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End-to-End Hyperspectral Image Classification Using Hybrid GCNN with SWIN Transformer 1

End-to-End Hyperspectral Image Classification Using Hybrid G...

1st IEEE International Conference on Ambient intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Ghotekar, Rahul Shaw, Kailash Rout, Minakhi Kiit University Department of Computer Science and Engineering Odisha India Department of Artificial Intelligence and Machine Learning Maharashtra Pune Lavale India

ISBN: (纸本)9798350316469

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional Neural Networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional machine learning and Deep learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets. © 2023 IEEE.

关键词： Deep learning Graph Convolution Neural Network Hybrid Model Hyperspectral Image Classification SWIN Transformer

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Review on Event Extraction for BioNLP with a Survey 2

Review on Event Extraction for BioNLP with a Survey

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Pattankar, Veena V Priyanga, P. Computer Science and Engineering Global Academy of Technology Bangalore India Artificial Intelligence and Machine Learning Global Academy of Technology Bangalore India

ISBN: (纸本)9781665475174

The scientific literature contains essential information connected to proteins, drugs, and symptoms. Researchers are extracting organized, brief, and understandable information from unstructured texts to keep up with the growing number of publications. Within the context of the biomedical area, this study investigates event extraction and natural language comprehension. We will build a flexible description of an event by first outlining several terminological methods. Second, we demonstrate event extraction, its challenges, and annotated corpora. Third, we investigate representative methodologies and provide an up-to-date analysis with performance discussion. We provide a taxonomy to assist academics in navigating the deluge of event extraction works. Fourth, we compare biomedical solutions to those in other domains to uncover fresh research opportunities and approaches. Finally, we discuss potential applications, explainability, and knowledge injection. © 2023 IEEE.

关键词： Semantics

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Pushing one pair of labels apart each time in multi-label learning: from single positive to full labels

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science China(Information sciences) 2025年第6期 268-285页

作者： Xiang LI Xinrui WANG Songcan CHEN MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics

In multi-label learning(MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be single positive multi-label learning(SPMLL), where only one positive label needs to be provided per sample. Existing SPMLL methods usually assume unknown labels as negatives, which inevitably introduces false negatives as noisy labels. More seriously, binary cross entropy(BCE) loss is often used for training, which is notoriously not robust to noisy labels. To mitigate this issue, we customize an objective function for SPMLL by pushing only one pair of labels apart each time to suppress the domination of negative labels, which is the main culprit of fitting noisy labels in SPMLL. To further combat such noisy labels, we explore the high-rankness of the label matrix, which can also push apart different labels. By directly extending from SPMLL to MLL with full labels, a unified loss applicable to both settings is derived. As a byproduct, the proposed loss can alleviate the imbalance inherent in MLL. Experiments on real datasets demonstrate that the proposed loss not only performs more robustly to noisy labels for SPMLL but also works well for full labels. Besides, we empirically discover that high-rankness can mitigate the dramatic performance drop in SPMLL. Most surprisingly, even without any regularization or fine-tuned label correction, only adopting our loss defeats state-of-the-art SPMLL methods on CUB, a dataset that severely lacks labels.

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Investigating Youtube's Educational Videos: Patterns, User Conduct and Suggestion Techniques

Investigating Youtube's Educational Videos: Patterns, User C...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sundar, Koushik Eugene Berna, I. Vijayakumar, R. Ambeth Kumar, V.D. Vijay, K. Citibank NA Jersey CityNJ United States Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Design Chennai India Mizoram University Department of Computer Engineering Mizoram Aizawl India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331522988

This research voyage drew us far into the world of instructional videos on YouTube, a platform that promotes learning and discovery. Our goal was to identify patterns and trends in instructional films so that we might improve the user experience by suggesting better content. We conducted an analysis of data that we directly gathered from YouTube using certain technologies. The varied lifespans of trending videos across nations were one fascinating discovery. Certain videos were more well-known for a longer period of time than others. We were captivated by the cultural diversity found in video lifespans. We also looked at likes, dislikes, views, and comments to find robust user involvement worldwide. Through engagement data, different countries demonstrated their preferences for expressing oneself. It was discovered that a video's popularity was greatly influenced by the timing of the content's release. Content producers can stay relevant by keeping in mind this correlation. Sentiment analysis also uncovered emotional overtones connected to video tags, offering perceptions into what appeals to viewers. We also identified the optimal time, based on user involvement and behavior, to watch instructional videos. Ultimately, we used recommendation algorithms to tailor the YouTube learning experience by fusing content-based and collaborative filtering. All things considered, clarifies the complex world of instructional content and offers advice to users and producers on this dynamic platform. Our research will continue to influence the direction of more interesting instructional content as YouTube develops. © 2024 IEEE.

关键词： Video analysis

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Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation

Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augme...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Wang, Letian Liu, Xianggen Lv, Jiancheng College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence China

ISBN: (纸本)9798891761148

We propose Label Creative Generation (LCG), a new paradigm in multi-label data augmentation. Beyond repeating data points with fixed labels, LCG creates new data by exploring innovative label combinations. Within LCG, we introduce Tail-Driven Conditional Augmentation (TDCA), combining tail-driven label sampling and label-conditioned text generation for balanced, consistent data augmentation. Our approach has demonstrated a 100.21% increase in PSP@1 across three datasets, successfully mitigating the long-tail effect in MLTC and markedly enhancing model performance. © 2024 Association for Computational Linguistics.

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Detection of Medicinal Plants Using machine learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Predicting Agricultural Crop Damage Caused by Unexpected Rainfall Using Deep learning

Predicting Agricultural Crop Damage Caused by Unexpected Rai...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Fulkar, Bhushan Patil, Pawan Srivastav, Gaurav Mahale, Promod Maharashtra Wardha India Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350375237

Efficient allocation of resources and timely agricultural interventions depend on the precise identification of crop loss at the field parcel level. Using recent data from 2018 to 2023, this study investigates the integration of deep learning techniques with real-time field photography to classify agricultural field parcels into those experiencing crop loss and those not. We build and assess deep learning models specifically for crop loss classification using a dataset from the Finnish Food Authority (FFA) that combines on-the-ground photos taken during field visits with comprehensive field parcel information. In this work, we use convolutional neural networks (CNNs) to examine the visual characteristics that we have extracted from field photos in order to identify the subtleties that may indicate crop health or possible loss. Our goal is to improve the model's generalization capabilities over a variety of crop types and agricultural landscapes by training it on a combination of field parcel data and corresponding photographs. © 2024 IEEE.

关键词： Image segmentation

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Hybrid Deep learning Cloud Intrusion Detection 3

Hybrid Deep Learning Cloud Intrusion Detection

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Karnan, Lokanayaki Mahalakshmi, S.B. Thirunavukkarasu, V. Christ University Department of Computer Science Karnataka Bangalore560029 India Coimbatore Institute of Technology Department of Artificial Intelligence & Machine Learning Tamil Nadu Coimbatore641014 India

ISBN: (纸本)9798350361186

The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. © 2024 IEEE.

关键词： Intrusion detection

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Leveraging Patient Flow Data to Reduce Hospital Readmissions: A Study Using Medical Dashboards 4

Leveraging Patient Flow Data to Reduce Hospital Readmissions...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Deshmukh, Santoshi Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Machine Learning Wardha India

ISBN: (纸本)9798331523923

Transparent reporting of patient outcomes is becoming more and more common as the inequalities in health outcomes amongst groups are being more widely acknowledged. In addition, documenting health outcomes over time across subgroups will be necessary for pay-for-equity programs like those put forth by the Centers for Medicare and Medicaid. One way to visualize data in the context of healthcare is through dashboards, which may be used to identify important differences in clinical outcomes, direct focused efforts toward quality improvement, and eventually enhance health equity. We assess every study that has documented the effective creation of a disparity dashboard in this viewpoint, and we also publish the information gathered and any unexpected repercussions that have been mentioned. We offer a framework for the systematic enhancement of equity via reward systems to lessen health inequalities and incentives for gathering and reporting health data. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these executives serve as the company's role models. The beliefs, vision, and leadership style of healthcare professionals greatly influence their behavior and performance. Competent leaders who inspire and empower their staff foster a culture of responsibility, creativity, and cooperation. They exhort healthcare professionals to put their patients' safety, satisfaction, and well-being first. Strategic planning, resource allocation, and decision-making are all components of good leadership that support the hospital's goal of offering the highest quality of care. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these ex

关键词： Resource allocation

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