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检索条件"任意字段=9th IEEE International Conference on Engineering of Complex Computer Systems"

共 16756 条记录，以下是21-30 订阅

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Designing of Smart Pothole Detection System 9

Designing of Smart Pothole Detection System

引用

9th international conference on Communication and Electronics systems, ICCES 2024

作者： Maji, Anushka Sinha, Sagnik Banerjee, Subhabrata Das, Amit Kumar Gangopadhyay, Malay Rateria, Roshan Institute of Engineering & Management Department of Computer Science and Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Electronics & Communication Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350377972

Recently, Artificial Intelligence (AI) algorithms have been used to detect the potholes. In this article, a machine learning algorithm has been designed to solve the problem efficiently. A software model has also been created for drivers, enabling them to detect and recognize potholes in advance through a mobile application. this mobile app utilizes various sensors to detect potholes and provides timely alerts to the driver. Moreover, the system is capable of responding to newly detected potholes and alerts the driver about them. the proactive notification system improves road safety by ensuring drivers are informed about upcoming road hazards. © 2024 ieee.

关键词： Mobile applications

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Convolutional Neural Network Modeling for Pest Detection in Corn Crops: Optimization for Monitoring Efficiency 9

Convolutional Neural Network Modeling for Pest Detection in ...

引用

9th international conference on Control and Robotics engineering (ICCRE)

作者： Ocampo Bonilla, Oscar Samuel Reyes Duke, Alicia Maria Univ Tecnol Ctr Amer UNITEC Fac Ingn San Pedro Sula Cortes Ecuador

ISBN: (纸本)9798350372694;9798350372700

this project aims to develop automated techniques for early detection of pests in maize crops using computer vision and deep learning. Early detection of pests is key to crop success, but is typically done manually, which is time consuming and resource intensive. Accurate methods are needed to obtain data quickly and non-invasively. the implemented method consists of analysing images of maize crops using convolutional neural networks(CNN) trained to identify insects. the results show that the developed approach achieves an accuracy of over 95% in detecting the main pests studied, surpassing the accuracy of manual methods. the data generated by computer vision and deep learning techniques can be useful to farmers in making decisions about preventive pest control.

关键词： Pest detection Fall Army-worm Maize Crops computer vision Deep learning Predictive control

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Robust Stacked Ensemble Model for Lung Cancer Diagnosis 9

Robust Stacked Ensemble Model for Lung Cancer Diagnosis

引用

9th international conference on computer Science and engineering, UBMK 2024

作者： Akbas, Ayhan Buyrukoglu, Gonca Buyrukoglu, Selim Institute for Communication Systems University of Surrey Guildford United Kingdom Karatekin University Department of Statistics Cankiri Cankiri Turkey Cankiri Karatekin University Computer Engineering Department Cankiri Turkey

ISBN: (纸本)9798350365887

In this paper, we introduce a stacked ensemble model for lung cancer diagnosis, combining Random Forest, Support Vector Machine, and Artificial Neural Networks. Our model, validated on two datasets and achieving diagnostic accuracies of 92.6% and 88.9%, outperforms traditional algorithms. We also highlight the model's robustness through advanced feature selection methods like ReliefF and Chi-Square. the results we achieved showcase the potential of machine learning techniques in medical diagnostics and contribute significantly to the field of computational oncology, offering a promising approach for early lung cancer detection and prognosis. © 2024 ieee.

关键词： Lung cancer

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Incorporating Knowledge Graph Embeddings into Graph Neural Networks for Sequential Recommender systems 9

Incorporating Knowledge Graph Embeddings into Graph Neural N...

引用

9th international conference on computer Science and engineering, UBMK 2024

作者： Yüksel, Kazim Emre Üsküdarli, Susan Bogazici University Department of Computer Engineering Istanbul Turkey

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365887

Traditional recommendation methods often struggle to capture the evolving nature of user interactions and the rich contextual information available in external data sources. this paper presents an innovative approach to sequential recommendations by integrating knowledge graph embeddings into a graph neural network (GNN) architecture. We utilize the MovieLens dataset as our primary data source and enrich it with additional contextual information from Wikidata, an open-source comprehensive knowledge base by incorporating the translation-based knowledge graph embeddings. Experimental results demonstrate that our approach improves recommendation accuracy, particularly in terms of Hit@K metrics, by leveraging the enriched movie embeddings. this study highlights the potential of utilizing open-source knowledge bases to enhance the predictive capabilities of recommendation systems, providing a more nuanced and comprehensive understanding of user preferences. © 2024 ieee.

关键词： Knowledge graph

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Demo: WalkingWizard - A truly wearable EEG headwear for everyday use

Demo: WalkingWizard - A truly wearable EEG headwear for ever...

引用

9th ieee/ACM international conference on Connected Health - Applications, systems and engineering Technologies (CHASE)

作者： Goh, Teck Lun Peh, Li-Shiuan Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9798350345025;9798350345018

Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG headwear that can be used under motion in everyday scenarios. We tested WalkingWizard using SSVEP experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0km/hr, beating state-of-the-art.

关键词： brain-computer interface (BCI) dry-contact electrode Mobile electroencephalogram (EEG) SSVEP wearable sensors

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Object Detection Algorithm Evaluation in the Context of Indian Transportation 9

Object Detection Algorithm Evaluation in the Context of Indi...

引用

9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Faslu Rahman, T.P. Kabeer, Marwan Pookattil Abdul Mohammed, Danish Deepak, A.V. Malayil, Shanid Mea Engineering College Computer Science And Engineering Vengur Perithalmanna India

ISBN: (纸本)9798350394474

computer vision plays a crucial role in detecting objects. Has various applications, including traffic management and autonomous vehicles. this study aims to evaluate the performance of different object identification algorithms in the context of transportation in India. the unique traffic patterns, diverse vehicle types, and infrastructure challenges make it an interesting and demanding scenario for object detection systems. Our research focuses on assessing the precision, robustness, and suitability of state of the art object recognition algorithms like YOLO V6, YOLO V7, YOLO V8 and Faster R-CNN in identifying and categorizing objects commonly seen in traffic scenes. the findings from this study provide insights for developing transportation systems specifically designed for the Indian environment. By understanding the strengths and weaknesses of algorithms when dealing with traffic situations, varying lighting conditions and a wide range of vehicle types this research contributes to advancing computer vision technology for transportation, in India. © 2024 ieee.

关键词： Object detection

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Marching Forward: Redefining Human-Machine Interactions in Conversational AI through Hybrid Intelligence, Blockchain Security, and Autonomous Agents 9

Marching Forward: Redefining Human-Machine Interactions in C...

引用

9th international conference on Communication and Electronics systems, ICCES 2024

作者： Charanya, P. Santhos Raj, B.S. Naveen Kumar, T. Pradheeban, A. Sri Eshwar College of Engineering Department of Artificial Intelligence and Machine Learning Coimbatore India Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350377972

Conversational AI has emerged as an essential instrument in enhancing human-computer interaction, with applications spanning customer service to personal assistants. this paper offers a comprehensive examination of recent developments in conversational AI, including novel techniques in natural language generation (NLG), dialogue systems, and dynamic response optimization. We analyze advancements including transformer-based architectures, reinforcement learning for dialogue generation, and retrieval-augmented models, emphasizing their roles in enhancing the quality and contextual accuracy of conversations. Additionally, we examine the feasibility of blockchain technology as the foundation for decentralized AI, promoting secure, transparent, and distributed frameworks for AI applications, hence improving privacy and control in data exchanges. Hybrid methodologies that combine symbolic reasoning with deep learning are analyzed, highlighting their effectiveness in addressing complex dialogues. these advancements, supported by empirical research and benchmark performance, signify the evolution towards more personalized, intelligent, and decentralized conversational systems. © 2024 ieee.

关键词： Chatbots

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Intelligent Control of Power Converters using Reinforcement Learning 9

Intelligent Control of Power Converters using Reinforcement ...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Krishna, R. Sathya Barna Sudharani, R. Devi, S. Rukmani Kulkarni, Pramodkumar H. Rohini, G. Sindhu, M. Sathyabama Institute of Science And Technology Department of Computer Science Engineering Tamil Nadu Chennai600119 India Panimalar Engineering College Department of Mathematics Tamil Nadu Poonamallee600123 India Saveetha College of Liberal Arts And Sciences Deemed To Be University SIMATS Department of Computer Science Tamil Nadu Chennai600124 India Dr. D. Y. Patil Institute of Technology Department of Electronics And Telecommunications Maharashtra Pimpri411018 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Thiruverkadu600077 India R.M.K. Engineering College Department of Computer Science And Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798350365092

Power converter control techniques must evolve to accommodate power system complexity and renewable energy integration. Traditional control methods may be difficult to adapt to these dynamic and uncertain systems. Reinforcement learning (RL) can build optimal control policies in complex and unpredictable situations, making it a promising intelligent control solution in this scenario. this research examines how reinforcement learning can control power converters in energy systems. Advanced reinforcement learning methods, notably deep reinforcement learning, are used to improve power converter efficiency in varied operational scenarios. this research examines the integration of reinforcement learning (RL)-based controllers into power converter systems, focusing on their autonomous learning and adaptation. the explored method uses Reinforcement Learning (RL) flexibility to address power system time-varying conditions, non-linearities, and uncertainties. this research considers practical concerns such RL-based control's effects on real-time execution, processing requirements, and safety. the report concludes with suggestions for future research including expanding RL-based intelligent control systems to power system applications. Reinforcement learning keywords include stability, efficiency, reliability, renewable energy, deep reinforcement learning, power converters, intelligent control, and energy systems. © 2024 ieee.

关键词： Power converters

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throughput Estimation of OBSS WLANs using Ensemble Graph Neural Networks 9

Throughput Estimation of OBSS WLANs using Ensemble Graph Neu...

引用

9th international conference on computer and Communication systems (ICCCS)

作者： Meti, Ashish Deepti, B. Athreya, K. J. Anagha, B. C. Mohan, Rajasekar PES Univ Bangalore Karnataka India

ISBN: (纸本)9798350350227;9798350350210

Predicting throughput in dense and dynamic Wireless Local Area Networks (WLAN) deployments is a critical challenge hampered by complex protocols and interference. Traditional methods struggle with high runtime or significant prediction errors, hindering their practical applications. this paper proposes an approach employing Graph Neural Networks (GNNs) for accurate throughput estimation in Overlapping Basic Service Sets (OBSS) within WLANs. Leveraging signal strength, airtime, Signal-to-Interference + Noise Ratio (SINR), and interference levels as features, the proposed method utilizes GNN models like GraphSAGE, Graph Convolution Networks (GCN), (Graph Attention Networks (GAT), and ChebNet. An ensemble stacking model with a random forest meta-model is introduced to enhance prediction accuracy. Addressing the decentralized nature of ieee 802.11 networks, our approach enables reliable throughput projections for optimized network design. this study paves the way for improved WLAN efficiency and reliability by facilitating real-time throughput predictions with minimal error. Our dataset and proposed stacking model achieve an impressive R2 score of 0.957, demonstrating its effectiveness and promising potential for real-world applications.

关键词： ieee 802.11ac/ax Komondor performance prediction throughput Next generation WLANs Graph Neural Network Stacking Ensemble

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Viability of Viola-Jones Algorithm for Drowsiness Detection in Drivers 9

Viability of Viola-Jones Algorithm for Drowsiness Detection ...

引用

9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Joshi, Kireet Jain, Paras Dhiman, Gamini Gupta, Vishan Kumar Singh, Harvinder Sharma, Ujjwal Graphic Era Deemed To Be University Department of Computer Science & Engineering Dehradun India Vit Bhopal University School of Computing Science And Engineering Madhya Pradesh Sehore India Amity University Punjab Amity School of Engineering & Technology Mohali India

ISBN: (纸本)9798350394474

this research paper investigates the viability ofusing the Viola-Jones algorithm for drowsiness detection in drivers. the algorithm is designed to detect the presence of a face, locate the eyes within the face region, and then calculate the Eye Aspect Ratio (EAR) to determine if the eyes are open or closed. the study uses a dataset of images of the driver's face and tests the algorithm's performance under various lighting, pose, and expression variations. the results show that the Viola-Jones algorithm is a robust and effective method for detecting drowsiness in drivers, achieving high accuracy rates even in challenging conditions. the paper concludes by discussing the potential applications of this technology in real-world settings, such as in the development of driver assistance systems for improving road safety. © 2024 ieee.

关键词： computer vision

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