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Innovation in Technology (INOCON), IEEE International Conference for

作者： Debashish Mondal Ravinder Saini CSE Department Chandigarh university Mohali Punjab India

Cardiovascular ailments have become a principal cause of mortality globally in recent decades. It then goes on to discuss the use of big data and machine learning algorithms for predictive analysis within the medical field. The five different algorithms discussed are Logistic Regression (LR), Support Vector machines (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT) and Random Forest (RF). Finally, it states that out these methods Decision Tree was found to be most accurate when predicting potential cases of cardiac illness. The practical implications of this paper are that it could help medical professionals make more informed decisions about patient care. By using machine learning algorithms to analyse large volumes of data, clinicians can better predict the likelihood of an individual developing heart disease and take appropriate action accordingly. This research also provides insight into how big data can be used for predictive analysis in healthcare settings, which could lead to improved outcomes for patients with cardiac illness.

关键词： Support vector machines Heart Technological innovation machine learning algorithms Big Data Prediction algorithms Decision trees

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Detecting breast cancer using machine learning algorithms: The efficient and accurate way

Detecting breast cancer using machine learning algorithms: T...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Satyabrata Patro B. Vasantha Lakshmi V. Sailaja Bhavani Sankar Panda Devvret Verma Department of CSE GIET University Gunupur India Department of ECE Pragati Engineering College Suramapalem India Department of CSE Raghu Engineering College Visakhapatnam India Department of Biotechnology Graphic Era Deemed to be University Dehradun India

Breast cancer is one of the most prevalent diseases that claims the lives of thousands of women every year. Artificial intelligence has been used to identify breast cancer early, fast, and correctly (AI). The objective of this essay is to assess current classification work on these tumours. Using machine learning techniques like Support Vector machine (SVM), K Nearest Neighbor (K-NN), and Random Forest, medical pictures are divided into benign and malignant categories (RF). Convolutional Neural Network C Nearest Neighbor (CNN) is one of the deep learning techniques recently employed for comparable purposes. Due to its high mortality and morbidity rates, breast cancer presents a particular concern to female patients. Therefore, it is essential to have an algorithm that can recognise the early symptoms of breast cancer. In order to predict breast cancer, the results were assessed using the four techniques: Convolutional Neural Network, Decision Trees, Logistic Regression and random forests is essential for identifying the early signs of breast cancer. Three distinct classification ML techniques will be employed in this investigation. The effectiveness and accuracy of each algorithm will next be assessed. For classification systems, data with unbalanced classes constitute a substantial problem, requiring careful management and pre-processing. Using a dataset of breast cancer patients, we'll train a variety of machine learning models. The best solution for this issue is finally found by evaluating the accuracy and performance of each algorithm. In order to choose the most effective course of action, this research will display the effectiveness of multiple ways for categorising breast cancer.

关键词： Support vector machines Radio frequency machine learning algorithms Forestry Prediction algorithms Breast cancer Classification algorithms

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Amazon Employee Access System using machine learning algorithms

Amazon Employee Access System using Machine Learning Algorit...

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： G Elizabeth Rani M Sakthimohan M Navaneethakrishnan S Mahendran S Dhivya M. Jayaprakash Department of CSBS KGiSL Institute of Technology Coimbatore Tamilnadu India Department of ECE KGiSL Institute of Technology Coimbatore Tamilnadu India Dept of CSE St. Joseph College of Engineering Sriperumbudur Tamilnadu India Department of CSE Kalasalingam Academy of Research and Education Tamilnadu India

Employees that desire to work for Amazon will find Amazon employee access to be quite useful. Any individual on this duty who begins evolved work at a firm should obtain the access necessary to carry out their responsibilities. When a worker requests access, the manager will select that employee's request at random and provide access; this may take longer time. To provide access without human intervention, created a task utilizing the Catboost, Decision Tree, and Random Forest algorithms. This, too, takes time. Due of the performance constraint, then used ensemble algorithms such as random forest and gradient boosting. Furthermore, apply a linear classifier and obtain good results by using one-hot encoding, which converts categorical data into binary values. Finally, combine the top three predictions from Decision tree, Catboosting, and Random Forest.

关键词： machine learning algorithms Prediction algorithms Boosting Encoding Data models Classification algorithms Decision trees

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Smart Temperature Management in Buildings using Predictive Analysis by machine learning algorithms

Smart Temperature Management in Buildings using Predictive A...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Ritika Dharmkar Jones Yeboah Isaac Kofi Nti School Of Information Technology University Of Cincinnati USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

In the contemporary world, energy management stands as a critical environmental imperative, necessitating a balanced approach that addresses both ecological and economic considerations. In the scope of this research paper, we employ machine learning techniques to tackle a specific facet of this challenge: temperature and energy management within buildings. Our primary objective is to predict the heating and cooling loads of buildings using different regression models, namely linear regression, decision tree, and random forest. We examine the performance of our models with open-source data from Kaggle-based performance metrics such as Mean Absolute Error, Mean Squared Error, and R-squared. Our findings demonstrate that all three machine learning algorithms perform effectively in predicting Heating and Cooling Loads. Notably, the decision tree regressor and random forest regressor surpass the linear regression model, exhibiting superior accuracy, as indicated by their lower Mean Absolute Error and Mean Squared Error, along with higher R-squared values. Consequently, the decision tree-based algorithm emerges as the most suitable choice for this particular dataset.

关键词： Heating systems machine learning algorithms Cooling Biological system modeling Buildings Linear regression Predictive models

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machine learning algorithms In User Authentication Schemes

Machine Learning Algorithms In User Authentication Schemes

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Laura Pryor Rushit Dave Jim Seliya Evelyn Sowells Boone University of Wisconsin - Eau Claire Eau Claire WI USA North Carolina A&T State University Greensboro NC USA

ISBN: (纸本)9781665442329

In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different machine learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.

关键词： Measurement machine learning algorithms Biometrics (access control) Heuristic algorithms Computational modeling Biological system modeling Authentication

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Integration of machine learning algorithms Classifiers and Sequential Forward Selection Features in Diabetes Prediction

Integration of Machine Learning Algorithms Classifiers and S...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Mohammad Abdullah Tahir Zamam Farhat Mohd Umair Rizwan Khan Jaafar Gaber Musheer Anwar Prosper Eguono Ovuoraye Université Bourgogne Franche Comté France Eötvös Loránd University Budapest Hungary Aligarh Muslim University Aligarh India Lúniversité de technologie deBelfort-Montbeliard Belfort France Lovely Professional University Phagwara India Federal University of Petroleum Resources Effurun Nigeria

Diabetes mellitus, commonly known as diabetes, is a medical condition in which blood sugar levels rise for an extended period. Diabetes is a chronic condition that affects thousands of people. It has resulted in the deaths of millions of people across the world. The only goal of this research is to create a machine-learning model for the early detection of diabetes utilizing supervised machine-learning algorithms and various feature selection strategies for the optimal number of features. On the diabetes dataset obtained from the online platform Kaggle, we employed three distinct machine learning classification algorithms, including Logistic Regression, Decision Tree classifier, and Random Forest classifier. All machine learning models scored roughly 86% accuracy, but the Random Forest model achieved 86.51 % accuracy on a test set with only a subset of four features. The first features are High Blood Pressure, High Cholesterol, Body Mass Index, and General Health, with an accuracy of 86.57% on the train set. The Sequential forward Selection algorithm was applied for the feature.

关键词： machine learning algorithms Forestry Feature extraction Prediction algorithms Blood pressure Diabetes Classification algorithms

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machine learning algorithms in 5g Cellular Network for Efficient Cognition

Machine Learning Algorithms in 5g Cellular Network for Effic...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： Chandra Sekhar Pasumarthi Akash Jain Narender Ravula Shanthi Palaniappan Sankararao Majji S K Mydhili Microsoft India Pvt Ltd Hyderabad India Electronics & Telecommunication SSIPMT Raipur Chhattisgarh India GITAM (Deemed to be University) Hyderabad India Sri Krishna College of Engineering and Technology Coimbatore India GRIET Hyderabad India KGiSL institute of technology Coimbatore India

ISBN: (纸本)9781665414487

In today's communications and information technology business, cellular mobile networks are one of the technologies that has had the most significant impact on the industry. As part of the steps made to improve the overall quality of life, many aspects of everyday living, as well as technical breakthroughs, are becoming increasingly reliant on smart gadgets, which are becoming increasingly common. It is projected that, in the near future, every electric gadget will be a smart device that can be connected to the internet on a regular basis. A new network paradigm known as the vast cellular Internet of Things is created as a result, in which a large number of simple battery-powered heterogeneous devices work together for the improvement of humanity in all aspects. This system was developed in accordance with the standard simulation specifications for such systems, and the realistic data that will be extracted from it will aid in demonstrating the effectiveness of the proposed algorithms in order for them to be included in the 5G cellular communications technology.

关键词： Industries machine learning algorithms 5G mobile communication Neural networks Routing Data models Information and communication technology

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Twitter Sentiment Analysis using machine learning algorithms for COVID-19 Outbreak in New Zealand

Twitter Sentiment Analysis using Machine Learning Algorithms...

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11th IEEE International Conference on System Engineering and Technology, ICSET 2021

作者： Baker, Oras Liu, Jay Gosai, Mayur Sitoula, Suyog Southern Institute of Technology School of Computing Invercargill New Zealand

ISBN: (纸本)9781665437660

The use of data obtained from social media for data mining has benefited the process of analysing public opinions. Sentiment analysis, also referred to as opinion mining, helps public health officials and other governmental agencies to understand the public's concerns, panics, emotions and interactions to provide effective services and information. This research focuses on the sentiment analysis of the COVID-19 pandemic outbreak in New Zealand using Twitter data. The analyses derived from several machine learning classification techniques, in particular Naive Bayes, K-Nearest Neighbour (KNN), Convolutional Neural Network (CNN), and Support Vector machine (SVM). In addition, the researchers implemented these algorithms in two different data mining platforms, namely Python and RapidMiner, then compared the metrics obtained from these techniques and platforms to identify the best algorithm suited for sentiment analysis. Finally, the researchers illustrate the experimental results that show the performance of Naïve Bayes and SVM, which indicates a longer computational time and led to an improved Twitter sentiment analysis result that outperforms the other models. After that, validate these models' effectiveness by comparing the obtained results for the same models in Python and RapidMiner. © 2021 IEEE.

关键词： Support vector machines COVID-19 Sentiment analysis machine learning algorithms Social networking (online) Pandemics Computational modeling

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Comparative analysis of machine learning algorithms for Intrusion Detection

Comparative analysis of Machine Learning algorithms for Intr...

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2020 International Conference on Futuristic Trends in Mechanical Engineering, ICOFTIME 2020

作者： Pai, Vasudeva Devidas Adesh, N.D. NMAM Institute of Technology Nitte Udupi Karnataka India Manipal Institute of Technology Manipal Academy of Higher Education Manipal Udupi Karnataka India

In this modern era, the network related applications, programs and services are growing enormously but the network security issues also grow along with them. Keeping the network secure is a challenging and a crucial task. To maintain the secure network there must be some system which can detect and identify any malicious activity happening in network. This system is called as Intrusion Detection System. There are many traditional network security tools and techniques of preventing intrusion like firewalls, anti-virus, encryption-decryption, access control etc. But all are not effective in protecting network from increasing attacks. The network traffic can be categories into normal and intrusive traffic using machine learning (ML) algorithms. Here, the preliminary comparative study regarding which type of machine learning algorithm performs better in identifying the attacks namely Denial of Service, Probe, User to Root and Remote to Local. The NSL-KDD dataset is used to study features and behavior of malicious attacker using machine learning techniques. This study can be taken as reference for mechanical engineers for developing a safe automation in industrial atmosphere and automation in automobile. © Published under licence by IOP Publishing Ltd.

关键词： feature selection Intrusion Detection System machine learning algorithms NSL-KDD dataset

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An efficient gene bigdata analysis using machine learning algorithms

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第15-16期79卷 9847-9870页

作者： Wang, Ge Pu, Pengbo Shen, Tingyan Shandong Univ Sci & Technol Dept Informat Engn Tai An Shandong Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Sch Chinese PLA Beijing 100853 Peoples R China

Bioinformatics is one of the emerging and rapidly developing research areas that is predominantly used for genetic data analysis and processing. Bioinformatics is characterized by its huge and voluminous data that is growing in nature which in turn complicates data analysis. In most cases, Bioinformatics data analysis and processing involve big data analytics due to the complex nature of the data. Previous research works handled data analytics using traditional tools and conventional big data analytical methods. However, it can be proved that machine learning algorithms and approaches can be effectively deployed to perform parallel, distributed and incremental processing of complex big data analytics especially in the case of gene big data analytics to enhance the efficiency in processing this large chunk of Bioinformatics-based gene big data. This paper provides a machine learning algorithm-based Convolution Neural Network (ML-CNN) approach for the process of identifying potential target genes, predicting miRNAs, visualizing the unique miRNA patterns, and validating genomes. The proposed approach has experimented with MATLAB software using deep learning toolbox on the pre - miRNA dataset. Experimental results indicate that machine learning algorithms certainly increases the efficiency of Bioinformatics-based methods of processing gene data in terms of prediction accuracy and reduced processing time. The mean performance of ML-CNN is improved 7% high than the existing system.

关键词： Big data analytics Gene big data machine learning algorithms Bioinformatics Pattern recognition Prediction

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