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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Rui Wang Xiaohong Ma Zesheng Liu Taiyu Zhang Li Huo Tsinghua University Beijing China System Engineering Research Institute China Shipbuilding Corporation Beijing China

In this paper, a method combining LIBS technology and machine learning algorithms was proposed to identify Ejiao rapidly and accurately. The experiment was divided into sample preparation, spectra acquisition, data preprocessing, feature selection, model training and model evaluation. Several Ejiao samples from different Ejiao manufacturers were collected, and there were two fake samples made by illegal manufacturers. All the samples were appropriately cut without other processing. The LIBS spectra of these samples were detected in the wavelength from 200 nm to 430 nm. Noise removal and intensity normalization were carried out; then the PCA method was used to select the features. machine learning algorithms like SVM, RF and ANN were used to build the Ejiao identification model, and the model performance was evaluated to explore the feasibility of using the LIBS spectrum to label Ejiao. The result showed that, for the Ejiao manufacturer identification problem, the best model accuracy of the three algorithms is 96.67%, 96.19% and 97.62% respectively; for the Ejiao authenticity identification problem, good model performance was obtained, for instance, the average Fl-measure for the three methods was 98.33 %. It also showed that the model performance would be further improved as the size of the training data set increased. The ANN algorithm obtained the best model performance, and the manufacturer identification accuracy was 98.57%, and the authenticity identification Fl-measure was 99.01%. The result indicated that our method could establish a practicable model for the manufacturer and authenticity identification of Ejiao.

关键词： Support vector machines Radio frequency machine learning algorithms Training data machine learning Data models Principal component analysis

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Predicting Workplace Injuries Using machine learning algorithms

Predicting Workplace Injuries Using Machine Learning Algorit...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Divya Sukumar Ji Zhang Xiaohui Tao Xin Wang Wenbin Zhang The University of Southern Queensland Australia Zhejiang Lab China Southwest Petroleum University China Carnegie Mellon University USA

ISBN: (数字)9781728182063

ISBN: (纸本)9781728182070

Predicting workplace injury using automated techniques opens newer possibilities in evidence-based research. This paper presents our preliminary research in a PhD project in predicting workplace incidents using machine learning algorithms. The analysis on the model performance using several mainstream machine learning algorithms including random forest, k-nearest neighbor and decision tree indicated that the general performance of the decision tree model was found to be statistically higher than that of the other two algorithms.

关键词： Injuries Employment Predictive models Random forests Data models machine learning algorithms Decision trees

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A Preliminary Investigation into Using machine learning algorithms to Identify Minimal and Equivalent Mutants

A Preliminary Investigation into Using Machine Learning Algo...

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Claudinei Brito Vinicius H. S. Durelli Rafael S. Durelli Simone R. S. de Souza Auri M. R. Vincenzi Marcio Eduardo Delamaro University of São Paulo Sao Carlos Brazil Federal University of São João del Rei Federal University of Lavras São Carlos Brazil Federal University of Lavras Lavras Brazil University of São Paulo (USP) São Carlos Brazil Federal University of São Carlos São Carlos Brazil

ISBN: (数字)9781728110752

ISBN: (纸本)9781728110769

Two issues that have been hampering the widespread adoption of mutation testing are redundant and equivalent mutants. Minimal mutation has been recently introduced to mitigate these two issues by generating and selecting only a subset of non-redundant mutants. Equivalent mutants are syntactically different from the original program, but functionally identical, so it is impossible to come up with test data capable of making equivalent mutants behave differently from the original program under test. In order to mitigate the cost of applying mutation testing, we set out to investigate how machine learning algorithms that generate predictive models can be used to classify mutants as belonging to the minimal set or equivalent. More specifically, we extract a set of features (i.e., properties) from programs, mutants, and test cases, which in turn serve as input to the creation of predictive models. To shed some light on the effectiveness of our approach, we carried out an experiment in which we trained seven different machine learning classifiers, the best of which obtained 81.88% and 80.30% accuracy to classify minimal and equivalent mutants, respectively. Results from our experiment would seem to indicate that our approach can effectively mitigate some of the costs associated with mutation testing by relying on the identification of minimal sets and equivalent mutants.

关键词： Testing machine learning algorithms machine learning Predictive models Measurement Supervised learning Feature extraction

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Performance Analysis of machine learning Classification algorithms in Static Object Detection for Video Surveillance Applications

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WIRELESS PERSONAL COMMUNICATIONS 2020年第2期115卷 1291-1307页

作者： Ariffa Begum, S. Askarunisa, A. KLN Coll Informat Technol Dept Comp Sci & Engn Pottapalayam Tamil Nadu India

Video surveillance system plays a pivotal role in automatic detection of abandoned luggage/bag in public places which causes threats to the public. As, the terrorist attacks are increasing world-wide, the detection and prevention of such attack is necessary to safeguard the people in public places. In this, a novel framework for the detection and classification of static object is proposed. In the proposed work first the static objects are identified and then it is classified to check the detected object is bag or anything else. In this study, the performance of machine learning techniques like Support Vector machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbour, and Random Forest methods are analyzed. The performance is tested in standard (PETS 2006, PETS 2007 and AVSS i-LIDS) and custom datasets. The SVM and ANN produce best results in terms of classification and accuracy. Applications of various machine learning algorithms could clearly assist for identification and prevention of terrorist attacks in public places.

关键词： Video surveillance system Static objects machine learning algorithms

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Diagnosis of Breast Cancer using machine learning algorithms-A Review

Diagnosis of Breast Cancer using Machine Learning Algorithms...

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Emerging Trends in Communication, Control and Computing (ICONC3), International Conference on

作者： Aditi Kajala V K Jain Computer Science and Engineering School of Engineering and Technology Mody University of Science and Technology Lakshmangarh India

ISBN: (数字)9781728114200

ISBN: (纸本)9781728114217

Breast cancer is one of the common diseases specifically in women now days. It has become the second main reason of cancer death in females. Every year 4.5-5% new cancer cases are recorded and increasing the morbidity at worldwide. It has proved that early detection of any cancer when followed up with appropriate diagnosis and treatment can increase the survival rate of the patients. Breast cancer is diagnosed by mammography. Mammograms are films generated by radiologist with a device. These mammograms are observed and diagnosed by the oncologist for further treatment. Since all general hospitals do not have the specialist and patients used to wait for their report. So waiting for diagnosing a breast cancer may take time. This delay may be responsible for cancer spreading and reducing the survival rate of the patient. Therefore machine learning can be used to diagnose breast cancer by a computer to make the diagnosing efficient and effective. This does not mean to replace expert or physician by computer but it means that computer can assist the expert for better understanding the particular case and the results can be produced early. This paper presents a brief summary on breast cancer diagnosis using machine learning algorithms used to increase the efficiency and effectiveness of predicting cancer. The correct diagnosis and accurate classification are the main objective of the reviewed papers.

关键词： Mammograms machine learning algorithm Wisconsin Breast Cancer Dataset (WBCD) Breast Cancer machine learning algorithms Digital Mammography Survival Rate Computers Diagnosis Professional personnel Artificial Intelligence machine learning breast neoplasm diagnosis General Hospitals Oncologists

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Anomaly Detection on MIL-STD-1553 Dataset using machine learning algorithms

Anomaly Detection on MIL-STD-1553 Dataset using Machine Lear...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Francis Onodueze Darsana Josyula Department of Computer Science Bowie State University Bowie USA

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

This paper evaluates the ability of several machine learning algorithms to detect attacks that emulate normal nonperiodical messages in the MIL-STD-1553 communication traffic. The dataset for this research is highly imbalanced and most algorithms fail or simply produce poor results when classifying the data. We conduct several experiments with different machine learning algorithms to correctly classify MIL-STD-1553 dataset. We identify appropriate metrics to judge the performance of the models applied to this dataset. Using these metrics, we compare the performance of different machine learning models that we generated.

关键词： Military standards Measurement Support vector machines machine learning algorithms machine learning Classification algorithms Security

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Utilizing Smartphone Usage Pattern for Gender Recognition applying machine learning algorithms

Utilizing Smartphone Usage Pattern for Gender Recognition ap...

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

作者： Johora Akter Polin Omayer Khan Ahmed Al Marouf Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

Smartphones can connect people over long distance within few seconds and perform critical tasks easily. The concept of global village has turned into reality through the optimum usage of smartphones. Smartphone is not just being used as a helping hand, it also contains the user's identity and behavior through its usage pattern. This paper has utilized the smartphone usage pattern to identify the gender of its user. In this process, the proposed research work has analyzed 1284 instances of male and female of a specific age group (18-30 years) are collected with a survey that incorporates relevant features from the subjects and their gender to create a labelled dataset. For better approximation and experimentation, 10 different machine learning algorithms such as Naïve Bayes, decision tree, random forest etc. are compared. The compared algorithms are taken from five different categories of classification algorithms such as tree-based classifiers, Bayes, function-based etc. To evaluate performance of different algorithms, accuracy, precision, recall and f-score measurements are utilized. Among these algorithms, random forest has performed better by showing accuracy of about 84.11%. From this research, the factors involved in smartphone are known and its usage is impactful for predicting users' gender. With the understanding of this traits, security, biometric, and privacy concerns can be developed to a higher extent.

关键词： Face recognition Support vector machines Smart phones Classification algorithms Social networking (online) Principal component analysis machine learning algorithms

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A Fundamental Study on Suicides and Rainfall Datasets Using basic machine learning algorithms

A Fundamental Study on Suicides and Rainfall Datasets Using ...

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

作者： U. Harita V. Uday Kumar Dorababu Sudarsa G Rama Krishna Cmak Zeelan Basha B Srinivasa S P Kumar Koneru Lakshmaiah Education Foundation Vaddeswaram India C.B.I.T Gandipet India

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

Suicides in India have been registering at an alarming rate. The rainfall rate in India is unpredictable due to changes in environment. Farmers faces huge losses due to this unpredictable rains. This leads into loss of lives and results the farmers into suicide state. Relative analysis of rainfall and suicide rate will support farmers to avoid losses due to rainfall and further suicides can be avoided. Prediction of rainfall and suicide rate is achieved in India using machine learning algorithms such as Linear regression, Logistic regression, Support Vector machine and Random Forest. Relative analysis provides better prediction results which greatly supports the farmers and avoid losses.

关键词： Linear regression Logistics Support vector machines Random forests machine learning machine learning algorithms Classification algorithms

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Test Set Optimization by machine learning algorithms

Test Set Optimization by Machine Learning Algorithms

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IEEE International Conference on Big Data

作者： Kaiming Fu Yulu Jin Zhousheng Chen Electrical and Computer Engineering University of California Davis CA USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Diagnosis results are highly dependent on the volume of test set. To derive the most efficient test set, we propose several machine learning based methods to predict the minimum amount of test data that produces relatively accurate diagnosis. By collecting outputs from failing circuits, the feature matrix and label vector are generated, which involves the inference information of the test termination point. Thus we develop a prediction model to fit the data and determine when to terminate testing. The considered methods include LASSO and Support Vector machine(SVM) where the relationship between goals(label) and predictors(feature matrix) are considered to be linear in LASSO and nonlinear in SVM. Numerical results show that SVM reaches a diagnosis accuracy of 90.4% while deducting the volume of test set by 35.24%.

关键词： Support vector machines machine learning algorithms Big Data Predictive models Numerical models Optimization Testing

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Demystifying and Anticipating Graduate School Admissions using machine learning algorithms

Demystifying and Anticipating Graduate School Admissions usi...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Mohd Aijaj Khan Manish Dixit Aaradhya Dixit Madhav Institute of Technology and Science Gwalior India Electronics & Communication Engg Birla Institute of Technology Mesra India

ISBN: (数字)9781728149769

ISBN: (纸本)9781728149776

One of the many aspirations of undergraduate students in India is going for further graduate studies. Unfortunately, many students spend months and years of preparation focusing on things that unfortunately won't improve their chances of getting into a good graduate school. This paper evaluates the chances of applicants to get into a particular graduate program using various classification and regression approaches of machine learning. Various algorithms have been pitted against each other and also the most important features have been extracted which are useful to get into a graduate school program. Using unsupervised approach, this paper finds various categories of students and pool them together to find if they are perfect fit for admission or not. A novel approach of predicting the chances for admission in graduate school is introduced in this paper.

关键词： graduate machine learning algorithms schools Students Chance

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