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Boosting-Based Machine Learning Approaches for Diabetes Prediction using Indian Demographic and Health Survey Data 3

Boosting-Based Machine Learning Approaches for Diabetes Pred...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India

ISBN: (纸本)9798350364569

Traditional diagnostic techniques are time-consuming and prone to error. Performance is added however time and cost are reduced by using computer-aided diagnostic methods. This study aims to calculate how healthy boosting-based machine learning classifiers perform in automatically identifying diabetes in the Indian context. For this experiment, Indian demographic and health survey 2021 data on women aged 19 to 79 are considered. This data includes clinical, anthropometric, and biochemical components. 12,103 of the 706,504 responders who tested positive for diabetic testing were considered. The predictive exploration-based boosting machine learning models are adopted, such as adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Kernel Principal Component Analysis is employed for feature extraction. The Extreme Gradient Boosting model demonstrated strong performance on the diabetes dataset both before and after applying Kernel PCA. The metrics including accuracy, F1-score, precision, and recall for the XGBoost model showed improvements: accuracy increased from 81% to 83%, F1-score from 81% to 82%, precision from 81% to 88%, and recall from 81% to 76%.In contrast, Adaptive Boosting performed poorly, while Categorical Boosting, Gradient Boosting, and Light Gradient Boosting models achieved moderate performance levels. Overall, the model attained optimal performance across metrics such as accuracy, precision, Area Under the Curve (AUC), and recall. © 2024 IEEE.

关键词： Adaptive boosting

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Smart Contract Cloning Identification Using BERT Model Based Representation Learning 20

Smart Contract Cloning Identification Using BERT Model Based...

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20th IEEE India Council International Conference, INDICON 2023

作者： Kumar, Vishnu Pranav, Pisipati Srinivas, M. National Institute of Technology Warangal Dept. of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350305593

The use of Smart Contracts and Blockchains has experienced a significant rise, leading to the automation of various financial and corporate operations. However, the occurrence of flaws and vulnerabilities in smart contracts has resulted in significant financial losses, raising concerns about their security. It is crucial to enhance the maintenance and reliability of smart contract codes. To detect the clones and bugs in a smart contract, in this paper, we propose a deep learning based model that learns the embedding as a continuous numeric-valued vector for each smart contract. Our approach involves five steps. First, we divide the smart contract Solidity program into streams along with their corresponding structural information. Second, we tokenize the textual data of the smart contract and create a corpus. Third, we employ the BERT word embedding model on the smart contract's tokens corpus to learn feature representation for each token, generating a fixed-length vector of size 768. Fourth, we compute the embedding of each smart contract by considering the weighted sum of tokens within the contract. Finally, to measure clone similarity, we use hierarchical clustering on the feature vectors of smart contracts. The experimental results obtained using our approach on 22,000 Ethereum blockchain Solidity contracts show a clone ratio of 92.14%, and a precision of 93% in detecting clone-related bugs, representing significantly higher accuracy compared to conventional algorithms. © 2023 IEEE.

关键词： BERT Model Blockchain Bug Detection Clone Detection Ethereum Smart Contract Solidity programming

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Nutrition Detection during Gestation Period using ML Algorithms 14

Nutrition Detection during Gestation Period using ML Algorit...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Dixit, Sunanda Sriram, Navayug Ashritha, S.B. Datta, Paidi Venkata Revanth Krishna, Bolem Jagadesh Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

The issue of low birth weight in infants is a significant concern in prenatal care, and it can have adverse effects on the newborn's health, sometimes leading to mortality. This problem contributes to high rates of child mortality worldwide. Artificial intelligence, particularly ML, offers potential solutions for predicting whether a fetus will be born small for its gestational age. Early detection of fetal developmental issues is critical, as timely intervention can increase gestation days and improve fetal weight at birth, reducing the risk of neonatal morbidity and mortality. This proposal aims to explore various machine learning methods for predicting small-for-gestational-age infants, emphasizing the importance of early detection and intervention to improve outcomes. © Grenze Scientific Society, 2023.

关键词： Machine learning

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Recognition of Fes Using A Novel Detection Method Based on Convolutional Neural Networks 1

Recognition of Fes Using A Novel Detection Method Based on C...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Thakur, Sumit Raza Khan, Nikhat IES UNIVERSITY Department of Computer Science and Engineering Bhopal India IES COLLEGE of TECHNOLOGY Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350345957

Currently, the wireless access method utilized by systems is 5G technology. In this study, a lightweight NN for emotion recognition is proposed, where a visual system can be incorporated into devices with a low computational capability to perform face expression classification while simultaneously minimizing the amount of parameters utilized in the classification process. To improve the reliability of the results, it is helpful to package the models as a service that other collaborating systems can use. Face expression resource development frameworks can be tailored to address the requirements of different security models, such as criminal threats, government secrets, etc. This model will have the opposite influence on detection and classification and produces good detection results by identifying photographs inside the dataset, which indicates that the model built into this research is suitable for multi- categorization of facial emotions. In general, comprehended a visual system that can be incorporated on devices with minimal computer capacity to attain result classification and decrease many factors. Following comparison with the algo in recent years, the accuracy of our algo will be higher than theirs, and it has achieved good detection results in photographs inside the dataset after the experimental results. © 2023 IEEE.

关键词： Face recognition

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An Indicative Demanding Teaching Scenario for Primary Education with the Support of a Multi-layer Fog Computing Architecture 26

An Indicative Demanding Teaching Scenario for Primary Educat...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Giannou, Vasiliki Mamalis, Basilis University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary Education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed. © 2022 ACM.

关键词： Fog computing

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Comparative Analysis of Multi-Model and Uni-Model Approaches using Time Distributed Bidirectional LSTM for Multidata Sentiment Analysis 14

Comparative Analysis of Multi-Model and Uni-Model Approaches...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Gupta, Krati Parmar, Mahesh Madhav Institute of Technology & Science Dept. of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350335095

The sentiment of the data contained in a text can be determined by analysis. This sentiment can be good, negative, or neutral. When carrying out this research, one may choose to employ either a uni-model or a multi-model technique. In the uni-model method, both the training and the analysis are performed using just a single model. The application of this strategy is uncomplicated and uncomplicated, but it might not be able to make sense of complicated or different facts, which would lead to wrong findings. Instead, the multi-model technique utilizes a number of different models, each of which is designed to examine a particular aspect of the data or a particular kind of attitude. This allows the multi-model strategy to produce more accurate results. The results of several different models might be combined in order to obtain a more in-dept. understanding of feelings. This method is more accurate overall and captures more complex expressions of emotion, but it requires more time and money to train personnel and put into practice. The evaluation of metrics was carried out using Multimodal and mono Model with Time Distributed Bidirectional LSTM in this research. Both the loss and the accuracy. The Multi-Model achieves the highest accuracy of 99.71 percent for the train, while the validation accuracy achieves 99.32 percent, and the Multi-Model achieves the least loss of 0.010 percent. © 2023 IEEE.

关键词： Sentiment analysis

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On Min-Max Graph Balancing with Strict Negative Correlation Constraints 34

On Min-Max Graph Balancing with Strict Negative Correlation ...

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Kuo, Ting-Yu Chen, Yu-Han Frosini, Andrea Hsieh, Sun-Yuan Tsai, Shi-Chun Kao, Mong-Jen Dept. of Computer Science National Yang-Ming Chiao-Tung University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Dept. of Mathematics and Informatics University of Florence Italy Dept. of Computer Science and Information Engineering National Chi-Nan University Puli Taiwan

ISBN: (纸本)9783959772891

We consider the min-max graph balancing problem with strict negative correlation (SNC) constraints. The graph balancing problem arises as an equivalent formulation of the classic unrelated machine scheduling problem, where we are given a hypergraph G = (V, E) with vertex-dependent edge weight function p: E × V 7→ Z≥0 that represents the processing time of the edges (jobs). The SNC constraints, which are given as edge subsets C1, C2, . . ., Ck, require that the edges in the same subset cannot be assigned to the same vertex at the same time. Under these constraints, the goal is to compute an edge orientation (assignment) that minimizes the maximum workload of the vertices. In this paper, we conduct a general study on the approximability of this problem. First, we show that, in the presence of SNC constraints, the case with maxe∈E |e| = maxi |Ci| = 2 is the only case for which approximation solutions can be obtained. Further generalization on either direction, e.g., maxe∈E |e| or maxi |Ci|, will directly make computing a feasible solution an NP-complete problem to solve. Then, we present a 2-approximation algorithm for the case with maxe∈E |e| = maxi |Ci| = 2, based on a set of structural simplifications and a tailored assignment LP for this problem. We note that our approach is general and can be applied to similar settings, e.g., scheduling with SNC constraints to minimize the weighted completion time, to obtain similar approximation guarantees. Further cases are discussed to describe the landscape of the approximability of this prbolem. For the case with |V | ≤ 2, which is already known to be NP-hard, we present a fully-polynomial time approximation scheme (FPTAS). On the other hand, we show that the problem is at least as hard as vertex cover to approximate when |V | ≥ 3. © Ting-Yu Kuo, Yu-Han Chen, Andrea Frosini, Sun-Yuan Hsieh, Shi-Chun Tsai, and Mong-Jen Kao;licensed under Creative Commons License CC-BY 4.0.

关键词： Computational complexity

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Quantum Resistance Blockchain Algorithm using QHF and Novel Consensus Mechanism

Quantum Resistance Blockchain Algorithm using QHF and Novel ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Paul, Soumyadip Mazumdar, Mausumi Maitra Chakraborty, Sarit Government College of Engineering & Ceramic Technology Dept. of Information Technology Kolkata700010 India Government College of Engineering and Leather Technology Department of Computer Science and Engineering Kolkata700106 India

ISBN: (纸本)9798331529833

The emergence of quantum computing poses a significant threat to the security of traditional blockchain systems, which rely heavily on classical cryptographic algorithms. To safeguard the integrity and reliability of blockchain technology in the upcoming era, this work proposes a novel quantum-resistant blockchain architecture. This approach integrates a robust consensus mechanism, with advanced quantum cryptographic primitives, including quantum digital signatures based on a Quantum Hash Function (QHF). Incorporating a quantum hash function enhances the blockchain resistance, providing an additional layer of security against quantum attacks. By combining these elements, our method creates a blockchain system fortified against the potential vulnerabilities introduced by quantum computers. The proposed solution leverages the power of QHFs to safeguard transaction integrity, while the consensus mechanism ensures the reliability and decentralization of the network. This paper also delves into the theoretical underpinnings of quantum blockchains, exploring their design, feasibility, and potential applications, emphasizing their promising future, particularly in areas such as decentralized identity authentication. Finally, it contributes to the development of a secure and resilient blockchain ecosystem capable of thriving in the coming era. © 2025 IEEE.

关键词： Quantum computers

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HealthPredictor: Accurate Health Insights at Your Fingertips 3

HealthPredictor: Accurate Health Insights at Your Fingertips

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sajjan, Mahantesh M. Ammanagi, Nausheen Pujari, Laxmi Kamatar, Vishwanath S Belavatgi, Meenaz K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580 027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi580 027 India

ISBN: (纸本)9798350364569

The "HealthPredictor: Accurate Health Insights at Your Fingertips"system transforms healthcare by predicting diseases based on user symptoms. Utilizing advanced machine learning models such as Decision Tree, SVM, Random Forest, and Logistic Regression, it assesses the likelihood of various illnesses. The system compares symptoms to a comprehensive dataset to provide accurate predictions, aiding in early detection. Users can consult with specialist doctors through an integrated chat feature, ensuring quick access to medical advice. By continuously learning from historical data, the system enhances its predictive capabilities, identifying new health patterns and trends. This proactive approach empowers users with timely information and personalized consultations, leading to better health outcomes. © 2024 IEEE.

关键词： Electronic health record

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Indian Sign Language Recognition for Static Gestures using DenseNet169 Model 8

Indian Sign Language Recognition for Static Gestures using D...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Keren Beulah, I. Raimond, Kumudha Litisha Miraclin, G. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350396638

It is usually difficult to communicate with someone who has hearing loss. Sign language is a way of communication used by dumb and deaf people. Each country has its own unique sign language. Being an Indian, it's a pride and identity to develop a system for Indian sign language (ISL) recognition. This proposed system aims to examine the difficulties in classifying signs in ISL and intends to take a step forward in this field by utilizing machine learning (ML) models for gesture prediction. This model is trained using a convolutional neural network (CNN), and then the Densenet169 architecture is utilized to recognize the images. This model has been tested and has yielded the highest accuracy of 99.95% in the classification of signs. © 2023 IEEE.

关键词： Machine learning

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