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Predictive Modeling for Osteoporosis Risk Assessment from DXA Scans 10

Predictive Modeling for Osteoporosis Risk Assessment from DX...

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Varshney, Neeraj Pokhariya, Hemant Singh Shrivastava, Anurag Shalini, N. Kumar, Mukesh Singh, Pankaj Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

A common skeletal condition marked by decreased bone density, osteoporosis is a major global health concern, especially for elderly populations. This study aims to provide a novel method for evaluating osteoporosis risk by combining relevant clinical data with information from Dual-energy X-ray Absorptiometry (DXA) scans. Using a deductive technique and an interpretive attitude, we compiled a large dataset of clinical data and DXA scans from various patient groups. To ensure data integrity, sophisticated data preparation procedures were used, such as alignment, standardization, and management of missing data. To extract pertinent data, characteristic engineering and selection procedures were used, producing a cleaned dataset for modeling. The comprehensive predictive model was created using machine learning techniques such as support vector machines, random forests, and logistic regression. Thorough analysis produced encouraging findings, with R-squared values showing that the model fit the data very well. Furthermore, interpretability methods like Partial Dependence Plots (PDPs) and Shapley Additive explanations (SHAP) values offered insightful information about the significance and connections between features. © 2023 IEEE.

关键词： DXA data fractures learning medical imaging osteoporosis risk

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Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

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International Journal of Information Technology (Singapore) 2023年第1期15卷 87-100页

作者： Uthirapathy, Samson Ebenezar Sandanam, Domnic Department of Computer Applications National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India Department of Computing Science and Engineering Vignan’s Foundation for Science Technology & Research Vadlamudi Andra Pradesh Guntur 522213 India

Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer applications and Management.

关键词： Cuckoo search Forest fire algorithm Fuzzy C-means clustering Information diffusion Opinion analysis Social network

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Non Linear Grid Based Search Energy Optimization for AUV Aided Underwater Acoustic Sensor Network 10

Non Linear Grid Based Search Energy Optimization for AUV Aid...

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

作者： Ayyadurai, M. Maniraj, S.P. Sujatha, K. Deeptha, R. Anu, D. Suresh Babu, T.N. Rajalakshmi Engineering College Department of Cse Chennai India Srm Institute of Science and Technology Department of Cse Chennai Ramapuram India Srm Institute of Science and Technology Department of It Chennai Ramapuram India Presidency College Department of Computer Applications Bangalore India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Melmaruvathur India

ISBN: (纸本)9798350353068

Recently, the significance of the Underwater Acoustic Sensor Network (UWASN) attracts the researchers due to the huge benefits of data collection obtained from the sensor nodes under the ocean. Resource exploration in underwater uses a sensor node for data sensing, collecting, processing and forwarding. These sensor nodes are energy constrained and often depleted, then becomes inactive. This occurrence may lead to network failure while computing optimal node selection, hence proposes non linear Grid-Based Search Energy Optimization (GBSEO) algorithm to find the optimal forwarding node with Autonomous Underwater Vehicle (AUV). The GBSEO algorithm considers the underwater Doppler Effect, sedimental drift and water current based on non linear searching and thereby improves the overall efficiency of the network by optimizing the energy of sensor nodes. AUV utilizes non linear optimization to improve the path selection based on GBSEO and thereby increases the network lifetime, energy efficiency and reduces the time delay. The results show that the proposed GBSEO algorithm outperforms the traditional algorithm Clustering Algorithm Optimization (CAO). © 2024 IEEE.

关键词： Autonomous underwater vehicles

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Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehensive Study using XGBoost Classifier and Feature Selection Techniques

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Procedia computer science 2024年 235卷 3458-3467页

作者： Rekha R Nair Tina Babu Kishore S Vishnu Vilashini S Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru Karnataka India

Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue.

关键词： XGBoost Classifier SelectKBest Singular Value Decomposition Principal Component Analysis hepatocellular carcinoma

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Distributed Deep Learning for Medical Image Processing in Cloud Environments 1

Distributed Deep Learning for Medical Image Processing in Cl...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Varshney, Neeraj Madan, Parul Shrivastava, Anurag Kumar, C Praveen Rao, A Kakoli Srivastava, Amit Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The incorporation of distributed deep learning for medical image processing in cloud settings is the subject of this study. The findings demonstrate the high viability and significant performance advantages realized by cloud-based distributed systems, notably significant processing time savings, outstanding diagnostic accuracy, as well as improved scalability. The consequences for security and privacy have been discussed, with a focus on effective safeguards for private medical information. There is a void in the literature about resource and cost-effectiveness optimization tactics used in cloud-based systems. Future research must concentrate on resource optimization tactics for economic sustainability, study developing security risks and privacy techniques, and incorporate real-world implementations in order to improve this topic. This study informs the use of distributed deep learning in cloud-based medical image processing as well as adds to the body of knowledge in healthcare technology. © 2023 IEEE.

关键词： cloud computing Distributed deep learning medical image processing privacy resource optimization security

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Ensemble-Based Big Data Analytics for Disease Prediction in Iot 1

Ensemble-Based Big Data Analytics for Disease Prediction in ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Rakesh Madan, Parul Shrivastava, Anurag Kumar, C Praveen Rao, A.L.N. Sankhyan, Akhil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The developing nexus between Internet of Things (IoT) technologies, healthcare, as well as analytics for prediction is the subject of this study. A deductive method is used to investigate how IoT-generated medical data and group learning approaches might be combined for predicting diseases. The study is guided by a descriptive research approach that emphasizes the necessity to thoroughly evaluate any possible overlap between these categories. This investigation's foundation is secondary data collecting from several IoT-enabled healthcare device sources. Studying the effects of picking features overall engineering, researchers found that doing so significantly increased the accuracy of models by 7% and precision by 8%. The evident superiority using ensemble methods is demonstrated by comparison with conventional models, having Random Forest surpassing logarithms approximately 15% in frequency. The study also assesses each model's efficiency of resources and scaling, emphasizing how practically applicable they are to high-throughput, practical applications. The results demonstrate the intriguing possibility of ensemble-based informatics for predicting illnesses using data provided by the Internet of Things. The report makes recommendations for further research, such as investigating cutting-edge ensemble approaches and incorporating other data modalities. To further improve and validate the suggested approach, undertaking real-world validation tests and incorporating methods for interpretation are advised. The ultimate goal of this research is to transform illness management overall patient outcomes by advancing medical predictive analytics. © 2023 IEEE.

关键词： complicated interdependencies Internet of Things machine learning medical systems untapped market

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Mathematical Morphology Adopted Automatic Detection of Ground Glass Opacities in Lung CT Images of COVID-19 Patients

Mathematical Morphology Adopted Automatic Detection of Groun...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Chatterjee, Soma Chatterjee, Rohit Kamal Goswami, R.T. Techno International New Town Dept. of Computer Applications Kolkata India Birla Institute of Technology Dept. of Computer Science & Engineering Mesra Ranchi India Techno International New Town Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350399387

The severity of the nCOVID infection relies on the presence of Ground Glass Opacities (GGO) present in the patient's chest CT scan images. Although, detecting and delineating the precise boundaries of GGO in the chest CT images is challenging. Here, we proposed a fast and novel technique to automatically segment the regions containing GGO in lung CT images using mathematical morphology. We have tested our algorithm on the chest CT images of 145 Covid-positive cases. This unique segmentation approach correctly segments the lung field from chest CT images and identifies GGO with average sensitivity, specificity, and accuracy of 96.89%, 95.23%, and 97.22%, respectively. We used expert radiologists' hand-curated segmentation of GGO as ground truth for quantificational performance analysis. Our research results indicate that this algorithm performs well found in the literature. © 2023 IEEE.

关键词： COVID-19

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Artistic Style Transfer Using Generative Adversarial Networks: A Pix2Pix Implementation

Artistic Style Transfer Using Generative Adversarial Network...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kumar, Dubasi Pavan Bhadula, Shuchi Al-Farouni, Mohammed Varshney, Neeraj Sharma, Vishal Saraswat, Manish National Institute Of Technology Integrated Master Of Science Mathematics Department Of Mathematics Patna India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This investigation explores the viability of four noticeable models, specifically Pix2Pix, Neural Style Transfer (NST), Fast Neural Style Transfer (FastNST), and CycleGAN, within the space of aesthetic style exchange. The think about envelops a fastidious assessment of these models, investigating their capabilities in generating outwardly engaging and elaborately reliable pictures. Through broad tests and quantitative evaluations, Pix2Pix developed as a strong choice, accomplishing a Crest Signal-to-Noise Ratio (PSNR) of 26.78 dB, a Basic Likeness List (SSIM) of 0.832, and a Fréchet Initiation Separate (FID) of 54.21. NST, exceeding expectations in style devotion, achieved a PSNR of 29.45 dB, an SSIM of 0.905, and an FID of 72.03. FastNST, known for its real-time preparation, illustrated an adjusted execution with a PSNR of 28.12 dB, an SSIM of 0.892, and an FID of 68.54. CycleGAN, outlined for unpaired picture interpretation, accomplished a PSNR of 27.65 dB, an SSIM of 0.874, and an FID of 63.72. The results give profitable experiences into the comparative qualities and weaknesses of these models, educating their appropriateness in different artistic assignments. © 2024 IEEE.

关键词： Generative adversarial networks

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Patient Monitoring and Data Analytics with Fog Computing in Healthcare Iot 1

Patient Monitoring and Data Analytics with Fog Computing in ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Rakesh Pundir, Sumit Shrivastava, Anurag Kumar, C Praveen Rao, A Kakoli Sankhyan, Akhil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

A substantial advancement in healthcare technological advances, particularly in the area of patient monitoring, has been made with the combined use of fog computing and IoT. This study examines how this combination may be transformative in order to improve the efficacy as well as the effectiveness of medical service delivery. Fog Computing lowers latency and enables immediate data processing, which is necessary for quick treatment. Fog Computing extends processing power to the outer edges of the network. This connection transforms monitoring procedures by providing healthcare providers with ongoing, contextually rich patient data. IoT-driven monitoring of patients further expands care beyond of conventional clinical settings by enabling continuous, individualized monitoring in homes of patients. This not only promotes patient involvement but also a feeling of empowerment as well as independence in managing their wellness. Despite the claims, problems with interoperability as well as data security continue to exist. This study offers helpful insights for applying these innovations in practical situations by objectively reviewing the benefits and drawbacks of merging fog computing and the internet of things in healthcare. This study seeks to provide thorough guidance on how to implement the technology of fog technology and IoT in healthcare using an interpretative lens, a method based on inference, and the use of a descriptive design alongside secondary data collecting. In the end, this integration aims to transform patient monitoring procedures and bring in a new era of individualized and preventive healthcare delivery. © 2023 IEEE.

关键词： computing healthcare healthcare systems internet medicine

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Cluster Head Selection for Single and Multiple data Sinks in Heterogeneous WSN using Wild Horse Optimization 2

Cluster Head Selection for Single and Multiple data Sinks in...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Kumar, Gotte Ranjith Nayana, B.P. Venkatramulu, S. Saranya, N. Naga Universiti Tun Hussein Onn Malaysia Electrical and Electronic Engineering Johor Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India Kakatiya Institute of Technology and Science CSE Dept Telangana Warangal India Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Enhancing a network lifetime by balancing energy as well as minimizing delay is a significant aim of Wireless Sensor Network (WSN). Besides, number of clustering approaches are developed to this process through selecting the optimum Cluster Heads (CHs) in heterogeneous WSN, but it has a significant challenge. In this research, the Wild Horse Optimization (WHO)-based Optimized Clustering approach for single and multiple data sinks is proposed for optimizing CH selection through combining the parameters of sink distance, residual energy in its formulated fitness function. The proposed method achieves better results and it is evaluated by various performance metrices like residual energy, network lifetime, throughput, alive nodes, FND as well as LND and it achieves the values of about each as 61.37, 98.36, 7.8, 96.39, 6129 and 8347 respectively when compared to the existing methods such as Energy-Efficient CH selection utilizing Improved version of Grey Wolf Optimization-based (EECHIGWO) and Enhanced Pelican Optimization Algorithm for the CH Selection (EPOA-CHS). © 2024 IEEE.

关键词： Cluster Head selection Multiple data sinks Network lifetime Wild Horse Optimization and Wireless Sensor Network

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