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检索条件"主题词=Machine Learning Algorithms"

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Devanagari Handwritten Compound Character Recognition Using Various machine learning algorithms

Devanagari Handwritten Compound Character Recognition Using ...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： Shalaka P. Deore M.E.S. College of Engineering Pune India

ISBN: (纸本)9781665420143

Handwriting is unique for each person. The formation of letters, compound characters, spaces and numbers by each person is so unique that handwriting is considered as legal evidence in forgery cases. Hence accurate recognition of handwritten characters holds huge significance. Especially compound characters are differing in writing ways and there are some characters which can be written in many ways so it is complex to recognize. Segmentation is another issue occurs due to structure of the character is very complex. This paper proposes a method to recognize compound handwritten Devanagari characters using several machine learning algorithms like K-Nearest Neighbours (K-NN) and Support Vector machine (SVM). Techniques used for extracting features from the images are playing a significant role in the identification of compound characters. In this paper, the Zoning feature extraction technique is used combined with other features like eccentricity, equivalent diameter and angle. We created a new dataset of 45 compound characters. Using 10 cross-validation we achieved the highest 96.62% accuracy by SVM classifier with training time 1.56 seconds.

关键词： Support vector machines Training machine learning algorithms Law Computational modeling Writing Feature extraction

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Use of machine learning algorithms for Android App Malware Detection

Use of Machine Learning Algorithms for Android App Malware D...

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International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： Shaurya Rawat Rushang Phira Prachi Natu MPSTME NMIMS University Mumbai India

ISBN: (纸本)9781665432733

Malware attacks in mobile phones have become more frequent with time. One of the most common ways to infect a phone system with malware is through apps that look benign but are actually malicious. machine learning has been employed in the past to detect malware in phones. This has been achieved through supervised learning algorithms and Deep learning. This paper analyses the performance of various classification algorithms to detect malware in android apps based on permissions requested from the user. The intention was to find as accurate a model as possible to make predictions based on the type of android package. Through implementation of multiple models, XGBoost classifier was found to yield the highest accuracy with 80.03%, followed by Cat Boost Classifier and Random Forest Classifier with 79.58% and 78.67% respectively. Even in most other metrics, XGBoost classifier gave the best results. The data was thoroughly cleaned and the relevant preprocessing steps were implemented, following which classification was performed along with detailed hyperparameter tuning. Various metrics were used to compare the results between the different algorithms, which were accuracy, precision, recall, F1 score and logistic loss function. The findings could help make people aware about the kind of permissions required of malicious applications.

关键词： Measurement Support vector machines machine learning algorithms Malware Classification algorithms Kernel Smart phones

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A Comprehensive Review on Email Spam Classification using machine learning algorithms

A Comprehensive Review on Email Spam Classification using Ma...

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International Conference on Information Networking

作者： Mansoor RAZA Nathali Dilshani Jayasinghe Muhana Magboul Ali Muslam School of Computing and Mathematics Charles Sturt University Study Centre Melbourne VIC Australia College of Computer and Information Sciences AI Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia

ISBN: (数字)9781728191010

ISBN: (纸本)9781728191027

Email is the most used source of official communication method for business purposes. The usage of the email continuously increases despite of other methods of communications. Automated management of emails is important in the today's context as the volume of emails grows day by day. Out of the total emails, more than 55 percent is identified as spam. This shows that these spams consume email user time and resources generating no useful output. The spammers use developed and creative methods in order to fulfil their criminal activities using spam emails, Therefore, it is vital to understand different spam email classification techniques and their mechanism. This paper mainly focuses on the spam classification approached using machine learning algorithms. Furthermore, this study provides a comprehensive analysis and review of research done on different machine learning techniques and email features used in different machine learning approaches. Also provides future research directions and the challenges in the spam classification field that can be useful for future researchers.

关键词： Support vector machines machine learning algorithms Unsolicited e-mail machine learning Feature extraction Information filters Classification algorithms

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Analysis and Performance of machine learning algorithms on Disease Diagnosis

Analysis and Performance of Machine Learning Algorithms on D...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Sumathi M S C Soujanya Joshi Rithika Rose Thomas Reethu G Electronics and Telecommunication BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9781665430869

Healthcare is one of the most significant and challenging fields since the history of mankind. It can be defined as the maintenance or improvement of health via the prevention, diagnosis, treatment, recovery, or cure of disease, injury, illness, and other physical or mental deteriorations and disablements in people. The best way to deliver healthcare would be by utilizing all the current resources and techniques used in all sectors, particularly the medical and technical fields. This project aims at embedding machine learning in healthcare to give a more sophisticated, accurate and well-defined approach to medical prognosis. The first part of the project aims at collecting various data about the prognosis and diagnosis of diseases from across the globe. This data is then methodically saved, stored and cleaned. The second part of the project introduces this problem statement into various ML algorithms and uses an ensemble of these algorithms.

关键词： machine learning algorithms machine learning Maintenance engineering Medical diagnosis History Prognostics and health management Medical diagnostic imaging

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Comparative Analysis of Predictive machine learning algorithms for Chronic Kidney Disease

Comparative Analysis of Predictive Machine Learning Algorith...

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Computational Performance Evaluation (ComPE), International Conference on

作者： Kirti Kangra Jaswinder Singh Guru Jambheshwar University of Science and Technology Hisar India

ISBN: (纸本)9781665436571

Chronic Kidney Disease (CKD) is a serious lifelong ailment caused kidney malfunction. To diagnose CKD, number of EHRs generated and manually process them is difficult. So, to process these EHRs and to diagnose CKD machine learning approaches can assist. The main motive of this study is to use machine learning algorithms to diagnose CKD in order to assist people to avoid it. The CKD Dataset acquired from the UCI machine learning repository is analyzed using machine learning techniques such as Support Vector machine (SVM), Naive Bayes (NB), Decision Tree (DT), Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Logistic Regression (LR). WEKA v3.8.3 was used to conduct the experiment. The mean of the values was used to replace missing values. With 99 % accuracy, RF outperforms other approaches. Against the CKD dataset, the ROC curves of LR and ANN yielded 0.999 and 0.998 results, respectively. This research can assist healthcare institutions and researchers in predicting CKD at an early stage.

关键词： Support vector machines Radio frequency Performance evaluation machine learning algorithms Artificial neural networks Medical services Kidney

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Lung Cancer Recurrence Prediction Using Radiomics Features of PET Tumor Sub-Volumes and Multi-machine learning algorithms

Lung Cancer Recurrence Prediction Using Radiomics Features o...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Seyyed Ali Hosseini Ghasem Hajianfar Isaac Shiri Habib Zaidi Department of Medical Physics and Biomedical Engineering Tehran University of Medical Sciences Tehran Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Science Tehran Iran Department of Medical Imaging Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Geneva 4 Switzerland Geneva University Neurocenter Geneva University Geneva Switzerland Department of Nuclear Medicine and Molecular Imaging University of Groningen University Medical Center Groningen Netherlands Department of Nuclear Medicine University of Southern Denmark Odense Denmark

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

We aimed to predict recurrence in lung cancer patients using PET radiomics features and machine learning algorithms. In this work, 136 non-small cell lung cancer (NSCLC) patients were enrolled. To study the impact of tumor sub-volume on recurrence prediction's accuracy, five sub-regions or contours were delineated manually, were extended with different distances (1, 2, 3, 4, and 5 mm). Three different feature selections and ten classifiers with 100 bootstraps were utilized. Our results illustrated that contourPlus1mm with the Minimum Redundancy Maximum Relevance (mrmr) feature selection and Random Forest (RF) classifier, contourPlus1mm with the MRMR feature selection and Linear Discriminant Analysis (LDA) classifier, and contourPlus4mm with the Recursive Feature Elimination (RFE) feature selection and Logistic regression (LR) classifier, had the highest performance (AUC= 0.65). The results of this study illustrated that an extended sub-volume of a manual contour boosts the performance of recurrence prediction in patients with lung cancer. This study demonstrated that the use of contour extending method can be effective in increasing the predictive accuracy of different machine learning classifier methods.

关键词： machine learning algorithms Redundancy Lung cancer Manuals Feature extraction Prediction algorithms Random forests

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Utilization of machine learning algorithms for Prediction of Diseases

Utilization of Machine Learning Algorithms for Prediction of...

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Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Farha Deeba Siddarama R. Patil Electronics & Communication Engineering P.D.A College of Engineering Kalaburagi India

ISBN: (纸本)9781665426923

For analysis of any health related problems or diseases, on time investigation and accuracy of prediction plays a vital role. machine learning technology has become the most significant field that has various applications in the healthcare field. The healthcare professionals are looking forward for most advanced and reliable healthcare systems that assist in providing best possible prognosis and treatment to patients with great accuracy in stipulated amount of time. There are various data mining techniques which are being developed so as to extract useful information from the health dataset collected. The main motto behind the development of prediction system using machine learning algorithms is to find the best possible solution for the health related issues during diagnosis. The sample dataset utilized for the implementation consist a record of about 4922 patients'. The prognosis was carried out based on 132 symptoms for prediction of 42 commonly occurring diseases. The paper also discusses the system design required for prediction of common diseases using ML algorithms where the doctors/clinicians just need to enter the symptoms with which patient is suffering. The best ML algorithms found so far for implementation in the field of healthcare are Decision Tree, Random Forest Classifier and Naive Bayes Classifier. The paper presents the comparison results between various algorithms. The system model yields a high accuracy rate of 95.12% for prediction of diseases.

关键词： machine learning algorithms Medical services Predictive models Prediction algorithms Classification algorithms Naive Bayes methods Data mining

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Early Diagnosis and Comparative Analysis of Different machine learning algorithms for Myocardial Infarction Prediction

Early Diagnosis and Comparative Analysis of Different Machin...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Sharmin Akter Mahdia Amina Nafees Mansoor University of Liberal Arts Bangladesh

Heart attack alternatively known as Myocardial Infarction is one of the primary reasons of morbidity on the planet. Therefore, the diagnosis and prediction of heart disease is persuading many researchers to develop intelligent medical decision support systems. machine learning has been demonstrated to be viable in helping with decision making and predictions from the huge amount of clinical data delivered by the medical care. This paper aims to improve the accuracy of machine learning models which can help to make informed decision and prediction of heart attack. We have applied six machine learning classification algorithms: Support Vector machine, Random Forest, K Nearest Neighbors, Gaussian Naive Bayes, Decision Tree and Logistic Regression. Additionally, an extensive comparison of machine learning techniques has been carried out. Our research work suggests that machine learning methods with data balancing techniques are effective tools for stroke prediction with imbalanced data. Therefore, Synthetic Minority Over-Sampling Technique (SMOTE) has been applied in our model. Hence, it is anticipated that Random Forest excels with the highest accuracy of 96% in heart attack prediction regarding performance metrics.

关键词： Support vector machines machine learning algorithms Planets Cardiac arrest Medical services Myocardium Predictive models

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Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using machine learning algorithms

Seed Architectural Phenes Prediction and Variety Classificat...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Christan Hail Mendigoria Ronnie Concepcion Elmer Dadios Heinrick Aquino Oliver John Alaias Edwin Sybingco Argel Bandala Ryan Rhay Vicerra Joel Cuello Electronics and Communications Engineering Department De La Salle UniversityManilaPhilippines De La Salle University Manila Philippines The University of Arizona Tucson USA

Proper identification and categorization of seeds at an earlier stage of the cultivation process is an imperative procedure that contributes to better crop quality and higher production yield. As a strategy to supplement this procedure, integration of computer vision approach and machine learning algorithms including gaussian process regression (GPR), decision trees for regression (RT) and classification (CT), support vector machine regression (SVMR), k-nearest neighbors (KNN), linear discriminant analysis (LDA) classifier, and Naïve Bayes (NB) classifier are explored in this study to predict the extended morphological features (solidity, roundness, compactness) and variety classification of dry bean (Phaseolus vulgaris L.). A total of 13,611 image samples were used. CIELab color channel thresholding was applied in segmenting bean pixels and region properties for extracting the morphological features (bean biomass area, perimeter, major and minor axis lengths, convex area, eccentricity, extent, equivalent diameter, and axis length proportionality, shape factors, roundness, solidity, compactness). Based on RMSE and MAE performances, the optimized GPR is the most reliable model for predicting seed solidity, and regression tree for both seed roundness and compactness. Classification models with seven morphological predictors (LDA7, KNN7, CT7, NB 7 ) exhibited sensitive classification performance, all having accuracies greater than 90%. Further, KNN7 bested out other models with 93.69% accuracy, 93.64% precision, 93.66% specificity, and 93.69% f1-score. The developed machine learning models are innovative approaches in the seed variety classification and phenotyping of dry bean seeds.

关键词： Support vector machines Computer vision machine learning algorithms Computational modeling Crops machine learning Predictive models

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Comparison of Effectiveness of machine learning algorithms for Vehicle Path Prediction

Comparison of Effectiveness of Machine Learning Algorithms f...

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IEEE Integrated STEM Education Conference (ISEC)

作者： Sumanth R Moole Thomas Jefferson High School for Science and Technology USA

ISBN: (纸本)9781665413848

In modern warfare, intercepting moving enemy targets such as tanks, aircraft, missiles, and drones plays a crucial role. These targets are either controlled by enemy personnel or by sophisticated electronic systems. Therefore, their movements are best characterized by random motion subject to certain physical laws. Predicting these motions is extremely complex and often requires continuous tracking through sophisticated radar equipment. machine learning algorithms, such as Artificial Neural Networks, have proven to be effective in learning many real world motions of vehicles on the roads and have been extensively used in the autonomous vehicles. Artificial Neural Networks use activation functions to determine the output of a model from the given observations. After training the model with appropriate activation function, the model can be used for predictions. In this process, the activation functions play a crucial role. Selecting the correct activation function is critical to the success of the model. This project simulates the moving enemy target using a BristleBot (a brush-head fitted with vibrating motor which generates vibrations in the bristles thus propelling the BristleBot) which moves on a flat surface. The motion of the BristleBot is digitized by recording the X-Y coordinates on the path it has taken from the beginning of the run to the end of the run. These runs are repeated and data from multiple runs is stored in a database. Using R Programming language, a neural network training algorithm is simulated where the activation function can be changed (slope-intercept linear function y = mx + b with various slopes and intercepts, quadratic function y = a x 2 + bx + c with various a, b, and c values). The resulting models corresponding to each training session are compared with each other to find their similarity to the paths taken by the BristleBot. The effectiveness of these activation functions is then measured by the similarity score. The trained model (

关键词： Training Vibrations Missiles machine learning algorithms Target tracking Artificial neural networks Predictive models

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