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共 17301 条记录，以下是281-290 订阅

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Machine Learning in Healthcare: Prognosis Heart Disease Prediction 4

Machine Learning in Healthcare: Prognosis Heart Disease Pred...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Ramnath, M. Revathi, B. Selva Birunda, S. Usharani, C. Ramana, R. Tyson Masillamani, J. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Thamirabharani Engineernig College Department of Computer Science and Engineering Tirunelveli India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

The heart, being a crucial organ, necessitates meticulous care. Accurate information is essential for identifying heart-related disorders. Precise patient data is vital for hospitals to effectively predict and treat cardiac issues. Utilizing advanced Machine Learning algorithms, such as Logistic Regression, Random Forest, decision trees, K-Nearest Neighbours, Support Vector Machine, Naive Bayes along with statistical models, makes the accurate prediction of heart diseases feasible and accuracy comparison has been made. Finally, a better model has been developed to predict heart disease predictions. Medical practitioners can efficiently detect and address cardiac problems by leveraging these sophisticated algorithms, thereby maximizing the benefits of current medical technology. The application of these algorithms has notably improved patient health and the overall quality of care. Furthermore, hospitals to allocate the resources more precisely perform the predictive analytics more efficiently and enhance patient outcomes. By adopting these technological advancements, medical professionals can stay ahead of potential heart-related issues and deliver better care to their patients. © 2024 IEEE.

关键词： Logistic regression

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A Survey on Deep Learning Techniques for Secure and Accurate Mobile Sink Position Prediction in Vehicular Pattern WSN 8

A Survey on Deep Learning Techniques for Secure and Accurate...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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Harnessing QGIS and Random Forest Analysis for Precision Storm Drain Reinforcement 2

Harnessing QGIS and Random Forest Analysis for Precision Sto...

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

作者： Ramachandran, Ramya Arthi, D. Sudha, D. Anil, Abhirami B Babu, Aravind Md, Haripriya KCG College of Technology Artificial Intelligence and Data Science Chennai India KCG College of Technology Department of Computer Science and Engineering India KCG College of Technology Science Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331534950

Amidst the backdrop of increased urbanization and shifting climate patterns, this research delves into the growing vulnerability of landscapes to floods. The focus is on storm drain placement, emphasizing the identification of suitable locations and the need to reinforce drainage networks. The analysis takes into account parameters linked to flood sensitivity, including total rainfall, decadal growth rate, and soil types in each district within a state. Leveraging Geographic Information Systems (GIS) and relevant Coordinate Reference Systems (CRS), the study integrates a diverse array of datasets to inform precise cartographic projections. Employing a Random Forest Classifier, the study dissects the influence of these parameters on storm drain placement. The findings offer valuable insights for decision-makers to improve stormwater management systems, underscoring the importance of ongoing maintenance. Overcoming the challenge of obtaining official government data for recommendations, the research advocates the adoption of new technologies and data integration methods. By combining various data sources, such as topography, rainfall patterns, historical flood records, land use, and infrastructure data, it lays the groundwork for more advanced urban water management strategies, fortifying cities against flooding. © 2024 IEEE.

关键词： Rain

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Transforming Heart Disease Detection with BERT: Novel Architectures and Fine-Tuning Techniques

Transforming Heart Disease Detection with BERT: Novel Archit...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Javeed, Muhammad Usman Ali, Muhammad Shafqat Iqbal, Asma Azhar, Muhammad Aslam, Shafqat Maria Shabbir, Ifra Department of Computer Science Comsats University Islamabad Sahiwal Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong

ISBN: (纸本)9798331510503

The prediction of heart disease is crucial for effective prevention and treatment. However, extracting clinical information such as CAD, smoking, hypertension, hyperlipidemia, obesity, and family history of CAD from unstructured electronic health records (EHRs) poses significant challenges to clinicians. This research introduces a novel approach that leverages an ensemble of transfer learning algorithms combined with a multi-head attention mechanism to automatically extract heart disease risk factors from EHRs. Various deep learning models, including BERT, BioBERT, BioClinical BERT, RoBERTa, and XLNet, were initially trained on medical data sets and subsequently fine-tuned on the i2b2 clinical data set. Individual models delivered strong results, with RoBERTa achieving the highest accuracy of 95. 27% and an F1 score of 94. 94%. BioBERT, BioClinical BERT, XLNet, and BERT also performed well, with precision ranging from 94. 73% to 95. 03%. However, the proposed ensemble model with multi-head attention outperformed all, achieving an accuracy of 96.35% and the F1-score of 95.76%. These findings highlight the superior ability of the ensemble model to capture complex inter-dependencies between heart disease risk factors, making it a robust tool for clinical prediction. © 2024 IEEE.

关键词： Electronic health record

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Multi-task Learning with Pre-trained Language Models for Mental Illness Screening

Multi-task Learning with Pre-trained Language Models for Men...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Shrestha, Avantika Tlachac, M.L. Flores, Ricardo Hickey, Kevin Rundensteiner, Elke A. Worcester Polytechnic Institute Data Science Department WorcesterMA United States Bryant University Center for Health and Behavioral Sciences Department of Information Systems and Analytics SmithfieldRI United States University of Concepcion Department of Computer Science Biobio Concepcion Chile Worcester Polytechnic Institute Data Science Department Computer Science Department WorcesterMA United States

ISBN: (纸本)9798350362480

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illness screening potential of language models on modalities such as transcripts. Such datasets suffer from a limited number of participants. To overcome these challenges, we take a two-pronged approach: (1) we leverage multi-task learning to model multiple illnesses concurrently namely MDD and PTSD screening, and (2) we plug in pre-trained language models as a backbone, namely, Bidirectional Encoder Representations from Transformers (BERT) variants - which are capable of learning the linguistic content of clinical interview transcripts. In particular, we experiment with three multi-task weighting strategies and five BERT variants, applying them to 15 transcript sets extracted from the Distress Analysis Interview Corpus. Our results indicate that leveraging multi-task learning, especially with meta-weighting, increases the screening performance compared to single-task learning. Our multi-task learning model strategies improved the F1 scores for all 15 datasets for both MDD and PTSD screening. Notably, multitask learning improved MDD screening ability by 20 percent with transcripts regarding 'regret' and achieved F1 of 0.89 and 0.82 respectively for MDD and PTSD screening with transcripts regarding 'medical history'. Our findings may help researchers develop more effective mental illness screening models. © 2024 IEEE.

关键词： Multi-task learning

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Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fusion for Breakthrough Early Detection of Alzheimer's 9

Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fu...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Selvi, G. Anbu Karthick, T. Srmist Department of Computer Science And Engineering Chennai Kattankulathur India School of Computing Srmist Department of Data Science And Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350365092

Several medical investigations have demonstrated that Alzheimer's disease (AD) manifests itself long before the formal diagnosis of dementia. These studies have led to the identification of numerous ideal biomarkers associated with Alzheimer's symptoms. Consequently, the imperative for early diagnosis has become evident, necessitating a high-performance computational tool capable of efficiently processing vast amounts of data. Early detection of AD not only offers valuable insights into the condition's progression but also presents a significant opportunity to benefit from timely and effective treatment. This ground-breaking study pioneers an innovative approach to the early detection and diagnosis of AD by crafting a sophisticated model SVGGL, which blends the power of stacked VGG19 neural networks with the interpretability of logistic regression. Leveraging neuro-imaging data, specifically the insights provided MRI, and our model aims to discern subtle structural and functional alterations in the brains of individuals at risk for or in the early stages of AD. The combination of feature extraction from stacked VGG19 models and subsequent logistic regression classification yields a precise and efficient classifier with an outstanding accuracy of 98.24%. With such a high degree of accuracy, the model can reliably differentiate among healthy people and those with Alzheimer's, opening the door to potential early intervention and individualized treatment plans. The study not only sheds light on the intricate neuro pathological landscape of AD but also propels us toward a future where cutting-edge technology and data-driven insights, exemplified by our high-accuracy model, converge to tackle the challenges posed by this prevalent neurodegenerative disorder. © 2024 IEEE.

关键词： Neuroimaging

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Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting 3rd

Unveiling Consumer Segmentation: Harnessing K-means Clusteri...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Saraswat, Shweta Agrohi, Vaibhav Kumar, Mahesh Lamba, Monica Kaur, Raminder Department of Artificial Intelligence and Data Science Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Graphics Era Deemed to Be University Dehradun India Department of Computer Science and Engineering Department JECRC University Jaipur India

ISBN: (纸本)9789819708918

Consumer segmentation is essential for accurate targeting and successful marketing efforts in today’s competitive business environment. Modern marketing groups individuals by interests and attributes. Segmentation drives targeting, personalization, and ROI. This article segments customers using K-means clustering. The Elbow approach and Silhouette score determine the appropriate number of clusters and increase segmentation accuracy. They also examine the possibility of precision targeting and customized marketing techniques across sectors. Businesses may optimize marketing, improve customer happiness, and increase profits by using K-means clustering. To compete in today’s market, this research helps marketers enhance targeting. Elbow and Silhouette K-means clustering may enhance client segmentation, engagement, loyalty, and economic success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Comparative Analysis of LoRa Antennae for Low-Cost IoT Deployments

Comparative Analysis of LoRa Antennae for Low-Cost IoT Deplo...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： MacKo-Dobrovic, Alexandria Verhosek, Justin Buxton, Joshua Krupp, Brian Baldwin Wallace University Computer Science Department Berea United States Case Western Reserve University Computer and Data Sciences Department Cleveland United States

ISBN: (纸本)9798350364910

As the use and application of Internet of Things (IoT) expands, the search for reliable, long-range, low-cost, and low-power communication between devices in order to provide real-time data is essential. Given these requirements, LoRa is a favorable choice for IoT devices that have a low-bandwidth requirement such as those used in sensing applications. However, the potential distance of LoRa can be misleading for projects that use popular boards and low-cost antenna options such as spring and simple copper wire antennas. In this paper, we investigate the potential distance using a variety of popular LoRa boards with different types of low-cost antennas. We provide an analysis using a variety of boards including a LoRa RFM9X board, LoRa RP2040 board, and a Raspberry Pi 3 with a LoRa HAT. We utilize a variety of antennas including a pigtail antenna, a directly soldered spring antenna, and a simple copper wire antenna including a dipole antenna. We hypothesized that the pigtail antenna would outperform the spring and simple copper wire antennae due to its higher cost. However, through a series of experiments conducted on our campus that test various distances and obstructions, our experiments reveal that in most cases the spring antenna yielded higher signal strength measured in RSSI. These results will benefit IoT enthusiasts, practitioners, and engineers to make informed decisions about the hardware they utilize, thereby enhancing the overall performance and reliability of LoRa-enabled devices in diverse environments. © 2024 IEEE.

关键词： Dipole antennas

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A study on Electronic Health Records (EHR) in India: Security challenges, initiatives and blockchain integration 4th

A study on Electronic Health Records (EHR) in India: Securit...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Bhargava, Misha Mahi, Samarjit Rao, Vidya Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9781032911571

With its vast population and extensive healthcare requirements, India is moving toward digitalizing its health records. Safeguarding Electronic Health Records (EHRs) is crucial given the increase in security breaches and attacks, with India’s healthcare sector at the forefront. This study provides an overview of EHR security in India by exploring the EHR adoption rate in India and the barriers to its use. It focuses on the significant security challenges they bring, and the initiatives currently being implemented in India, such as the Ayushman Bharat Digital Mission (ABDM) and the recently released National Accreditation Board for Hospitals (NABH) Digital Health Standards. Integrating Blockchain in current EHR systems to resolve the observed challenges is then explored. The findings of this study show the urgency of integrating emerging technologies such as blockchain which have the potential for patient-controlled data sharing and data immutability possibly within the framework of the ABDM. This study aims to serve as a valuable resource for healthcare professionals, researchers and other regions seeking to understand and improve EHR security. © 2025 the Author(s).

关键词： Electronic health record

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Extracting Valuable Insights from The Handwritten Feedback 7

Extracting Valuable Insights from The Handwritten Feedback

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Chawale, Yugandhar Patwardhan, Prasanna Dewani, Rahul More, Ankit Jadhav, Varsha Pandey, Mrityunjoy Sharma, Neha Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Maharashtra Pune India Army Institute of Technology Department of Computer Engineering Maharashtra Pune India Society for Data Science Pune India

ISBN: (纸本)9798331527822

This paper describes the collection and analysis of feedback data which is crucial for evaluating the success of any event and identifying areas of improvement. However, when feedback forms are distributed in paper format, extracting the text responses into a machine-readable format for analysis can be a tedious and error-prone manual process. Optical Character Recognition (OCR) Technology offers an automated solution for digitizing text from scanned documents, but its accuracy can be impacted by various factors like image quality, font styles, and language complexities. This study investigates the use of Gemini 1.5 Pro, a multimodal AI, to extract text data from PDF scans of collected handwritten feedback forms. After evaluation of Gemini's performance across a range of PDF qualities and comparing its output to manually transcribed data, the results demonstrate Gemini's high accuracy rates, even on lower quality scans, enabling efficient extraction of feedback responses while minimizing manual effort. This solution offers a scalable and reliable approach for educational institutions, professional organizations and other bodies to digitize survey responses for deeper qualitative and quantitative computational techniques. © 2024 IEEE.

关键词： data accuracy

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