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Enhanced U-Net with Transformer-Driven Encoder for Medical Image Segmentation

Enhanced U-Net with Transformer-Driven Encoder for Medical I...

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2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Palaniappan, Ramya Surendran, Suseendran Balakrishnan, Saranya Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India KGiSL Institute of Technology Department of Artificial Intelligence and Data science Coimbatore India

ISBN: (纸本)9798331540067

The automatic segmentation of tumours or lesions from magnetic resonance imaging (MRI) pictures is a critical but difficult task in clinical settings, sometimes necessitating laborious and time-consuming techniques. Deep learning has made a significant advancement in the medical imaging field in recent years. U-Net has emerged as the predominant model used for segmenting medical images. Although the standard U-Net framework has shown excellent performance in segmenting multimodal medical pictures, it has been noticed that it lags in certain aspects when dealing with tough datasets. We propose specific improvements to the state-of-the-art U-Net model. We have introduced attention block in the skipped connections to highlight relevant features. Additionally, we have incorporated transformers in the encoding path to capture temporal dependencies in medical images. In this research work, we have introduced an innovative architecture that has the potential to replace the U-Net model for segmenting regions of interest (ROI) in medical images. This study utilizes a total of 1760 samples obtained from the ISBI 2015 and PH2 medical repositories. The experimental findings have demonstrated that our proposed model has obtained highly encouraging results of 78.02 Jaccard coefficient and IoU of 65.14. In addition, we conducted tests and compared the performance of the proposed model with the classical U-Net model using the same dataset. As a result, we observed a relative improvement in the performance of both 2.36 and 1.85, respectively. The proposed technology will function as a sophisticated toolkit for radiologists to precisely detect and diagnose anomalous tumour cells. © 2024 IEEE.

关键词： Semantic Segmentation

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Robust Sign Language Recognition Leveraging Bi-LSTM with CNN 2

Robust Sign Language Recognition Leveraging Bi-LSTM with CNN

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Jansirani, S. Savitha, N. Kiruba Priyadharshini, V. Sruthi Sree, S.G. Jansons Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Jansons Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

People who are partially or completely deaf are considered hearing impaired or silent. Communication is the primary obstacle they encounter. Sign Language Recognition (SLR) model provides a mechanism to improve accessibility and inclusion by facilitating smooth communication, which is used to address this issue. Traditional SLR systems frequently use alternative designs, such as CNNs alone or regular Recurrent Neural Networks (RNNs). However, that may not be able to handle long-term dependencies in video data or interpret sequence data. This paper uses a model which improves sequential pattern recognition and outperforms conventional RNNs in continuous recognition by combining CNN for visual feature extraction from ISL video frames with BiLSTM to capture temporal relationships. © 2024 IEEE.

关键词： Recurrent neural networks

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Assessment of Heart-Attack Prediction using Fuzzy Rule Based System 8

Assessment of Heart-Attack Prediction using Fuzzy Rule Based...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saratkar, Saniya Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Kumar, Hemant Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442107 India

ISBN: (纸本)9798350391770

This paper evaluates the prediction of heart attacks with a fuzzy rule-based system (FRBS). Since heart disease is the world's leading cause of death, precise prediction models are crucial for prevention and early intervention. With its reputation for managing imprecise and ambiguous data, fuzzy logic presents a viable method for raising prediction accuracy in medical diagnostics. In order to capture the complexity and unpredictability of cardiovascular risk variables, fuzzy rules are used in this work to investigate the creation and assessment of a fuzzy rule-based system (FRBS) for heart attack prediction. The efficacy and dependability of the FRBS in identifying those at high risk of heart attack are shown via testing and comparison with conventional prediction models. The results highlight fuzzy logic's potential to improve cardiovascular predictive analytics. © 2024 IEEE.

关键词： Prediction models

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Small Object Detection Algorithm for Unmanned Aerial Vehicle Based on Multifaceted Feature Information Extraction and Fusion 8

Small Object Detection Algorithm for Unmanned Aerial Vehicle...

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8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： Yin, Shasha Ge, Yueran Tian, Lingling Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin China Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Tianjin China

ISBN: (纸本)9798350380286

Unmanned Aerial Vehicle (UAV) image object detection has been widely applied in security and disaster relief and other aspects. For the sake of solving the problem of many small objects, this paper proposes a Multifaceted feature information extraction and fusion algorithm (MFIEF). Firstly, the multi-information feature extraction convolution module (MFECM) is adopted, which retains more small object feature information and surrounding spatial information. Secondly, the optimized FPN structure (OFPN) is used, the fusion of horizontal and vertical information avoids the loss of information, and the addition of lightweight attention convolution module (LACM) reduces the number of parameters while improving attention to key object information. The experimental results on the VisDrone datasets demonstrate that the MFIEF algorithm achieves a obvious improvement in performance compared to the benchmark algorithm. Specifically, the mAP@0.5 value of MFIEF is 9.7 percentage points higher, while the mAP@0.5:0.95 value of MFIEF is 6.7 percentage points higher than the benchmark algorithm. These findings highlight the superior performance of MFIEF in object detection tasks on the VisDrone datasets. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Comprehensive Methodologies for Breast Cancer Classification: Leveraging XAI LIME, SHAP Bagging Boosting, and Diverse Single Classifiers 4

Comprehensive Methodologies for Breast Cancer Classification...

引用

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

作者： Avlani, Dhruvi T. Abhijna, M.B. Sai Disha, G.S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Information Science and Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence and & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350354218

Breast cancer is one of the most common causes for deaths owed to cancer. It is very crucial and necessary to detect cancer at early phases. There are various Machine Learning techniques and computational techniques available for the aim of diagnosis of breast cancer data. This paper offers a Machine Learning model to perform automated diagnosis for the classification of breast cancer. This method employs single classifier model and bagging boosting for feature selection. Also, four algorithms SVM, Decision Tree, KNN and Logistic Regression which gives the highest accuracy of 88%. The system was trailed on BCGENES dataset. The performance of the system is measured on the basis of accuracy and precision. The incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. The amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers. © 2024 IEEE.

关键词： Logistic regression

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Leveraging Support Vector Machines for Detection of Network Traffic Attacks 4

Leveraging Support Vector Machines for Detection of Network ...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tiwari, Anurag Indian Institute of Technology School of Artificial Intelligence & Data Science Jodhpur India Babu Banarasi das Institute of Technology& Management Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350364729

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities. © 2024 IEEE.

关键词： Network intrusion

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI 1

Detection of PCOS Leveraging Machine Learning and Interpreta...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Madhavi, Bindu V. Firdouse, Asfiya Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability. © 2024 IEEE.

关键词： Adversarial machine learning

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS 4

Symptoms-based Classification of Disease Using Various ML Al...

引用

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

作者： Soujanya, K.J. Kodipalli, Ashwini Gosai, Sanjeev Sneha, B.J. Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354218

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimization of Fracture Reduction Robot Controller 8

Optimization of Fracture Reduction Robot Controller

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8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： An, Baichuan Chen, Jianwen Liu, Minghe Yin, Minghuan Song, Zicheng Zhuang, Chao Chang, Cheng Sun, Hao Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin China Southern University of Science and Technology Hospital Limb Deformity Correction and Function Reconstruction Department Shenzhen China Hebei University of Technology School of Artificial Intelligence and Data Science Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of Education Control Engineering Technology Innovation Center of Hebei Province Tianjin China

ISBN: (纸本)9798350380286

To improve the accuracy of fracture reduction, combining inverse kinematic analysis with robot dynamics modelling might lead to a more effective controller for the fracture reduction process. The trajectory of the actuator driving rod's motion and any deviations from it are calculated using Simulink simulation. The Levy-SSA-optimized fracture reduction robot's positioning inaccuracy is two orders of magnitude lower than the PID adjustment approach. Using this method also improves the reduction trajectory's accuracy and speeds up the convergence of the fracture-resetting robot control system to the ideal solution. © 2024 IEEE.

关键词： Inverse kinematics

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Stratification of Depressed and Non-Depressed Texts from Social Media using LSTM and its Variants 2

Stratification of Depressed and Non-Depressed Texts from Soc...

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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Kumar, Keerthan G.T. Anoop, R. Koolagudi, Shashidhar G. Rao, Trupthi Kodipalli, Ashwini Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Artificial Intelligence and Data Science Global academy of Technology Bengaluru India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models' precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

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