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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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Transformative Trends in Reinforcement Learning: From Deep Q-Learning to Real-World applications 2

Transformative Trends in Reinforcement Learning: From Deep Q...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mani, Chandra Sharma, Tarun Kumar Yadav, Kanchan Gupta, Divya Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Miet Department of Bca UP Meerut India

ISBN: (纸本)9798350371055

Reinforcement studying (RL) is a type of machine studying that maximizes an agent's cumulative praise in an environment. As such, it has been more and more used to address a ramification of sequential selection-making issues successfully. Currently, there were most important breakthroughs within the RL space that have led to expanded research activity and the development of several novel techniques and packages. Those transformative traits include deep learning, model-free exploration, off-policy gaining knowledge of hierarchical techniques, meta-gaining knowledge of multi-agent training, deep reinforcement learning (DRL), and transfer learning. These new strategies to RL allow for the development of more efficient and powerful algorithms, which in flip allow more a hit education and alertness of reinforcement mastering marketers in lots of one of a kind domain names. Similarly, those additives of RL permit faster, more flexible, and stronger schooling for RL marketers. As a result, RL is rapidly turning into a part of many one of a kind areas of studies that require powerful choice-making systems, inclusive of robotics, herbal language processing, and healthcare. © 2024 IEEE.

关键词： Environment Healthcare Hierarchical Reinforcement Strategies

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Diabetes Mellitus Prediction and Classification Using Firefly Optimization Based Support Vector Machine 2

Diabetes Mellitus Prediction and Classification Using Firefl...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Chitradevi, B. Supriya Chandra, N. Subhash Chitradevi, T.N. Alabdeli, Haider Department of Computer Applications Trichy India Nitte Meenakshi Institute of Technology Dept of ISE Bengaluru India Cvr College of Engineering Department of Cse Telangana Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The Diabetes mellitus is the most dangerous disease all over the world and it is deadly disease determined by the rapid growth levels of the glucose in a blood. The Machine Learning (ML) approaches supports in the prediction and detection of the diabetes. This research aims to propose a Firefly Optimized-based Support Vector Machine (FSVM) for the prediction and classification of diabetes. The datasets utilized for diabetic prediction are PIMA Indian Diabetic dataset. The pre-processing is done by using the data normalization, cleaning as well as data splitting techniques. The Principal Component Analysis (PCA)-based feature selection approach is used for selecting the features in a acquired dataset. Finally, the ML-based algorithms are used for the classification of the diabetes disease. The effectiveness of proposed method is evaluated by PIMA dataset with the various performance metrices such as accuracy, precision, recall, and F1-score of values about 98.45%, 98.69%, 99.37%, and 98.25% respectively when compared to previous approaches such as Naïve Bayes, Integrated kernel as well as K-Nearest Neighbour. © 2024 IEEE.

关键词： Support vector machines

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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches 4

An Efficient Model for Early Detection of Maize Leaf Disease...

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

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. K.L.E.Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K.L.E.Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, Data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule 30

Effective Integration of Text Diffusion and Pre-Trained Lang...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ou, Yimin Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous diffusion models (CDMs) and pre-trained language models (PLMs). That is, the performance of diffusion models even degrades when combined with PLMs. To alleviate this issue, we propose to utilize a pre-trained decoder to convert the denoised embedding vectors into natural language instead of using the widely used rounding operation. In this way, CDMs can be more effectively combined with PLMs. Additionally, considering that existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation, we propose a linguistic easy-first schedule that incorporates the measure of word importance, conforming to easy-first-generation linguistic features and bringing about improved generation quality. Experiment results on the E2E dataset and five controllable tasks show that our approach can combine the merits of CDMs and PLMs, significantly outperforming other diffusion-based models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

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ReFuSeg: Regularized Multi-modal Fusion for Precise Brain Tumour Segmentation 9th

ReFuSeg: Regularized Multi-modal Fusion for Precise Brain T...

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9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Kasliwal, Aditya Sagaram, Sankarshanaa Srivastava, Laven Seth, Pratinav Khan, Adil Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031761591

Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for medical diagnosis and treatment planning. However, failure to acquire specific MRI imaging modalities can prevent applications from operating in critical situations, raising concerns about their reliability and overall trustworthiness. This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help address the challenges posed by artifacts in medical imagery due to data acquisition errors (such as patient motion) or a reconstruction algorithm’s inability to represent the anatomy while ensuring a trade-off in accuracy. Our proposed regularization module makes it robust to these scenarios and ensures the reliability of lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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Improving Implicit Discourse Relation Recognition with Semantics Confrontation 30

Improving Implicit Discourse Relation Recognition with Seman...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cai, Mingyang Yang, Zhen Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Implicit Discourse Relation Recognition (IDRR), which infers discourse logical relations without explicit connectives, is one of the most challenging tasks in natural language processing (NLP). Recently, pre-trained language models (PLMs) have yielded impressive results across numerous NLP tasks, but their performance still remains unsatisfactory in IDRR. We argue that prior studies have not fully harnessed the potential of PLMs, thereby resulting in a mixture of logical semantics, which determine the logical relations between discourse arguments, and general semantics, which encapsulate the non-logical contextual aspects (detailed in Sec.1). Such a mixture would inevitably compromise the logic reasoning ability of PLMs. Therefore, we propose a novel method that trains the PLMs through two semantics enhancers to implicitly differentiate logical and general semantics, ultimately achieving logical semantics enhancement. Due to the characteristic of PLM in word representation learning, these two semantics enhancers will inherently confront with each other, facilitating an augmentation of logical semantics by disentangling them from general semantics. The experimental results on PDTB 2.0 dataset show that the confrontation approach exceeds our baseline by 3.81% F1 score, and the effectiveness of the semantics confrontation method is validated by comprehensive ablation experiments. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information Processing 2024.

关键词： Sentiment analysis

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A comparative analysis of using Deep Learning and Machine Learning technologies for intrusion detection for effective network traffic analysis

A comparative analysis of using Deep Learning and Machine Le...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Divya S B S. Prince Mary Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai Department Of Computer Applications Mangalam College Of Engineering

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020–2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered.

关键词： Deep learning Performance evaluation Reviews Computational modeling Intrusion detection Telecommunication traffic Security

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Harris Hawks Optimization Based Feature Selection on High Dimensional data 2

Harris Hawks Optimization Based Feature Selection on High Di...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Jena, Suvendu Kumar Kumar, Gotte Ranjith Al-Fatlawy, Ramy Riad Ali, S.Rasheed Mansoor Aruna, T.M. Indira Gandhi Institute of Technology Department of Computer Science Engineering and Applications Odisha Sarang India School of Computer Science & Artificial Intelligence Sr University Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Jamal Mohamed College Affiliated to Bharathidasan University Department of Computer Science Tiruchirappalli India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350373110

In recent times, evolutionary algorithms have been demonstrating significant benefits in feature selection due to their simplicity and global search capability. However, most of the existing evolutionary algorithms are computationally intensive for high-dimensional data. In order to decrease computational costs, effective evaluation methods are crucial. In this paper, Harris Hawks Optimization (HHO) technique is proposed for effective feature selection on high dimensional data. Firstly, a Multi-Layer Perceptron (MLP) is used to form an integer optimization problem for efficient feature selection process. Then the proposed HHO method is applied for the process of feature selection. This method has three steps such as Initialization, solutions update and classification. The performance of proposed HHO algorithm is compared with the existing classifiers such as Binary Waterwheel Plant Algorithm (BWWP A) and Community detection based Genetic Algorithm (CDGAFS) and achieved 98.2% of accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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