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Analysis of Nutritional Status of Children in Karnataka Based on Machine Learning Techniques Using Indian Demographic and Health Survey data 1

Analysis of Nutritional Status of Children in Karnataka Base...

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1st International Conference on Computational Intelligence for Information, Security and Communication applications, CIISCA 2023

作者： Sharma, Anjali Rohitaksha, K. JSS Academy of Technical Education Bangalore Dept. of Computer Science and Engineering Karnataka Bengaluru India JSS Academy of Technical Education Bangalore Dept. of Master of Computer Application Karnataka Bengaluru India

ISBN: (纸本)9798350339727

Malnutrition is caused when an individual gets very little or too many nutrients, resulting in health problems. In particular, 'a deficit, excess, or imbalance of energy, protein, and other nutrients' negatively impacts the body's tissues and form. Malnutrition significantly contributes to infant mortality rates in developing nations, and India, as a developing country, experiences challenges associated with malnutrition in children under the age of five. The primary objective of this study is to analyze the factors influencing the nutritional status of under-five children in Karnataka, India, using data from the 2015-2016 Indian Demographic and Health Survey (IDHS). Logistic regression (LR), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), and Naïve Bayes (NB) are five prominent techniques in machine learning that are being utilized to successfully predict the factors affecting children's nutritional status. Body Mass Index (BMI), Height for Age Z score (HAZ), Weight for Age Z score (WAZ), and Weight for Height Z score (WHZ) are the main factors considered. Additionally, accuracy is used to do a systematic evaluation of the algorithms. Finally, the key factor impacting nutritional status in children was discovered to be HAZ based on accuracy, and the Random Forest produced the greatest findings and it was moderately superior to any other ML algorithms applied in this analysis. © 2023 IEEE.

关键词： Support vector machines

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Image Segmentation Survey: Classical and Deep Learning Methods 4

Image Segmentation Survey: Classical and Deep Learning Metho...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bachani, Viren Roychowdhury, Anish Dasgupta, Abhijit Ei-Alfy, Hazem BSc. Data Science SP Jain School of Global Management Sydney Australia SP Jain School of Global Management Data Science Dept. Sydney Australia SP Jain School of Global Management Data Science Dept. Mumbai India School of Computer Science University of Sydney Sydney Australia Alexandria University Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9798350395914

Image segmentation is a prerequisite to almost all computer vision applications. It enables the extraction of meaningful information from visual inputs by partitioning images into segments with shared features. This review paper begins by outlining different image segmentation tasks. It then surveys a broad spectrum of segmentation techniques, ranging from classical frameworks to deep-learning (DL) architectures. We perform this through a systematic literature analysis across a handful of applications as part of our methodology. A key contribution of our survey is that it elaborates on a few situations where classical approaches outperform DL-based methods in accuracy, along with the added advantage of computational efficiency. In some cases, these findings also provoke interest in revisiting classical methods, prompting strategies that leverage the strengths of both paradigms. © 2024 IEEE.

关键词： Markov processes

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Blood Test Report-Based Prediction and Classification of Diseases Using Artificial Neural Networks 1

Blood Test Report-Based Prediction and Classification of Dis...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Kodipalli, Ashwini Prajwal, N. Vignesh, Vamshi B. Rao, Trupthi Gargi, N. Devamane, Shridhar B. Global Academy of Technology Dept. Artificial Intelligence & Data Science Karnataka Bangalore India School of Computing Amrita Vishwavidyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Through early intervention and individualized treatment plans, timely disease detection and personalized healthcare can advance patient results and reduce healthcare costs. With the aim to categorize medical conditions, we present a dataset in this study that includes blood sample parameters like haemoglobin, cholesterol, and glucose. To analyse these parameters, we created a three-layered artificial neural network (ANN). Achieving an accuracy of 93.42% on the test set, the ANN was trained and validated on the dataset. With the aim of reducing overfitting and enhance model generalization, dropout layers were added. Furthermore, a comparative analysis with various optimizers was carried out. The results highlight the ANN's potential as a means for timly diagnosis, personalized healthcare, improving patient outcomes and lowering long-term healthcare costs. They also demonstrate the ANN's ability to accurately classify diseases. © 2024 IEEE.

关键词： Anaemia ANN Artificial Neural Networks Blood Test Classification Deep learning

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Generalizing Clinical De-identification Models by Privacy-safe data Augmentation using GPT-4

Generalizing Clinical De-identification Models by Privacy-sa...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kim, Woojin Hahm, Sungeun Lee, Jaejin Korean National Police Agency Seoul Korea Republic of Dept. of Data Science Seoul National University Seoul Korea Republic of Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798891761643

De-identification (de-ID) refers to removing the association between a set of identifying data and the data subject. In clinical data management, the de-ID of Protected Health Information (PHI) is critical for patient confidentiality. However, state-of-the-art de-ID models show poor generalization on a new dataset. This is due to the difficulty of retaining training corpora. Additionally, labeling standards and the formats of patient records vary across different institutions. Our study addresses these issues by exploiting GPT-4 for data augmentation through one-shot and zero-shot prompts. Our approach effectively circumvents the problem of PHI leakage, ensuring privacy by redacting PHI before processing. To evaluate the effectiveness of our proposal, we conduct cross-dataset testing. The experimental result demonstrates significant improvements across three types of F1 scores. © 2024 Association for Computational Linguistics.

关键词： Phishing

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics application 25

Investigating Principle Component Analysis Impact on the Per...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Almatarneh, Sattam Bulkrock, Olla Alazaidah, Raed Almahasneh, Hossam Samara, Ghassan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates Zarqa University Data Science & Artificial Intelligence Dept. Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798331540012

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion. © 2024 IEEE.

关键词： Dimensionality reduction

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HCB Machine Learning Approach For Movie Recommendation System 6

HCB Machine Learning Approach For Movie Recommendation Syste...

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Gourammolla, Samheeta Gokila, S. Hindustan Insititute of Technology and Science Dept. of Computer Application Chennai603103 India

ISBN: (纸本)9781665410359

The Recommendation System (RS) is a very powerful technique which supports the user to derive decision quickly. The RS is essential in any business model like B2B, B2C and C2C. The user wants to get the quality and essential information suitable to perception in an entertainment which falls on C2C category. The recommendation system can achieve this by filtering the essential choices from the large amount of rating of given by other users. The Hybrid Collaborative-Based (HCB) recommendation system in proposed work is experimented. The proposed experimental results on movie rating and review rating with movie lens data set shows high result than SVD, KNN and Co-Clustering Machine Learning (ML) algorithms. © 2022 IEEE.

关键词： Collaborative filtering

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Enhanced Identification of Care Preference Documentation in Patients’ Discharge Summaries Using Pre-trained Large Language Models 1

Enhanced Identification of Care Preference Documentation in ...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Arora, Saksham Khayal, Inas S. Department of Computer Science Dartmouth College HanoverNH United States Dartmouth Institute & Biomedical Data Science Geisel School of Medicine at Dartmouth Dept. of Computer Science Dartmouth College HanoverNH United States

ISBN: (纸本)9798350371987

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than the more appropriately broad population of ‘seriously ill’ patients. This study addresses this gap, first by matching decedent patients to non-decedent counterparts by using propensity score matching, accounting for important variables like age, gender, primary diagnoses, and patient co-morbidities, and then by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods. We trained and fine-tuned Bio_ClinicalBERT and ClinicalLongformer models on a large dataset of patient discharge summaries, enhancing their signal-to-noise ratio by focusing on key textual aspects, thereby capturing contextually nuanced mentions of care preference documentation. These models demonstrated high sensitivity and specificity compared to industry-standard keyword-search methods, proving adept.at interpreting complex clinical concepts. This study highlights the potential of transformer-based models specifically trained for clinical domain tasks. Our findings not only contribute to enhancing end-of-life communication and aligning treatment with patients’ care objectives but also pave the way for future research in this promising domain, with potential implications for improving patient care quality. ©2024 IEEE.

关键词： Large datasets

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Unmasking the Reality: A Comprehensive Study of Deepfake Identification Techniques and Their Expanding Terrain 2

Unmasking the Reality: A Comprehensive Study of Deepfake Ide...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Shubh Pathak, Disha Mohini Jain, Dhyanendra Srivastava, Somya Vats, Prashant Upadhyay, Govind Murari Department of Computer Science ABES Engineering College U.P. Ghaziabad India Department of Computer Science Engineering-AIML ABES Engineering College U.P. Ghaziabad India Dept. of CSE School of CSE Manipal University Jaipur Rajasthan Jaipur India Dept. of Computer Application Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798331530389

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity. © 2025 IEEE.

关键词： Contrastive Learning

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU 4

Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GR...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mouthami, K. Yuvaraj, N. Thilaheswaran, K.K. Lokeshvar, K.J. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kpr Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300093

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques;with Keras API and the Internet Movie database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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