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检索条件"任意字段=2nd International Conference on Pattern Recognition and Machine Intelligence"

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Pythagorean Fuzzy pattern recognition Model in the Assessment of Social Inclusion Index for Azerbaijan 2nd

Pythagorean Fuzzy Pattern Recognition Model in the Assessmen...

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2nd international conference on Information Technologies and Their Applications

作者： Imanov, Gorkhmaz Aliyev, Asif Azerbaijan Republ Inst Control Syst Minist Sci & Educ AZ-1141 Baku Azerbaijan

ISBN: (纸本)9783031734168;9783031734175

This paper introduces a novel Pythagorean fuzzy pattern recognition-(PFPR) model for the evaluation of the Social Inclusion Index (SII) in Azerbaijan, a crucial component of the Social Quality framework. The approach takes into account the fuzziness of input data and the fuzziness generated throughout the computation process, utilizing Pythagorean fuzzy (PF) logic tools. The proposed model integrates operations framing the PFPR process. Compared to existing multiple-criteria decision-making methods, the contemplated algorithm enhances the computation of socio-economic indices. The paper explores the multidimensional nature of social inclusion, emphasizing its role in creating equal opportunities and facilitating engagement across various societal spheres. To measure social inclusion, the study addresses challenges related to defining social inclusion and identifying indicators, drawing on approaches from institutions such as the European Commission and Eurostat. The results obtained reveal the SII level in Azerbaijan, and the proposed approach demonstrates its applicability in analyzing and estimating various socio-economic phenomena.

关键词： Social Inclusion Index Pythagorean fuzzification Pythagorean fuzzy relation matrix aggregation Pythagorean fuzzy pattern recognition model

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2nd international conference on Cyber intelligence and Information Retrieval, CIIR 2023

2nd International Conference on Cyber Intelligence and Infor...

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2nd international conference on Cyber intelligence and Information Retrieval, CIIR 2023

ISBN: (纸本)9789819776023

The proceedings contain 40 papers. The special focus in this conference is on Cyber intelligence and Information Retrieval. The topics include: A Performance Analysis to Detect Synthetic Images with Deep Learning-Based Image Classification;A Study of AI-Based Systems in the Judicial Interpretation of the Law;broken Authentication and Its Significance in Protecting Online Applications: An Overview Paper;skin Lesion Segmentation Using Deep Learning;AI in Subconscious Language Learning for Error Remediation;amazon Sales Sentiment Prediction and Price Forecasting Using Facebook Prophet;house Price Prediction Using Random Forest Regression Considering the Nearest In-Demand Location;Enhancing Legal Document Understanding Through Text Summarization: A Study on NLP and Wavelet Tree Techniques;Neural Network Participation to Enhance Hydrogen-Biofuel CI Engine Performance and Combat Emissions;Analyzing MACD Divergence: Challenges in Forex and Commodity Trading;GIS-Based Analysis of Water Integrity Differences Within the Madurai Region;a machine Learning Approach to Cardiovascular Disease Prevention in Smart Healthcare;examination of Sustainability 4.0 with Special Reference to international Legal Doctrines;a Study into the Application of Ecological Drones for Wildlife Conservation;the Transformative Role of Artificial intelligence in the Legal Profession and international Arbitration;impact of Health Information Systems on Healthcare Service Quality in Supporting Non-communicable Disease Management in Kosovo;contemporary Challenges in Metaverse: A Techno-Legal Analysis;artificial intelligence in Disease Diagnostics: Rethinking Risk Factors for Cervical Cancer;an Analytical Survey on Credit Scoring Mechanism in India;the Study of the Effectiveness and Sensitivity of Stochastic Divergence in Foreign Exchange Market;conceptual Framework and Impact of Blended Education System on Employability Skills;adversarial Attacks on Neural Networks;preface.

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Dynamical Feature Extraction and pattern recognition for Mental Workload Level with FNIRS 17th

Dynamical Feature Extraction and Pattern Recognition for Men...

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17th international conference on Intelligent Robotics and Applications

作者： Zhang, Lianchi Zhang, Jingting Mu, Fengjun Huang, Rui Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9789819607822;9789819607839

Mental Workload Level (MWL) is an important indicator reflecting the human's cognitive state in human-computer interaction, such as the applications of vehicle driving and mental state assessment. A useful method of MWL assessment is by utilizing functional near-infrared spectroscopy (fNIRS) to monitor the brain tissue blood oxygen concentration during human's cognitive process. However, existing fNIRS-based MWL classification methods still have limited performance in terms of classification accuracy and interpretability, since they have not precisely extracted the feature describing human's cognitive behavior, e.g., cerebral hemodynamics. To address this issue, this paper proposes a new dynamical feature extraction and pattern recognition method for MWL classification by using Dynamic System Theory (DST) and Deterministic Learning (DL) technique. A so-called dynamical feature is extracted by modeling the cerebral hemodynamics using fNIRS in human's cognitive process, which is mathematically interpretable and specific to the human'sMWLaccording to DST. Dynamical pattern recognition scheme is then proposed by combining DL and Support Vector machine, aiming to identify high and low levels of MWL with the dynamical feature of fNIRS. Compared to methods such as EEGNet, DeepConvNet, DCNN, SVM, fNIRS-PreT, and Logistic Regression, our method achieved a performance improvement of 2% to 6% for classifying mental workload levels.

关键词： Functional Near-infrared Spectroscopy Mental Workload Level Dynamical pattern recognition Deterministic Learning Dynamic System Theory

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Design of a university English teaching system based on artificial intelligence technology 2

Design of a university English teaching system based on arti...

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2nd international conference on Big Data, Computational intelligence, and Applications, BDCIA 2024

作者： Wang, Qi Shandong Vocational College of Science and Technology Shandong Province Weifang261053 China

ISBN: (纸本)9781510689053

The rapid development of artificial intelligence (AI) technology has introduced extensive potential applications in the field of education. Currently, university English teaching often faces challenges due to large class sizes, where teachers find it difficult to provide personalized guidance and feedback tailored to the specific needs of each student, thus hindering improvements in teaching quality and efficiency. To address this issue, this paper designs and develops a university English teaching system based on AI technology. The system employs advanced machine learning algorithms to intelligently assess student assignments, generate personalized learning suggestions, and automatically correct grammar and pronunciation. Its functional modules include learning task analysis, assignment evaluation and feedback, and voice learning assistance, aiming to comprehensively enhance students' learning experience and teachers' instructional efficiency. Experimental results indicate that the system demonstrates robust performance and high stability under high-concurrency user scenarios, with consistent operational behavior at different time points, effectively adapting to the practical teaching demands of higher education environments. This research provides theoretical support and practical reference for the application of AI technology in university English teaching, offering significant contributions to advancing educational informatization. © 2025 SPIE.

关键词： Adversarial machine learning

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Analysis of Cloud Service Providers and Computing Services in Modern IT Infrastructure 2

Analysis of Cloud Service Providers and Computing Services i...

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2nd international conference on Computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Himanshu Chopra, Taruna School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9798331530389

In recent years, a growth of cloud computing services has revolutionized modern IT infrastructure and left organizations with difficult choices of what service provider to use and how to implement such services. Much research is done on the level of individual cloud services, yet an important gap still exists with regard to comprehensive comparative analyses that evaluate major cloud service providers across multiple dimensions. This research systematically assesses market leaders: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)-by taking into account their computing services, pricing models, metrics for performance, and security frameworks. Utilizing a mixed methods approach, we analyzed the available quantitative performance data of 2,500 cloud deployments across 12 regions from all over the world, in addition to carrying out qualitative assessments by structured interviews with 150 IT decision-makers. Our results have large differences in service reliability (±15%), cost-effectiveness (23% variance in similar workloads), and integration capabilities across providers. Of course, in compute-intensive workloads, AWS did better, but in integrating enterprises, Azure won, and in machine learning, one could see clear advantages with GCP. The study concludes that optimal cloud service selection is workload-specific rather than provider-centric, so that organizations can benefit from the multi-cloud strategy. Such insights define a decision frame for organizations that are using or planning to adopt cloud services, thereby contributing to the growing body of knowledge in cloud computing infrastructure management. © 2025 IEEE.

关键词： Cloud platforms

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LaiDA: Linguistics-Aware In-Context Learning with Data Augmentation for Metaphor Components Identification 13th

LaiDA: Linguistics-Aware In-Context Learning with Data Augme...

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13th international conference on Natural Language Processing and Chinese Computing

作者： Liu, Hongde He, Chenyuan Meng, Feiyang Niu, Changyong Jia, Yuxiang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Metaphor Components Identification (MCI) contributes to enhancing machine understanding of metaphors, thereby advancing downstream natural language processing tasks. However, the complexity, diversity, and dependency on context and background knowledge pose significant challenges for MCI. Large language models (LLMs) offer new avenues for accurate comprehension of complex natural language texts due to their strong semantic analysis and extensive commonsense knowledge. In this research, a new LLM-based framework is proposed, named Linguistics-aware In-context Learning with Data Augmentation (LaiDA). Specifically, ChatGPT and supervised fine-tuning are utilized to tailor a high-quality dataset. LaiDA incorporates a simile dataset for pre-training. A graph attention network encoder generates linguistically rich feature representations to retrieve similar examples. Subsequently, LLM is fine-tuned with prompts that integrate linguistically similar examples. LaiDA ranked 2nd in Subtask 2 of NLPCC2024 Shared Task 9, demonstrating its effectiveness. Code and data are available at https://***/WXLJZ/LaiDA.

关键词： Metaphor Components Identification Linguistics-aware In-context Learning Data Augmentation Large Language Models

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2nd international conference on Applied Algorithms, ICAA 2025

2nd International Conference on Applied Algorithms, ICAA 202...

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2nd international conference on Applied Algorithms, ICAA 2025

ISBN: (纸本)9783031845420

The proceedings contain 29 papers. The special focus in this conference is on Applied Algorithms. The topics include: On the Characterization of Eulerian es-Splitting p-Matroids;approximability of Edge-Vertex Domination in Unit Disk Graphs;density Extrema of Integer Points in Standard Hexagons;Dilution with Digital Microfluidic Biochips: Unbalanced Split-Error Correction with SIMOP;social Evolution of Published Text and the Emergence of Artificial intelligence Through Large Language Models and the Problem of Toxicity and Bias;HITgram: A Platform for Experimenting with n-Gram Language Models;Overlapping Community Detection Using Dynamic Residual Deep GCN;DeepUIR-Net: Underwater Image Restoration Using Residual-UNet with Optimized Efficient Channel Attention Network Integration;deepfake Image Detection Using Light-Weight Attention Integrated MobileNetV3 Model;AI-Driven Monitoring System for Detecting People Using Mobile Phones in Restricted Zone;integrated Analysis of Voice patterns and Semantic Features for Emotion recognition;Using Various machine Learning Algorithms and xLSTM-UNet for Crop Recommendation and Disease Prediction;he-Li Graph Convolutional Neural Network: Deep and Enhanced Adaptation of Interest Modeling for Personalized Recommendation System;creation of Cartoon Face Images of Celebrities: Automation;deployable Solution for Real-Time Children Face Emotion Prediction System;vision Transformers in Evaluating Bread Edibility;MobileNet Based Fruit Classification Using UNet Generated Segmented Images: GRAD-CAM Visualization;RExAS: Relation Extraction Using Adaptive Self-attention;Kontho: An AI-Driven Smart Glove for Enhanced Sign Language Communication;leveraging EfficientNetB4 Model with Multi-head Attentions for Maize Leaf Disease Detection;automated Module for Image Quality Assessment from Narrow-Banding Imaging Endoscopy Cameras;does Varied Developer Interactions Cause Bugs to Be Resolved Faster? A Study of Open Source Software Ecosystems;movie Recommen

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Prediction Of Student Dropout Analysis For Education using machine Learning 2

Prediction Of Student Dropout Analysis For Education using M...

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2nd international conference on Computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Shrirao, Vedant Satokar, Parth Gulhane, Monali Rakesh, Nitin Agrawal, Pratik Kaur, Mandeep Dixit, Saurav Department of CSE Symbiosis Institute of Technology Symbiosis International Deemed University Nagpur Campus Pune India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura India

ISBN: (纸本)9798331530389

In previous years the quantity of understudy dropout from the instructive organization is expanding quickly. The high pace of understudy's dropout in an enlisted course has been a significant danger to numerous instructive establishments or colleges. The understudy enters the establishment with bunches of dreams and assumptions. At the point when their assumptions are not satisfying or certain elements like socio economics will impact and makes them drop from their enlisted program. It is a significant danger to all instructive organization. The different procedure of the dimensionality decreases, which incorporates highlight determination and component extraction. Include choice is bit by bit strategy that is utilized to choose the right trait from a given property sets. For the component extraction process, it includes the change of higher aspects' information in comparing lower aspects. Include choice comprises of elements like Scholastics, demographical factors, mental variables, medical problems, instructor's perspective, understudy conduct. In this paper, we acquaint a strategy with foresee the understudy dropout involving Guileless Bayes Grouping Calculation in R language. And furthermore, inspect the justification for understudy drop out at an early state and foresee regardless of whether the understudy will drop. There are many elements that influence an understudy to commit dropout as we referenced previously. Early dropout expectation assists the association with holding the understudies from the particular scholastic program. © 2025 IEEE.

关键词： Learning systems

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Conservation of Wildlife in Hunting Tourism Using Artificial intelligence and Image Processing in Smart Firearms 2nd

Conservation of Wildlife in Hunting Tourism Using Artificial...

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2nd international conference on Information Technologies and Their Applications

作者： Asil, Ufuk Nasibov, Efendi Dokuz Eylul Univ Izmir Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificial intelligence (AI) and advanced image processing techniques to prevent firing by activating the safety pin in various situations. Through an integrated camera module and a minicomputer from the Jetson family, animals can be detected in real-time, and various data analysis techniques, along with object recognition algorithms, allow for ethical intervention in many situations that are invisible to the human eye and could be exploited by hunters. Particularly in various national parks, the frequent reports of accidental or uninformed hunting of rare and endangered species, especially during their breeding seasons, underline the importance of this technology. Furthermore, hunters' preference for the most dominant animal in a herd as a trophy poses a significant threat in evolutionary terms. This technology contributes significantly to the conservation of wildlife while promoting ethical and sustainable practices in hunting tourism. The study addresses the development, testing, and implementation of this technology and adds a technological dimension to wildlife conservation strategies.

关键词： Artificial intelligence Image Processing Smart Firearms Wildlife Conservation Hunting Tourism

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PolyNMT: Neural machine Translation Model with Seq2Seq Encoder-Decoder System 6th

PolyNMT: Neural Machine Translation Model with Seq2Seq Encod...

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6th international conference on Computational intelligence in pattern recognition, CIPR 2024

作者： Pandya, Kshitiz Patel, Priyanka Gujarat Changa India

ISBN: (纸本)9789819780891

This chapter looks on the critical role of machine Translation (MT) in bridging communication gaps across various businesses in the Artificial intelligence sphere. A thorough examination of conventional MT models, such as Statistical machine Translation (SMT), Rule-Based machine Translation (RBMT), and Neural machine Translation (NMT), reveals NMT’s higher performance. While translating, these models suffer from a lack of emotive judgments. In support of this, we present PolyNMT, an improved model with its own seq2seq encoder-decoder system and an integrated attention mechanism. PolyNMT outperforms previously existing models in rigorous testing on broad datasets comprising statements from various areas. It includes adverbs and frequent Indic phrases. Notably, PolyNMT’s encoder-decoder system accurately translates sentences like "he is sitting" to "vaha baitha hai" in Hindi, demonstrating its ability to handle sophisticated language expressions. With a huge amount of dataset training and testing, PloyNMT gets better with those profound judgments that were ignored between the statements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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