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检索条件"主题词=Machine Learning Algorithms"

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Survivability Prediction for Patients with Tonsil Cancer Utilizing machine learning algorithms

Survivability Prediction for Patients with Tonsil Cancer Uti...

引用

Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Rasel Hider Nobin Mokhlesur Rahman Mohammad Jahangir Alam Dept. of CSE Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798350399141

The most prevalent oropharyngeal malignancy is tonsil cancer, and as cancers caused by the human papillomavirus (HPV) are becoming more common, their incidence is also significantly increasing recently. Several popular machine learning classifiers have been used to predict numerous types of cancer survivability. And it can make a substantial contribution to the field of cancer prediction. In accordance with that and finding the best effective models, we applied ten machine learning classifiers to the Surveillance, Epidemiology, and End Results (SEER) program database by choosing pertinent features to predict tonsil cancer survival status and evaluated the performance of each of these algorithms using accuracy, and F1 score, precision, recall, specificity, confusion matrix, and cross-validations. Additionally, we balance the Alive and Death classes using the Synthetic Minority Oversampling Technique (SMOTE). These methods have been used to analyze tonsil cancer patients’ chances of surviving and help doctors make a prognosis about the phase of the illness. According to our result, the Random Forest classifier outperformed others and obtained the highest score of 93.88%. Besides this, the Extra Tree classifier, and LGBM classifier also can be helpful in predicting the survivability of tonsil cancer patients. In addition, rather of referring to several publications, this resercah will also assist the researchers in quickly reviewing the relevant literature.

关键词： machine learning algorithms Databases Surveillance Roads Medical services Predictive models Prediction algorithms

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Analyzing various machine learning algorithms with SMOTE and ADASYN for Image Classification having Imbalanced Data

Analyzing various Machine Learning Algorithms with SMOTE and...

引用

Current Development in Engineering and Technology (CCET), IEEE International Conference on

作者： Yakshit Gagandeep Kaur Veerpal Kaur Yashika Sharma Vishnu Bansal Chitkara University Institute of Engineering and Technology Punjab India Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

Oversampling is a strategy employed in machine learning to handle imbalanced datasets by creating copies of the minority class instances to balance the dataset, thus reducing bias and enhancing the accuracy of the model. The work presents various machine learning techniques to classify images from an imbalanced dataset. In this paper various oversampling techniques such as ADASYN and SMOTE are blended with the classification algorithms i.e., SVM and CNN with SVM in order to balance imbalanced datasets. The experimentation is performed on the Google Colab using different machine learning techniques: SVM, and (CNN and SVM). The results of the experimentation suggests that the amalgamation of SVM and CNN is better than the SVM and SMOTE is better than ADASYN on the basis of performance matrices such as recall, precision, F1 score.

关键词： Support vector machines machine learning algorithms Neural networks Support vector machine classification machine learning Classification algorithms Internet

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Implementation of machine learning algorithms to classify university academic success

Implementation of Machine Learning algorithms to classify un...

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： Efré n Jimé nez Delgado Authors Linnette Roldá n Morales Yesenia Calvo Araya Department of Software Enginner Universidad T&#x00E9 cnica Nacional San Carlos Costa Rica

machine learning is a software tool that allows information present in databases to be converted into intelligent decisions based on data and not on experience or feelings. This work presents a review of the algorithms used in the classification of student performance present in the database of the national technical university, specifically in the software engineering career. Information classification analysis using ma- chine learning algorithm has become a modern and powerful instrument, which could help academic institutions improve retention and performance rate of courses by students and with this obtain a more general view of student performance before starting the course based on information from other courses and general student variables in order to reduce the risk of failure. The main objective of this work is to describe the variables used by the National Technical University and the application of different automated learning algorithms in order to obtain the metrics that allow demonstrating the best algorithm among those studied. Finally, the article concludes with a classification algorithm that provides an accuracy of around 80% to 82% that can be replicated in other university institutions.

关键词： machine learning algorithms Databases Software algorithms Education machine learning Prediction algorithms Classification algorithms

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Cardiovascular Disease Detection Using Multiple machine learning algorithms and their Performance Analysis

Cardiovascular Disease Detection Using Multiple Machine Lear...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Zainab Ali Noman Naseer Hammad Nazeer Department of Mechatronics and Biomedical Engineering Air University Islamabad Pakistan

ISBN: (纸本)9781665492430

Heart problems have proven to be lethal all around the world. Cardiovascular diseases like cardiac rhythm disorders, heart failure, congenital heart diseases, etc. are the leading cause of death. In this disease, the heart fails to provide enough blood to other body regions to allow it to perform its regular functions. Cardiovascular disease is detected by traditional invasive procedures such as CT and angiography but they have their limitation to combat such problems and limitations, therefore early and precise diagnosis of this disease is needed for avoiding further damage to patients and protecting their lives in advance. The modern world required intelligent and modern solutions thus in this regard computational strategies built on intelligent machine learning systems have been discovered to be more accurate and effective in the identification of heart disease. This study aimed to develop a system that integrates multiple machine learning algorithms, including K-nearest Neighbor, Naïve Byes, Linear Regression, Decision Tree, and Random Forest, which are used to detect cardiovascular disease. Five machine learning algorithm models were developed and their performances were observed based on several other performance indicators like accuracy, Precision, F1-score, Macro Average, and Weighted average among two target classes i.e. Presence and absence of cardiovascular disease. Classification reports generated against each model were utilized to assess the efficacy and strength of the constructed model.

关键词： Heart Analytical models machine learning algorithms Computational modeling Predictive models Prediction algorithms Rhythm

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An Intrusion Detection Model for CICIDS-2017 Dataset Using machine learning algorithms

An Intrusion Detection Model for CICIDS-2017 Dataset Using M...

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Advances in Computing, Communication & Materials (ICACCM), 2020 International Conference on

作者： Shailesh Singh Panwar Y. P. Raiwani Lokesh Singh Panwar Dept. of Computer Science and Engg. H.N.B. Garhwal University Srinagar Garhwal Uttarakhand India Dept. of Electronics and Comm. Engg. Graphic Era University Dehradun Uttarakhand India

ISBN: (纸本)9781665474405

Due to excessive use of the internet, keeping the network secure and transferring data securely across networks has become a difficult task. To recognize distinct types of network (internet) attacks, need machine learning strategies, to develop various types of the intrusion detection system. The main reason behind using different types of intrusion detection strategies is that the attackers constantly receiving information about the network traffic. Therefore, the intrusion detection system is used to easily identify the attacks and to thwart those attacks. In this paper, we have used the CICIDS-2017 dataset, which is a labeled dataset, for analyzing the result of eight different supervised classification techniques (GaussianNB (GNB), BernoulliNB (BNB), Decision Tree, KNN, Logistic Regression, SVM, Random Forest and SGD). The dataset is divided into five different days; each has different types of class attacks (DoS, PortScan, Botnet, Web Attacks, Infiltration, Heartbleed and DDoS). We have proposed a model with three stages. The first stage data-preprocessing, which includes several steps such as extracting independent and dependent variable, splitting dataset, finding missing value, feature scaling on the dataset and encoding categorical data, then in feature selection stage, finding out unique features using RFE (Recursive feature elimination) technique. Finally, test the result of eight different supervised classification techniques using cross-validation on the CICIDS-2017 dataset.

关键词： Support vector machines machine learning algorithms Intrusion detection Gaussian processes Telecommunication traffic Feature extraction Encoding

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A Review of the Various machine learning algorithms for Cloud Computing

A Review of the Various Machine Learning Algorithms for Clou...

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International Conference on Advanced Science and Engineering (ICOASE)

作者： Sandy Victor Amanuel Ibrahim M. Ahmed Information Technology Department Technical College of Informatic - Akre Duhok Polytechnic University Duhok Iraq College of Science University of Mosul Mosul Iraq

Cloud computing (CC) provides network services on request, especially data storage and processing capacity, without users' specific and direct management. CC recently became a collection of public and private data centers that provide the customer with a shared Internet network. Edge Computing is an emerging computing and knowledge storage model that puts end-users closer together to increase reaction times and save communication power. However, CC and edge computing face protection issues, including customer risk and corporate recognition, that hinder the swift implementation of computing modelling. One solution to this problem, because of its complexity and severity, is machine learning (ML) which consists of researching computational algorithms and naturally advancing knowledge. The problem and solution issues are raised by the overview article that analyses CC safety risks, problems, and solutions that use one or more ML algorithms. Study various ML algorithms, such as controlled, unmonitored, semi-supervised, and enforced training, to solve cloud protection problems. The paper assesses each technique's efficiency based on its characteristics, advantages, and drawbacks. In addition, it will have potential study guidance on safeguarding CC usage and applications.

关键词： Training Privacy machine learning algorithms Face recognition Computational modeling Memory machine learning

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Towards Smart Agriculture using machine learning algorithms

Towards Smart Agriculture using Machine Learning Algorithms

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Atla Devikarani Gumpena Veera Durga Jyothi J.Divya Lalitha Sri B.S. Kiruthika Devi Department of CSE Aditya College of Engineering and Technology Surampalem A.P India CL Educate Ltd New Delhi India

ISBN: (纸本)9781665462013

Indian society relies heavily on agriculture, making it a top global industry. The agricultural sector has been facing several challenges because of the effect of temperature fluctuations and their uncertainty. The accurate prediction of crop growth is a crucial factor in farm management. The current challenges faced by the agricultural sector will slow down the crop production. In order to make accurate forecasts, current conditions of the farmland are required. Hence, machine learning techniques are employed for analyzing the environmental factors such as crop yield recommendation, crop protection and customized support for farmers. This study clearly provides a detailed review on the various algorithms such as K-NN, Support Vector machine (SVM), Random Forest (RF), and Neural Network (NN). The smart agriculture system uses such machine learning algorithms along with IoT technology to provide high accuracy, precision and medical diagnosis of plants. From the studies, it is evident that neural network outperforms other existing classifiers with better accuracy. The comparison of various machine learning algorithms is carried out along with several other performance metrics such as precision, recall, and F-score.

关键词： Support vector machines Smart agriculture Radio frequency machine learning algorithms Uncertainty Temperature Plants (biology)

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Comparative Study on Pairs Trading Using machine learning algorithms

Comparative Study on Pairs Trading Using Machine Learning Al...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Anmol Agarwal Ankit Ankit Deepak Kumar Meena Rohit Beniwal Department of CSE Delhi Technological University Delhi India

Hedge funds employ the trading of pairs as one of their most lucrative trading strategies. It’s especially intriguing since it eliminates the time-consuming process of evaluating assets by focusing on comparable prices. Upon the convergence of the pair’s price, a profit can be achieved by buying a discounted investment and selling an overvalued investment. However, as the data became more widely available, finding rewarding pairs turned out to be a cumbersome process. Pair Trading is a two-step mechanism. The first step is to identify the pairs of stocks that belong to the same market segment or have similar market capitalization. It is like choosing the same fruit from a fruit platter that shares similar identical features. The second step is to keep a watch on the distance between them in the subsequent trading cycle. When the distance between them abruptly rises, it is assumed that the spread between them will converge. If the pairs are mean-reverting, then the overachieved stock is short and the under-achieved stock is long. When spreads converge, the investor liquidates all positions resulting in a profit. Results shows that we achieved accuracy of 95% pairs to be successful traded. Also, compared all results which are achieved from different machine learning algorithms.

关键词： machine learning algorithms Focusing Investment Convergence

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A Comparative Exploration of machine learning algorithms for Disease Detection

A Comparative Exploration of Machine Learning Algorithms for...

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International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： Ashendra Kumar Saxena Shashikala H. K. Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India Department of Computer Science and Engineering Jain University Bangalore India

Alzheimer's disease (Promotion) is an unsolvable ongoing issue that causes cerebral disintegration, which results in memory loss and mental deterioration. machine learning techniques are widely used in a variety of healthcare disciplines. A remarkable amount of research has gone into characterizing Alzheimer's disease. In order to provide a better understanding of the work that has been completed in the subject of Alzheimer's disease, we have reviewed several articles by various experts that use various machine learning techniques collectively. One of the major problems in recent years has been Alzheimer's disease. Due to this illness, people lost their capacity for comprehension and reasoning, among other things. Applying machine learning research to attractive reverberation imaging (X-ray) techniques can help determine promotion more quickly and may help predict the progression of the disease. It was also possible to predict individual dementia in older adults using Promotion screening data and ML classifiers. The X-ray segment data and the patient's prior states can help in improving the classifier execution to predict the Promotion subject status.

关键词： Image segmentation machine learning algorithms Imaging machine learning Medical services Prediction algorithms Reverberation

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MQTT equipped Intrusion detection system for IoT devices based machine learning algorithms

MQTT equipped Intrusion detection system for IoT devices bas...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Priya Sharma Sanjay Kumar Sharma Diksha Dani University School of Information & Communication Technology Gautam Buddha University Greater Noida Indraprastha Engineering College Ghaziabad India

ISBN: (纸本)9781665462013

In the era of COVID19, the world has shifted to an online presence and is now forced to embrace the usage of digital technology in their daily lives. With the meteoric rise of internet-based devices, there is a requirement for a protocol for secure communication between these devices. Message Queueing Telemetry Transport (MQTT) is the standard protocol for IoT devices. The MQTT implementation with IDS have very prominent usability and has huge potential for increasing efficiency. Therefore, in this paper, an IDS has been proposed with MQTT as a protocol for IoT devices, using machine learning to improve the pattern recognition of the IDS. The proposed system has been tested with three machine learning algorithms, namely, and the results show that they are adequate for the MQTT protocol

关键词： Protocols machine learning algorithms machine learning Real-time systems Pattern recognition Internet of Things Telemetry

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