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35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023

作者： Yang, Cheng-Zen Li, Jin-Jian Lin, Shu-Chang Department of Computer Science and Engineering Department of Foreign Languages and Applied Linguistics Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9789869576963

Lexical complexity is crucial for reading comprehension. In the past, research work of lexical complexity prediction mainly focuses on differentiating the complexity difference between two words. Moreover, most of the previous lexical complexity prediction approaches only consider traditional lexically relevant features. In this paper, we propose a novel supervised approach using word embeddings features to tackle the lexical complexity prediction problem as a single-label multiclassification problem. We discuss four word embeddings techniques including Word2Vec, fastText, GloVe, and BERT. We also discuss five classification models including k-Nearest Neighbors, Support Vector Machines, Multilayer Perception, Random Forest, and XGBoost. The prediction models are evaluated with three datasets in English, Traditional Chinese, and Japanese. The results show that SVM with fastText can achieve the highest accuracy of 66.23% for the English dataset. SVM with GloVe can achieve the highest accuracy of 53.84% for the Traditional Chinese dataset. SVM with Word2Vec can achieve the highest accuracy of 49.96% for the Japanese dataset. © 2023 ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Embeddings

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Revolutionizing Urban Waste Management: Cutting-Edge Image Processing for Precise Solid Waste Classification 5

Revolutionizing Urban Waste Management: Cutting-Edge Image P...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rama Krishna, S. Patan, Muzeeb Khan Sudarsan, J.S. Padmapriya, R. Bhagavathiperumal, S. Balaram, Allam Department of Computer Science & Engineering Hyderabad502329 India College of Engineering and Technology University of Technology and Applied Sciences Department of EEE Muscat Oman India Sathyabama Institute of Science and Technology Department of Civil Engineering Chennai India Sri Krishna College of Engineering and Technology Coimbatore India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798331530013

Due to rapid urbanization and population growth, urban waste management has become one of the major challenges faced by cities around the world. Solid waste classification and sorting are necessary for intensifying recycling, safeguarding environment and manage additional landfilling of materials that are reusable or recyclable, and are getting in landfill of solid waste in certain extent. In this study, an original ensemble model using the VGG19 and EfficientNet architectures is proposed to classify solid waste in urban area. This proposed model was applied on a dataset consists of 2,527 number of images in to six classes to increase the accuracy of classification as well as to maximize the efficiency of waste. It preprocesses the incoming data using various techniques such as image resizing, normalization and augmentation to make the dataset more diverse and robust. The ensemble mechanism performed better combining the detailed feature extraction skills of VGG19 and versatile and scalable architecture of EfficientNet. On test set, it outperformed individual models and the accuracy for the proposed model was 98.75% which closely followed, previously obtain the best result. © 2024 IEEE.

关键词： Population statistics

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Wavelet-Transformed K-NN Pipeline for EEG-Based Eye Blink Classification with Time Wrapping 2nd

Wavelet-Transformed K-NN Pipeline for EEG-Based Eye Blink C...

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2nd International Conference on Data science and Network engineering, ICDSNE 2024

作者： Priyadharshini Jayadurga, N. Chandralekha, M. Saleem, Kashif Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Sciences and Engineering College of Applied Studies and Community Service King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9789819783359

An innovative pipeline integrating wavelet transform, k-nearest neighbors (k-NN), and temporal wrapping approaches is presented in this work for the classification of eye blinks in electroencephalogram (EEG) recordings. The proposed process commences with raw EEG data preprocessing followed by wavelet treatment to extract significant time and frequency domain properties. These characteristics are subsequently utilized by a k-NN classifier to enhance the accuracy of eye blink recognition by using spatial relationships within EEG signals. Notably, the integration of a novel time wrapping technique deals with temporal dynamics, supplying the model with resilience to fluctuations in eye blink patterns across time. The pipeline’s enhanced efficiency can be observed through validation using a customized EEG dataset, particularly when navigating the time complexity associated with eye blink events. Evaluations performed against current techniques demonstrate the efficacy of the proposed solution. The outcomes not only demonstrate the methodology’s accuracy, but also its possible use in real-world settings featuring clinical diagnostics and human-computer interactions. This work conveys a strong basis for the discipline of EEG-based eye blink classification, having implications to enhance neuroscience and facilitating beneficial uses in healthcare and assistive technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wavelet transforms

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The Optimum Switching Bilateral of Various Mix Noise Filters to reduce Medical X-Ray Images 12

The Optimum Switching Bilateral of Various Mix Noise Filters...

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12th International Electrical engineering Congress, iEECON 2024

作者： Langampol, Kriengkri Prayote, Akara Lee, Wilaiporn Srisomboon, Kanabadee Rajamangala University Of Technology Phra Nakhon Faculty Of Engineering Department Of Computer Engineering Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Computer And Information Science Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Thailand

ISBN: (纸本)9798350383591

In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the symptom diagnosis applications. However, the medical image quality may drop due to the imperfection of devices and the environment. Noise is one of the most critical factors that reduces the image quality where the noise may form by combining two types of noises. In this paper, we considered four well-known mixed noise filters including TF, BM3D, NCSR and OSBF filters. Since the OSBF filter is the alternative version of SBF filter that requires to determine the optimal weights, therefore we first determine the optimal weights for the medical image under the combination of Gaussian noise and salt-and-pepper noise. Then, we evaluate the filtering performance under three metrics including PSNR, SSIM and filtering time. As a result, OSBF filter achieves the best filtering performance since it can reduce the noise effect, preserve image details and consumes the short filtering time. On the other hand, the filtering performance of the TF filter suffers from the combining of salt-and-pepper noise. © 2024 IEEE.

关键词： Salt and pepper noise

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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Quality of Service based Selfish Node Detection in Mobile Ad-Hoc Network 3

Quality of Service based Selfish Node Detection in Mobile Ad...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Kannan, B. Maruthu Vadivel, G. Viswanathan, C. Reddy, P. Kiran Kumar Sphoorthy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India University of Technology and Applied Science Department of Information Technology Salalah Oman Alpha College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9781665462167

Mobile ad hoc networks (MANETs) are wireless networks containing a collection of wireless nodes interrelated with multi-hop communication paths. Mobile nodes can freely create and dynamically self-organize random and temporary wireless networks without pre-existing infrastructure. Hence, the transmission connection between two nodes breaks repeatedly due to node mobility. This causes an additional delay in the network's performance. All mobile nodes work together entirely regarding the distribution of their memory space. In actuality, though, several nodes may inconsiderately choose to cooperate partially;otherwise, not at all with other nodes. These selfish nodes minimize the whole data's accessibility. To solve these issues, quality-of-service-based self-node detection (QSND) in MANET is proposed. In this approach, the Quality of Service (QoS) parameters verifies the operation of a node. This approach uses the QoS parameters like packet loss ratio, packet delay ratio, and remaining energy ratio as the most significant components to evaluate the selfish nodes in the MANET. This approach minimized the delay and improved the network throughput. © 2023 IEEE.

关键词： Quality of service

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Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

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computers, Materials & Continua 2022年第8期72卷 3585-3600页

作者： Avishek Banerjee Deepak Garg Victor Das Laxminarayan Sahoo Ira Nath Vijayakumar Varadarajan Ketan Kotecha Department of Computer Science and Engineering Bennett UniversityGreater Noida201310India Department of Information Technology Asansol Engineering CollegeAsansol713305India Department of Computer and Information Science Raiganj UniversityRaiganj733134India Department of Computer Science and Engineering JIS College of EngineeringKalyani741235India School of Computer Science and Engineering University of New South WalesSydneyAustralia Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)PuneIndia

In this paper,the establishment of efficientWireless Sensor Network(WSN)networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach(SMOWA)algorithm for solving a multi-objective *** Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficientWireless Sensor Network to minimize energy *** that,the cluster head for each cluster has been selected with the help of the duty *** configuring the WSN networks,the SMOWA algorithms have been developed to obtain the minimum energy consumption for the *** minimization,as well as the amount of day-saving,has been calculated for the differentWSNswhich has been configured through different deployment *** major finding of the research paper is to improve the durability of Wireless Sensor Network(i)applying different deployment strategies:(Random,S pattern and nautilus shell pattern),and(ii)using a new Meta-heuristic algorithm(SMOWA Algorithm).In this research,the lifetime of WSN has been increased to a significant *** choose the best result set from all the obtained results set some constraints such as“equivalent distribution”,“number of repetitions”,“maximum amount energy storage by a node”has been set to an allowable range.

关键词： Wireless Sensor Network(WSN) Self-adaptive Multi-Objective Weighted Approach(SMOWA) deployment strategies Meta-heuristicMethods energy minimization duty cycle

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Advanced Hyper-Tunning Strategies for Global Mean Based Feature Selection and Creation for enhanced Accuracy 4

Advanced Hyper-Tunning Strategies for Global Mean Based Feat...

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

作者： Prasad, Maradana Durga Venkata Srikanth, T. Mishra, Durgesh Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam India Symbiosis University of Applied Sciences Scsit Indore India

ISBN: (纸本)9798331518981

Clustering accuracy is crucial for effective data analysis in unsupervised learning tasks, with the usage of the simple and efficient cluster analysis technique named as K-means technique. However, standard K-means suffers from limitations such as sensitivity to random initialization and challenges in handling high-dimensional data with irrelevant features. This paper presents a hyper-tuned approach to improve K-means clustering accuracy by Unifying Global Mean-Based Nearest Feature Object Selection for robust Centroid initialization with a Feature Creation Method Based on Important Feature Selection. The proposed method first enhances Centroid initialization by selecting points close to global feature means, ensuring initial centroids are more representative of the data distribution. Additionally, it focuses on identifying and weighting the most important features while creating composite features that amplify cluster separability. Experimental results demonstrate a 1.36986% improvement in clustering accuracy, an increase in silhouette scores, faster convergence, and improved stability across multiple trials compared to traditional K-means. © 2024 IEEE.

关键词： Data accuracy

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach 31

HPC Application Parameter Autotuning on Edge Devices: A Band...

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31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

ISBN: (纸本)9798331509095

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2024 IEEE.

关键词： Edge computing

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