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Enhancing Diabetes Detection: A Weighted Averaging Approach for Combined Model Accuracy 9

Enhancing Diabetes Detection: A Weighted Averaging Approach ...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Sami, Areesha Naseer, Anum Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology Department of Computer Science Lahore Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan University Lahore Information Technology Pakistan

ISBN: (纸本)9798350394474

Diabetes, marked by prolonged high blood sugar levels, poses a significant global health challenge. Precise early prediction is vital but faces hurdles due to limited data and complexities like outliers. Uncontrolled diabetes heightens severe complications, driving research into advanced machine and deep learning techniques. Healthcare professionals recognize the crucial role of Machine Learning Algorithms (MLA) in enhancing disease identification. This study integrates deep and machine learning models through techniques like K-means, CNN, NB, and SVM. Combining their predictions using weighted averaging yields a 79.5% accuracy, improving diabetes prediction globally. © 2024 IEEE.

关键词： Support vector machines

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator

Closed-Loop System Identification of an Unstable Nonlinear S...

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First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Benjaset Maneeloke Sudchai Boonto Peerayot Sanposh Yodyium Tipsuwan Natthawut Chinthaned Warit Tanaprasitpattana Dept. of Electrical Engineering Kasetsart University Bangkok Thailand Dept. of Control Systems and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Computer Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331505400

ISBN: (纸本)9798331505417

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies.

关键词： Time-frequency analysis Adaptation models Regulators Accuracy Fitting Machine learning Robustness Closed loop systems Complex systems Frequency control

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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare 19

Cloud-Based Framework for Data Exchange to Enhance Global He...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information technology, SEIT 2024

作者： Naz, Ammerha Ali, Muhammad Cheema, Sehrish Munawar Pires, Ivan Miguel Dept. of Computer Science University of Sialkot Punjab Sialkot Pakistan Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries. © 2024 The Authors. Published by Elsevier B.V.

关键词： Electronic health record

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Reliability and Lifetime Analysis of a Standalone PV System Employing Fuzzy Logic and P&O MPPT Methods Under Faulty Conditions

Reliability and Lifetime Analysis of a Standalone PV System ...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Navid Dehghan Ali Amini Mohammad Hossein Shaabani Behrooz Vahidi Dept. Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Dept. Electrical engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

In light of the increasing need for solar power, fueled by the depletion of fossil resources and their ecological consequence, various studies have been conducted to improve the tracking of the maximum power point. This optimization aims to enhance the utilization of PV cell power, especially in standalone applications, given the vital role of electricity in agriculture and local consumption. Hence, considerations regarding reliability and lifetime management hold substantial importance. This study delves into the examination and analysis of MPPT controllers, including the conventional P&O and Fuzzy Logic, for their application in PV systems. To achieve this objective, a 100 kW off-grid PV system is employed. Following the implementation of the two mentioned MPPT techniques, their critical failure modes undergo investigation through lifetime analysis. In order to assess the reliability status of these MPPTs under faulty conditions, appropriate reliability criteria are selected. The outcomes of the reliability assessments lead to the derivation of Markov chains, and the study on reliability provides the Mean Time to Failure as a metric for lifetime estimation. Additionally, their performance in the face of rapid load changes is employed to analyze the stability of each method. Ultimately, the most reliable MPPT method is recommended for standalone PV system, serving as a guide for engineering applications.

关键词： Maximum power point trackers Fuzzy logic Photovoltaic systems Renewable energy sources Stability criteria Estimation Lifetime estimation

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Emotion Classification and Intensity Prediction using Hybrid Deep Learning Models

Emotion Classification and Intensity Prediction using Hybrid...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

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

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

The study of people's feelings, emotions, and opinions toward a specific event or issue is known as sentiment analysis. Individuals can utilize social media to share their views and opinions on any subject or issue, resulting in massive amounts of unstructured data. By processing and evaluating these feelings, you can get insights and understand them. Multiple Machine Learning (ML) based approaches were used to study various points of view. Due to changes in the sequential order, stream lengths, and advanced logic, determining the exact thoughts expressed in the consumer comments is still difficult. This paper proposed the emotion analysis model by Vectorizing all the training data and creating hybrid models with the Conv 1D model with the LSTM model. The proposed outcomes show outstanding performance as compared to the other base models. © 2022 IEEE.

关键词： Sentiment analysis

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers

Photoplethysmography Signal Quality Assessment Using Neighbo...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Zahir Khan M. Sabarimalai Manikandan Mufti Mahmud Dept. of Computer Science and Engineering Indian Institute of Technology Palakkad Palakkad Kerala INDIA Dept. of Electrical Engineering Indian Institute of Technology Palakkad Palakkad Kerala INDIA Dept. of Computer Science Nottingham Trent University Clifton Lane Nottingham UK

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: $99.09\%, 95.03\%, 96.56 \%$ and $84.63\%$ using the GNB classifier.

关键词： Machine learning algorithms Accuracy Gaussian processes Feature extraction Photoplethysmography Classification algorithms Quality assessment

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Timely Monitoring of Markov Chains Under Sampling Rate Constraints

Timely Monitoring of Markov Chains Under Sampling Rate Const...

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IEEE International Conference on Communications (ICC)

作者： Nail Akar Sennur Ulukus Electrical and Electronics Engineering Dept. Bilkent University Ankara Turkey Dept. of Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We study a pull-based monitoring system in which a common remote monitor queries the states of a collection of heterogeneous finite-state irreducible continuous time Markov chain (CTMC) based information sources, according to a Poisson process with different per-source sampling rates, in order to maintain remote estimates of the states. Three information freshness models are considered to quantify the accuracy of the remote estimates: fresh when equal (FWE), fresh when sampled (FWS) and fresh when close (FWC). For each of these freshness models, closed-form expressions are derived for mean information freshness for each source, as a function of the sampling rate. Using these expressions, optimum sampling rates for all sources are obtained using water-filling based optimization for maximizing the weighted sum freshness of the monitoring system, under an overall sampling rate constraint. Numerical examples are presented to validate the effectiveness of the proposed method by comparing it to several baseline sampling policies.

关键词： Closed-form solutions Accuracy Computational modeling Numerical models Computational complexity Monitoring Optimization

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Machine Learning Approach for Skin Melanoma Classification

Machine Learning Approach for Skin Melanoma Classification

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2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Sanvordekar, Manjusha Kuppili, Venkatanareshbabu Modi, Chirag National Institute of Technology Dept. of Computer Science & Engineering Goa Ponda India

ISBN: (纸本)9781665454995

Melanoma is the most fatal type of malignant skin cancer, posing a serious threat to people's physical health. If melanoma of the skin is detected early, the chances of survival are very high. Dermoscopic images can be used to make an early diagnosis. Dermoscopy magnifies the skin, allowing dermatologists to better evaluate morphological features that are not easy to see with the naked eye. Machine learning is now an important technique for detecting various types of skin cancers. Convolutional Neural Network (CNN) has the potential to greatly assist dermatologists in the diagnosis of melanoma accurately. This paper uses a machine learning based approach to detect benign and malignant forms of skin cancer in dermoscopic images. The EfficientNet CNN model used here can design the appropriate network architecture to extract features with greater better accuracy and efficiency. The model is evaluated using dataset from the ISIC Archive. © 2022 IEEE.

关键词： Convolutional neural networks

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Facial Attribute Analysis for Emotion Recognition using Deep Learning 6

Facial Attribute Analysis for Emotion Recognition using Deep...

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

作者： Arun, D.N. Joshua, S. Daniel, Esther Sujith Mithun, M. Galvin Richardson, V. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665410359

computerized test highlights are involved every day in an assortment of ways of understanding biomedical examination, picture control, between PC connections, electronic gadgets and other business exercises. The principal motivation behind the venture is to work on the workplace. Orientation uniformity, skin tone, and feeling are assumed a key part in deciding the course of a PC. This task requires the development of a Convolutional Neural Network (CNN) for age and orientation. The structure is intended to highlight a pay of around 8 years and 2 names of orientation. As well as counting the years, looks a significant part in PC vision. It is marked as 6 passionate and 6 racial characteristics. Non-verbal relational abilities, like looks, looks, and signals, are utilized in numerous human PC capacities. Utilizing the Viola-Jones calculation, pictures are eliminated from CNN. With the right utilization of the client, the normal outcomes will be apparent. Knowing individuals' feelings, the directing framework can be further developed. Assuming that the underlying face is impartial or quiet, present day strategies work best as a worldwide picture, and there is an absence old enough forecast when the facial highlights change. The information is taken from IMDb-WIKI by age and orientation, while Fer2013 and Kaggle depend on feeling and racial character. The undertaking has two models, one for age and one for foreseeing orientation correspondence through the Resnet vault, and the other for passionate mindfulness. Another closure such as old enough, feeling, skin tone and orientation, the presence of the world, spoiling and arranging resources are offered. Specifically, the huge changes in reality that are not sifted by the picture handling calculation that plans and cycles those pictures are considered before they are communicated on CNN. © 2022 IEEE.

关键词： Convolutional neural networks

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Image Denoising based on Multi Domain Feature Inspired Convolutional Neural Network

Image Denoising based on Multi Domain Feature Inspired Convo...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Thakur, Ramesh Kumar Maji, Suman Kumar Indian Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Blind Gaussian denoising is the process of removing noise from an image affected by additive white Gaussian noise (AWGN) of unknown variance. This paper proposes a multi domain feature inspired deep neural network for blindly (noise variance unknown) denoising AWGN corrupted images. The proposed network uses features of the noisy image in both spatial domain as well as frequency (discrete cosine transform) domain. Spatial and frequency domain features are concatenated to get a combined feature map from which we generate the residual noisy image. The residual noise is then subtracted from the input noisy image to obtain the final denoised image. By restricting the number of layers and trainable parameters in the proposed network, we ensure that it is lightweight in nature and provides fast denoising capability. Comparison with classical state-of-the-art methods and recent CNN based denoising approaches show that the proposed network gives superior denoising results both visually and quantitatively. Our code is uploaded at https://***/RTSIR/MDFBGDN. © 2022 IEEE.

关键词： Convolution

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