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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是61-70 订阅

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Strategies for Determining Effective Step Size of the Backpropagation Algorithm for On-Line learning 7

Strategies for Determining Effective Step Size of the Backpr...

引用

7th international conference of soft computing and Pattern Recognition (SoCPaR)

作者： Kaneda, Yuya Zhao, Qiangfu Liu, Yong Pei, Yan Univ Aizu Dept Comp & Informat Syst Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781467393607

In this paper, we investigate proper strategies for determining the step size of the backpropagation (BP) algorithm for on-line learning. It is known that for off-line learning, the step size can be determined adaptively during learning. For on-line learning, since the same data may never appear again, we cannot use the same strategy proposed for off-line learning. If we do not update the neural network with a proper step size for on-line learning, the performance of the network may not be improved steadily. Here, we investigate four strategies for updating the step size. they are 1) constant, 2) random, 3) linearly decreasing, and 4) inversely proportional, respectively. the first strategy uses a constant step size during learning, the second strategy uses a random step size, the third strategy decreases the step size linearly, and the fourth strategy updates the step size inversely proportional to time. Experimental results show that, the third and the fourth strategies are more effective. In addition, compared with the third strategy, the fourth one is more stable, and usually can improve the performance steadily.

关键词： Decision Boundary Making Multilayer Perceptron Backpropagation Algorithm On-Line learning

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Decision Model of Ship Intelligent Collision Avoidance Based on Automatic Information System Data and Generic Adversary Imitation learning-Deep Deterministic Policy Gradient 23

Decision Model of Ship Intelligent Collision Avoidance Based...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Liu, Jiao Shi, Guoyou Zhu, Kaige Shi, Jiahui Wang, Yuchuang Navigation College Dalian Maritime University Liaoning Dalian116026 China Key Laboratory of Navigation Safety Guarantee of Liaoning Province China

ISBN: (纸本)9781450398633

Aiming at the problems that the current decision-making model of ship collision avoidance does not consider international Regulations for Preventing Collisions at Sea (COLREGS), ship maneuverability, and the need for a lot of training time, combined with the advantages of reinforcement learning and imitation learning, a ship intelligent collision avoidance decision-making model based on Generic Adversary Imitation learning (GAIL) is proposed: Firstly, the collision avoidance data in Automatic Information System (AIS) data is extracted as expert data;Secondly, in the generator part, the environment model is established based on Mathematical Model Group (MMG) and S-57 chart rendering, and the state space, behaviour space and reward function of reinforcement learning are constructed. the deep deterministic policy gradient (DDPG) is used to interact with the environment model to generate ship trajectory data. At the same time, the generator can constantly learn expert data;Finally, a discriminator can distinguish the expert data from the data generated by the generator is constructed and trained. the model training is completed when the discriminator cannot distinguish the two. In order to verify the performance of the model, AIS data near the South China Sea is used to process and extract collision avoidance decision data, and a ship intelligent collision avoidance decision model based on GAIL is established. After the model converges, the final generated data is compared with the expert data. the experimental results verify that the model proposed in this paper can reproduce the expert collision avoidance trajectory and is a practical decision model of ship collision avoidance. © 2023 ACM.

关键词： Collision avoidance

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Joint Action Representation and Prioritized Experience Replay for Reinforcement learning in Large Discrete Action Spaces 23

Joint Action Representation and Prioritized Experience Repla...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Wei, Xueyu Xue, Wei Zhao, Wei Shen, Yuanxia Yu, Gaohang School of Computer Science and Technology Anhui University of Technology Maanshan243032 China Department of Mathematics School of Science Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781450398633

In dealing with the large discrete action spaces, a joint action representation and prioritized experience replay method is proposed in this paper, which consists of three modules. In the first module, we use the k-nearest neighbor method to reduce the dimensionality of the original action space, generating a compact action space, and then the critic network is introduced to further evaluate and filter this compact space to obtain the optimal action. Note that the optimal action may have inconsistency with the actual desired action. then in the second module, we introduce a multi-step update technique to reduce the training variance when storing data in the replay buffer. In the third module, considering the existence of correlation between samples when sampling data, we assign the corresponding weight to the sample experience by calculating the absolute value of temporal difference error and use such a non-uniform sampling method to prioritize the samples for sampling. Experimental results on four benchmark environments demonstrate the effectiveness and efficiency of the proposed method in dealing with the large discrete action spaces. © 2023 ACM.

关键词： Reinforcement learning

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Enhancing Cardiovascular Disease Prediction Using Ensemble learning 7

Enhancing Cardiovascular Disease Prediction Using Ensemble L...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ambade, Sharwari Chikmurge, Diptee School of Computer Engineering Mit Academy of Engineering Alandi India

ISBN: (纸本)9798350304268

the proposed ensemble learning framework integrates diverse machine learning algorithms. Each base model is trained on a diverse set of features derived from comprehensive patient data. To evaluate the ensemble model's performance, a large-scale dataset comprising anonymized electronic health records from a diverse patient population is employed. the dataset includes longitudinal data, allowing for the incorporation of temporal information. Preliminary results demonstrate that the ensemble learning approach outperforms individual models in terms of predictive accuracy and stability. Moreover, the model exhibits robustness across different subpopulations, indicating its potential for generalizability and applicability in diverse healthcare settings. the accuracy metrics is being utilized as a foundation for assessing the effectiveness of all classifications. With an accuracy of 93.01% the stacking classifier has proven to be the finest model. © 2023 IEEE.

关键词： Cardiovascular disease Ensemble learning machine learning algorithms

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Prediction on Impact of Electronic Gadgets in Students Life using machine learning 7

Prediction on Impact of Electronic Gadgets in Students Life ...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Saraswathi, K. Devadharshini, B. Kavina, S. Srinidhi, S. Department of Computer Technology Kongu Engineering College Erode India

ISBN: (纸本)9781665464086

Nowadays electronic gadgets play an important role in students' life as a source of learning. the Dependency of services provided by electronic gadgets has reached a large scale. Electronic gadgets like smart phones have a major impact on people in their day-to-day life. Among all, students are the important one, as they rely on electronic gadgets for their academic activities. the major impact is that it can affect the students mental and physical health. Students are getting addicted to these electronic gadgets as it becomes inevitable. this study uses machine learning techniques to demonstrate how gadgets affect students' daily lives. To examine the addictiveness of gadgets among the students, a questionnaire has been circulated to get to know the student's necessity on electronics. the parameters include how many electronic devices they use and how long they use them for, whether the usage of electronic gadgets shows any improvement in their academic performance. machine learning employs the preprogrammed algorithms, to predict output values for the given input data. It is considered to be an aspect of artificial intelligence. machine learning algorithms are used in a variety of fields, such as computer vision, voice recognition, medicine etc. Where it is difficult or impractical to create conventional algorithms to perform the necessary tasks. the collected dataset is taken to analyse the performance of prediction for various machine learning algorithms like K-Nearest Neighbour, Random Forest, Decision Tree, Logistic regression, Support Vector machine. As a result of this study, accuracy of several performance measures were evaluated. In the future the performance of an algorithm can be improved by using optimization techniques. © 2023 IEEE.

关键词： Students

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A Study of Challenges and Limitations to Applying machine learning to Highly Unstructured Data 7

A Study of Challenges and Limitations to Applying Machine Le...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Singh, Sahib Hooda, Susheela Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

In recent years, there has been a lot of curiosity about the use of machine learning algorithms to analyze unstructured data, including social media posts and videos. the limitations and difficulties posed by these technologies must be overcome in order to guarantee accurate and trustworthy results, despite the promise they hold. this study examines the restrictions and difficulties associated with using machine learning algorithms on unstructured data. We identify the main difficulties in working with unstructured data, such as the lack of standardization and the difficulty in handling noise and bias, through a literature review and case studies. We then look at methods for overcoming these obstacles, such as feature engineering, pre-processing techniques, and ensemble methods. the results of this study have implications for both theoretical and applied work in machine learning, particularly in the fields of feature engineering, model selection, and data cleaning. this paper's goal is to provide a thorough understanding of the constraints and difficulties associated with using machine learning algorithms on unstructured data, as well as to contribute to the creation of best practices for handling these data type © 2023 IEEE.

关键词： Deep learning machine learning Natural Language Processing (NLP) Sentiment Analysis Social Media Posts Text classification Unstructured Data

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international Joint conference SOCO’14-CISIS’14-ICEUTE’14: Bilbao, Spain, June 25th-27th, 2014, Proceedings 1

引用

丛书名： Advances in Intelligent Systems and computing

2014年

作者： José Gaviria de la Puerta, Iván García Ferreira, Pablo Garcia Bringas, Fanny Klett, Ajith Abraham, André C.P.L.F. de Carvalho, Álvaro Herrero, Bruno Baruque, Héctor Quintián, Emilio Corchado (eds.)

ISBN: (数字)9783319079950

ISBN: (纸本)9783319079943;9783319079950

this volume of Advances in Intelligent and soft computing contains accepted papers presented at SOCO 2014, CISIS 2014 and ICEUTE 2014, all conferences held in the beautiful and historic city of Bilbao (Spain), in June 2014. soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and *** a through peer-review process, the 9th SOCO 2014 international Program Committee selected 31 papers which are published in these conference proceedings. In this relevant edition a special emphasis was put on the organization of special sessions. One special session was organized related to relevant topics as: soft computing Methods in Manufacturing and Management *** aim of the 7th CISIS 2014 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. the need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall *** a through peer-review process, the CISIS 2014 international Program Committee selected 23 papers and the 5th ICEUTE 2014 international Program Committee selected 2 papers which are published in these conference proceedings as well.

关键词： soft computing Artificial intelligence

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DDoS Attack Detection on Cloud computing Services using Algorithms of machine learning: Survey 7

DDoS Attack Detection on Cloud Computing Services using Algo...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sathvika, Chilla Satwika, Vuyyuru Sruthi, Yarrapothu Geethika, Maddali Bulla, Suneetha Swathi, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9781665464086

Nowadays cloud computing services have become the most popular internet-based computing and many organizations use their services. Due to this, many cyberattacks are happening in the cloud. One of those attacks is the Distributed-Denial-Of-Service (DDoS) attack. It floods unreal traffic, hence troubles the availability of the resources. this article is about DDoS attacks and detection of DDoS attacks using machine learning. there are many famous machine learning algorithms such as naïve bayes, random forest, support vector machines etc. these machine learning algorithms can be used to detect the DDoS attacks on cloud. there are several datasets available for the researchers to test their proposed models which include NSL-KDD, ICDX, CIDDS-001, CICIDS 2017 etc. this paper presents a detailed study on different machine learning based techniques proposed by various authors to detect the DDoS attack in the cloud environment. A brief explanation has been provided on the available datasets and further discussed about the general methodology. © 2023 IEEE.

关键词： Denial-of-service attack

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Artificial Intelligence, machine learning, and Natural Language Processing Capabilities in Modern Business Intelligence 7

Artificial Intelligence, Machine Learning, and Natural Langu...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ukhalkar, Prakash Bhate, Manasi Hingane, Suraj Pimpri Chinchwad College of Engineering Mca Department Pune India MES's Institute of Management and Career Courses Mca Department Pune India Abhinav Education Society's Institute of Management and Research Mca Department Pune India

ISBN: (纸本)9798350304268

Operational business intelligence capabilities in modern business applications demand the simultaneous processing of analytical queries and business transactions using the same data. Mixed workloads become the result of this. the business sector is being disrupted by artificial intelligence, machine learning, and natural language processing. the voice-based search, high-tech and telecommunications, automotive and assembly, and financial services industries are just a few of the sectors setting the pace for adopting these particular technologies. this study demonstrates that real-time analysis, machine learning, and natural language processing are essential for current business intelligence systems to make well-informed decisions and provide company value across various industries. © 2023 IEEE.

关键词： Artificial Intelligence BI Tools Business Intelligence machine learning Natural Language Processing

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Hybrid ML Algorithms for learning Disability Forecast in School Going Children Using Python in machine learning Techniques 7

Hybrid ML Algorithms for Learning Disability Forecast in Sch...

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7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Margaret Mary, T. Prakash, V.S. Divya, K.S. George, Amalorpavam Kristu Jayanti College Karnataka Bengaluru India Affiliation Bangalore North University India Sambhram Academy of Management Studies Karnataka Bengaluru India

ISBN: (纸本)9798350340600

A learning disability is a neurological illness that impairs a child's ability to read, speak, and do a variety of other skills. the World Health Organization (WHO) estimates that learning disabilities impact 15% of youngsters [14]. the most important challenge for researchers to perform in order to identify learning disabilities early on is efficient prediction and accurate categorization. Our primary goal in this effort is to use soft computing to create a model for the prediction and categorization of learning disabilities. this study proposes a hybrid approach for enabling classification in order to enhance the performance of prediction and classification. this method incorporated classification's primary five techniques. Random Forest, Logistic Regression, Stochastic Gradient Descent, and K-Fold cross validation. In order to implement the system used python. Results analysis reveals the predict of learning disability in effectively © 2023 IEEE.

关键词： Python

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