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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Lin, Zhenkun Zhu, Yuhan Huang, Xing Yang, Liliang Liu, Genggeng Fuzhou University College of Computer and Data Science Fuzhou China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350312270

With the rapid development of the Industrial Internet of Things (IIoT), higher requirements have been put forward for hardware devices, and as the core part of hardware devices, the performance of chips is critical. The Obstacle-Avoiding Rectilinear Steiner Minimal Tree (OARSMT) problem is a classic issue in VLSI physical design, and having a shorter OARSMT can help reduce chip delays and energy consumption. This paper proposes a framework based on Deep Reinforcement Learning (DRL) to automatically learn and generate heuristic algorithms for solving the OARSMT problem. The core of our proposed method combines Graph Convolution Network (GCN) with DRL, where GCN extracts the graph-state feature information of Obstacle-Avoiding Steiner Tree (OAST) generated throughout the process, and DRL learns policy decisions under different graph states. Experimental results have confirmed our proposed framework outperforms existing manually designed work, indicating that our framework is a promising tool for solving the OARSMT problem. © 2023 IEEE.

关键词： Reinforcement learning

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach 38

DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowl...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Qian Chen, Ling State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a Deep Evolutionary Clustering jointed temporal knowledge graph Representation Learning approach (DECRL). Specifically, a deep evolutionary clustering module is proposed to capture the temporal evolution of high-order correlations among entities. Furthermore, a cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters across timestamps, thereby maintaining the temporal smoothness of clusters. In addition, an implicit correlation encoder is introduced to capture latent correlations between any pair of clusters under the guidance of a global graph. Extensive experiments on seven real-world datasets demonstrate that DECRL achieves the state-of-the-art performances, outperforming the best baseline by an average of 9.53%, 12.98%, 10.42%, and 14.68% in MRR, Hits@1, Hits@3, and Hits@10, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Evaluation of convolutional neural network models’ performance for estimating mango crop yield

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第1期9卷 1-18页

作者： Neethi, M.V. Raviraj, P. Department of Computer Science and Engineering-Data Science ATME College of Engineering Affiliated to Visvesvaraya Technological University (VTU) Belagavi India Department of Computer Science and Engineering GSSS Institute of Engineering and Technology for Women Affiliated to Visvesvaraya Technological University (VTU) Belagavi India

In agriculture, crop yield estimation is essential;producers, industrialists, and consumers all benefit from knowing the early yield. Manual mango counting typically involves the utilization of human labor. Experts visually examine each sample to complete the process, which is time-consuming, difficult, and lacks precision. For commercial mango production to produce high-quality fruits from the orchard to the consumer, a quick, non-destructive, and accurate variety classification is required. Because of its effectiveness in computer vision, a convolutional neural network—one of the deep learning techniques—was chosen for this investigation. For yield prediction, a total of eight popular mango cultivars were utilized. A comparison with previously trained models was used to assess the proposed model. The performance of the classifiers was evaluated using evaluation metrics such as accuracy, loss, area under the receiver operating characteristic curve score, precision, recall, F1-score, sensitivity, specificity, positive predictive value, negative predictive value, and Cohen’s Kappa performance measure. In terms of performance evaluation criteria, it was found that the proposed approach outperformed the pre-trained models. The suggested model achieved 98.85% accuracy in the test set, which had 800 images. This outcome demonstrates the tangible applicability of the proposed methodology for mango crop estimation. © (2025), (Society of Sytematic Innovation). All rights reserved.

关键词： Convolutional Neural Networks Deep Learning Early Detection Machine Learning

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Revealing Population Flows in Community and Urban Centrality Perspective: From Lockdown to Recovery in China 24

Revealing Population Flows in Community and Urban Centrality...

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2024 International Conference on Image Processing, Intelligent Control and computer Engineering, IPICE 2024

作者： Chen, Xuanyu Yin, Yanzhong Wu, Qunyong The Academy of Digital China FuZhou University China College of Computer and Data Science FuZhou University China

ISBN: (纸本)9798400710285

With the COVID-19 epidemic having a profound impact on global population mobility patterns, this study aims to explore the new pattern of population mobility in China after the end of the epidemic. Based on Baidu migration data, this study uses the Intercity Population Transcendence Index (S-index) and Louvain Community Discovery Algorithm to calculate urban centrality and detect communities in 366 cities in China. The study found that: after the full unsealing of the epidemic, urban centrality rose overall;rankings in the west and northeast generally rose, while rankings in the central and southeastern coastal regions fell;rankings in the south and north generally rose, and cities in the central region fell;the number of cohesive communities fell, and communities became more connected, with interprovincial fringe cities interacting more closely with neighbouring provinces. © 2024 Copyright held by the owner/author(s).

关键词： Community Detection Population Flows S-index Urban Centrality

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting 38

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shang, Zongjiang Chen, Ling Wu, Binqing Cui, Dongliang State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://***/shangzongjiang/Ada-MSHyper. © 2024 Neural information processing systems foundation. All rights reserved.

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Obstacle-Avoidance X-Architecture Steiner Minimal Tree Algorithm Based on Deep Reinforcement Learning

Obstacle-Avoidance X-Architecture Steiner Minimal Tree Algor...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： You, Jie Zhu, Yuhan Huang, Xing Yang, Liliang Liu, Genggeng Fuzhou University College of Computer and Data Science Fuzhou China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350312270

In recent years, we have witnessed the rapid development of the Internet of Things (IoT) paradigm. A new computing paradigm, predictive intelligence, has been introduced into many IoT applications. In IoT applications, predictive intelligent devices must consider power and delay consumption. The goal of Very Large Scale Integration (VLSI) circuit routing problem is to minimize the wirelength to optimize chip delay. Therefore, VLSI routing problems have been widely applied in IoT. The X-architecture routing technology presents a promising solution that can reduce the number of vias, wirelength, and mold size significantly, thereby proving to be a highly practical approach. Moreover, with the shift of modern VLSI design towards system-on-chip paradigm, many uncrossable obstacles appear on the plane, thus Obstacle-Avoiding X-architecture Steiner Minimal Tree (OAXSMT) problem has attracted more and more attention. Most existing algorithms are troubled by poor wirelength quality or high construction time cost. Therefore, this paper proposes a method based on Deep Reinforcement Learning to construct an OAXSMT. First, an encoder based on heterogeneous graph transformer is designed to generate fixed-length encodings for each node. Second, an actor-critic decoder based on pointer network is designed to generate obstacle-avoiding X-architecture Steiner redundant tree. Finally, a fast evaluation strategy is designed to evaluate the quality of the redundant tree. Experimental results show that the routing results generated by the algorithm are superior to existing heuristic algorithms in terms of wirelength and runtime, and the algorithm also has certain generalization ability. © 2023 IEEE.

关键词： Reinforcement learning

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Sign Comm: A Real-Time Indian Sign Language Recognition System Using Deep Learning for inclusive communication 2

Sign Comm: A Real-Time Indian Sign Language Recognition Syst...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ilakkia, A. Kanchanadevi, P. Abirami, A. Rajan, R. Muthukumaran, K. Artificial Intelligence and Data Science Sri Manakula Vinayagar Engineering Puducherry India St. Joseph College of Arts and Science Department of Computer Science Cuddalore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Individuals who are deaf often utilize sign language for communication. In Sign Language, the symbols or motions are arranged grammatically. Every motion is referred to as a sign. Indian Sign Language Recognition using Deep Learning model describes the creation of SignComm, which improves communication for those with hearing loss by translating Indian Sign Language (ISL) motions into text. To identify and interpret ISL gestures in real-time. The paper describes the difficulties the deaf community faces due to the dearth of real-time sign language interpretation services. It suggests a method that uses an Automatic Sign Detection system with deep learning models. The research describes current systems and their shortcomings, such as the lack of real-time translation tools and the difficulties the deaf community faces in obtaining sign language interpretation. Next, it presents the suggested system, SignComm, which has benefits including quick sign language decoding, a user-friendly platform that is easy to use and can foster an inclusive atmosphere in public areas and educational institutions a literature review that examines several deep learning models and their applicability to the project is included in the paper. It emphasizes the potential of the ResNet-50 architecture and the significance of a unique ISL dataset. The benefits of the suggested method, including its accuracy, inclusivity, and capacity to address communication barriers, are highlighted in the paper. Additionally, it outlines the steps involved in the implementation process, such as gathering data, training the ResNet model, and deploying in real time. A user-friendly experience for converting ISL gestures into text is the main goal of the intricate user interface design. © 2024 IEEE.

关键词： Deep learning

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JDC-GCN: joint diversity and consistency graph convolutional network

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Neural Computing and Applications 2025年第16期37卷 10407-10423页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

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