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检索条件"任意字段=International Conference on Advanced Research on Computer Science and Information Engineering"

共 160469 条记录，以下是4921-4930 订阅

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research on 3D Localization Technology Based on Hybrid Signal Models and Adaptive Dynamic Weight Whale Optimization Algorithm

Research on 3D Localization Technology Based on Hybrid Signa...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Zhengxin Li Yiyang Du Jingchen Ma Chengcheng Jin Zhen Zhang Bijie Power Supply Bureau Guizhou Power Grid Limited Liability Company Bijie China School of Computer and Data Engineering Zhejiang University of Technology Ningbo Institute Ningbo China School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland School of Computer Science and Technology Shenyang Institute of Engineering Shenyang China Jiulan Academy Nanchang University Nanchang China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper investigates a 3D localization technology based on hybrid signal models and the Adaptive Dynamic Weight Whale Optimization Algorithm (AWOA). By analyzing the physical characteristics of five types of opportunity signals (TOA, TDOA, DFD, AOA, and RSS), mathematical models are constructed to derive the position information. It is found that at least four signal sources are required for accurate localization using TOA and RSS methods, while TDOA and AOA methods need at least three signal sources. DFD requires at least three signal sources to determine the target's direction and velocity. To enhance localization accuracy, a hybrid localization model based on the least squares method is proposed and optimized in real-time using the AWOA. This algorithm, inspired by the hunting behavior of whales, effectively addresses localization errors in non-line-ofsight (NLOS) environments. Experimental results demonstrate that the technology can accurately track the target's trajectory in real-time over a 10-second interval, with the final localization error within an acceptable range. This study provides an effective solution for high-precision 3D localization in complex environments.

关键词： Location awareness Adaptation models Solid modeling Three-dimensional displays Accuracy Heuristic algorithms Whale optimization algorithms Real-time systems Robustness Optimization

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Sustainable Digital and Enterprise Transformation for Omni-Channel Experiences Empowered by Data and AI/ML 2nd

Sustainable Digital and Enterprise Transformation for Omni-C...

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2nd international conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Kolasani, Saydulu Singhal, Sangeeta Enterprise and Digital Operations and Transformation Manhattan BeachCA United States Infosys Engineering BridgewaterNJ United States

ISBN: (纸本)9783031717284

This research paper explores the convergence of sustainable practices with digital and enterprise transformation, aiming to redefine and enhance omnichannel experiences. In a rapidly evolving technological landscape, the integration of data, artificial intelligence (AI), and machine learning (ML) is central to this transformation. The study delves into the strategic incorporation of sustainability principles, emphasizing eco-conscious decision-making throughout the digital and enterprise journey. By investigating how sustainable practices can be seamlessly woven into the fabric of omnichannel experiences, the paper seeks to provide insights for organizations aspiring to navigate the delicate balance between technological innovation and environmental responsibility. Through an interdisciplinary approach, the research not only highlights the potential of data-driven technologies but also examines their implications on sustainable development, contributing to a holistic understanding of the transformative power of digitalization within a responsible and ethical framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sustainable development goals

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Diabetic Retinopathy Image Classification using Quasi Cross UNet Discrete Transform with Gold Rush Optimizer

Diabetic Retinopathy Image Classification using Quasi Cross ...

引用

Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： M.Siva Ramkumar Nivetha R Mathi Kannaiyan Mohammad Kanan S. Shankar Jayant Giri Dept of ECE SNS College of Technology Computer Science and Engineering Kumaraguru College of Technology lakshitha department of Mechanical Engineer University college of Kancheepuram Anna University kancheepuram campus Kanchipuram Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department of Mechanical Engineering Cambridge Institute of Technology Bengaluru India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The diagnosis of diabetic retinopathy faces three main challenges which include inconsistent image clarity and weak detection of small visual impairments and little ability to share information between different sets of data. The model suffers from two issues which limit its practical use: high expenses for operations as well as low decision accuracy because of complex tissue dynamics. Using the MESSIDOR dataset as input, this paper illustrates the application of the Quasi Cross UNet Discrete Transform with Gold Rush Optimizer (QCUNet-DT-GRO) algorithm for diabetic retinopathy picture categorization. The Unet Swin Transformer (UST) approach is used to precisely segment areas afflicted by diabetic retinopathy, while the Quasi-Cross Bilateral Filtering (QCBF) methodology improves image quality. The One-Dimensional Discrete Transform (ODDT) is used to extract features. The Gold Rush Optimizer (GdRO) does optimization, while the Gates-Controlled Deep Unfolding Network (GCDUNet) handles classification. The system detects diabetic retinopathy along with distinguishing between normal and diabetic retina structures. According to experimental results, QCUNet-DT-GRO performs better than current techniques, with 99.9% accuracy and 99.8% sensitivity. This method is shown to be a viable substitute for the manual diagnostic processes that are now in use, enabling robotic diagnosis systems in subsequent applications.

关键词： Diabetic retinopathy Gold Discrete transforms Accuracy Sensitivity Filtering Logic gates Transformers Feature extraction Robot sensing systems

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RECNN: A Novel Deep Learning Model to Predict 8-state Protein Secondary Structure 2nd

RECNN: A Novel Deep Learning Model to Predict 8-state Protei...

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2nd international conference on Applied Intelligence, ICAI 2024

作者： Cheng, Long Xia, Yiyi Lu, Yiming Shen, Jiyun Qiu, Yi Zhao, Haipeng Lu, Weizhong School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Tianping College of Suzhou University of Science and Technology Suzhou215009 China

ISBN: (纸本)9789819619061

Protein secondary structure prediction is an important problem in bioinformatics. Inspired by the success of deep neural networks recently, in this paper, we present a novel deep learning architecture which exploits an integrative synergy of prediction by the encoder, convolutional neural network, residual network, and bidirectional recurrent neural network to improve the performance of protein secondary structure prediction. Our architecture utilizes the attention mechanism to extract sequence features, while considering the long-range dependencies present in amino acid sequences using Bidirectional Gated Recurrent Units (BGRU) to capture contextual features. Additionally, we employ a one-dimensional CNN module to extract local relationships present in the sequence. Our model achieves 73.1% Q8 accuracy on the CullPDB test dataset, 67.1% Q8 accuracy on public benchmark dataset CB513, demonstrating its effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Early Detection of Lung Cancer Using Pyramid Vision Transformer (PVT v2): A Comparative Analysis of Deep Learning Models

Early Detection of Lung Cancer Using Pyramid Vision Transfor...

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international conference on Artificial Intelligence in information and Communication (ICAIIC)

作者： Md. Khurshid Jahan Ahmed Faizul Haque Dhrubo Maher Ali Rusho Ashfaqur Rahman Chowdhury Farhana Sharmin Mohammad Abdul Qayum Dept.of ECE North South University Dhaka Bangladesh Dept. of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dept. of Public Health North South University Dhaka Bangladesh

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Lung cancer remains one of the most prevalent and life-threatening diseases worldwide, with early detection being critical for improved survival rates. This paper explores the application of advanced vision transformers, specifically the Pyramid Vision Transformer (PVT v2), to enhance lung cancer detection from Computed Tomography (CT) scan images. We comprehensively compare state-of-the-art deep learning models, including MaxViT, XCiT, and NextViT, and we investigate their performance in classifying lung cancer into malignant, benign, and standard categories. Utilizing the IQ-OTH/NCCD dataset, we fine-tune PVT v2 alongside other models, implementing rigorous preprocessing, augmentation, and regularization techniques to achieve robust results. Our experimental findings demonstrate that PVT v2 achieves superior accuracy with 99.09% and generalization, outperforming other models on multiple evaluation metrics such as precision, recall, Receiver-operating Characteristic curve (ROC) and Area under the curve (AUC) compared to published research. Despite challenges like dataset imbalance and computational costs, our work highlights the potential of vision transformers to revolutionize lung cancer detection and its application in public health.

关键词： Deep learning computer vision Computed tomography Computational modeling Lung cancer Surgery Transformers Standards categories Radiation therapy Public healthcare

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Smart Healthcare Solutions: IoT Integration for Sustainable Management of Kidney Diseases Leveraging Machine Learning 2nd

Smart Healthcare Solutions: IoT Integration for Sustainable ...

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2nd international conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Mahajan, Radhika Arora, Renuka Jagannath University Haryana Bahadurgarh India Department of Engineering Jagannath University Haryana Bahadurgarh India

ISBN: (纸本)9783031717284

This research explores the convergence of machine learning, specifically employing the Random Forest Classifier algorithm, with Internet of Things (IoT) integration to predict kidney diseases. The study, conducted on a comprehensive dataset, yields promising results with an impressive accuracy of 98%. The utilization of the Random Forest Classifier demonstrates robust predictive capabilities, as evident in the confusion matrix, which reveals minimal instances of misclassification with only one false positive and one false negative. The classification report further emphasizes the model's precision and recall, showcasing high F1-scores of 99% for non-kidney disease instances and 98% for kidney disease instances. This research highlights the potential of the Random Forest Classifier algorithm in healthcare decision-making, especially when integrated with IoT technologies, offering a reliable and sustainable approach to kidney disease management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Bitcoin Price Prediction Using Deep Learning Models

Bitcoin Price Prediction Using Deep Learning Models

引用

Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Rasika Hajare Chetan Puri Pradnyawant M. Gote Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares the use of three deep learning models, namely recurrent neural networks (RNN), gated recurrent unit (GRU), and a convolutional neural network-long short-term memory (CNN-LSTM) for Bitcoin price prediction. Historical Bitcoin price data is used in the dataset, and every one of the models is trained and tested based on its performance to predict future prices based on past data. The proposed models include the RNN and GRU, which incorporate sequential dependency mappings to learn complex sequences, and the CNN-LSTM structures combine convolution layers for feature extraction and LSTM layers for sequencing. Different models' competency is discussed using the averaged Mean Squared Error (MSE) and graphs of the model's prediction against actual prices. The experimental results reveal that the proposed CNN-LSTM model has better prediction accuracy than RNN and GRU in time-series forecasting tasks, indicating that more complex architecture hybrid models might provide better forecasting performance. In analyzing and analyzing cryptocurrency price dynamics, this investigation gives information about the opportunities and challenges of these models to enhance future research.

关键词： Deep learning Analytical models Sequential analysis Accuracy Time series analysis Bitcoin Predictive models Convolutional neural networks Forecasting Long short term memory

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Wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine 8th

Wind Power Forecasting Model Based on Feature Processing and...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wei, Xiongfei Wang, Jing Ruan, Yi Fang, Yuanjie College of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China School of Electronic Engineering Chaohu University Anhui Chaohu238024 China

ISBN: (纸本)9789819602315

To improve the forecasting accuracy of wind power, a wind power forecasting (WPF) model based on feature cleaning combined with deep hybrid kernel extreme learning machine (DHKELM) is proposed. The Pearson correlation coefficient (PCC) method is used to optimize the features, eliminate irrelevant features, and the isolated forest (IF) algorithm is used to clean the anomalous data in the retained features, which enhances the correlation between the selected features and wind power, and then combines the DHKELM to predict the wind power. Finally, the proposed model is applied to predict the wind power of a 1MW wind turbine, and the results of the progressive forecasting of the proposed method and the forecasting results of different models are compared. The results show that the PCC-IF-DHKELM forecasting model has a root-mean-square error of 0.01928 MW, an average absolute error of 0.015254 MW, and a correlation coefficient of 0.99435, which is significantly better than the other forecasting models, proving the effectiveness of the PCC-IF-DHKELM forecasting method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind forecasting

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Metaheuristics for Automating Performance Measurement in Management

Metaheuristics for Automating Performance Measurement in Man...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： S. Paramasamy A. Mounika Rajeswari Haider Mohmmed Alabdeli Mahalakshmi R Yadala Kamala Raju B. R. Supreeth Department of Mechanical Engineering Sethu Institute of Technology Kariapatti Tamil Nadu India Department of CSE CMR College of Engineering & Technology Hyderabad Telangana India Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq Anaesthesiology Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Civil Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The “metaverse” is a novel concept that imagines a fully immersive virtual world where users can interact, communicate, and conduct business. The “metaverse” is an idea that represents the most immersive type of virtual world, one in which we can interact and exchange information with others or do business. However, with the further development of and wider applications for the metaverse, its effects on both corporate culture and communication are expected to be increasingly felt. A: This is a research project to explore how the metaverse affects corporate communication and culture. From the literature review, it can be seen that the metaverse will change how businesses operate-it not only creates new remote work and virtual gatherings, but also encourages greater social engagement; teamwork; and communication. These findings are supported by a second study, which showed that the metaverse improved working culture and communication. These results imply that if companies are to achieve maximum value from the metaverse opportunities, they will need to actively prepare for some of these changes.

关键词： Performance evaluation Metaverse Metaheuristics Velocity control Supply chains Remote working Teamwork Business Systematic literature review Intelligent control

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Deep Reinforcement Learning for Energy Efficiency Optimization using Autonomous Waste Management in Smart Cities

Deep Reinforcement Learning for Energy Efficiency Optimizati...

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Trends in Material science and Inventive Materials (ICTMIM), international conference on

作者： T. Kavitha Koushik Reddy Chaganti Sivananda Lahari Reddy Elicherla M. Ravi Kumar D. Chaithanya K Manikanta Assistant Professor Department of CSE Srinivasa Ramanujan Institute of Technology Research Scholar Dept. of Computer and Information Systems University of the Cumberlands Associate Professor Department of CSE Dayananda Sagar University Assistant Professor Department of Cyber Security & IoT Malla Reddy University Assistant Professor Department of CSE CVR College of Engineering Assistant Professor Department of CSE Gates Institute of Technology

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Smart cities face vital waste management challenges regarding efficiency and sustainability in their development period. The established waste collection approaches that depend on pre-set schedules together with unchanging routes create problems by possessing both inefficient resource usage and elevated fuel usage as well as delayed waste pickup times. The proposed framework builds a Deep Reinforcement Learning (DRL)-based system for dynamic waste collection route optimization using Autonomous Waste Collection Vehicles (AWCVs) with Internet of Things (IoT)-based smart bins. The system uses Deep Q-Networks (DQNs) to analyze traffic conditions data alongside vehicle battery level data with smart bin information from the system so vehicles carry out efficient waste collection operations using minimal energy. The newly proposed algorithm provides 30% distance reduction and 28% faster responses and 30% lower energy usage than baseline heuristic methods do. The conclusions show that AI-powered waste management strategies can create sustainable operations which increase both service performance and overall energy efficiency in cities. The research team plans to apply the proposed approach to actual waste collection systems while exploring methods of deploying multiple agents alongside renewable energy systems to enhance future urban waste management operations.

关键词： Waste management Schedules Renewable energy sources Smart cities Deep reinforcement learning Energy efficiency Real-time systems Vehicle dynamics Sustainable development Optimization

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