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A weakly supervised multiple instance learning approach for classification of breast cancer HER-2 status using whole slide images 3

A weakly supervised multiple instance learning approach for ...

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3rd international conference on Biomedical and intelligent Systems, IC-BIS 2024

作者： Gao, Zheng Chen, Tiandong Yang, Bei School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China National Super computing Center In Zhengzhou Henan Zhengzhou450001 China Department of Pathology The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital Henan Zhengzhou450008 China

ISBN: (纸本)9781510681279

Breast cancer, as the most common malignant tumor in women, poses great harm to women's health. Among breast cancer patients, there is a relatively high proportion of HER2-positive cases, making the prediction of HER2 status extremely important. Currently, many deep learning methods based on Whole Slide Images (WSIs) have been proposed, but these methods still face the problem of imbalanced datasets, with significant differences in the number of samples in each class, causing the model to overfit categories with an advantage in quantity while ignoring minority categories. Therefore, to address the above issues, this paper proposes a weakly supervised Dual-tier Cluster-based Multiple Instance learning (DCMIL) framework for the classification of breast cancer images. This framework uses ResNet-101 as the backbone and optimizes the pseudo bag partitioning process by introducing clustering mechanisms in the DTFD algorithm. A series of experiments were conducted on the HER2 dataset. The results showed that our model outperforms the latest algorithms in terms of performance. © 2024 SPIE.

关键词： Diseases

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Research on Automatic Guided Vehicle Scheduling Strategies based on Artificial intelligent 3

Research on Automatic Guided Vehicle Scheduling Strategies b...

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3rd international conference on Control and intelligent Robotics, ICCIR 2023

作者： Wang, Chenke School of Intelligence science and technology University of Science and Technology Beijing China

ISBN: (纸本)9781510671867

Automatic guided vehicles have widely utilized in the transportation works and assist to move the objects into certain areas. Therefore, avoiding the conflict of these vehicles and determine the effective scheduling strategies are an essential component to improve the effectiveness and guarantee the safe of these vehicles. Existing scheduling strategies are concentrated to utilize the mathematical models or train a machine learning algorithm to dispose the scheduling for each vehicles. In this work, we utilize the artificial neural network to solve the scheduling issue and achieve the high effectiveness of moving vehicles. Initially, we order the vehicles with the principle of priority and get the minimum Euclid distance for these vehicles. Subsequently, an artificial neural network is trained to obtain the scheduling routes for each vehicles. From our extensive simulation results and comparison analysis, we can conclude our proposed model can effectively achieve the scheduling routes for each vehicles and avoid the conflict or congestion among guided vehicles with reasonable computation and response time. © 2023 SPIE.

关键词： Automatic guided vehicles

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A comprehensive Review: Cardiac Disease Detection using IoT based Wearable Sensor and Deep learning Techniques 3

A comprehensive Review: Cardiac Disease Detection using IoT ...

引用

3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Chitra, B. Ambeth Kumar, V.D. Anna University Panimalar Engineering College Department of Cse Chennai600123 India Panimalar Engineering College Department of Cse Chennai600123 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The clinical field is making huge measure of information that doctors can't unravel and utilize productively. Additionally, rule-based master frameworks are wasteful in settling convoluted clinical assignments or for making experiences utilizing Big data. The Deep learning has arisen as a more precise and powerful innovation in a wide scope of clinical issues like analysis, expectation, and intercession. Deep learning is a portrayal learning technique that comprises of layers that change information nonlinearly, subsequently, uncovering various leveled connections and designs. Wearable technologies can provide creative solutions to medical care problems. Certain Wearable Technology uses, like weight management or ongoing work tracking, are intended to assist people in avoiding sickness and maintaining their health. These wearable gadgets were being used for other purposes by heart patients and executives who are unwell. Wearable apps can have a direct effect on treatment direction. In this audit, Wearable Technology applications in medical services and Deep learning application papers that utilization organized information, and sign and imaging modalities from cardiology are concentrated exhaustively. The benefits and constraints of condition of-craftsmanship strategies Deep learning in Cardiology and wearable advances that likewise apply in medication by and large, while proposing specific bearings as the most practical for clinical use. © 2022 IEEE.

关键词： Decision making

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Network Intrusion Detection: A Study on Various learning Approaches 3

Network Intrusion Detection: A Study on Various Learning App...

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3rd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Sadhwani, Sapna Verma, Avani Muthalagu, Raja Pawar, Pranav Mothabhau BITS Pilani Dubai Campus Dept. of Computer Science Dubai United Arab Emirates

ISBN: (纸本)9798350338263

Internet of Things (IoT) is a comprehensive paradigm where millions of devices are connected to a network. These interconnected devices create a network of intelligent systems that exchange data without the need for any computer or human communication. The devices gather data that is important to humans and businesses. Standard high-end security solutions are ineffective for safeguarding an IoT system because IoT devices have limited storage and processing capability. Due to the proliferation of innovative attacks, network security is finding it difficult to identify breaches with good accuracy. As a result, it becomes necessary to provide smart security solutions that are portable, widely dispersed, and provide long term services. The monitoring of network traffic by an intrusion detection system (IDS), which protects against prospective intrusions and preserves the network's confidentiality, integrity, and availability, is one solution. However, IDS still has difficulties detecting intrusions and improving detection accuracy while lowering false alarm rates. When dealing with heterogeneous data of varied sizes, Machine learning (ML) and Deep learning (DL) have already demonstrated their importance. Many modern IDS are ML based models. In this paper, ML and DL learning models like Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XG Boost (XGB), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) are used and compared. The best algorithm amongst these is compared with the existing state-of-Art models. The dataset used is UNSW-NB 15 Train set and Test set. The metrics used for comparison are Accuracy (Ac), Recall (Rc), Precision (Pr), F1 score, Mean Squared Error (MSE), training time, prediction time and total time. RF performs better than all other algorithms with Train Ac of 95.98% and Test Ac of 97.69%. It also outperforms the existing state-of-Art models achieving the highest a

关键词： Internet of things

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Predicting Stock Price and Market Direction Using Statistical and LSTM Models 3rd

Predicting Stock Price and Market Direction Using Statistica...

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3rd international conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Gupta, Yogesh Saraswat, Amit BML Munjal University Gurugram India Faculty of Engineering Manipal University Jaipur Rajasthan India

ISBN: (纸本)9789819784288

Global stock markets reflect key shifts in national economies, attracting an excess of investors. The market movements are based on past market changes. Predicting the direction of the stock market is the most crucial factor influencing financial investors’ decisions to buy, hold or sell shares. Thus, investors are always worried about analysing and projecting long-term stock market patterns to maximise earnings. In this paper, various models have been used to potentially replicate the behaviour of the stock market, and a comparison has been made. The models included are Statistical Analysis, Regression Analysis, SVM and LSTM. This paper also examines the effect of Twitter sentiment on the stock market. The proposed approach is assessed using RELIANCE and RCOM share price data. The experimental results illustrate that the proposed approach using LSTM shows better results in comparison to the state-of-the-art method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

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Investigation of Death Due COVID19 Based on Tweeter dataSet’s Sentiment Analysis Using Deep learning 3rd

Investigation of Death Due COVID19 Based on Tweeter DataSet...

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3rd international conference on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Kumar, Anubhav Kaushik, Priyanka Tarun, Sashi Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali India

The coronavirus disease, or COVID19, characters "CO" stand for for the corona, "VI" as virus, & "D" for disease in the context of the word COVID. It describes an infectious disease br... 详细信息

ISBN: (纸本)9789819725076

The coronavirus disease, or COVID19, characters "CO" stand for for the corona, "VI" as virus, & "D" for disease in the context of the word COVID. It describes an infectious disease brought on by a recently found coronavirus causing disruption in every country across the world. In all, over 230 nations become affected by COVID-19, and more than 1,330,000,000 individuals are diagnosed globally. Of those, around 7 million individuals have die as a result of this deadly illness. thus, COVID19 has been declared a global epidemic. For this study COVID-19 social media data is used. In this paper, the extraction of Twitter data, initial data processing, evaluation of sentiment were thoroughly investigated with respect to world, USA and India for which tweeter data set is used taen from WHO dataport website. Alter LSTM is used to investigate relation between people sentiment and death due to COVID19. For alculating sentiments NLTK is used along with some other libraries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Deep learning for High-Speed Laryngeal Imaging Analysis 3

Deep Learning for High-Speed Laryngeal Imaging Analysis

引用

3rd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Naghibolhosseini, Maryam Yousef, Ahmed M Zayernouri, Mohsen Zacharias, Stephanie Rc Deliyski, Dimitar D Michigan State University Department of Communicative Sciences and Disorders East LansingMI United States Michigan State University Department of Mechanical Engineering Statistics and Probability East LansingMI United States Head and Neck Regenerative Medicine Program Mayo Clinic Scottsdale Department of Otolaryngology-Head and Neck Surgery PhoenixAZ United States

ISBN: (纸本)9798350338263

High-speed imaging of the larynx provides a valuable means for studying vocal folds function and vibratory behaviors. Using laryngeal high-speed videoendoscopy (HSV) with a flexible nasolaryngoscope, one can record the detailed vibratory movements of vocal folds during connected speech. This high-speed imaging tool enables us to study the normal function of the vocal folds and how this function can be disrupted due to the presence of voice disorders. In this work, HSV data were utilized during connected speech from subjects with normophonic voices (no voice disorders) and a neurological voice disorder. The data were obtained using a high-speed camera, coupled with a flexible endoscope, at 4,000 frames per second. Deep learning was used for the analysis of the big HSV dataset to extract the vibratory behaviors of the vocal folds. This deep-learning-based tool achieved high levels of accuracy for analysis of challenging HSV data in connected speech. This tool provides a computationally cost-effective and an accurate measurement approach that could help design more advanced voice assessment protocols in future. © 2023 IEEE.

关键词： High speed cameras

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AI-Infused Sales Prediction for Smart Stock Maintenance 3rd

AI-Infused Sales Prediction for Smart Stock Maintenance

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3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Mervin, R. Ramyadevi, R. Department of CSE Saveetha Engineering College Chennai India

ISBN: (纸本)9789819737444

The "AI-Infused Sales Prediction for Smart Stock Maintenance" project utilizes advanced machine-learning techniques to predict sales trends, providing valuable insights for effective business planning. By employing ensemble learning with diverse algorithms, the model optimizes predictions, contributing to enhanced decision-making in the retail domain. The project encompasses crucial steps such as data preprocessing, feature engineering, and the deployment of powerful regression models like Random Forest and XGBoost. Noteworthy innovations, including the creation of a ‘Price:Category’ feature and the use of label encoding and alphanumeric transformation, contribute to improved data processing. Visualizations of predicted sales ratios through dynamic charts offer a clear understanding of the forecasting results. Beyond refining traditional methods, the project sets the stage for future enhancements, such as real-time prediction and seamless integration with external data sources, paving the way for more user-friendly interfaces. This project represents a significant advancement in leveraging AI for strategic decision-making in the retail sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Predictive maintenance

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Exploiting Cross-scale Consistency for Object Detection in Aerial Images 3

Exploiting Cross-scale Consistency for Object Detection in A...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Xu, Jingtao Li, Yali Wang, Shengjin Tsinghua University Electronic Engineering Beijing100084 China

ISBN: (纸本)9781665459112

It is challenging for convolution neural networks (CNN) to handle aerial images with extremely small objects. At each layer of the CNN, the limited receptive field leads to the contradiction between learning detailed features of the small objects and the global features of the large objects. Moreover, it is difficult to learn the feature representation for small objects occupying quite a few pixels. In this letter, we exploit the cross-scale consistency to enhance the feature representation of the objects at variant scales. We devise a double-stream training pipeline to capture the correspondence between objects at different scales and improve the adaptation of receptive field to scale variation. To explicitly formulate the model adaptability to object scales, we propose a novel scale consistency loss to learn the scale-invariant feature representation for object detection. To verify the effectiveness of our method, extensive experiments are conducted on the VisDrone-DET dataset which has large variance in object scale and quite small objects. Our method achieves state-of-the-art performance, outperforming existing methods by about 2.50%. © 2022 IEEE.

关键词： Object detection

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Improved UAV-to-Ground Small Object Detection Algorithm Based on Yolov5s 3rd

Improved UAV-to-Ground Small Object Detection Algorithm Base...

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3rd international conference on Machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Luo, Ling Wu, Zhang Han, Wei Sun, Xuefei Bai, Tingting Army Engineering University of PLA Jiangsu Nanjing210001 China

ISBN: (纸本)9789819624676

Small objects make up a high proportion of Unmanned aerial vehicle (UAV) images, but the existing detection algorithms have problems such as low detection accuracy and high leakage rate. To improve small object detection, an improved YOLOv5s model is proposed in this paper. In the Neck Network of the YOLOv5s model, a channel attention module attention (CBAM) mechanism is added. By adding CBAM after each CSP2_1 module and before the convolution operation, the model can automatically ignore non-critical information and focus highly on key features in the image. The improved YOLOv5s model was evaluated on the VisDrone2019-DET dataset and its performance was compared with YOLOv5s and YOLOv8s. The YOLOv5s-CBAM model has improved mAP_0.5 by 2.2% and mAP_0.5:0.95 by 1.2%. The loss convergence speed is faster and the stability is better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object tracking

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