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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Wu, Tung-Ling Kung, Chih-Ming Lin, Guan-Ting Tsai, Kuen-Yu Meng, Guang-Yun Huang, Yu-Che Cheng, Wan-Shu Institute of Transportation at Ministry of Transportation and Communications Taiwan Shin Chin Univeristy Kaohsiung Campus Dep. of Information Technology&Communication Taiwan Providence University Dep. of Computer Science and Information Management Taichung Taiwan National Taiwan Univeristy Department of Electrical Engineering Taipei Taiwan Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics Ntu Taiwan

ISBN: (纸本)9798350365856

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition

Grid Search Hyperparameters Tuning with Supervised Machine L...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Amogne Andualem Ayalew Gizachew Bitew Ayodeji Olalekan Salau Melaku Lake Tegegne Shashi Kant Gupta Mohammad Israr Tesfa Tegegne Tsehay Admassu Assegie Department of Information Technology School of Computing and Informatics Mizan-Tepi University Tepi Ethiopia Computer Science Department School of Computing Mizan Tepi University Mizan-Tepi Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Information Technology College of Engineering and Technology Injibara University Injibara Ethiopia Department of Computer Science and Engineering Eudoxia Research University USA Hotoro GRA Maryam Abacha American University of Nigeria Kano State Federal Republic of Nigeria Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dara Ethiopia Department of Computer Science College of Engineering and Technology Injibara University Injibara Ethiopia

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment.

关键词： Support vector machines Training Accuracy Machine learning algorithms Named entity recognition Feature extraction Conditional random fields Bayes methods Optimization Tuning

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Applying unsupervised keyphrase methods on concepts extracted from discharge sheets

arXiv

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arXiv 2023年

作者： Memarzadeh, Hoda Ghadiri, Nasser Samwald, Matthias Shahreza, Maryam Lotfi Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Department of Computer Engineering Shahreza Campus University of Isfahan Iran

Clinical notes containing valuable patient information are written by different health care providers with various scientific levels and writing styles. It might be helpful for clinicians and researchers to understand what information is essential when dealing with extensive electronic medical records. Entities recognizing and mapping them to standard terminologies is crucial in reducing ambiguity in processing clinical notes. Although named entity recognition and entity linking are critical steps in clinical natural language processing, they can also result in the production of repetitive and low-value concepts. In other hand, all parts of a clinical text do not share the same importance or content in predicting the patient's condition. As a result, it is necessary to identify the section in which each content is recorded and also to identify key concepts to extract meaning from clinical texts. In this study, these challenges have been addressed by using clinical natural language processing techniques. In addition, in order to identify key concepts, a set of popular unsupervised key phrase extraction methods has been verified and evaluated. Considering that most of the clinical concepts are in the form of multi-word expressions and their accurate identification requires the user to specify n-gram range, we have proposed a shortcut method to preserve the structure of the expression based on TF-IDF. In order to evaluate the pre-processing method and select the concepts, we have designed two types of downstream tasks (multiple and binary classification) using the capabilities of transformer-based models. The obtained results show the superiority of proposed method in combination with SciBERT model, also offer an insight into the efficacy of general extracting essential phrase methods for clinical *** Codes 68P20 Copyright © 2023, The Authors. All rights reserved.

关键词： Medical informatics

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An Exploratory Study of Diverse Models and Datasets for Transfer Learning based Image Classification on Sparse Data

An Exploratory Study of Diverse Models and Datasets for Tran...

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Moratuwa engineering Research Conference (MERCon)

作者： Jathurshan Pathmarasa Udaya Wijenayake Ruchire Eranga Wijesinghe Bhagya Nathali Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Transfer learning is a powerful technique for image classification, especially when dealing with limited data. However, selection of the best transfer learning approach and model remains challenging, since the strategy is highly influenced by the scenario. This work conducted a thorough analysis of four transfer learning approaches for binary classification, and multiclass classification using four pretrained models i.e. MobileNetV2, MobileNetV3Large, EfficientNetB0, and EfficientNetB5. The test accuracy was measured with respect to the sample size considering the effects of data augmentation and fine tuning. The key finding of the study revealed that data augmentation has improved the test accuracy by up to $11.8 \%$. Moreover, the lightweight models marked a breakthrough by outperforming large scale models’ accuracy for sparse data settings when fine tuning (FT) and data augmentation (DA) are applied appropriately. Hence, this work provides a comprehensive overview on selecting different transfer learning approaches for sparse data classification by exploring diverse models and datasets, while discussing the consequences and challenges for future real-world applications.

关键词： Knowledge engineering Accuracy Transfer learning Optical wavelength conversion Data augmentation Feature extraction Size measurement Data models Tuning Image classification

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Observation of Su-Schrieffer-Heeger Topological Model Band Structure in Integrated LNOI Coupled Ring Cavities 16

Observation of Su-Schrieffer-Heeger Topological Model Band S...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Dinh, Hiep X. Balcytis, Armandas Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Rmit University Integrated Photonics and Applications Centre MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan The University of Tokyo Research Center for Advanced Science and Technology Tokyo153-8904 Japan

We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonic integrated circuits

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The Effectiveness of a Simplified Model Structure for Crowd Counting

arXiv

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arXiv 2024年

作者： Chen, Lei Gao, Xinghang Chao, Fei Chang, Xiang Lin, Chih-Min Gao, Xingen Lin, Shaopeng Zhang, Hongyi Lin, Juqiang The School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen China The Department of Artificial Intelligence School of Informatics Xiamen University Xiamen China The Department of Computer Science Institute of Mathematics Physics and Computer Science Aberystwyth University Aberystwyth United Kingdom The Department of Electrical Engineering Yuan Ze University Taoyuan City320 Taiwan

In the field of crowd counting research, many recent deep learning based methods have demonstrated robust capabilities for accurately estimating crowd sizes. However, the enhancement in their performance often arises from an increase in the complexity of the model structure. This paper discusses how to construct high-performance crowd counting models using only simple structures. We proposes the Fuss-Free Network (FFNet) that is characterized by its simple and efficieny structure, consisting of only a backbone network and a multi-scale feature fusion structure. The multi-scale feature fusion structure is a simple structure consisting of three branches, each only equipped with a focus transition module, and combines the features from these branches through the concatenation operation. Our proposed crowd counting model is trained and evaluated on four widely used public datasets, and it achieves accuracy that is comparable to that of existing complex models. Furthermore, we conduct a comprehensive evaluation by replacing the existing backbones of various models such as FFNet and CCTrans with different networks, including MobileNet-v3, ConvNeXt-Tiny, and Swin-Transformer-Small. The experimental results further indicate that excellent crowd counting performance can be achieved with the simplied structure proposed by us. Copyright © 2024, The Authors. All rights reserved.

关键词： Complex networks

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Development of AI Chatbot for Tourism Promotion: A Case Study in Ranong and Chumphon, Thailand

Development of AI Chatbot for Tourism Promotion: A Case Stud...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Nasith Laosen Kanjana Laosen Piyawat Sriprasert Department of Digital Technology Faculty of Science and Technology Phuket Rajabhat University Phuket Thailand Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Computer Engineering Institute of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Ranong and Chumphon provinces in Thailand pos-sess significant tourism potential due to their natural beauty and archaeological heritage. However, tourism development in the region remains limited due to inadequate information infrastructure and limited technology integration. This research presents a chatbot designed to promote tourism in these locations. The chatbot leverages a graph database built using data from the University to Tambon (U2T) project. Functioning as a virtual travel companion, it provides information on tourist attractions, accommodations, and dining options. Advanced natural language processing and machine learning enable the chatbot to under-stand user inquiries and effectively retrieve relevant information. Evaluation demonstrates strong performance with an F1-score of 99.56 for intent classification and 99.96 for named entity recognition. Testing with user-designed test cases confirmed the chatbot's functionality.

关键词： Adaptation models Databases Training data Prototypes Machine learning Chatbots Data models

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Hybrid Heuristic Optimization of Deep Learning Models for Robust Plant Disease Detection

Hybrid Heuristic Optimization of Deep Learning Models for Ro...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： K. Ananthajothi Pravin R. Kshirsagar Tan Kuan Tak Kailash Kumar Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and error-prone. A multitude of deep learning algorithms have been developed to detect plant leaf diseases; however, most rely on low-resolution photos utilizing convolutional neural nets (CNNs). This research seeks to create an improved nine-layer the CNN network model for the accurate classification of leaf-related illnesses. A contrast enhancement approach is used to pre-process plant leaf pictures. Following binary thresholding, the pre-processed pictures are separated into leaf images and abnormality segmented using the "Enhanced U-Net (EU-Net)" approach. A Multilevel Feature Fusion Network Convolutional Neural Network (MFFN-CNN) is employed to categorize illnesses of leaves based on segmented images. The "Hybrid Leader Cat Swarm Optimization(HLCSO)" method improves U-Net parameter optimization. The effectiveness of the leaf diagnostic model is demonstrated experimentally employing a variety of factors through the use of numerous baseline approaches.

关键词： Deep learning Plant diseases Image segmentation Computational modeling Neural networks Convolutional neural networks Particle swarm optimization informatics Optimization Monitoring

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Light Weight Cryptography for Cloud-Based E-Health Records 7

Light Weight Cryptography for Cloud-Based E-Health Records

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Mittal, Shikha Monga, Chetna Upreti, Kamal Kumar, Nishant Raut, Ranjana Dinkar Alam, Mohammad Shabbir Chitkara University Institute of Engineering and Technology Punjab India Dr. Akhilesh das Gupta Institute of Technology and Management Department of Computer Science and Engineering India Amity University Amity School of Business Uttar Pradesh Noida India Sant Gadge Baba Amravati University Department of Applied Electronics Maharashtra India Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Cloud computing is currently a popular research topic among academics. It is an internet-based resource pool with a wide range of resources. The cloud environment is extremely dependable in terms of making resources available to internet users when they are needed. Reliable computing services can be provided without the need for any additional infrastructure, making it a cost-effective alternative. The majority of the businesses relied on cloud computing to host their apps. The health-care unit service is the most important service for the population. It is critical to keep sensitive information about a patient's medical history in a secure location. As a result, the most secure encryption and decryption should be used. The maintenance of Electronic Health Records is one of the advanced technologies in cloud computing. There is very little research and development in the areas of personal health records and electronic health records. The ultimate goal of this work is to offer a system for exchanging personal health information in a safe cloud setting. The medical information about a patient is likewise kept in this EHR by cloud environment in a dispersed manner. The saved information of user can be collected, shared, exchanged, and organised by other users. In addition, this research study demonstrates an effective way for securing an e-health cloud system in a cloud computing environment utilising identity-based cryptographic technique. © 2022 IEEE.

关键词： Cryptography

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Investigation of DSR protocol performance in Wireless MANET using Correlation Method 1

Investigation of DSR protocol performance in Wireless MANET ...

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1st International Conference on Advances in Computational Science and engineering, ICACSE 2020

作者： Rastogi, Umang Pattanaik, Balachandra Barani Sundaram, B. Mishra, Manish Kumar Thirumoorthy, Dhanabal Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India Department of Electronics and Communication Engineering Bule Hora University Bule Hora Ethiopia Department of Computer Science Bule Hora University Bule Hora Ethiopia Department of Computer Science University of Gondar Ethiopia College of Informatics Bule Hora University Bule Hora Ethiopia

As we know already MANET means Mobile ad-hoc network that works made by two words Mobile and Ad-hoc Network collection of moving nodes shows mobile and kind of temporarily network which do not have any fixed point or centralised administration. Here assortment of nodes forms a *** can be adjust mobility to bandwidth in high or low level. In this paper, we evaluate performance of DSR protocol. In this unique design shows the results in terms of how protocol changes quickly according to nodes movement along with compare by correlation method. The result of this paper is coming after analysing of different behaviour of DSR can be applied in other new protocols that are using DSR framework. In this paper I showed results in term of break time, PDF, delay, throughput etc by simulation. These simulation results are applied on correlation method and finding out in which point DSR protocol parameters are performing good performance. In this result shows when pause time is increases along with no of nodes then correlation will be high. © Published under licence by IOP Publishing Ltd.

关键词： Correlation methods

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