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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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Bento Packaging Activity recognition with Convolutional LSTM Using Autocorrelation Function and Majority Vote 1

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3rd international conference on Activity and Behavior Computing (ABC)

作者： Fujii, Atsuhiro Yoshida, Kazuki Shirai, Kiichi Murao, Kazuya Ritsumeikan Univ Grad Sch Informat Sci & Engn 1-1-1 Nojihigashi Kusatsu Shiga 5258577 Japan

ISBN: (数字)9789811903618

ISBN: (纸本)9789811903618;9789811903601

This paper reports Bento Packaging Activity recognition Challenge by team "RitsBen" held in the international conference on Activity and Behavior Computing (ABC 2021). Our approach used an autocorrelation function in the preprocessing to isolate the data since the dataset was given with repetitive activity. We then use a model that implements convolutional layers and LSTM. The final decision is made by majority vote using sigmoid predictions output from all body parts. The loss is calculated using BCEWithLogitsLoss for each body part. The evaluation results showed that average accuracy of 0.123 was achieved among subjects 1, 2, and 3 in leave-one-subject-out manner. However, we did not achieve high accuracy as the possibility that the extraction of repetitive actions was not correct.

关键词： pattern recognition

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An Explainable Artificial intelligence Strategy for Transparent Deep Learning in the Classification of Eye Diseases

An Explainable Artificial Intelligence Strategy for Transpar...

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2024 IEEE international conference on Computing, Applications and Systems, COMPAS 2024

作者： Shipra, Elmeeh Hasan Sazzadur Rahman, Md. Bangladesh University of Professionals Department of Ict Dhaka1216 Bangladesh Jahangirnagar University Institute of Information Technology Savar Dhaka1342 Bangladesh

ISBN: (纸本)9798331529765

Significant global health issues are posed by eye illnesses, especially in poor nations with inadequate financial and technological resources. Recent advances in pattern recognition for medical diagnosis have increased accuracy, but consistent health data collection and Artificial intelligence integration are critical for machine Learning model reliability. Explainable AI has made expert-level evaluation critical in sensitive areas. This study uses a hybrid approach to provide accurate categorization of eye diseases by utilizing deep learning assisted by Explainable Artificial intelligence. The key contributions include using a Sequential Convolutional Neural Network to categorize eye disease images, significantly boosting accuracy with pre-trained CNN models, and employing the Local Interpretable Model-Agnostic Explanations method for clear result interpretations. The study compares two experimental models, a black-box CNN model and a glass-box model using XAI algorithm LIME. While pre-trained models like Inception V3 and Xception showed training accuracies of 81.5% and 85.5% and validation accuracies of 78.8% and 73.8%, respectively, the sequence model obtained training accuracy of 80.48% and validation accuracy of 76.34%. When combined with LIME, these pre-trained models showed increased interpretability providing thorough insights into disease prediction. Future research can be done on improving the effectiveness of AI-driven ophthalmic decision-making by comparing pre-trained models and XAI algorithms and experimenting with different CNN architectures. © 2024 IEEE.

关键词： Convolutional neural networks

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SecureCut: Federated Gradient Boosting Decision Trees with Efficient machine Unlearning 27th

SecureCut: Federated Gradient Boosting Decision Trees with ...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Li, Bowen Zhang, Jian Li, Jie Wu, Chentao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Yancheng Blockchain Research Institute Yancheng China Blockchain Advanced Research Center Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai China

ISBN: (纸本)9783031781681

In response to legislation, companies are now mandated to honor the right to be forgotten by erasing user data. Consequently, it has become imperative to enable data removal in Vertical Federated Learning (VFL), where multiple parties provide private features for model training. In VFL, data removal, i.e., machine unlearning, requires removing specific features across all samples, ensuring privacy in federated learning. To address this challenge, we propose SecureCut, a novel Gradient Boosting Decision Tree (GBDT) framework that effectively enables both instance unlearning and feature unlearning without the need for retraining from scratch. Leveraging a robust GBDT structure, we enable effective data deletion while reducing degradation of model performance. Extensive experimental results on popular datasets demonstrate that our method achieves superior model utility and forgetfulness compared to the state-of-the-art methods. To the best of our knowledge, this is the first work that investigates machine unlearning in VFL scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Autonomous Vehicle Lane Detection Using Hyperbolic Neural Network with Bi-Directional Long Short-Term Memory 3

Autonomous Vehicle Lane Detection Using Hyperbolic Neural Ne...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Alzubaidi, Laith H. Almoussawi, Zainab Abed Jagadish, Sripelli Madhavan, Sowmya Alassedi, Zainab The Islamic University Najaf Iraq College of Islamic Science Ahl Al Bayt University Karbala Iraq School of Computer Science & Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350317022

Lane detection is a feature of autonomous vehicles and is an essential technology for environmental awareness. While many academics have successfully used Deep Learning (DL) for lane detection, their application cases were rather straightforward. When a lane is blocked, lost, or has other problems, the accuracy of lane recognition diminishes significantly. In this paper, to solve the above-mentioned problems, a Hyperbolic Neural Network (HNN) with a Bi-directional Long Short-Term Memory (B-LSTM) model is proposed for efficient autonomous vehicle lane detection. At first, hyperbolic space is used to identify the space changes in the frame, and the HNN is used to extract the spatial information in the frames. Then, finally, the B-LSTM is used to perform the sequential data to detect the autonomous vehicle lane in an effective way. The implemented HNN with the B-LSTM model demonstrates superior performance, achieving a Precision of 0.0311, Recall of 0.0253, F1-Score of 97.57%, and Accuracy of 96.74%. When compared to previous models like the Lightweight Attention Deep Neural Network (LA-DNN), ResNest and Self-attention Distillation (RS-Lane), and Convolutional Neural Network-Multi Layer Perceptron (CNN-MLP). © 2023 IEEE.

关键词： Autonomous Vehicle Deep Learning Hyperbolic Neural Network Long Short-Term Memory Spatial Information

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Face Mask Detection and Face recognition of Unmasked People in Organizations 3

Face Mask Detection and Face Recognition of Unmasked People ...

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Bennur, Sushma Bharadwaj, Dhanyaa Anand, Piyush Premananda, B.S. Rv College of Engineering Dept. of Ete Bengaluru India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

With the onset of the global pandemic of COVID-19 and with the infection rates on the rise, face masks have become a necessity in public spaces to prevent further spread of the virus. Therefore, it is necessary to develop a system which enforces the need to wear a facemask in public places and identify the ones who are without it. This paper describes a face mask detection and face recognition system. Masked faces are detected using Convolutional Neural Networks and individuals without a face mask are recognized using the LBPH algorithm. The models use OpenCV and TensorFlow and are trained and tested. For doing so, two sets of datasets are used, where the majority of the images are obtained from the Kaggle dataset. 20% of the images are considered for training purposes and other 80% is for testing. The experimental results have confirmed an average face mask detection and face recognition rate of 99%. Accuracy and loss plots for the proposed model are compared with the existing model. Testing for mask detection and facial recognition of masked and unmasked faces are done elaborately. © 2022 IEEE.

关键词： Local binary pattern

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Dual-Channel Decoupling Marching with Density Relationship Global Fusion for Few-shot Object Detection 3

Dual-Channel Decoupling Marching with Density Relationship G...

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3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Wang, Futian Yi, Yu Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China AHU-IAI Ai Joint Laboratory Anhui University Hefei China

ISBN: (纸本)9798350372052

Traditional deep learning approaches to object recognition typically require large datasets, and acquiring high-quality annotated data prohibitively expensive. Consequently, there has been a growing interest in few-shot object detection, which aims to classify and detect novel classification with only a limited number of samples. Existing methods do not fully exploit feature matching between support images and query images. We propose called the dual-channel decoupled matching with density relationship global fusion method for few-shot object detection. The dual-channel decoupled matching backbone network is specifically designed to mitigate the mutual interference between support features and query features, enabling the extraction of diverse information and improving the model's generalization capability. In order to perceive a more comprehensive feature information, the density relationship global fusion module utilizes feature information of different scales to obtain a more comprehensive feature representation. Extensive experiments conducted on the PASCAL VOC datasets validate the efficacy of our proposed method. © 2023 IEEE.

关键词： Object detection

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Satellite Communication System Resource Scheduling Algorithm Based on Artificial intelligence 3

Satellite Communication System Resource Scheduling Algorithm...

引用

3rd international conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Chen, Linli Zheng, Yufu School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China

With the rapid growth of social economy, the requirements for resource utilization are becoming higher and higher. How to effectively solve the problem of energy shortage has become a hot topic. However, there are still many deficiencies in the current resource scheduling algorithm. For example, for different types of satellites with different needs and requirements, their performance indicators are different, and in the satellite communication system, the interaction between multiple nodes needs to be considered. Therefore, this paper studies the artificial intelligence algorithm, and proposes an improved particle complementary scheduling strategy for a single user in view of the shortcomings of the existing particle swarm optimization (PSO) algorithm. On this basis, this paper applies this method to evaluate the system performance and verify its effectiveness. The verification results show that the utilization efficiency of the satellite communication system resource scheduling algorithm is above 93%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Satellite communication systems

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Combating Fake News in Social Media by Enabling Deep Convolutional Neural Network-Based Detection and Mitigation Strategies 3

Combating Fake News in Social Media by Enabling Deep Convolu...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sabera, G. Manoranjini, J. Aarif, Mohd Rao, Vuda Sreenivasa Anandaram, Harishchander Valavan, Ponni Ravindra College of Engineering for Women Department of Humanities and Sciences Kurnool India Chennai India Aligarh Muslim University Department of Commerce Uttar Pradesh Aligarh India Koneru Lakshmaiah Education Foundation Department of Cse Andhra Pradesh Vaddeswaram India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Coimbatore India K.Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350369083

The extensive transmission of fake information on social media platforms has grown to be a serious social problem that affects freedom of speech, democracy, and individual safety. The amount of fake information available and the constant evolution of approaches have rendered traditional ways of recognizing and controlling disinformation ineffective. As an outcome, this study offers a thorough framework that uses Deep Convolutional Neural Networks to detect and prevent the spread of false information in social media settings. This methodology relies heavily on the Fake and Real News Dataset that we obtained from Kaggle. This dataset is a broad range of news stories that have been classified as "real"or "fake". The DCNN models are able to differentiate between real and fake material using linguistic, semantic, and contextual aspects owing to the foundational dataset that use to train and fine-tune them. Through the testing and training. the effectiveness of the DCNN-driven methodology in precisely detecting false information on diverse social media channels. This framework focused mitigation plans that limit the spread and influence of false information, taking into account the knowledge gained during the detection stage. These tactics include proactive content filtering, user education programs, and algorithmic changes intended to promote the accuracy and integrity of online information. This research contributes to the ongoing initiatives to prevent misleading information in social media contexts by combining cutting-edge machine learning algorithms with proactive mitigation strategies. The proposed DCNN model achieved an accuracy of 98.98% promote a more robust and reliable digital ecosystem where genuine information predominates. © 2024 IEEE.

关键词： Algorithmic changes Content filtering Deep Convolutional Neural Networks Fake and Real News Dataset Mitigation strategies

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Enhancing Weather Forecast Accuracy Using Deep Learning Models 15th

Enhancing Weather Forecast Accuracy Using Deep Learning Mode...

引用

15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, Naween Dash, Yajnaseni Abraham, Ajith Ahmad, Shaik Irfan Saifullah, Shaik School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810824

The weather has a significant impact on a wide range of businesses and daily activities, including transportation, agriculture, and disaster relief. Planning for risk mitigation, allocating resources, and making every decision depends on accurate and precise weather pattern forecasts. In recent years, deep learning algorithms have become effective tools in improving the accuracy of weather forecasting. The model uses neural networks to uncover complex patterns from weather data. This study presents the use of deep learning models to predict climate change. This article highlights the shortcomings of traditional methods and the benefits of using deep learning, laying the foundation for our study of how deep learning can improve the accuracy of weather forecasting. Recurrent neural networks (RNN) and Convolutional neural networks (CNN) were used to predict weather patterns. The results indicate that RNN produces accurate results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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An Ensemble Technique for Classification of Oral Cancer by Using Histopathological Imaging 3rd

An Ensemble Technique for Classification of Oral Cancer by...

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Saikia, Trishna Dhamaniya, Ashutosh Gupta, Puneet Singh, Koushlendra Kumar Department of Computer Science and Engineering IIT Indore Indore India Machine Vision and Intelligence Lab NIT Jamshedpur Jamshedpur India

ISBN: (纸本)9789819743582

Oral cancer primarily affects the oral chamber within the head and neck area, and underscores the critical need for effective classification to initiate timely treatment. Deep learning (DL)-based computer-aided diagnostic (CAD) systems have demonstrated notable success in various applications, offering accurate and prompt diagnosis of oral squamous cell carcinomas (OSCC). One of the challenges in biomedical image classification is the acquisition of a sufficiently large training dataset. Transfer learning presents an efficient solution by extracting general features from natural image datasets and adapting them to new image datasets. In this study, we focus on classifying OSCC histopathology images to develop a productive DL-based CAD solution. To this end, we employ an average weighted ensemble technique, harnessing the strengths of deep learning-based models. To address the limitation of a small dataset, we fine-tune pre-trained deep CNN models, specifically VGG-16, VGG-19, MobileNet-V2, and Inception-V3, within our proposed method. Additionally, we conduct a comprehensive comparative analysis of these models, considering classification accuracy, precision, recall, and F-score as metrics. Our experimental findings reveal that VGG-19 consistently delivers substantially superior performance compared to the other fine-tuned deep CNN models. However, our proposed weighted ensemble technique outperforms all these deep CNN models, particularly when employing the RMSProp optimizer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ensemble Histopathological imaging Inception-V3 MobileNet-V2 OSCC Transfer learning VGG-16 VGG-19

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