14 research outputs found

    A study of ocular manifestations in systemic lupus erythematosus

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    INTRODUCTION: Systemic Lupus Erythematosus is a chronic progressive autoimmune disease with multisystem manifestation. The underlying abnormalities in systemic lupus erythematosus is due to the production of a number of pathogenic autoantibodies and immune complexes and to an inability to suppress and clear them. The disease can present in a wide variety of forms, degrees, and manifestation, ranging from relatively mild cutaneous and joint involvement to lethal cardiac, renal and cerebral involvement. Systemic lupus erythematosus commonly presents in young and middle aged woman who comprise upto 90% of all systemic lupus erythematosus sufferers. Systemic lupus erythematosus is three times more common in blacks than in other races and Asians display an increased incidence of systemic lupus erythematosus against Caucasians. AIM OF THE STUDY: To determine the spectrum and prevalence of ocular manifestation of systemic lupus erythematosus. To identify potentially sight threatening lesions in ocular systemic lupus erythematosus. MATERIALS AND METHODS: The study was carried out at Government Rajaji Hospital Madurai. A standardized ophthalmic examination on Madurai patients with systemic lupus erythematosus referred from the Dermatology Department and Rheumatology Department from Madurai were included in the study. The study was carried out prospectively and all ophthalmic examinations were carried out. SUMMARY: This clinical study was done at Department of ophthalmology Govt. Rajaji Hospital Madurai. A total of 33 patients were examined and out of which 31 (93.9%) were females and 2 (6.06%) were males. CONCLUSION: Eye is a highly sensitive Barometer for the onset and reactivation of autoimmune phenomenon. In this study the prevalence of ocular manifestation in systemic lupus erythematosus was 63.63%. This stresses the importance of ocular manifestation in systemic lupus erythematosus. Sight threatening complication can occur in systemic lupus erythematosus which if intervened at appropriate time will prevent serious visual loss. Patients with retinopathy in systemic lupus erythematosus had higher serum creatinine levels than patients without retinopathy. Hence from these observations, ocular manifestation in systemic lupus erythematosus had led us to the belief that ocular inflammation should be added to the criteria for classification of systemic lupus erythematosus. Such inclusion may lead to better awareness of systemic lupus erythematosus ocular disease for the physician & may yield better outcome in diagnostic prognostic therapeutic ways for the patient

    BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

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    Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis

    An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

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    In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

    An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

    Get PDF
    In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

    Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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    Funding Information: Funding Statement: This work was funded by the Researchers Supporting Project Number (RSP2023R 509) King Saud University, Riyadh, Saudi Arabia. This work was supported in part by the Higher Education Sprout Project from the Ministry of Education (MOE) and National Science and Technology Council, Taiwan, (109-2628-E-224-001-MY3), and in part by Isuzu Optics Corporation. Dr. Shih-Yu Chen is the corresponding author. Publisher Copyright: © 2023 Tech Science Press. All rights reserved.Peer reviewedPublisher PD

    First reformulated Zagreb indices of some classes of graphs

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    A topological index of a graph is a parameter related to the graph; it does not depend on labeling or pictorial representation of the graph. Graph operations plays a vital role to analyze the structure and properties of a large graph which is derived from the smaller graphs. The Zagreb indices are the important topological indices found to have the applications in Quantitative Structure Property Relationship(QSPR) and Quantitative Structure Activity Relationship(QSAR) studies as well. There are various study of different versions of Zagreb indices. One of the most important Zagreb indices is the reformulated Zagreb index which is used in QSPR study. In this paper, we obtain the first reformulated Zagreb indices of some derived graphs such as double graph, extended double graph, thorn graph, subdivision vertex corona graph, subdivision graph and triangle parallel graph. In addition, we compute the first reformulated Zagreb indices of two important transformation graphs such as the generalized transformation graph and generalized Mycielskian graph

    Optimizing Personalized and Context-Aware Recommendations in Pervasive Computing Environments

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    Abstract The researchers in the current era provided many new recommendation methodologies. Though various recommendation techniques exist, there is a need to develop a unique technique for capturing latent factors and patterns from sparse and high-dimensional data in pervasive environments, specifically for optimizing dynamic recommendations. This study proposes a hybrid approach for optimizing dynamic recommendations in pervasive environments by combining Non-Negative Matrix Factorization (NMF) with deep learning and reinforcement learning techniques. The goal is to overcome the challenge of capturing latent factors and patterns from sparse and high-dimensional data. By leveraging NMF, meaningful latent factors are extracted, while deep learning, specifically Faster recurrent neural networks (FRNNs), learns complex feature representations. Reinforcement learning algorithms optimize the recommendation policy based on user feedback. This Hybrid Context-Aware Optimized Recommendation (HCOR) approach improves recommendation accuracy and relevance in pervasive environments, adapts to changing contexts, and enhances user experiences. The performance benefits are achieved by effectively capturing latent factors and patterns, resulting in improved accuracy and the ability to provide personalized and context-aware recommendations. The performance indicators to validate the research work include the recommendations' accuracy, relevance, and adaptability in pervasive environments. Additionally, metrics, such as precision, recall, and F1-score, are used to evaluate the effectiveness of the hybrid approach in capturing latent factors and patterns. User feedback and satisfaction are also measured to assess the impact on user experiences. The HCOR approach shows substantial performance gains, measuring a precision of 0.932, a recall of 0.922, and an F1-score of 0.943, which indicates the excellent ability of the approach to deliver accurate and personalized recommendations in a pervasive environment

    Smart Decision-Making and Communication Strategy in Industrial Internet of Things

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    Smart machine-machine (M2M) interactions, such as those enabled by the Internet of Things (IoT), have enabled people and machines to communicate and make decisions together. Furthermore, these systems have become increasingly important in the commercial and industrial sectors over the previous two decades. The Industrial Internet of Things (IIoT) is a smart system comprising engineering equipment which can connect to one another to improve manufacturing operations. This task would become more complicated if the amount of energy used by the IIoT ecosystems, as well as the amount of network traffic they generate, increased dramatically. Consequently, decision-making processes during communication are essential for autonomous interaction in critical IoT infrastructure. Smart factories employ communication technology to track and gather information in real-time to enhance the output, effectiveness, and predictability while lowering the overall cost of vital operations. In this context, Industry 4.0 not only limits to addresses the issues of integrating technologies, but it also focuses on data collection, dissemination, utilization, and organization and also improves the delivery of the solution or services quicker with more sustainability. This study intends to create an NF-based communication system for IIoT platforms to leverage those benefits. The proposed model includes smart decision-making procedures to deal with communication issues. Compared with the many methods already in use, the suggested mechanism’s functional viability in the automated system is found to be optimal. Outcomes from simulations reveal that the suggested method has improved the accuracy and communication reliability of the IIoT platforms in comparison with the previous methods. Aside from these, the suggested model keeps the throughput of the local automation unit at 96.03% and the throughput of the production hall at 95.58% on average while maintaining the lowest average PLR of about 26.48% across different data rates
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