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Application of Social and Private Interests Coordination Engine Model in State Development Programs 3

Application of Social and Private Interests Coordination Eng...

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3rd international conference on Control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Gorbaneva, Olga I. Murzin, A.D. Ougolnitsky, Gennady A. Institute of Mathematics Mechanics and Computer Sciences Southern Federal University Rostov-on-Don Russia Southern Federal University Don State Technical University Management Department Rostov-on-Don Russia

ISBN: (纸本)9781665439817

An implementation of federal development programs affects the interests of many economic agents. At the regional level, such programs can be implemented in the form of joint activities' regulation of municipal districts for the purpose of their intensive development. This work solves the problem of applying well-known models of the coordination of social and private interests (SPICE-models) to the management of specific government programs implementation. The study simulates the interaction of several municipal districts of the Rostov region in the framework of the program 'Development of agriculture and regulation of markets for agricultural products, raw materials and food'. The authors considered the mechanisms of administrative, economic and proportional management. As a result of the above calculations, the expediency of the administrative management mechanism is substantiated, which makes it possible to solve all managerial tasks in the given conditions of all the districts under consideration. © 2021 IEEE.

关键词： Agricultural products

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A Study of the Ecological Dynamics of Batumi, on the Black Sea Coast, Analyzing Environmental Factors and Their Impact

A Study of the Ecological Dynamics of Batumi, on the Black S...

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international Black Sea conference on Communications and Networking (BlackSeaCom)

作者： Lily Petriashvili Irine Khomeriki Mzia Kiknadze Tamar Lominadze Nona Otkhozoria Dept. of Program Engineering Georgian Technical University Tbilisi Georgia Dept. of Interdisciplinary Informatics Georgian Technical University Tbilisi Georgia Dept. of Computer Engineering Georgian Technical University Tbilisi Georgia Dept. of Information Systems Georgian Technical University Tbilisi Georgia Dept. of Microprocessor and Measurement Systems Georgian Technical University Tbilisi Georgia

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

This paper studies the complex ecological dynamics of Batumi on the Black Sea coast, through adetailed analysis of environmental factors and their impact. We use Business Intelligence (BI) methods to explore the complex interactions between natural and human impacts on the local ecosystems. By using data-focused approaches, including statistical analysis and predictive modeling, we aim to distinguish patterns and trends in ecological dynamics. This study aspires to make clear the complex relationships between environmental factors such as climate change, pollution levels, habitat degradation, and biodiversity loss, and their impacts on the coastal ecosystem of Batumi. Through this study, we aim to provide valuable information on sustainable management practices and conservation measures to protect the ecological integrity of this important coastal region.

关键词： Sea coast Pollution Statistical analysis Ecosystems Habitats Sea measurements Predictive models

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Employing Feature Engineering for River Stage Forecasting to Improve Hybrid Model Performance

Employing Feature Engineering for River Stage Forecasting to...

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international conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Anna Maria Mihel Špela Pečnik Grega Vrbančič Jonatan Lerga Nino Krvavica Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Rijeka Croatia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data.

关键词： measurement Weather forecasting Predictive models Wavelet analysis Data models Rivers Numerical models Forecasting Spectrogram Water resources

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Research on the Application of CIoU and CNN joint Compression in Autonomous Driving

Research on the Application of CIoU and CNN Joint Compressio...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Xintong Liu Gu Gong Fan Jiang Gongyu Shang School of Computer Science and Technology Jiangsu Normal University Xuzhou China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

With the rapid development and application of autonomous driving technology, vehicle computing devices are facing increasingly daunting challenges. These systems need to process a large amount of sensory data in real-time, plan routes, and make decisions, which requires more computational resources. Therefore, effective model compression methods have become crucial. Our study aims to reduce the size and computational complexity of models through weight pruning and convolutional split compression methods, thereby reducing the consumption of computational resources while maintaining model performance and accuracy. We also utilize the CIoU loss function to improve the accuracy and efficiency of obstacle detection in autonomous driving systems. In simulated experiments, we tested and evaluated the improved model using a dataset of simulated road scenarios. The results of the experiments validate the effectiveness of model compression and demonstrate the advantages of the improved model in increasing obstacle detection rates and reducing computational costs. These improvements can help autonomous driving systems operate efficiently with limited computational resources, thereby enhancing system reliability and practicality. Our research provides valuable theoretical and practical support for advancing autonomous driving technology.

关键词： YOLO Accuracy Computational modeling Roads Real-time systems Robustness Computational efficiency Autonomous vehicles Robots Mobile computing

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YOLOv8 for Bangla License Plate Recognition: Advancing Real-Time Object Detection in Localized Contexts

YOLOv8 for Bangla License Plate Recognition: Advancing Real-...

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international conference on Informatics and Computational Sciences (ICICoS)

作者： Utsha Saha Imam Uddin Ahamed M Iffat Hossain Dept. of Computer Science North Dakota State University Fargo ND USA School of Computer Science and Mathematics Liverpool John Moores University Liverpool UK Dept. of Electrical and Computer Engineering University of Central Florida Orlando FL USA

ISBN: (数字)9798350375886

ISBN: (纸本)9798350375893

Automatic License Plate Recognition (ALPR) systems play a crucial role in traffic management and security applications, and the development of accurate and efficient ALPR models is essential for their success. This study investigates the application of YOLOv8, a state-of-the-art object detection architecture, for recognizing Bangladeshi license plates. A comprehensive dataset, combining images from various vehicle categories and environments, was curated and augmented to train five variants of the YOLOv8 model: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv81, and YOLOv8x. The models were rigorously evaluated using metrics such as mean Average Precision (mAP), precision, recall, and F 1 -score. The results demonstrate the effectiveness of YOLOv8 in detecting and recognizing Bangladeshi license plates, with YOLOv8x achieving the highest mAP50 and mAP5095 scores of 0.96 and 0.75, respectively. The study highlights the adaptability of YOLOv8 to the unique characteristics of Bangladeshi license plates and provides insights into model selection criteria based on computational resources and performance requirements. Future research directions include expanding the dataset to cover a wider range of environments and integrating the YOLOv8 models into a comprehensive ALPR system for enhanced traffic management and security applications.

关键词： measurement Adaptation models Computational modeling Neural networks Object detection computer architecture Real-time systems

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Learning Content-Aware Multi-Modal joint Input Pruning via Birds'-Eye-View Representation

Learning Content-Aware Multi-Modal Joint Input Pruning via B...

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international conference on Intelligent Transportation

作者： Yuxin Li Yiheng Li Xulei Yang Mengying Yu Zihang Huang Xiaojun Wu Chai Kiat Yeo School of Computer Science and Engineering Nanyang Technological University Singapore Desay SV Automotive Singapore Institute for Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In the landscape of autonomous driving, Bird's-Eye-View (BEV) representation has recently garnered substantial attention, serving as a transformative framework for the fusion of multi-modal sensor inputs. The BEV paradigm effectively shifts the sensor fusion challenge from a rulebased methodology to a data-centric approach, thereby facilitating more nuanced feature extraction from an array of heterogeneous sensors. Notwithstanding its evident merits, the computational overhead associated with BEV-based techniques often mandates high-capacity hardware infrastructure, thus posing challenges for practical, real-world implementations. To mitigate this limitation, we introduce a novel contentaware multi-modal joint input pruning technique. Our method leverages BEV as a shared anchor to algorithmically identify and eliminate non-essential sensor regions prior to their introduction into the perception model's backbone. We validate the efficacy of our approach through extensive experiments on the NuScenes dataset, demonstrating substantial computational efficiency without sacrificing perception accuracy. To the best of our knowledge, this work represents the first attempt to alleviate the computational burden from the input pruning point.

关键词： Training Multimodal sensors Computational modeling Pipelines Sensor fusion Feature extraction Hardware Complexity theory Computational efficiency Intelligent transportation systems

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Measuring Subjective Image Quality using Machine Learning Techniques

Measuring Subjective Image Quality using Machine Learning Te...

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international conference on Computing and Networking Technology (ICCNT)

作者： Anvesha Garg V. Pushparajesh Manisha P. Mali D. Anandhasilambarasan Gunveen Ahluwalia S. Yuvaraj Quantum University Research Center Quantum University Electrical and Electronics Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Computer Engg Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical abstract discusses the use of gadget mastering strategies to degree the subjective first-class of digital pictures. Traditionally, rating subjective picture satisfaction has required manual intervention by means of human observers. However, gadget studying may be used to measure photographs in a speedy and goal manner. Such structures may be educated to assign rankings based totally on person possibilities, image content, digicam parameters, or any combination of those. The utility of gadget getting-to-know strategies to purchasers and photo satisfaction scores can be useful to both producers and consumers. For manufacturers, gadget getting-to-know structures offer the ability to quickly stumble on and greatly lessen exceptional control problems. For purchasers, device learning systems can supply extra accurate, varied, and in-intensity rankings of products; this may cause greater knowledgeable purchasing choices and higher outcomes.

关键词： measurement Learning systems Image quality Adaptation models Accuracy Computational modeling Manuals Machine learning Observers

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Early Identification of Mild Cognitive Impairment with Graph Convolutional Networks

Early Identification of Mild Cognitive Impairment with Graph...

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international conference on Information systems and computer Aided Education (ICISCAE)

作者： Xincen Wu Groton School Massachusetts USA

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Early identification of Mild Cognitive Impairment (MCI) is crucial for treatment to be efficient and effective. The convergence of detailed medical imaging and patient background selected from the Alzheimer's Disease Neuroimaging Initiative has provided abundant data, serving as the bedrock for MCI early diagnosis with machine learning models. In search of enhanced early detection, this paper aims to outline an effective and more efficient methodology, leveraging insights gleaned from high-definition brain scans and data extraction. The process begins with Freesurfer, a software that processes brain imaging data, obtaining intricate information from these high-resolution scans into a myriad of metrics and variables. This initial step sets the stage for the application of Confidence-based Graph Convolutional Networks (GCNs), which operate on the multimodal graph derived from the amalgamation of brain imaging data. Compared to pre-existing models, this model proposes several key enhancements. These GCNs, utilizing confidence-based mechanisms recognize subtle patterns and deviations indicative of cognitive impairment. This model selects for the optimal activation function, or a convex activation that combines multiple functions such as Sigmoid, tanh, Relu, and others. This nuanced approach iteratively refines the model, assimilates insights from previous diagnostic instances, and progressively hones its ability to discern early signs of MCI.

关键词： Neuroimaging measurement Accuracy Graph convolutional networks Magnetic resonance imaging Brain modeling Data models Software Singular value decomposition Overfitting

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Detection and Classification of Brain Tumor Using Machine Learning

Detection and Classification of Brain Tumor Using Machine Le...

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Smart Technologies and systems for Next Generation Computing (ICSTSN), international conference on

作者： Prachi V. Kale Ajay B. Gadicha G. D. Dalvi Department of Computer Science and Engineering P R Pote College of Engineering & Management Amravati Maharashtra India Department of AI P R Pote College of Engineering & Management Amravati Maharashtra India Department of EXTC Allard College Pune Maharashtra India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Diagnosing a brain tumor typically necessitates the expertise of a radiologist, whose skills and knowledge are essential to the process. Since the overall number of individuals has grown, so too has the quantity of medical data required to be managed, rendering outdated methods extremely costly and inefficient. Many investigators looked into different techniques that had been quick and effective in identifying and categorizing brain tumours. Currently, Machine learning (ML) techniques have gained popularity in the development of computerized systems that can quickly and effectively diagnose or segment brain tumours. The deep learning (DL) enables the application of ML frameworks for brain tumor identification. This paper proposed various ML models to analyze the MRI images. First, preprocessing and augmentation algorithms were applied to the MRI brain images. We evaluated the performance of several models, including LR, SVC, kNN, NB, NN, RF, and K-means clustering. The analysis focused on accuracy, precision, recall, F1-score, and AUC. LR (96%), NN (95%), and RF (96%) showed the highest performance across all metrics, effectively distinguishing between tumor and no-tumor images and providing reliable results.

关键词： Radio frequency measurement Magnetic resonance imaging Static VAr compensators Brain modeling Rendering (computer graphics) Reliability

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Lower Limb Trajectory Prediction for Exoskeleton with AutoEncoder-Deep Gaussian Process

Lower Limb Trajectory Prediction for Exoskeleton with AutoEn...

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Pingping Lv Chi Zhang Feng Yi Ting Yuan Shupei Li Meitong Zhang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Chinese Academy of Sciences Ningbo China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Gait trajectory prediction is a crucial component in compensating control system delays, reducing interactive resistance, and achieving real-time assistance in exoskeleton robotics. In this paper, an adaptive gait prediction model based on the autoencoder deep Gaussian process (AE-DGP) is proposed for joint angle estimation using measurement data from inertial measurement units (IMUs). The DGP process involves stochastic gradient Hamiltonian Monte Carlo (SGHMC) calculations and Monte Carlo Expectation-maximization (MCEM) optimization at a lower computational cost. The kernel of the model is the multi-feature fusion input based on autoencoder and the construction of the DGP model with an adaptive sliding window. To suppress spikes in predicted deviations, a weighted averaging strategy is employed to estimate future stable gait trajectories. Benefiting from the design, the proposed model can enhance the accuracy and adaptability of gait prediction at different speeds. Experiments involving four participants at four speeds indicate that the proposed method achieves hip and knee joint prediction with less than 0.16° and 0.35° root mean squared errors respectively. Compared to the Gaussian process (GP) and long short-term memory (LSTM) models, the proposed method exhibits superior performance in adaptive gait trajectory prediction at different speeds.

关键词： Adaptation models Monte Carlo methods Computational modeling Exoskeletons Gaussian processes Predictive models Real-time systems

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