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

共 35345 条记录，以下是71-80 订阅

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Multi-Label Classification of CS Papers Using Natural Language Processing Models 22

Multi-Label Classification of CS Papers Using Natural Langua...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Das, Amit Rahgouy, Mostafa Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task. © 2023 IEEE.

关键词： BERT Deep Learning LSTM Machine learning multi-label classification

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning 3

A Comprehensive Analysis on Skin Cancer Classification Using...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Renu, Israt Zerin Haque, Md. Momenul Paul, Subrata Kumer Mou, Mahmuda Shirin Rahman, Md Nahid Gupta, Sudipta Sen Paul, Rakhi Rani Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Qadirabad Dept. of Computer Science and Engineering Dayarampur Natore6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Skin cancer, a prevalent and potentially life-threatening disease, necessitates accurate and timely diagnosis for effective treatment. In addressing this critical healthcare challenge, this research proposes a novel approach to skin cancer classification. The study identifies the need for enhanced diagnostic accuracy and introduces a solution by integrating the efficiency of transfer learning, specifically utilizing the EfficientNet V2 architecture. The proposed methodology leverages pre-trained convolutional neural networks (CNNs) to extract high-level features from dermatological images, which are subsequently input into a sophisticated classifier, forming a robust framework for precise skin cancer classification. The research employs a diverse dataset comprising 3297 images sourced from the publicly available Skin Cancer ISIC archive dataset, ensuring comprehensive coverage of benign and malignant skin lesions. During the training phase, the proposed EfficientNet V2 transfer learning approach outperforms conventional methods, achieving an impressive accuracy of 84%, surpassing both Inception V3 (82%) and a generic CNN model (81%). Interpretability analysis is conducted to elucidate the decision-making process of the EfficientNet V2 model, providing insights into the key features influencing its predictions. This transparency is crucial for building trust in the model's recommendations and facilitating its seamless integration into clinical workflows. The EfficientNet V2 transfer learning approach emerges as a promising and effective solution for advancing skin cancer classification systems, contributing significantly to the ongoing efforts to improve diagnostic accuracy in dermatology. © 2024 IEEE.

关键词： Decision making

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Towards the Diagnosis of Heart Disease Using an Ensemble Learning Approach 22

Towards the Diagnosis of Heart Disease Using an Ensemble Lea...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Xu, Liangliang Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

Heart disease is one of the leading causes of death worldwide. Understanding the presence of heart disease is crucial for timely intervention and effective management. However, it is still a challenging task to accurately diagnose and treat heart disease. In this paper, we propose an ensemble machine learning model that combines the predictive power of three state-of-the-art machine learning algorithms: BERT (Bidirectional Encoder Representations from Transformers), FT-transformer, and XG-Boost. Through extensive training and evaluation, we assess the performance of our model using established metrics such as accuracy, precision, recall, F1-score, and receiver operating char-acteristic (ROC) curve analysis. The results obtained demonstrate the effectiveness and validity of our machine learning system in predicting heart disease accurately. This paper presents a promising approach that can assist healthcare professionals in making informed decisions and improving patient outcomes, enhances our understanding of heart disease patterns, and contributes to the development of effective diagnostic and treatment approaches. © 2023 IEEE.

关键词： Autoencoder BERT Heart Disease Machine Learning Transformer XG-Boost

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A Transformer-Based Model for Image Caption Generation with Memory Enhancement

A Transformer-Based Model for Image Caption Generation with ...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Ome, Shadika Afroze Azhar, Tanvir Asaduzzaman East Delta University Dept. of Computer Science & Engineering Chattogram Bangladesh Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798331535476

This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory Transformer is integrated for generating captions. The Vision Transformer splits images into fixed-size patches for extracting visual features, which are further processed by a memory-augmented encoder to integrate prior knowledge. It ensures key features are preserved and reused and the meshed decoder combines the text embedding with the outputs from each encoding layer to generate more contextually rich and accurate captions compared to conventional approaches. The model's performance is evaluated on the Flickr30K and the MSCOCO datasets, demonstrating that the Meshed Memory architecture in our approach improves both the image encoding and caption generation step. Our model outperforms the conventional 'CNN-LSTM' models and 'CNN-Transformer' methods, achieving a 1.02% improvement in METEOR and 1.31% in SPICE on the MSCOCO dataset. Additionally, on the Flickr30k dataset, our approach exhibits superior performance across most metrics, notably a 6.37% increase in the METEOR score. © 2024 IEEE.

关键词： Encoding (symbols)

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Enhancing Brain Tumor Classification Using Data-Efficient Image Transformer

Enhancing Brain Tumor Classification Using Data-Efficient Im...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Yang, Jiafeng Rusak, Alena Belozubov, Alexander ITMO University Software Engineering and Computer Technology Dept. Saint-Petersburg Russia

ISBN: (纸本)9798350395044

Medical image analysis is essential in the diagnosis and treatment of many diseases, including brain tumors. This work investigates the effectiveness of transformer models in the multiclass classification of brain tumor MRI images. We use a combination of three publicly available brain tumor datasets classified into four categories and appropriately modify existing transformer variants, including adjustments to patch size, number of categories, dropout, etc. The results indicate that, due to the condition of insufficient data volume, most transformer variants are still inferior to the benchmark model VGG-16. The DeiT model with knowledge distillation has significantly improved various indices on the test set, verifying its effectiveness in the classification of brain tumor MRI images under limited data. The article supports the application of transformer-based models to medical image analysis. Follow-up work will verify the generalization performance of the model on other datasets and explore methods to reduce the dependence of the transformer on the data, making it more applicable to tasks such as medical images classification. © 2024 IEEE.

关键词： Distillation

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Generative Adversarial Networks for Anomaly Detection: A Systematic Literature Review 4

Generative Adversarial Networks for Anomaly Detection: A Sys...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Noor, Shah Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Akram, Saira Ali, Fatima Dept. Computer Science Quetta Pakistan Pakistan Television Corporation Dept. of Electrical Engineering Superior University Lahore Pakistan Dept. of Software Engineering Quetta Pakistan Dept. of Computer Engineering Quetta Pakistan

ISBN: (纸本)9798350335316

In numerous research areas, anomaly identification is a major problem. Identifying and properly classifying data as anomalous is a challenging task that is resolved in various manners over the years. Different approaches like traditional, supervised, unsupervised, and semi-supervised are used for anomaly detection. In the literature, various machine learning-based anomaly detection algorithms exist. It is challenging to choose one anomaly detection algorithm from the several available algorithms because each algorithm puts forward its good detection performance. In recent years, generative adversarial networks have shown remarkable results for anomaly classification. This paper aims to represent a systematic literature review of generative adversarial network-based approaches for anomaly detection and highlights their pros. © 2023 IEEE.

关键词： Anomaly detection

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DC Fault Detection Using Time Derivative of High-Pass Filter Response in MTDC Systems 50

DC Fault Detection Using Time Derivative of High-Pass Filter...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Khan, Saad Ahmed Ukil, Abhisek Nair, Nirmal-Kumar C. Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

In the DC fault event, DC link capacitors immediately discharge a colossal current of high frequency. These large currents of high frequencies can instantly damage the components attached to the DC transmission system. Therefore, a rapid and robust DC fault detection topology is necessary, facilitating protection coordination in forthcoming Multi-Terminal Direct Current (MTDC) systems. This study introduces a novel fault detection technique using the time derivative of the High Pass Filter (HPF) response to identify fault currents in just a few microseconds (µs). This topology enables protection coordination for modern MTDC systems. Furthermore, this technique can identify different types of DC faults and remains unaffected by AC faults and load variation. © 2024 IEEE.

关键词： Chebyshev filters

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Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks using Reinforcement Learning 35

Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks usi...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Ara, Tarana Liscano, Ramiro Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350362244

This paper presents an innovative MAC scheduling algorithm to achieve energy savings in IEEE 802.15.4e TSCH sensor networks leveraging reinforcement learning to determine an optimal number of slots to keep active in order to maintain a specific packet delivery ratio for the network. The goal is to turn off slots in the 802.15.4e TSCH frame that are not highly utilized. Each node determines the slots that should be deactivated based on a threshold Q value. This scheduling strategy allows the nodes to conserve energy effectively by finding the optimal active timeslots for both transmission and reception. This algorithm we name it as RAST Reinforcement-based Slot utilization Technique) algorithm. Through extensive simulations and evaluations across various network configurations, the RAST algorithm reveals a significant packet delivery ratio improvement as compared to Orchestra utilizing less active slots. © 2024 IEEE.

关键词： Scheduling algorithms

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Non-Intrusive Alert For Drivers With Fatigue 4

Non-Intrusive Alert For Drivers With Fatigue

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Amrutha, K. Shreekumar, T. Resmi, K.R. Vineetha, K.R. Dept. of Computer Science Bangalore India Mite Dept of Computer Science and Engineering Mangalore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354218

Fatigue during driving significantly impairs a driver's reaction time, awareness, and decision-making abilities, leading to an increased risk of accidents. Recognising and mitigating driver fatigue is crucial for enhancing road safety and preventing potential collisions. This research introduces a non-intrusive method to reduce road accidents caused by driver fatigue. Recognising that accidents often occur due to lapses in driver alertness, the system utilises an affordable, high-quality webcam and dashcam. The approach involves extracting essential information through a multi-stage monitoring process to evaluate the driver's state thoroughly. The study includes a detailed analysis of webcam specifications, such as resolution, frame rate, and additional features. It also compares the traditional cascading technique with the advanced MediaPipe method, incorporating facial recognition, eye movement tracking, and head position analysis to detect driver fatigue accurately. © 2024 IEEE.

关键词： Eye movements

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