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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Gavin, Rory Cao, Ming Paarporn, Keith University of Groningen Faculty of Science and Engineering Netherlands University of Colorado Department of Computer Science Colorado Springs United States

ISBN: (纸本)9798350316339

The well-known replicator equation in evolutionary game theory describes how population-level behaviors change over time when individuals make decisions using simple imitation learning rules. In this paper, we study evolutionary dynamics based on a fundamentally different class of learning rules known as logit learning. Numerous previous studies on logit dynamics provide numerical evidence of bifurcations of multiple fixed points for several types of games. Our results here provide a more explicit analysis of the logit fixed points and their stability properties for the entire class of two-strategy population games - by way of the r-Lambert function. We find that for Prisoner's Dilemma and anti-coordination games, there is only a single fixed point for all rationality levels. However, coordination games exhibit a pitchfork bifurcation: there is a single fixed point in a low-rationality regime, and three fixed points in a high-rationality regime. We provide an implicit characterization for the level of rationality where this bifurcation occurs. In all cases, the set of logit fixed points converges to the full set of Nash equilibria in the high rationality limit. © 2024 IEEE.

关键词： Bifurcation (mathematics)

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Iot - Enabled Technologies for Sustainable Smart Agriculture and their Comprehensive Survey

Iot - Enabled Technologies for Sustainable Smart Agriculture...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Santosh, S. Raghavendra, R. Deemed-to-be University Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bangalore India Deemed to Be University Department of Computer Science and IT Jain Bangalore India

ISBN: (纸本)9798350322309

Both the quality and amount of food demanded have increased, necessitating agricultural technology and intensification. Agricultural innovation is booming thanks to the Internet of Things, or IoT, is a cutting-edge technology. Research institutions and scientific organizations are creating IoT-based goods and solutions to solve a range of agricultural concerns. This study offers a thorough literature assessment by examining IoT technology and their current applications in a range of farming-related businesses. The comprehensive reviews of the literature conducted for this study were entirely based a review of the literature publications published in government publications during the previous ten years. Carefully selected articles from a wide variety have been arranged into courses. The major objective gathers all pertinent studies on IoT agricultural applications, sensors/devices, communication protocols, and community types. Additionally, it addresses the main problems and challenges that are the focus of modern agricultural research. © 2023 IEEE.

关键词： Internet of things

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A Survey on Cryptocurrencies Utilizing the Blockchain 11

A Survey on Cryptocurrencies Utilizing the Blockchain

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Manikandan, V. Babu, S. Anantha Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665487320

This study examines public perception of blockchains. The data is stored in block on the ledger, and each piece includes the checksum associated with the coin. After completing this scientific study, you will have a better understanding of the history of cash, an overview to the electronic cash system, how data is stored in blocks as cryptographic hashes, how various crypto currencies were created, and exactly how to utilise them. Additionally, bitcoin may be employed as a tradeable asset class. Numerous crypto trading platforms that provided crypto trading have existed since 2011. The general state of cryptocurrency is increasing every day. © 2022 IEEE.

关键词： Electronic money

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AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making

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International Journal of Advanced computer science and Applications 2025年第4期16卷 552-563页

作者： Gaidhani, Yashika Ramesh, Janjhyam Venkata Naga Singh, Sanjit Dagar, Reetika Rao, T. Subha Mastan Godla, Sanjiv Rao Baker El-Ebiary, Yousef A. Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of MBA-Sanjivani College of Engineering Savitribai Phule Pune University Pune India GD Goenka University Haryana Gurugram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Faculty of Informatics and Computing UniSZA University Malaysia

The advent of Artificial Intelligence (AI) has dramatically altered Customer Relationship Management (CRM) by allowing organizations to anticipate customer behavior, customize interactions and automate service delivery. This research introduces an extensive AI-based predictive analytics framework aimed at improving customer engagement, retention and satisfaction using advanced Machine Learning (ML) and Natural Language Processing (NLP) methodologies. By using XGBoost for churn prediction and BERT-based models for sentiment analysis, the system efficiently handles both structured and unstructured customer data. The methodology involves sophisticated feature engineering, customer segmentation via K-Means clustering, and Customer Lifetime Value (CLV) prediction to aid data-driven business strategies. An NLP-driven chatbot offers real-time, personalized support, response time and improving user experience. Evaluation metrics such as accuracy, precision, recall and F1-score demonstrate the better performance of the proposed system compared to conventional CRM approaches. This work also addresses important issues such as data privacy compliance, algorithmic bias and explainability of AI decision-making. Ethical deployment and transparency of AI are emphasized for building confidence in automated CRM systems. Future evolution will tackle the use of reinforcement learning to facilitate learning-based interaction schemes and federated learning for trusted, decentralized management of data. This architecture does not only provide better CRM functionality but also builds a platform towards intelligent, responsible and scalable solutions for customer relations across industries. © (2025), (science and Information Organization). All rights reserved.

关键词： Reinforcement learning

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Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network

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SN computer science 2024年第8期5卷 992页

作者： Sonavale, Amruta Chakkaravarthy, Midhun Srinivasa Rao, Surampudi Salleh, Hishamuddin Bin M. Jadhav, Jagannath Department of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Faculty of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia ECE Malla Reddy College of Engineering and Technology Hyderabad India Faculty of Electronics and Communication Engineering KLE College of Engineering and Technology Chikodi India

Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is *** process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The propos

关键词： Adaptive median filtering Flood detection LSTM Neural network algorithm Structural object son the RSI Versatile Linear Edge Detection (VLED) algorithm

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Non Linear Homogenization of Laminate Magnetic Material by Computing Equivalent Magnetic Reluctivity 14

Non Linear Homogenization of Laminate Magnetic Material by C...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Yousfi, Ghania Mohellebi, Hassane Electrotechnics Department Faculty of Electrical Engineering and Computer Science Mouloud Mammeri University Tizi-Ouzou Algeria

ISBN: (纸本)9789897587061

In the present study we present a numerical modeling of a laminate magnetic material using an homogenization technique which exploit an inverse problem resolution. The laminate magnetic material is consisting of an alternate of magnetic and insulates layers. The equivalent magnetic reluctivity is computed for the homogenized domain by considering a non linear behavior of the magnetic layers. A finite element method is used to solve the 2D non-linear electromagnetic partial differential equation. An optimization problem is constructed and solved with the association of the 2D finite element resolution and a conjugate bi-gradient algorithm. The computation of the equivalent magnetic reluctivity is then performed for different excitation field value according to B-H curve. The comparison of the obtained B-H curves of the laminate and the homogenized domains to the theoretical B-H curve (experimental data) show a good agreement of laminate results. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Inverse problems

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Employing Adbdconvolutional KNN, Diagnosis of Irregular Driver Behavior 11

Employing Adbdconvolutional KNN, Diagnosis of Irregular Driv...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chandramma Manjunath, C.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665487320

Actual observation of unusual aggressive driving is essential for enhancing car safety. to enhance riding habits and behaviour in order to keep fatal crashes. The utilization of perception abnormal driving things that tend is becoming more and more common since it is essential to both the current level of driverless vehicles and the health of both passengers and motorists in vehicles. Recent developments in deep learning techniques, such as the impressive extension capacity of modern deep neural networks and the vast quantities of film clips necessary for fully retraining the data-driven models, may substantially help with this challenging problem. To conclude the study, new massive continuing to learn models are provided. These methods are motivated by the recently created and extensively used networked cnn model known as the Excessive Drive Control System in place (ADBD Net). © 2022 IEEE.

关键词： Deep neural networks

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Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

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computers, Materials & Continua 2024年第8期80卷 1753-1808页

作者： Zheyi Chen Liuchang Xu Hongting Zheng Luyao Chen Amr Tolba Liang Zhao Keping Yu Hailin Feng College of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenofia UniversityShebin El KomMenoufia Governorate32511Egypt School of Computer Science Shenyang Aerospace UniversityShenyang110136China Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training *** the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models *** advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal *** survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language ***,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI ***,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

关键词： Artificial intelligence large language models large multimodal models foundation models

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Track Damage And Obstacle Detection Using Multi Sensor Fusion Framework 15

Track Damage And Obstacle Detection Using Multi Sensor Fusio...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Priya, S.Padma Abarna, P.S. Devi, C.Manjula Hammed, S. Shahul Karthikeyan, M.P. Saravanan, K.Muthu Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India Velammal college of Engineering and Technology Department of Information Technology Madurai India

ISBN: (纸本)9798350370249

The integration of Internet of Things (IoT) technology into railway track monitoring systems marks a pivotal advancement in ensuring the safety and efficiency of railway operations. Through strategic deployment of sensors along the tracks, this innovative system offers real-time detection of obstacles, furnishing vital data for timely intervention to avert potential accidents. This capability not only elevates passenger and crew safety but also bolsters the reliability of railway transportation systems as a whole. The system constantly scans the track ahead of the train for any anomalies using sensors positioned toward the front of the vehicle. The system immediately notifies the application faults it detects, enabling prompt action. Additionally, it updates the status of an IoT platform and displays it for personnel on board via an LCD panel. An audio alarm is generated in the event of critical sensor readings to ensure prompt attention. By lowering potential risks and responding quickly to track problems, This IoT-based solution holds the promise of revolutionizing railway operations by furnishing augmented safety, efficiency, and reliability, thereby addressing the burgeoning demands of contemporary transportation infrastructure. © 2024 IEEE.

关键词： InfraRed Sensor Railway safety reliability track monitoring Ultrasonic Sensor

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BnGraphemizer: A Grapheme-based Tokenizer for Bengali Handwritten Text Recognition 9

BnGraphemizer: A Grapheme-based Tokenizer for Bengali Handwr...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Basher, Mohammad Jahid Ibna Noor, Mohammad Raghib Afroze, Sadia Ahmed, Ikbal Hoque, Mohammed Moshiul Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Dhaka Department of Management Information Systems Dhaka Bangladesh CCN University of Engineering & Technology Computer Science and Engineering Cumilla Bangladesh

ISBN: (纸本)9798350319651

Bengali Handwritten Text Recognition (HTR) is a challenging task due to the text's cursive disposition and significant variations in the graphemic unit, the smallest written unit in alphasyllabary languages. These graphemic units, or simply graphemes, may consist of multiple characters and diacritics, and the order of characters or diacritics between textual and visual representations is only sometimes preserved. Moreover, Bengali conjunct characters, a subset of the graphemes, often have unique visual representations vastly different from the characters they comprise. Because of these inherent characteristics, a character-based tokenizer fails to tokenize the label text appropriately for BHTR in a supervised setting, making it challenging to adapt the existing sequence-to-sequence frameworks often used for Handwritten Text Recog-nition (HTR). To address this, this paper proposes a trie- based text tokenizer (called BnGraphemizer) that allows one to tokenize text into a set of graphemes instead of characters. Experimental investigations show that the BnGraphemizer-based BHTR system performs better regarding higher Word Recognition Rate (WRR) than its character-based counterpart in two of three Bengali handwritten datasets. © 2023 IEEE.

关键词： Character recognition

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