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2022 International Conference on Optical and Photonic engineering, icOPEN 2022

作者： Yu, Changzhi Zhang, Lianxin Chen, Dongsheng Chen, Niannian The Institute of Machinery Manufacturing Technology China Academy of Engineering Physics Mianyang China School of Computer Science Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9781510661905

With the development of robot technology, automatic assembly technology based on industrial robots has been greatly applied. Considering the high-precision automatic grasping of heterogeneous products, robot grasping detection technology is put forward. Although the current method of using camera is relatively mature, it is greatly affected by the ambient light and the surface material of the object, resulting in the limited versatility. Since the 3D structured light method based on the laser scanning galvanometer has the characteristics of high speed and high accuracy, strong environmental adaptability and weak influence by the surface material of the object, the structured light equipment using laser scanning galvanometer was installed at the end of the robot to realize the grasping detection of the products. Through hand eye calibration, point cloud acquisition, point cloud processing, point cloud matching and other links, the point cloud collection and accurate matching of local features are realized. The experiment shows that the grasping detection accuracy is less than 0.32mm. It can not only achieve accurate grasping of different products, but also has the characteristics of strong adaptability to surface materials, environmental lighting, and high accuracy. © 2022 SPIE.

关键词： Galvanometers

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ChatGPT: Opportunities, Features and Future Prospects 7

ChatGPT: Opportunities, Features and Future Prospects

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Santhosh, R. Abinaya, M. Anusuya, V. Gowthami, D. Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India Ramco Institute of Technology Department of Computer Science and Engineering Tamil Nadu Rajapalayam India

ISBN: (纸本)9798350397284

ChatGPT is an AI-based Natural Language Generation (NLG) system developed by Microsoft that enables users to converse with virtual agents in a conversational manner. ChatGPT is based on the transformer-based architecture, a type of deep learning algorithm that can be used to generate text directly from raw inputs. ChatGPT is designed to enable natural conversations, with the goal of making it easier for people to interact with virtual agents in a conversational manner. ChatGPT has the potential to revolutionize the way people interact with virtual agents, as it can be used to generate natural language responses to userinput. The system could also be used to create virtual agents that can respond to user queries in a more natural manner, allowing for more efficient customer service interactions. In the future, ChatGPT could be used to create more sophisticated virtual agents that can understand user intent and provide more accurate responses. Additionally, ChatGPT could be used to generate more engaging content for virtual agents, such as stories, jokes, and conversations. This paper provides the power of ChatGPT with its various features, opportunities and impact of ChatGPT in various sectors. © 2023 IEEE.

关键词： Learning algorithms

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An Improved Imitation Learning Algorithm Based on Markov Chain

An Improved Imitation Learning Algorithm Based on Markov Cha...

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2024 IEEE International Conference on Control science and Systems engineering, ICCSSE 2024

作者： Yu, Jiyang Huang, Dan He, Xiaobao Han, Yong Wang, Jianyu Sun, Kaiwen Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China China Research and Development Academy of Machinery Equipment Chongqing University of Technology Beijing Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9798331517199

An improved imitation learning algorithm based on Markov chain is proposed, aiming to enhance the performance of autonomous driving in complex environments. Imitation learning learns task execution by observing expert behavior, while Markov chains are used to model state transition processes. The existing imitation learning algorithms have limited ability to handle complex tasks, especially in natural environments. The algorithm proposed in this article improves learning efficiency and autonomous control performance by introducing strategy iteration and reward function design for sudden obstacles. The core idea is to introduce Markov decision processes into traditional imitation learning frameworks to improve learning efficiency and performance. By acquiring a refined strategy, the objective is to mimic the agent's actions within the environment to closely resemble those of the expert. To achieve this, first train an initial strategy using expert examples. Then, use this strategy to generate new examples and update the strategy using these examples. This process iterates continuously until the strategy converges. The experimental results show that on the lunar environment autonomous driving datasets, the accuracy of our algorithm reaches 95% with a mean square error of 0.02, while the accuracy of the comparative algorithm is the highest at 90% with a mean square error of 0.05. This indicates that the improved algorithm can more accurately learn and imitate the driving behavior of experts. In addition, the performance of the algorithm is stable at different iteration times, indicating good convergence. © 2024 IEEE.

关键词： Contrastive Learning

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A multiscale circular geodesic model for image segmentation 15

A multiscale circular geodesic model for image segmentation

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15th International Conference on Graphics and Image Processing, ICGIP 2023

作者： Chen, Xin Liu, Li Zhou, ShuWang Wang, YaLin School of Mathematics and Statistics Qilu University of Technology Shandong Academy of Sciences China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510675018

It is a popular way to incorporate the active contour evolution scheme into the multiscale image decomposition and reconstruction procedure, so as to enhance the image segmentation accuracy. However, most of these models are carried out by the level set formulation which cost much computation time. In this paper, we propose a new image segmentation model that combines the circular geodesic model with an adaptive cut and the multiscale image processing. As a consequence, the proposed model can blend the benefits from both of the geodesic models and the multiscale image analysis method. Experimental results show that the proposed multiscale geodesic model indeed outperforms the circular geodesic model with an adaptive cut in solving the image segmentation problem in the presence of strong noise. © 2024 SPIE. All rights reserved.

关键词： Image segmentation

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Alzheimer Disease Prediction Using Recursive Feature Elimination and Artificial Neural Network

Alzheimer Disease Prediction Using Recursive Feature Elimina...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Balakrishnan, D. Mariappan, Umasree Sai Prudhvi, Bodempudi Saikumar, Patha Mohan Mahesh, Boggavarapu Mani Sai, Polisetty Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Krishnankoil626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering TamilNadu Virudhunagar626005 India

ISBN: (纸本)9798350300826

Alzheimer's Disease (AD) is a devastating neurodegenerative disorder that affects a significant portion of the aging population worldwide. Early prediction and detection of Alzheimer's disease are crucial for providing timely interventions, personalized treatment plans, and improved patient care. In recent years, machine learning techniques have shown promise in aiding the prediction of Alzheimer's disease by leveraging the analysis of diverse data sources, including demographic information, genetic markers, biomarkers, neuroimaging data, and cognitive assessments. In this paper, a combined technique called Recursive feature elimination with Artificial Neural Network (R-ANN) is introduced for fast processing and better prediction of AD. In the combined technique, the Recursive Feature Elimination (RFE) selects the most important features in the dataset for reducing the computational complexity and improves the disease prediction accuracy. RFE can handle non-linear relationships between features and the target variable by utilizing machine learning models capable of capturing complex interactions. This allows RFE to potentially capture non-linear dependencies between features and the disease being predicted. The selected features are used in ANN for prediction of AD. The empirical results prove that the accuracy, precision, recall and F-measure of proposed R-ANN is 4.71%, 4.6%, 3.41%and 3.37% better than Regularized Extreme Learning Machine (RELM)technique respectively for AD prediction. © 2023 IEEE.

关键词： Forecasting

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Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection 38

Suitable is the Best: Task-Oriented Knowledge Fusion in Vuln...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Jingjing Huang, Minhuan Nie, Yuanpin Li, Xiang Du, Qianjin Kong, Wei Deng, Huan Kuang, Xiaohui Institute of Systems Engineering Academy of Military Sciences PLA China Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Zhejiang Sci-Tech University China

Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code comprising multiple functional modules or diverse vulnerability subtypes. In this paper, we present a knowledge fusion-based vulnerability detection method (KF-GVD) that integrates specific vulnerability knowledge into the Graph Neural Network feature learning process. KF-GVD achieves accurate vulnerability detection across different functional modules of the Linux kernel and vulnerability subtypes without compromising general task performance. Extensive experiments demonstrate that KF-GVD outperforms SOTAs on function-level and statement-level vulnerability detection across various target tasks, with an average increase of 40.9% in precision and 26.1% in recall. Notably, KF-GVD discovered 9 undisclosed vulnerabilities when employing on C/C++ open-source projects without ground truth. © 2024 Neural information processing systems foundation. All rights reserved.

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SSAT: Scientific Storyboarding Framework Using Artificial Intelligence Techniques

SSAT: Scientific Storyboarding Framework Using Artificial In...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Hariharan, Nitin Deepak, Gerard Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Tamil Nadu Karaikudi India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819709748

This paper introduces a scientific storyboarding framework that uses artificial intelligence for learning and inferencing. It works by classifying a dataset of documents using the Bi-LSTM classifier to identify different instances. The results are then used to create metadata, which is further classified using deep belief networks. Next, the user's topic of interest is pre-processed, and an intermediate ontology model is generated. This model helps build knowledge by gathering information from the World Wide Web using recurrent neural networks. The framework then combines the results from two distinct classifiers—one from the dataset and the other from the user's query. These results are analyzed through explicit semantic analysis to select relevant entities. The monkey algorithm is utilized for optimization, resulting in an optimal solution set. To ensure semantic reasoning and entity regulation, Morisita index and explicit semantic analysis is applied. The proposed SSAT model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 95.58, 96.83, 96.205, and 96.2009397%, respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.05%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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Advancements in Cancer Disease Prediction Using Modified Multi Model Deep Neural Networks 3

Advancements in Cancer Disease Prediction Using Modified Mul...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Balakrishnan, D. Mariappan, Umasree Reddy, Sangam Sravya Nikitha, Ishnumolakala Likitha, S. Sirisha, S. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Krishnankoil626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering TamilNadu Virudhunagar626001 India

ISBN: (纸本)9798350302288

Cancer disease prediction is a critical task in healthcare that can significantly impact patient outcomes. This abstract presents a multimodal deep neural network (DNN) approach for cancer disease prediction, implemented in MATLAB. The proposed model leverages the power of DNNs to handle diverse types of data, such as medical images, genomic profiles, clinical records, and patient demographics. By integrating these multimodal data sources, the model aims to capture a comprehensive view of the disease, incorporating both genetic and phenotypic information. The MATLAB implementation allows for efficient data processing, model training, and evaluation. The performance of the proposed multimodal DNN model is assessed using benchmark datasets, and the results demonstrate its potential in accurate cancer disease prediction. Further research is needed to explore the interpretability of the model's predictions and expand its application to real-world clinical settings. Overall, this work contributes to advancing the field of cancer diagnostics by utilizing a multimodal DNN approach implemented in MATLAB for improved disease prediction. © 2023 IEEE.

关键词： Deep neural networks

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Earlier Detection of Plant Disease and Recommending Pesticides using Convolutional Neural Network

Earlier Detection of Plant Disease and Recommending Pesticid...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Balakrishnan, D. Mariappan, Umasree Kavya, Dyapa Sindhuja, Nerella Srija, Anugu Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798350315455

Plant disease detection systems may face challenges in accurately distinguishing between various diseases, especially when symptoms overlap or environmental factors introduce ambiguity, impacting the system's reliability. The agricultural innovation described involves an integrated system for early plant disease detection and targeted pesticide recommendations. It utilizes preprocessing, feature extraction, and advanced classification algorithms. Data from IoT sensors and high-resolution imaging devices are collected to monitor crop conditions and remove image noise using Gaussian smoothing. Feature extraction, particularly the Histogram Oriented Gradients method, is employed to identify disease symptoms and environmental parameters. Advanced machine learning algorithms, including Convolutional Neural Networks (CNN), are used to classify diseases and recommend pesticides based on disease type, severity, crop, and environmental conditions. This innovation enhances timely disease identification and promotes more precise and sustainable pesticide use, leading to increased agricultural productivity and reduced environmental impact. MATLAB is used for research evaluation, showing 7% superior performance compared to existing methodologies. © 2023 IEEE.

关键词： Agricultural Innovation Convolutional neural network Gaussian smoothing Histogram oriented gradients pesticides Plant disease

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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