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Using Bayesian Networks For Predicting Hypertension Risk Factors 10

Using Bayesian Networks For Predicting Hypertension Risk Fac...

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

作者： Varshney, Neeraj Madan, Parul Michaelson, Jacob Laxminarayanamma, K. Yadav, Vijay Kumar Kumar, Navneet 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 Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

Hypertension, a primary cause of mortality as well as morbidity in the cardiovascular system, demands accurate prediction models to ensure effective treatment and preventative measures. This study predicts hypertension associated risk factors using Bayesian Systems, an interpretative mindset, a deductive approach, and an exploratory approach. Using secondary data including genetic, lifestyle, as well as environmental characteristics, the study provides a comparison to established statistical methodologies. The Bayesian neural network model performs admirably in terms of prediction, with an accuracy of more than 85%. Both sensitivity and specificity show values above 80% and 90%, respectively, suggesting proficiency in diagnosing hypertensive people. Furthermore, sensitivity analysis shows that the model is resistant to changes in input parameters. Key contributions to hypertension risk are found, with biological factors, lifestyle choices, as well as exposure to environmental stressors all playing a substantial role. These factors' complicated relationships are explored, revealing their combined impact on antihypertensive susceptibility. Bayesian networks were able to beat traditional statistical methods in terms of accuracy and selectivity in predicting the incidence of hypertension variables. This demonstrates how Bayesian techniques have a potential to transform risk assessment as well as prevention tactics in cardiovascular health. Finally, this study demonstrates the effectiveness of Bayesian models in predicting risk for hypertension using multiple variables, giving a solid framework for focused interventions and preventative measures. The findings hold great promise for furthering public health measures aimed at controlling hypertensive and its effects. © 2023 IEEE.

关键词： Bayesian Networks global health problem Hypertension lifestyle factors using probabilities

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Predictive Analytics for Breast Cancer Survival Rates Using Random Forest 10

Predictive Analytics for Breast Cancer Survival Rates Using ...

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

作者： Kumar, Rakesh Bordoloi, Dibyhash Michaelson, Jacob Laxminarayanamma, K. Kalra, Ravi Kumar, Anil 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 Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

The Random Forest algorithm was used in this study to build a prediction model for breast cancer survival rates. The study used a deductive methodology, a descriptive design, including secondary data collecting in accordance with the interpretivism philosophy. The dataset was painstakingly preprocessed, and numerous metrics were used to gauge the model's effectiveness. The significance of the model's features was investigated as well to offer insight into the factors affecting survival forecasts. The study emphasizes how the model's practical applications might improve patient care by guiding treatment choices. It assesses the model's resilience under various circumstances, admits its shortcomings, and suggests future study trajectories. The study demonstrates the way predictive models have the ability to greatly affect patient outcomes and breast cancer care. © 2023 IEEE.

关键词： Breast cancer clinical implications data preprocessing model robustness predictive modelling Random Forest algorithm

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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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Latent Dirichlet Allocation for Topic Discovery and Segmentation in Big Data 2

Latent Dirichlet Allocation for Topic Discovery and Segmenta...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Clementking, A. Rani, S. Roseline, R. Purushothaman, K.E. Kavitha, G. Murugan, S. Karnataka Bengaluru India Department of BCA/IT Tamil Nadu Chennai India Pg Department of Computer Applications Tamil Nadu Cuddalore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

Using Latent Dirichlet Allocation (LDA) for topic identification and segmentation in big data helps identify significant patterns and topics from large text corpora. LDA will be implemented and optimized to rapidly process and analyze large datasets, revealing hidden subjects and enhancing content structure. Creating a strong framework for accurate and scalable subject modeling would improve analysis and decision-making in social media analytics, consumer feedback, and academic research. The LDA technique must be refined to accommodate large data's great dimensionality and complexity while being computationally efficient. An innovative topic identification tool that processes large-scale text data quickly and reliably will reveal theme patterns and improve big data management and use. The Bigdata Corpus results demonstrate the results for Topic Distribution Across Documents in a sample of 5 topics and 5 documents vary from 0.1 to 0.25. The same dataset also has Top Words per Topic. 10 example words for 10 subjects the identical dataset with another instance has values from 0.03-0.15. Document clustering based on topic proportions in a sample of 5 documents clustering 5 topics yields 0.1-0.75. © 2024 IEEE.

关键词： Data assimilation

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MEET: Advancing Mobility-Enhanced Edge Intelligence for Efficient and Sustainable 6G Networks 7

MEET: Advancing Mobility-Enhanced Edge Intelligence for Effi...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Vanteru, Madhu Kumar Dasari, Kavitha Asha, V. Singh, Navdeep Abdul-Zahra, Dalael Saad Ramprakash, R. Balaji Institute of Technology and Science Department of Ece India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Anurag University Department of Ece Telangana Hyderabad India

ISBN: (纸本)9798350350067

A machine learning technique known as federated learning, or collaborative learning, allows edge devices to cooperatively integrate to a global model while retaining local copies of their data. The integration of edge AI, IoT, and communication technologies can be greatly improved via federated learning. Because of the abundance of devices, apps, and services available in the modern digital era, users have higher expectations and demands for quality of service (QoS) from network service providers. As a result, extensive deployment of base stations (BSs) overall edge servers (ESs) is required, which raises the cost of deployment and operation - particularly energy expenditures. In order to add flexibility to the creation of the traffic along with mobility management algorithms, the current work makes use of fuzzy logic. Enhancing the quality of service (QoS) of current mobility management solutions and making optimum use of available network resources are the main goals of the research project. In this study, we examine a novel architecture for the efficient and environmentally friendly 6G systems dubbed Mobility-Enhanced Edge inTelligence (MEET), that takes advantage of the detection, communication, computation, and automatic features of intelligent linked vehicles. © 2024 IEEE.

关键词： Federated learning

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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Performance Assessment of Various Machine Learning Algorithms in Recommendation

Performance Assessment of Various Machine Learning Algorithm...

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International Conference on Inventive Computing and Informatics (ICICI)

作者： Siddharth Ranjan Trilok Nath Pandey Bibhuti Bhusan Dash Manoj Ranjan Mishra Utpal Chandra De Sudhansu Shekhar Patra School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Recommendation system has become an inevitable tool for businesses over the years. Its significance is widely recognized for both products as well as services. This study offers a thorough examination of several machine learning algorithms appropriate for recommendation systems designed for diverse domains, such as music and movies. Although there are several algorithms available for creating suggestions, some jobs may benefit from the use of a particular method. This article examines a number of basic and sophisticated algorithms used in recommendation systems, explains their applications, and analyses their advantages and disadvantages. This research compares the implementation of movie recommendation using single value decomposition plus-plus (SVD++) with popular machine learning techniques like k-nearest neighbor (K-NN) and singular value decomposition (SVD). Using the MovieLens 100 K and 1M datasets, it is experimentally proven by measurement of the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The outcome demonstrates that the SVD++ provides a lower error rate.

关键词： Machine learning algorithms Error analysis Measurement uncertainty Machine learning Nearest neighbor methods Motion pictures Root mean square

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Transformer Monitoring and Security System Using IoT 3

Transformer Monitoring and Security System Using IoT

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Anakal, Sudhir Kar, Sangita Rani Sangeetha, A. Sritha, P. Satish, Dekka Sajitha, P.L. Prasad, A. Rajendra Sharnbasva University Department of Computer Applications Karnataka India ITER Siksha 'o' Anusandhan Department of Electrical & Electronics Odisha India R.M.K College of Engineering & Technology Department of Computer Science Chennai India Bannari Amman Institute of Technology Department of Electrical & Electronics Chennai India Lendi Institute of Engineering & Technology Department of Computer Science Andhra Pradesh India R. M. K Engineering College Department of Computer Science Chennai India Sri Sai Ram Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350336238

Temperature increases are a common source of problems in transformers. Temperature increases as load current increases. There are two ways to monitor the rise in load current. Use a potential transformer and a temperature sensor, respectively. Voltage drops and winding temperature rises as load current increases. By measuring these two variables with a voltage sensor and a temperature sensor, the transformer problem can be resolved. The quantity detected by the sensor will be relayed over IoT to the control room in order to inform it of the transformer's state. It operates totally on autopilot. The Node MCU8266 board (which consist of microcontroller and built-in Wi-Fi board) included with the sensor will activate the circuit breaker to protect the transformer when the fault level is high. © 2023 IEEE.

关键词： Temperature sensors

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A Constructive Role for Social science in the Development of Automated Vehicles Based on LFM-BiGRU Approach 3

A Constructive Role for Social Science in the Development of...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Srikanth, J. Nitheesha, N. Khetree, Surekha Josephson, P Joel Akila, D. Divya, V. Kommuri Pratap Reddy Institute of Technology Computer Science and Engineering Telangana Ghatkesar India Telangana Hyderabad India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Navi Mumbai India Department of Ece Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Applications Tamilnadu Chennai India Department of Information Technology Chennai India

ISBN: (纸本)9798350381931

This proposed system delves into the transformative realm of autonomous vehicles, a technological marvel that has captivated society's anticipation for its profound societal implications. While existing social science research predominantly focuses on regulatory, safety, and efficiency considerations, this method contends that these discussions fall short in comprehensively appreciating the extensive social ramifications of autonomous vehicles. Addressing this gap, the system aims to provide a more nuanced understanding of the broader societal impacts that autonomous vehicles are poised to instigate. The proposed methodology comprises three integral modules: preprocessing, feature extraction, and model training. Preprocessing involves essential tasks such as punctuation removal, URL elimination, and batch normalization. The feature extraction stage leverages TF-IDF, contributing to the discernment of significant features. For model training, the paper employs the LFM-BiGRU architecture, a distinctive aspect of the proposed method. Comparative analysis against conventional approaches, namely BiGRU-LSTM and BiLSTM, underscores the superior performance exhibited by the proposed method. This suggests that the LFM-BiGRU approach holds promise for advancing the capabilities of autonomous vehicle research and, consequently, societal understanding of this groundbreaking technology. © 2024 IEEE.

关键词： Autonomous vehicles

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Low Resource Chinese Geological Text Named Entity Recognition Based on Prompt Learning

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Journal of Earth science 2024年第3期35卷 1035-1043页

作者： Hang He Chao Ma Shan Ye Wenqiang Tang Yuxuan Zhou Zhen Yu Jiaxin Yi Li Hou Mingcai Hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Institute of Sedimentary GeologyCollege of Computer Science and Cyber SecurityChengdu University of TechnologyChengdu 610059China Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources Chengdu University of TechnologyChengdu 610059China School of Information Engineering China University of Geosciences(Beijing)Beijing 100083China

Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual *** the rapid development of science and technology,a large number of textual reports have accumulated in the field of ***,many non-hot topics and non-English speaking regions are neglected in mainstream geoscience databases for geological information mining,making it more challenging for some researchers to extract necessary information from these *** Language Processing(NLP)has obvious advantages in processing large amounts of textual *** objective of this paper is to identify geological named entities from Chinese geological texts using NLP *** propose the RoBERTa-Prompt-Tuning-NER method,which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named entities in low-resource dataset *** RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word ***,we conducted experiments on the constructed Geological Named Entity Recognition(GNER)*** experimental results show that the proposed model achieves the highest F1 score of 80.64%among the four baseline algorithms,demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.

关键词： Prompt Learning Named Entity Recognition(NER) low resource geological text text information mining big data geology.

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