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检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

共 3023 条记录，以下是521-530 订阅

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Incorporating text and emoticon features in sarcasm and irony detection: A deep learning-based approach

Incorporating text and emoticon features in sarcasm and iron...

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data science and computer application (icdsca), ieee international conference on

作者： Xinjun Ma Tianning Li Zhida Jin College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China College of Computer Science and Engineering Northwest Normal University Lanzhou China

Emotions play a crucial role in social media interactions, and sarcasm and irony often convey complex emotions that can be challenging to detect accurately. This study focuses on automating the recognition of sarcasm and irony in Chinese through various computational methods using machine learning algorithms. This study utilizes both text and emoticon/emoji feature to improve detection accuracy. The study employs a combination of linguistic rules, word embeddings, and neural network models for detecting irony in Chinese microblog comments. The results highlight the advantages of deep learning techniques and pre-trained word embeddings in enhancing the accuracy of irony detection. Furthermore, the study demonstrates the viability of the proposed models by focusing on microblog posts related to social events. These contextualized experiments show promising results and pave the way for future studies to explore other domains and data sources. Overall, this study has made significant contributions to the field of irony recognition research and laid the groundwork for more complex models in the future, with practical implications for enhancing sentiment analysis in various fields.

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application of Coleoptera Elytra for Protection of Winged Drones

Application of Coleoptera Elytra for Protection of Winged Dr...

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data science and computer application (icdsca), ieee international conference on

作者： Daniel Bao Concordia International School Shanghai Shanghai China

Small UAVs are used in a multitude of contexts, including surveillance or transportation of goods. Fixed winged drones in certain environments risk damage to flight components, but no well-utilized solution has been created. Multirotor copters can use enveloping cages but a solution for winged drones is more complex. The purpose of this research is to determine the viability of using Coleoptera elytra as an inspiration for a protection mechanism. The general framework for conducting this research centered around fabricating numerous prototypes, improving on the shortcomings of the previous ones. Early prototypes centered more on individual mechanisms while later prototypes focused on integrating all components. Simulations and tests were conducted on each prototype to determine durability and flight characteristics. The elytra in most tests even with low impact-tolerance plastics showed effective protection capabilities. However, the unoptimized aerodynamic qualities of wing surfaces and the body and unoptimized weight prevented the drone from becoming airborne. This research shows promise in using beetle elytra as an inspiration for protection of winged drones. At a smaller scale, elytra are a viable option, but needs to be optimized more in terms of materials usage and shape and tested further with larger scale prototypes.

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Maintaining the Integrity of Online Exams using computer Vision and Image Processing 3

Maintaining the Integrity of Online Exams using Computer Vis...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Nanda Kumar, K. Guthikonda, Nandini Abdullah, Mohammad Adil Sagar Imambi, S. Mohan, V. Murali Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram Guntur India

ISBN: (纸本)9781665462167

Due to the growing popularity of online learning, there is a need for efficient methods to assure the validity of online tests, especially multiple-choice exams (MCQs). The application of computer vision and picture pre-processing techniques is one strategy that has shown potential in this situation. With the use of these methods, cheating may be identified and stopped by analysing photos of the test-taker and their surroundings. In the context of online MCQ tests, this article addresses the possible uses of computer vision and picture pre-processing, including the use of facial recognition algorithms and image segmentation to detect and extract particular items or characteristics. The employment of these strategies offers enormous potential for maintaining the integrity of online education, even if there are still issues to be resolved, such as privacy concerns and the requirement for precise and trustworthy algorithms. © 2023 ieee.

关键词： Image segmentation

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A Comparative Study of Anti-Spoofing Detection Based on Deep Learning 3

A Comparative Study of Anti-Spoofing Detection Based on Deep...

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3rd international conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2023

作者： Abdelouahed, Sabri My Hasnae, El Khoukhi Assia, Ennouni Abdellah, Aarab University Sidi Mohamed Ben Abdallah Faculty of Sciences Computer Science Department Morocco University Sidi Mohamed Ben Abdallah Faculty of Sciences Physics Department Morocco

ISBN: (纸本)9798350398366

In the developing field of biometrics, technology is improving our ability to automatically recognize persons and protect access. The face is an excellent option for an authentication application because most devices can be equipped with a camera. Fraudsters can falsify biometric data by using, for example, photos of faces instead of the real face. So, it is essential to confirm that the biometric is that of a real, live person. Face anti-spoofing is one of the methods to combat this type of fraud. The objective of this work is to propose a comparative study between 5 powerful deep learning architectures for anti-spoofing namely, DenseNet201, DenseNet169, VGG16, MiniVGG, InceptionV3, and ResNet50. We used in this study the ROSE-Youtu Face Liveness Detection dataset. Experimental results show the effectiveness of the use of deep learning and especially the MiniVGG architecture in terms of maximum accuracy and traditional performance evaluation measures. © 2023 ieee.

关键词： Face recognition

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A Comprehensive Study of Robotic Healthcare System 3

A Comprehensive Study of Robotic Healthcare System

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3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Sai, Sanam Geethika Akhil, Raparthy V.K.Sh. Sushma, V.Sasi Preetham, S.Harsha Prasad, K.Venkata Kumar Kannaiah, Sathish Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9781665464871

Health Care 4.0 envisions a smart and connected healthcare practices. The emerging Cyber Physical Systems (CPS) based robotic healthcare systems is the recent technological advancement that revolutionizes the healthcare industry. This study reports the innovative concepts and capabilities of the CPS-based robotic healthcare systems. The technology that empowers health care industry include, Artificial intelligence (AI), distributed computing and big data. Finally, this study discusses about the potential design and development challenges faced by CPS-based robotic healthcare systems. © 2023 ieee.

关键词： Telemedicine

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Natural Language Processing to Improve Optimal Customized Treatment in Clinical Decision Support Systems 3

Natural Language Processing to Improve Optimal Customized Tr...

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3rd ieee international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Malviya, Sachin Dubey, Shivendra Verma, Dinesh Kumar Sharma, Anubhav Nair, Rajit Chauhan, Pushpraj Singh Computer Science and Engineering Parul University of Engineering and Technology Parul University Gujrat Vadodara India Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna India Ies Institute of Technology and Management Ies University Bhopal Department of Computer Science and Engineering Madhya Pradesh 462044 India School of Computing Science & Engineering Vit Bhopal University Madhya Pradesh India Sagar Institute of Science and Technology Department of Computer Science and Engineering Madhya Pradesh Bhopal India

ISBN: (纸本)9798350343274

Through computational methods and natural language processing, sentiment analysis recognizes and classifies the sentiments conveyed by personal statements. The current effort aims to apply entity semantic analysis to construct a test case for a healthcare system for individualized medication. To assess if a patient's response to a cure, service, therapy, etc., is positive, negative, or neutral pharmaceutical data analysis is used. After a more thorough investigation of the patient surveys and an excellent diagnostic decision system, the gathered orientations were compared. To classify the pharmaceutical data analysis and contrast, sentiment polarity methods based on machine learning are also applied. Support vector machines (SVM), random forest classification, linear SVC, and multinomial NB are the most used analytic Sklearn techniques. It has been discovered that the SVM method achieves better accuracy than other algorithms. © 2023 ieee.

关键词： Support vector machines

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Object detection of double-sided copper laminates based on YOLOv5 3

Object detection of double-sided copper laminates based on Y...

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3rd ieee international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Wu, Qian Wang, Chengrui Han, Yu Kang, Qinhao Li, Jianglong Lu, Xinyao University of Technology School of Computer Science and Engineering Chongqing Chongqing400065 China School of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9781665472784

A YOLOv5-based object detection inventory algorithm was proposed to address the problem of low efficiency and high cost of inventorying the number of stacked compact double-sided laminates by manual means. The algorithm first collected images of laminates in a real factory environment and annotated them with the network;then constructed a double-sided copper clad laminate inspection dataset with a real application background and trained the dataset with the YOLOv5 object detection inventory algorithm to obtain a training model;finally, detected the number of laminates inventoried based on the validation set. The experimental data verified that the trained model could achieve 99% accuracy in detecting different numbers of sheets in stacked scenarios, which own more advantageous than manual inventory methods in terms of both detection accuracy and speed. The results show that the proposed YOLOv5-based object detection counting algorithm is reasonable, feasible and effective for counting stacked compact double-sided copper clad laminates. © 2023 ieee.

关键词： Object detection

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Optimization of random forest algorithm based on mixed sampling additional feature selection 3

Optimization of random forest algorithm based on mixed sampl...

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3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Cui, Haobo Xu, Hongmei Li, Jingxin Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798350331578

Because of the poor performance of the Random Forest algorithm in processing the classification of high-dimensional unbalanced data, a Hybrid Samping&Feature Selection Random Forest optimization strategy (Hybrid Samping&Feature Selection Random Forest (HFRF) is proposed in this paper. First, from the data level, the high-dimensional unbalanced data set is preprocessed by SMOTE algorithm combined with random undersampling to achieve balanced unbalanced data. At the same time, the clustering algorithm is combined with SMOTE algorithm to improve the processing ability of the algorithm for negative samples;On the algorithm level, through the Relief F algorithm, different weight values are given to the preprocessed high-dimensional data, irrelevant and redundant features are eliminated, and high-dimensional data is reduced for dimensionality;Finally, the weighted voting principle is used to further elevate the predictive performance of HFRF. The experimental results show that compared with the traditional algorithm, the proposed algorithm has higher indicators when dealing with high-dimensional unbalanced data, which proves that the HFRF proposed in this paper is The correctness of the algorithm and its effectiveness in improving the classification performance of high-dimensional unbalanced data. © 2023 ieee.

关键词： Feature Selection

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Analysis and Prediction of Road Accidents in Nepal by Grey System Theory 3

Analysis and Prediction of Road Accidents in Nepal by Grey S...

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3rd ieee international conference on computer Systems, ICCS 2023

作者： Khatiwada, Smriti Liu, Zhaohui Shandong University of Science and Technology College of Transportation Qingdao266590 China

ISBN: (纸本)9798350313666

Although the road safety status of Nepal has improved in recent years, it still falls among the countries with high fatality rates in traffic accidents. Statistics from the traffic police division of Nepal shows that an average of 2,500 people are killed and thousands are injured annually. Despite road traffic accidents being a major public health issue, there are limited studies that focus on probabilistic and statistical analysis of accident data. The application of statistical, probabilistic, and machine learning methodologies plays a pivotal role in forecasting time series, with their effectiveness contingent upon the precision, volume, and accuracy of the employed datasets. Realizing that part of the solution is to find an appropriate methodology to understand the actual trends in the collected data, the grey theory is hereby used to estimate the accident risks in Nepal. Grey theory has proven to be effective because of its quick, brief, and accurate predictions even for vague, incomplete, or imprecise data sets. Therefore, the grey one-order one variable model GM (1,1) is used in this paper as a precise technique to analyze the accident rate for the past decade and predict the next five years. For the available data sets, the efficiency of the constructed GM (1,1) model is compared with the exponential smoothing approach, which is one of the most popular and well-known forecasting techniques. The grey forecasting model is found to perform better with a mean relative simulation accuracy of 92.59%. The reasonable accuracy of the obtained model shows that the grey theory is an effective and efficient approach for predicting road traffic accidents in Nepal using limited available data. © 2023 ieee.

关键词： Forecasting

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The application of BP Neural Network Algorithm in the Performance Evaluation of College Teachers 3

The Application of BP Neural Network Algorithm in the Perfor...

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3rd international conference on Digital data Processing, DDP 2023

作者： Zhou, Yingli Sun, Jie Li, Guangpeng Liu, Lirong School of Cold Chain Logistics and Supply Chain Shandong Institute of Commerce and Technology China

ISBN: (纸本)9798350329018

With the promotion of the strategy of rejuvenating the country through science and education, the national government and colleges and universities have continuously promoted the emphasis and investment in the work performance of scientific research personnel, and the performance evaluation of university teachers has gradually become a social development trend. This paper mainly analyzes each university development pattern and each teacher performance appraisal situation, carries on the demonstrates to each performance rating, the field inspection, the quantitative analysis and the vector master close union and so on method verification, obtains the university teacher scientific research performance appraisal system, simultaneously proposes the BP neural network algorithm carries on the computation each appraisal index, establishes the application model under the BP neural network action, obtains the maximization result and its error minimum range. Based on the sample data collection and analysis, the effect of the algorithm and teacher performance evaluation is simulated. The effect of the BP model, the influence of the index of teachers' performance evaluation, and the optimization mode of teachers' comprehensive ability are investigated in the field. All the error problems in the algorithm process are improved in the example. © 2023 ieee.

关键词： Neural networks

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