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检索条件"主题词=Machine Learning Algorithms"

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Mammogram Image Classification Using Various machine learning algorithms

Mammogram Image Classification Using Various Machine Learnin...

引用

IEEE International Conference on Computing, Communication and Security (ICCCS)

作者： Arpita Joshi Ashish Mehta Department of Computer Science Soban Singh Jeena University Almora India Department of Computer Science Kumaun University Nainital India

The leading cause of death in women is still breast *** cancer in its early stages is crucial. For the purpose of diagnosing breast cancer data, a variety of machine learning algorithms are *** this study, performance comparisons between different machine learning algorithms: Extra Trees, Random Forest, Support Vector machine (SVM),Decision Tree, Logistic Regression Bagging, Gradient Boosting, and AdaBoost have been conducted on mammography images of MIAS(Mammographic Image Analysis Society) *** is observed that Bagging outperformed all other algorithms and achieved the highest accuracy (0.9678).All the work is done in the Kaggle environment based on the python programming language.

关键词： Support vector machines machine learning algorithms Boosting Breast cancer Mammography Security Random forests

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Loss Function Computation using machine learning algorithms based on the effects of Natural Disasters and Plant Diseases on Plant Growth

Loss Function Computation using Machine Learning Algorithms ...

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Computer Science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Mohammad faizul Kabir Farzana Chowdhury Raisa Shakib Mahmud Dipto Arif Shakil Md Ashraful Alam Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

Many machine learning(ML) architectures have been in use for detecting soil structure, plant diseases and other plant related tasks for many years. So here using machine learning we are trying to compute a “Loss Function” which will provide a Loss factor value between (0–1) for determining how different Natural Disasters(particular weather factors) and Plant Diseases affect plant structure (growth) and get an output of how much of the crop is affected. Moreover, the Loss function mainly will help us get a value which will quickly determine plant growth in a particular condition. In addition, for the Disease detection we are going to use images with real life backgrounds which will ensure that there are plants in a particular background and we can still detect the disease. Therefore, our main target will be making the “Loss Function” depending on two factors namely Natural Disasters (particular weather factors) and Plant Diseases. By using our “Loss Function” a Loss factor value will be given as output which considers effects of these two parameters on plant growth.

关键词： Plant diseases machine learning algorithms Insects Crops machine learning Soil Data engineering

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Performance Comparison of machine learning algorithms for Human Activity Recognition

Performance Comparison of Machine Learning Algorithms for Hu...

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： Berkan Bostan Yavuz Ş enol Gö kmen Aş cioğ lu Fen Bilimleri Enstit&#x00FC s&#x00FC Dokuz Eyl&#x00FC l &#x00DC niversitesi &#x0130 zmir T&#x00FC rkiye Elektrik Elektronik M&#x00FC hendisli&#x011F i Dokuz Eyl&#x00FC

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

Human activity monitoring and recognition is one of the significant focuses for researchers recently due to the concerns regarding improvements of the healthcare, surveillance and many others that includes human-machine interactions. The data collected because of advances in sensor technology and the machine learning algorithms enable the work in the field of recognition of human activities to progress. Life quality of especially elderly people highly benefits from the activity monitoring and recognition research. This study aims to collect data from multiple activities and train multiple machine learning algorithms using the recorded data. For this purpose, a total of two wireless sensors were placed on the ankle and calf regions of the subjects on one leg, and the data obtained from these sensors were used in decision tree, artificial neural network, and deep learning algorithms. The performance of the algorithms was evaluated with the F1 score metric. The results show that the most suitable algorithm for this dataset is the deep learning algorithm with an average F1 score of 0.989.

关键词： Deep learning Wireless communication Measurement Wireless sensor networks machine learning algorithms Surveillance Signal processing algorithms

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A Comparative Study of Supervised machine learning algorithms for Fake Review Detection

A Comparative Study of Supervised Machine Learning Algorithm...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Frederic Setievi Jessica Natalia Theodore Raynard Tjhang Ivan Sebastian Edbert Derwin Suhartono Binus University Indonesia

The behavior of shopping has shifted into online shopping. Especially after Coronavirus Disease of 2019 (COVID-19), people choose online shopping rather than going to the market for economic and hygienic reasons. Reviews help the seller to make customers trust their products, but since some sellers are not honest, they use fake reviews to help boost their products. Fake reviews are commonly generated randomly by a computer bot or someone not using the product. Some researchers are already working on fake review detection to help this problem using many methods. In this paper, we compared three supervised machine learning algorithms: Support Vector machine (SVM), Logistic Regression (LR), and Random Forest (RF). By preprocessing the data and using the Term Frequency-Inverse Document Frequency (TF-IDF) feature, we begin the experiment process without tuning. We apply the tuning parameters to each algorithm for the other experiments using 5-fold cross-validation. The result showed that SVM algorithms outperform the best algorithms of the three before and after tuning, with 88.89% and 89.77%, respectively.

关键词： Support vector machines COVID-19 Seminars machine learning algorithms Biological system modeling Classification algorithms Electronic commerce

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Crop Fertilizer Prediction using Regression analysis and machine learning algorithms

Crop Fertilizer Prediction using Regression analysis and Mac...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： K Monika Balakrishnan Ramprakash Sankayya Muthuramalingam K Mirdula Department of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India Department of Information Technology Thiagarajar College Of Engineering Madurai India

India is one among the well-known countries whose socio-economic status is dates by agriculture. The rise of sedentary human civilization is the key role achieved in the development of agricultural practice. The problems in agriculture revolves not only about the productivity of the crop but also the amount of fertilizer, herbicide, insecticide used and also include the amount of water that are required to get a good productivity to the farmers. machine learning is used in agriculture to find solutions to the problems with high accuracy for the benefit of the farmers by recommending fertilizers, pesticides, etc., according to their needs. There are several machine learning algorithms that are widely used in our day-today life in various domains. This paper gives a detailed description of predicting the suitable variety of fertilizer based on the machine learning technique like multiple linear regression and lasso regression as it takes the relationship between the dependent and independent variables that affects the resultant prediction. It gives a brief overview for recommending suitable fertilizer for the conditions given by the farmers like temperature, humidity, soil type, crop type, quantity of fertilizers like nitrogen, potassium, phosphorus that are present in the soil, etc., This paper implies that the lasso regression is the most suitable regression algorithm for predicting the suitable variety of fertilizer for the climatic and field conditions given by the farmers with an accuracy of 89%. The developed model helps the farmers to find the most suitable fertilizer with respect to the soil and climatic conditions of the field to have higher yield, which in turn increases the profit for the farmers. The application of the suitable variety of fertilizer to the soil also helps to increases the nutrient contents that are present in the soil.

关键词： Productivity machine learning algorithms Tongue Crops machine learning Soil User interfaces

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An Efficient Phishing Attack Detection using machine learning algorithms

An Efficient Phishing Attack Detection using Machine Learnin...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： P. Chinnasamy N. Kumaresan R. Selvaraj S. Dhanasekaran K Ramprathap Sruthi Boddu Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Dr. N. G. P Institute of Technology Coimbatore India Department of Information Technology Kalasalingam Academy of Research andEducation Srivilliputtur India Department of Management Studies M. Kumarasamy College of Engineering Karur India

Phishing is an illegal method which involves user's personal information at high risk. Phishing websites prey individuals, the cloud storage hosting companies and government agencies. Though there are various anti-phishing approaches like hardware as they are not cost effective and they don't choose these approaches. To overcome this, many software-based techniques are used. Zero-day phishing problem cannot be omitted with the existing models. To prevail over these issues and detect phishing attack an approach using heuristic methodology has been proposed. We classify whether a link is phishing or non-phishing based on the input features we take like Web Traffic and Uniform Resource Locator (URL). The proposed methodology is executed by retrieving datasets from phishing cases and machine learning model using algorithms like Random Forest, SVM, Genetic.

关键词： Uniform resource locators Support vector machines Privacy machine learning algorithms Phishing Government Training data

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Hybrid Android Malware Detection Model using machine learning algorithms

Hybrid Android Malware Detection Model using Machine learnin...

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Information and Computing Research (iCORE), International Conference in

作者： Andrew Kobe T. Lee Yam John Michael R. Ballesta Joyce Ann H. Lanceta Moises Kairon T. Mogol Rogel Labanan College of Computing and Information Technologies National University Philippines

Malware in general, is a serious threat that affects a wide range of users on an everyday basis. Since Android is the most popular mobile operating system, it became a target for fraudulent and malicious activities caused by malware developers that can result in devastating effects. With that, many Android malware applications continue to come into existence and throughout the years, several solutions have emerged, while Android malware has also smartly improved its capabilities to avoid being detected. Hence, a stronger detection model should be developed by improving the current existing Android malware detection techniques. The study created a multi-class hybrid Android malware detection model which investigates the effectiveness of combining system permissions as the static feature and network traffic features as the dynamic feature whereas K-Nearest Neighbor, Random Forest, and XGBoost as the machine learning classifying algorithms. The said classifiers are compared based on various evaluation metrics such as recall, precision, accuracy, F1-score, and Receiver Operating Characteristics Curve (ROC). All classifiers produced a result higher than 90% in accuracy, precision, recall, specificity, and F1-Score. Meanwhile, it is also revealed that our created model can compete with other models that are existing from the previous works wherein the best model is the Random Forest setup C which has a total of 18 features. The said model got a 98.3% recall, 98.4% precision, 98.3% accuracy, 98.3% F1-Score, and 99.9% ROC-AUC.

关键词： Measurement machine learning algorithms Operating systems Computational modeling Heuristic algorithms Telecommunication traffic Receivers

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Comparative Evaluation of machine learning algorithms on Lung Cancer Type Classification from DNA Microarray Data

Comparative Evaluation of Machine Learning Algorithms on Lun...

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International Conference on Biomedical Innovations and Applications (BIA)

作者： Ferid Ben Ali Doraid Alrifai Maria Braoudaki Sola Adeleke Iosif Mporas School of Eng/ing &amp Computer Science University of Hertfordshire Hatfield United Kingdom St George&#x2019 s University Hospitals NHS Foundation Trust London United Kingdom School of Life &amp Medical Sciences University of Hertfordshire Hatfield United Kingdom Curenetics Ltd Essex United Kingdom

ISBN: (纸本)9781665445825

machine learning algorithms, such as Support Vector machines or Deep learning, can help in the task of diagnosing diseases. An evaluation of classification algorithms to classify among four lung cancer types and healthy control from DNA microarray data is presented. Microarray data were collected from the Gene Expression Omnibus database. A comparison has been made between five well-known and widely used machine learning algorithms for classification with and without using the Synthetic Minority Oversampling Technique (SMOTE) for data oversampling. Principal Component Analysis (PCA) was applied to reduce the number of features in the microarray data. The machine learning algorithms for classification were tested for different numbers of PCA-based features in terms of their F1 score. The use of SMOTE oversampling was found to improve the overall performance by approximately 1%. The best performing algorithms were the Support Vector machines and the Deep Neural Networks with F1 scores 89.43 and 89.03, respectively.

关键词： Support vector machines Deep learning Technological innovation machine learning algorithms Neural networks Lung cancer DNA

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Dynamic Flight Price Prediction Using machine learning algorithms

Dynamic Flight Price Prediction Using Machine Learning Algor...

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

作者： Chitranjan Kumar Gupta Om Nath Kushwaha Ayush Singh Yadav Vinod Kumar Department of Computer Engineering Delhi Technological University Delhi India

In terms of convenience in Mode of Transportation, Airplane has become one of the primary methods for long as well as short-distance travel. But flight prices are dynamic and to predict them for normal people is so hectic. As the price keeps on changing on the demand, flight prices are different during morning time and evening time of the day. It also keeps on changing by seeing the holidays as well as the special festive season. The price of flight tickets gets affected by so many factors. Most distributors have data or information on many parameters, but purchasers only have limited knowledge, which is insufficient to anticipate flight prices. In this paper, we will explore a machine learning model that will take into account features such as departure location, number of stops, departure time, arrival time, the number of days till departure, and time of day to determine the optimal time to purchase tickets. The goal of this research work is to identify the elements that influence changes in travel prices and how these factors are linked to changes in values.

关键词： Analytical models Airplanes machine learning algorithms Heuristic algorithms Computational modeling Transportation machine learning

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HVDC GIS/GIL - machine learning algorithms for Online PD Classification at DC Voltage

HVDC GIS/GIL - Machine Learning Algorithms for Online PD Cla...

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2022 9th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： B. Schober U. Schichler Institute of High Voltage Engineering and System Performance Graz University of Technology Graz Austria

ISBN: (数字)9784886864314

ISBN: (纸本)9781665470155

The measurement of partial discharges (PD) is one of the most important diagnosis methods for investigating the condition of high-voltage (HV) equipment. The interpretation and classification of PD defects can be done either manually by human experts using visual interpretation tools or automatically by machine learning (ML) algorithms. The latter has many advantages, especially for online PD monitoring applications. Reliable PD monitoring systems with automatic PD classification already exists for AC applications, unlike for DC applications. The increasing use of HVDC systems makes it necessary to develop a significant and reliable automatic classification tool for PD monitoring at DC voltage as well. Therefore, ML algorithms are trained to automatically recognise hidden pattern in the data without visualisation, interpret the data and to initiate further steps. To achieve good and reliable classification results, feature extraction and selection are the first important steps in training an algorithm. Based on the fundamental parameters of PD measurements at DC voltage, the time of occurrence of each PD pulse ti and the PD magnitude qi, several features were extracted and already analysed in previous publications. This contribution deals with PD classification using ML algorithms and is the next crucial step towards an automatic PD classification at DC voltage. Several algorithms with different parameters, support-vector machines (SVM) and artificial neural networks (ANN), were trained and investigated. The feasibility and reliability of the algorithms were determined using online PD measurements of typical PD defects of gas-insulated systems in a laboratory test setup.

关键词： Support vector machines Partial discharges Voltage measurement machine learning algorithms HVDC transmission Artificial neural networks Feature extraction

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