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Predictive Analytics for Heart Disease Detection: A machine learning Approach 4

Predictive Analytics for Heart Disease Detection: A Machine ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Praveena Rachel Kamala, S. Gayathri, S. Muthuvairavan Pillai, N. Anto Gracious, L.A. Varun, C.M. Siva Subramanian, R. Easwari Engineering College Dept of It India S.A Engineering College Dept of Cse India Rmd Engineering College Dept of Csbs India Rmk College of Engg and Tech Dept of Cse India R.M.K.Engineering College Dept of Csbs India

ISBN: (纸本)9798350300093

One of the most life-threatening diseases that causes human beings to suffer is heart disease. According to WHO report, more than 17.9 million people are affected and raised to death due to cardiovascular diseases around the globe. Many factors that influence the disease are high blood pressure, LDL cholesterol, obesity, physical inactivity, diabetes, etc. An earlier diagnosis of the disease helps to change the lifestyle and the start of treatment helps to save the human being life. machine learning techniques are used in this work to analyze heart disease effectively. the purpose of the research is to perform efficient heart disease analysis using ML models. machine learning is a sub-category of AI that makes decisions based on past and historical data. ML can be sub-classified into 3 types. In this research, supervised ML models are considered for analysis. In supervised ML models: K-NN, SVM, and RF are applied. the experimental approach is conducted with the heart disease dataset with three different ML models. the results demonstrate that the RF approach has the best performance across all metrics, subsequent to the SVM model and the KNN model. Among the three models, the RF model has the greatest sensitivity (0.8654), precision (0.8182), specificity (0.7674), and accuracy (0.82105) compared to other approaches in this particular heart disease dataset. © 2023 IEEE.

关键词： Diseases

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From Models to Microservices: Easily Operationalizing machine learning models 5

From Models to Microservices: Easily Operationalizing Machin...

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5th international conference on computer and applications, ICCA 2023

作者： Panchal, Deven Baran, Isilay Musgrove, Dan Lu, David AT&T MiddletownNJ United States AT&T DallasTX United States

ISBN: (纸本)9798350303254

Although machine learning and Deep learning have advanced significantly in their ability to perform supervised, unsupervised and reinforcement learning tasks quite well, integrating them into applications for which they are meant to provide intelligence is not as seamless as it should be. there are many reasons for this - primary being that the kind of skills needed to understand the business need, understand the data, develop models, develop applications, and integrate models with applications are different and it is very difficult for one person or just a group of application developers or just a group of data scientists to have all these skills. this makes it challenging to productionalize the developed ML models fast or at all in order to experiment with them further. this paper demonstrates how certain components of the Acumos AI platform project can be used to take models developed using H2o, Java, Spark to production by deploying them as microservices, automatically. the same concept can be (and has been extended by Acumos) to Python, R and ONNX models. What this enables is - multiple heterogeneous models written by different developers or different teams or different organizations in different languages and frameworks becoming functioning microservices that would provide intelligent APIs to the business application in question. these models can then be easily shared with different individuals and organizations and operationalized easily. Using Acumos, these microservices can be deployed such that they also communicate and co-ordinate with each other to do much more complex tasks. We will talk about the 5G Network Slicing usecase, create an ML model for 5G Network Slicing and use certain Acumos components to make it shareable and operationalize it as a predicting microservice. © 2023 IEEE.

关键词： 5G mobile communication systems

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

ISBN: (纸本)9783031505768

the proceedings contain 121 papers. the special focus in this conference is on Multimedia Technology and Enhanced learning. the topics include: Security Awareness Method of Opportunistic Network Routing Protocol Based on Deep learning and Knowledge Graph;research on Pedestrian Intrusion Detection Method in Coal Mine Based on Deep learning;personalized Recommendation Method of College Art Education Resources Based on Deep learning;global Planning Method of Village Public Space Based on Deep Neural Network;a Multi Stage Data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet;machine learning Based Method for Mining Anomaly Features of Network Multi Source Data;data Anti-jamming Method for Ad Hoc Networks Based on machine learning Algorithm;a Data Fusion Method of Information Teaching Feedback Based on Heuristic Firefly Algorithm;research on Pain Information Management System Based on Deep learning;research on Database Language Query Method Based on Cloud Computing Platform;reliability Evaluation Method of Intelligent Transportation System Based on Deep learning;forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing;power Consumption Behavior Analysis Method Based on Improved Clustering Algorithm of Big Data Technology;anomaly Detection of Big Data Based on Improved Fast Density Peak Clustering Algorithm;data Privacy Access Control Method Based on Ciphertext Policy Attribute-Based Encryption Algorithm;Evaluation Method of Enterprise Circular Economy Development Level Based on AHP Fuzzy Inference;research on Performance Evaluation of Industrial Economic Management Based on Improved machine learning;application of Big Data Processing Technology in Power Consumption Information Acquisition.

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Skin Melanoma diagnosis using machine learning and ABCD rule 4

Skin Melanoma diagnosis using machine learning and ABCD rule

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4th international conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Kadeem, Zahra B. Mosa, Qusay O. College of Computer Science and Information Technology Dept. Computer Science Diwaniyah Iraq

ISBN: (纸本)9798350376111

Skin cancer, being the most predominant type of malignant growth, represents more than half of all cancer instances. While melanoma is less common than other skin cancers, it poses the highest risk due to its tendency to metastasize and potentially result in mortality. Nonetheless, early detection significantly increases the chances of curing melanoma. the detection of melanoma poses a considerable challenge, primarily attributed to the minimal interclass variance observed between images of melanoma and non-melanoma, alongside the heightened interclass variance within melanoma images. Hence, it is imperative to formulate a computer-aided Diagnosis (CAD) to identify such lesions and facilitate early disease diagnosis to enhance the probability of patient survival. the proposed methodology consists of a sequence of four distinct phases. the initial stage involves preprocessing, which incorporates the application of filtering and contrast enhancement algorithms such as the Median filter, Dull razor, and Histogram Equalization. the segmentation process proceeds by utilizing Otsu methods in order to detect any anomalies. Subsequently, the following phase involves the extraction of features through GLCM for textural attributes and ABCD base parameters, encompassing asymmetry, border irregularity, color, and diameter. Following this, a model based on artificial neural networks (ANN) is developed by leveraging these extracted traits to discern instances of skin cancer within the provided dataset. this methodology achieves a commendable overall accuracy rate of 99%. Dermatologists may derive significant assistance in their clinical decision-making procedures from this promising breakthrough. © 2024 IEEE.

关键词： Lung cancer

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Self-Driving Car Simulation Using Reinforcement learning and Xception Model Tuning 4

Self-Driving Car Simulation Using Reinforcement Learning and...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Balasubramaniam, Aparna Singh, Rabindra Kumar Tyagi, Amit Kumar School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai600127 India National Institute of Fashion Technology Department of Fashion Technology Delhi New Delhi India

ISBN: (纸本)9798350300888

the main idea is to have the environment (server) and then agents (clients). the server/client architecture means that the user can of course run both the server and client locally on the same machines, but it could also run the environment (server) on one machine and multiple clients on multiple other machines. For training and predicting at the same time, reinforcement learning is used. To obtain a convincing result in the training process would mean promising improvement in the field of automation and automotives. this simulation will be very helpful in all manners when developed with more real-life objects and circumstances that get into the simulation asset. the improvement in self-driving cars comes along with the advancement in the field of deep learning and data handling. Due to the sheer amount of multidimensional data that a self-driving car could create, a more efficient data handling methods are required to compute better results. there is always a chance to add layers to this particular deep learning framework and make it more efficient as the data captured evolves. the data may evolve in the fields of addition in new sensors, new obstacle identification, addition of more real time features. Progressing further into this study and implementation of a deep learning framework for self-driving cars, it is observed that the sensor data provides sufficient parameters for the estimation of new class variables. the model accuracy and results are compared in a way that is projected towards higher volume of data along with high computational power. this study recommends using cloud service to accommodate the graphical and CPU requirements as the computation for this particular multidimensional data could be very demanding. the understanding of how autonomous vehicles and how well it would be accepted is discussed below, along with the complications it would bring. It's a social change which many will oppose in the beginning but will slowly adopt as science impro

关键词： Autonomous vehicles

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Supervised and Unsupervised Coupled Generative Adversarial Networks in Multivariate Time Series 4

Supervised and Unsupervised Coupled Generative Adversarial N...

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4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Wang, Qinfen Li, Yifei Xia, Yong School of Computer Science and Engineering Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China

ISBN: (纸本)9798350337181

Generative adversarial networks (GAN) have been successful in demonstrating the efficiency of generative time series. In this paper, we proposed a novel end-to-end supervised and unsupervised coupled generative adversarial networks (SUC-GAN) framework that consists of a gated recurrent unit (GRU) for generating multivariate time series. We test SUC-GAN on three datasets (i.e. Sines, Energy, Stocks) to investigate the efficacy and properties. the preliminary results show the potential power of supervised, unsupervised, and coupled learning for data generation. © 2023 IEEE.

关键词： Generative adversarial networks

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Optimization of Appointment Scheduling for Hospitals Based on a Multilingual Virtual Assistant in an Instant Messaging application 4

Optimization of Appointment Scheduling for Hospitals Based o...

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4th IEEE international conference on Advanced learning Technologies for Education and Research, ICALTER 2024

作者： Benites, Jefferson Abner Cuenca Ramos, Bruno Humberto Gracey Trujillo, Emilio Antonio Herrera Universidad Peruana de Ciencias Aplicadas Departamento de Ingeniería de Sistemas Lima Peru

ISBN: (纸本)9798331510244

In Peru's public healthcare system, the process of scheduling medical appointments faces inefficiencies that lead to long waiting times and communication difficulties, especially for patients who speak different languages. these problems cause frustration and anxiety, delaying access to medical care and worsening health conditions. Poor communication can result in misunderstandings about treatment, increasing the risk of errors and making the patient experience more challenging. Moreover, lengthy phone calls to schedule appointments overwhelm administrative staff, reducing their efficiency. To address these issues, a chatbot was developed to operate through a messaging app, using artificial intelligence to understand various languages in a hospital setting. the chatbot is designed to improve the user experience by answering questions, providing information, and solving problems automatically. AI in this context refers to a branch of computer science focused on creating systems that perform tasks requiring human-like intelligence. the chatbot was tested in a hospital environment with a group of local and foreign users. the results showed that the majority of users completed the booking process quickly and without errors. Most participants expressed satisfaction with the booking experience, while foreign users found it particularly helpful to use the chatbot in their preferred language. these outcomes demonstrate the chatbot's effectiveness in enhancing accessibility, reducing waiting times, and meeting patient needs in a healthcare setting. © 2024 IEEE.

关键词： Instant messaging applications

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Explainable Artificial Intelligence-Based Disease Prediction with Symptoms Using machine learning Models 4th

Explainable Artificial Intelligence-Based Disease Prediction...

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4th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Sannala, Gayatri Sanjana Rohith, K.V.G. Vyas, Aashutosh G. Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819965854

Artificial intelligence (AI) has the potential to revolutionize the field of healthcare by automating many tasks, enabling more efficient diagnosis and treatment. However, one of the challenges with AI in healthcare is the need for explainability, as the decisions made by these systems can have serious consequences for patients. AI can be particularly useful in the classification of diseases based on symptoms. this involves using machine learning algorithms to analyze a patient’s symptoms and classify them as having a particular disease or condition. While using black box machine learning algorithms can be highly accurate, there is little to no understanding on how these models work. therefore, using techniques such as feature importance analysis and Explainable AI, it is possible to provide clear explanations for the decision-making process, which can improve trust and understanding among healthcare providers and patients. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Research on Ornamental Fish Recognition Base on Improved Shufflenet V2 Network 4

Research on Ornamental Fish Recognition Base on Improved Shu...

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4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Zhang, Xingyun Chen, Hongming Zhang, Longqing Chen, Qian Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Guangdong University of Science and Technology School of Computer Science Dongguan523083 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350329490

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. Based on the ShuffleNet V2 network, we incorporate Transfer learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. the final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome. © 2023 IEEE.

关键词： computer vision

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7th international conference on Wireless Communications, Networking and applications, WCNA 2023

7th International Conference on Wireless Communications, Net...

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7th international conference on Wireless Communications, Networking and applications, WCNA 2023

ISBN: (纸本)9789819624089

the proceedings contain 38 papers. the special focus in this conference is on Wireless Communications, Networking and applications. the topics include: EffiNet: An Efficient, Low-Complexity Neural Network for Monocular Depth Estimation on Embedded Devices;adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection;a Cache Scheduling Method Based on Adaptive Expiration for Data Process System;Research on Airport Communication and Navigation Equipment Inspection Assistance System Based on AR Technology;application of Quality Function Deployment in Assessing Unmanned Assault Capabilities for Mountain Counter-Terrorism Operations;research on the application of Internet of things Technology in Power Grid Limited Space Security Monitoring;research on the application of computer Vision Based on Deep learning in Autonomous Driving Technology;research on Cybersecurity System of Scientific Research Institutions Based on National Cybersecurity Classified Protection 2.0: Take the Chinese Academy of Agricultural Sciences as an Example;Research on DOA Estimation Method of Underwater Acoustic Signal Based on machine learning;Design and Implementation of Smart Home Control System Based on STM32;communication Anti-jamming System Based on Deep Reinforcement learning;fault Diagnosis of Infrared Sensor Based on Convolutional Neural Network;graph Neural Network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information;an Approach to Microenvironment-Based Particle Swarm Optimization Algorithm;a Verifiable Ciphertext Retrieval Scheme for Smart Grids;panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap;An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN;recognition and Calculation of Fish Rafts in Mariculture on the Basis of Artificial Intelligence;digital Twin for Power Load Forecasting.

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