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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Identification of Sign Language Alphabets on Static Images Using Deep learning 6

Identification of Sign Language Alphabets on Static Images U...

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6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Jayasingh, Bipin Bihari Mallick, Sumitra CVR College of Engineering IT Department Telangana Hyderabad India Guru Nanak Institute of Technical Campus Department of Computer Science Telangana Hyderabad India

ISBN: (纸本)9798350306613

Deaf and mute individuals often rely on sign language interpreters to facilitate communication. However, finding proficient interpreters can be challenging and costly. To address these challenges, a computerized interpreter could serve as a reliable and cost-effective alternative. Such a system would be capable of translating the sign language into plain text or audio in real-time, aiding in seamless communication. This paper integrates computer vision and employs deep learning techniques such as Convolutional Neural Networks (CNNs) to facilitate the real-time translation of sign language. These models quickly translate the user's sign into text by using computer vision to quickly record the user's sign. Through image processing, Convolutional Neural Networks facilitate the classification and prediction of various sign symbols. This integration of technology enables a seamless and instantaneous translation of sign language into textual form, promoting enhanced communication accessibility. By leveraging more amounts of data, deep learning algorithms aim to achieve high accuracy and generate predictions. The validation accuracy increased to 95.2% after 43 epochs and the validation loss dropped to 0.19. This demonstrates that the model was able to distinguish between Sign Language Recognition (SLR) and non-SLR photos with high accuracy and minimal error rates. The research intends to develop an automated system that can precisely recognize and translate sign language motions into written text by integrating deep learning and computer vision. © 2024 IEEE.

关键词： computer vision data learning deep learning gestures hearing impairments image processing motion detection neural networks sign language recognition textual translation

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Multi-View Human Action Recognition using Adaptive Optimization Algorithm with Skeleton Based Graph Neural Networks 2

Multi-View Human Action Recognition using Adaptive Optimizat...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Srilakshmi, N. Radha, N. Psgr Krishnammal College for Women Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350306415

Recognizing human actions from multi-viewpoint video data is a complex task with applications in various fields, including surveillance, robotics, and human-computer interaction. An innovative approach is presented in this study to Multi-View Human Action Recognition (MV-HAR) using an Adaptive Optimization Algorithm (AOA) combined with a Skeleton-based Graph Neural Network (SGNN). The proposed architecture aims to leverage the complementary information from multiple viewpoints while effectively capturing the temporal and spatial dependencies inherent in human actions. The pre-processing pipeline aligns and fuses skeleton data extracted from different viewpoints, producing a coherent representation of the action across cameras. Each skeleton sequence is transformed into a graph structure, where joints represent nodes and edges encapsulate the relationships between joints over time. This sequence of graphs is then processed by the SGNN, which learns to capture the evolving dynamics of the action through multiple graph convolutional layers. On benchmark datasets, the proposed approach proves effective with an accuracy of 96.8%. © 2024 IEEE.

关键词： Adaptive Optimization Algorithm Deep learning Multi-View Human Action Recognition Skeleton-based Graph Neural Network Temporal and Spatial Dependencies

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Rice Leaf Disease Detection for Precision Agriculture 10

Rice Leaf Disease Detection for Precision Agriculture

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10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Bakshi, Manan Chugh, Priyal Arulalan, V. Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350353068

Rice being crucial for sustaining over half of the global population, is recognized as one of the foremost plants in agriculture worldwide. The presence of diseases affecting rice plants can significantly influence both the quantity and quality of the harvest, sometimes resulting in crop losses ranging from 30 to 60%. This work focuses on automating the recognition and categorization of common rice leaf diseases using deep learning, particularly with respect to the DenseNet architecture. Preprocessing methods, the deployment of DenseNet for disease diagnosis, and the gathering of an extensive dataset are all part of this work. The efficacy of the model is demonstrated by the high recall, accuracy, and precision rates of the experimental data. This work presents the effectiveness of DenseNet, explores the implications of the results for precision agriculture, and identifies future directions for deep learning-based plant disease detection research © 2024 IEEE.

关键词： Precision agriculture

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Zenvgg1X: An Advance Hybrid Framework based on Gene Sequencing for Analysis and Prediction of Lung Cancers using KNN-GNN and Custom VGG19

Zenvgg1X: An Advance Hybrid Framework based on Gene Sequenci...

引用

2024 IEEE international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Pandit, Kalpa Chatterjee, Ahan Chowdhury, Debtan Chatterjee, Debraj Techno International New Town Dept of Artificial Intelligence and Machine Learning Kolkata India Techno International New Town Dept of Computer Science Engineering Kolkata India

ISBN: (纸本)9798350390759

Lung cancer affects about 238,340 people annually, of which men account for 117,550 cases whereas women have 120,790. Lung cancer causes roughly 127,070 deaths every year. The survival rate for lung cancer is only 56 percent if it can be detected within the lungs;however, it drops to just 5 percentage where there are distant tumors. As few as 16 percentage of cases can be diagnosed if the cancer is identified early. The study created a new lung cancer detection framework, Zenvgg1X a diagnosis and predictive framework combining VGG19, which is a deep convolutional neural network (CNN), and K-Nearest Neighbors - Graph Neural Network (KNN-GNN) hybrid. The combination of these two allows analyzing both images and texts, thus increasing the success rate of lung cancer detection. By working with medical images for identification purposes only the VGG19 model that is well recognized in terms of image classification analysis for lung cancer while KNN-GNN determine patterns from text related to diseases-which if you ask me, the combinations sometimes provide statistical outputs that are mindbogglingly high in accuracy such as 87.5 percent using only 30 percent of the data or more impressively reaching up to an astonishing figure around 98 percent with larger dataset. Further inclusion of Tp53 gene mutation data can enable early-stage prediction of lung cancer which may lead to improved survival rates. It goes without saying that this hybrid model represents a giant stride in medical diagnostics because it offers hope for what could eventually become a simple routine test for early diagnosis and treatment of lung cancer. © 2024 IEEE.

关键词： Lung cancer

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A Classification of Inflammatory Bowel Disease using Ensemble learning Model 1

A Classification of Inflammatory Bowel Disease using Ensembl...

引用

1st international conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sitaraman, Surendar Rama Adnan, Myasar Mundher Maharajan, K. Krishna Prakash, R. Dhilipkumar, R. Intel Corporation CA United States The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Academy of Research and Education School of Computing Kalasalingam Department of Computer Science and Engineering Krishnankoil India Sona College of Technology Department of Information Technology Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Inflammatory Bowel Disease (IBD) is an enduring, intermittent bowel disorder with unidentified mechanism and etiology. It typically contains Ulcerative colitis (UC) and Crohn's disease (CD) that are categorized by several bowel ulcers. An impedance phase displaces dynamic development, accumulating susceptibility to exterior pathogens and theoretically leading to neurotic symptoms which encompasses bowel blockade, intestinal tear and liver lesions, with high disability ratio. The existing models have more overfitting, and class imbalance problems. To overcome that, an Ensemble learning (EL) model such as Logistic Regression, Random Forest and Gaussian Naive Bayes is proposed in this research. Initially, the collected Gene Expression Series 106817 (GSE106817) dataset is preprocessed with Recurrent MultiArray Average (RMA) Normalization and Log2 data Transformation to eliminate the noises. Next, Recursive Feature Elimination (RFE) technique is used to reduce high-dimensional data for selecting optimal features;and finally, the proposed EL model gives a precise rate in detecting and classifying IBD as CD, UC and Healthy controls. The proposed EL model gives better results than the other compared existing methods Random Forest-Support Vector machine (RF-SVM) in the context of recall, accuracy, f1 score, and precision as 99.38%, 99.75%, 94.72%, and 97.94% respectively. © 2024 IEEE.

关键词： Contrastive learning

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Parallel Approaches to Accelerate Deep learning Processes Using Heterogeneous computing 1

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23rd international conference on Next Generation Wired/Wireless Networks and Systems (NEW2AN) / 16th conference on Internet of Things and Smart Spaces (RuSMART)

作者： Nasimov, Rashid Rakhimov, Mekhriddin Javliev, Shakhzod Abdullaeva, Malika Tashkent State Univ Econ Tashkent Uzbekistan Tashkent Univ Informat Technol Tashkent Uzbekistan

ISBN: (数字)9783031609978

ISBN: (纸本)9783031609961;9783031609978

In the current context, the rise of artificial intelligence (AI) emphasizes the need to expedite training procedures, especially when dealing with extensive data, particularly in deep learning. This research primarily aims to significantly improve the time efficiency of deep learning processes. While it's widely recognized that graphics processing units (GPUs) offer notably faster performance for specific data tasks compared to a computer's central processing unit (CPU), this study explores heterogeneous computing systems for situations where GPUs are unavailable. Here, we investigate strategies to achieve enhanced processing speed using advanced technologies. The study concludes by presenting comparative results from various approaches and providing important recommendations for future endeavors.

关键词： artificial intelligence deep learning heterogeneous computing systems OpenCL CUDA technology parallel processing

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Adaptive Service Function Chain Orchestration Algorithm Based on Deep Reinforcement learning 2

Adaptive Service Function Chain Orchestration Algorithm Base...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Zhang, Wenbo Zhang, Yangyang Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

In traditional networks, service function chains consist of multiple network service devices that data flows traverse from source to destination, which leads to unnecessary resource waste due to issues like fixed network topology and deployments. SDN/NFV breakthroughs in decoupled control and hardware virtualization provide server virtualization techniques that lift limitations of traditional network bottom layer resources. This enables possibilities for reducing link latency and resource overhead to achieve adaptive *** propose DeepSFC, a distributed self-learning service chain orchestration method based on DRL. After offline training, the intelligent agents apply online with minimal overhead. Without requiring expert knowledge, they can make orchestration decisions by periodically observing environmental monitoring information. Each agent only observes its direct neighbors rather than requiring global knowledge. Compared to state-of-the-art heuristic and DRL algorithms, DeepSFC significantly improves throughput on real-world network topology and traffic traces (11% and 15%), while each online decision takes less time (1 ms). © 2024 IEEE.

关键词： Intelligent agents

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'Liver Disease Risk Prediction Using Regularized Deep learning: A Novel Approach' 2

'Liver Disease Risk Prediction Using Regularized Deep Learni...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350390179

Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subjectivity and laborious amounts of work. It introduces a DNN-based model with L2 regularization to predict risk categorization of liver diseases based on five categories of categorizations: Healthy-Liver, Low-Risk, Moderate-Risk, High-Risk, and Severe-Liver-Disease. The model includes the hidden layers with rectified linear unit (ReLU) activation functions, batch normalization, and dropout for regularizing to achieve robust learning. The DNN was trained in a very comprehensive Kaggle dataset with many physiological and biochemical features and has achieved an impressive 94.07% classification accuracy. Healthcare professionals will be equipped with a significant resource to develop personalized treatment strategies facilitated by this investigation, which highlights the capabilities of deep learning methodologies in the automation of liver disease assessment. Subsequent research will focus on integrating additional data sources to improve the model's scalability and predictive precision, thus promoting the advancement of automated liver disease evaluation in clinical settings. © 2024 IEEE.

关键词： Prediction models

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Identifying High Influential Nodes in Social Networks Based on GCN 24

Identifying High Influential Nodes in Social Networks Based ...

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2024 international conference on machine learning, Pattern Recognition and Automation Engineering, MLPRAE 2024

作者： Yu, Bo Yang, Ruisi Xue, Liang Yue, Qiang Zhang, Yunfei School of Information Engineering Kunming University China Faculty of Science University of Malaya Malaysia

ISBN: (纸本)9798400709876

In the field of social computing, identifying highly influential users is of paramount theoretical and practical importance. Traditional methods often rely on centrality measures such as degree, closeness, betweenness, and K-shell within social networks. While straightforward, these approaches typically fall short due to their dependence on static topological structures. To address this shortcoming, we introduce a novel method that calculates users' potential influence based on social network topology and constructs feature-rich ego networks from historical data and network structure. Utilizing the robust feature extraction capabilities of Graph Convolutional Networks (GCNs), we developed a classifier named GCNA to identify highly influential users. This classifier is trained using the constructed ego networks as source data. Our model demonstrates superior performance over existing methods in two real-world social network datasets. By comprehensively leveraging both historical data and dynamic propagation mechanisms, this approach provides a more effective strategy for pinpointing highly influential users. © 2024 ACM.

关键词： Network topology

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Federated learning for Real-Time Decentralized Smile Detection in Virtual Reality Environments 2

Federated Learning for Real-Time Decentralized Smile Detecti...

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2024 international conference on Metaverse computing, Networking, and Application

作者： Khan, Salabat Ghani, Anwar Naqvi, Syed Shehreyar Ali Khan, Murad Ali Faseeh, Muhammad Kim, Do Hyeun Sharif, Muhammad Jeju Natl Univ Dept Comp Engn Big Data Res Ctr Jeju South Korea Jeju Natl Univ Dept Comp Engn Jeju South Korea

ISBN: (纸本)9798331516000;9798331515997

Facial expression detection is paramount in real-world applications, including patient monitoring, photo selection, and facial protection within the metaverse and virtual reality environments. Among various expressions, detecting smiles poses a significant challenge. Traditional methods relying on low-level face descriptors are being superseded by advancements leveraging Convolutional Neural Networks (CNNs) to enhance smile detection performance. In this research, we introduce and implement three CNN architectures tailored for smile detection from facial images. Our proposed model incorporates a fully convolutional neural network featuring two deep convolutional layers dedicated to smile detection. Additionally, pre-trained LeNet and DenseNet CNNs are employed for this task. The effectiveness of our approach is demonstrated through extensive evaluations of the Hromada dataset, encompassing images with and without smiles within the virtual reality environment. Moreover, we delve into detailed discussions on the practicality and efficacy of CNNs in facial expression detection, explicitly focusing on smile detection in real-time scenarios. Our experiments are conducted within a federated learning framework, enabling model inference and optimization for privacy preservation and scalability of the smile detection system in large-scale virtual reality applications. Through this decentralized approach, we address challenges related to real-time detection, model inference, and collaboration in the metaverse.

关键词： decentralized face smile detection federated learning convolutional neural network privacy preservation model inference and collaborative learning

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