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Diabetes Mellitus Prediction and Severity Calculation Using Stacked Ensemble Method

SN computer science 2024年第8期5卷 1001页

作者： Ananthi, G. Santhiya, S. Gokila, V. Department of Computer Science and Engineering Mepco Schlenk Engineering College (Autonomous) Sivakasi India

Millions of individuals throughout the world suffer with diabetes mellitus, a chronic condition that can be effectively managed with early detection and precise prognosis. In this work, a machine learning method for predicting diabetes utilizes stacking ensemble algorithms with hyperparameter tuning. The proposed method uses three fundamental classifiers such as Random Forest, logistic regression, gradient boosting and tunes certain hyperparameters to improve the performance of each algorithm. Another classifier, called support vector machine, with hyperparameter tweaking is used as a meta-classifier to aggregate the predictions of the basic classifiers. It has been demonstrated that the stacking ensemble technique, especially when combined with hyperparameter tuning, enhances the prediction performance of machine learning models. The proposed technique is evaluated using two datasets, namely PIMA Indian Diabetes dataset, and dataset 2. Hierarchical clustering is used to forecast the disease's severity level. Severity levels are assigned based on the model's clustering results. The findings show that the suggested method accurately predicts diabetes and performs better than the individual base classifiers. To evaluate the severity level, a model created using a machine learning classification technique is chosen for further development. The model is finally included with the web application. The efficiency of the model is evaluated using a variety of performance criteria for evaluation such as accuracy, precision, recall, and F1-score. The proposed stacked ensemble method results in the accuracy of 97% which shows the improvement in the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accuracy Diabetes Severity Stacked ensemble method Web application

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Empowering Kubernetes with Platform engineering to Automate IoT Processing Workflows Orchestration at the Edge 29

Empowering Kubernetes with Platform Engineering to Automate ...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Tazzioli, Davide Sabbioni, Andrea Bujari, Armir Foschini, Luca University of Bologna Department of Computer Science and Engineering Italy

ISBN: (纸本)9798350377644

The proliferation of IoT devices provides access to an unprecedented amount of data coming from sensors and smart devices, characterized by high volumes and strong locality. The Edge Computing paradigm, by providing low-latency, privacy-aware, resilient, and distributed computational resources is considered a fundamental enabler for the processing of the massive data volume incoming from IoT devices. However, the heterogeneous and decentralized edge resource poses a significant barrier to seamless device integration and management. In this paper, we propose a Platform engineering architecture that extends the Kubernetes control plane to simplify the orchestration and management of the processing workflow that produces data incoming from IoT devices. The proposed platform integrates management capabilities that address the complexity of provisioning het-erogeneous network, storage, and compute resources, enabling smart management of edge services. © 2024 IEEE.

关键词： Storage management

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Enhancing Crop Health Monitoring: A ResNet50 Approach to Automated Plant Disease Severity Prediction

Enhancing Crop Health Monitoring: A ResNet50 Approach to Aut...

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2025 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2025

作者： Gunavathi, H.S. Priya, Ishani Sree, Kothakota Bindhu Kushwaha, Aparna Bangalore Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798331529833

Plant diseases significantly impact agricultural productivity, making it essential to accurately assess not only the presence of disease but also its severity. This paper presents a deep learning-based framework using ResNet50 to predict plant disease severity across multiple levels (Healthy, Mild, Moderate, and Severe). The system is designed to process plant leaf images, perform augmentation to improve robustness and classify disease severity with high accuracy. The proposed method leverages labelled and pseudo- labelled datasets to enhance the model's performance through data augmentation and training on unlabelled images. The results demonstrate the model's effectiveness in distinguishing between varying levels of disease progression, providing valuable insights for early intervention and optimized crop management strategies. This platform is crucial for automating plant disease severity monitoring, and aiding decision-making processes for timely interventions in agriculture. © 2025 IEEE.

关键词： agriculture data augmentation deep learning image classification plant disease ResNet50 severity prediction

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Investigating the Benefits of Using Non-Orthogonal Multiple Access for Double Connetion LiFi-RF Networks 5

Investigating the Benefits of Using Non-Orthogonal Multiple ...

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5th International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Kassahun, Ermias Jember, Kehali A. Ayelign, Paulos University of Gondar Department of Electrical and Computer Engineering Gondar Ethiopia Mettu University Department of Electrical and Computer Engineering Metu Ethiopia

ISBN: (纸本)9798350328486

Recently, light fidelity (LiFi) networks have emerged as a preferred option for indoor data access due to their high data speeds, low installation costs, and lack of radio frequency (RF) interference. They are expected to play a key role in the next generation of indoor networking. Non-orthogonal multiple access (NOMA) is another approach that is being considered for the future generation of wireless networks. NOMA has a higher spectrum efficiency compared to orthogonal access techniques, making it an attractive option. This study investigates the impact of interference on a double connected LiFi-RF network that uses NOMA for both LiFi and RF subsystems simultaneously. computer simulations demonstrate the validity of the investigation, showcasing the advantages provided by the proposed scheme over traditional opportunistic methods while also demonstrating its robustness with respect to the number of users in the network. © 2023 IEEE.

关键词： Frequency division multiple access

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Soundtrack Success: Unveiling Song Popularity Patterns Using Machine Learning Implementation

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SN computer science 2024年第3期5卷 278页

作者： Arora, Shruti Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Music streaming services are getting increasingly popular as a result of the prevalent need for web and smart gadgets. Melophiles are drawn toward a range of musical genres and create a unique digital footprint. The emotional reactions of the music listeners may cause physiological changes as well as neurological benefits. However, any musician should avoid being influenced by an algorithmic recommendation presented in the research. The most prominent aspect for any musician is to stay true to their own musical style and to be novel, honest, and sincere. The main objective of this research work is to provide precise music predictions to the artists before they launch their music in the market. This work also aims to improve the accuracy of the prediction models using some feature-engineering-based techniques to manipulate the Spotify dataset and predict whether the song will be a hit or not. The proposed work leverages state-of-the-art machine learning models, including Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest, GBM, and neural networks to explore the predictive factors influencing the inclusion of a particular music track in the featured Spotify Hit-50 list. Furthermore, it offers descriptive statistical analysis of the features that can help in popularity prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Hit song science audio features Music information retrieval Music streaming data Regression

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Improved YOLOv8n Models for Object Detection in Remote Sensing Images

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IEEE Access 2025年 13卷 97889-97900页

作者： Chen, Young-Long Hung, Kai-Chun Zhang, Jia-Yun Lin, Ling-Wei National Taichung University of Science and Technology Department of Computer Science and Information Engineering China National Taichung University of Science and Technology College of Information and Distribution Science China

In recent years, numerous efficient object detectors have emerged in computer vision. However, applying these models to remote sensing images remains challenging due to complex backgrounds, high object scale variation, and the difficulty of detecting small objects. Many existing approaches prioritize accuracy but overlook the trade-off between precision and efficiency, limiting their usability in resource-constrained environments. To address these challenges, we propose a lightweight model based on the original YOLOv8n, culminating in the Transformer with Bi-directional and Coordinate Attention-YOLOv8n (TBC-YOLO8n) model. Our approach introduces a weighted bidirectional feature pyramid network that adaptively assigns importance to different feature maps, enhancing multi-scale object detection. Additionally, we integrate transformer blocks to improve global feature representation and capture long-range dependencies, alongside a coordinate attention block before each detection head to enhance localization by incorporating spatial information. Unlike previous YOLO variants, our method optimizes feature fusion strategies and integrates advanced attention mechanisms specifically tailored for remote sensing images, effectively addressing small-object detection, large-scale variations, and complex backgrounds. Compared to the original YOLOv8n, our proposed TBC-YOLO8n model improves mAP@50 from 90.2% to 94.5%, with a slight increase in inference time from 3.0 ms to 4.0 ms. Experimental results confirm the robustness and effectiveness of our model, making it a practical choice for real-time remote sensing applications with limited computational resources. © 2013 IEEE.

关键词： Remote sensing

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Enhancing Diagnostic Accuracy in Breast Ultrasound Imaging through Deep Learning and Image Augmentation Techniques 2

Enhancing Diagnostic Accuracy in Breast Ultrasound Imaging t...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mahesh, R.T. Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798331534950

This study investigates the application of deep learning models, specifically EfficientNet-B7, combined with advanced image augmentation strategies to improve the diagnostic accuracy of breast ultrasound image classification. The research utilizes a comprehensive dataset of breast ultrasound images labelled as benign, malignant, and normal, supplemented by corresponding masks to highlight areas of interest. By applying image changes such as Random Horizontal Flip, Random Rotation, and Color Jitter, the study intends to address class imbalances and boost the model's generalizability. The EfficientNet-B7 model, fine-tuned on the expanded dataset, attained a classification accuracy of 99% with a precision of 99%, recall of 99%, and F1-score of 99%, demonstrating a considerable improvement in recognizing various circumstances. These metrics underscore the model's effectiveness in minimizing diagnostic errors, crucial for clinical applications. This research highlights the potential of combining high-capacity neural networks with targeted image augmentations to improve outcomes in medical imaging diagnostics. © 2024 IEEE.

关键词： Ultrasonic imaging

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Epileptic Seizure Prediction through ML And DL Models: A Survey 8

Epileptic Seizure Prediction through ML And DL Models: A Sur...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Viswanath, J. Annamalai, S. Ramesh, S. Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350367904

Epileptic seizures are unpredictable and pose significant risks to individuals affected by epilepsy. Electroencephalogram (EEG) signals offer a promising avenue for early seizure prediction, enabling timely intervention and improved quality of life for patients. This survey paper provides a comprehensive review of Deep learning (DL) and Machine learning (ML) learning techniques applied to EEG-based epileptic seizure prediction. It covers traditional ML models, advanced DL architectures, and optimization algorithms, highlighting their effectiveness and limitations. Additionally, the paper discusses feature extraction methods, signal processing techniques, and evaluation metrics used in this domain. Real-world applications, current challenges, and future research directions are explored to offer a holistic view of the field. By examining state-of-the-art methods, this survey aims to provide valuable insights into the development of robust, real-time seizure prediction systems, fostering advancements in clinical applications and patient care. © 2024 IEEE.

关键词： Prediction models

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Modeling a novel modified DeepLabV3Plus network for localization of tampered region in an image and video

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Multimedia Tools and Applications 2025年 1-28页

作者： Anbu, T. Joe, M. Milton Murugeswari, G. Department of Computer Science and Engineering Manonmaniam Sundaranar University Tamil Nadu Tirunelveli India Tamil Nadu Tirunelveli India

Digital Image and Digital Video forgery describes the process of manipulating or modifying digital photos or videos in order to trick viewers or misrepresent reality. Due to the widespread availability of multimedia editing tools, it becomes simple to manipulate digital images and videos which cause many problems in real life. Using the manipulated images or videos people might spread wrong information or fake news and also can cause trouble in courts, hurt people’s reputation or lead to financial scams. The challenge of detecting and localizing forged regions in images and videos has become increasingly significant. Existing forgery detection techniques often rely on format-dependent forgery localization approach and also inefficient in feature extraction. In this research, we propose a novel methodology for localizing forged regions in an image and video through semantic segmentation using Modified DeepLabV3Plus Network. The DeepLabV3Plus Network is modified using MobileNetV2 network and Attention layer. This work improves feature extraction by empowering the network to actually focus on appropriate spatial regions and catch both local as well global features effectively and also it works on different format images. The result demonstrates that the tampered regions are localized accurately in tampered image and video. The experimental evaluations on diverse dataset demonstrates the proposed model’s superior performance in accurately localizing forged regions with an global accuracy of 98.56% and 98.07% in an image and video, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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