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Application of machine learning algorithm for prediction of heart disease

Application of machine learning algorithm for prediction of ...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ankit Kumar Upadhyay Sharzeel Saleem Adarsh Anubhav Shrddha Sagar Department of Computer Science and Engineering Galgotias University Greater Noida India

Healthcare is the one the domain in which every organization is working on it globally. It need to make it better and better via different researches and the innovations in this sector. Machine learning is the most used algorithm in every sector and organization. It not merely transforming the field of healthcare but also able to emulate the various human intelligence from the surrounding in order to solve various problems. Machine learning in health care sector has shown its various application in it. Disease prediction is one of the applications of it. this study helps to predict heart diseases with the help of its algorithm to determine the risk and treat the patient in the healthcare system. In this paper we have taken out the data from the kaggle and implemented it using the Jupiter notebook tool. the three algorithm we have implemented (naïve bayes, decision tree and random forest) out of which random forest is giving. Hence, with the help of the following algorithms it is possible for us to predict the heart disease with more accuracy. this research will able to predict various disease over its various symptoms more easily and accurately.

关键词： Heart Technological innovation Machine learning algorithms Jupiter Human intelligence Medical services Organizations

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A Systematic Study on methods of Spatiotemporal Hotspot Detection and Evaluation metrics

A Systematic Study on methods of Spatiotemporal Hotspot Dete...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rishabh Jain Aruna Bhat Department of Computer Science Delhi Technological University New Delhi India

A Spatial hotspot is a confined region/space inside a geographical area which has a higher concentration of activity points than outside the hotspot region within the study area. With technological breakthroughs in spatial data collection and increased computation capabilities, several research publications presented novel methods for Spatial Hotspot detection. the relationship between Spatial Hotspots and time (temporal effect) has also been investigated by various researchers, expanding the horizon of the spatial hotspot to spatiotemporal hotspot. the idea of Spatiotemporal hotspot detection gained considerable interest among researchers because of its practical utility in public health, public safety, traffic volume analysis, crime zone analysis, and other essential applications. However, it is also vital to analyze the research literature. Our research aims to organize the existing comprehensive literature into a well-organized hierarchical framework to comprehend better the various methodologies widely adopted around the world. We propose a framework that suitably categorizes the research effort of various scholars into several appropriate categories based on their differences in primary algorithmic approaches. Furthermore, we also present an extensive analysis of the widely utilized evaluation measures adopted in this research domain.

关键词： Systematics Gain measurement Extraterrestrial measurements Time measurement Spatial databases Spatiotemporal phenomena Safety

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An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques

An Ingenious Method to Detect COVID in X-Ray Images Using Ma...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Palak Kumari Neha Rani N. Suresh Kumar School of Computing Science and Engineering Galgotias University Greater Noida India

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. the World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. the COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. the global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. this paper proposes a deep neural network modification based on the Xception model. the model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. the layer addition is intended to avoid overfitting the proposed model. the proposed as a result, we compare the model’s loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. the architecture of this study has been presented for the diagnosis of COVID-19.

关键词： COVID-19 Deep learning Machine learning algorithms Pandemics Computational modeling Predictive models data models

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Towards an Understanding of the Lipophilicity of Non-Coded Amino Acids: Computational Simulations of Proline Analogs

Towards an Understanding of the Lipophilicity of Non-Coded A...

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BioInspired Processing (BIP), IEEE international conference on

作者： Paulina Matamoros Silvana Pinheiro Antonio Viayna William Zamora CBio3 Laboratory School of Chemistry San Pedro San José Costa Rica CBio3 Laboratory Faculty of Pharmacy University of Costa Rica San Pedro San José Costa Rica Department of Nutrition Food Science and Gastronomy Faculty of Pharmacy and Food Sciences University of Barcelona Santa Coloma de Gramenet Spain CBio3 Laboratory School of Chemistry Advanced Computing Lab (CNCA) National High Technology Center San Pedro San José Costa Rica

ISBN: (纸本)9781665470490

Amino acids are the backbone for the formation of biopolymers known as proteins. One of them, proline, can be found in high concentrations in collagen, being this protein the most supportive for the skin, tendons, bones, and connective tissue, promoting their health and recovery. Here, we compute the lipophilicity of 25 non-coded proline analogs using the quantum mechanics implicit solvation model SMD. Comparison with the experimental data provided for partition coefficients yielded a root-mean square error (rmse) of 0.72 (log P units). Overall, the results support the appropriateness of SMD solvation model for computing the n-octanol/water partition coefficient in proline analogs, which can be used to tune the hydrophobic properties of bioinspired synthetic peptides.

关键词： Proteins Connective tissue Quantum computing Computational modeling Biological system modeling Peptides Quantum mechanics

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Tweet Summarization Using Clustering Mechanisms

Tweet Summarization Using Clustering Mechanisms

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Karthikeyan Ampili Srinivas Kanakala Department of Computer Science and Engineering VNR VJIET Hyderabad India

Due to the growth of online sites like Twitter, this’s resulted in a rise in the quantity of user-generated content. It is difficult for users to absorb so enormous amounts of data. the outcome is creating automated technologies to summarise massive problematic. Twitter is a well-known platform for microblogging. Users post *** form of twitter posts. It serves as a framework for compiling tweet summaries in this article. Occasionally, in times of catastrophes or environmental hazards. Users tweet frequently. Just a few twitter posts will be beneficial. Displaced people find these twitter posts useful. the details can be gleaned out of those twitter posts. Emergency team can execute rescue efforts with an information gleaned from such twitter posts. the categorization of tweets serves as the preliminary step of the suggested strategy dividing twitter posts into relevant/irrelevant categories accompanied by a clustering mechanism. BERT paradigm, also referred to as the "transformers classifier," is utilized for categorization. this system considered clustering mechanism is DB SCAN. An innovative technique relying on neural networks is used to separate and simplify relevant Twitter posts. An unguided approach for categorizing Twitter posts as relevant/irrelevant will be used to build this model.

关键词： Social networking (online) Computational modeling Blogs User-generated content Neural networks Bit error rate Transformers

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Food Detection and Recognition Using Deep Learning – A Review

Food Detection and Recognition Using Deep Learning – A Revi...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Anushrie Banerjee Palak Bansal K.T. thomas Department. of Data Science Christ University Pune Lavasa India

Studies show poor lifestyle choices and unhealthy eating patterns cause issues like obesity and other ongoing illnesses that raise the risk of heart attacks, such as hypertension, abnormal blood sugar levels, and diabetes. To improve this situation a lot of health apps have been built which use modern dietary monitoring systems that automatically evaluate dietary intake using machine learning and deep learning techniques rather. For these reasons indepth investigations on food detection, classification, and analysis have been conducted. Some of the top methods for automatic food recognition created have been discussed in this paper. We also propose an idea for detection of Indian food items using image classification. According to our findings of the papers we reviewed, convolutional neural networks (CNN) have been extensively been used in food detection as it has been giving better results compared to other models. We also observed that Vision transformers perform better in situations where the dataset is large and a hybrid model would give better accuracy. A review of potential applications for food image analysis, shortfalls in the area, and open issues concludes the paper.

关键词： Deep learning Support vector machines Performance evaluation Obesity Image recognition Computational modeling Benchmark testing

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Challenges of Digital Transformation in Education in India

Challenges of Digital Transformation in Education in India

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Saurabh Shukla Lija Jacob Department of Data Science Christ Deemed to be University Pune India

Online learning has been present since the 1960s and has risen in popularity over time. World-class universities have been using online teaching-learning methodologies to fulfill the needs of students who reside far away from academic institutions for more than a decade. Many people predicted that online education would be the way of the future, but with the arrival of COVID-19, online education was imposed upon stakeholders far sooner and more suddenly than expected. When the COVID-19 pandemic broke out, educational institutions began to explore digital ways to keep students studying even when they couldn't be together in person as governments enacted legislation prohibiting large groups of people from gathering for any reason, including education. the future of such a transition looks promising. However, transitioning from one mode of education to another is not easy. Historically, when educators adopt new tools, learning still continues in the conventional manner. Based on the responses of 176 students, this paper studies the challenges of Digital transformation in the Education sector. the research is extremely beneficial in evaluating the scope of societal opposition to change.

关键词： COVID-19 Machine learning algorithms Pandemics Digital transformation Education Anxiety disorders Legislation

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Intrusion Detection in Cloud Architecture Using Machine Learning

Intrusion Detection in Cloud Architecture Using Machine Lear...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Govinda Rajulu. G R. Santhoshkumar D. Venkatesan Kalvikkarasi. S P. Santosh Kumar Patra Department of Computer Science and Design St. Martin’s Engineering College Department of Artificial Intelligence and Data Science St. Martin’s Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE St. Martin’s Engineering College Professor in ECE Department Podhagai College of Engineering & Technology St. Martin’s Engineering College

the research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. the purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ.

关键词： databases Cloud computing security Neural networks Intrusion detection Computer architecture Passwords Decision trees

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Multimodal Emotion Recognition Using Deep Learning Techniques

Multimodal Emotion Recognition Using Deep Learning Technique...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： S Jerald James Lija Jacob Department of Data Science Christ University Pune Lavasa Pune India

Humans have the ability to perceive and depict a wide range of emotions. there are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). this can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. this paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. the CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. this project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today’s world.

关键词： Training Emotion recognition Adaptation models Schedules Social networking (online) Computer architecture Real-time systems

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Federated Learning for Detecting COVID-19 in Chest CT Images: A Lightweight Federated Learning Approach

Federated Learning for Detecting COVID-19 in Chest CT Images...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Weimin Lai Qiao Yan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

the novel coronavirus is spreading rapidly worldwide, and finding an effective and rapid diagnostic method is apriority. Medical data involves patient privacy, and the centralized collection of large amounts of medical data is impossible. Federated learning is a privacy-preserving machine learning paradigm that can be well applied to smart healthcare by coordinating multiple hospitals to perform deep learning training without transmitting data. this paper demonstrates the feasibility of a federated learning approach for detecting COVID-19 through chest CT images. We propose a lightweight federated learning method that normalizes the local training process by globally averaged feature vectors. In the federated training process, the models' parameters do not need to be transmitted, and the local client only uploads the average of the feature vectors of each class. Clients can choose different local models according to their computing capabilities. We performed a comprehensive evaluation using various deep-learning models on COVID-19 chest CT images. the results show that our approach can effectively reduce the communication load of federated learning while having high accuracy for detecting COVID-19 on chest CT images.

关键词： COVID-19 Training Image recognition Text analysis Federated learning Computational modeling Computed tomography

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