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Coronary Artery Disease Prediction using data mining Techniques

Coronary Artery Disease Prediction using Data Mining Techniq...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Santhosh Kumar K L Nida Kousar G Madhurya J A GITAM University Bengaluru India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

The health industry produces a gigantic amount of data and by using this huge data many diseases can be detected and predicted at early stage and can be even cured. Diseases like coronary artery, cancer, and tumour disease cause a big threat to human. In this regard, the proposed research work has been carried out to predict coronary disease and it has also been outlined below. The cardiac illness is often identified by using AI strategies. The medical statistics are taken as input and then those capabilities are modelled for prediction. This model can then be used to envision subsequent medical data. The three different data mining techniques are used in our proposed model. The accuracy of the model using each of the algorithms is intended. Then the one with adequate accuracy is taken as the model for predicting the coronary *** this new-fangled era in healthcare domains, generally the statistics mining techniques are used for prediction of diseases. It is very challenging task for the researchers to predict the medical diseases using gigantic medical databases. To overcome from this issue the researchers use various statistics mining techniques for the disease prediction. In our paper, the main intention of the research work is to predict coronary diseases using machine learning algorithms. In this paper, SVM, Naïve Bayes and Random Forest model have been used to predict the heart diseases which will be helpful for the medical sector. Early prediction of coronary illness is very vital to save human life and take proper steps to control the disorder. The main aim fo the proposed research work is to explore the new methods for early prediction of coronary disease by using statistic mining techniques.

关键词： Heart Diseases data mining Support vector machines Random forests Medical services databases

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A Comparative Analysis of machine learning and Deep learning Algorithms for Skin Cancer Detection and Classification: Exploring Performance Across Multiple Parameters

A Comparative Analysis of Machine Learning and Deep Learning...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Summi Goindi Khushal Thakur Divneet Singh Kapoor Electronics and Communication Engineering Chandigarh University Punjab India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Skin cancer is a substantial health concern, with a yearly incidence of 123,000 new cases and melanoma being the most prevalent variant. Identifying skin diseases is becoming increasingly challenging due to visual similarities. Automated skin cancer screening devices have the potential to enhance accuracy in identifying skin cancers. Advancements in technology and processing capacities have enabled the use of machine and deep learning algorithms for skin lesion examination. However, there is a pressing need for expedited diagnostic systems for life-threatening illnesses. This study provides an overview of various methodologies used in diagnosing skin cancer using image analysis and examines lessons and insights from previous studies. Future directions and scope will help address issues in autonomous detection of skin cancer. In a comparative analysis of various machine learning models, different performance metrics were observed. The Support Vector machines (SVM) yielded an accuracy of 75.23%, with an AUC of 90.22%. While effective in high-dimensional spaces, SVMs may face limitations with non-linear or noisy data. Random Forest, an ensemble method, demonstrated superior performance with an accuracy of 80.12% and an AUC of 93.22%, possibly due to its proficiency in handling non-linear data structures. Neural Networks, with their ability to model intricate relationships, achieved an accuracy of 81.34%. Multi-Layer Perceptron (MLP), a subset of neural networks, slightly varied in performance, indicating potential differences in architecture or training. Convolutional Neural Network (CNN), renowned for image recognition tasks, showcased an impressive accuracy of 93.23%, hinting at the dataset's spatial structures. The Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) emerged as the top performer with an accuracy of 94.344%, suggesting the dataset's potential temporal or sequential patterns. This comparative study underscores the importance of selecting the app

关键词： Deep learning Support vector machines Accuracy Biological system modeling Skin data models Lesions

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Advancements in Disease Diagnosis and Prediction Leveraging Insights Extraction and Computational learning Techniques

Advancements in Disease Diagnosis and Prediction Leveraging ...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Niveditha Konda Koprada Pydi Durga Lakshmi Sri Vidya Varshith Chavalam Karthikeya Vadde K B Venkata Brahma Rao Department Of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Technology has revolutionized the health domain largely. Accurate and on-time analysis of any health-related problem It is crucial for the prevention and treatment of the ailment. The traditional way of diagnosis may in the event of a severe illness, it may not prove to be satisfactory. This project aims to design a diagnostic framework for different diseases based on their symptoms. This system utilizes data mining methodologies including classification techniquesin order to achieve such a mode and also ML algorithms for prediction of any diseases can help in a more accurate diagnosis than the conventional method. We have introduced a system for disease prediction that employs several machine learning algorithms. The dataset contains information on symptoms, age, blood group, and gender of individuals, which is used for processing by the diagnosis system. The output of the system is the disease. Here, the SympoDx algorithm, which incorporates Random Forest, Decision Tree, KNN, and Support Vector Classifier, is employed for classification tasks and computes the probabilities of diseases. Following the results, patients can contact the doctor accordingly for further treatment.

关键词： Accuracy machine learning algorithms Medical services Prediction algorithms Vectors Classification algorithms Decision trees

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Leveraging Big data Analytics for Enhancing Corn, Tomato, and Pepper Crop Disease Detection in Ghana

Leveraging Big Data Analytics for Enhancing Corn, Tomato, an...

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Advancement in Technology (ICONAT), international conference for

作者： Padmja Salvi Dhvani Patel Rashida Kadiwala Soham Vyas Rajeevkumar Gupta Department of CSE Pandit Deendayal Energy University Gujarat India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

In the context of Ghana’s agricultural sector, which faces significant challenges due to climate change and the prevalence of plant diseases, this paper proposes a novel approach to enhance crop disease detection and management leveraging big data analytics. We introduce an “Afrocentric” dataset comprising annotated photographs of leaves from various crops, focusing on tomato, corn, and pepper, which are vital crops in Ghana. Utilizing this dataset, we employ advanced machine learning and image recognition techniques, particularly the YOLOv7 architecture, for early disease detection. Our methodology involves dataset collection, model training, implementation, and deployment, followed by evaluation and validation. Performance metrics such as precision, recall, and mean Average Precision (mAP) are analyzed, revealing insights into the model’s effectiveness. Despite its strengths, including decent precision and mAP scores, the model exhibits room for improvement, particularly in recall rates for certain classes of crop diseases. We discuss future research directions, including the exploration of advanced deep learning methodologies and the integration of multispectral imaging data, aiming to enhance model accuracy and efficiency. Collaborative efforts with agricultural experts and stakeholders are emphasized to refine datasets and develop contextually relevant solutions. By embracing these diverse directions, our research aims to contribute to food security and agricultural sustainability, not only in Ghana but also in other regions facing similar challenges.

关键词： Training Measurement Plant diseases Image recognition Multispectral imaging Crops Food security Big data Stakeholders Sustainable development

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Authentication using Biometric data from Mobile Cloud Computing in Smart Cities

Authentication using Biometric Data from Mobile Cloud Comput...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Artificial Intelligence Engineering Dept AI and Robotics Institute Near Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and A1anagement(GLBITAM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them.

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Speech Age-Gender Classification Using Long Short-Term Memory

Speech Age-Gender Classification Using Long Short-Term Memor...

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international conference on Information and Communications Technology (ICOIACT)

作者： Galih Rahagi Nitisara Suyanto Suyanto Kurniawan Nur Ramadhani School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9781728173566

ISBN: (纸本)9781728173573

Recognizing the age and gender of a person with certainty from a media has a significant advantage. For example, the perpetrators recorded on a CCTV camera can be easily recognized, or someone used to lie about age on social media or job application applications can be easily detected. However, detecting the exact age of a person is still a tricky thing because of the quality media and the characteristics of the person who seems deceptive. In machine learning, the neural-based methods are commonly used for classification and recognition. However, age and gender classifications still produce unsatisfactory results, even age and gender classifications by speech are rarely discussed. Hence, the right approach is needed to create a good age and gender classification model. One of the solutions is using Recurrent Neural Network (RNN), which is made for sequential data like speech. In this paper, a speech age-gender classification model is developed using one of the popular RNN models called Long Short-Term Memory (LSTM). The experimental results show that the proposed model is trapped on the overfitting problem so that the accuracy of the testing set is lower than the training set. Regularization can reduce the difference between the accuracies of both training and testing sets but it cannot increase them. The data augmentation is able to slightly solve the overfitting problem.

关键词： Training Recurrent neural networks Biometrics (access control) Social networking (online) machine learning Media Testing

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The study of identification of fishing vessel behavior based on VMS data 19

The study of identification of fishing vessel behavior based...

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Proceedings of the 3rd international conference on Telecommunications and Communication Engineering

作者： Yuan Feng Xueli Zhao Mingxu Han Tianying Sun Chen Li Ocean University of China Qingdao Shandong

ISBN: (纸本)9781450371803

Accurate identification of different behaviours of fishing vessels is important for fisheries management and fisheries ecology, which can enhance the management of overfishing and marine resources. In this study we identify fishing vessel fishing behavior through Vessel Monitoring System (VMS) data and BP neural networks. The change trend of the direction angle and speed of the fishing vessel is selected as the input parameters of the model, and the accuracy of identifying the fishing behavior is 79%. The fishery distribution is drawn according to the behavior of the fishing vessel identified by the model, which is similar to the distribution of the actual fishery and the fishing density. This laid the foundation for the deep exploration of the spatio-temporal characteristics of VMS in the future and the high-precision prediction of the distribution of fishing areas in China's offshore fisheries.

关键词： machine learning VMS neural networks fishing vessel track recognition vessel fishing

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A Study on Apparent Age Estimation

A Study on Apparent Age Estimation

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international conference on Computer Vision Workshops (ICCV Workshops)

作者： Yu Zhu Yan Li Guowang Mu Guodong Guo Lane Department of CSEE West Virginia University Morgantown WV USA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from?the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age labels are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are?not many face images available with annotated age labels. Further, the annotated age labels for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age labels to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age labels, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.

关键词： Estimation Face databases Training Neural networks data models Computer architecture

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Computer Vision and Fuzzy Logic for Sustainable Indonesian Fisheries 3

Computer Vision and Fuzzy Logic for Sustainable Indonesian F...

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3rd international conference on Eco Engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Saputra, Adi Sakinah, Putri Purwoko, Hery Zulardi, Indra Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

Tuna fishing in Indonesia is excessive and if left unchecked, some types of tuna will become extinct within 3-10 years. There is an urgent need to help MMAF for producing more accurate fish catches data. This paper proposed a system that utilizes computer vision, fuzzy logic and Global Positioning System (GPS) for supporting a sustainableIndonesian fishery by recognizing and measuring fish length, determine fish age and where is the catch location. The system can help authority to manage sustainable fisheries management in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： machine learning

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AI Driven Blockchain Reward Insights Using ML

AI Driven Blockchain Reward Insights Using ML

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), international conference on

作者： Abeera Malik Faisal Bukhari Hafiz Muhammad Talha Department of Information Technology Faculty of Computing and Information Technology University of the Punjab PUCIT Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Several industries have embraced blockchain technology due to the change it has brought, especially in the way records are kept. However, one major problem inherent in the concept of blockchain from the participants perspective is the presence of unpredictable rewards in proof-of-work and proof- of-stake models. That is why this research aims at developing a new strategy to solve this problem by incorporating artificial intelligence (AI) as well as machine learning (ML) algorithms into blockchain compensation prediction. This project seeks to analyze how artificial intelligence (AI) and machine learning (ML) methods may be incorporated into blockchain technology to solve the problem of the unpredictability of rewards. Using the EtherScan API, we have obtained 1000 timestamps and block reward values, and for prediction of rewards for certain timestamps, we use the KNN algorithm, linear regression algorithm, and random forest regressor algorithm. Thus, a What We KNN attained an accuracy of performance specifies, and a random forest regressor attained an impression accuracy range of 74% particular timestamps. These outcomes show the benefits of advanced ML to the blockchain ecosystem as a tool that helps stakeholders make informed decisions concerning activities like mining, staking, or investing in blockchains with potentially high rewards.

关键词： Technological innovation machine learning algorithms Accuracy Biological system modeling Nearest neighbor methods Prediction algorithms Proof of Work Blockchains Stakeholders Random forests

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