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International Conference on Mathematics of Intelligent Computing and Data Science, ICMICDS 2022

作者： Ramkumar, P.B. Dhanya, P.M. Department of Mathematics Rajagiri School of Engineering and Technology Kerala Kakkanad India Department of Computer Science and Business Systems Rajagiri School of Engineering and Technology Kerala Kakkanad India

ISBN: (纸本)9789819615049

A hypergraph is a type of graph, H=(V,E) where V is the set of nodes and E is the set of hyperedges;where one edge consists of may nodes. Morphological operators are nonlinear operators which can be applied on images, graphs, hypergraphs etc. Dilation and erosion are morphological operators and other operators like opening, closing can be obtained by repeated application of these operators. Topological space consists of a set X together with topology τ defined on X, which satisfies the axioms namely Nullset, arbitrary union of opensets and finite intersection of opensets. Such a topological space S=(X,T) is compatible with a hypergraph H if X=VH∪EH where VH and EH are the vertex set and edge set respectively. The morphological operators induces several topology on hypergraph with its compatible topological space S=(X,T). Neighborhood of a node is defined in many ways which can be considered as open sets in the corresponding topological space. The purpose of this paper is to apply topological properties on hypergraphs using morphological operators. Separation axiom and closure function are illustrated using hypergraph structure. The work is extended to crime analysis, text processing and patient disease analysis. Topological feature extraction is done on Barcodes created from crime hypergraphs. This gives a better visualization of crime analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph theory

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Message Control for Blockchain Rewriting

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1864-1876页

作者： Li, Yingjiu Sengupta, Binanda Tian, Yangguang Yuan, Jiaming Yuen, Tsz Hon School of Computer and Information Science University of Oregon EugeneOR United States School of Computer Science and Electronic Engineering University of Surrey Guilford Surrey United Kingdom Indian Institute of Engineering Science and Technology Shibpur India Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia

Blockchain rewriting is necessary for modifying illegal or messages included in blockchain transactions, while maintaining the consistency of subsequent blocks in the blockchain. However, arbitrary blockchain rewriting is not desirable as it defeats the purpose of blockchain rewriting. In this work, we propose a new security primitive named message-controlled chameleon hash (MCH) and apply it for message control in blockchain rewriting to ensure that no unspecified messages are generated from blockchain rewriting. The proposed MCH enables permitted parties to select candidate messages from designated message sets for blockchain rewriting at the message level. Our evaluation shows that the proposed MCH can be easily integrated into both permissioned and permissionless blockchains. © 2024 IEEE.

关键词： Smart contract

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Lumbar Spine Degenerative Classification

Lumbar Spine Degenerative Classification

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Sudha, S. Adharsh Narayana, S. Yadhveer, R.M. Rajasekaran, Rithesh School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Vellore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

In the year 2020, the World Health Organization reported that lower back pain constituted the primary cause of disability on a global scale, impacting 619 million individuals. Magnetic resonance imaging (MRI) offers a comprehensive perspective of the lumbar spine, encompassing vertebrae, discs, and nerves, thereby allowing radiologists to evaluate both the presence and severity of these conditions. This paper classifies five degenerative conditions regarding the lumbar spine, which are the following: Left Neural Foraminal Narrowing, Right Neural Foraminal Narrowing, Left Subarticular Stenosis, Right Subarticular Stenosis, and Spinal Canal Stenosis. Each study from the imaging dataset is represented by severity scores categorized as either Normal/Mild, Moderate, or Severe, and every one of these five conditions are classified over the five intervertebral disc levels: L1/L2, L2/L3, L3/L4, L4/L5, and L5/S1. We will use DenseNet, a convolutional neural network architecture known for its effectiveness in feature propagation and optimization of parameters, to overcome this classification challenge. © 2025 IEEE.

关键词： Diagnosis

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Enhanced path planning for robot navigation in Gaussian noise environments with YOLO v10 and depth deterministic strategies

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Discover Artificial Intelligence 2025年第1期5卷 1-22页

作者： Xiao, Feng Chu, Shiwei Guo, Xing Zhang, Youhai Huang, Rubing School of Computer and Information Technology Anhui Vocational and Technical College Anhui Hefei230011 China School of Electronic Information Engineering Anhui University Hefei230039 China School of Computer Science and Technology Anhui University Hefei230039 China

Facing Gaussian noise from severe rain and haze, we employ multi-scale YOLO v10 for obstacle detection amidst high noise and DDPG (Deep Deterministic Policy Gradient) for enhanced path planning. YOLO v10 is used to annotate obstacles, and different input scales are set to deal with Gaussian noise. Recognition accuracy is optimized using cross entropy loss function. The DDPG algorithm is applied to update the path planning strategy, define the state space of robot position, velocity, and Laser Radar (LiDAR) readings, and output possible movement directions and velocities as action outputs. A reward function is designed to motivate the agent to learn. The weighted average method is used to fuse the visual information of YOLO v10 with LiDAR data. The robot’s status and surrounding environment are tracked through real-time feedback mechanism. The path planning is dynamically adjusted, and an intelligent navigation system is built. The research results show that the average recognition rate of the system for obstacles is 0.95, and the success rate of avoidance under extreme weather conditions of heavy rainfall is 88.0%. The system can effectively cope with various Gaussian noise environments and achieve precise path planning decisions. © The Author(s) 2025.

关键词： Gaussian noise (electronic)

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Optimized cross-corpus speech emotion recognition framework based on Normalized 1D Convolutional Neural Network

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第1期9卷 98-115页

作者： Barsainyan, Nishant Singh, Dileep Kumar Department of Computer Science & Engineering JLU School of Engineering and Technology Jagran Lakecity University Chandanpura Madhya Pradesh Bhopal 462044 India

Speech Emotion Recognition (SER) technology is essential for determining human emotions based on voice signals. However, combining knowledge from disparate domains, such as speech-emotion recognition and applied psychology, poses considerable problems. Many traditional systems rely on handcrafted features to accurately categorize emotions in multi-lingual situations. Deep learning algorithms have evolved as self-learning feature extractors from voice data, but they can fail when dealing with complex and challenging speech inputs. To address these issues, we present a novel SER framework that bridges various interdisciplinary disciplines. Our method introduces data augmentation techniques that are purposefully used before feature extraction to provide informative and discriminative feature sets for each spoken sentence. These best features are combined to form a feature vector, fed into a Normalized 1D Convolutional Neural Network (1D-CNN) for emotion recognition. Furthermore, using test data from a corpus trained on a different dataset, we evaluate the performance of an XGBoost (XGB) classifier for multi-lingual emotion recognition. Our results show that the proposed SER design outperforms previous approaches, especially in multi-lingual environments, achieving 96.88% of multi-class classification accuracy. Our framework demonstrates promising advances in voice emotion recognition by leveraging the power of deep learning, data augmentation, and feature selection. This study is essential to improving human-computer interaction by developing more accurate and adaptive voice-based emotion recognition systems. © (2025), (Society of Sytematic Innovation). All rights reserved.

关键词： Convolutional Neural Networks Cross-Corpus Deep Learning Feature Extraction Signal Processing Speech Emotion Recognition XGB Classifier

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MSCC-RetNet: a multi-scale color corrected retinex network for underwater image enhancement

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Multimedia Systems 2025年第3期31卷 1-18页

作者： Sun, Benxue Chen, Mingxuan Hu, Liming Wang, Anjie Fang, Zhijun School of Electronic and Electrical Engineering Shanghai University of Engineering Science 333 Longteng Road Shanghai201620 China School of Electronic and Computer Engineering Peking University 2199 lishui road Shenzhen518055 China School of Computer Science and Technology Donghua University 2899 Renming North Road Shanghai201620 China

High-quality underwater images are crucial for applications such as seafloor exploration, resource monitoring, and object detection. However, underwater imaging faces significant challenges, including poor visibility, low contrast, and severe color distortion. To address these issues, we propose a novel deep learning framework for Underwater Image Enhancement (UIE), incorporating color correction, cross-attention mechanism, and visibility enhancement. The proposed Multi-Scale Color Corrected Retinex Network (MSCC-RetNet) consists of three key modules: a multi-scale color correction module, an information complementarity module, and a multi-scale feature fusion module. The multi-scale color correction module integrates the Inception (Inc) and Squeeze-and-Excitation (SE) modules to enhance feature extraction. The multi-scale feature fusion module combines Convolutional Neural Networks (CNNs) with Transformers to improve representation learning. Experimental results indicate that the multi-scale color correction module effectively mitigates color distortions, while the cross-attention mechanism in the Information Complementarity module selectively emphasizes important regions. Additionally, the multi-scale fusion module enables end-to-end processing of illumination variations, significantly enhancing image visibility. Comprehensive evaluations on five benchmark underwater image datasets confirm that MSCC-RetNet outperforms four traditional approaches and five deep learning-based methods in key image quality assessment metrics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Extended Multi-scale Feature Fusion and Balanced Generative Adversarial Network for Image Inpainting under Limited Data

Extended Multi-scale Feature Fusion and Balanced Generative ...

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2024 Workshop on Electronics Communication engineering, WECE 2024

作者： Xiao, Ting Sun, Minqian Chen, Haoyuan Li, Jiaqi Qiu, Yixing Wang, Zhe Lu, Dan Department of Computer Science and Engineering East China University of Science and Technology China School of Artificial Intelligence OPtics and ElectroNics Northwestern Polytechnical University China School of Computer Science and Technology Harbin Engineering University China

ISBN: (数字)9781510689145

ISBN: (纸本)9781510689138

Image inpainting has made remarkable progress in recent years due to the availability of sufficient training data. However, when the training data is insufficient, i.e., image inpainting in few-shot regimes, the performance of existing image inpainting methods degrades drastically. Two challenges impede image inpainting under few-shot regimes, adequate information and unstable training. To address the above issues, this paper proposes a new method for image inpainting under limited data. Specifically, an Extended Multi-scale Feature Fusion (EMFF) module is proposed to capture representative contextual information by progressively reducing the number of channels for features with increasing receptive fields, thereby utilizing effective information. Additionally, we leverage a multi-scale discriminator whose cross-layer features are mixed for supplementation to formulate a Balanced Generative Adversarial Network (BGAN) to stabilize the training dynamics. Comprehensive experiments demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Image fusion Image quality Feature fusion Education and training Image restoration Convolution Data modeling Image enhancement Gallium nitride Semantics

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Towards Unbiased Minimal Cluster Analysis of Categorical-and-Numerical Attribute Data 27th

Towards Unbiased Minimal Cluster Analysis of Categorical-an...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Yunfan Luo, Xiaopeng Chen, Qingsheng Zou, Rong Zhang, Yiqun Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Computer Engineering Guangzhou Huali College Guangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9783031781650

Categorical and numerical attributes occur frequently in cluster analysis tasks. To bridge the information gap between the heterogeneous categorical and numerical attributes in cluster analysis, the existing approaches usually adopt prior assumptions to distance definition and cluster distribution, which unavoidably introduce bias to the clustering process. To address this issue, we propose to analyze mixed data comprising both categorical and numerical attributes by forming minimal clusters through neighborhood set theory. As the minimal clusters are the smallest cluster units that can be obtained without relying on prior assumptions, unbiased cluster analysis can be facilitated accordingly. To avoid information loss, distance and density metrics that are unified on both numerical and categorical attributes are also proposed and utilized to merge the minimal clusters hierarchically. It turns out that our proposed approach is highly interpretable, and is capable of accurately and robustly clustering data sets composed of any combination of numerical and categorical attributes. Extensive experimental evaluations demonstrate its efficacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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