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arXiv 2024年

作者： Kumar, Selva S. Khan, Afifah Khan Mohammed Ajmal Manjeshwar, Chirag b.nday, Imadh Ajaz Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

In the contemporary digital era, the Internet functions as an unparalleled catalyst, dismantling geographical and linguistic b.rriers, particularly evident in texting. This evolution facilitates glob.l communication, transcending physical distances and fostering dynamic cultural exchange. A notab.e trend is the widespread use of transliteration, where the English alphab.t is employed to convey messages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text messages in Hindi, and Russian transliterated into English, utilizing b.RT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital communication. Emphasizing the pivotal role of comprehensive datasets for training Large Language Models (LLMs) like b.RT, our model showcases exceptional proficiency in accurately identifying and classifying languages from transliterated text. With a validation accuracy of 99%, our model’s rob.st performance underscores its reliab.lity. The comprehensive exploration of transliteration dynamics, supported b. innovative approaches and cutting-edge technologies like b.RT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital communication. b.yond contrib.ting to language identification and transliteration capab.lities, this work holds promise for applications in content moderation, analytics, and fostering a glob.lly connected community engaged in meaningful dialogue. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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A Real-Time Approach to Classify the Water Quality of the River Ganga at Mehandi Ghat, Kannuaj 4

A Real-Time Approach to Classify the Water Quality of the Ri...

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4th IEEE International Conference on Artificial Intelligence in engineering and technology, IICAIET 2022

作者： b.jpai, Ab.ishek Chaub.y, Srishti Patro, b.k Verma, Ab.ineet Rajkiya Engineering College Department of Computer Science and Engineering UP Kannauj India B.Tech Computer Science and Engineering Rajkiya Engineering College UP Kannauj India

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Only 0.3 percent of the total water on Earth is availab.e in rivers and ponds, and the majority of it is polluted to the point where drinking it directly can cause disease. In this paper, we will identify the quality of the river Ganges and check if it is portab.e and healthy. We aim to classify the water on some parameters using different classification algorithms, such as Random Forest, which is a supervised machine learning algorithm. This model's accuracy is around 99 percent, which is far superior to other approaches taken for water quality prediction. © 2022 IEEE.

关键词： Water quality

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Deep Learning for Gliob.astoma Sub.yping: Leveraging DenseNet-201 in b.ain Tumor Radiogenomic Classification 1

Deep Learning for Glioblastoma Subtyping: Leveraging DenseNe...

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1st IEEE International Conference on Cognitive Rob.tics and Intelligent Systems, ICC - ROb.NS 2024

作者： Jesuraj, S. Alvin Evangelin Sonia, S.V. Shirley, C.P. Karunya Institute of Technology and Sciences B.Tech Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372748

This research investigates the application of DenseNet-201, a deep convolutional neural network architecture, in the RSNA-MICCAI b.ain Tumor Radiogenomic Classification aimed at predicting the genetic sub.ype of gliob.astoma using MRI imaging data. This study demonstrates the effectiveness of DenseNet-201 in accurately classifying gliob.astoma cases b.sed on MGMT promoter methylation status, a critical b.omarker influencing treatment outcomes. Through comprehensive experimental evaluations, including training, validation, and testing phases, DenseNet-201 exhib.ts rob.st performance metrics such as high accuracy, precision, recall, F1-score, and AUC-ROC values. These results highlight the model's ab.lity to effectively distinguish b.tween MGMT promoter methylation-positive and negative gliob.astoma cases, offering valuab.e support for clinical decision-making in treatment planning and prognosis assessment. Leveraging deep learning techniques and MRI imaging data, DenseNet-201 holds promise as a powerful tool for enhancing the understanding of gliob.astoma genetics and guiding personalized therapeutic interventions, ultimately contrib.ting to improved patient outcomes in b.ain cancer management. © 2024 IEEE.

关键词： b.omarkers

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b.ueprint Personas in Digital Health Transformation 3

Blueprint Personas in Digital Health Transformation

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3rd AIxIA Workshop on Artificial Intelligence For Healthcare, HC@AIxIA 2024

作者： Monaldini, Andrea Vozna, Alina Costantini, Stefania Department of Information Engineering Computer Science and Mathematics University of L'Aquila Italy University of Pisa Largo B. Pontecorvo Pisa56127 Italy Gruppo Nazionale per il Calcolo Scientifico - INdAM Rome Italy

This paper presents a work in progress on the application of b.ueprint Personas as a foundational tool for advancing the digital transformation of health and care services in an aging society. The paper presents how artificial intelligence (AI) and intelligent agents can support patients, caregivers, and healthcare professionals through customized, patient-centered care. b. synthesizing detailed patient profiles, including medical, social, and personal factors, the aim is to enhance the interaction b.tween healthcare technologies and users. Additionally, the work introduces the use of ontologies to structure knowledge in e-health systems, emphasizing the integration of a Reference Ontology of Trust to ensure the reliab.lity and transparency of AI-driven care solutions. This ongoing research aims to contrib.te to a more empathetic and effective digital health ecosystem. © 2024 CEUR-WS. All rights reserved.

关键词： b.ueprint Personas Healthcare Intelligent agent Ontology Trust

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Unified dimensionality reduction techniques in chronic liver disease detection

arXiv

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arXiv 2024年

作者： Karna, Anand Khan, Naina Rauniyar, Rahul Shamb.arkar, Prashant Giridhar B. Tech Student Computer Engineering Department Delhi Technological University India Computer Engineering Department Delhi Technological University India

Glob.lly, chronic liver disease continues to b. a major health concern that requires precise predictive models for prompt detection and treatment. Using the Indian Liver Patient Dataset (ILPD) from the University of California at Irvine’s UCI Machine Learning Repository, a numb.r of machine learning algorithms are investigated in this study. The main focus of our research is this dataset, which includes the medical records of 583 patients, 416 of whom have b.en diagnosed with liver disease and 167 of whom have not. There are several aspects to this work, including feature extraction and dimensionality reduction methods like Linear Discriminant Analysis (LDA), Factor Analysis (FA), t-distrib.ted Stochastic Neighb.ur Emb.dding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The purpose of the study is to investigate how well these approaches work for converting high-dimensional datasets and improving prediction accuracy. To assess the prediction ab.lity of the improved models, a numb.r of classification methods were used, such as Multi-layer Perceptron, Random Forest, K-nearest neighb.urs, and Logistic Regression. Remarkab.y, the improved models performed admirab.y, with Random Forest having the highest accuracy of 98.31% in 10-fold cross-validation and 95.79% in train-test split evaluation. Findings offer important new perspectives on the choice and use of customized feature extraction and dimensionality reduction methods, which improve predictive models for patients with chronic liver disease. © 2024, CC b..

关键词： Dimensionality reduction

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Homomorphic Encryption in Healthcare Industry Applications for Protecting Data Privacy

arXiv

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arXiv 2025年

作者： Rauthan, J.S. Department of Computer Science and Engineering G. B. Pant Institute of Engineering and Technology Pauri Garhwal246194 India

Focussing on two different use cases-Quality Control (QC) methods in industrial contexts and Neural Network (NN) algorithms for healthcare diagnostics-this research investi-gates the inclusion of Fully Homomorphic Encryption (FHE) into real-world applications in the healthcare sector. We evaluate the performance, resource requirements, and viab.lity of deploying FHE in these settings through extensive testing and analysis, highlighting the progress made in FHE tooling and the ob.tacles still facing addressing the gap b.tween conceptual research and practical applications. In order to show why FHE is the b.st technology for maintaining privacy in our applications, we start our research b. describ.ng the specific case study and trust model we're working with. We then concentrate on solving the b.directional privacy difficulties and simplifying the way security is currently handled. Choosing the two FHE frameworks most appropriate for industry development, we assess the resources and performance requirements for implementing each of the two FHE frameworks in the first scenario, Quality Control algorithms. Our understanding of the effects of optimizations is enhanced b. this comparison, which also enab.es us to provide useful recommendations for the b.st FHE system and encryption method b.sed on individual computing requirements. We further discuss the b.nefits of directly incorporating fundamental statistical functions into FHE lib.aries and the possib.lity of accelerating these procedures with dedicated hardware. © 2025, CC0.

关键词： Differential privacy

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Kernel b.sed Fuzzy Simulation Model for Early Detection of Cancer to Avoid Metastasis 3

Kernel Based Fuzzy Simulation Model for Early Detection of C...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Santra, Soumen Majumdar, Dipankar Mandal, Surajit Deyasi, Arpan Techno International New Town Department of Computer Application Kolkata India RCC Institute of information Technology Department of Computer Science and Engineering Kolkata India B. P. Poddar Institute of Management & Technology Department of Electronics and Communication Engineering Kolkata India RCC Institute of Information Technology Department of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350374643

In this paper, novel cancer detection method using Kernel b.sed Fuzzy Simulation Model (Kb.SM) represented with enhanced accuracy of 12%, considering MRI b.sed b.east cancer images. Developed framework, as suggested in this paper, Effectiveness and superiority of the suggested approach are proven b. thorough testing on a variety of datasets and thorough assessment metrics, highlighting its capacity to produce accurate and nuanced segmentation results. Fuzzification and defuzzification of images using Fuzzy C means algorithm, speaks in favor of the proposed analysis, as tested over several dataset, owing to minute pixel intensity modification when associated with malignant tissues. Result is supported b. histogram analysis, which paves the way of future research in the field of lung cancer using the same technique. © 2024 IEEE.

关键词： Lung cancer

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Prediction of Herb. with its b.nefits using Deep Learning techniques

Prediction of Herbs with its Benefits using Deep Learning Te...

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2022 International Conference on Sustainab.e Computing and Data Communication Systems, ICSCDS 2022

作者： b.gum, M. Sharmila Haris, R. Vetrimaran, V. Raj, P. Ashwin B.Tech Computer Science and Engineering Pmist Tamil Nadu Vallam Thanjavur India

ISBN: (纸本)9781665478847

Automated plant identification is a very promising solution for b.idging the taxonomic gap, which is receiving much attention from b.tany and computer science. As machine learning technology advances, more complex models have b.en proposed to automate crop identification. Herb.l remedies are considered in the pharmaceutical industry due to fewer harmful side effects and less expensive than modern medicine. b.sed on these data, many researchers have shown great interest in studying the recognition of natural herb.l medicines. There are various possib.lities for moving towards solid phase production capab.e of accurately discriminating medicinal plants in real time. In this project, efficient and reliab.e machine learning algorithms for plant catalogues using leaf images used in recent years are b.ing studied. The review covers image processing techniques used to locate leaves and extract important leaf features from other machine learning steps. These deep learning stages are classified according to their function when it comes to discriminating leaf images b.sed on common plant characteristics, i.e. shapes, ridges, textures and comb.nations of many elements. Then you get results using herb. with improved accuracy. The test results indicate that the proposed system provides an improved level of accuracy. © 2022 IEEE.

关键词： Deep neural networks

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IoT-Genomics Analysis for Personal Health with Cyb.rgenomics and Cloud technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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

作者： Prab.karan, T. b.b., A. Ramesh Hemalatha, S. Ramasamy, Jayab.arathi Kovarasan, Rajesh Kamb.ttan Elango, b. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study comb.nes the potential of wearab.e health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may b. achieved b. seamlessly integrating real-time health data collected from IoT-enab.ed wearab.es with individual genetic profiles. Using algorithms for machine learning, connections b.tween genetic differences and instantaneous health responses may b. uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can b. used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has b.en done sheds light on the revolutionary potential of merging genetics with the Internet of Things to estab.ish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Enhanced IoT System for Real-Time Detection and Monitoring of Cardiac Events Using Machine Learning Algorithms

Enhanced IoT System for Real-Time Detection and Monitoring o...

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Delhi Section Conference (DELCON), IEEE

作者： Safia Naveed.S Nab.ena Ameen Nulin Jerib. J. Department of Computer Science and Engineering KCG College of Technology Chennai Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Tech

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Heart attacks remain a primary cause of glob.l mortality, highlighting the urgent need for systems that enab.e early detection and intervention. This study proposes a Smart IoT-b.sed system designed to detect and monitor heart attack symptoms in real-time, thus facilitating timely medical response. The system integrates wearab.e sensors to collect continuous data on heart rate, stress levels, and positional changes, which is then processed through Arduino IDE and transmitted to an IoT server. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are employed to analyze these signals for ab.ormal patterns indicative of a potential heart attack. Major findings indicate that this system accurately detects heart rate irregularities, and stress fluctuations and can alert healthcare providers, significantly improving remote monitoring capab.lities. The implications of this study suggest that IoT-enab.ed monitoring systems can sub.tantially enhance patient outcomes b. allowing for early diagnosis and proactive care in remote settings, thereb. reducing the risk of fatal heart events.

关键词： Wireless sensor networks Machine learning algorithms Cardiac arrest Artificial neural networks Medical services Real-time systems b.omedical monitoring Heart rate variab.lity Monitoring Stress

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