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Research on Complex Decision Making Strategies Based on Improved Particle Swarm Optimisation and Machine learning Techniques

Research on Complex Decision Making Strategies Based on Impr...

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Electrical, Automation and Computer Engineering (ICEACE), international conference on

作者： Zixi Wang Ocean University of China Qingdao China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

In this paper, we focus on the problem of formulating an optimal decision-making strategy, with special attention to the use of multidimensional data and information, taking into account site distribution characteristics, market risk dynamics and cross-price elasticity between products [1]. Firstly, we propose an innovative decision-making method for the stability problem, i.e., a nonlinear planning model and an improved particle swarm optimisation (PSO) algorithm for solving the problem, which shows an increase in annual return of about 15% [2], [3]. Secondly, to cope with the risk volatility problem, a truncated normal distribution is introduced and dynamically optimised using the PSO algorithm with penalties, and the 7-year prediction results show a significant improvement in the robustness of the system [4]. Finally, considering the substitutability and complementarity between products, a constrained particle swarm optimisation model was constructed and a sensitivity analysis was carried out using the cross-price elasticity and Spearman's correlation coefficient matrix optimisation algorithms, and the optimisation scheme reduced the overall volatility of returns by about 10% [5]. By comprehensively applying advanced optimisation algorithms and machine learning techniques in computer science, this study effectively solves multidimensional problems in complex decision-making, demonstrating the great potential and effectiveness of these techniques in practical applications [6].

关键词： Machine learning algorithms Sensitivity analysis Heuristic algorithms Decision making Machine learning Elasticity Prediction algorithms Robustness Particle swarm optimization optimization

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Karting racing: A revisit to PPO and SAC algorithm 4

Karting racing: A revisit to PPO and SAC algorithm

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4th international conference on Computer Information Science and Artificial Intelligence, CISAI 2021

作者： Xu, Chengyuan Zhu, Ruijie Yang, Dongce Beijing University of Post and Telecommunication BUPT School of Communication Engineering Beijing China Northwestern Polytechnical University NWPU Honors College Xi'an China Inner Mongolia University School of Software Engineering Chengdu China

ISBN: (纸本)9781665406925

Proximal Policy optimization (PPO) is a classical algorithm in reinforcement learning, which has been tested in a collection of benchmark tasks. In this paper, we test PPO in Unity environment to train Karting racing agent, with different parameters and different training settings. In our experiments, the improved PPO algorithm has a better performance in convergence rate and practical results (the average speed of agent) than baseline and other algorithms, such as Soft Actor-Critic (SAC). Visually, the agent trained by PPO appears more stable, which is able to cope with challenging tracks and has better generalization ability when we turned our trained model into a new environment. Our improved algorithm enables several applications, such as auto-pilot and UAV navigation. © 2021 IEEE.

关键词： Reinforcement learning

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Design and Evaluation of a Deep learning Model for Counting Objects in Satellite Images

Design and Evaluation of a Deep Learning Model for Counting ...

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Data Engineering and Communication Systems (ICDECS), international conference on

作者： R. Raja Subramanian Nithish Paidimarri Balaji Navuluri Bobby Sai Vignesh Oleti Mohan Mahesh Boogavarapu Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhnagar Tamilnadu India

Images obtained by satellites orbiting in space of the surface of the Earth or other celestial bodies are known as satellite images. these photos are an excellent resource for a variety of applications and have several uses. applications are of earth observation, remote sensing and GIS etc. they are essential to tackling global issues and expanding scientific knowledge, and they play a crucial part in modern technology. Automated object counting in satellite images is a computer vision and image processing technique used to identify and quantify specific objects or features within images captured by Earth-observing satellites. After performing image preparation steps like using the CNN method and reducing noise, we must then recognize objects in the picture. Object counting has wide range of applications like Urban planning development, Military and Defence etc… In essence, automatic item counting in satellite photos helps businesses and researchers effectively obtain important information and make defensible choices. It considerably improves the capacity to manage and analyse extensive geographic data for a variety of applications.

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Gene Expression Analysis of Solanum lycopersicum - Bacillus megaterium Interaction to Identify Informative Genes Using Machine learning Classifiers 2nd

Gene Expression Analysis of Solanum lycopersicum - Bacillus ...

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2nd international conference on optimization, learning algorithms and applications (OL2A)

作者： Rodrigues, Vania Deusdado, Sergio USAL Univ Salamanca Salamanca 37008 Spain Inst Politecn Braganca CIMO Ctr Invest Montanha P-5301855 Braganca Portugal

ISBN: (纸本)9783031232350;9783031232367

there has been a growing interest in identifying specific plant growth-promoting rhizobacteria that confer health, growth, and protective benefits to plant host. Understanding the mechanisms of this association as well as the differences that determine the different outcomes can be exploited to optimize beneficial interactions. To this end, we developed a classifier capable of predicting the presence of Bacillus megaterium inoculated in tomato root tissue and identify potential informative genes related to their interaction. Two machine learning models, Kernel Logistic Regression and Multilayer Perceptron were studied. From the 4 Multilayer Perceptron classifiers tested (MLP-a, MLP-b, MLP-c and MLP-d) with different parameters, MLP-a and MLP-c achieved near optimal performance considering all the relevant metrics. then, these classifiers were used as attribute evaluators to identify two sets of informative genes (IGs). MLP-a showed 216 highest-rated attributes. Among these IGs, 173 were identified as Solanum lycopersicum genes, 37 were assigned to 5 Bacillus subtilis protein, 4 were assigned to 1 Escherichia coli protein and 2 were unidentified. On the other hand, MLP-c showed the same highest-rated attributes adding 27 new attributes. Based on the results of MLP-a and MLP-c, considering the identified tomato IGs, a functional enrichment analysis was developed showing nine and eight biological pathways, respectively. Furthermore, the same IGs were used to compose biological networks from Arabidopsis thaliana orthologous genes. the biological networks identified for the first set were co-expression, shared protein domains, predicted interaction and co-localization. the second set presented the same networks adding physical interaction.

关键词： Machine learning Informative genes Tomato - Bacillus megaterium interaction

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Map Reduce Techniques for Machine learning in Big Data Analytics

Map Reduce Techniques for Machine Learning in Big Data Analy...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Sanjeev Kukreti Hassan M. Al-Jawahry Anurag Shrivastava Saloni Bansal Hemanth Raju Rowsonara Begum Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Engineering and Applications GLA University Mathura Uttar Pradesh India Department of Mechanical Engineering New Horizon College of Engineering Bangalore Karnataka India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

One of the most groundbreaking approaches to the processing and analysis of large datasets has been created as a result of the combination of MapReduce techniques and machine learning algorithms within the framework of big data analytics. the problems of traditional data processing in terms of handling the volume, velocity, and variety of big data are addressed by the distributed computing architecture introduced by MapReduce. this research investigates the ways in which MapReduce may be used to simplify the process of machine learning, with a particular focus on the capabilities of the framework to manage enormous volumes of data and to parallelize operations. By contrasting the performance of MapReduce with that of more traditional methods, the study solves the problems of scalability and computing efficiency that are brought about by massive amounts of data. through an in-depth analysis, the paper highlights the optimization potential of MapReduce as well as its implications for next-generation machine learning applications across a variety of industries.

关键词： Industries Technological innovation Machine learning algorithms Scalability Machine learning Computer architecture Big Data Informatics optimization Next generation networking

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Leveraging Machine learning for Anomaly Detection Methods in Cryptocurrency: A Data-Driven Study

Leveraging Machine Learning for Anomaly Detection Methods in...

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international conference on optimization and applications (ICOA)

作者： Achraf Yahia Yassine Mouhssine Abdelkader Elalaoui Said Ouatik Elalaoui Engineering Sciences Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco Laboratory of Analyzing and Modeling Systems for Decision Support University Hassan I Settat Morocco Rabat Business School International University of Rabat Rabat Morocco

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

Cryptocurrencies like Bitcoin and Ethereum have experienced remarkable growth but also high volatility, making anomaly detection crucial for predictive modeling and risk management. this study evaluates three unsupervised machine learning algorithms - Local Outlier Factor, One-Class Support Vector Machine, and Isolation Forest - for detecting anomalies in Bitcoin and Ethereum prices and returns from January 2017 to May 2024. Local Outlier Factor is benchmarked against One-Class Support Vector Machine and Isolation Forest using mean absolute error (MAE), root mean squared error (RMSE), and explained variance score (EVS) as performance metrics. Results demonstrate Local Outlier Factor outperforms One-Class Support Vector Machine and Isolation Forest in detecting return anomalies for both cryptocurrencies. While Local Outlier Factor and Isolation Forest exhibit similar global anomaly detection capability, Local Outlier Factor achieves lower RMSE, indicating superior efficiency in capturing cryptocurrency market dynamics. One-Class Support Vector Machine lagged the other two methods across all metrics. the findings highlight the efficacy of density-based local outlier detection techniques like Local Outlier Factor for cryptocurrency applications. this study provides valuable insights for developing robust anomaly detection systems to enhance risk management and trading strategies in the highly volatile cryptocurrency market.

关键词： Support vector machines Measurement Machine learning algorithms Decision making Forestry Bitcoin Machine learning Predictive models Risk management Anomaly detection

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Enhanced Ransomware Detection Techniques using Machine learning algorithms 4

Enhanced Ransomware Detection Techniques using Machine Learn...

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4th international conference on Computing and Communications Technologies, ICCCT 2021

作者： Usha, G. Madhavan, P. Vimal Cruz, Meenalosini Vinoth, N A S Veena Nancy, Maria Srm Ist Ktr Dept. of Computing Technology India Allen E. Paulson Georgia Southern University Dep of Information Tehnology United States

ISBN: (纸本)9781665414470

A challenge that governments, enterprises as well as individuals are constantly facing is the growing threat of ransomware attacks. Ransomware is a type of malware that encrypts the user's files and then demands a huge sum of money from the user. this increasing complexity calls for more advancement and innovative ideas in defensive strategies used to tackle the problems. In this paper, firstly we discuss the existing research in the field of ransomware detection techniques and their shortcomings. Secondly, a juxtaposed study on various machine learning algorithms to detect ransomware attacks is compared for ransomware dataset. thirdly, various behavioral data such as API Calls, Target files, Registry Operations, Signature, Network Accesses are collected for each ransomware and benign sample and the results are compared for various attributes to understand the behavior of the attack. In order to understand the behavior of the attack various Machine learning algorithms like KNN, Naïve Bayes, Random Forest, Decision Trees are used for training and testing the dataset. Further optimization was done using hyper parameters to control the learning process. Finally, we have used the model(s) Accuracy, F1 Score, Precision and Recall to compare the results observed and suggesting how the roadmap for how efficiently the attacks can be prevented in future. © 2021 IEEE.

关键词： Decision trees

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Machine learning algorithms used in Crop Yield Prediction for Precision Agriculture

Machine Learning Algorithms used in Crop Yield Prediction fo...

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Soft Computing for Security applications (ICSCSA), international conference on

作者： Umamaheswari.R S.Shanmugapriya P.Vimala R.Ganesan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Chennai Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. these algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture.

关键词： Productivity Training Surveys Machine learning algorithms Rain Soil Predictive models Prediction algorithms Crop yield Meteorology

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A Music Recommender System to Identify the Emotional State of a Person Using Machine learning algorithms

A Music Recommender System to Identify the Emotional State o...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Muruganantham A C. Malarvizhi Divya Vahini. S D. Saranya A. Uthiramoorthy Department of Computer Science (PG) Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science PSG Institute of Advanced Studies Coimbatore India Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Department of Information Technology Sri Eshwar College of Engineering Coimbatore Department of Computer Science Rathinam College of Arts and Science

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. the system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods--with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. this study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be--make tailored entertainment paths in the digital era.

关键词： Support vector machines Emotion recognition Recurrent neural networks Machine learning algorithms Accuracy Mood Face recognition Computational modeling Convolutional neural networks Recommender systems

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17th conference on the theory of Quantum Computation, Communication and Cryptography, TQC 2022

17th Conference on the Theory of Quantum Computation, Commun...

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17th conference on the theory of Quantum Computation, Communication and Cryptography, TQC 2022

ISBN: (纸本)9783959772372

the proceedings contain 12 papers. the topics discussed include: quantum algorithms for learning a hidden graph;quantum algorithm for stochastic optimal stopping problems with applications in finance;the parametrized complexity of quantum verification;averaged circuit eigenvalue sampling;classical simulation of quantum circuits with partial and graphical stabilizer decompositions;on converses to the polynomial method;the quantum approximate optimization algorithm at high depth for MaxCut on large-girth regular graphs and the Sherrington-Kirkpatrick model;a constant lower bound for any quantum protocol for secure function evaluation;approximating output probabilities of shallow quantum circuits which are geometrically-local in any fixed dimension;and memory compression with quantum random-access gates.

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