AdaBoost, Gradient Boosting, Bagging, XGBoost, and K-Nearest Neighbors. Using a dataset encompassing student family background, personal information, and recent academic grades, we trained and evaluated these models to predict future academic performance. Experimental results demonstrated that Ridge Regression outperformed all other models in predictive accuracy with the highest r2 score of 0.9036 and lowest RMSE of 1.4623. Furthermore, we employed SHAP values to interpret the predictions made by the Ridge Regression model, revealing key feature contributions to the model’s output. This research underscores the potential of Ridge Regression and SHAP in building predictive and interpretable models in the educational domain. This fusion of predictive accuracy and interpretability provides invaluable insights for educators and policy-makers alike in understanding and enhancing student academic achievement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Human-cantered Artificial intelligence in Education, Present and future opportunities 3

Human-cantered Artificial intelligence in Education, Present...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Mittal, Arun Ramachandran, K.K. Lakshmi, K.K. Hasbullah, Nornajihah Nadia Ravichand, M. Lourens, Melanie Birla Institute of Technology Department of Management Uttar Pradesh India Dr G R D College of Science Management/Commerce/International Business India Dwaraka Doss Goverdhan Doss Vaishnav College The Department of English India Faculty of Business and Management Melaka Malaysia Mohan Babu University School of Liberal Arts and Allied Sciences India Durban University of Technology Faculty of Management Sciences South Africa

ISBN: (纸本)9798350399264

Artificial intelligence (AI), which was once only a pipe dream, is now a reality, permeating every facet of our life - including education - and becoming a regular part of our daily existence. Although it is still a young field, as time goes on, we shall see how AI develops and discover its unrealized potential. In light of the foregoing, this chapter explores the most recent developments in AI and its potential applications in a variety of fields, including Natural language, computer vision, and supervised learning. To achieve this, networking websites analysis (SNA) is used as a basis for the comprehension of the most central themes in Academic research from the perspective of education. Three main topics arose from the investigation: (1) individualized training, learning styles, and adaptive learning;(2) advanced tutoring systems and adviser systems;and (3) Intelligence as a possible improvement to educational institutions, practices in the future. Benefits of e - learning (LMS) offer the best alternative for supplying teaching programs in higher education by giving students from all across the world top-notch resources. As a bridge, student-teacher learning management programmes are utilized. This paradigm is altered by intelligent agents who emulate the clever strategies employed by the teacher and the pupils. Ai - powered technologies are constructed on the duplication of principles that are necessary for human understanding. This analysis includes the analysis of the Modules that utilize AI methods. The emphasis also includes more instances of the huge variation. Learning administration system techniques related to AI have been researched to help students, faculty and staff, and administrations and to overcome some LMS issues. © 2023 IEEE.

关键词： E-learning

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A design of a fishing net depth sensor applied to wireless trawl monitoring system 3

A design of a fishing net depth sensor applied to wireless t...

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3rd international conference on Digital Signal and computer Communications, DSCC 2023

作者： Du, Peng Ni, Xiuhui Zheng, Yi Gong, Xiaodong 37 Peremohy Ave. Kyiv03056 Ukraine 37 Peremohy Ave. Kyiv03056 Ukraine National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Ukraine Qingdao Maritech Technology Co. Ltd. China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

In order to enable fishermen to better obtain information about the distance of the net from the bottom of the sea and the distance between the upper and lower net outlines, this paper proposes a wireless trawl monitoring system fishing net depth sensor with MSP430FR5994 as the main processor, which has wireless communication capability and overcomes the inconvenience of cable operation. The wireless trawl monitoring system fishing net depth sensor can collect the depth information of fishing net underwater and transmit the information back to the deck computer on the fishing vessel through wireless communication. This allows the fishermen on board to obtain information about the underwater position of the nets. The paper specifically describes the design of the transceiver algorithm and the design of the fishing net depth sensor. © 2023 SPIE.

关键词： Radio transceivers

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Reducing The Assessment Cost of "Two Rules" Through Bi-level Programming Configuration of Energy Storage in Wind Farms 3

Reducing The Assessment Cost of "Two Rules" Through Bi-level...

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3rd IEEE international conference on Intelligent technology and Embedded Systems, ICITES 2023

作者： Dang, Zhehui Gao, Jiaqi Yang, Pengcheng Wang, Yaying Tian, Songtao Hu, Bangan Datang Liangshan Renewable Power Company Limited Sichuan China College of Electrical Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350343755

The National Energy Administration of China has issued the "Implementation Measures for Power Grid Operation and Management" and the "Implementation Measures for Auxiliary Power Services Management" (Two Rules), which assess the relevant indicators of power generation plants involved in grid safety and stability. Due to the significant uncertainties caused by factors such as climate and environment, the output power of wind farms faces higher assessment costs in terms of accuracy of power prediction and power fluctuations. To reduce the assessment costs for wind farms under the Two Rules, this paper proposes a method for optimizing the allocation of energy storage system (ESS) capacity in wind farms. It generates and reduces the description of uncertainties through wind power output scenarios, and solves the optimal configuration of ESS capacity by utilizing an improved Grey Wolf Optimization (GWO) and the Yalmip toolbox for iterative bilevel programming. The results of the case analysis demonstrate that the proposed method effectively solves the optimal configuration of ESS capacity in wind farms, and exhibits good applicability in reducing assessment costs, improving wind farm prediction accuracy, and mitigating wind power fluctuations. © 2023 IEEE.

关键词： Energy storage

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Research on Community Discovery Method of Complex Network Based on Density Peak Clustering and Its Application 3

Research on Community Discovery Method of Complex Network Ba...

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3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Wang, Teng Guo, Shijie He, Miao Li, Yatong School of Mathematics and Physics Southwest University of Science and Technology Mianyang621010 China School of Civil Engineering and Architecture Southwest University of Science and Technology Mianyang621010 China School of Electrical Information Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9798350331387

para>Nowadays, examples of complex networks are everywhere, such as biological networks, transportation networks and power networks. Community discovery is a basic task of complex network analysis. In complex network analysis, analyzing community structure and extracting information are crucial research tasks. The existing research starts with the density peak clustering algorithm and obtains a variety of methods to divide complex network communities, but the sensitivity of cutoff distance dc is not considered in the research of density peak clustering algorithm. Based on this, this paper proposes a new method of density peak clustering in complex network communities (GWO-DPC-Node2vec) based on grey wolf optimization. Firstly, the grey wolf optimization algorithm is iterated to find the optimal dc value of the density peak species silhouette coefficient. Compared with the classical algorithm, the stability and superiority of the improved density peak clustering algorithm are proved. Then, the nodes in the network are transformed into vectors through Node2vec, and the grey wolf optimization algorithm is run to search the cutoff distance with the maximization of Q value of modularity as the optimization goal. The searched dc results are applied to the density peak clustering algorithm, so that the division results can be obtained. Finally, the stability and superiority of the complex network community discovery method proposed in this paper are demonstrated by real network data sets. © 2023 IEEE.

关键词： Complex networks

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