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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20256 条记录，以下是741-750 订阅

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An enhanced LSTM-CRF based approach for Knowledge Integration based Sentiment Analysis 3

An enhanced LSTM-CRF based approach for Knowledge Integratio...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Balaram, Allam Silparaj, M. Sree, K. Likhitha Pramodh Kumar, V. Kumar, D. Vinay Venu Kumar Reddy, K. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9798331523893

Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This paper proposes an approach that will fuse syntactic and contextual knowledge to respond to these requirements. Steps for preprocessing include tokenization, stop word removal, lemmatization, followed by feature extraction using BERT embedding. In addressing high-dimensional output, the Relief algorithm is then used to extract the relevant features. The classification model utilizes a combination of Deep learning for sequence LSTM networks and Maximum entropy methods, i.e., Conditional Random Fields (CRF), and enables sequential dependency learning to give better accuracy. This application of data pertaining to time and context can effectively address subtle nuances such as sarcasm and irony and classify the text as positive, negative, or neutral. It brings several advancements with respect to traditional models, thus improving the understanding of emotion in text. © 2025 IEEE.

关键词： Emotion Recognition

来源：评论

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Deep learning Framework for Constellation Signal Classification in Underwater Optical Wireless Communication Systems 3

Deep Learning Framework for Constellation Signal Classificat...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Bisla, Nidhi Chauhan, Dushyant Singh Singh, Deepali Maurya, Arun Kumar, Naresh Garg, Amit Jindal Global Business School Op Jindal Global University Sonipat India Ajay Kumar Garg Engineering College Department of Ece Ghaziabad India Indian Institute of Technology Department of Ece Delhi India Ajay Kumar Garg Engineering College Department of EN Ghaziabad India

ISBN: (纸本)9798331528201

Underwater optical wireless communication (UOWC) is a nascent technology facilitating communication and data exchange among underwater sensors. However, it faces challenges like limited bandwidth and frequent transmission failures. Modulation classification plays a crucial role in optimising spectrum allocation, ensuring reliable communication, reducing interference, enhancing network security, and enabling diverse applications in UOWC. Deep learning (DL) has succeeded in various domains but has not been extensively explored in UOWC. This study uses a Convolutional Neural Network (CNN) to classify modulation techniques in UOWC. Raw modulated signals are converted into constellation signal images and fed into the CNN for training. The performance is evaluated on a CNN pre-trained model like SqueezeNet. Simulation results show that this method achieves better classification accuracy without selecting features manually. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

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Environmental Monitoring in Hospitals Using IoT and Deep learning 3

Environmental Monitoring in Hospitals Using IoT and Deep Lea...

引用

3rd international conference on Trends in Electrical, Electronics and Computer engineering, TEECCON 2024

作者： Das, Devikrishna Balabantaray, Sidhanta Kumar Mishra, Sambit Kumar Dash, Ritesh Gandhi Institute for Education and Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Reva University School of Eee Bangalore India

ISBN: (纸本)9798331505714

Hospital environmental monitoring is essential for maintaining regulatory compliance, preventing infections, and guaranteeing patient safety. Conventional techniques for keeping an eye on environmental factors including temperature, humidity, air quality, and noise levels are frequently time-consuming and labor-intensive. This research offers a novel strategy that improves the effectiveness and precision of environmental monitoring in hospital settings by utilizing deep learning and the Internet of Things (IoT). We suggest an all-inclusive system in which real-time data on several environmental parameters is continuously collected by IoT sensors. After that, this data is sent to a centralized cloud-based processing platform. The gathered data is analyzed using deep learning algorithms, which find patterns and abnormalities that can point to possible risks or inefficiencies. Predictive analytics is also integrated into the system to estimate future environmental conditions and give hospital management useful insights. © 2024 IEEE.

关键词： Environmental monitoring

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Research on Sports Competition Result Prediction System Based on Computer Big data Machine learning Model 3

Research on Sports Competition Result Prediction System Base...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lu, Xiaolong Li, Ming Ye, Chao Yunnan Vocational College of Sports Yunnan Kunming China Yunnan Youth Sports Training Center Yunnan Kunming China

ISBN: (纸本)9798350380989

Using big data theory to analyze athletes' performance data, this paper finds out the changing characteristics of athletes' performance. Using the principle of big data, we find the performance characteristics of athletes in the training process. The particle swarm optimization (PSO) method is used to predict the network parameters of support vector machine (SVM) online. The validity of this method is verified by testing on college students. This method can track athletes' performance characteristics more accurately, so as to build an excellent sports performance prediction model. The model can detect future trends from athletes' performance data in data mining and machine learning. This kind of system has a wide application prospect, and can greatly improve the efficiency and quality. The method combines machine learning theory with support vector machine network, which can effectively improve the prediction accuracy. This method has a very broad application prospect. © 2024 IEEE.

关键词： Athlete performance changing situation machine learning model prediction model

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Counterfactual Explanations for Sustainable Tourism Indicators 25th

Counterfactual Explanations for Sustainable Tourism Indicato...

引用

25th international conference on intelligent data engineering and automated learning

作者： Saugar, Javier Lancho, Carmen Cuesta, Marina Cano, Emilio L. Martin de Diego, Isaac Amado, Antonio Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain Dephimatica SL C Rios Rosas 44 3 C Madrid 28002 Spain

ISBN: (纸本)9783031777301;9783031777318

Counterfactual explanations are a well-known technique in Explainable Machine learning (XML) to provide simple explanations on complex Machine learning (ML) models. Through understandable "what if" scenarios, counterfactuals explore how changes in the input data affect the results of a model. This article leverages counterfactual explanations for sustainable tourism, an emerging approach within the tourism industry to mitigate the negative impacts of mass tourism on ecological systems and local communities. The proposed method analyzes the relationships between several Sustainable Tourism Indicators (STIs) defined for a specific tourist destination and its general sustainability assessment. It identifies the key changes needed in the STIs to achieve an improved global sustainability score. As a result, a decision-making system is offered for sustainable tourism management, which domain experts can use to make more informed decisions. The effectiveness of the proposed method is illustrated through its application to Mallorca, a popular Spanish tourist destination.

关键词： Counterfactuals Explainable Machine learning Explainable Artificial Intelligence Sustainable Tourism

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Unveiling the Intricacies of Cyber Harassment Intentions on Social Media Platforms 3

Unveiling the Intricacies of Cyber Harassment Intentions on ...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Shraya, Bejgum Reddy Dopathi, Hari Charan Anitha, G. Pattanaik, Balachandra Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Wallaga University Nekemte Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350381931

Cyberbullying is a severe issue that impacts teens and adults alike. Errors like hopelessness and suicide have resulted from it. The demand for material on social media platforms to be regulated is growing. In the work that follows, machine learning and natural language processing are used to construct a model to identify cyberbullying in text data. The program leverages data from two different types of cyberbullying: personal attacks on Wikipedia forums and tweets containing hate speech. To determine the optimal approach, three feature extraction techniques and four classifiers are examined. The method produces more than 80% accurate data from Wikipedia and more than 90% correct data from Twitter. © 2024 IEEE.

关键词： Machine learning

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Exploratory Analysis of Zero-Shot Lunar Crater Detection using SAM for Chandrayaan-2 Images 3

Exploratory Analysis of Zero-Shot Lunar Crater Detection usi...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Koranglekar, Kaustubh Prateek, K. Kumar, Shankar Biswas, Dheeraj Kumar Khanna, Nitin Department of Mechatronics Bhilai India Department of Electrical Engineering Bhilai India Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798331528201

This paper explores the application of a zero-shot lunar crater detection method based on the Segment Anything Model (SAM) for high-resolution Digital Terrain Model (DTM) images from ISRO's Chandrayaan-II mission. A pre-trained transformer-based instance segmentation model, SAM, is used to segment and identify lunar craters without requiring training data. The proposed method is capable of detecting craters in varying crater sizes and shapes, highlighting its potential for generalizable planetary feature detection. This work investigates the applicability of general-purpose deep learning models in planetary exploration and offers insights for efficient lunar surface analysis. © 2024 IEEE.

关键词： Lunar surface analysis

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Integrated intelligent Surveillance System Using Deep learning 3

Integrated Intelligent Surveillance System Using Deep Learni...

引用

3rd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Paul, Arya Paul, Sona Manikandan, A.R. Jose, Katharin P Sabarinath, M.S. Adi Shankara Institute of Engineering and Technology Department of Electronics and Communication Engineering Kerala India

ISBN: (纸本)9798350346169

Nowadays the surveillance systems are widely used to find out the suspicious events that have occurred. In conventional systems, there are a lot of limitations such as storage, bandwidth, cost, the short lifespan of hardware devices, loading issues, etc. We developed an intelligent surveillance system using deep learning in which the video footage of suspicious events is extracted. Transfer learning, a part of machine learning, is used for face detection which involves the reuse of a pre-trained model on new data. The abnormal activity detection is done using a multi person MoveNet Light model and the face detection is done using VGG16. The suspicious objects found in the frame (gun, mask) are identified using corner detection. This system offers less bandwidth, high security, effective storage, and reduced load-balancing issues. In this paper, we detailed the face detection, object detection and anomaly detection used in our system. © 2023 IEEE.

关键词： Deep learning

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Large Scale Image data Processing and Content Recognition Based on Deep learning 3

Large Scale Image Data Processing and Content Recognition Ba...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Wan, Ruoyan Wei, Xiuzhuo Ma, Rui Zhao, Huinan Wu, Feng Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

As the cornerstone of computer vision (CV), image recognition is of great importance. It not only profoundly affects daily life fields such as facial recognition, intelligent security, and medical image analysis, but also plays a key role in breakthroughs in high-tech fields such as autonomous driving technology, intelligent robots, and industrial inspection. With the vigorous rise of artificial intelligence (AI) technology, especially the rapid development of deep learning (DL) technology, image recognition has ushered in unprecedented changes. This article focuses on the latest applications of DL in the field of image recognition and proposes an innovative large-scale image data processing and content recognition algorithm. This algorithm effectively captures complex features in images by constructing deep neural network models, and continuously optimizes model parameters using massive training data, thereby improving recognition accuracy and significantly enhancing the algorithm's ability to understand image content. The experimental results show that the algorithm exhibits excellent performance in processing large-scale image datasets, with fast recognition speed and industry-leading recognition accuracy, laying a solid foundation for the further application of image recognition technology. © 2024 IEEE.

关键词： Medical robotics

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A Novel Approach of Image Caption Generator using Deep learning 3

A Novel Approach of Image Caption Generator using Deep Learn...

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Saini, DilipKumar Jang Bahadur Kumar, Sunil Joshi, Kapil Pathak, Abhishek Kumar Jain, Saksham Singh, Anupam Pimpri Chinchwad University Department of Computer Science and Engineering Pune412106 India Meerut India Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India Upes Dehradun India Meerut India Graphic Era Hill University Department of Computer Science and Engineering dehradun India

ISBN: (纸本)9798350306989

Image caption generation is an emerging field of study for researchers that mainly focuses on developing systems that can generate captions of an image. In today's World, Image captioning is a very useful tool. Moreover, some systems use machine learning models such as deep learning models which include CNNs and RNNs models used to analyze images and generate captions. There are some late growths in caption generation that have focused on transfer learning, reinforcement learning, and multimodal approaches. The proposed system has 5 phases which are data cleaning, extraction, layering, training, and testing. The proposed model is tested on the recent Flicker8k_dataset for image caption generation and it is implemented using Python software. © 2023 IEEE.

关键词： Long short-term memory

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