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检索条件"机构=Computer Science and Engineering Technology"

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Integration of Explainable Artificial Intelligence (XAI) in the Development of Disease Prediction and Medicine Recommendation System 2

Integration of Explainable Artificial Intelligence (XAI) in ...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mathew, Joel Chitra, R. Stephen, Caleb Koshy, Richie Suresh Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350360523

The contemporary healthcare landscape necessitates innovative solutions to improve transparency and understanding in medical decision-making. This paper proposes an advanced medicine recommendation system and a robust disease prediction model integrating explainable AI (XAI). Recognizing the prevalence of misinformation and the urgent need for user-friendly applications, the system empowers users to manage their health proactively. It can be extended beyond common diseases, encompassing rare diseases, and employs XAI algorithms, specifically SHAP and LIME, to enhance transparency. The system incorporates Random Forest Classifier and Decision Tree models, showcasing high accuracy and robustness. The explanation models contribute to user understanding, while performance metrics offer insights into model strengths and generalization abilities. Figures depict SHAP outputs for Decision Tree and Random Forest models, emphasizing transparency in medical predictions. This proposed system addresses critical healthcare challenges, fostering informed decision-making and user trust. © 2024 IEEE.

关键词： Transparency

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Breast Cancer Image Classification using Convolutional Neural Networks 2

Breast Cancer Image Classification using Convolutional Neura...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Ravi Teja, S. Joshi, Sujata Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798331504960

This study aimed to develop and evaluate a deep learning-based classification model for breast ultrasound images. The primary objectives were to classify ultrasound images into three categories: benign, malignant, and normal. Breast cancer detection using ultrasound imaging is challenging due to class imbalance and insufficient annotations in datasets. This research focuses on enhancing classification accuracy by leveraging a pre-trained ResNet-50 model. Methods included data augmentation to address class imbalance and the integration of masks overlayed on ultrasound images to emphasize regions of interest. These enhancements significantly improved the model’s ability to distinguish between benign, malignant, and normal breast tissue. The model achieved an accuracy of 98%, up from an initial 86% on unseen data, demonstrating the impact of detailed annotations and fine-tuning. The results underscore the potential of AI-driven models in improving diagnostic accuracy. Future work involves incorporating additional data sources and validating the model across diverse clinical settings, with the goal of advancing early detection and personalized treatment strategies in breast cancer care. © 2024 IEEE.

关键词： Ultrasonic imaging

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Generating Book Suggestions Using a Hybrid Model with Sentiment Analysis

Generating Book Suggestions Using a Hybrid Model with Sentim...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Pagadala, Manvitha Cheekatla, Sreshta Karthik, N. Vani, V. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Recommendation Systems are ubiquitous, whether it is in social media posts, advertisements, or digital book and movie libraries. Many recommender systems suggest items based on the product or its use and do not consider the user's sentiments towards the item or user preferences. This paper employs a hybrid model comprising a mix of popularity and content-based approaches to generate book suggestions along with sentiment analysis. Sentiment analysis of the book descriptions and user feedback enhances the recommendations. This recommendation system also incorporates classification and filtering of explicit books based on the user's age to generate the top 10 recommendations of books. The measurement metrics implemented in testing this proposed model are Precision, Recall, Mean Absolute Error, and Mean Squared Error to test the recommendation system's performance. The proposed system with high precision and low error results in notable additions to existing recommendation systems. © 2024 IEEE.

关键词： classification content-based hybrid model recommendation system sentiment analysis

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Food Oil Quality and Usage Detection Using AI 10

Food Oil Quality and Usage Detection Using AI

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sanjay, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353068

The proposed system revolutionizes food oil quality assessment through an intelligent integration of Artificial Intelligence (AI) and Convolutional Neural Networks (CNNs). Overcoming the limitations of traditional methods, the system employs a robust CNN model trained on a diverse dataset of labeled food oil images. This innovative approach enables swift and reliable quality classification, addressing the challenges of real-time monitoring in food production and distribution. The system's architecture emphasizes user-friendliness, offering a seamless interface for diverse stakeholders, including food manufacturers and regulatory bodies. Its adaptability and scalability make it applicable across various food oil types and production settings. By leveraging the efficiency of CNNs, the proposed system heralds a paradigm shift in food oil quality assessment, fostering rapid and accurate decision-making in the ever-evolving landscape of the food industry. © 2024 IEEE.

关键词： Convolutional neural networks

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

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Optimizing Species Recognition in Medicinal Plants: A Comprehensive Evaluation of Deep Learning Models 14

Optimizing Species Recognition in Medicinal Plants: A Compre...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Sharma, Vishal Chaurasia, Kuldeep Bansal, Abhay School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350344837

Medicinal plants have the potential to both significantly progress the pharmaceutical and medical industries and shield people from fatal diseases like cancer and cardiovascular conditions. Since there are hundreds of kinds of plants in the globe and because their characteristics: -like color, texture, and form are all the same, it might be difficult to identify them. The manual process is laborious and time-consuming. In view of current technological breakthroughs, more efficient methods for meeting the need for species identification have required to be devised. Pattern recognition and digital image processing are two examples of these methods. Despite several prior research, there are still difficulties in reliably automating plant species identification in a sequential and adaptive manner to identify a variety of plant species. In this article, the most recent deep learning models (VGG19, InceptionV3, LeNet, and Resnet50) are compared for medicinal plant identification and categorization. The system is trained using 1835 leaf samples from 30 distinct medicinal plants. For every model, confusion and performance metrics were used. In contrast to the other models in this investigation VGG19 fared better, with an accuracy rate of 99.85%. © 2024 IEEE.

关键词： Plants (botany)

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Sarcasm Detection with BiLSTM Multihead Attention 9

Sarcasm Detection with BiLSTM Multihead Attention

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Thakur, Harish Srivastava, Jyoti National Institute of Technology Computer Science And Engineering Himachal Pradesh Hamirpur India

ISBN: (纸本)9798350394474

Sarcasm detection is a difficult task in natural language processing because it requires interpreting nuanced details and contextual indications that often challenge simple explanation. This research explores the implementation of a sarcasm detection model using advanced ways, involving Bidirectional Long Short-Term Memory(BiLSTM) networks and Multihead Attention mechanisms. The bidirectional flow of information in the BiLSTM allows the model to capture dependencies in both directions, aiding in the understanding of long-term contextual connections. The model's capacity to focus on different parts of the input sequence is further improved with the addition of Multihead Attention, resulting in a more complex contextual representation. In prior research, a Multihead attention-based bidirectional long-short memory (MHA-BiLSTM) network had been built to detect sarcastic remarks in a given corpus. To increase the model performance, we perform fine tunning, varying the hyperparameters using low learning rate optimizer and sigmoid activation function. Results show that our model give better results than previous MHA-BilSTM model. We'll look at misclassifications to show the model's advantages and possible drawbacks. The findings contribute to the ongoing efforts to develop robust sarcasm detection models, providing insights into the intricate nature of language comprehension and interpretation. © 2024 IEEE.

关键词： Natural language processing systems

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Dimensionality Reduction and Gradient Boosting for In-Vivo Hyperspectral Brain Image Classification

Dimensionality Reduction and Gradient Boosting for In-Vivo H...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Singh, Raj Bahadur Datta, Aloke The Lnm Institute of Information Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350376876

Hyperspectral imaging (HSI) datasets contain hundreds of contiguous narrow spectral bands, which can create challenges in data analysis due to the curse of dimensionality. However, much of this data is redundant, necessitating the application of dimensionality reduction techniques to extract the most relevant features before the classification task. Two feature extraction methods, principal component analysis (PCA) and non-negative matrix factorization (NMF), are explored. Then, the reduced feature sets are used to train three gradient-boosting algorithms, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), for the classification of in-vivo hyperspectral brain images. The experimental results show the effectiveness of gradient boosting over support vector machine (SVM) and random forest (RF) in performance measures like accuracy, sensitivity, specificity, and F1-score. © 2024 IEEE.

关键词： Non-negative matrix factorization

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DAMPSO: Dynamic Accelerated Memory-Based PSO for Hyperparameter Tuning of Plant Disease Classifiers 5th

DAMPSO: Dynamic Accelerated Memory-Based PSO for Hyperparam...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Nagpal, Jyoti Goel, Lavika Department of Computer Science & Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9789819760350

Plant disorders cause substantial threats to agriculture, impacting crop conditions and output. In recent years, the recognition and utilization of machine learning have grown substantially as a valuable asset, particularly in automating the recognition and categorization of foliage diseases. This advancement has resulted in enhanced agricultural practices. However, traditional detection methods suffer from drawbacks such as high costs, error-proneness, inconsistency, time-consuming procedures, and the need for enhanced efficiency. To overcome these constraints, this paper introduces a model that utilizes imaging techniques to detect and categorize foliar diseases in maize and cotton plants. The study utilizes a dataset comprising 2543 images of maize leaves and 1443 images of cotton leaves with distinct disease classes sourced from online platforms. The study incorporates hyperparameter tuning of the aforementioned classifiers using machine learning-based hyperparameter tuning techniques such as random search, grid search, and nature-inspired algorithms. In particular, a novel algorithm named Dynamic Accelerated Memory-Based PSO (DAMPSO) is proposed for hyperparameter tuning. Experimental analysis reveals that DAMPSO surpasses other tuning techniques, yielding the highest accuracy of 95% when applied to the XGBoost classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cotton

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ST-DAAE: An Enhanced Swin Transformer Attention Mechanism for Leaf Disease Classification

ST-DAAE: An Enhanced Swin Transformer Attention Mechanism fo...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Madhavi Singh, Thipendra P Singh, Shailendra Pratap Bennett University School of Computer Science Engineering & Technology Greater Noida India

ISBN: (纸本)9798331542344

This work presents a novel dual-technique method that uses Drop Attention (DA) for regularization and Attention Entropy Loss (AE) for optimization in the Swin Transformer framework to improve leaf disease classification. We have introduced sophisticated mathematical formulations that improve the model's foundations and performance, these methods provide notable improvements over previous approaches. The experimental findings show significant gains in accuracy: the Swin Transformer equipped with Drop Attention obtained a remarkable accuracy of 98.91%, whereas the variation using Attention Entropy Loss only managed 85%. These improvements increase the precision of leaf disease classification systems but also provide important tools for the progress of agricultural technology. © 2024 IEEE.

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