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Treatment Prediction using Dual Adaptive Sequential Networks 3

Treatment Prediction using Dual Adaptive Sequential Networks

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Benedict, Salva Sachin Reyana, A. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The application of advanced forecasting techniques has attracted major interest in the area of medicinal study and treatment as a method of increasing quiet care. This research study introduces a new method for predicting the best course of treatment based on individual persistent profiles, called Double Adaptive Sequential Networks (DASN). The DASN enhances the accuracy and strength of treatment predictions by combining the control of successive neural systems with adaptable learning components. The system prepares and fine-tunes two parallel successive systems using verifiable persistent information, including genomic data, treatment outcomes, and restorative records. While the moment arrange focuses on encoding flexible designs that evolve over time as patients respond to various medications, the primary arrange records different conditions inside persistent information. DASN's adaptable character is then achieved. DASN's adaptability is accomplished by a method of active learning that continually adjust the network's weights in response to ongoing data from responses. This enables the display to detect slight modifications to uniform responses and adjust predictions accordingly. Furthermore, DASN is integrated with a fortification learning component, which enables it to iteratively improve treatment recommendations by simulating possible persistent reactions was carried out using various runs of understanding cases and treatment scenarios in order to validate the viability of DASN. The results demonstrate that, in terms of treatment precision and flexibility, DASN consistently outperforms current predictive models. The model has the potential to revolutionize personalized medication due to its ability to adapt to changing quiet elements and treatment patterns. © 2024 IEEE.

关键词： Forecasting

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Fetal Health Status Classification Using Machine Learning Algorithms-A Comparative Analysis

Fetal Health Status Classification Using Machine Learning Al...

引用

2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Nisha, S.M. Brindha, D. Rajeswari, M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331528126

Fetal health status classification is essential for monitoring and confirming the well-being of the fetus through pregnancy. This study evaluates various Machine Learning techniques for classifying fetal health status using the Cardiotocography (CTG) dataset from the University of California Irvine Machine Learning Repository(UCI) Repository and the same is available in in Kaggle community also. The dataset is obtained by an automated processing system which was used to measure the diagnostic features of 2126 fetal Cardiotocograms (CTGs) and a consensus classification label(normal, risky and pathological) was issued to each of the CTGs by three expert obstetricians in this dataset. Assessment and comparison of the performance of multiple models, including Decision Tree (DT), Gradient Boosting (GB), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), AdaBoost, and XGBoost are performed. Key performance indicators like accuracy, precision, recall, and F-measure are calculated for each model to determine their effectiveness in fetal health classification. The results reveal significant variations in performance across the models, with certain techniques demonstrating superior classification accuracy and robustness. This comparative analysis provides insights into the strengths and limitations of several Machine Learning (ML) approaches for fetal health status classification, guiding future research and practical applications in prenatal care. © 2024 IEEE.

关键词： Electronic health record

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Swarm Based Searching Method for Effective Sinkhole Attack Detection in Internet of Things 1

Swarm Based Searching Method for Effective Sinkhole Attack D...

引用

1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mona, R. Manoranjitham, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

Over the last several decades, the IoT and WSN have seen a fast proliferation, which has resulted in astonishing innovation and integrated possibilities. Since the developments in networking technology and nanodevices, the transition from IPv4 to IPv6 has further increased connection. Due to this change, it becomes easier to connect a greater variety of devices to servers. The growing interconnectedness has resulted in challenges in effectively managing and evaluating the massively generated data across all levels of IoT. Considering the massive IoT devices and their tiny size, the demand of comprehensive security management is especially significant for these devices. In IoT layered design, the network layer is of utmost significance in terms of assuring security. This layer is responsible for storing routing information and carrying out choices that match to specific routing information. Among the several security assaults that are being handled, the sinkhole attack is one that is commonly observed and poses a considerable problem. In this study, the ramifications of the sinkhole attack on IoT are investigated in detail, and the effect of the attack is meticulously analyzed. In addition to this, it introduces a unique mitigation method that is specially built to effectively combat threats of this kind. To conduct comprehensive simulations, the study makes use of MATLAB v9. This has made it possible to evaluate the throughput of the network as well as the End-to-End delay and network lifetime. © 2024 IEEE.

关键词： MATLAB

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Automatic Kidney Stone Detection using XResNet 152 Model for CT Images 2

Automatic Kidney Stone Detection using XResNet 152 Model for...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Manudhas, S.M. Manoranjitham, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

This study provides an automatic kidney stone identification using computed tomography (CT) scan images and cutting-edge deep learning techniques. The identification and categorization of diverse kidney disorders, such as stones and normal structures, are the main goals of this study. Our research intends to transform the diagnosis process by offering an accurate and effective automated system for renal health evaluation using the resilience of the XResNet152 architecture. The methodology includes a well-selected dataset with a variety of kidney diseases, which makes thorough model training, validation and assessment possible. The model used augmentation approaches and preprocessing processes specific to medical imaging data to improve its capacity to identify complex patterns and features that correspond to various kidney abnormalities. The model demonstrated remarkable precision in categorization, demonstrating encouraging outcomes in discerning and differentiating various renal ailments. The model demonstrated remarkable accuracy in distinguishing between stones and normal structures, and it demonstrated resilience in intricate medical imaging situations. In this study, we present a thorough explanation of our approach, dataset preparation, model architecture, and in-depth performance analysis. We critically assess the advantages and disadvantages of our methodology and suggest directions for further development. By advancing the field of medical image analysis, this research opens the door to more advanced precision medicine and better healthcare diagnostics. © 2024 IEEE.

关键词： Medical imaging

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Social Media Text Analysis for Disaster Management Using DistilBERT Model

Social Media Text Analysis for Disaster Management Using Dis...

引用

2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Varghese, Sherin R. Juliet, Sujitha Athish, N.S. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350376913

There is no doubt on the fact that disasters like floods, earthquakes and other man-made ones have a destructive impact on human lives. Timely response is crucial to minimize the casualties and aid recovery. Social media in such times, are vital as people share experiences, news and request assistance. This study investigates the efficiency of Transformer-based models for fine-grained sentiment analysis of disaster tweets on social media. Five models, including Distil-BERT, are compared and analyzed using Twitter data. The analysis categorises tweets into ten classes reflecting human sentiments during a disaster. Model performance is evaluated using accuracy, precision, recall, F1-score and execution time. While all the models demonstrate strong performance, Distil-BERT achieved comparable accuracy with a significant reduction in training and execution time (45%-55%). These findings suggest the potential of Distil-BERT (Distilled BERT) as the core component in disaster management applications;enabling organizations to gain a deeper understanding of the multifaceted public sentiment during disasters and make informed decisions for improved public aid and resource allocation. © 2024 IEEE.

关键词： Sentiment analysis

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Deep Learning Approaches in Anticipating Diabetic Retinopathy Progression 10

Deep Learning Approaches in Anticipating Diabetic Retinopath...

引用

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

作者： Thanush, M. Denisha, M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

One of the most prevalent complications caused by diabetes that still causes loss of vision and blindness worldwide is diabetic retinopathy. For treatments to be effective, early and correct identification is essential. Despite advancements in medicine, a complex automated solution is still required to address the impending threat of blindness due to the difficult early diagnosis of diabetes-related retinopathy, a key contributor to visual impairment. Using a well-selected diabetic retinopathy dataset, our novel approach applies transfer learning techniques using EfficientNetB3, ResNet50, and DenseNet169. Our model is strengthened by a new hybrid oversampling method that addresses class imbalance, and its training incorporates cutting-edge components like differential regularization, adaptive dropout, and Adamax optimization for increased *** results demonstrate the remarkable sensitivity, specificity, accuracy, and F1 score of our model at various degrees of retinopathy severity, establishing a new standard for diagnostic dependability in the identification of diabetic retinopathy. By utilizing cutting-edge deep learning architectures, our method establishes a strong basis upon which future developments will be built. These developments will include adaptive optimization strategies, ensemble learning to improve resilience, and self-supervised pre-training, all of which will accelerate the ongoing advancement of diabetic retinopathy detection accuracy. © 2024 IEEE.

关键词： Diagnosis

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A Comparative Analysis on Fashion Cloth Feature Extraction Methods using Text and Image Data 11

A Comparative Analysis on Fashion Cloth Feature Extraction M...

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Attoor, Lemya Sainudeen Chitra, R. Anusha Bamini, A.M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350379136

Modern methods of extracting relevant features from images and texts are crucial to the recovery of fashionable garments. In this research, we compare and contrast a number of different approaches to extracting features from images and text. We utilize Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) to efficiently process and represent textual descriptions of fashion products for text feature extraction. This allows us to take advantage of GPT's generative capabilities and contextual understanding. In order to extract features from photographs of fashion sketch images, we use a mix of Residual Networks (ResNet) and Visual Geometry Group (VGG) networks. This allows us to pick up complex visual patterns and details. Our method is designed to enhance the accuracy of text and image matching by combining the best features of these state-of-the-art models. Accuracy, recall, F1-score, and retrieval efficiency are some of the assessment metrics that were examined on a wide dataset that included different types of fashion clothing. The hybrid model improves feature extraction capabilities, producing robust and discriminative text and picture representations, according to preliminary findings. More intelligent and user-friendly retrieval systems in digital fashion platforms and e-commerce might be possible thanks to the study's useful insights into the possibilities of advanced deep learning architectures in the field of fashion product retrieval. © 2024 IEEE.

关键词： Image matching

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Forecasting Bitcoin Price Trends: Integrating Sentiment Analysis and Machine Learning 3

Forecasting Bitcoin Price Trends: Integrating Sentiment Anal...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Aakash, Kasukurthy Reyana, A. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

With the increasing scrutiny of Bitcoin and other cryptocurrencies in recent years, there is a growing significance in studying methods to predict their values, including sentiment analysis on Twitter-collected data. The primary objective is to perform sentiment analysis on Bitcoin prices and examine the predictive accuracy of various machine learning algorithms. The approach cleans up Twitter data that is relevant to Bitcoin. Followed by quantifying people's opinions on Bitcoin using sentiment analysis. With this data and Bitcoin's price history, a binary target variable for price movement is built to specify the price variations. In order to predict the changes in Bitcoin price, several machine learning models are developed and evaluated. Among these models are Long Short-Term Memory networks, Decision Trees, Random Forests, and Linear Discriminant Analysis are mostly applied. The proposed approach is the enhanced Random Forest base classifier to improve the prediction accuracy. With an F1-score of 64% and 70% accuracy, the Enhanced Random Forest model performs better than the existing models. This model illustrates how sentiment analysis and machine learning may be combined to forecast cryptocurrency prices. © 2024 IEEE.

关键词： Machine learning

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Al-Proctored Exam Portal with Mobile Companion Application 3

Al-Proctored Exam Portal with Mobile Companion Application

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Daniel, Ronald C Caleb Andrew, H. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

Upholding academic integrity during exams has always been difficult in the world of online learning. We provide a novel approach to solve this problem: an AI-proctored exam Portal combined with a mobile companion app. This solution uses artificial intelligence for proctoring and enables instructors to prepare and conduct exams safely. The site includes elements like multiple-choice exams, written assessments with a question bank, and integrated course modules with quizzes and videos. With the use of AI algorithms, the exam process is tracked in real time, and each student receives a thorough report. Additionally, the mobile companion app improves the assessment process's integrity by acting as an additional monitoring tool and giving a side view of the student during the test. This article describes the architecture, functionality, and use of the AI-Proctored Exam Portal and its accompanying mobile application, emphasizing the platform's effectiveness in guaranteeing transparent and equitable testing in the online learning environment. © 2024 IEEE.

关键词： E-learning

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Enhanced Air Quality Prediction Using Optimized Sarimax Model for Coimbatore Region

Enhanced Air Quality Prediction Using Optimized Sarimax Mode...

引用

2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Revanth, Bande Manoranjitham, R. Division of Computer Science and Engineering Karunnya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350376913

This research delves into examining the forecast of air quality in Coimbatore, India, according to data gleaned from the Central Pollution Control Board (CPCB) specific to the area. The gathered information underwent a preprocessing step, using interpolation to ensure uniformity and completeness. Forecasting methods such as Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM) models were utilized to predict air quality levels. The SARIMAX model was fine-tuned through a grid search process, while the LSTM underwent hyperparameter adjustments to improve air quality predictions. Performance evaluation of these models involved metrics like R-squared, Mean absolute error, and Root mean square error. Based on the selected evaluation criteria, the outcome shows that SARIMAX outperformed LSTM in Coimbatore when it came to predicting the air quality. This analysis improves our understanding of air quality patterns in Coimbatore. Highlights SARIMAX's effectiveness as a modeling tool for atmospheric science applications. © 2024 IEEE.

关键词： Long short-term memory

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