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Modeling Growth and Spread of Dandelion through Integrodifferential Equations 24

Modeling Growth and Spread of Dandelion through Integrodiffe...

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3rd international conference on Artificial intelligence and Intelligent Information Processing, AIIIP 2024

作者： Chen, Yuming Luo, Ruihan Gao, Rong Sun, Xuehan Basis International School Nanjing Jiangsu Nanjing China RCF experimental school Beijing China Shanghai Pinghe School Shanghai China Oxford University Oxfordshire Oxford United Kingdom

ISBN: (纸本)9798400707308

Dandelion is remarked for its unique seed wind-dispersal strategy and resilience to environment. To better understand biological and ecological traits of dandelions, this study models the population spread of dandelion spread under different conditions. An Integrodifferential Equation (IDE) models the spread of dandelions over time on an open one-hectare plot of land. The IDE breaks down population spread into growth and dispersal. Population growth is governed by a set of Delayed Differential Equations which models the seeds, germinated seedlings, blooming, and mature stages of dandelions. Each stage is delayed from the previous one according to the biological pattern, and the proportion of successful conversion between states is determined based on daily temperature and sunshine level. The Wald Kernel is used to simulate wind dispersal of dandelion seeds through a one-hectare field. The kernel is adjusted based on wind speed distribution, wind direction, and precipitation. It is found that 15◦C, sufficient sunshine, and warm winters are ideal for dandelion spread. The model is then applied to simulate the spread of dandelions with environmental data in Edinburgh and Sydney, representatives of inclement and tropical climate. Rate Description Rs(t) Seed survival rate. The majority of seeds are eaten by insects and animals, mostly beetles. There is also a portion that fails to dispose of the puffball. Overall, the seed survival rate is determined to be 10 percent under all conditions [2]. Rg(t) Germination rate. Germination is the most environmentally demanding stage, as seeds are very sensitive to temperature and sunshine, according to Figure 1. Fourteen to eighteen degrees is the optimal temperature of germination. Eight to ten hours of sunlight is optimal for germination. A complete lack of sunlight can reduce the germination rate by one-fourth [3]. rd(t) Death rate. The average lifespan of a dandelion plant is 2 years, so we set rd(t) = 1/2x365. However, when the

关键词： Integrodifferential equations

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Role of Artificial intelligence in Fostering Internal Stakeholders' Performance in Academia 3

Role of Artificial Intelligence in Fostering Internal Stakeh...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Rao, Batani Raghavendra Vinoth, S. Batni, Anusha R. Navaratna, Raghavendra S. Jain Deemed-to-be University Faculty of Management Bengaluru India Deloitte Dublin Ireland Princeton University Princeton Neuroscience Institute United States

ISBN: (纸本)9798331515911

The study aims to examine the role of Artificial intelligence (AI) in furthering the performance of internal stakeholders in the academia. It aims to explore the positive impact of leveraging AI in fostering the development of students, faculty members and admin or operational excellence. The study also exposes the challenges and the risks in AI adoption. AI's role in the corporate world, especially in banking, insurance, health etc., is widely discussed and therefore, this study is a significant one as it is concerning the academia. Primary data was collected by conducting the survey. 205 respondents participated in the survey. The respondents were categorized into three clusters, namely, emerging, engaged and experienced based on the degree of adoption of AI. To examine the academic stakeholders' behaviour, K-Means clustering method was employed. Multiple regression tool was used to study the perceived benefits of AI adoption in academia. Decision tree was used to develop challenges matrix and to study the interrelated associations. Sentiment analysis was leveraged to examine the AI adoption sentiments among stakeholders of the academia. The study revealed that AI has a positive influence on all the stakeholders of academia. AI assists teachers, helps students and admin team so that the learning is epibranchial and fruitful. AI should be adopted in a calibrated way after considering the AI challenges. The outcome is win-win for the academic world if AI is appropriately implemented. © 2025 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Legal intelligence: Advances in Neural Networks and Language Models for Legal NLP 3

Revolutionizing Legal Intelligence: Advances in Neural Netwo...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Noronha, Raoul Samuel Alenchery, Alex Stanley Deepa, S. Jayapriya, J. Vinay, M. Christ University Department of Computer Science Bengaluru India

ISBN: (纸本)9798331523893

As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers' workflows to support them in their decision-making processes. © 2025 IEEE.

关键词： BERT Contract Clause Extraction GPT Large Language Models (LLMs) Legal Document Classification Legal NLP Legal Text Summarization machine Learning Named Entity recognition (NER) Neural Networks Transformer Models

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EB-SNN: An Ensemble Binary Spiking Neural Network for Visual recognition 27th

EB-SNN: An Ensemble Binary Spiking Neural Network for Visua...

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27th international conference on pattern recognition, ICPR 2024

作者： Li, Xinjie Tang, Jianxiong Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangzhou510555 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783031781858

In recent years, spiking neural networks (SNNs) have gained significant attention in visual recognition tasks due to the low computational energy. However, most SNNs have a large number of parameters, which limits their use on resource-limited devices. In this paper, we propose an Ensemble Binary Spiking Neural Network (EB-SNN) for accurate and memory-friendly visual recognition. The EB-SNN is modeled by Ensemble Binary Weights (EBW) module, which integrates multiple binary weights for lightweight SNN modeling. Meanwhile, we propose Knowledge Alignment Strategy to ensure that the EB-SNN can approximate a well-trained SNN for good performance. Experimental results show that the EB-SNN can achieve accuracy of 95.39% on CIFAR10, using 9.3% memory of full-precision SNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Local binary pattern

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Artificial intelligence in Handwriting Analysis for Personality Trait Identification 3

Artificial Intelligence in Handwriting Analysis for Personal...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kotharkar, Riya Pradeep Rohini, T. Bnm Institute of Techonology Dept. of Ece Bengaluru India

ISBN: (纸本)9798331515911

Handwriting is a unique characteristic that distinguishes a person's identity and serves as a method for identifying various physical and psychological attributes of the writer. It reflects genuine personality traits, including behavior, emotional responses, self-perception, creativity, honesty, fears, and many other characteristics. In this paper, we propose a multi-layered approach for analyzing personality traits through handwriting analysis, utilizing a Voting Classifier, which integrates predictions from multiple base models to enhance overall performance. Specifically, our Voting Classifier combines Logistic Regression, Support Vector machine, Random Forest Classifier, and Gradient Boosting Classifier. Each base model is carefully tuned using RandomizedSearchCV to optimize hyperparameters such as regularization strength for Logistic Regression, kernel type for Support Vector machine, and number of estimators for Random Forest and Gradient Boosting Classifiers. The model's performance evaluation reveals impressive results, with a 93% accuracy in classifying handwriting samples. These findings highlight the model's effectiveness and its ability to handle various handwriting styles with consistency. The strong accuracy underscores the model's potential for real-world applications, offering trustworthy insights into personality traits through handwriting analysis. © 2025 IEEE.

关键词： Decision trees

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The personalized university English learning system in computer-driven research 3

The personalized university English learning system in compu...

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3rd international conference on Artificial intelligence and Education, ICAIE 2024

作者： Ming, Jing School of Public Fundamental Courses Wuhan Institute of Design and Sciences Hubei Wuhan China

ISBN: (纸本)9798400712692

The aim of this study is to develop a computer-assisted personalised university English learning system to improve learning efficiency and effectiveness. machine learning algorithms were used to analyse student data and build a personalised learning model. Experimental results show that the system significantly outperforms traditional methods in vocabulary acquisition (22% improvement), listening comprehension (18% improvement) and oral fluency (15% improvement). The conclusion suggests that a personalised learning system based on artificial intelligence and big data can effectively improve university English teaching, and provide a new direction for the future development of educational technology. During the Eastern Han period, the production of everyday pottery, pottery sculptures, architectural bricks and tiles, a variety of ceramic sculptures, living statues, horses, acrobatics and other funerary objects, as well as new products such as large lofts, chariots and horses, became the main features of the industry. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adaptive Learning Artificial intelligence Data Mining Educational Technology Language Acquisition

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Automated Duplicate Question Analysis with NLP 3

Automated Duplicate Question Analysis with NLP

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohmmad, Sallauddin Chittimalla, Rishika Cholleti, Alekhya Gunda, Sanjana ChandanaMirupuri, Siri Namani, Vaishnavi School of Computer Science and Artificaial Intelligence SR University Telangana Warangal India SR University Telangana Warangal India

ISBN: (纸本)9798331527549

As the capacity of user-generated content endures to upsurge, question duplication has emerged as a must-have for increasing efficiency in question-answering systems. The aim of this paper is to explore how to look for duplicate questions using machine and deep learning methods, reducing duplication in large sets of question data with great accuracy and thus improving user experience at knowledge platforms. This exploration includes models of Logistic Regression, Naive Bayes, Decision Trees, LSTM networks, BERT embeddings, and advanced ensemble methods, such as XGBoost. From above, it is seen that the performance metrics vary across different models, and its accuracy is shown as follows - accuracy of Logistic Regression at 78.9%, 76.4% accuracy of Naive Bayes, 72.3% of Decision Trees, and 82.6% in the LSTM model;the best accuracy has been obtained with the model of XGBoostoptimised by Optuna at a rate of 91.5%, even higher than some deep learning models such as BERT. The results depicted above indicate the outcome of hyperparameter tuning via Optuna on model performance. Our results highlight better effectiveness from optimized ensemble learning techniques in question deduplication, very useful in the implementation of efficient question-answering and knowledge management solutions. © 2025 IEEE.

关键词： Question answering

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A Hybrid Deep Learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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Design Thinking in Electrical Engineering: A Pathway to Human-Centered Innovations 3

Design Thinking in Electrical Engineering: A Pathway to Huma...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, D. Siva Ramkumar, M. Arun, M. Baig, Rahmath Ulla Sivaramkrishnan, M. India Sns College of Technology Dept of Ece India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Karpagam COllege of Engineering Dept of Eee India

ISBN: (纸本)9798331509675

Design thinking is a useful technique in many technical domains since it is a novel methodology that emphasizes comprehending and meeting customer demands. Using design thinking in electrical engineering provides a fresh approach to creating user-focused solutions in a field where technological complexity is constantly changing. In order to foster creativity and useful problem-solving, this study explores how applying design thinking concepts might transform electrical engineering's conventional procedures. Engineers may produce more efficient and user-friendly designs by utilizing crucial phases like empathy, problem identification, ideation, prototyping, and iterative testing. In order to demonstrate how this strategy results in more flexible and sustainable solutions, the discussion cites particular instances, such as developments in consumer electronics, electric power systems, and renewable energy. The study also emphasizes how teamwork and ongoing iteration foster creativity while maintaining designs' alignment with user requirements and societal impact. © 2025 IEEE.

关键词： Systems thinking

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Data Analytics on Blitz E-sports: Enhancing Gaming Experience through Personalization and Prediction 3

Data Analytics on Blitz E-sports: Enhancing Gaming Experienc...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Kusuma, T. Shruthi, G. Avanish Rao, B.S. Anand, Nikhil Abhishek, M. Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India Ramaiah Institute of Technology Department of Information Science Bangalore India

ISBN: (纸本)9798331515911

This paper delves into the comprehensive data analytics of Blitz E-sports, a dynamic gaming cafe, to enhance operational efficiency and customer satisfaction. The study focuses on three pivotal objectives: revenue optimization, game recommendations, and churn prediction. Through meticulous analysis of customer data, gaming trends, and operational metrics, this proposal equips Blitz E-sports with action able insights to enhance profitability, customer engagement, and long-term sustainability. The potential of data-driven decision-making in the competitive e-sports industry is demonstrated by leveraging advanced machine learning techniques and big data technologies. This paper aims to reveal significant improvements in personalized user experiences, optimized revenue strategies, and effective customer retention measures. © 2025 IEEE.

关键词： Sales

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