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Proceedings - 2024 3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

Proceedings - 2024 3rd International Conference on Artificia...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

ISBN: (纸本)9798350376173

The proceedings contain 179 papers. The topics discussed include: signal detection and demodulation algorithm based on deep learning in communication network;design and implementation of intelligent control algorithm for energy power system combined with reinforcement learning;application of artificial intelligence technology in flood early warning system;implementation of a machine intelligence pronunciation system based on Mul Tran to improve the basic English speaking skills;design and optimization algorithm of interactive education system based on virtual reality technology;application of artificial intelligence technology in intelligent environment design system;intelligent anti-collision algorithm of electric bicycle helmet based on Arduino and sensor data;and neural network algorithm in automatic allocation system of water resources.

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ADMIT 2024 - conference Proceedings: 2024 3rd international conference on Algorithms, data Mining, and Information Technology

ADMIT 2024 - Conference Proceedings: 2024 3rd International ...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

ISBN: (纸本)9798400718120

The proceedings contain 70 papers. The topics discussed include: research on the optimization of driving strategy of intelligent networked vehicles based on big data technology;a tennis momentum analysis method based on Gaussian dynamics and machine learning;sentiment and user churn analysis for online platform business using yelp studies;research on commercial vehicle replacement prediction model based on improved attention mechanism;cross-sectional industrialization factors’ contribution to heat wave risk classification;cross-domain person search data augmentation based on style transfer;recurrent convolutional fact graph neural networks for temporal knowledge graph reasoning;and research on network quality evaluation system based on the entropy weight method.

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Visualization of Hidden Patterns in WhatsApp Chat using Sentimental Analysis 3

Visualization of Hidden Patterns in WhatsApp Chat using Sent...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vinora, A. Suryakumar, L.S.S. Harivishnu, T.S. Jaspin, K. Selvaraj, Arikumar K. Nancy Deborah, M. Velammal College of Engineering and Technology Madurai India St. Joseph's Institute of Technology Chennai India SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Challenges include handling unstructured chat data, ensuring privacy and ethical standards, managing large data volumes, accurately interpreting sentiment despite context, sarcasm, and irony, and creating effective visualizations. The proposed paper aims to develop a robust chat analyzer for WhatsApp conversations, leveraging Python libraries like pandas, matplotlib, seaborn, and natural language processing techniques. By filtering out irrelevant messages and group notifications, the tool focuses on meaningful interactions and employs sentiment analysis to provide insights into user dynamics. This comprehensive analyzer offers visualization and statistical analysis of chat data through a user-friendly application deployed on the Heroku web platform. The project improves comprehension of WhatsApp messages by integrating machine learning and NLP, allowing users to extract important data and handle unwanted content effectively. The suggested procedure involves utilizing a machine learning algorithm that combines VADER sentiment analysis for accurate emotion detection with keyword frequency analysis for trend identification. The new system shows a 15% boost in accuracy and a 20% increase in processing efficiency for sentiment analysis and user activity detection when compared to current models. © 2025 IEEE.

关键词： data visualization matplotlib Natural language processing (NLP) Python libraries (pandas seaborn) Sentiment analysis User behavior insights VADER WhatsApp chat analyzer

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Research on Disease Prediction Based on Daily data Analysis of the Elderly Based on Neural Network Algorithms 3

Research on Disease Prediction Based on Daily Data Analysis ...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lei, Liu Guangdong Open University Guangdong Polytechnic Institute Guangzhou China

ISBN: (纸本)9798350380989

In recent years, with the gradual arrival of an aging society, the health status of the elderly has attracted widespread attention. Against this background, this study aims to explore the potential value of these data for disease prediction by collecting and analyzing daily data of the elderly based on neural network algorithms. First, by using technical means such as smart sensing devices and mobile applications, we collected multi-dimensional data on the elderly's daily life, including but not limited to physiological indicators, activity levels, social interactions, and sleep patterns. These data are considered potential disease predictors because they provide comprehensive information about the physical condition and lifestyle of older adults. Second, we apply the collected data to a neural network algorithm, which has powerful learning and pattern recognition capabilities. By training models, neural networks can learn potential patterns and regularities from large amounts of complex data, thereby providing strong support for disease prediction. We pay special attention to the superiority of neural networks in processing multi-source heterogeneous data to more comprehensively understand the multi-faceted information of the elderly's health status. Through experiments and verification, we evaluate the performance of neural network algorithms in disease prediction in the elderly. This study explores disease prediction based on the daily data of the elderly based on neural network algorithms, aiming to provide scientific basis for establishing a more intelligent and accurate elderly health management system to promote the health and well-being of the elderly. © 2024 IEEE.

关键词： data analysis elderly health neural networks

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Pesticide Recommender System for Detecting the Paddy Crop Diseases through SVM 3

Pesticide Recommender System for Detecting the Paddy Crop Di...

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3rd IEEE international conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Jeyanathan, Josephine Selle Kumar, Manoranjan Reddy, P.Ravi Kumar Reddy, G.Chakradhar Reddy, D.Karthik Rabbani, Y.Shaik Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India Karnataka Bengaluru560001 India

ISBN: (纸本)9798350361186

Rice cultivation is pivotal in India, facing various color-related issues during different growth stages. Identifying these conditions manually can be challenging, especially for less experienced farmers. Recent advancements in Deep learning (DL) demonstrate the effectiveness of automated image recognition systems. These systems utilize Convolutional Neural Networks (CNN), Transfer learning (TL), and Support Vector Machine (SVM) models for similar agricultural problems. Due to the unavailability of readily accessible rice disease image datasets, a custom dataset was created. Consequently, a deep learning model was devised using transfer learning. The proposed CNN architecture, based on MobileNet, was trained and tested using datasets from paddy fields and the internet. © 2024 IEEE.

关键词： Support vector machines

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Efficient Chromosome Abnormal Detection using Yolo-V8 model 3

Efficient Chromosome Abnormal Detection using Yolo-V8 model

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

作者： Umarani, V. Isaac, Augustian Premnath, K. Kannan, G. Sanjay, A. Hari Haran, L.K. Saveetha Engineering College Dept of Computer Science and Engineering TamilNadu Chennai India Saveetha Engineering College Dept of Artificial Intelligence & Machine Learning TamilNadu Chennai India

ISBN: (纸本)9798331523893

Human chromosomes exhibit complex and intricate patterns, making their analysis and data interpretation challenging. Studying chromosomes is crucial for identifying genetic disorders, such as Down syndrome, and various types of cancer. Traditional karyotyping methods depend heavily on manual method, which causes human error and high cost. To address these challenges, a deep learning-based approach is proposed to automate the karyotyping process, improving both accuracy and efficiency while minimizing human involvement and errors. This research leverages advanced deep learning techniques, specifically the YOLOv8 object detection framework, to enhance chromosomal analysis. From experiments, the proposed YOLO V8 model achieves a 95.2% accuracy, surpassing other models such as DCNN, CNN, and VGG-19. This work represents a significant step toward developing a more reliable and efficient diagnostic tool for detecting genetic disorders. © 2025 IEEE.

关键词： Deep learning

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Natural Language Processing in Geographic Information Systems 3

Natural Language Processing in Geographic Information System...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic Information Systems (GIS) with Natural Language Processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 IEEE.

关键词： Structured Query Language

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Water Leakage Detection and Recognition System 3

Water Leakage Detection and Recognition System

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvaprakash, J. Prem Arasu, S.K. Vetri Xavier, S. Orlin Rudina Maria, R. Manimegalai, M. Sebasthirani, K. National Engineering College Kovilpatti India Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An IoT-enabled water leak detection system is proposed. The proposed system uses real-time data collection and advanced machine learning techniques for continuous monitoring. It consists of two leak sensors integrated with Arduino nodes. It also uses NodeMCU to enable IoT communication and alerts. The sensors continuously monitor water flow and pressure to detect any anomalies that may indicate a leak. The system collects real-time datasets, which are then processed using a modified Long Short-Term Memory (LSTM) model for accurate leak prediction. The modified LSTM uses a new attention mechanism to increase the model's ability to focus on significant data patterns. This integration is used to increase the prediction accuracy. The IoT-enabled system sends immediate alerts to users upon detecting a leak. This ensures swift response and minimizing water loss. This approach increases leak detection efficiency, resource management, and reduces manual intervention. Experimental results show that the proposed system achieves higher accuracy and best suitable for both residential and industrial water distribution networks. © 2025 IEEE.

关键词： Liquid crystal displays

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Advancing Adaptive Assessment in English Language Teaching: A Deep learning-based Approach within intelligent Tutoring Systems 3

Advancing Adaptive Assessment in English Language Teaching: ...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Greeni, A. Chitkara, Preeti Pathak, Punit Orosoo, Myagmarsuren Rengarajan, Manikandan Bala, B. Kiran St.Martin's Engineering College Department of English Department of Freshmen Engineering Telangana Secundrabad India Kiet Group of Institutions Department of Humanities and Social Sciences Delhi-NCR Ghaziabad India Auro University Department of School of Liberal Arts and Human Sciences Surat India Mongolian National University of Education Mongolia Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350369083

The integration of deep learning techniques into intelligent Tutoring Systems (ITS) is a unique strategy to advance adaptive assessment in English Language Teaching (ELT) presented in this research. Personalised learning experiences are hampered by the lack of flexibility of traditional ELT assessment systems. The efficacy of current methods is limited because they find it difficult to adjust evaluations to the skill levels and learning paths of specific students. On address this, provide an adaptive assessment system based on deep learning that modifies assessment problems in real time according on performance data. Deep learning algorithms can detect minute trends in language usage and understanding by examining enormous volumes of student data. This allows them to provide more precise and thorough feedback. Using deep neural networks trained on a massive corpus of language data, this study creates models that can evaluate several facets of language competency. According to preliminary findings, this method greatly increases the efficacy and flexibility of assessment in ELT. The system can offer tailored learning experiences and targeted interventions based on ongoing analysis of student performance, which improves learning outcomes and boosts student engagement. By providing knowledge about the possibility of data-driven techniques to revolutionize language learning and assessment processes, this research adds to the increasing body of literature on the convergence of education technology and deep learning. © 2024 IEEE.

关键词： Contrastive learning

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Proceedings - 2023 3rd international conference on Electronic and Electrical engineering and intelligent System: Responsible Technology for Sustainable Humanity, ICE3IS 2023

Proceedings - 2023 3rd International Conference on Electroni...

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3rd international conference on Electronic and Electrical engineering and intelligent System, ICE3IS 2023

ISBN: (纸本)9798350327762

The proceedings contain 94 papers. The topics discussed include: development of deep learning model base on modified CNN architectures for brain tumors early diagnosis;metaverse-based learning media to increase wireless security awareness;comparative study of maximum-power-point tracking with multiplier-based step-size variation;IoT based AC electric vehicle supply equipment;the usage of machine learning on penetration testing automation;implementation of design conformance measurement process between data entry form and class on SRS penguin;computer vision for determining trajectory and impact position of fighter-aircraft bombing;and sentiment analysis and topic modeling of e-grocery application reviews using naive bayes and support vector machine: a case study of Segari data review on the google play store.

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