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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Enhancing Brain Tumour Classification from MRI's Dataset Using CNNs with Attention Mechanism for Improved Accuracy 5

Enhancing Brain Tumour Classification from MRI's Dataset Usi...

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5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Balaji, V. Dinesh Kumar, J. Sanjai Kumar, N. Sakthivel Raj, N. Sasi Kumar, P. Sri Krishna College of Engineering and Technology Dept. of ECE Tamil Nadu Coimbatore India

ISBN: (纸本)9798331530013

the human brain serves as the central processing unit of the body, and abnormal cellular proliferation within the brain can lead to tumourigenesis, often culminating in malignancies such as brain cancer. In the domain of medical imaging, Computer Vision (CV) plays a pivotal role by providing objective, data-driven insights that minimize the impact of human cognitive biases. this study focuses on utilizing advanced CV techniques for the automated detection of brain tumours from MRI scans. the methodology leverages bilateral filtering to effectively reduce image noise, followed by binary thresholding for enhanced contrast, and Convolutional Neural Network (CNN) segmentation to accurately delineate tumour regions. the model's robustness is ensured through extensive training, testing, and validation using diverse datasets. Key performance metrics, including accuracy, sensitivity, specificity, precision, recall, and F1 score, are utilized to evaluate the model's diagnostic capability, with the goal of outperforming existing methods. the proposed Hybrid CNN (HCNN) model achieves an accuracy of 96.4%, precision of 93.2%, recall of 94%, and an F1 score of 92%, demonstrating its superior efficacy in brain tumour detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Essay Scoring: A Comparative Study of Machine Learning and Deep Learning Approaches 5

Automated Essay Scoring: A Comparative Study of Machine Lear...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Bansal, Bhuvi Gupta, Jhalak Singh, Manasvi Rani, Ritu Jaiswal, Garima Sharma, Arun Indira Gandhi Delhi Technical University for Women Delhi India Indira Gandhi Delhi Technical University for Women Delhi India Bhagwan Parshuram Institute of Technology Delhi India Bennett University Noida India

ISBN: (纸本)9798350356236

Automated Essay Scoring (AES) refers to the use of technology to automatically assign scores to essays. It serves as a viable alternative to traditional human grading methods and has become an important tool in high-stakes educational assessments. It has received considerable research attention in academic institutions as it accelerates the process and alleviates the workload on human evaluators. Hence, we studied the Artificial Intelligence and Machine Learning methods that were employed to develop automatic essay scoring systems. We have discussed the limitations in the existing research and explored emerging trends in this area. this study seeks to compare the performance of several common AES frameworks, emphasizing on machine learning with complex language features and deep neural algorithms. through rigorous testing, the strengths and weaknesses of both traditional and modern models were assessed under varying circumstances and the results were evaluated using the efficiency score which were compared to the grades given by human raters. the results revealed that the neural network model, trained on comprehensive prompt data and employing Natural Language processing technique for feature extraction, outperformed all the other models. this indicates that it gave a closer alignment with results of the human raters as compared to the conventional Machine Learning models. © 2025 IEEE.

关键词： Deep neural networks

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Data-driven Gradient Mapping Adjustment Method for Color Optimization in Digital Illustrations 5

Data-driven Gradient Mapping Adjustment Method for Color Opt...

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5th international conference on Image, Video processing, and Artificial Intelligence, IVPAI 2023

作者： Xie, Hanna Chen, Siyi Li, Jie School of Information Science and Technology North China University of Technology China

ISBN: (纸本)9781510674660

In recent years, gradient mapping based on colormaps has gained popularity in image editing and digital painting. However, this mapping often results in the loss of detailed information in the image. In response to this challenge, we propose a method aimed at revealing hidden details without deviating from the original color tendencies of the gradient colormaps. To achieve this goal, we employ a non-linear gradient mapping combined with a generative adversarial network to iteratively adjust the colormap parameters guided by changes in the color space of the image. through a series of experiments, we demonstrate that our method not only fulfills the desired objectives but also exhibits outstanding performance in color representation. © 2024 SPIE. All rights reserved.

关键词： Generative adversarial networks

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Machine Learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional networks 5

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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5th IEEE international conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Radhamahendran, S. Dogra, Akriti Mendhe, Dinesh Tilak Babu, S.B.G. Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology and Advanced Studies School of Life Sciences Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India State University of New Jersey Computer and Information Research Scientist Office of Research Computing Rutgers New BrunswickNJ United States Aditya Engineering College Department of ECE Surampalem India Vellore Institute of Technology School of Computer Science Engineering Tamil Nadu Vellore India UPUMS Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350351378

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. the interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. the atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize the model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. the results of our research suggest that Graph Convolutional networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins. © 2024 IEEE.

关键词： Proteins

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Endoscopic Image processing: Evaluating the Robustness of Deep Neural networks 5

Endoscopic Image Processing: Evaluating the Robustness of De...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Gireesh Babu, C.N. Babu, Kaparapu Satish Suneel, Sajja Rao, K.Srinivasa Muralitharan, Divya Aarthi, R. Bms Institute of Technology and Management Dept.of CSE/ISE Karnataka Bengaluru India Geetanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Hyderabad Telangana Dundigal500043 India K.S.R.M. College of Engineering Department of Ai&ml Andhra Pradesh Kadapa516003 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798331504403

the Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Indian Sign Language Alphanumeric Hand Gestures Recognition Using Deep Learning Techniques 5

Indian Sign Language Alphanumeric Hand Gestures Recognition ...

引用

5th international conference on Advanced Research in Computing, ICARC 2025

作者： Mahendran, Shakana Mahendran, Ramya Vavuniya Campus of the University of Jaffna Department of Physical Science Fas Vavuniya Sri Lanka EUS. Information Technology Center Trincomalee Campus Trincomalee Sri Lanka

ISBN: (纸本)9798331530983

Alphanumeric sign language recognition plays a vital role in enabling effective communication between individuals who are deaf and the general population. this research focuses on classifying alphanumeric hand gestures in Indian Sign Language using real-time image processing. the study collects diverse hand shape images captured under varying backgrounds and lighting conditions, making it suitable for real-world applications. In the preprocessing stage, images are cropped and resized to a standardized 128×128 pixel format to ensure uniformity in the dataset. A two-pronged approach is adopted to classify the collected images. Sequential and parallel CNNs are employed to capture spatial and channel information effectively. Additionally, a channel attention module enhances the model's ability to identify crucial channel information. the Deep Convolutional Neural Network (DCNN) employed in this study achieved remarkable accuracy, reaching 99.976%. this outcome demonstrates the superior performance of this approach compared to other classification methods, whether utilizing real-time or non-laboratory experimental data. this research contributes to developing an efficient, accurate, and cost-effective system for classifying alphanumeric signs in Indian Sign Language. It facilitates easy and fast communication between individuals who are deaf and those who are not, promoting inclusive interactions. © 2025 IEEE.

关键词： Convolutional neural networks

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Dynamic Optimization Deployment of Wireless sensor networks Based on TPWRLS Graph Construction and VFA 1

引用

5th international conference on Artificial Intelligence in China, AIC 2023

作者： Cui, Naiyu Gao, Hefei Wang, Wei Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin300387 China

ISBN: (数字)9789819975457

ISBN: (纸本)9789819975440

With the in-depth research and rapid development of wireless sensor networks (WSNs), dynamic optimization deployment technology has emerged as a research hotspot, aiming to improve the network monitoring effect and reduce the network deployment cost. this paper proposes a novel dynamic optimization deployment strategy for WSNs based on the Two Phase Weighted Regularized Least Square (TPWRLS) graph construction and Virtual Force Algorithm (VFA). Firstly, a mobile node selection strategy based on the TPWRLS graph construction is proposed to select nodes that hold higher values for movement. Secondly, a node movement strategy based on the VFA is proposed to move the selected nodes to appropriate positions, realizing dynamic optimization deployment with the coverage rate as the optimization objective. Simulation results demonstrate that the proposed strategy significantly enhances the coverage rate of WSNs and achieves dynamic optimization deployment of WSNs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： sensor nodes

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Node Deployment Strategy Based on Improved Particle Swarm Algorithm in three-dimensional Underwater sensor networks 5

Node Deployment Strategy Based on Improved Particle Swarm Al...

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2023 5th international conference on Wireless Communications and Smart Grid, ICWCSG 2023

作者： Dai, Dongping Yi, Jinwang School of Optoelectronics and Communication Engineering Xiamen University of Technology Xiamen361024 China

To enhance the monitoring quality of the underwater three-dimensional target area and address issues such as uneven node distribution, inadequate coverage, and poor connectivity in traditional deployment strategies, a new node deployment strategy based on grid deployment with a variable weight particle swarm algorithm has been proposed in this research paper. Its goal is to minimize the quantity of deployed nodes while maintaining the coverage and connectivity of the three-dimensional wireless sensor networks (UWSNs). Simulation results demonstrate that this deployment strategy leads to good performance of the sensor network, and our approach ensures maximum coverage with the same number of nodes. © Published under licence by IOP Publishing Ltd.

关键词： sensor nodes

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Fuzzy Cognitive Security Techniques for Data Aggregation in Capillary networks 5th

Fuzzy Cognitive Security Techniques for Data Aggregation in ...

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5th international conference on Computer and Communication Technologies, IC3T 2023

作者： Mallapur, Jayshree D. Balavalad, Kirankumar B. Bendigeri, Kirankumar Y. Kumbalavati, Santosh B. Sidnal, Nandini Department of Electronics and Communication Engineering Basaveshwar Engineering College Karnataka Bagalkot587103 India Software Engineering and IT Torrens University Ultimo Sydney Australia

ISBN: (纸本)9789819997039

IoT is a revolutionary area of computing technology that allows many different devices to work together over existing internet infrastructure. Capillary networks are the intermediate interface between the IoT devices and cellular e-nodes of the present IoT development, making sensor network nodes to connect and to make use of the capacity of cellular networks through the gateways. the global connectivity can also be enabled with short-range frequency and with the well-managed security and virtual services of the network. But biggest challenge they all face is security provisioning while data aggregation. Our paper has come with unique solution for security problem for data aggregation in capillary networks. We have proposed fuzzy cognitive security system, which considers few influential parameters for fuzzification to normalize the uncertainty and carrying out data aggregation. the proposed scheme shows that there is an increase in throughput and a decrease in the packet loss ratio. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： sensor nodes

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Research on the Application of Intelligent Algorithms in the Automation of Music Generation and Composition 5

Research on the Application of Intelligent Algorithms in the...

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5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Ye, Hanxiao Fuzhou University of International Studies and Trade Fuzhou China

ISBN: (纸本)9798331527662

this study explores recent advances in intelligent algorithms for the automation of music generation and arrangement, with a particular focus on the potential applications of Markov decision processes (MDP) and Deep Q-networks (DQN) in this area. We propose an innovative framework for music composition that uses MDPS to construct dynamic environments for music generation, where transition probabilities describe the likelihood of moving from one musical state to another, and reward functions quantify the degree of improvement in music quality. then DQN uses deep neural network to approximate the Q-value function, so as to effectively learn the optimal action strategy in the highdimensional music state space. In the process of interaction with the environment, it gradually learns how to choose the most appropriate action at a given music state to maximize the long-term cumulative reward. the framework combining the two can not only significantly improve the intelligence level of music creation. It could also push the music industry in a more personal and innovative direction. © 2024 IEEE.

关键词： Markov processes

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