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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sadhasivam, G Sudha Aadhithyashri, M.S. Gowdhaman, Dhanush Palanisamy, Nishanth G Shri Vignesh, S. Samrddhi, M. PSG College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Sign languages play a crucial role in the lives of individuals with speech impairments, offering a unique way to communicate through gestures. However, the challenge lies in standardizing this visual language. American Sign Language is one of these rich languages, but each sign language has its own alphabet and unique expressions, reflecting diverse cultures. © 2024 IEEE.

关键词： Visual languages

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Cooperative Channel and Optimized Route Selection in Adhoc Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1547-1560页

作者： D.Manohari M.S.Kavitha K.Periyakaruppan B.Chellapraba Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119TamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035TamilnaduIndia Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107TamilnaduIndia Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is *** uses have expanded in lockstep with its *** to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is *** this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the conﬁdence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization *** that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated *** simulation work is validated in this section using the MATLAB ***,steps show how the node acts as a conﬁdent forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between *** cal-culate the conﬁdence of the node at the start of the network by combining the reliability report for theﬁrst hop and the reliability report for the secondary *** then refer to the same node as the conﬁdent node in order to operate as a *** a result,we witness an increase in the leftover energy in the *** percentage of data packets delivered has also increased.

关键词： Adhoc Network conﬁdent forwarder one-hop optimized route selection secondary report channel selection

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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Flood Detection and Segmentation Using Deep Learning Models

Flood Detection and Segmentation Using Deep Learning Models

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Santhi, V. Krishnamurthi, Ilango Madhumitha, N.H. PSG College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350378177

A flood occurs when water overflows onto normally dry land, often caused by a variety of factors starting from natural events or human actions, resulting in significant losses affecting both human lives and economic stability. This project aims to introduce a flood detection and model by utilizing various deep learning methods. This high clarity proves pivotal for precise flood detection, particularly in segmentation urban locales where buildings and infrastructure might obscure floodwaters. Typically, ordinary media images become accessible immediately following a flood event, unlike satellite images, which may necessitate processing time. This immediacy is crucial for prompt emergency response actions and decision-making processes. The augmentation process applied to non-flooded images encompasses various techniques aimed at enriching the diversity and resilience of the training dataset. Techniques such as image data augmentation are being implemented to improve the model's effectiveness and resilience. The CNN model with attention mechanism demonstrates exceptional performance in accurately categorizing flood images and segmenting flood regions, while the U-Net with a MobileNet base exhibits promising potential. The model achieves impressive scores across various metrics, including accuracy, precision, recall, and Cohen's kappa score, with values of 0.98, 0.97, 0.98, and 0.96 respectively. Additionally, for the segmentation task, the U-Net with MobileNet base algorithm is employed, achieving an accuracy of 0.90, with Mean IOU and Dice Coefficient reaching 0.84 and 0.82 respectively. The CNN model incorporating an attention mechanism exhibits exceptional performance in accurately detecting and segmenting flood regions, while the U-Net with a MobileNet base highlights its potential for flood segmentation. Bayesian Optimization was used to fine tune the hyperparameters and accuracy improved from 90.33% to 91.01%. © 2024 IEEE.

关键词： Image enhancement

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Key-Point Driven Animation of Stick Figures and Children's Drawings using ResNet-50 and Shape Manipulation

Key-Point Driven Animation of Stick Figures and Children's D...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Prakash, J. Nashita, V. Shivani Sri, S. Shri Shivathmika, V. Sivesh, K. Ashwin, V. PSG College of Technology Department of Computer Science & Engineering Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378177

This paper focuses on advancing animated drawing techniques through precise key-point identification. The methodology utilizes ResNet-50, integrated with image segmentation and As Rigid As Possible (ARAP) shape manipulation for animated stick figures. Results demonstrate the efficacy of ResNet-50 in accurate key-point identification and seamless integration with image segmentation and ARAP shape manipulation. The ResNet-50 model trained has an accuracy of 0.76. In conclusion, the developed methodology provides a robust solution for dynamic animations and precise key-point identification, showcasing its potential for advancements in animated drawing techniques. © 2024 IEEE.

关键词： Animation

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Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest

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Multimedia Tools and Applications 2024年 1-26页

作者： Yarrarapu, Manasa Leelavathy, Narkedamilly Haritha, Dasari Department of Computer Science and Engineering Jawaharlal Nehru Technological University Andhra Pradesh Kakinada533003 India Department of Computer Science and Engineering Godavari Institute of Engineering and Technology Andhra Pradesh Rajahmundry533296 India Department of Computer Science and Engineering University College of Engineering Jawaharlal Nehru Technological University Andhra Pradesh Kakinada533003 India

Now-a-days, the generation of videos has increased dramatically due to the quick growth of multimedia and the internet. The need for effective ways to store, manage, and index the massive numbers of videos has become imperative due to this expansion. As a result, a method needs to be proposed that collects only the necessary data from the original recording. In computer vision, Video summarization is a significant task, and its primary goal is to give a quick summary of the video by removing irrelevant information and capturing key frames from the video. Many approaches have developed over the last several decades, using the most recent deep neural network architectures that represent the current state-of-the-art. Our method involves extracting vital key frames from the input video using the MobileNetSSD model, which is well-known for its efficient recognition and localization of objects of interest. These highlighted frames are essential in creating a detailed video summary. Furthermore, a method of temporal analysis is applied to guarantee that the summary accurately reflects the relevant events in the order in which they occurred, contributing to a coherent and meaningful representation of the information. We evaluated the proposed approach on TV Sum and SUM me video datasets, comparing the results against cutting-edge video summarization techniques. Our approach works effectively to produce clear and meaningful video summaries. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

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Chinese Aspect-Level Sentiment Analysis Based on Graph Convolutional Networks with Syntactic Distance 6

Chinese Aspect-Level Sentiment Analysis Based on Graph Convo...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Huang, Shufen Liu, Changhui Zhang, Yinglin Wuhan Institute of Technology College of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350353174

Aspect-level sentiment analysis tasks aimed at identifying the sentiment polarity of multiple specific aspects of a sentence. Currently most researches based on syntactic analysis have ignored the effect of different relationship types in the dependency tree to the classification results, and most of them are unable to distinguish the importance of node features to the target aspect items. To address these problems, we propose a graph convolutional network model (RD-GCN) that combines relational types and syntactic distances, where the model mainly consists of a Dependency Graph Convolutional Network (DepGCN), a Syntactic Distance Graph Convolutional Network (DisGCN), and an Affine Interaction Module. The DepGCN imports relationship type and dependency probability matrix in the feature transfer process so as to abundantly extract the structural information between the nodes;The DisGCN obtains the importance scores of each word in the sentence according to the syntactic distance matrix from the dependency tree, and setting the distance thresholds to reduce the weight proportion of the long-distance features. The RD-GCN model firstly uses the BiLSTM network as an embedding layer to obtain the initial vectors of the text, then inputs them into two different graph convolutional networks with different syntactic analyses to extract the node features respectively, finally, the affine interaction layer is used as a bridge to make the two different node features interactively learning, to improve the efficiency of the model. The experimental results show that this model on the NLP&CC2012 Chinese dataset improves the Acc and F1 values to 84.36% and 82.58% compared with the ATAE-LSTM and BiGCN comparison models. The Acc and F1 values on the two Chinese datasets of SemEval-2016 task 5 reached 83.60%, 71.99%, and 84.62%, 72.71%, respectively. © 2024 IEEE.

关键词： Network theory (graphs)

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Enhancing Alzheimer's Disease Diagnosis via Open Set Domain Adaptation-Based Representation Learning on the Hypersphere 6

Enhancing Alzheimer's Disease Diagnosis via Open Set Domain ...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Zhang, Qiongmin Shi, Yin Yu, Siyi Tan, Xiaowei College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9798331541750

Structural magnetic resonance imaging (sMRI) is commonly employed for Alzheimer's disease (AD) diagnosis. However, sMRI datasets often suffer from domain shifts due to variations in scanning protocols, and real-world clinical scenarios frequently involve unknown categories, presenting challenges for conventional domain adaptation methods. To address these issues, we propose a novel Open Set Domain Adaptation (OSDA) framework that not only detects unknown class but also improves AD diagnosis. The method integrates demographic information as auxiliary features alongside sMRI-derived features to enhance the recognition of high-dimensional data. Features are projected onto a hyperspherical space via a spherical variational auto-encoder (S-VAE), enabling efficient feature representations. Domain alignment is achieved using a von Mises-Fisher kernel-based maximum mean discrepancy function, while A-softmax loss enables the model learn angularly discriminative feature representations. Additionally, an entropy minimization strategy ensures greater prediction confidence in the target domain. Experimental results show that the proposed method achieves OS of 92.29%, 85.28%, and 80.37% on AD vs. CN vs. Unk, MCl vs. CN vs. Unk, and AD vs. MCl vs. Unk tasks, respectively. This framework extends the applicability of OSDA techniques to medical imaging, providing enhanced diagnostic accuracy and enabling more robust AD detection. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Smoke and Flame Detection Method in Multiple Scenarios 5

Smoke and Flame Detection Method in Multiple Scenarios

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Xie, Yucong Luo, Hongtao College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9798331530334

Nowadays, global fire outbreaks occur frequently, and timely detection before a fire outbreak is imminent to reduce its harmful effects. Addressing the current issues of poor effectiveness, high false alarm rate, slow detection speed, and limited detection environment in smoke and flame detection, this paper proposes an improved approach based on the YOLOv7 algorithm. Employing EIOU as the boundary loss function enhances the model's convergence speed and regression accuracy. Furthermore, in the neck section of the structure, a one-time aggregation method is utilized to design the cross-level sub- network (GSCSP) module VoV-GSCSP, which reduces the com- plexity and computational burden of the network structure while maintaining sufficient accuracy. Finally, the Channel Attention (CA) mechanism is integrated into the backbone to enhance the extraction capability of features related to the edges and dense distribution positions of smoke and flames. The improved algorithm achieves a 2.3% increase in mAP@0.5, reduces the overall network model parameters by 5.17MB, and improves detection speed to meet the real-time requirements of smoke and flame detection in multiple scenarios. © 2024 IEEE.

关键词： Premixed flames

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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