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Policy Generation from Latent Embeddings for Reinforcement learning 3rd

Policy Generation from Latent Embeddings for Reinforcement...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Artaud, Corentin Pina, Rafael Shi, Xiyu De-Silva, Varuna Institute for Digital Technologies Loughborough University London The Broadcast Centre Here East 3 Lesney Ave E20 3BS London United Kingdom

ISBN: (纸本)9783031463372

The human brain endows us with extraordinary capabilities that enable us to create, imagine, and generate anything we desire. Specifically, we have fascinating imaginative skills allowing us to generate fundamental knowledge from abstract concepts. Motivated by these traits, numerous areas of machine learning, notably unsupervised learning and reinforcement learning, have started using such ideas at their core. Nevertheless, these methods do not come without fault. A fundamental issue with reinforcement learning especially now when used with neural networks as function approximators is their limited achievable optimality compared to its uses from tabula rasa. Due to the nature of learning with neural networks, the behaviours achievable for each task are inconsistent and providing a unified approach that enables such optimal policies to exist within a parameter space would facilitate both the learning procedure and the behaviour outcomes. Consequently, we are interested in discovering whether reinforcement learning can be facilitated with unsupervised learning methods in a manner to alleviate this downfall. This work aims to provide an analysis of the feasibility of using generative models to extract learnt reinforcement learning policies (i.e. model parameters) with the intention of conditionally sampling the learnt policy-latent space to generate new policies. We demonstrate that under the current proposed architecture, these models are able to recreate policies on simple tasks whereas fail on more complex ones. We therefore provide a critical analysis of these failures and discuss further improvements which would aid the proliferation of this work. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Students’ Performance Prediction Using Feature Selection and Supervised machine learning Algorithms 3rd

Students’ Performance Prediction Using Feature Selection an...

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3rd international conference on Innovative Computing and Communication, ICICC 2020

作者： Gajwani, Juhi Chakraborty, Pinaki Division of Computer Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (纸本)9789811551123

Educational data mining involves finding patterns in educational data which can be obtained from various e-learning systems or can be gathered using traditional surveys. In this paper, our focus is to predict the academic performance of a student based on certain attributes of an educational dataset. The attributes can be demographic, behavioural or academic. We propose a method to classify a student’s performance based on a subset of behavioural and academic parameters using feature selection and supervised machine learning algorithms such as logistic regression, decision tree, naïve Bayes classifier and ensemble machine learning algorithms like boosting, bagging, voting and random forest classifier. For selection of the attributes, we plotted various graphs and determined the attributes that were most likely to affect and improve prediction. Experiments with different algorithms show that ensemble machine learning algorithms provide best results with our dataset with an accuracy of up to 75%. This has widespread applications such as assisting students in improving their academic performance, customizing e-learning courses to better suit students’ needs and providing tailor-made solutions for different groups of students. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Students

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Comparison between Fisher's Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition 3

Comparison between Fisher's Ratio and Information Gain with ...

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3rd international conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019

作者： Rustam, Z. Kristina, Andrea Laksmirani Satria, Y. Department of Mathematics Universitas Indonesia Depok16424 Indonesia

The enthusiasm level of a person is an important measurement in real-world problems. This paper, therefore, presents face recognition classification for enthusiasm level, based on supervised machine learning, using Support Vector machine (SVM) as a classifier, with a one-vs-one method because the data consists of more than two classes. In addition, Fisher's Ratio and Information Gain are applied in the selection of contributive features, and the goals were to present an accuracy comparison between SVM and Fisher's Ratio, as wells as with Information Gain, and the results showed the accuracy at 88,89%, and 80,95238%, respectively. This indicates the combination of SVM with Fisher Ratio to be better. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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3rd international conference on Big-data and Cloud Computing, ICBDCC 2019

3rd International Conference on Big-Data and Cloud Computing...

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3rd international conference on Big-data and Cloud Computing, ICBDCC 2019

ISBN: (纸本)9789811552847

The proceedings contain 62 papers. The special focus in this conference is on Big-data and Cloud Computing. The topics include: Classification of gender from face images and voice;an outlier detection approach on credit card fraud detection using machine learning: A comparative analysis on supervised and unsupervised learning;unmasking file-based cryptojacking;selection of a virtual machine within a scheduler (dispatcher) using enhanced join idle queue (ejiq) in cloud data center;an analysis of remotely triggered malware exploits in content management system-based web applications;gsm-based design and implementation of women safety device using internet of things;a novel approach on various routing protocols for wsn;fraud detection for credit card transactions using random forest algorithm;deep learning application in iot health care: A survey;L1 norm SVD-based ranking scheme: A novel method in big data mining;context aware text classification and recommendation model for toxic comments using logistic regression;self-supervised representation learning framework for remote crop monitoring using sparse autoencoder;determination of elements in human urine for transient biometrics;optimal placement of green power generation in the radial distribution system using harmony search algorithm;data security and privacy protection in cloud computing: A review;Re-ranking ODI batsman using JJ metric;intelligent cloud load balancing using elephant herd optimization;a quest for best: A detailed comparison between drakvuf-vmi-based and cuckoo sandbox-based technique for dynamic malware analysis;cloudstore: A framework for developing progressive streaming games;classification of dga botnet detection techniques based on dns traffic and parallel detection technique for dga botnet.

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Sinc-based Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis under Small Samples 3

Sinc-based Multiplication-Convolution Network for Equipment ...

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3rd international conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Liu, Rui Ding, Xiaoxi Wu, Qihang Xiang, Hao Tan, Huaping Shao, Yimin College of Mechanical and Vehicle Engineering State Key Laboratory of Vehicle NVH and Safety Technology Chongqing University Chongqing China Chongqing University The State Key Lab of Mechanical Transmissions Chongqing China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665492812

data-driven intelligent diagnosis models need massive monitoring data to train themselves for desired performance. However, in many engineering scenarios, collecting fault data is often expensive and time-consuming, which leads to few-shot learning becoming a valuable research hotspot for intelligent diagnosis. Inspired by mode characteristics and feature enhancement learning, this study propose a Sinc-based multiplication-convolution network (SincMCN) for intelligent fault diagnosis under small samples. It works in frequency domain, and consists of only three layers, including a feature separator, a feature extractor and a classifier. In the feature separator, a series of Sinc-based multiplication filtering kernels (SincMFKs) are designed for improving the utilization of fault information of spectrum samples. The products between SincMFKs and spectrum samples are stacked into activated mode spectrum images (AMSIs) with rich fault-related features retained. Since AMSIs are concise enough, this study employs only a 2D convolutional layer and a fully connected layer as the feature extractor and the classifier for achieving a fast and precise pattern recognition. Experimental results show SincMCN has better diagnosis accuracy and stronger potentials for few-shot diagnosis compared other cutting-edge models. Specially, analytic filtering kernels not only cut down the model parameters for edge diagnosis and provide powerful application potentials and engineering value for online monitoring of rotating machinery. © 2022 IEEE.

关键词： feature enhancement Few-shot learning intelligent edge diagnosis multiplication-convolution network Sinc filtering

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The Forecasting About Bitcoin and Other Digital Currency Markets: The Effects of data mining and Other Emerging Technologies

The Forecasting About Bitcoin and Other Digital Currency Mar...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Anil Kumar Bhuyan Darshana A. Naik Sachin Sharma Anita Gehlot A. Jafersadhiq Dhiraj Kapila Birla Global University Bhubaneswar Odisha Department of CSE Ramaiah Institute of Technology Bangalore Department of Computer Applications MRIIRS Faridabad Haryana Uttaranchal Institute of Technology Uttaranchal University India Department of Business Administration Kalasalingam Academy of Research and Education (Deemed to be University) Virudhunagar Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

We examine the bitcoin currency's predictability over prediction timeframes of 1 to 15 minutes. As a result, we evaluate a variety of machine learning methods and discover that, although all models perform better than a random predictor, Bidirectional recurrent neural networks and extreme Gradient Boosting Classifier (XGB Classifier) are particularly effective for the investigated prediction tasks. We employ a wide range of features, including technological, asset-based, interest-based, and block chain technology characteristics. Our findings indicate that technical characteristics continue to be the most important for the majority of techniques, followed by a few blockchain- and interest-based features. Furthermore, we see that reliability rises with increasing prediction horizons. Although a quantile-based long-short trading strategy may provide market return of up to 34% before transaction costs, the very brief holding durations result in negative yields after transaction expenses are incurred.

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From data Pre-processing to Hate Speech Detection: An Interdisciplinary Study on Women-targeted Online Abuse

From Data Pre-processing to Hate Speech Detection: An Interd...

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Intelligent Technologies (CONIT), international conference on

作者： Rutuja G. Rathod Yashoda Barve Jatinderkumar R. Saini Sourav Rathod Surayadatta College of Management Information & Research Technology Pune India Symbiosis Institute of Computer Studies and Research Symbiosis International (Deemed University) Pune India AI&ML Deparment Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

The term “misogyny” conveys hatred and disrespect for women. It is a type of sexism that stems from the idea that women are less valuable than men and can take many forms, such as verbal abuse, sexual harassment, and physical violence. One area where misogyny is particularly prevalent is in online spaces. Women who speak out and share their experiences online often face vicious attacks, including violence, rape, and death threats. This phenomenon, often referred to as “online harassment,” can have devastating effects on women’s mental health and well-being. Despite the growing recognition of this issue, there is still a lack of understanding about the nature and extent of hate speech toward women on social media. In this paper, the authors aim to address this gap by analyzing a dataset of tweets on Twitter containing hate speech toward women. The authors introduced the Measuring Hate Speech corpus, a dataset used while studying hate speech towards women. From this dataset, the authors extract some of them to build the model. And authors proposed machine learning algorithms like Logistic Regression with 0.92% accuracy. And to look further to fill the gap, the authors developed Support Vector machine (SVM) algorithm, Topic Modeling technique to extract the topics from the corpus. This study contributes to a better understanding of hate speech on social media platforms as Twitter informs strategies for resisting this pervasive problem.

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Optimization Research on Abnormal Diagnosis of Transformer Voiceprint recognition based on Improved Wasserstein GAN 3

Optimization Research on Abnormal Diagnosis of Transformer V...

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2020 3rd international conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Zhu, Kejia Ma, Huiyuan Wang, Jianing Yu, Changshu Guo, Chunlin Xiao, Ce State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China State Grid Beijing Electric Power Company Beijing China Hunan Power Transmission and Transformation Engineering Company Limited China

Transformer is an important infrastructure equipment of power system, and fault monitoring is of great significance to its operation and maintenance, which has received wide attention and much research. However, the existing methods at home and abroad are based on model analysis for detection and diagnosis techniques, and their application is limited by the maturity and applicability of expert knowledge. In recent years, deep learning artificial intelligence has made important breakthroughs, however, the relevant research is still mainly focused on the field of speech, and intelligent diagnostic techniques on machine voice patterns have just begun. Therefore, this article adopts an unsupervised learning anomaly detection method based on WGAN-GP to diagnose the electrical and mechanical anomalies of transformer equipment. The experimental results show that the method can effectively identify transformer anomalies and provide an idea for artificial intelligence-based machine voice pattern recognition. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Importance of data Re-Sampling and Dimensionality Reduction in Predicting Students' Success 3

Importance of Data Re-Sampling and Dimensionality Reduction ...

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3rd international conference on Electrical, Communication and Computer Engineering, ICECCE 2021

作者： Buraimoh, Eluwumi Ajoodha, Ritesh Padayachee, Kershree School of Computer Science and Applied Mathematics The University of the Witwatersrand Johannesburg South Africa The University of the Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa

ISBN: (纸本)9781665438971

In this paper, we present the importance of data pre-processing in predicting students' success. We implemented Principal Component Analysis for dimensionality reduction to achieve better model performance. data re-sampling technique was also utilized to handle the imbalanced class problem that is one of the significant issues in effective classification in Educational data mining due to the nature of the data from educational settings. We also performed a comparative analysis on the impacts of Random Under-Sampling (RUS), Random Over-Sampling (ROS), and Synthetic Minority Over-Sampling Technique (SMOTE) to an imbalanced dataset used in this study. SMOTE and PCA techniques application offer better performance compared to RUS and ROS with PCA. Support Vector machine had the best accuracy value of 0.94 after the application of SMOTE and PCA. The application of PCA on the imbalanced data also positively affected the accuracy of the models used in this study. We used other performance metrics to evaluate our models: Kappa, Area Under Curve, and Precision-Recall curve. Our finding shows that the predictive models can predict student success with the proper application of PCA and data re-samnling techniques. © 2021 IEEE.

关键词： Principal component analysis

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Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP

Revolutionizing Legal Intelligence: Advances in Neural Netwo...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Raoul Samuel Noronha Alex Stanley Alenchery Deepa S Jayapriya J Vinay M Department of Computer Science Christ University Bengaluru India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers’ workflows to support them in their decision-making processes.

关键词： Analytical models Law Large language models Neural networks Text summarization Transforms Transformers data models Complexity theory Contracts

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