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Research on logistics dispatch algorithm based on ant colony optimization neural network 22

Research on logistics dispatch algorithm based on ant colony...

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3rd Asia-Pacific conference on Image processing, Electronics and Computers, IPEC 2022

作者： Zeng, Xiaojiao Hubei Three Gorges Polytechnic Yichang443000 China

ISBN: (纸本)9781450395786

Logistics dispatch mainly refers to the reasonable arrangement and scheduling of vehicles and personnel by the logistics company according to the weight, destination, specification and urgency of the goods to be delivered. The good logistics dispatch of logistics companies can quickly deliver the goods entrusted by customers to the receiver in time and in good condition. The shortest path problem and dynamic flow assignment problem in logistics dispatch are important links in the field of logistics dispatch system, optimization and control, especially in the analysis and optimization of logistics dispatch. In large-scale logistics dispatch network, the traditional algorithm can not meet the real-time requirements of logistics system. At present, the rapidly developing and increasingly mature intelligent algorithm provides a new way for the research and implementation of shortest path problem and dynamic flow assignment problem. In view of the above situation, this paper introduces Ant colony optimization (ACO). ACO is a new simulated evolutionary algorithm proposed in the s. The algorithm not only has excellent random search optimization ability and adaptive distributed computing characteristics, but also the ant path optimization process is very similar to the vehicle path selection process. Therefore, this paper studies the use of ACO to solve the shortest path problem and dynamic flow assignment problem in logistics dispatch. For the shortest path problem, an adaptive ACO is designed. For the dynamic assignment problem, based on the existing dynamic assignment model and the characteristics of ACO, a discrete dynamic user optimal assignment model is established. Experiments show that the improved algorithm is reasonable and effective, which provides a new idea and method to solve such problems, and has certain theoretical reference and application value. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Ant colony optimization

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Design and Implementation of Lung Abnormality Detection Through Sparse Projection Features Enabled distributed Convolution network

Design and Implementation of Lung Abnormality Detection Thro...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International conference on

作者： K.V. Ravikumar U. Kumaran B Teja Sree B Prashanth Kumar Reddy Boligala Rethaswi M G Lavanya Department of Computer Science and Engineering Mother Theresa Institute of Engineering and Technology Palamaner Andhra Pradesh

Due to various factors in recent days the lung infection has become a common disease in humans. The lung abnormality needs to be detected to avoid life threatening problems. Various researches are undergone to implement the early detection of lung abnormality. The evaluation of various techniques such as machine learning Deep learning are helpful to analyze the lung images in a more accurate way. The accurate localization and segmentation of lung abnormality become an interesting area of research. The presented system is focused on creating a robust architecture that considers sparse projection (SP) features enabled distributed convolution neural networks for making the classification of lung abnormality. Various intensity levels of lung images are considered as an important factor for differentiating the normal lung image and abnormal lung image. The proposed system considered axial CT are PET images for analysis. During the pre-processing stage the lung intensity pixel intensity is collected and morphological segmentation is applied to extract the land abnormality tissue from the background image. These inputs are fetched into ensemble distributed feature mapped convolutional neural networks (DFM-CNN) to make the classification effective. The performance evaluation of the model is developed by calculating the accuracy error rate and prediction time of the lung abnormality.

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Architectural Vision for Quantum Computing in the Edge-Cloud Continuum

Architectural Vision for Quantum Computing in the Edge-Cloud...

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Quantum Software (QSW), IEEE International conference on

作者： Alireza Furutanpey Johanna Barzen Marvin Bechtold Schahram Dustdar Frank Leymann Philipp Raith Felix Truger Distributed Systems Group TU Vienna Institute of Architecture of Application Systems University of Stuttgart

Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.

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On the role of noise in the sample complexity of learning recurrent neural networks: exponential gaps for long sequences 23

On the role of noise in the sample complexity of learning re...

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Proceedings of the 37th International conference on neural Information processing Systems

作者： Alireza F. Pour Hassan Ashtiani Cheriton School of Computer Science University of Waterloo Department of Computing and Software McMaster University

We consider the class of noisy multi-layered sigmoid recurrent neural networks with w (unbounded) weights for classification of sequences of length T, where independent noise distributed according to N(0, σ2) is added to the output of each neuron in the network. Our main result shows that the sample complexity of PAC learning this class can be bounded by O(w log(T/σ)). For the non-noisy version of the same class (i.e., σ = 0), we prove a lower bound of Ω(wT) for the sample complexity. Our results indicate an exponential gap in the dependence of sample complexity on T for noisy versus non-noisy networks. Moreover, given the mild logarithmic dependence of the upper bound on 1/σ, this gap still holds even for numerically negligible values of σ.

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Fusion of Pyramid Real Image Denoising network and Residual Encoder-Decoder for Cutting-Edge Image Restoration for Advancing Industry 7th

Fusion of Pyramid Real Image Denoising Network and Residual ...

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7th International conference on Soft Computing and Signal processing, ICSCSP 2024

作者： Kumar, Chevella Anil Neelima, N. Pravallika, Ambati Kumar, Chinthakindi Kiran VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Mallareddy College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819609239

Image denoising is a vital process in improving the visual quality by eliminating unnecessary noise from images, to restore the original clarity images. Conventional methods often assume Gaussian-distributed noise, which may not represent accurately for real-world noise complexities. This proposed research focuses on optimizing blind noise, prevalent in real-world images using deep learning techniques. The denoising model learns the features directly from the noisy samples by capturing the noise distribution within the image. This study employs the deep learning fusion-based method by integrating the noise prioritization using the Pyramid Real Image Denoising network (PRID Net), multi-scale feature extraction, and Residual Encoder-Decoder network (RED net) to reduce the blind noise. Both PRID network and RED network use the convolution neural network to efficiently lower the noise in order to enhance the noisy image’s quality. The experimental analysis compares the proposed model with the individual PRID and RED network models in terms of objective measurements such as the Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Signal-to-Noise Ratio (SNR) on various datasets such as RENOIR, SIDD, and NIND datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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NEMO-CNN: An Efficient Near-Memory Accelerator for Convolutional neural networks 32

NEMO-CNN: An Efficient Near-Memory Accelerator for Convoluti...

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32nd IEEE International conference on Application-specific Systems, Architectures and Processors (ASAP)

作者： Brown, Grant Tenace, Valerio Gaillardon, Pierre-Emmanuel Univ Utah Salt Lake City UT 84112 USA

ISBN: (纸本)9781665427012

The relevance of Deep Learning applications has skyrocketed in the last few years, exposing key weaknesses of traditional Von Neumann hardware architectures. With high amounts of data to be fetched from memory, the efficiency of these systems gets adversely impacted by an order of magnitude for each memory hierarchy that is traversed (e.g., data cache, on-chip SRAM, and off-chip DRAM). Such an issue is even more relevant when we consider that Convolutional neural networks (CNNs) are composed of tens of millions of parameters that imply billions of operations per second to achieve an acceptable performance. In order to remove this so-called memory wall problem, we introduce NEMO-CNN: a high-performance hardware accelerator built around the Near-Memory Computing paradigm, i.e., a design methodology based on distributed memory blocks enhanced with nearby processing elements. Coupled with a smart mapping strategy that slices the CNN structure along its depth, our solution drastically reduces the amount of data exchanged between off- and on-chip memories by executing each slice concurrently on dedicated processing elements that only leverage local data. Experimental results using VGG-16, DarkNet-19, and TinyYOLOv2 networks demonstrate that our solution achieves a top efficiency of 60.7 FPS/W, outperforming existing CNN accelerators.

关键词： Near-Memory Computing Convolutional neural networks Hardware Accelerators

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User Mobility Prediction in 5G networks Using Recurrent neural networks

User Mobility Prediction in 5G Networks Using Recurrent Neur...

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Communication, Computing and Signal processing (IICCCS), IEEE International conference on

作者： Anubhav Shrestha Vikalp Sharma Layth Hussein M.Aishwarya A N Satyanarayana T. Saimanohar Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India ECE Department Sreenidhi Institute of Science and Technology Hyderabad India Department CSE-AI Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The use of new possibilities introduced by 5G networks also creates new problems and concerns, specifically in the field of user mobility in wireless communication systems. In this paper, the Authors investigate the effectiveness of Recurrent neural networks (RNNs) in the mobility prediction context in order to exploit the temporal component in the users movement data for improved prediction performance. To drive this process, the input datasets are obtained from the GeoLife GPS Trajectories dataset, then follow the data pre-processing steps, constructing an LSTM-based RNN model, and using Python with TensorFlow and Keras. From the analyses, we detected that the accomplishment of using LSTM and hybrid RNN-CNN is significant and higher than the traditional approaches of EN, RFM, and CL. The test results demonstrate that the LSTM model can reduce the MAE and RMSE values by 19. 49% and 14. 82%, respectively. However, the execution of these models has some issues that include compute time, instantaneous prediction that is needed in many real-world applications, and case. Such problems can be solved with the help of such approaches as edge computing and distributed processing. Besides, it is pivotal to put measures in place to maintain the confidentiality and security of user information. This paper highlights the applicative potential of RNNs in redesigning user mobility prediction in 5G networks, and thus helping toward a better ownership of the network players and assets.

关键词： Recurrent neural networks 5G mobile communication Computational modeling Wireless networks Transforms Predictive models Data models Security Long short term memory Edge computing

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Enhancing robustness of graph neural networks on social media with explainable inverse reinforcement learning 24

Enhancing robustness of graph neural networks on social medi...

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Proceedings of the 38th International conference on neural Information processing Systems

作者： Yuefei Lyu Chaozhuo Li Sihong Xie Xi Zhang Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications China Artificial Intelligence Thrust The Hong Kong University of Science and Technology (Guangzhou) China

ISBN: (纸本)9798331314385

Adversarial attacks against graph neural networks (GNNs) through perturbations of the graph structure are increasingly common in social network tasks like rumor detection. Social media platforms capture diverse attack sequence samples through both machine and manual screening processes. Investigating effective ways to leverage these adversarial samples to enhance robustness is imperative. We improve the maximum entropy inverse reinforcement learning (IRL) method with the mixture-of-experts approach to address multi-source graph adversarial attacks. This method reconstructs the attack policy, integrating various attack models and providing feature-level explanations, subsequently generating additional adversarial samples to fortify the robustness of detection models. We develop precise sample guidance and a bidirectional update mechanism to reduce the deviation caused by imprecise feature representation and negative sampling within the large action space of social graphs, while also accelerating policy learning. We take rumor detector as an example targeted GNN model on real-world rumor datasets. By utilizing a small subset of samples generated by various graph adversarial attack methods, we reconstruct the attack policy, closely approximating the performance of the original attack method. We validate that samples generated by the learned policy enhance model robustness through adversarial training and data augmentation.

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30th International conference on Parallel and distributed processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

30th International Conference on Parallel and Distributed Pr...

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30th International conference on Parallel and distributed processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031856372

The proceedings contain 24 papers. The special focus in this conference is on Parallel and distributed processing Techniques. The topics include: Parallel N-Body Performance Comparison: Julia, Rust, and More;REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments;An Efficient Data Provenance Collection Framework for HPC I/O Workloads;using Minicasts for Efficient Asynchronous Causal Unicast and Byzantine Tolerance;a Comparative Study of Two Matrix Multiplication Algorithms Under Current Hardware Architectures;Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance;towards Automatic, Predictable and High-Performance Parallel Code Generation;Attack Graph Generation on HPC Clusters;analyzing the Influence of File Formats on I/O Patterns in Deep Learning;inference of Cell–Cell Interactions Through Spatial Transcriptomics Data Using Graph Convolutional neural networks;natural Product-Like Compound Generation with Chemical Language Models;improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters;Improved Method for Similar Music Recommendation Using Spotify API;Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits;Building Simulation Environment of Reconfigurable Virtual Accelerator (ReVA);vector Register Sharing Mechanism for High Performance Hardware Acceleration;Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading;introducing Competitive Mechanism to Differential Evolution for Numerical Optimization;hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization;jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term network;vision Transformer-Based Meta Loss Landscape Exploration with Actor-Critic Method;Fast Computation Method for Stopping Condition of Range Restricted

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BRCars: a Dataset for Fine-Grained Classification of Car Images 34

BRCars: a Dataset for Fine-Grained Classification of Car Ima...

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34th SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI)

作者： Kuhn, Daniel M. Moreira, Viviane P. Univ Fed Rio Grande do Sul Porto Alegre RS Brazil

ISBN: (纸本)9781665423540

Fine-grained computer vision tasks refer to the ability of distinguishing objects that belong to the same parent class, differentiating themselves by subtle visual elements. Image classification in car models is considered a fine-grained classification task. In this work, we introduce BRCars, a dataset that seeks to replicate the main challenges inherent to the task of classifying car images in many practical applications. BRCars contains around 300K images collected from a Brazilian car advertising website. The images correspond to 52K car instances and are distributed among 427 different models. The images are both from the exterior and the interior of the cars and present an unbalanced distribution across the different models. In addition, they are characterized by a lack of standardization in terms of perspective. We adopted a semi-automated annotation pipeline with the help of the new CLIP neural network, which enabled distinguishing thousands of images among different perspectives using textual queries. Experiments with standard deep learning classifiers were performed to serve as baseline results for future work on this topic.

关键词： Deep learning Visualization Computer vision Computational modeling Pipelines neural networks Automobiles

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