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Development of a Support system for Situation Awareness and Decision of Beginner Navigators Using Voice Information

Development of a Support System for Situation Awareness and ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Chihiro Nishizaki Shunsuke Hamanaka Dept. of Maritime System Engineering Tokyo University of Marine Science and Technology Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Marine accidents often result from errors in situation awareness (SA) among navigators. Despite the ongoing unmanned ship projects worldwide, the implementation of unmanned systems on many ships is expected to take a considerable amount of time. Therefore, the support technology for shipboard navigators and maritime autonomous surface ship's operators will continue to be crucial in the future. Additionally, to address the challenges posed by the shortage and aging of seafarers, supporting the SA of beginner navigators is essential. In this study, we propose a support system for enhancing the SA of beginner navigators using voice information. This system aims to encourage the gathering of information on target ships and, if necessary, prompt actions against target ships. The results of simulator experiments with cadets, using the Situation Awareness Global Assessment Technique demonstrated the effectiveness of the proposed support system in enhancing SA and decision-making among beginner navigators through voice information. However, to enhance the practicality of the system, it is essential to adjust the timing, contents, and frequency of the voice information according to the skill levels of beginner navigators.

关键词： Navigation Autonomous systems Decision making Aging Timing Safety Marine vehicles Marine accidents Cybernetics

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Sparse Long Short-Term Memory Approach for Energy-Efficient Adaptive Cluster Fuzzy-based Controller in Wireless Sensor Network 5

Sparse Long Short-Term Memory Approach for Energy-Efficient ...

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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Ramanan, K. Ramesh, S.P. Kingsly, C. Sahaya Rajkumar, G. Vinoth Ponkumar, D. David Neels Vargheese, M. Npr College of Engineering and Technology Dept. of Ece Dindigul India Amet University Department of Computer Science and Engineering Tamil Nadu Chennai India Mangalam College of Engineering Department of Computer Science and Engineering Kottayam Ettumanoor India Jp College of Engineering Department of Ece Tamil Nadu Tenkasi India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Dept. of Ece Chennai India Psn College of Engineering and Technology Department of Cse Tamil Nadu Tirunelveli India

ISBN: (纸本)9781665474672

Wireless Sensor Network (WSN) gadgets began with limited-scope WSNs and have expanded to larger-scope and Internet of Things -based WSNs. Clustering increases WSN activity. Before picking Cluster Heads (CHs), Nodes are clustered. Nodes in a clustered WSN, transmit CH environmental variables. Current sink-selection approaches presume unlimited data transfer. Multiple CHs using the same instance, might create complications. Insufficient drop-off space causes package loss. Sinks should have airy, less-restrictive regulators. SIS reduces sink nodes. Looser regulators choose the CH. R-recommendations are used to standardize. Reductions standardize regulators. These methods reduce energy, residual energy, First Node Dead, Half Nodes Dead, Last Node Dead, packet loss, and latency. Good throughput, latency, reliability, packet loss, durability, power consumption, and end-to-end delay are accomplished. The objective of this work is to examine the accessible clustering systems for further developing sensor network execution due to different plan limits and streamlining philosophies. © 2023 IEEE.

关键词： Packet loss

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Efficient Stochastic Error Injection for Optimizing Large Language Models in Arabic Spelling Correction

Efficient Stochastic Error Injection for Optimizing Large La...

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Moath R. Khaleel Gheith A. Abandah Computer Engineering Department The University of Jordan Amman Jordan Dept. of Computer Science and Engineering American University of Sharjah UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Spelling mistakes in written communication among Arabic speakers have become more common, especially with the rise of social media. Correcting these errors requires effective natural language processing (NLP) tools. This paper introduces a ByT5-based approach to address two types of spelling mistakes: directed and general. ByT5, a token-free, pre-trained transformer model, processes UTF-8 text as raw bytes, which minimizes preprocessing and enhances robustness. We perform experiments with varying error injection rates to identify the optimal rate for correction. Our results are evaluated on two test sets: Test200 and TSMTS. The findings demonstrate ByT5's strong performance in spelling error correction, reducing the character error rate (CER) from 5% to 1.37% on the Test200 set and from 5% to 1.77% on the TSMTS set. These test sets include real Arabic sentences containing actual spelling mistakes. Overall, our results show that this approach offers a promising solution for spelling error correction and contributes to generating effective synthetic datasets for training large language models.

关键词： Training Social networking (online) Large language models Stochastic processes Transformers Market research Natural language processing Robustness Error correction Synthetic data

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EMS-BERT: A Pre-Trained Language Representation Model for the Emergency Medical Services (EMS) Domain 23

EMS-BERT: A Pre-Trained Language Representation Model for th...

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Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, systems and engineering Technologies

作者： M Arif Rahman Sarah Masud Preum Ronald D. Williams Homa Alemzadeh John Stankovic Dept. of Computer Science University of Virginia Charlottesville VA USA Dept. of Computer Science Dartmouth College Hanover NH USA Dept. of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

ISBN: (纸本)9798400701023

Emergency Medical Services (EMS) is an important domain of healthcare. First responders save millions of lives per year. Machine learning and sensing technologies are actively being developed to support first responders in their EMS activities. However, there are significant challenges to overcome in developing these new solutions. One of the main challenges is the limitations of existing methods for EMS text mining, and developing a highly accurate language model for the EMS domain. Several important Bidirectional Encoder Representations from Transformer (BERT) models for medical domains, i.e., BioBERT and ClinicalBERT, have significantly influenced biomedical text mining tasks. But extracting information from the EMS domain is a separate challenge due to the uniqueness of the EMS domain, and the significant scarcity of a high-quality EMS corpus. In this research, we propose EMS-BERT - a BERT model specifically developed for EMS text-mining tasks. For data augmentation on our small, classified EMS corpus which consists of nearly 2.4M words, we use a simultaneous pre-training method for transfer-learning relevant information from medical, bio-medical, and clinical domains; and train a high-performance BERT model. Our thorough evaluation shows at least 2% to as much as 11% improvement of F-1 scores for EMS-BERT on different classification tasks, i.e., entity recognition, relation extraction, and inferring missing information when compared both with existing state-of-the-art clinical entity recognition tools, and with various medical BERT models.

关键词： emergency medical services (EMS) data processing and analysis

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Impact of Quaternary Pumped Storage Hydropower on Frequency Response of U.S. Western Interconnection with High Renewable Penetrations

Impact of Quaternary Pumped Storage Hydropower on Frequency ...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Nag, Soumyadeep Dong, Zerui Tan, Jin Kim, Jinho Muljadi, Eduard Lee, Kwang Y. Jacobson, Mark Power System Engineering Center National Renewable Energy Laboratory GoldenCO United States Baylor University Dept. of Electrical and Computer Engineering WacoTX United States Auburn University Dept. of Electrical and Computer Engineering AuburnAL United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

As renewable penetrations increase in the United States, maintaining the stability and reliability of low-inertia power grids by providing sufficient frequency control capabilities becomes a challenge. Advanced pumped storage hydropower (A-PSH) technologies will be expected to play an essential role in the future grid to provide energy and ancillary services. This paper studies the impact of using quaternary pumped storage hydropower (Q-PSH) as a newly proposed A-PSH technology to provide primary frequency response. To quantify the impact of Q-PSH on the frequency response of the U.S. Western Interconnection, a user-defined dynamic model of Q-PSH is developed in the GE Positive Sequence Load Flow (PSLF) software and is implemented in a set of detailed U.S. Western Electricity Coordinating Council planning cases, in which renewable penetration levels are 20%, 40%, 60%, and 80%. Simulation results show that Q-PSH can help improve the frequency nadir and the settling frequency compared to conventional PSH. © 2022 IEEE.

关键词： Frequency response

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Sentimental Analysis of Various TV Serials in Society

Sentimental Analysis of Various TV Serials in Society

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Ripa Sarkar Ratna R. Sarkar Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh Dept. of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

Sentiment analysis which involves identifying emotions in the text is a widely studied topic of natural language processing. Many researchers focus on social media content like posts, tweets and reviews for their studies. In this paper, a large number of data from comments about serials on social media and internet forums in Banglish is collected and analyzed. We use NLP techniques to do the sentiment analysis of the comments. This study explores how different kinds of serials have impacted society and concentrates on how they have influenced the creation of Banglish comments. This study aims to conduct a sentiment analysis of Banglish comments generated in response to different genres of serials and to analyze their potential societal implications. Along with Long Short-Term Memory (LSTM), we use several machine-learning algorithms such as Logistic Regression (LR), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes, Decision Tree (DT), AdaBoost, Random Forest (RF) and Support Vector Machine (SVM) to create this model. The SVM gives the highest accuracy among all of the machine learning algorithms.

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Detecting Retinal Damage From OCT Images

Detecting Retinal Damage From OCT Images

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International Conference on Computing and Networking Technology (ICCNT)

作者： Kundrapu Vineetha Md Jaffer Ali Narisetty Prathima Tripty Singh Prakash Duraisamy Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering University of Wisconsin- Green Bay Wisconsin USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Optical coherence tomography (OCT) is a crucial imaging modality in ophthalmology, offering detailed retinal images essential for diagnosing various retinal disorders. In this study, we investigate the application of convolutional neural networks (CNNs) and transfer learning to classify retinal OCT images. We compare a custom CNN model with two pretrained models, MobileNet and VGG16, to explore their effectiveness in categorizing retinal diseases. Our study focuses on improving classification accuracy through image enhancement techniques, training methodologies, and model evaluation using confusion matrices. The results highlight the VGG16-based CNN model as the most effective, demonstrating its potential for accurate classification of retinal disorders based on OCT images. Furthermore, we delve into strategies to further enhance the performance of these models, such as data augmentation, finetuning, and hyperparameter tuning. We discuss the importance of these techniques in optimizing model performance and reducing overfitting. Additionally, we emphasize the significance of evaluating models using confusion matrices to gain insights into their performance across different classes. Overall, our findings suggest that CNNs, particularly the VGG16based model, hold promise for improving the automated diagnosis of retinal diseases using OCT imaging, which could lead to better patient outcomes in ophthalmology.

关键词： Training Accuracy Optical coherence tomography Transfer learning Retina Feature extraction Ophthalmology Convolutional neural networks Tuning Diseases

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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Detection of Potato Leaves Using Convolutional Neural Networks (CNNs) 3

Detection of Potato Leaves Using Convolutional Neural Networ...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Kaushik, Priyanka Kakkar, Manav Yadav, Meenakshi Sathish, M. Mago, Beenu Kaushik, Manas Manglik Chandigarh University Dept. of Cse (AIML) Chandigarh India Dhanalakshmi Srinivasan College Dept. of Civil Engineering Coimbatore India Skyline University College Dept. of Computer Science Sharjah United Arab Emirates Manipal Institute of Technology Dept. of Electrical and Electronics Karnataka India

ISBN: (纸本)9798331521691

Agricultural productivity plays a vital role in India's economy, contributing approximately 17-18% to the nation's GDP and serving as a primary livelihood for a significant portion of the population. With India possessing the largest net cropped area globally, effective pest and disease management is crucial to ensuring stable agricultural yields. This study utilizes advanced technologies, including machine learning, computer vision, and deep learning, to detect and identify common diseases such as early blight and late blight in potato plant leaves. Using a Kaggle-sourced dataset comprising roughly 2,000 images, rigorous data augmentation techniques were applied to enhance the robustness of the model. A customized convolutional neural network (CNN) was deployed, achieving an impressive accuracy of 97.12% after just 32 epochs. This performance surpasses many existing Kaggle models, which typically reach around 95% accuracy. © 2025 IEEE.

关键词： Data Augmentation Deep Learning Kaggle Dataset Machine Learning Pests Potato Disease Detection

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An Efficient Natural Merge Sort Incorporating Array Decomposition

An Efficient Natural Merge Sort Incorporating Array Decompos...

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International Conference on computer and Information Technology (ICCIT)

作者： Kanchon Gharami Kazi Mehrab Rashid Golam Rabbi Farhana Amin Swakkhar Shatabda S.M. Shovan Md. Al Mehedi Hasan Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science & Engineering United International University Dhaka Bangladesh Dept. of Computer Science & Engineering Missouri University of Science & Technology Missouri United States

Modifying an algorithm that has been established over many years and making it even faster has always been a fascinating and challenging area in the field of algorithms, which motivated us to take the challenge of improving the performance of Knuth’s NaturalMergeSort by reducing the runtime with considering both ascending and descending runs. The way we optimize it is by taking advantage of both ascending and descending runs, i.e., increasing the potential of the decomposition method compared to the existing algorithm. The proposed algorithm was implemented in C++, and the experiment was conducted with some random and manually prepared datasets that resulted in improving the worst case of NaturalMergeSort by an exceedingly large margin of 97.5%, demonstrating the efficiency and flexibility of our algorithm. Even for the average case, our proposed algorithm beats Knuth’s NaturalMergeSort by a slight margin, and it also outperforms traditional merge sort with 17.5% improvements. The performance and efficiency of our algorithm have been recorded and presented in graphical form by comparing time and space complexity with other competitor sorting algorithms.

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