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检索条件"任意字段=4th International Conference on Computer Science and Network Technology, ICCSNT 2015"

共 8928 条记录，以下是61-70 订阅

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Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leveraging Spatial and Temporal Dependencies 4

Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leve...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vallileka, N. Rajkumar, G.Vinoth Krishnan, R. Santhana Shankar, S. Vijay Francis Raj, J. Relin Karthikeyan, M. Saravana Psna College of Engineering and Technology Department of Computer Applications Dindigul India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798331523923

Accurate weather forecasting is critical for decision-making in agriculture, disaster management, urban planning, and numerous other domains. Traditional forecasting methods often struggle to capture the intricate spatial and temporal dependencies in weather data. this work proposes a hybrid deep learning model that integrates Convolutional Neural networks (CNNs) and Long Short-Term Memory (LSTM) networks to address these challenges. the CNN component efficiently extracts spatial features from satellite images, such as cloud formations and precipitation zones, while the LSTM network models the temporal dependencies inherent in meteorological data, such as temperature, humidity, and wind speed. the model is trained on a dataset comprising historical weather records and satellite imagery, spanning several years. PyTorch, a dynamic deep learning framework, is used to implement and train the hybrid architecture. Data preprocessing involves normalization, image augmentation, and sequence structuring to prepare inputs for the model. the CNN layers, built with convolutional and pooling operations, extract spatial features, which are combined with temporal weather data and passed to the LSTM network. the LSTM layers capture long-term dependencies and trends in weather patterns. Fully connected layers map the learned features to final predictions, including temperature, rainfall, and other key weather parameters. the hybrid model is evaluated using metrics such as Root Mean Square Error (RMSE) for numerical predictions and accuracy for classification tasks. Results indicate that the proposed hybrid CNN-LSTM model outperforms standalone models and traditional statistical approaches, demonstrating its ability to leverage spatial and temporal dependencies for accurate weather forecasting. this work highlights the potential of hybrid deep learning models in advancing weather forecasting capabilities, offering a scalable and reliable solution for real-world applications such as e

关键词： Convolutional neural networks

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Research on improvement strategy of detr real time object detection algorithm for small devices 4

Research on improvement strategy of detr real time object de...

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4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Gao, Chao Gao, Jing Cao, Lili Zhao, Long Gao, Shaojie School of Information Engineering East University of Heilongjiang Harbin China School of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin China Gannan County Second Middle School Qiqihar China Wuxi Vocational Institute of Commerce Wuxi China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

With the rapid development of deep learning technology, object detection, as an important task in the field of computer vision, has also been widely applied in the ROS[1](Robot Operating System) robot field. ROS, as an open-source robot operating platform[1], provides a favorable operating environment for object detection algorithms. this article studies the improvement strategy of real-time object detection algorithm (RT-DETR) [2] for small devices, which provides a better solution for the field of robotics. through the application analysis of the RT-DETR[2] algorithm, we found that there are certain issues with detection accuracy and efficiency, as well as the ability to detect small objects, when the performance of small devices is limited. Especially when the camera is not in focus or the image is ghosted, the issues of detection accuracy and efficiency are particularly prominent. For this purpose, we propose a dynamic and irregular deformable convolution kernel strategy to address the performance issues of small edge devices in terms of detection accuracy. In response to efficiency concerns, we propose an enhanced non-linear network structure to achieve greater non-linear capability with fewer parameters applied during the operation process. Finally, we combine the two methods to form a DenNet network (Deformable and Enhanced Nonlinear Convolutional Kernel networks). through experimental verification, our improvement strategy can greatly improve the detection accuracy and efficiency of small devices, solve the efficiency problem of insufficient performance of small devices, and has important practical application value. © 2025 SPIE.

关键词： Robot Operating System

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Minimizing the Maximum Link Utilization for Traffic Engineering in SDN: A Comparative Analysis 4

Minimizing the Maximum Link Utilization for Traffic Engineer...

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4th international conference on Evolutionary Computing and Mobile Sustainable networks, ICECMSN 2024

作者： Prabu, U. Geetha, V. Department of Computer Science and Engineering Puducherry Technological University Puducherry605014 India Department of Information Technology Puducherry Technological University Puducherry605014 India

Software Defined network modernizes network architecture by supporting scalability, significant flexibility, and programmability. Its functioning has been extended to several areas such as cloud computing, edge computing, enterprise networks, data centers, and the Internet of things (IoT). It also successfully enhances the functioning of traffic engineering. Traffic engineering involves optimization and dynamic control of traffic flows, minimizing congestion and maximum link utilization, centralized control, and network traffic management. It also enables the effective utilization of resources, boosts the user experience, and increases network performance. Minimizing maximum link utilization implies the distribution of network traffic across respective links or paths to use the offered resources of the network by preventing congestion. Its major goal is to guarantee that none of the individual links gets overloaded. Various approaches have been proposed in the past several years to address this problem. In this article, the state-of-the-art approaches are reviewed and analyzed. the problem formulation of each approach for minimizing the maximum link utilization is detailed with significant importance. the proposed algorithms, its type, and results are also depicted to have a better comparative analysis. Additionally, the type of graph considered while formulating the problem, topologies used to investigate the efficiency of the algorithm, performance metrics, and implementation platforms are listed to have a clear understanding of various approaches. © 2025 the Authors. Published by Elsevier B.V.

关键词： Deep reinforcement learning

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Multi-class Generative Adversarial network for Hyperspectral Image Classification for Addressing Class Imbalance Problem 4th

Multi-class Generative Adversarial Network for Hyperspectral...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kumar, Deepak Kumar, Dharmender Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9789819751990

Class-imbalanced datasets are commonly observed in hyperspectral image classification and deep learning models often tend to demonstrate a bias toward the dominant class, resulting in very low efficiency for minority classes. Data augmentation has been the most widely used strategy to mitigate class-imbalanced challenge. therefore, in this paper, the authors have introduced a data augmentation approach utilizing a multi-class generative adversarial network (MCGAN) to deal with this issue. In the proposed approach a generative adversarial network is utilized to specifically generate fake minority class samples and integrating these samples with the existing class samples. Further, a convolutional neural network (CNN) has been employed for classification of hyperspectral image samples. A series of experiments, utilizing the proposed approach, has been conducted with two most popular and publicly available datasets. the experiment results obtained are then compared with four state-of-the-art data augmentation techniques. the comparison results prove the supremacy of the proposed approach in terms of the quality of the generated samples and the classification accuracy over the state-of-the-art techniques. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Influencer Ranking Framework Using th-DCNN for influence maximization 4

Influencer Ranking Framework Using TH-DCNN for influence max...

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4th international conference on Evolutionary Computing and Mobile Sustainable networks, ICECMSN 2024

作者： Shelke, Vishakha Jadhav, Ashish Department of Computer Engineering Ramrao Adik Institute of Technology D.Y. Patil Deemed to Be University Nerul Maharashtra Navi Mumbai400706 India Department of Information Technology Ramrao Adik Institute of Technology D.Y. Patil Deemed to Be University Nerul Maharashtra Navi Mumbai400706 India

As the influencer gains more significance in social media marketing, companies raise their budgets for influencer campaigns. With business increasing day by day, finding efficient influencers is becoming the most prominent factor for success, but choosing the right influencer from these social media users is quite a challenge. this manuscript proposes a novel method to rank influencers by their effectiveness based on their posting behavior and social relations over time. Initially, the data from Twitter is collected from the Indian politics tweets and reactions dataset. this raw data undergoes preprocessing using various techniques including, tokenization, stemming, lemmatization, stop word removal, and data normalization using the Min-Max normalization approach to ensure the data is relevant and suitable format for analysis. Next, construct a heterogeneous network to represent the complex interactions between entities like users, tweets, hashtags, and mentions. then Tree Hierarchical Deep Convolutional Neural network (th-DCNN) is applied to these networks to derive information representation for each influencer at each period. Finally, a Cosine similarity (CS) is used to learn from the network and predict the influencer rankings. the performance metrics such as accuracy, f1-score, mean average precision (MAP), Normalized Discounted Cumulative Gain (NDCG), Receiver Operating characteristic (ROC), Mean Reciprocal Rank (MRR), and Hit Rate are analyzed in experimental evaluations. the proposed method improved the accuracy compared with existing techniques. © 2025 the Authors. Published by Elsevier B.V.

关键词： Tweets

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Dual-Channel Convolutional Neural network with Attention Pooling and Sand Cat Swarm Optimization for Incremental Land-Cover Classification in Satellite Image Patches 4

Dual-Channel Convolutional Neural Network with Attention Poo...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Anushkannan, N.K. Karpagavalli, C. Jagadesan, A. Kavya, R.V. Geetha, K. Devi, S. Rukmani Kathir College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641062 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India R.M.K Engineering College Department of Physics Tamil Nadu Thiruvallur601206 India J J College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli620009 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

Incremental Land-Cover Classification in satellite image patches concerns the categorization of different types of land cover in satellite images. Land cover mapping from satellite imagery has applications in environmental observation, planning and resource management, and disaster response. Current land cover change classification paradigms involve heavily sampled and trained state vector data models, which do not have provisions for dynamic updates consequent upon variation in the cover density sizes, seasonal changes, incremental growth of urban sprawl, deforestation, and other natural calamities. Since new satellite images become available often, retraining your models from the very beginning is both time-consuming and computationally expensive. To tackle these issues, we propose a Dual-Channel Convolutional Neural network with Attention Pooling and Sand Cat Swarm Optimization (DualCCNNet-AP+SCSO) for incremental classification of land cover in satellite imagery patches. this methodology utilizes satellite image patches from the So2Sat image patch dataset, where images are initially preprocessed with Iterative Self-Guided Image Filtering (ISGIF). After preprocessing, the Spike-Driven Transformer (SDT) method extracts key features, which are then classified through the Dual-Channel Convolutional Neural network with Attention Pooling (DualCCNNet-AP), fine-tuned by Sand Cat Swarm Optimization (SCSO) for accurate incremental land-cover classification. the DualCCNNet-AP+SCSO method is implemented in Python. the effectiveness of the proposed method was assessed on the So2Sat image patch dataset, where it achieved an impressive 99.9% accuracy and 99.5% recall, outperforming current techniques. Based on these conclusions about classification accuracy, it demonstrated that the proposed approach enables the effective classification of the change, especially in the smallest class of the land cover in the satellite image patches. the results of the proposed method were bett

关键词： Convolutional neural networks

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Optimal Transport Flow Distribution and Construction of Wardrop Optimal networks 4th

Optimal Transport Flow Distribution and Construction of Wa...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Bagdasaryan, Armen Kalampakas, Antonios Saburov, Mansoor College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Institute of Mathematics National Academy of Sciences of Armenia 24/5 Baghramyan ave. Yerevan0019 Armenia

ISBN: (纸本)9783031812439

this paper is motivated by the problem of optimal flow allocation in transportation networks. We consider equilibrium and optimal flow distribution on networks of parallel links. In a parallel network, a user (or Wardrop) equilibrium is the optimal distribution of flow across alternative parallel links that minimize the effective costs of the links, while the system optimum is the optimal distribution of flow for which the average effective cost is minimal. We study Wardrop optimal networks, i.e., the networks that admit Wardrop optimal flows which satisfy both the Wardrop equilibrium and the system optimum. We first present the characterization of Wardrop optimal flows, and then propose a matrix approach for constructing Wardrop optimal networks by means of convex, strictly increasing, and continuously differentiable functions. Some special classes of Wardrop optimal networks are also introduced. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Construction of Wardrop optimal networks Optimal flow distribution Price of anarchy Transport network Wardrop optimal flow Wardrop optimal network

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A Review Analysis on Smart Blind Stick Using IoT 4

A Review Analysis on Smart Blind Stick Using IoT

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Asha, P.K. Shanmugha Priya, R.K. Nagaraj, D. Aswini, S. Wilson, G Preethi Vinoth Kumar, K. New Horizon College of Engineering Department of Chemistry Bengaluru India Jai Shriram Engineering College Avinashipalayam Department of Information Technology Tirupur India Dhanalakshmi College of Engineering Department of Cse Chennai India Bangalore Technological Institute Department of Cse Bengaluru India Jerusalem College of Engineering Department of Computer Science & Business Systems Chennai India New Horizon College of Engineering Department of Eee Bengaluru India

ISBN: (纸本)9798331523923

Millions of individuals worldwide struggle with blindness and other forms of visual impairment. 940 million individuals will suffer some degree of vision loss by 2015, according to the WHO (World Health Organization). 246 million people experience fleeting apparitions, while 39 million people are blind. Most people with bad vision are older than 50 and live in industrialized cultures. the main challenge that blind individuals encounter is mobility. they are cut off from their environment. Since they are oblivious to what is going on around them, they require assistance. Technological advancements have been crucial in increasing the mobility and freedom of those who are blind. One such innovation is the Smart Blind Stick, a high-tech assistance gadget made to improve the safety and navigation of the blind. the goal of this review article is to examine the many facets of Smart Blind Stick technology, including its functionality, benefits, drawbacks, and overall influence on users' life. this review aims to shed light on the current status of Smart Blind Stick technology and its potential future advances by reviewing existing literature and advancements. © 2025 IEEE.

关键词： Obstacle detectors

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Automated Skin Cancer Classification and Detection Using Convolutional Neural networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th international conference on Evolutionary Computing and Mobile Sustainable networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

the uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the international Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 the Authors. Published by Elsevier B.V.

关键词： Lung cancer

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Pluggable Compensation Module for Twofold Propagation-based Hologram Generation 4

Pluggable Compensation Module for Twofold Propagation-based ...

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4th international Computational Imaging conference, CITA 2024

作者： Yuan, Ganzhangqin Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (纸本)9781510688834

Recent advancements in learning-based computer-generated holography (CGH) have enabled sophisticated methods for generating phase-only holograms (POHs). However, a key challenge remains unaddressed: ringing artifacts, which arise due to the Gibbs phenomenon during the diffraction propagation process, significantly degrade the quality of reconstructed holographic images. To address this, we introduce a novel pluggable compensation module designed to predict and mitigate these artifacts by adjusting for residual values during propagation. this pluggable module, easily integrated with existing CGH networks, alleviates the network's burden by aligning the diffraction process more closely with ideal conditions. Our results demonstrate that embedding this module in state-of-the-art CGH frameworks, such as HoloNet and CCNN, elevates the PSNR by up to 3.9 dB and 0.6 dB, respectively. Notably, our method achieves considerable artifact reduction in real-world scenarios, thereby enhancing the overall fidelity of holographic displays. this pluggable module represents a substantial advancement, offering versatility and improved performance across diverse CGH algorithms. © 2025 SPIE.

关键词： Holograms

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