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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Uban, Ana-Sabina Cristea [Ciobanu], Alina Dinu, Liviu P. Faculty of Mathematics and Computer Science University of Bucharest Bucharest Romania Human Language Technologies Research Center University of Bucharest Bucharest Romania Data Science Center University of Bucharest Bucharest Romania

ISBN: (纸本)9783031243394

Meaning is the foundation stone of intercultural communication. Languages are continuously changing, and words shift their meanings for various reasons. Semantic divergence in related languages is a key concern of historical linguistics. In this paper we investigate semantic divergence across languages by measuring the semantic similarity of cognate sets in multiple languages. The method that we propose is based on cross-lingual word embeddings. In this paper we implement and evaluate our method on English and five Romance languages, but it can be extended easily to any language pair, requiring only large monolingual corpora for the involved languages and a small bilingual dictionary for the pair. This language-agnostic method facilitates a quantitative analysis of cognates divergence – by computing degrees of semantic similarity between cognate pairs – and provides insights for identifying false friends. As a second contribution, we formulate a straightforward method for detecting false friends, and introduce the notion of "soft false friend" and "hard false friend", as well as a measure of the degree of "falseness" of a false friends pair. Additionally, we propose an algorithm that can output suggestions for correcting false friends, which could result in a very helpful tool for language learning or translation. © 2023, Springer Nature Switzerland AG.

关键词： Semantics

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Outage Prediction and Improvement in 6G for UAV Swarm Relays Using Machine Learning

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Progress In Electromagnetics Research B 2024年 107卷 33-45页

作者： Khalil, Hisham Ali, Gauhar Rahman, Saeed Ur Asim, Muhammad Affendi, Mohammed El Department of Intelligent Systems Faculty of Information Technology University of Lahore Pakistan EIAS Data Science and Block chain Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia School of Electronic Engineering Xidian University Xi’an China

The issue of signal outages in sub-THz frequency communication for future 6G networks is addressed by this research. A machine learning method is proposed, employing Random Forest and K-Means algorithms to predict the optimal frequency band and outage probabilities for UAV relays. Both space and frequency diversity are explored to enhance signal strength, and metasurface-carrying UAVs are introduced with a 16 × 16 mm2 design. This design significantly reduces the predicted outage probability from 0.1% to 0.0178%. Finally, triangular and hexagonal UAV swarm formations with metasurfaces are investigated, demonstrating improved performance through heatmap results. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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PM2.5Forecasting Model based on Linear and Non-linear Hybrid Algorithm 15

PM2.5Forecasting Model based on Linear and Non-linear Hybrid...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Banjongkan, Anupong Kerdprasop, Nittaya Hirunyawanakul, Anusara Kerdprasop, Kittisak Suranaree University of Technology School of Computer Engineering Thailand King Mongkut's University of Technology North Faculty of Science Energy and Environment Data Science and Computation Rayong Campus Bangkok Thailand

ISBN: (纸本)9781665477123

Air pollution is one of the harmful problems that the world has focused on and needs to be solved urgently because air pollution has a direct impact on humans leading to premature death caused by various diseases such as asthma inflammatory respiratory disease, lung cancer, and so on. The air pollutants, especially tiny particulate matter (PM), are currently receiving attention because they are a major problem in many large and populated cities around the world. This paper proposed a time-series model for forecasting PM2.5 in advance through a machine learning process with a linear and non-linear hybrid algorithm. A hybrid algorithm that brings together the capabilities of autoregressive integrated moving average (ARIMA) and the adaptive-neuro fuzzy inference system (ANFIS) is used to find the linear and nonlinear correlation of the PM2.5 time-series data. The proposed model is called ARIMA-FIS which uses the gradient descent (GD) method in the learning process. The dataset used in this research is the daily recorded of PM2.5 values in Rayong province, which is the industrial city in Thailand. The results showed that the ARIMA-FIS model had the best performance in forecasting PM2.5 in advance with the least error at 3.46 of mean absolute error (MAE) and 5.11 of root mean square error (RMSE). The proposed model gave the percentage of RMSE almost 3% better than the other standard time-series models. © 2023 IEEE.

关键词： Time series

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Remote Sensing data-Based Satellite Image Analysis in Water Quality Detection for Public Health data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

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computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： Information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Extremely Weak Feedback Method for Controlling Chaotic Resonance

Extremely Weak Feedback Method for Controlling Chaotic Reson...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Iinuma, Takahiro Ebato, Yudai Nobukawa, Sou Tran, Anh Tu Wagatsuma, Nobuhiko Inagaki, Keiichiro Doho, Hirotaka Yamanishi, Teruya Nishimura, Haruhiko Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Chiba Institute of Technology Department of Computer Science Japan Toho University Faculty of Science Department of Information Science Chiba Japan Chubu University Department of Artificial Intelligence and Robotics Aichi Japan Kouchi University Faculty of Education Teacher Training Division Kochi Japan Osaka Seikei University Faculty of Data Science Osaka Japan Yamato University Faculty of Informatics Osaka Japan

ISBN: (纸本)9798350337020

Chaotic resonance, resembling stochastic resonance, is induced by internal fluctuations (i.e., chaos). This phenomenon has been observed in numerous systems. The most representative form of chaotic resonance is the synchronization with respect to weak applied signals under chaos-chaos intermittency, where a chaotic orbit moves among multiple attractors. Chaotic resonance exhibits higher sensitivity than stochastic resonance, but its engineering applications are limited by concerns such as the requirement to adjust the state of chaos by internal system parameters to induce chaotic resonance rather than the external noise strength. However, achieving this adjustment is challenging, especially in biological systems. Therefore, to handle this limitation, we proposed a novel double-Gaussian-filtered reduced region of orbit (RRO) method (called the DG-RRO method) for obtaining perturbed feedback signals lower than the conventional RRO method. This DG-RRO feedback signal is determined by the inverse sign of the map function and double-Gaussian filters around the local maximum/minimum values of the map. Because of its fine local specification, the DG-RRO feedback signal induces a chaotic resonance by one-third the feedback strength of the conventional signal. This method may pave the way for using chaotic resonance in engineering applications. © 2023 IEEE.

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Box facets and cut facets of lifted multicut polytopes 24

Box facets and cut facets of lifted multicut polytopes

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Proceedings of the 41st International Conference on Machine Learning

作者： Lucas Fabian Naumann Jannik Irmai Shengxian Zhao Bjoern Andres Faculty of Computer Science TU Dresden Faculty of Computer Science TU Dresden and Center for Scalable Data Analytics and AI Dresden/Leipzig

The lifted multicut problem has diverse applications in the field of computer vision. Exact algorithms based on linear programming require an understanding of lifted multicut polytopes. Despite recent progress, two fundamental questions about these polytopes have remained open: Which lower box inequalities define facets, and which cut inequalities define facets? In this article, we answer the first question by establishing conditions that are necessary, sufficient and efficiently decidable. Toward the second question, we show that deciding facet-definingness of cut inequalities is NP-hard. This completes the analysis of canonical facets of lifted multicut polytopes.

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Contrastive Learning for Chest X-ray Classification: A Fusion of Topological data Analysis and ResNet 9

Contrastive Learning for Chest X-ray Classification: A Fusio...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Ren, Hao Luo, Zeyu Jing, Fengshi Zhang, Xinyue He, Han Yu, Yonghao Zhao, Dawei City University of Macau Faculty of Data Science China Minzu University of China Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Lung diseases such as pneumonia and tuberculosis present significant global health challenges, leading to millions of deaths annually. Chest X-ray imaging is crucial for diagnosing these conditions, but interpretation often relies on specialized radiologists, creating barriers in resource-limited areas. This study introduces a novel framework integrating topological data analysis (TDA), deep learning with ResNet-50, and contrastive learning to enhance the accuracy and computational efficiency of chest X-ray analysis. TDA captures global topological structures, while ResNet-50 extracts detailed local features. Contrastive learning then optimizes the feature space, enhancing the model's discriminative power and generalization capabilities. Comprehensive evaluations on multiple datasets demonstrate the framework's robustness and effectiveness, showing substantial improvements in diagnostic performance and computational efficiency. These findings suggest that the proposed approach is well-suited for real-time clinical applications, especially in resource-limited settings. Future work will focus on further optimizing the model and exploring its application to other medical imaging modalities, broadening its clinical utility. © 2024 IEEE.

关键词： Contrastive Learning

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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