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Forest Monitoring and Protection using Motion Sensors and Metal Detection 2

Forest Monitoring and Protection using Motion Sensors and Me...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Manikandan, N. K. Muktha, Chaturya Abhinai, Yegi Manivannan, D. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & CSE Dept Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

This research explores the implementation of advanced monitoring and protection systems to safeguard forests from illegal activities. By leveraging devices, remote sensing, and drones equipped with high-resolution cameras, these systems enable real-time surveillance and data analysis. Artificial intelligence is employed to detect suspicious activities, triggering alerts to forest rangers and conservation authorities. Community engagement and education programs are integrated to foster sustainable practices among local populations. This combined approach aims to preserve biodiversity, combat climate change, and ensure the long-term health of forest habitats. The proposed system, combining motion and metal detection, provides a comprehensive solution for monitoring high- risk areas. By detecting suspicious activities and sending immediate alerts, this system enhances vigilance and discourages illegal activities, ultimately contributing to the preservation of forests.

关键词： wireless sensor networks (WSNs) Light Detection and Ranging (LiDAR) conference on Computer Vision and Pattern Recognition (CVPR)

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A hybrid CNN-GNN approach for enhanced early detection of schizophrenia using EEG data 3

A hybrid CNN-GNN approach for enhanced early detection of sc...

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3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Reetha, L. Gnanajeyaraman, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of CSE TN Chennai602105 India

ISBN: (纸本)9798331523893

Schizophrenia, a complex neuropsychiatric disorder, poses significant challenges in early detection due to its impact on cognitive, emotional, and social functioning. Hybrid approach integrating CNNs and Graph Neural Networks has shown promise in addressing this challenge. CNNs excel at capturing local spatial-temporal features from EEG signals, while GNNs are designed to model the brain's connectivity patterns, providing a holistic representation of brain function. This study presents a hybrid CNN-GNN model that extracts both spatial-temporal features and models global brain connectivity for schizophrenia detection. Using EEG data from the Lomonosov Moscow State University (LMSU) dataset, this model achieves high accuracy (99.12%), sensitivity (98.83%), and specificity (99.35%). An improvement in schizophrenia detection, the CNN-GNN hybrid model gives doctors a more precise and trustworthy instrument for early diagnosis. © 2025 ieee.

关键词： Convolutional neural networks

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Enhanced Real-Time Analysis and Anomaly Detection in Smart Health Monitoring systems through Integration of Deep Learning Algorithm with IoT-Edge computing 1

Enhanced Real-Time Analysis and Anomaly Detection in Smart H...

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1st ieee international conference on computing and data Science, ICCDS 2024

作者： Antony Pradeesh, D. Subiramaniyam, N.P. Nehru Arts and Science College Department of Electronics and Communication Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350365337

In this research, the proposed system HealthEdge, a new IoT-based intelligent health monitoring framework based on edge computing and a diverse sensor array that includes breathing frequency (RR), pulse rate (PR), oxygen saturation level in blood (SpO2), galvanic skin response (GSR), body temperature and hemoencephalography as vital signs sensors, as well as a formaldehyde sensor to gauge environmental influence on health. HealthEdge utilizes a powerful Deep Convolutional Neural Network (DCNN) to perform and analyze the data in real-time at the edge, ensuring ultra-low latency and amplified privacy. The model is capable of identifying health anomalies with a high accuracy of 97% which is demonstrating its potential in real-time health observation and anomaly detection. The potential role of environmental quality measurements in particular formaldehyde levels, in imposing on health conditions, thereby spreading the purview of the monitoring system beyond the traditional set of parameters have been explored. The integration of edge computing not only addresses the challenges that conventional systems face in maintaining a balance between privacy concerns and processing speed, it is also a state-of-the-art technology which enables HealthEdge to become the new norm for complete and real-time health monitoring solutions. © 2024 ieee.

关键词： Internet of things

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An intelligent Invoice Processing System Using Tesseract OCR

An Intelligent Invoice Processing System Using Tesseract OCR

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2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Ashlin Deepa, R.N. Chinta, Suhas Ashili, Nikhil Kumar Babu, B. Sankara Vydugula, Revanth Reddy Raj Sripada, V.S.L. Gokaraju Rangaraju Institute Of Engineering And Technology Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350364828

Invoice processing is a time-consuming and tedious task that can be automated using optical character recognition (OCR) technology. Tesseract is a popular open-source OCR engine that can be used to extract text from scanned invoices. In this paper, we propose a method for invoice processing using Tesseract OCR. The method involves pre-processing the image of the invoice to remove noise and improve the quality of the text. The pre-processed image is then passed to Tesseract OCR to extract the text. The extracted text is then parsed to extract the relevant invoice information. The results showed that the method was able to extract the invoice information with high accuracy. © 2024 ieee.

关键词： Optical character recognition

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Exploring the potential of Non Terrestrial Networks in next generation intelligent Transport systems 8

Exploring the potential of Non Terrestrial Networks in next ...

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8th ieee international conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Djihane, Maadoud Abdelkrim, Hamza Mohamed, Becherif University of Science and Technologie Houari Boumedienne LISIC Laboratory Dept. Telecommunications Algiers Algeria University of Bourgogne Franche-Comte FEMTO-ST Institute UTBM CNRS Belfort France

ISBN: (纸本)9798350309249

Our study investigates the fusion of Non-Terrestrial Networks (NTNs) with advanced intelligent Transport systems (ITS), highlighting the capability of NTNs, including UAVs, HAPS, and satellites, to boost coverage, reliability, and performance. The study showcases how, despite atmospheric attenuation challenges, Free-Space Optical (FSO) communication remains a robust channel for high data rate achievements. It offers an in-depth examination of path loss models and coverage areas, culminating in a case study aimed at enhancing the ITS infrastructure along the Hauts Plateaux highway in Algeria. © 2024 ieee.

关键词： Orbits

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Enhancing Engineering Cost Risk Management Through Artificial Intelligence-Based Warning and Control systems 6

Enhancing Engineering Cost Risk Management Through Artificia...

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6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Xu, Weiyi Fushun Vocational and Technical College Department of Civil Engineering Fushun China

ISBN: (纸本)9798350374315

With the growing importance of engineering cost risk management in the construction sector, the integration of artificial intelligence (AI) technology has emerged as a crucial tool for enhancing the effectiveness of risk alerts and control measures. This research endeavors to delve into the utility of AI in managing engineering cost risks and to suggest appropriate warning and control approaches. By conducting a thorough literature review and case studies, the study identified and compared three AI models: the Long Short-term Memory (LSTM) Network, Support Vector Machine (SVM), and Random Forest (RF), evaluating their performance against key metrics such as Cost Variance Ratio (CVR), Schedule Performance Index (SPI), and Risk Response Efficiency (RRE). The research methodology encompassed data collection and preprocessing, model training and validation, and comprehensive performance assessment. The findings revealed that the LSTM model achieved a minimum CVR of 1.1%, a maximum SPI of 1.03, and a remarkable risk response efficiency as fast as 12.5 minutes. The study concludes that LSTM significantly enhances the precision of engineering cost risk warnings, offering a novel insight and robust technical foundation for project management in the construction industry. © 2024 ieee.

关键词： Risk management

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Enhancing Summarization of Legal Text Documents using Pre-trained Models 5

Enhancing Summarization of Legal Text Documents using Pre-tr...

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5th international conference on Emerging systems and intelligent computing, ESIC 2025

作者： Kasar, Ashish Matade, Suyash Rasal, Durvankur Shinde, Swapnil Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331522100

A very relevant challenge of summarizing legal text documents often exceeds the length, therefore being complex, was addressed using pre-trained models such as BART and PEGASUS. In the paper, the issue is discussed-the challenge that these pre-trained models face while being effective in general text summarization tasks, which becomes difficult in legal contexts due to specialized vocabulary and structure of legal documents. The authors fine-tune these models using the BillSum dataset-provided US Congressional bills as a proxy for court documents. Evaluation on ROUGE metrics reveals that BART outperforms PEGASUS in summation of content structures of legal texts to the greatest extent intended. The results suggest that the pre-trained models significantly improve the process of summarizing legal documents, both for professionals in law and general readers who would otherwise have to wade through an extensive amount of legal information. © 2025 ieee.

关键词： Natural language processing systems

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A novel approach for optical RIS aided VLC communication in 5G IoT networks 1

A novel approach for optical RIS aided VLC communication in ...

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1st international conference on advanced computing and Emerging Technologies, ACET 2024

作者： Taneja, Ashu Soni, Dhruv Kumar Thakur, Kunal Rana, Ankita Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350367720

With the emergence of Internet-of-things (IoT) enabled consumer applications, there is widespread augmentation of IoT devices, smart wearables and smart consumer electronics. Owing to the limited capabilities of 5G sub 6GHz technology and millimeter wave (mmWave) technology, optical wireless communication (OWC) is widely adopted in the IoT networks for high data rates and high bandwidth applications. This paper presents a visible light communication (VLC) 5G IoT network that uses VLC transmitters to serve the network IoT nodes fitted with photodiodes (PDs). An optical reconfigurable intelligent surface (ORIS) is used to overcome the line-of-sight (LoS) signal obstructions through controlled reflection. The performance of the VLC network is investigated for achievable sum rate under different ORIS elements and different optical transmit powers. It is observed that 90% of the user nodes in the network achieve higher sum rate with increased N. Further, the effect of ORIS phase shift is evaluated. In the end, the performance comparison of different VLC communication setups is also investigated. © 2024 ieee.

关键词： 5G mobile communication systems

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Feature Selection in Learning to Rank for Listwise Approach using Multiobjective Evolutionary Algorithm

Feature Selection in Learning to Rank for Listwise Approach ...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Information retrieval systems commonly depend on ranking models to prioritize search results, which requires efficient feature selection to improve accuracy. Feature selection improves the model's query relevance ranking by selecting the most relevant features. This unique method avoids diluting the ranking model with unnecessary variables, resulting in more accurate ranks. The proposed approach consists of three separate stages: multi-objective instance selection, feature selection, and ensemble method. By utilizing a listwise methodology, the algorithm directly enhances the optimization of ranking criteria resulting in enhanced performance. The effectiveness of FS-listwise was evaluated using standardized datasets. After conducting experiments, results shown that the proposed method have peak performance than MOFSRank and classical ranking algorithms, such as ListNet and LambdaMART, in terms of Normalized Discounted Cumulative Gain and consistently selecting a minimal yet highly effective feature set. © 2024 ieee.

关键词： Contrastive Learning

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intelligent Transportation systems: Exploring Digital Twin Technologies in Smart Grid, Transportation systems and Smart Cities

Intelligent Transportation Systems: Exploring Digital Twin T...

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2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Kanimozhi, N. Sumathy, G. Maheshwari, A. Arunarani, A.R. Sherin Shibi, C. School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence TamilNadu Kattankulathur603203 India

ISBN: (纸本)9798350364828

The recent advancement of 'digital twins (DT)', which have the potential to completely transform all modern sectors, has drawn the attention of numerous researchers due to recent advancements in (ICT), The term 'DT concept' describes a digital depiction of a physical object by utilizing platforms and real-time, bidirectional data flow, which can mirror its physical behavior. The power grid's impressive use of the Internet of Things has made information dependable, enhancing its functionality and providing it with an effective tool for managing and analyzing data in real time. This study attempts to follow the ongoing research and offer useful suggestions for creating and expanding DT technology, taking into account the many power system application domains. It also outlines suggested remedies for the problems that arise from DT. As contemporary cities grow, 'new energy layers-such as microgrids, smart grids, and transportation systems'-have emerged to confront a variety of problems that put the multifaceted energy management system to the test. For instance, traffic is a significant issue in transportation systems that needs to be managed, planned, and analyzed in real time. Some of the current issues facing the field of power grids include remote data transfer within the system and different analyses requiring actual data. An actual twin framework should be provided and examined in each part to solve these issues. This survey's overall goal is to examine the numerous uses of DT in the development of a city's transit, electricity, and microgrid systems, among other areas of energy management. Additionally, the security of ML-based DT technology is explored. Additionally, it gives readers a comprehensive understanding of how to create and implement DT technology for different power systems. © 2024 ieee.

关键词： intelligent systems

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