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Cardiac Arrest Prediction Model Using Ensemble Technique 4

Cardiac Arrest Prediction Model Using Ensemble Technique

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sinha, Dibakar More, Sarla Jeyakrishnan, V. Mishra, Durgesh Manipal University Jaipur Department of Computer Science Engineering Rajasthan Jaipur India Symbiosis University of Applied Sciences School of Computer Science and Information Technology MP Indore India

ISBN: (纸本)9798331518981

The physician employs a diverse range of laboratory diagnostics, physical examinations, and on occasion, even invasive procedures to ascertain the presence of diseases. It is necessary to have a physician that possesses exceptional training, expertise and skill in the specific field, for detection of the disease in its initial phases. Despite the widespread use of ensemble techniques in disease prediction, here is a notable absence of comprehensive evaluation of regularly utilized ensemble algorithms for highly studied diseases. Consumers typically resort to seeking medical assistance from a doctor or other medical expert only after exhausting all other home remedies without finding relief for their ailments. This is probably a result of insufficient comprehension, as well as other factors related to behavior. This paper introduces a conventional ensemble LSTM model that incorporates data preparation and feature engineering. The model proposed in this research represents major gains in numerous statistical parameters like precision, f score, accuracy and ROC curve in comparison to the other models. © 2024 IEEE.

关键词： Diseases

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Leveraging Semantic Parsing using Text Embeddings and Reinforcement Learning

Leveraging Semantic Parsing using Text Embeddings and Reinfo...

引用

2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Piza-Londono, Jessenia Andrade-Lotero, Edgar J. Universidad Del Rosario Applied Mathematics and Computer Science Bogotá Colombia Universidad Del Rosario School of Engineering Science and Technology Bogotá Colombia

ISBN: (纸本)9798350374575

Representing natural language information is a key challenge in Artificial Intelligence and Cognitive science, requiring the transformation of unstructured data into formats suitable for computational tasks. While logical formalisms offer robust methods for information representation, their complexity often limits widespread adoption. Conversely, transformer architectures provide strong generalization capabilities but struggle with logical inference tasks. To address both the need for generalization and reliable logical inference, we propose a novel approach using deep reinforcement learning, enabling agents to autonomously learn the rules of semantic parsing. Our preliminary results indicate successful generation of appropriate representations for simple queries. Future work will extend the environment to handle a wider range of real-world sentences. © 2024 IEEE.

关键词： Deep reinforcement learning

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An Analysis of the Use of Machine Learning to Enhance Online Digital Learning 8

An Analysis of the Use of Machine Learning to Enhance Online...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Acharjya, Pinaki Pratim Majumdar, Jayeeta Bera, Mihir Baran Barman, Subhabrata Sinha, Jayashri Deb Das, Moumita Haldia Institute of Technology Computer Science and Engineering Haldia India Haldia Institute of Technology School of Applied Science Haldia India Haldia India

ISBN: (纸本)9798350367904

Academic institutions' interest in online learning is rising at an exponential rate due to the rapid advancement of expertise. The Learning Management System (LMS) is a key component of the online learning architecture and is also used to mine student data. This study reviews and analyses the literature on machine learning (ML) in online education and machine learning integrated learning management systems (LMS). The goal of this study is to comprehend the different efforts made to employ different ML techniques to lessen the difficulties associated with online learning environments, or online LMSs. Increasing student performance by projecting future grades is just one of several obstacles. Other issues that can be resolved using machine learning include student retention and behavior analysis for performance enhancement. The paper presents the research that different authors have done in the relevant topic. The chosen time frame for the project remains close to the Covid19 years. © 2024 IEEE.

关键词： Federated learning

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A Machine Learning-Based Approach for Network Optimization in WSNs

A Machine Learning-Based Approach for Network Optimization i...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Bhattad, Bharti Sethi, Kamal Kumar Pal Singh Rathore, Narendra Bhanodia, Praveen Mishra, Durgesh Kumar Computer Science Engineering Acropolis Institute of Technology and Research Indore India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Madhya Pradesh Indore India

ISBN: (纸本)9789819709748

With the advancements in interconnected devices and automation, wireless sensor networks have gained substantial importance. Energy constraint is one of the main issues that wireless sensor networks (WSNs) must deal with. Moreover, to enhance the effectiveness, it is necessary to reduce the latency as much as possible. Wireless sensor networks can be optimized for latency and energy consumption using a variety of protocols, with their own pros and cons. This paper investigates different aspects related to wireless sensor networks along with the relevant approaches. The intent of this paper is to present a reinforcement learning-based approach for optimized routing in WSNs. The protocol is designed to cater to the constraints of limited energy and latency, with the evaluation of the proposed algorithm in terms of network lifetime and latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Multiband strain balanced superlattice material system for third generation infrared detectors

Multiband strain balanced superlattice material system for t...

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2024 IEEE Research and Applications of Photonics in Defense Conference, RAPID 2024

作者： Dehzangi, Arash Robert R. McCormick School of Engineering and Applied Science Northwestern University Department of Electrical and Computer Engineering EvanstonIL United States

ISBN: (纸本)9798350373097

Recent increasing interest in strain balanced Type-II superlattices material causing close attention from industry. Tremendous investment was drawn toward establishing strain balanced superlattice (SLS) as new alternative for infrared photodetectors across the broad range of infrared spectrum. In recent years, SLS material system has shown capability in particular specifications to compete with mature and standard mercury cadmium telluride for mid-wavelength and long-wavelength infrared detection. It has been great interest in SLS material system for applications aligned with the standard of third generation of infrared detectors. In this presentation, some recent progress in three color infrared photodetectors based on SLS material system will be presented. © 2024 IEEE.

关键词： Infrared detectors

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Deep RL-Based Smart Signaling Using Space-Time Vehicular Features Under C-V2X Scenario 16

Deep RL-Based Smart Signaling Using Space-Time Vehicular Fea...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Shah, Kashish D. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India LIT Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

As of recent, the majority of developing countries have faced traffic congestion issues. As a result, vehicular communication technologies such as DSRC and Cellular-V2X are standardized by the 3GPP to support the transportation community. The C-V2X standard proposed in 3GPP Re114 provides high data transmission facilities for V2V and V2I communication. In this paper, we attempt to solve the traffic congestion problem by considering the V2I communication facility as per the C-V2X standard. We propose an online learning strategy based on Deep Reinforcement Learning for smart signaling to avoid traffic congestion at the intersections. These are the key aspects to address the congestion issue. We conduct extensive near-real-time simulations in PTV VISSIM considering the Swastik Cross Road intersection at Ahmedabad, Gujarat, India, imported via OpenStreetMap. With the proposed smart signaling strategy, we found significant improvement in vehicles' average travel time and average space occupancy compared to existing traffic signaling methods. Uplifting this research and implementing such real-time strategies will significantly impact the current transportation system. © 2024 IEEE.

关键词： Cellular-V2X Deep Reinforcement Learning Intelligent Transportation Systems Smart Signaling Vehicular Communication

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Enhancing 6G mmWave Beam Prediction in V2I with Class Imbalance Mitigation 35

Enhancing 6G mmWave Beam Prediction in V2I with Class Imbala...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. Ahmedabad University School of Engineering and Applied Science India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Powai Mumbai India

ISBN: (纸本)9798350362244

The increase in the use of autonomous vehicles has motivated a paradigm shift in the transportation domain as it redefines the boundaries of urban mobility by augmenting safety measures. This research paper explores an innovative approach to enhance 6G millimeter-wave (mmWave) beam prediction for vehicle-to-infrastructure (V2I) communications by using generative adversarial networks (GANs). By generating synthetic data samples effectively and balancing the real-world dataset, we improve the accuracy of beam prediction models significantly. Our proposed method of training random forests on synthetic data (RFGAN) to predict beam indices provides the solution for imbalanced class issues and significantly improves the predictive performance of mmWave beam selection, contributing to more reliable and efficient V2I communications. This work also performs comparative analysis with state-of-the-art models in top-K evaluation metrics, average power loss, and overhead savings related to adapting to the new approach. © 2024 IEEE.

关键词： Generative adversarial networks

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Millimeter- Wave Vehicle-to-Infrastructure Communications for Autonomous Vehicles: Location-Aided Beam Forecasting in 6G 16

Millimeter- Wave Vehicle-to-Infrastructure Communications fo...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Shah, Kashish D. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

The rise of autonomous vehicles has generated substantial interest due to their potential to transform transportation, enhance safety, and reduce traffic congestion in urban areas. This research investigates strategies to facilitate communication among various elements in driving environments, such as vehicles, pedestrians, and emergency responders, primarily focusing on vehicle-to-infrastructure (V2I) communication. Furthermore, advancements in wireless technology, including 6G and millimetre waves, offer improved connectivity but face challenges in signal propagation. The study emphasizes the importance of beam-forming as a solution, exploring alternative, resource-efficient methods to predict optimal signal directions using user positions. Positive simulation results suggest real-world applicability, and the study addresses critical questions about utilizing future positions and their impact on overhead complexity. This research provides valuable insights into position-based approaches for enhancing wireless communication in autonomous driving scenarios. © 2024 IEEE.

关键词： 6G Autonomous vehicles beam prediction overhead complexity training overhead

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Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study 1

Efficacious Routing Approaches in Vehicular Ad hoc Network: ...

引用

1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Janet Kumari, J Jesy Thangam, S. Raja, A. Saleem Amrita Vishwa Vidhyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Technology and Applied Science-Shinas Department of Computer Science and Engineering Oman

ISBN: (纸本)9798350342796

VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques. © 2023 IEEE.

关键词： ant colony optimization hybrid protocol proactive reactive VANET

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Degree reduction of ball Said-Ball surfaces using an enhanced coot optimization algorithm

Degree reduction of ball Said-Ball surfaces using an enhance...

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作者： Hu, Gang Dou, Wanting Du, Bo Department of Applied Mathematics Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China

Degree reduction of ball Said-Ball (BSB) surface is a complex and unsolved problem in computer-aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geometric modeling. In this paper, the degree reduction of BSB surface is transformed into the optimization problems of center surface and radius function, and the improved coot optimization algorithm is used to solve it. An effective method to solve the degree reduction of BSB surface by intelligent optimization algorithm is proposed. Firstly, the degree reduction of BSB surface is decomposed into center surface reduction and radius function reduction, and the objective function and constraint conditions are determined according to the optimization idea, then the degree reduction optimization model of center surface and radius function is established. Secondly, an enhanced coot (ECOOT for short) algorithm is proposed, which combines chain strategy, dispersed foraging strategy, and quadratic interpolation strategy. Compared with the original algorithm, ECOOT algorithm has better convergence performance and higher calculation accuracy. Finally, the proposed ECOOT algorithm is used to solve the degree reduction optimization model, and the optimal center surface and minimum distance radius function of the BSB surface after degree reduction are obtained. The experimental results show that the ECOOT algorithm can effectively solve the problem of degree reduction for BSB surface, the degree reduction error is reduced by about 20% compared to the original COOT algorithm, and is superior to other intelligent optimization algorithms in accuracy, robustness, and convergence. © 2023 John Wiley & Sons, Ltd.

关键词： Optimization

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