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Using IoT Enabled Predictive Maintenance to Enhance Business Operations: Benefits and Challenges 7

Using IoT Enabled Predictive Maintenance to Enhance Business...

引用

7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Ansari, Seema Ansari, Adeel Institute of Business Management Dept. of Electrical Engineering Karachi Pakistan Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798331534271

In today's changing world incorporating Internet of Things (IoT) technologies, into maintenance strategies is seen as a crucial innovation to improve business operations. This study delves into the use of driven maintenance systems and their notable impact on enhancing business efficiency cutting costs and ensuring operational dependability. By making use of real time data from sensors and devices these systems can predict equipment failures in advance reducing downtime and prolonging the lifespan of vital assets. The study provides a look at how maintenance strategies have evolved over time the technology behind powered predictive maintenance and how it revolutionizes business processes. The insights gathered from case studies and analysis of current executions reveals how business have transformed and achieved enhancement in operations. The paper also reflects challenges with their related solutions and suggested strategies to solve them. The findings show that implementing IoT for maintenance is not a technology hop but a necessity for companies struggling to stay in today's competitive market. In addition to this it investigates developments and possible innovations in this area emphasizing the evolvement and rising significance of IoT, in the upkeep of business industry. This research has explored into the competences of this technology highlighting its capability to reduce prices, improve efficiency, lift equipment reliability and upkeep making decision depending on data. © 2024 IEEE.

关键词： Predictive maintenance

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Genetic Algorithm-Based Path Planning for Autonomous Mobile Robots

Genetic Algorithm-Based Path Planning for Autonomous Mobile ...

引用

2023 IEEE Jordan International Joint Conference on electrical engineering and Information technology, JEEIT 2023

作者： Alabbadi, Areej Kanan, Awos Princess Sumaya University for Technology Dept. of Electrical Engineering Amman11941 Jordan Princess Sumaya University for Technology Dept. of Computer Engineering Amman11941 Jordan

ISBN: (纸本)9798350324051

In this paper, a Genetic Algorithm is used to solve the path planning problem for autonomous mobile robots in static environments. The goal of the path planning problem is to find a valid and practical path between two points while avoiding obstacles and optimizing a number of criteria including path length, safety, and distance from obstacles. A quality function is proposed to evaluate the optimization approach for different scenarios. Experimental results show that enhanced solutions can be achieved in less time using optimal values of the search algorithm parameters. © 2023 IEEE.

关键词： Genetic algorithms

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Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model

Personalized Movie Recommendation Based on User Preferences ...

引用

2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350379716

This study explores the application of a Transformer based model to the 'MovieLens 1M' and 'MovieLens 10M' datasets provided by GroupLens, which includes one million and ten movie ratings. The Transformer model, known for its proficiency in handling sequence-to-sequence tasks, employs multi-head self-attention mechanisms and position-wise feed-forward networks. Our implementation, developed in PyTorch, benefits from its capacity for automatic differentiation and GPU acceleration, which significantly enhances the training efficiency. The model underwent rigorous hyperparameter tuning and optimization to adapt optimally to the characteristics of the dataset. Performance evaluation using the Normalized Discounted Cumulative Gain (NDCG) metric demonstrates that the Transformer model substantially outperforms a conventional popular recommendation system across various top-K rankings. Specifically, for the MovieLens 1M dataset, the Transformer model achieved NDCG scores of 0.0455, 0.0805, 0.0995, and 0.1273 at top 1, 3, 5, and 10, respectively. Comparatively, the popular recommendation system scored 0.0023, 0.0044, 0.0061, and 0.0091 at these same rankings. This trend of superior performance by the Transformer model is also consistent across the MovieLens 10M dataset, further confirming its effectiveness in ranking highly relevant items at the top of the list. Also, to address quality and utility of recommendations, MAP, Precision, Recall, F1 Score, Coverage and Serendipity metrics evaluated. These results underscore the effectiveness of Transformer models in extracting and leveraging complex patterns in sequence data, demonstrating a clear superiority over traditional methods in recommendation systems. This study highlights the potential of deep learning architectures to revolutionize fields reliant on understanding user preferences and sequential data interactions. © 2024 IEEE.

关键词： Recommender systems

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Using Structured Pruning to Find Winning Lottery Tickets 28

Using Structured Pruning to Find Winning Lottery Tickets

引用

28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Azizi, Kamyab Taheri, Hassan Khooyooz, Soheil Amirkabir University of Technology Dept. Electrical Engineering Tehran Iran Sharif University of Technology Dept. Electrical Engineering Tehran Iran

ISBN: (纸本)9798350338195

In recent years, deep neural networks have successfully solved artificial intelligence problems. However, large models need more memory and computational resources. Recent research has proved that the deep models are over-parameterized and have redundancy in their parameterization. The lottery ticket hypothesis paper by Frankle and Carbin offers that based on pruning, we can achieve subnetworks with initializations that are capable of training from scratch. Still, they have used unstructured pruning, and the resulting architectures are sparse that need special hardware/software for compression and speedup. On the other hand, structured pruning methods in the convolutional neural networks (CNNs) preserved the structure of the convolution layers. Therefore, we do not need special hardware/software (HW/SW) libraries. In this work, we examined the lottery ticket hypothesis with structured pruning techniques and used these methods with different architectures. © 2023 IEEE.

关键词： Deep neural networks

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Predicting the Lifespan of Lithium-Ion Batteries: Integrating Initial Data with Machine Learning Approaches 2

Predicting the Lifespan of Lithium-Ion Batteries: Integratin...

引用

2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Hossian, Md. Ismail Uz Zaman, Md Towfiq Riyad, Tarifuzzaman Anonto, Hasanur Zaman Das, Hridoy Shufian, Abu American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Lamar University Dept. of Electrical and Computer Engineering BeaumontTX United States Texas Tech University Dept. of Electrical and Comnuter Engineering LubbockTX United States

ISBN: (纸本)9798331506995

Accurate lithium-ion battery cycle life prediction from early operational data is essential for optimizing manufacturing processes, ensuring product reliability, and enabling proactive maintenance. This study presents a machine learning-based approach that predicts the remaining cycle life using features derived from the first 100 operation cycles, including discharge capacity trends, internal resistance changes, and voltage characteristics. A linear regression model with elastic net regularization was trained on data from 124 cells, with hyperparameters optimized through cross-validation. The model achieved a root mean square error (RMSE) of 210 cycles and an average percentage error of 9.96% on test data, demonstrating strong predictive performance for cycle lives between 500 and 1200 cycles despite underestimations for longer-lived cells due to data distribution imbalances. This work highlights the promise of data-driven methods for early-stage battery life cycle prediction, offering a scalable and interpretable framework to support advancements in battery technology and maintenance strategies. © 2025 IEEE.

关键词： Linear regression

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EHRGPT: Leveraging language models for generating synthetic health records

EHRGPT: Leveraging language models for generating synthetic ...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Pamisetty, Giridhar Batreddy, Subbareddy Verma, Priya Babu, Ch Sobhan Indian Institute of Technology Dept. of Electrical Engineering Hyderabad India Administrative Staff College of India Centre for Management Studies Hyderabad India Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350362480

Electronic health records (EHRs) are the comprehensive digital records containing patient health information, which help in various domains like public health monitoring, predictive modeling, data-driven research, etc. However, there are strict regulations about data sharing due to the sensitivity of the EHR data. So, there is an increasing demand for generating high-quality synthetic EHR data that mitigates privacy concerns. Most of the existing approaches to EHR generation are limited as they predominantly generate binary decisions about affecting a disease or count-based summaries of disease occurrences for a patient, failing to capture the full patient trajectory contained in the real data and hence reducing the downstream applications. With the success of language models in generative approaches, we propose a language modeling approach using transformers to generate the complete health record of a patient for each admission. We propose to group the patient's EHRs as it enhances the model to capture temporal coherence and the medical history of the corresponding patient. The proposed model can generate high-fidelity privacy preserved EHR data with the statistical properties of the real data. © 2024 IEEE.

关键词： Electronic health record

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Real-Time Incident Monitoring for Smart Cities using IoT 4

Real-Time Incident Monitoring for Smart Cities using IoT

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Pati, Aloukik Praharaj, Ayushi Swain, Kunjabihari Lenka, Manas Ranjan Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9798350349856

In the ever-evolving urban landscape, the concept of the 'smart city' has emerged as a solution to enhance the quality of life for its residents and visitors. Central to this vision is the utilization of the Internet of Things (IoT) technology, which enables the seamless integration of various urban systems and services. Among the critical aspects of smart city management is real-time incident monitoring, which encompasses the rapid detection and response to diverse urban events, including traffic congestion, environmental issues, and public safety concerns. This paper presents a comprehensive exploration of real-time incident monitoring in smart cities through the deployment of IoT-enabled sensors and devices. Leveraging IoT's data collection capabilities, our research delves into the creation of a sophisticated incident monitoring system capable of aggregating, analyzing, and responding to real-time data. The proposed architectural model highlights the data processing algorithms and communication protocols that facilitate the collection and transmission of pertinent data. In the pursuit of resilient and responsive urban environments, this research underscores the pivotal role of IoT-enabled real-time incident monitoring, offering insights into the potential of this technology to enhance the efficiency, safety, and overall well-being of smart city inhabitants. © 2024 IEEE.

关键词： Smart city

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BioPoint: Enhancing Human-computer Interaction through Single-Site, Multi-Sensor Gesture Recognition 46

BioPoint: Enhancing Human-Computer Interaction through Singl...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Isabel, Xavier Campbell, Evan Olivier, Loïc Gagné, Gabriel Scheme, Erik Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laval University Dept. of Electrical and Computer Engineering QC Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B 5A3 Canada University of Oslo Dept. of Technology Systems Oslo Norway

ISBN: (纸本)9798350371499

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. This study also introduces "Myo Hero", an online environment inspired by "Guitar Hero."Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process's appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG's potential for human-computer interaction. © 2024 IEEE.

关键词： Electromyography

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Converging Diagnostic Paths: A Unified Approach to Acid-Base Imbalances in COPD Patients 2

Converging Diagnostic Paths: A Unified Approach to Acid-Base...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Kinikar, Atharva Shah, Manan Pune Institute of Computer Technology Dept. Information Technology Pune India Dwarkadas J Sanghvi Coe Dept. Computer Engineering Mumbai India

ISBN: (纸本)9798350360523

Chronic Obstructive Pulmonary Disease (COPD) significantly impacts patient quality of life and is a leading cause of morbidity and mortality. This paper introduces an innovative algorithm for diagnosing acid-base disorders in COPD patients, incorporating traditional and advanced methodologies like the Henderson-Hasselbalch equation and Bedside Stewart Method. The algorithm enhances the accuracy and speed of Arterial Blood Gas (ABG) analysis, crucial for COPD management. We present a comprehensive approach that includes a Gamblegram for visualizing acid-base balance and a sophisticated system architecture employing Optical Character Recognition (OCR) for efficient ABG report processing. Our findings demonstrate the algorithm's efficacy in providing rapid, detailed analysis, markedly reducing treatment initiation time. This advancement holds significant potential in revolutionizing COPD patient care by improving diagnostic accuracy and treatment strategies for acid-base disorders. © 2024 IEEE.

关键词： Gases

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Bounded Rationality Equilibrium Learning in Mean Field Games 39

Bounded Rationality Equilibrium Learning in Mean Field Games

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Eich, Yannick Fabian, Christian Cui, Kai Koeppl, Heinz Dept. of Electrical Engineering and Information Technology Technische Universität Darmstadt Germany

ISBN: (纸本)157735897X

Mean field games (MFGs) tractably model behavior in large agent populations. The literature on learning MFG equilibria typically focuses on finding Nash equilibria (NE), which assume perfectly rational agents and are hence implausible in many realistic situations. To overcome these limitations, we incorporate bounded rationality into MFGs by leveraging the well-known concept of quantal response equilibria (QRE). Two novel types of MFG QRE enable the modeling of large agent populations where individuals only noisily estimate the true objective. We also introduce a second source of bounded rationality to MFGs by restricting the agents’ planning horizon. The resulting novel receding horizon (RH) MFGs are combined with QRE and existing approaches to model different aspects of bounded rationality in MFGs. We formally define MFG QRE and RH MFGs and compare them to existing equilibrium concepts such as entropy-regularized NE. Subsequently, we design generalized fixed-point iteration and fictitious play algorithms to learn QRE and RH equilibria. After a theoretical analysis, we give different examples to evaluate the capabilities of our learning algorithms and outline practical differences between the equilibrium concepts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning algorithms

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