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FBG Sensor Design and Analysis for Early Detection of Cancer 18

FBG Sensor Design and Analysis for Early Detection of Cancer

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sharan, S.C. Dhruva, H.K. Mary Anitha, T. Roy, Neil Sharan, Preeta Madhwesha Moudgalya, R. The Oxford College of Engineering Dept. of Computer Science and Engineering Bangalore India The Oxford College of Engineering Dept. of Mca Bangalore India Delhi Technological University Dept. of Computer Science and Engineering New Delhi India The Oxford College of Engineering Dept. of Electronics and Communication Engineering Bangalore India The Oxford College of Engineering Dept. of Science and Humanities Bangalore India

ISBN: (纸本)9789380544519

Several of the current procedures for detecting cancer, such as mammography, ultrasound, MRI, and biopsy, are either expensive, painful, intrusive, or have limitations in accuracy and sensitivity. As a result, there is a need for a simple, noninvasive, and cost-effective tool for detecting cancer at an early stage. Fiber Bragg grating (FBG) sensors offer a wide range of uses in primary care and biomedical applications for intelligent sensing. Furthermore, fiber-optic FBGs have several benefits that set them apart. The most noteworthy of these applications, of course, are incredibly valuable human health indicators such as blood pressure, heart rate, and body temperature. Temperature and blood pressure vary depending on a person's physical, involuntary, nervous, and mental state. Therefore, measuring vital parameters, especially temperature can help in the early identification of symptoms of a disease. Research suggests that temperature variation is observed in cancerous cells (breast, cervix, and blood vessels). FBGs can be utilized as thermal sensors to measure temperature changes with great sensitivity and precision. FBG sensors also face some challenges, such as low signal-to-noise ratio, cross-sensitivity, and environmental interference. Therefore, optimizing the Q factor of FBG sensors is crucial for improving their performance and reliability for cancer identification. In the current study, an optimized FBG sensor was designed and simulated. The average Q-factor obtained in 244.26. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Blood cancer FBG Fiberoptic Hela cells MCF-7 cells modulation OptiGrating SMF

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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

引用

2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Islam, Md. Muhiminul Shuvo, M.I.R. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350350289

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field. © 2024 IEEE.

关键词： COVID-19

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SFNet: Synergistic Fusion Network for Text Localization in Natural Scene Images 3

SFNet: Synergistic Fusion Network for Text Localization in N...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Suman, Suman Champa, H.N. University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350381931

Prior methodologies for the detection of scene text have demonstrated promising outcomes across many evaluation metrics. Nevertheless, despite the utilization of deep neural network models, their effectiveness is sometimes limited in complicated situations due to the intricate interplay of several stages and components within the pipelines, which ultimately governs overall performance. This study presents a straightforward yet robust methodology for identifying textual content in practical environments. Phrases or text lines may be predicted in images of varying orientations and quadrilateral forms using the suggested pipeline's single neural network. This eliminates the need for superfluous intermediary steps such as candidate aggregation and word division. Due to the streamlined nature of our SFNet(Synergistic Fusion Network), our primary emphasis is directed at the development of loss functions and neural network design. The suggested approach has been evaluated using a well-established dataset including ICDAR 2015. The results of these experiments demonstrate that the new technique outperforms existing techniques in terms of both accuracy and efficiency. The proposed methodology demonstrates a performance metric, namely an F-score of 0.89 while operating at a frame rate of 13.2 frames per second and a resolution of 720p using the ICDAR 2015 dataset. © 2024 IEEE.

关键词： Pipelines

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Image Segmentation based Approach for Skin Disease Detection and Classification using Machine Learning Algorithms 2

Image Segmentation based Approach for Skin Disease Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sany, Nazmul Hasan Chandra Shill, Pintu Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant prices restrict accessibility. To address this, this paper describes an image processing strategy for dermatological screening and procedure that comprises obtaining digital photographs of damaged skin patches and applying image processing to detect the disease type. Moreover, when conducted manually by medical specialists, identifying skin illnesses is time-intensive and subjective. As a consequence, both patients and physicians want automated skin disease prediction, which makes the treatment plan faster. This cost-effective approach depends on a basic setup: a camera and a computer. Operating on colour photos, a solution combines resizing for feature extraction using a pertained convolutional neural network, followed by classification using a multiclass SVM. The technology efficiently recognises four separate skin illnesses with an astonishing 90.8% accuracy, giving users complete findings spanning disease kind, extent, and severity. © 2024 IEEE.

关键词： Decision Tree Image Processing K-NN Machine Learning Skin Disorder SVM

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Smart Gadgets Cockpit Using IoT 1

Smart Gadgets Cockpit Using IoT

引用

1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Eapen, Maya Mercy, H. Beulah Jayakumari, R. Lavanya Mala, A. Indumathi, S. Pooja, S. Jerusalem College oif Engineering Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365337

The Smart Device Cockpit collects data from IoT enabled household appliances like Air conditioner, refrigerators, microwave ovens, and washing machines. Once transmitted to the Smart Device Cockpit portal, the data undergoes analysis and is then presented in the form of both usage data and error data. This platform facilitates a thorough comprehension of the utilization patterns of these household devices and offers insights into any possible errors or malfunctions that may occur. In essence, the Smart Device Cockpit serves as a powerful tool for homeowners to not only monitor and manage their smart appliances but also to make informed decisions about usage optimization and timely maintenance. © 2024 IEEE.

关键词： Internet of things

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A Fusion-Based Approach to Multiclass Retinal Disease Classification: Transfer Learning Strategy with CBAM Integration 2

A Fusion-Based Approach to Multiclass Retinal Disease Classi...

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2nd International Conference on Power, Electrical, Electronics and Industrial applications, PEEIACON 2024

作者： Nath, Bijoy Deb Afroge, Shyla Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Eye diseases pose significant global public health challenges, often resulting to irreversible blindness if untreated. Early, precise diagnosis of these disorders is critical. In this paper, we address this crucial issue using transfer learning methods for the categorization of multiple eye disorders. The proposed architecture features a fusion-based technique that combines EfficientNetB0 and MobileNetV3Large convolutional neural networks (CNNs) for feature extraction and incorporates Convolutional Block Attention Module (CBAM), which dynamically focuses on significant regions of the images, enhancing the model's discriminative capability. Our proposed model achieves outstanding performance on two separate datasets: 97.85% accuracy on the Eye Disease Classification (EDC) dataset and 91.96% on the Ocular Disease Intelligent Recognition (ODIR) dataset, surpassing existing state-of-the-art approaches in ocular disease classification. By developing the proposed model, we intend to contribute to the creation of more effective diagnostic tools and eventually enhance patient outcomes in ophthalmology. © 2024 IEEE.

关键词： Ophthalmology

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WebTrek Learner : AI Integrated Cloud Based Learning Platform

WebTrek Learner : AI Integrated Cloud Based Learning Platfor...

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Bhandari, Arjun Harisha, A. Rishav, Kumar Shifana, Mariyam Sameer, Jazil Sahyadri College of Engineering and Management Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331517892

In an era defined by the rapid proliferation of digital technology and connectivity, the need for virtual education has never been more critical. This article, referred to hereon as 'WebTrek Learner,' is a forward-thinking initiative aimed at catering quality virtual education through a dynamic and engaging approach of gaming. To further enhance the learning experience, WebTrek Learner incorporates cutting-edge LLM based AI technology to curate internal insights and content based on glossary documents. Cloud based a gaming platform enables lightweight experience to all potential learners. © 2024 IEEE.

关键词： Adversarial machine learning

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LegalAI Across Languages: A Comprehensive Survey on Tasks, Methodologies and Challenges 1

LegalAI Across Languages: A Comprehensive Survey on Tasks, M...

引用

1st International Conference on AIML-applications for engineering and Technology, ICAET 2025

作者： Deshpande, Anjali Deshmukh, Pratiksha COEP Technological University Dept. of Computer Science and Engineering Pune India PICT Dept. of AI & DS Pune India School of Computational Sciences COEP Technological University Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Natural Language Processing (NLP) is revolutionizing the legal domain, enabling tasks such as predicting legal outcomes, summarizing complex documents, identifying key entities, and assessing bail risks. This survey paper presents a comprehensive review of the existing research on NLP tasks applied to legal documents and judgments across various languages, with a special emphasis on low-resource languages. Despite the extensive work done in widely spoken languages like English, there remains a critical need to extend these advancements to languages with limited resources and data availability. This paper categorizes and analyzes the NLP tasks in the legal domain based on the languages they target. It highlights the methodologies employed, datasets utilized, and the performance of different models across diverse languages. Furthermore, it identifies the gaps in current research and proposes potential directions for future work, particularly in improving the accessibility and applicability of NLP solutions for legal texts in underrepresented languages. By providing a comprehensive overview of advancements and obstacles, this survey seeks to assist future research endeavors that bridge the disparity between high-resource and low-resource languages in legalAI applications. © 2025 IEEE.

关键词： Risk assessment

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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images 5

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Nazim Mia, Md Sajib Uddin, Mohammed Nasir Mahmud, Prince Chandpur Science and Technology University Dept. of Information and Communication Technology Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%. © 2023 IEEE.

关键词： Optical tomography

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Exploring Machine Learning Techniques for Accurate Heart Disease Detection: A Comprehensive Study

Exploring Machine Learning Techniques for Accurate Heart Dis...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of Data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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