scholarly journals Optimizing Computer Worm Detection Using Ensembles

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Nelson Ochieng ◽  
Waweru Mwangi ◽  
Ismail Ateya
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Computer File ◽  
Worm Detection ◽  
Final Model ◽  
Computer Worms ◽  
Space Network ◽  
Modern Computer ◽  
Computer Worm ◽  
Dark Space

The scope of this research is computer worm detection. Computer worm has been defined as a process that can cause a possibly evolved copy of it to execute on a remote computer. It does not require human intervention to propagate neither does it attach itself to an existing computer file. It spreads very rapidly. Modern computer worm authors obfuscate the code to make it difficult to detect the computer worm. This research proposes to use machine learning methodology for the detection of computer worms. More specifically, ensembles are used. The research deviates from existing detection approaches by using dark space network traffic attributed to an actual worm attack to train and validate the machine learning algorithms. It is also obtained that the various ensembles perform comparatively well. Each of them is therefore a candidate for the final model. The algorithms also perform just as well as similar studies reported in the literature.

Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms

2020 ◽  
Vol 102-B (6_Supple_A) ◽  
pp. 101-106
Author(s):  
Romil F. Shah ◽  
Stefano A. Bini ◽  
Alejandro M. Martinez ◽  
Valentina Pedoia ◽  
Thomas P. Vail
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Clinical Decision Making ◽  
De Novo ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Fine Tuning ◽  
Final Model ◽  
Prosthetic Loosening

Aims The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. Methods A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. Results The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an accuracy of 70%. The final model, which was built by fine-tuning a publicly available model named DenseNet, combining the AP and lateral radiographs, and incorporating information from the patient’s history, had an accuracy, sensitivity, and specificity of 88.3%, 70.2%, and 95.6% on the independent test dataset. It performed better for cases of revision THA with an accuracy of 90.1%, than for cases of revision TKA with an accuracy of 85.8%. Conclusion This study showed that machine learning can detect prosthetic loosening from radiographs. Its accuracy is enhanced when using highly trained public algorithms, and when adding clinical data to the algorithm. While this algorithm may not be sufficient in its present state of development as a standalone metric of loosening, it is currently a useful augment for clinical decision making. Cite this article: Bone Joint J 2020;102-B(6 Supple A):101–106.

scholarly journals Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria

2019 ◽  
Author(s):  
Colby T. Ford ◽  
Daniel Janies
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Artemisinin Resistance ◽  
Parasite Clearance ◽  
Machine Learning Algorithms ◽  
Final Model ◽  
Ensemble Machine Learning ◽  
Massively Parallel Processing ◽  
Underlying Mechanisms

ABSTRACTAntiparasitic resistance in malaria is a growing concern affecting many areas of the eastern world. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway.The 2019 Malaria Dream Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict Artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles.In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Abstract 14846: Artificial Intelligence and Clinical Stability After the Norwood Operation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Alaa Aljiffry ◽  
Yanbo Xu ◽  
Shenda Hong ◽  
Justin Long ◽  
Jimeng Sun ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Care ◽  
Clinical Status ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Norwood Procedure ◽  
Norwood Operation ◽  
Waveform Data ◽  
Final Model

Background: Postoperative management of the neonate following the Norwood operation is among the most complex and challenging in pediatric critical care. Artificial intelligence is poised to assist in management of this complex population. Methods: We developed a convolutional neural network (CNN) model trained on electrocardiogram (ECG) waveforms collected from 45 neonates after the Norwood procedure. Waveforms from the first two postoperative days (critical) and the day prior to transfer out of the ICU (stable) were used for training. The model was validated on 10 post-Norwood neonates. Models were compared to traditional machine learning algorithms on non-waveform data, heart rate variability, and then combined in a final model to optimize performance. Retrospective clinical observation scoring was completed for comparison. Results: The CNN model on 3-lead ECG yielded an area under the curve of the receiver operating characteristic (AUC-ROC) of 0.97 ( 0.02). The final model combining predictions from the CNN, random forest on vital signs, and logistic regression on pH, lactic acid, and base deficit values achieved 0.98 ( 0.02). Retrospective clinical observations agreed with the final model 78% of the time. Conclusions: Application of this novel combined machine learning model can accurately detect changes in clinical status as patients progress from critically ill to stable following the Norwood procedure, yielding impressive clinical insights from the ECG beyond what is currently possible. This work provides the basis for the development of a novel bedside monitoring tool and suggests new ways artificial intelligence may influence clinical care beyond predicting deterioration events.

Integration of IoT and MLA In Prediction of Diabetes : An Overview

2021 ◽  
pp. 146-153
Author(s):  
A. Prathap ◽  
Dr. R. Jemima Priyadarsini
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Diabetes Management ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Machine Learning Classification ◽  
Modern Computer

A Healthcare system that employs modern computer techniques is the most investigated area in Research. For many years, researchers in the disciplines of Healthcare have collaborated to improve such systems technologically. A number of Internet-based apps on diabetes management have been proposed as a result of rapid developments in wireless and web technology. According to a recent World Health Organization Survey the number of persons affected with diabetics has increased. Diabetes chronic symptoms are the most common Health Problems. Large volumes of medical data are being created. These patients' health data should be recorded and preserved so that continual monitoring and technology advancements can be used to interpret, learn, and anticipate. Internet of Things (IoT) is used to implement numerous applications. IoT can be used in numerous domains, like the health surveillance system of patients. Various successful machine learning methods can be used to forecast diabetes, allowing people to avoid it and receive treatment as soon as possible. Different machine learning classification algorithms for diabetes are investigated in depth in this work. Machine learning algorithms applied on the diabetes data set include K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and others.

scholarly journals DEVELOPMENT AND VALIDATION OF A MODEL FOR THE PREDICTION OF MORTALITY IN CHILDREN UNDER FIVE YEARS WITH CLINICAL PNEUMONIA IN RURAL GAMBIA

2021 ◽  
Author(s):  
Alexander Jarde ◽  
David Jeffries ◽  
Grant A Mackenzie
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Final Model ◽  
Machine Learning Methods ◽  
Prediction Of Mortality

Background: Pneumonia is the leading cause of death in children aged 1-59 months. Prediction models for child pneumonia mortality have been developed using regression methods but their performance is insufficient for clinical use. Methods: We used a variety of machine learning methods to develop a predictive model for mortality in children with clinical pneumonia enrolled in population-based surveillance in the Basse Health and Demographic Surveillance System in rural Gambia (n=11,012). Four machine learning algorithms (support vector machine, random forest, artifical neural network, and regularized logistic regression) were implemented, fitting all possible combinations of two or more of 16 selected features. Models were shortlisted based on their training set performance , the number of included features, and the reliability of feature measurement. The final model was selected considering its clinical interpretability. Results: When we applied the final model to the test set (55 deaths), the area under the Receiver Operating Characteristic Curve was 0.88 (95% confidence interval: 0.84, 0.91), sensitivity was 0.78 and specificity was 0.77. Conclusions: Our evaluation of multiple machine learning methods combined with minimal and pragmatic feature selection led to a predictive model with very good performance. We plan further validation of our model in different populations.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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