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检索条件"任意字段=International Conference on Advanced Research on Computer Education, Simulation and Modeling"

共 3733 条记录，以下是61-70 订阅

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Real-Time Optimization and Lightweight Architecture of Face Detection Models for Embedded Systems

Real-Time Optimization and Lightweight Architecture of Face ...

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

作者： Min Ki Son Seungkeun Park Convergence Security for Automobile Soonchunhyang University Asan Republic of Korea Smart Automotive Engineering Department Soonchunhyang University Asan Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This study presents optimization and lightweighting techniques to improve the performance of real-time face detection models in resource-constrained environments such as embedded systems. The selected model, RetinaFace, based on the MobileNetV1 0.25 backbone, was streamlined into SlimLite and RFBLite networks, and the components and layers of the model were efficiently restructured to effectively reduce model size and inference time. These optimizations achieved a balanced tradeoff between performance and real-time processing capabilities in a resource-constrained environment. The ONNX optimization further improved the inference speed of the RetinaFace model by approximately 37.7%, reducing the average inference time from 524.89 ms to 327.24 ms on an embedded platform (Raspberry Pi 4). In addition, resource efficiency was maximized by reducing the input resolution to 320×240, resulting in an additional 67.5% reduction in inference time to 106.16 ms while maintaining face detection accuracy. This demonstrates that the model retains its detection capabilities even at reduced input resolution, ensuring reliable performance. Accuracy evaluations were performed using the WIDER FACE dataset, highlighting the importance of tailoring optimization strategies to achieve optimal performance in constrained environments. This research is expected to broaden the applicability of lightweight face detection models, enabling potential integration in areas such as software-defined vehicles (SDVs), multi-object tracking, gesture recognition, and advanced driver assistance systems (ADAS).

关键词： Embedded systems Image resolution Computational modeling Face recognition Gesture recognition computer architecture Real-time systems Face detection Optimization Load modeling

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Enhancing the Autonomy of Soft Robots in Minimally Invasive Surgeries Through computer Vision

Enhancing the Autonomy of Soft Robots in Minimally Invasive ...

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advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Hams Alsirhani Salma M Elhag King Abdulaziz and His Companions Foundation for Giftedness and Creativity “Mawhiba” Riyadh Saudi Arabia Department of Information Systems King Abdul-Aziz University Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

researchers have recognized the potential of employing soft robots in minimally invasive surgeries (MIS), which could significantly reduce the side effects associated with traditional surgical methods and enhance patient outcomes. However, the extent to which this potential is realized depends on the level of autonomy achieved by the soft robots. Lower levels of autonomy necessitate increased hands-on involvement during MIS, potentially compromising the robots' ability to perform procedures with consistent accuracy. Consequently, achieving high levels of accuracy in the autonomy of soft robots remains a significant challenge. The autonomy of these robots is influenced by various factors, including their capacity to accurately classify their surroundings, particularly anatomical structures, which is crucial for effective decision-making. To address the challenge posed by our research question, How can the application of Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL) improve the autonomy of soft robots in performing complex tasks in MIS, particularly in the precise classification of anatomical structures and decision making?, we conducted an in-depth literature review and investigation into the integration of DNNs and DRL within this context. Our study used a modeling and simulation methodology to evaluate and quantify the benefits of incorporating these advanced AI techniques. Through this approach, we measured the effects of DNNs and DRL on enhancing the autonomy of soft robots, particularly in their ability to perform complex tasks in MIS with improved precision and decision-making capabilities. This work represents a step toward optimizing robotic autonomy in surgical environments, potentially leading to more efficient and accurate outcomes in minimally invasive procedures.

关键词： Technological innovation Accuracy Minimally invasive surgery Decision making Artificial neural networks Anatomical structure Transforms Soft robotics Deep reinforcement learning Real-time systems

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Stock Market Analysis and Forecasting Using Deep Learning

Stock Market Analysis and Forecasting Using Deep Learning

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

作者： Karthika V P.E. Anusree Master of Computer Application Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Stock market forecasting and analysis through Deep Learning (DL) methods is the objective of this study, which aims to address the complexity of financial data through the use of advanced predictive modeling. The current research explores Deep Learning (DL) methods used in the stock market analysis and forecasting, tackling financial data complexities through innovative predictive modeling. The intrinsic volatility of stock prices based on numerous economic variables poses an important challenge for which conventional methods of analysis typically fail to achieve a breakthrough. To address the same, an FFNN model was created using historical stock price data to predict future price movement with greater precision. For stock prediction accuracy to be enhanced, a Feedforward Neural Network (FFNN) has been trained using historical stock price data. An FFNN with ReLU activation and Adam optimizer was built and trained following the methodology that comprised data preprocessing, feature selection, normalization, and time-series sequence generation. With a Mean Squared Error (MSE) loss and 80/20 train-test split, the model was trained for more than 50 epochs. Predicted and actual stock prices showed high correlation with slight variations at the peak values. The findings indicate that DL has a great potential for financial prediction and can offer analysts and investors a robust tool. Incorporating sophisticated architectures such as LSTM or GRU, sentiment analysis, and macroeconomic variables might be included in future enhancements to make the predictions even more accurate and versatile.

关键词： Deep learning Analytical models Adaptation models Sentiment analysis Accuracy Predictive models Data models Complexity theory Stock markets Forecasting

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international conference on advanced research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024

International Conference on Advanced Research in Technologie...

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international conference on advanced research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024

ISBN: (纸本)9783031834349

The proceedings contain 65 papers. The special focus in this conference is on advanced research in Technologies, Information, Innovation and Sustainability. The topics include: Depth Completion Enhancement with Morphological Filtering and a Variation of the Infinity Laplacian;blockchain Application in Smart Tourism;fuzzy Theory in Credit Scoring: A Literature Review;Mobile Application for Managing Service Payments in SMEs;Bibliometric Insights into AI and Emerging Technologies for Sustainability Accounting and Reporting;urban Governance Models in Smart Cities: Case of Quito-Ecuador;the Use of Artificial Intelligence in Governance: A Systematic Review;smart Chatbot for the Optimization of the Use of Natural Resources in Agricultural Production;Optimizing Autonomous Vehicle Control Through Deep Q-Learning: A simulation-Based Approach with CARLA;analysis of the Correct Execution of Free Squats for Injury Prevention: A Genetic Algorithm Approach;lane Direction by Implementing Random Sampling Algorithms;a Model for Recognizing Mexican Sign Language Using Convolutional Neural Networks;AI-Based Decision Support Systems to Improve Diagnostic Accuracy via Medical Imaging;predicting Employment Outcomes Based on Health Status and Socioeconomic Factors Using Neural Network Analysis;Explainability of Disease Classification from Medical Images Using XGrad-Cam;MRI-Based Brain Tumor Classification: A Texture Analysis and Machine Learning Approach;Leveraging Neuromarketing and AI: Developing an Algorithm to Detect Consumer Joy Through Brain Signals;femoral Head Surface Segmentation Using Ultrasound Images as Input Source: A Local Phase and Rigid Filtering Approach;Interpreting CNN for Brain Tumor Classification Using XGrad-Cam;d.R.E.A.M. App to Promote the Mental Health in Higher education Students;FGPE - An Evolving Framework for Gamified Programming Learning;Enhancing Gamified LMS with the Integration of Games Experiences;visitor Perspectives on Event Marketing’s Role in Local Tou

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international conference on advanced research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024

International Conference on Advanced Research in Technologie...

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international conference on advanced research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024

ISBN: (纸本)9783031834318

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A Critical Study on Hybrid Computing and Its Current Influences and Future Challenges

A Critical Study on Hybrid Computing and Its Current Influen...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Praveen K V Joe Prathap P. M L Sherin Beevi W Vinil Dani Department of Information Technology St. Peter’s College of Engg. and Tech. Chennai India Department of Computer Science and Engineering R.M.D. Engineering College India Department of EEE St. Peter’s College of Engg. and Tech. Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computing Program processors Scalability Computational modeling Biological system modeling Standards organizations Machine learning Organizations Hardware Optimization

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Distributed MPC Compensation-Based LFC for Heterogeneous Multi-area Interconnected Power Systems 8th

Distributed MPC Compensation-Based LFC for Heterogeneous Mul...

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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： Chi, Xiaobo Li, Xinyi Liu, Xiaolin Jia, Xinchun School of Automation and Software Engineering Shanxi University Taiyuan030031 China School of Mathematical Sciences Shanxi University Taiyuan030006 China The Key Laboratory of Complex Systems and Data Science Ministry of Education Taiyuan030006 China

ISBN: (纸本)9789819602247

This paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) with thermal and wind power generations, considering both inter-area and intra-area transmission delays and random packet losses induced by communication networks. Firstly, a heterogeneous framework of MAIPSs is constructed, where a networked distributed model predictive control (DMPC) scheme is proposed to compensate for transmission delays and random packet losses. Based on this framework, we then developed a distributed closed-loop LFC system model for heterogeneous MAIPS. Subsequently, a Luenberger observer is constructed to estimate the system state of the local area, and a networked feedback controller is designed to ensure stability with a certain H∞ performance level for the networked MAIPSs. Moreover, stability conditions and controller design criteria are provided by using the Lyapunov-Krasovskii stability theory. Finally, the control performance of the proposed method is verified through an example of a two-area interconnected power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive control systems

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Big Data for Enhancing Cybersecurity in Enterprise Environments Proactive Threat Detection and Prevention

Big Data for Enhancing Cybersecurity in Enterprise Environme...

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Automation and Computation (AUTOCOM), international conference on

作者： Upender Reddy Chityala Ammar H. Shnain Manoj Govindaraj Prashant Johri T. Kuppuraj N.Lavanya Devi Equinox IT Solutions LLC (Senior ServiceNow Developer - IHG) Department Of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Management Studies Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Department of ECE PSNA College of Engineering and Technology India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This research explores the integration of big data technologies and machine learning to enhance cybersecurity in enterprise environments focusing on proactive threat detection and prevention. Traditional security systems often struggle to keep up with the growing complexity and volume of cyber threats, necessitating a more advanced, data-driven approach. The proposed methodology leverages real-time data processing, predictive modeling, and anomaly detection to improve threat detection accuracy, reduce response times, and enhance system scalability. By collecting and analyzing data from various network sources, including logs from security devices and endpoint protection software, the system detects potential threats more efficiently than conventional methods. Machine learning algorithms such as supervised and unsupervised learning, along with big data platforms like Apache Spark, enable real-time analysis of large volumes of data, ensuring timely identification of both known and unknown cyber threats. The results demonstrate significant improvements in detection accuracy, system scalability, and incident resolution time, with the proposed methodology outperforming traditional security systems in key performance metrics. This research highlights the potential of big data-driven cybersecurity solutions in addressing modern challenges and offers a framework for organizations to implement more effective, scalable, and proactive security measures to safeguard their digital assets.

关键词： Scalability Prevention and mitigation Machine learning Organizations Big Data Predictive models Data processing Threat assessment Real-time systems computer security

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Enhancing Sales Forecasting in E-Commerce Through advanced Predictive Analytics and Deep Neural Networks

Enhancing Sales Forecasting in E-Commerce Through Advanced P...

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Automation and Computation (AUTOCOM), international conference on

作者： Mahesh Kotha Gnanajeyaraman Rajaram K. Sathishkumar Mohammed Hussein Fallah J. Karpagam Anil P S Department of CSE (AI&ML) CMR Technical Campus Hyderabad India Department of CSE SIMATS Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical sciences Chennai India Dept. of Computer Science Erode Arts and Science College Erode Tamilnadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of Management Gnanam School of Business Thanjavur Tamilnadu

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Within this context, this research is aimed at enhancing the sales forecasting in e-commerce using the newer approaches of predictive analytics and deep neural networks. There usually is significant variability of results as compared to traditional approaches that are based on historical data and simple econometric models. This research evaluates how deep learning approaches, LSTM and Transformer models, improve precisely the forecast by noticing the behaviour dynamics and more complex patterns in the business environment. The actual approach includes sales history data, understanding customers, trends, and related promotions to train as well as test neural networks. The basic hybrid approach integrates deep learning and statistical methods to forecast the maximum potential outcomes. The performance of models in this study is evaluated by Mean Absolute Error (MAE), Root Mean Squared Error(RMSE) and R-Squared to compare the forecasted sales values with the actual ones. It is established that incorporating the deep learning models that have been described enhances the forecast precision concerning the baseline and conventional methods of approach significantly, especially the hybrid model. The precise accuracy, and factors that cause it to fluctuate from one product category to another, and the impact of promotional activities on the forecast's precision, are addressed. Thus, the study contributes to the knowledge that adopting innovative techniques such as advanced predictive analytics and deep learning to improve sales forecasting can potentiate strategic business courses of action and the firm's performance in today's cut-throat e-commerce marketplace.

关键词： Deep learning Biological system modeling Predictive models Transformers Market research Data models Electronic commerce Forecasting Predictive analytics Long short term memory

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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

作者： L. Selvam R. Dhamotharan R. Santhana Krishnan J. Relin Francis Raj G. Ram Sankar G. Yamini Department of Computer Science and Engineering SRM Madurai College for Engineering and Technology Pottapalayam Sivaganga India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of English Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies.

关键词： Deep learning Support vector machines Solid modeling Analytical models Three-dimensional displays Accuracy Attention mechanisms Brain modeling Data models Alzheimer's disease

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