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检索条件"机构=Dept. of Computer Science and Data Science"

共 71763 条记录，以下是491-500 订阅

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Extraction of Conditional Belief Bases and the System Z Ranking Model From Multilayer Perceptrons for Binary Classification 22

Extraction of Conditional Belief Bases and the System Z Rank...

引用

22nd International Workshop on Nonmonotonic Reasoning, NMR 2024

作者： Wilhelm, Marco Hahn, Alexander Kern-Isberner, Gabriele Dept. of Computer Science TU Dortmund University Dortmund Germany

We extract propositional conditional belief bases from multilayer perceptrons, a basic type of feedforward neural networks, and investigate the relation between these two prevalent formalisms from knowledge representation and reasoning (KRR) and machine learning (ML), respectively. The ultimate goal of our work is to imitate with the extracted belief base the main information flow in the original multilayer perceptron detached from specific input data. For this, we introduce a notion of sufficient (in)activators of neurons which reflect the most relevant connections within the multilayer perceptron that lead to the (in)activation of the subsequent neurons. While focusing on the binary multi-class classification task, we show that our approach produces consistent belief bases from which principled inferences can be drawn, for instance under System Z. In particular, no inferences are invented by the System Z ranking model that are not in accordance with the initial neural network. © 2024 Copyright for this paper by its authors.

关键词： Multilayer neural networks

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Minimizing Sorting Networks at the Sub-Comparator Level 25th

Minimizing Sorting Networks at the Sub-Comparator Level

引用

25th Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR 2024

作者： Cruz-Filipe, Luís Schneider-Kam, Peter Dept. Mathematics and Computer Science University of Southern Denmark Denmark

Sorting networks are sorting algorithms that execute a sequence of operations independently of the input. Since they can be implemented directly as circuits, sorting networks are easy to implement in hardware – but they are also used often in software to improve performance of base cases of standard recursive sorting algorithms. For this purpose, they are translated into machine-code instructions in a systematic way. Recently, a deep-learning system discovered better implementations than previously known of some sorting networks with up to 8 inputs. In this article, we show that all these examples are instances of a general pattern whereby some instructions are removed. We show that this removal can be done when a particular set of constraints on integers is satisfiable, and identify conditions where we can reduce this problem to propositional satisfiability. We systematically apply this general construction to improve the best-known implementations of sorting networks of size up to 128, which are the ones most commonly found in software implementations. © 2024, EasyChair. All rights reserved.

关键词： Deep learning

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Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN and Hybrid Models with Ultrasound Imaging

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN a...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Sharma, Sanchit Manavi, J. Vachana, U. Bhargavi, M.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges due to its varied clinical manifestations, which can lead to misdiagnosis or higher perforation rates stemming from atypical symptoms. This research investigates the use of deep learning, more especially Convolutional Neural Networks (CNNs), by utilizing ultrasonic imaging data, which is less expensive, non-invasive, and quicker than the other approaches now in use. Several variants are evaluated including a basic CNN and more advanced hybrid models such as CNN-Support Vector Regression, CNN-Transformer, and Hierarchical Deep CNN, to identify the most optimal model to significantly enhance diagnostic accuracy, for young patients facing this condition. The study's main goal is to develop a highly accurate and dependable diagnostic tool for evaluating the severity of pediatric appendicitis by differentiating between simple and complex cases and identifying whether surgery is required to inform treatment plans. Various challenges that need to be tackled are managing significant dataset imbalances, enhancing interpretability and transparency for real-world clinical settings, and ensuring the robustness and adaptability of all these models across various healthcare environments. © 2025 IEEE.

关键词： Pediatrics

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Enhancing Brain Tumor Identification through Convolutional Neural Networks and Transformer Models 15

Enhancing Brain Tumor Identification through Convolutional N...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Dudam, Nerajdattu Chato, Lina Motupalli, Nagamani Kashyap, Kristipati Thoyajaksha Sane, Madhusree Bollina, Mounika University of South Dakota Dept. Computer Science Vermillion United States

ISBN: (纸本)9798331507695

An automatic diagnosis of brain tumors based on Deep Learning (DL) is a promising research field to enhance the diagnosis and prognosis of brain tumors. This paper presents an exploration into the use of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) for brain tumor classification, specifically identifying glioma, meningioma, pituitary tumors, and no tumor from brain Magnetic Reasons Imaging (MRI) scans. We implemented different approaches, such as 1) developing a custom CNN model from scratch, 2) leveraging pre-trained models based on Transfer Learning (TL) (including VGG16 and ResNet50), 3) developing a custom ViT model from scratch, and 4) leveraging pre-trained model in vision transformers, such as ViT B16. A publicly available labeled dataset is used to develop and test our proposed classification Deep Learning models. Our findings reveal that both custom-trained and pre-trained CNN and ViT models achieve high accuracy. The model ViT B16 based on transferred learning achieves the best accuracy of 97.6%, which is slightly better than the CNN models. To enhance the accuracy of the prediction model and reduce the misclassifications, ensemble model based on the combination of ViT B16 and CNN model;this approach increases the accuracy to 98%. © 2025 IEEE.

关键词： Convolutional neural networks

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Handcrafted Feature Assisted Light-Weight Encoder Decoder Based Classifier for Yoga Posture Recognition 2

Handcrafted Feature Assisted Light-Weight Encoder Decoder Ba...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Sarma, Prasiddha Ibotombi Singh, S. Tezpur University Dept. of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350372847

The trend for the need for at-home workout regimens and the potential for technology to support the trend is rising as remote employment becomes more and more common. One such activity that has benefits like increased flexibility and stress alleviation is yoga. Presently, numerous self-guided yoga courses in the form of pictures and videos are available online. Consequently, there is an urgent requirement for a system that can recognize and evaluate the precision of yoga posture execution in these instructional materials. In the current scenario lightweight prediction models are very much essential because devices like smartphones, edge devices, and embedded systems that have limited processing power or battery life, lightweight models are the best option as they require less resources to train and operate. One particular approach of making the recognition system lightweight is the use of keypoint-based pose/action classifiers. In this paper, a lightweight classifier for yoga postures based on neural network architecture using MoveNet pose estimation technique is proposed. Our model is able to classify the yoga postures by learning the human joints information in a efficient manner. © 2024 IEEE.

关键词： Classification (of information)

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Cross Platform Multiplayer using JavaScript and UDP 15

Cross Platform Multiplayer using JavaScript and UDP

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Johnson, Nobin Jaison Monika, Polaki Dept. of Computer Science Dayananda Sagar University Bangalore India

ISBN: (纸本)9798350370249

This paper attempts to create a system for online multiplayer using concepts such as a game, devops, and backend development. It uses a JavaScript tech stack for multiplayer. This system is compatible with all platforms that can access the internet as the system is not restricted by OS structures and cross-platform communication. Online Multiplayer has been used in Video Games for players to join each other’s sessions and interact with each other. However, it has become popular in many other applications such as document editing, Live streaming, and video calling applications. Traditional online multiplayer systems rely on the use of sockets and web sockets. The proposed system for this application relies on backend development concepts and avoids using web sockets. The system is user-friendly and efficient. ©2024 IEEE.

关键词： Video streaming

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An In-dept. Exploration of Machine Learning Algorithms and Performance Evaluation Approaches for Personalized Diabetes Prediction

An In-Depth Exploration of Machine Learning Algorithms and P...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kaur, Inderdeep Ali, Aleem Chandigarh University Dept. of Computer Science and Engineering Kharar India

ISBN: (纸本)9798350376470

Diabetes is now recognised as a major worldwide health problem, demanding effective early detection and prediction measures to limit its repercussions. Machine learning algorithms have shown to be excellent tools in healthcare for creating prediction models. This paper delves into an extensive investigation of machine learning (ML) algorithms and methodologies for assessing their performance in the context of personalized diabetes prediction. Different machine learning models are rigorously explored and evaluated for how well they can customise forecasts to the unique characteristics of individual patients. The study also critically assesses several performance evaluation techniques in an effort to improve the accuracy and reliability of diabetes prediction algorithms. Furthermore, interpretability problems and class imbalance are addressed as challenges and limitations to the application of machine learning in the prediction of diabetes. This study aims to motivate researchers, facilitating a deeper understanding of disease prediction algorithms and conduct related research. © 2024 IEEE.

关键词： Prediction models

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Driver Drowsiness Alert System 1

Driver Drowsiness Alert System

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Kumar, Vinay Dwivedi, Aarav Vishwakarma, Aryan Gupta, Nidhi Sharda University Dept. of Computer Science Greater Noida India

ISBN: (纸本)9798350353648

A distracted driver poses a serious risk and leads to serious traffic accidents. This article presents a method using computer vision technology, specifically Python, OpenCV, and a DLib, to detect driver fatigue and warning issues. The system can measure sleepiness by analyzing facial expressions such as yawning, closed eyes, and head movements and can give audio warnings accordingly. Alerts range from mild drowsiness warnings to emergency contacts if the driver continues to shows drowsiness signs. The architecture of the system includes processing video from cameras and monitoring vital signs such as eye closing time and yawning frequency. In addition, the design of the system prioritizes the safety of the driver, helping to increase road safety and reduce accidents due to drowsy driving. © 2024 IEEE.

关键词： Face recognition

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Sentiment Analysis of Customer Reviews on Food Ordering Portals of Bangladesh using Natural Language Processing 6

Sentiment Analysis of Customer Reviews on Food Ordering Port...

引用

6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Deb, Priom Bhuiyan, Asibur Rahman Ahmed, Farhan Hossain, Md. Rakib Mahrin, Habiba Ahmed, Md Faisal Karim, Dewan Ziaul Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331505790

In recent years, the popularity of online food ordering services has surged, offering consumers a convenient way to order food from restaurants and have it delivered to their doorstep. During this period, HungryNaki and Foodpanda Bangladesh have been identified as key contributors to the growth and advancement of the online food delivery market. This study aims to anticipate the sentiments of Bangladeshi customers towards online meal ordering services, with a specific focus on Foodpanda Bangladesh and HungryNaki. We created a new dataset for this research, subjected it to preprocessing and employed six machine learning models and three deep neural network models. In the machine learning approach, the Random Forest Classifier demonstrated excellence in accuracy, precision, and recall, achieving an accuracy rate of 80.31%. On the other hand, the BERT Classifier performed effectively in the deep learning approach, reaching a peak accuracy of 89%. Despite challenges such as data ambiguity and imbalances in the dataset, our findings underscore the potential of the BERT model in sentiment analysis, offering valuable insights for future research in this field. © 2024 IEEE.

关键词： data accuracy

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EmoBlend Fusion: Leveraging Handcrafted and Deep Features for Emotion Detection 4

EmoBlend Fusion: Leveraging Handcrafted and Deep Features fo...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Anzum, Fahim Gavrilova, Marina L. University Of Calgary Dept. Of Computer Science CalgaryAB Canada

ISBN: (纸本)9798350315790

Correct detection of human emotions plays a vital role in human-machine communication and decision-making. Recent advancements in large language models (LLMs) have sparked research in opinion mining, emotion detection, and sentiment analysis. While features extracted from texts using pre-trained LLMs can capture patterns effectively, integrating domain-specific handcrafted features remains crucial for constructing predictive models using user-generated microblogs. This paper proposes EmoBlend Fusion, a hybrid emotion detection model incorporating handcrafted features and latent deep features using pre-trained embeddings extracted from tweets. While the handcrafted features are trained using an ensemble of random forest, XGBoost, and linear SVM classifiers, a new bidirectional convolutional neural network-based model is trained using the deep features. Model predictions are then fused through a weighted ensemble mechanism for classifying emotions into one of the six distinct classes: sadness, joy, love, anger, fear, and surprise. The experimental results demonstrate that the proposed model outperforms the state-of-the-art techniques, achieving the highest recall (0.94), precision (0.95), accuracy (0.94), and F1-score (0.94) on a benchmark Twitter emotion detection dataset. © 2024 IEEE.

关键词： Sentiment analysis

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