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检索条件"主题词=Machine Learning Algorithms"

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Comparative Performance Analysis of Quantum Algorithm with machine learning algorithms on Diabetes Mellitus

Comparative Performance Analysis of Quantum Algorithm with M...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： BBNS. Prakash B. Naveen MD. Akhiluzzama Pothuraju Rajarajeswari Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

This paper shows the distinction between Quantum and Classical machine learning techniques appeal to a diabetic mellitus dataset. Diabetes mellitus is like series of diseases that affect the body and deplete blood sugar levels (insulin). In our bodies, glucose is an important source of energy for powering mitochondria, the cells' function that make muscles and tissues strong. We use many machine learning classifiers such as Support Vector machine, Kernel Principal Component Analysis, Bayesian Network and Decision Tree etc. These models are able predict a certain amount of data, while in case of large data there is significant amount of error and low accuracy rate, A new method known as quantum machine learning (QML) is nothing but the collaboration of machine learning in the way of quantum algorithms the term used for machine learning algorithms executed in a quantum computer for analysis of classical data is known as quantum-enhanced machine learning. In this method we are predicting diabetes mellitus using quantum machine learning algorithms which able to handle the huge data with high accuracy rate of 97% and F1-Score of 68%.Our study having implemented models of Predicted & Enhanced models.

关键词： Support vector machines machine learning algorithms Quantum algorithm Computational modeling machine learning Predictive models Data models

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Comparative Analysis of machine learning algorithms for Weather Prediction using Error Detection

Comparative Analysis of Machine Learning Algorithms for Weat...

引用

International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Navneet Kaur Navdeep Singh Computer Science Engineering Chandigarh University Mohali India

Weather prediction is actually a tedious task, it is a tough task not only for the common man but also for the researcher scientists. So as to predict the most accurate weather, machine learning techniques are used to make an error prediction based on the given data. In this study, the meteorological and geographic data of the past 30 years were collected, and five machine learning algorithms were used: linear regression (LR), polynomial regression (PR), random forest regression (RFR), decision tree regression (DTR), random forest dept. The dataset used in the research is taken from Kaggle and contains 96454 instances and 12 attributes. machine-learning algorithms were selected as regression models to predict weather. Additionally, grid search and five-fold cross-validation techniques were used to improve learning performance. Model performance was evaluated by comparing the observed and predicted data. It was observed that the Random Forest (RF) with maximum Dept-50 model accurately predicted the error with accuracy of 0.0019 compared with other models with various statistical criteria.

关键词： Radio frequency machine learning algorithms Weather forecasting Null value Predictive models Data models Cleaning

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Improving the Accuracy of Heart Disease Prediction Approach of machine learning algorithms

Improving the Accuracy of Heart Disease Prediction Approach ...

引用

Delhi Section Conference (DELCON), IEEE

作者： Md. Belal Hossain Mohammed Nasir Uddin Syada Tasmia Alvi Chowdhury Abida Anjum Era Dept. of CSE Jagannath University Dhaka Bangladesh Dept. of CSE Daffodil International University Dhaka Bangladesh

The work is about forecasting heart disease. First and foremost, we gathered data from various sources and divided it into two portions, one of which is 80% and the other is 20%, where the first part is for training and the remainder is reserved for the test dataset. After collecting this dataset, we applied the pre-processing formula and different classifier algorithms. K-Nearest Neighbor, Support Vector machine, Decision Tree, Random Forest, Naive Bayes & Logistic Regression are the techniques utilized here. When compared to other algorithms, Logistic Regression, KNN, and SVM provided the same or superior accuracy. Precision, Recall, F1 score, and ERR are used to measure accuracy. Gender, Glycogen, BP, and Heartrate are some of the prefixes used while training and found to be different major vulnerable factors of heart diseases. The direction of this work is real-life experiments and clinical trials using different devices.

关键词： Heart Training Support vector machines machine learning algorithms IEEE Sections Predictive models Classification algorithms

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Breast Cancer Detection Using Advanced machine learning algorithms: A Comparative Analysis

Breast Cancer Detection Using Advanced Machine Learning Algo...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Pinaki Pritam Singha Debaleena Datta Mahmoud Odeh Savitri Bevinakoppa Muhamamd Fazal Ijaz Department of CSE University of Engineering and Management Kolkata India Information Technology Department Zarqa University Jordan Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia School of IT and Engineering Melbourne Institute of Technology Melbourne Australia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Breast cancer is the disease that affects 24.5% of all women found with cancer worldwide, as stated by Globocan 2020 statistics. Early cancer diagnosis has become a crucial concern as a result of the current population rise in medical research. Therefore, early cancer prediction is crucial for revising standards for survival and treatment options. Therefore, a computer assisted detection (CAD) system that employs a machine learning technique to provide an accurate diagnosis of breast cancer is necessary. Our proposed research aims for the detection of malignant and benign cancer using the Wisconsin Breast Cancer dataset through a novel approach. Our work is segmented into two parts: (1) Use of six classic ML models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), k-Nearest neighbor (k-NN), Support vector machine (SVM), and (2) Use of four advanced ML models: Adaptive boosting (AdaBoost), Extreme gradient boosting (XGBoost), Gradient boosting machines (GBM), Extreme learning machine (ELM). Experimental results show that the advanced ML classifiers are technically more optimized and accurate than the classic ones, as the former are capable of dealing with multiple complex features and a large dataset. AdaBoost outperforms all the other ML strategies used in this research by scoring approximately 98.7% accuracy.

关键词： Support vector machines Radio frequency Solid modeling Adaptation models Accuracy machine learning algorithms Boosting

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Remote Health Prediction System Using machine learning algorithms

Remote Health Prediction System Using Machine Learning Algor...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： P Purushotham A. Punitha Ajmeera Kiran D Divya Priya Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Annamalai University TamilNadu India

The “Remote health prediction System employing machine learning algorithms” uses machine learning in healthcare. Better diagnostic and treatment decisions require better medical facilities. machine learning helps clinicians analyse massive and complex medical data. This information helps doctors treat patients. Thus, machine learning in healthcare improves patient happiness. This project integrates healthcare machine learning functions. Illness prediction using machine learning predictive algorithms can make healthcare smarter than diagnosis. machine learning algorithms and libraries will build this project. Due to its rich library and usability, Python is recommended. syntax. We used different categorization approaches to train machine learning models and then chose the best model that avoided overfitting and underfitting and had low variance and bias.

关键词： Schedules machine learning algorithms Medical services machine learning Syntactics Prediction algorithms Libraries

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A Visionary Approach to Detect Spoofing Website using machine learning algorithms

A Visionary Approach to Detect Spoofing Website using Machin...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： T. Kavitha S. Hemalatha R. Mounica V. Niveda Yasvant Kumar Department of Computer Applications Kongu Engineering College Perundurai Erode

Today, everyone is addicted to the internet. Web searching has become important for everyone despite their age, location or occupation. However, as the use of the internet is increasing, the spoofing attacks through malicious websites are also increasing. All conducted online purchases and online pursuits being online amount transaction, e-reservation, recharge online and moreonline. As the number of Internet users grows, automatic URL detection is becoming increasingly more important for protected users. The proposed research will compare the contrasting classification models such as Random Forest, Decision Tree and Logistic Regression which have the highest accuracy of differentiation between legitimate sites from phishing sites. The intention of the research is to find the attacking sites and best ML algorithm which has the highest accuracy.

关键词： Uniform resource locators machine learning algorithms Phishing Forestry Internet Classification algorithms Informatics

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Performance Evaluation of machine learning algorithms to Predict the Medication Prescription Errors in Intensive Care Units

Performance Evaluation of Machine Learning Algorithms to Pre...

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Advancement in Technology (ICONAT), International Conference for

作者： Vineetha Pais Santhosha Rao Balachandra Muniyal Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

Medication error can be considered as one of the major issues in healthcare. Critical care unit is considered as the most crucial unit where medication errors if occurred can be dangerous at the cost of life of a patient. Artificial intelligence has the capacity to significantly reduce prescription errors by helping to identify potential mistakes before they take place. This study is an attempt to compare and choose a machine learning algorithm for the machine learning model that will help the doctors and clinicians working in the intensive care unit to reduce the prescription errors in intensive care unit. In this study machine learning classification techniques have been applied to choose the best machine learning algorithm for the problem of predicting the prescription errors in intensive care unit. The result shows that K Nearest Neighbors (KNN) shows the best accuracy (99.13 %).

关键词： Performance evaluation Drugs Deep learning machine learning algorithms Costs Hospitals Measurement uncertainty

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machine learning algorithms for the Prediction of Daily Mean Solar Power

Machine Learning Algorithms for the Prediction of Daily Mean...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Rashmi Bachulkar Rohan Bhatkande Akshata Patil Bibi Fatima Telgi IT Executive Global Energy Pvt Ltd AITM Belagavi

ISBN: (纸本)9781665435253

Daily Mean Solar Power is the main factor that is taken into consideration while a solar panel is being installed. The average solar irradiance data which is collected from the NASA satellite is being used as input to the model. The results obtained from the model showed the amount of solar energy that can be generated before any installation of solar panel at a given location by using the machine learning(ML) algorithms. To predict the mean solar irradiance three regression algorithms are being used along with its parameters that are linked to solar irradiance. In the given work there is representation of a comparative analysis among these machine learning algorithms. Furthermore, the data is divided into four parts i.e. prediction of solar irradiance for one week, one month, one year and fifteen years data. This data is applied for three algorithms namely Decision Tree Regression, Linear Regression, Support Vector Regression. And the algorithm which produces least root mean square error is selected for prediction.

关键词： Support vector machines machine learning algorithms Satellites Linear regression Solar energy Prediction algorithms Solar panels

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A Comparative Study of machine learning algorithms for Detecting Breast Cancer

A Comparative Study of Machine Learning Algorithms for Detec...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Razib Hayat Khan Jonayet Miah Md Minhazur Rahman Maliha Tayaba Department of Computer science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science University of South Dakota South Dakota USA Department of Physics University of South Dakota South Dakota USA

Breast cancer poses a major hazard to women, with high morbidity and fatality rates, because there is a lack of reliable prognostic models, clinicians find it challenging to develop a treatment regimen that could increase patient life expectancy. There are required to detect breast cancer early stages so the necessary steps should be taken as early as possible to stop this disease first we need more research in this field. So, in this work, we are trying to build a sustainable machine-learning model which can detect the type of breast cancer whether benign or malignant. Through the detection, we proposed the best model which can detect this outbreak efficiently. In our study, we examined the performance of five machine learning algorithms (XGBoost, Naïve Bayes, Decision Tree, Random Forest, and Logistic Regression) in predicting human health behavior. Among these algorithms, XGBoost had the highest accuracy (95.42%) and performed well in terms of sensitivity (98.5%), specificity (97.5%), and F-1 score (99%). Our findings suggest that XGBoost has promising potential in predicting breast cancer, but further research is needed to develop and apply it for commercial use in the healthcare industry.

关键词： Industries machine learning algorithms Sensitivity Medical services Prediction algorithms Breast cancer Hazards

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Classification and Contrast of Supervised machine learning algorithms

Classification and Contrast of Supervised Machine Learning A...

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

作者： Ramakrishnan Raman Rejuwan Shamim Shaik Vaseem Akram Lalit Thakur Biju G. Pillai R. Ponnusamy Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering with Data Science Maharishi University of Information Technology Noida Uttaranchal Institute of Technology Uttaranchal University Department of CIVIL Parul University Department of IT & Business Analytics BIIB & Dean - Faculty of Management Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs performed by intelligent systems most commonly is supervised classification. Based on the data set, number of occurrences and variables in this study, the most efficient classification algorithm is selected. It is comprehensively described and contrasted with various supervised learning methods. Seven distinct machine learning algorithms were taken into consideration utilizing the Waikato Environment for Data Analysis (WEDA) machine learning tool. For the identification process, the data set was used with 780 instances, eight independent variables (quality), and one dependent variable (variable). According to the data, SVM was the technique with the highest degree of precision and accuracy. Accordingly, the next accurate classification algorithms after SVM were determined to be Naive Bayes and Random Forest. The study demonstrates that precision (accuracy) and model construction time are two factors, whereas kappa statistics and mean error percentage (MAE) are two other factors. Therefore, controlled predictive machine learning requires precision, accuracy, and minimal error in ML algorithms.

关键词： Support vector machines machine learning algorithms Supervised learning Training data Prediction algorithms Time measurement Classification algorithms

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