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Learning to Communicate Using Action Probabilities for Multi-Agent Cooperation

Learning to Communicate Using Action Probabilities for Multi...

引用

2023 IEEE International Conference on Agents, ICA 2023

作者： Bai, Yidong Sugawara, Toshiharu Waseda University Dept. Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350312782

While communication is an essential tool for cooperation in multi-agent systems (MAS), existing approaches generally assume high-dimensional messages genereated by deep networks, which are uninterpretable for humans and require expensive transmission and complex encoding/decoding networks in agents. Uninterpretability may in turn raise reliability and security issues for the systems. To achieve low-dimensional and interpretable communication, we demonstrate that each agent's action probabilities can be used as messages, inspired by the fact that humans often share likely actions during collaboration. Our proposed method, communication based on action probabilities (CAP), is a simple yet effective communication architecture for coordination and collaboration in MAS, which facilitates our understanding of the agents' learned coordinated and cooperative behaviors. Moreover, because messages can be generated by the actor network, which is usually implemented in deep reinforcement learning agents, we can eliminate the requirement of extra message-generator networks. Our experiments show that CAP can achieve comparable performance to those of the state-of-the-art methods, with quicker convergence, simpler network structures and better interpretability. © 2023 IEEE.

关键词： Reinforcement learning

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Shanketik: Revolutionizing Communication for the Deaf and Mute - A Reinforcement Learning-Enhanced Bangla AAC Application 26

Shanketik: Revolutionizing Communication for the Deaf and Mu...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Monalisa, Natasha Tanzila Himi, Shinthi Tasnim Parvin, Kohinur Uddin, Mohammad Shorif Jahangirnagar University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

This research addresses the communication challenges faced by the deaf and mute community, leveraging advanced technology to enhance augmentative and alternative communication (AAC). In light of the limitations of existing methods, we propose a novel approach, 'Shanketik', that integrates Reinforcement Learning (RL) into an AAC mobile application in Bangla language. This system bridges the gap in accessible communication tools, particularly in languages like Bangla, where options are limited. The 'Shanketik' system functions as an agent, interacting with users through symbols in AAC. It incorporates a dynamic feedback loop, learning from positive and negative user responses. The system's core architecture involves the innovative use of the Deep Q-Network (DQN) algorithm, a fusion of Q-learning and deep neural networks. The 'Shanketik' application not only refines text and speech generation for accuracy and contextual relevance but also offers a user-friendly interface, ensuring navigation flexibility and enhanced efficiency in usability. The research presents a significant advancement in AAC technology, marking a transformative step toward inclusive communication for the deaf and mute community. © 2023 IEEE.

关键词： Augmented and Alternative Communication (AAC) Bangla Language Processing Human-computer Interaction Mobile Application Reinforcement Learning

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Enhanced Rainfall Prediction: Leveraging Ensembling Models for Maximum Forecasting Performance 1

Enhanced Rainfall Prediction: Leveraging Ensembling Models f...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dasari, Krishna Kumar Nuthanakanti, Bhaskar Raju, K.Srujan Madhusudhan, Mudimela Nazare, Sanjana S Raji Reddy, Avala Cmr Technical Campus Dept. Computer Science and Engineering Telangana Hyderabad India Cmr Dept. of Mechanical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350367720

Many important fields rely on accurate rainfall predictions, including agriculture, water resource management, and emergency preparation. The many nonlinear interactions included in weather data are notoriously difficult for traditional approaches to grasp. Agriculture, water resource management, and disaster preparation are just a few of the many sectors that might benefit greatly from accurate rainfall predictions. Because meteorological data is both complicated and dynamic, traditional methods often get it wrong. Recent years have seen encouraging results in the use of machine learning (ML) approaches, especially ensemble models, to enhance the precision and consistency of precipitation forecasts. This research provides a new method for predicting precipitation using an ensemble of ML models. The ensemble model EERP algorithm combines the predictions of numerous base learners to create a more reliable and accurate forecast. Decision trees, random forests, gradient boosting machines, neural networks, and other ML approaches serve as the ensemble's base learners. Assess the effectiveness of the proposed approach and then compare it with some existing classifiers in which the proposed classifier gave the best accuracy when compared with the other i.e., 96 %. © 2024 IEEE.

关键词： Prediction models

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Hybrid ANN-Based Model for Lung Cancer Disease Prediction 3

Hybrid ANN-Based Model for Lung Cancer Disease Prediction

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Paul, Banibrata Mir, Tawseef Ahmad Murugan, Stalin Alliance University Dept. of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350317022

Currently, lung cancer ranks as the world's leading cause of cancer-related fatalities in both men and women. The primary cause of lung cancer may be attributed to smoking. Although lung cancer can develop in any part of the lung, 90% to 95% of cases are believed to start in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles). This study's main goal is to identify lung cancer by employing artificial neural networks with scaled conjugate gradient back propagation and k-fold cross validation. We have utilized the Lung Cancer dataset through kaggle. data from 1000 lung cancer patients, ages 14 to 73, is used to train the network. How many neurons are in the hidden layer affects how accurate the results are. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, yielding a minimum accuracy percentage of 83.1429% and a maximum accuracy percentage of 100%. © 2023 IEEE.

关键词： Artificial Neural Network Back propagation Lung Cancer Lung cancer dataset Scaled Conjugate Gradient Algorithm

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Bringing computer science Education to Schools in Qatar: Challenges of the implementation 5

Bringing Computer Science Education to Schools in Qatar: Cha...

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Alhazbi, Saleh Qatar University Dept. of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350303254

Many countries have started national curriculum changes to make computer science (CS) as a compulsory subject for all students in schools. In 2018, a new national computing curriculum has been developed in Qatar for all k-12 grades. Comparing to the old curriculum, the new one focuses on teaching computer science instead of digital literacy. This paper discusses the challenges of implementing this new curriculum from CS teachers' perspectives in Qatar public schools. A survey was conducted of over 392 CS teachers who taught the new curriculum during the academic year 2018/2019. From the analysis of the collected data, it is found that teachers face different challenges including more working loads, lack of support to master the new contents, as well as the need of professional developments to improve their pedagogical skills in order to deliver the new CS concepts effectively. Moreover, the results shows that teachers in preparatory and secondary stages struggle with these challenges more than those in the elementary stage. Although all teachers are computing majors, the majority indicated that programming subjects in all grades are the topics where they need the most support. Accordingly, more support in term of improving teachers' content knowledge, and pedagogical content knowledge is essential in order to achieve the objectives of this reform. © 2023 IEEE.

关键词： Curricula

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Smart Video Retrieval and Question Answering System 2

Smart Video Retrieval and Question Answering System

引用

2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Likhith Dubey, Mahima Dashora, Mahim Kusuma Shree, V. Jayashree, R. PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665475174

More and more video content is being produced on daily basis and utilizing it to its fullest has never been easier. In this paper we propose a new solution for this problem as a smart video retrieval system, leveraging the progress made in extractive question-answering models like RoBERTa and DistilBERT. The system is designed mainly for educational videos in English, with major use cases in schools and colleges. The problem is divided mainly into two parts: 1. Video retrieval and 2. Timestamps retrieval within the video. The paper discusses different variations of the proposed architecture involving slide-text extraction, TFIDF and semantics-based indexing while also speaking about the implementation. We also introduce two new datasets based on educational YouTube videos and Cloud-computing course videos in the paper which are used to generate results and evaluate performance across all the variations implemented. We are able to achieve the best of 89.35% and 84.83% accuracy in video retrieval, and just 1% and 1.7% error in timestamp retrieval considering outliers across the two datasets respectively. © 2023 IEEE.

关键词： Semantics

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Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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Examining Amazon Customer Reviews using PySpark and AWS: A data Lake Approach 14

Examining Amazon Customer Reviews using PySpark and AWS: A D...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Remya, S. Karthik, Abburi Sai Karthik, Maddala H S M Krishna Jayasurya, Marasani Yashwanth, Samudrala Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350335095

In the contemporary world, big data analysis with PySpark and AWS has become quite handy. Large multinational companies, including Walmart, trivago, and countless others are using big data. And with the assistance of AWS, which offers a variety of services like glue, Athena, S3, etc., becoming extremely beneficial for cloud storage purposes and also for creating data migration pipelines. Any RDBMS database server might send tens of gigabytes of data to an Amazon S3 bucket using the ETL architecture. Our primary goal in this project is to use PySpark to construct an ETL pipeline for data migration. Amazon wireless devices review analysis, Amazon watches review analysis, Amazon books review analysis, Amazon shoes review analysis, and Amazon musical instruments review analysis are the five separate datasets on which we conducted our analysis. The datasets considered for the analysis are Amazon watch reviews, book reviews, shoe reviews, wireless device reviews and musical instrument reviews. The goal of the project is to obtain the ratio of the number of trustworthy, real reviews to untrusted reviews and a comparison in done in the form of graphs. © 2023 IEEE.

关键词： Digital storage

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Improved Sampling Methods for Evaluation of Classification Performance

Improved Sampling Methods for Evaluation of Classification P...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Lee, Yue-Shi Yen, Show-Jane Tang, Yi-Jie Ming Chuan University Dept. of Computer Science & Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798331507022

To evaluate the classification accuracy of a classification algorithm on a certain dataset, it is necessary to sample training and test data from the original dataset. After building the classification model from the training data, the test data can be used to evaluate the classification accuracy. However, in order to evaluate the classification accuracy of a classification algorithm on an original dataset, it is usually necessary to sample the training/test data many times, build the classification model many times, and then average the accuracies of all the evaluation, which will consume a lot of time and resources. To address this, we propose a sampling method so that the sampled training/test data can replace the results of building multiple models and averaging multiple classification accuracy, that is close to the evaluation results of the original data. Our approach introduces different methods for calculating the similarity of data distributions and incorporates feature weights in the similarity calculation process to select training/test sets that are close to the distribution of the original dataset. © 2025 IEEE.

关键词： Taxonomies

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A Comparative Analysis of Brain Atlases and DNN-Based Autism Spectrum Disorder Detection Using IMPAC dataset

A Comparative Analysis of Brain Atlases and DNN-Based Autism...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Kabir, Mir Arnab Arafin, Protiva Barman, Shawon Hussein, Molla Rashied Tasnim, Rumana Zonayed, Md University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798350369106

An autism spectrum disorder (ASD) is a physiological condition that impairs an individual's ability to acquire skills, engage in social interactions, empathize, and socialize. Early intervention is crucial to alleviate intellectual, behavioral, and functional challenges related to ASD. The significance of early ASD assessment cannot be overstated, as it is rapidly addressed through cognitive progress, yielding equally favorable outcomes. This research work developed a machine learning (ML) model for early assessments of autism spectrum disorder (ASD) utilizing functional magnetic resonance imaging (fMRI) data and deep neural networks (DNNs). A comprehensive analysis is carried out utilizing seven distinct brain atlases from the IMPAC dataset. The results indicate that among the seven atlases, Craddock 200 (CC200) attains the highest ROC AUC of 85.091% and an impressive accuracy of 82.759%, which outperforms numerous current state-of-the-art models. These findings emphasize the potential of fMRI-based machine learning models, especially deep neural networks, in improving the precision and efficacy of early autism spectrum disorder diagnosis. Such enhancements offer potential for enhancing the lives of individuals with ASD, as prompt treatments can profoundly influence their developmental trajectory and general well-being. © 2024 IEEE.

关键词： Diseases

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