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IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME)

作者： Hnatiuc, M. Ghita, S. Inge, L. Chetehouna, K. Maritime Univ Constanta Constanta Romania INSA Ctr Val Loire Blois Loir & Cher France

ISBN: (纸本)9781728133300

Simulation of waterfall in nature can be done using a fluid level control system in a pool. This system is made of two mini-pools at a height of 50 cm from each other. The water monitoring system consists of a control and control board that reads the temperature and the flow rate of the water and pumps the water from the lower container to the top. In the pools are introduced different types of substrate (e.g. naval steel, liquid wood) that simulate the resistance of materials in contact with sea water and the evolution of biofouling formation on these materials. The aim of the project is to detect the appearance of biofilm layers on the materials that are integrated into a ship's body, during the time. A comparative analysis is presented in this paper for different type of material and different environmental condition.

关键词： water flow/microcosm system water pump classification algorithm microbiota

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Optical biomarkers for detection of malignant tissue using Digital Holographic Microscopy 2

Optical biomarkers for detection of malignant tissue using D...

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Conference on Advances in Microscopic Imaging II

作者： Calin, Violeta L. Mihailescu, Mona Costea, Radu V. Dumitru, Adrian Patrascu, Oana-Maria Brehar, Felix Petrescu, George Lisievici, Mihai Comanescu, Brindus Grigore, Viorela N. Butmalai, Dan Savopol, Tudor Moisescu, Mihaela G. Carol Davila Univ Med & Pharm 8 Eroii Sanitari Blvd Bucharest 050474 Romania Politehn Univ 313 Spl Independentei Bucharest 060042 Romania Univ Emergency Hosp Bucharest 169 Spl Independentei Bucharest 050098 Romania Bagdasar Arseni Clin Emergency Hosp 12 Berceni St Bucharest 041915 Romania Optoelect SA 409 Atomistilor St Magurele 077125 Romania

ISBN: (数字)9781510628465

ISBN: (纸本)9781510628465

The histopathological diagnosis in malignances requests well trained specialists and multi-step operational procedures for sample preparation. Faster and more objective evaluation protocols should be implemented to give support to the pathologists. The Quantitative Phase Imaging based methods are biological-proved to be efficient in revealing important characteristics of the living structures without any labeling. These can be further exploited for an automatic evaluation of complex tissues. Using an off-axis Digital Holographic Microscopy setup, biopsies of two histological origins: cerebral (grade II glioma and grade IV glioblastoma) and colonic malignancies (dysplastic and malignant colonic adenomatous polyps), were investigated. Various parameters of quantitative phase shift maps (QPMs) were computed (mean, variance, median, kurtosis, skewness, energy, entropy). The possibility of automatic discrimination of tumor tissues having different structural complexity and presenting various malignancy grades was evaluated using supervised machine learning algorithms. The analysis of phase shift maps has successfully discriminated between levels of malignancy with high statistical confidence in the case of gliomas. Moreover an algorithm with the ability to classify the tissue biopsies in different malignant stages using parameters based on QPMs has been implemented on glioma tissues having a high level of homogeneity. In case of colonic polyps, the heterogeneity of the multilayered tissue demanded QPMs analysis to be performed on selected area of interest even though some statistical differences were obtained for global evaluation of phase shift distributions. In case of colonic polyps, for a good accuracy of classification algorithm a larger library of QPMs is under construction.

关键词： Digital Holographic Microscopy quantitative phase maps colonic adenomatous polyps glioblastomas classification algorithm

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Embedding naïve Bayes algorithm data model in predicting student graduation 19

Embedding naïve Bayes algorithm data model in predicting st...

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Proceedings of the 3rd International Conference on Telecommunications and Communication Engineering

作者： Ace C. Lagman Joseph Q. Calleja Corazon G. Fernando Joseph G. Gonzales John Benedict Legaspi John Heland Jasper C. Ortega Ronel F. Ramos Maria Vicky S. Solomo Regina C. Santos FEU Institute of Technology Manila Philippines

ISBN: (纸本)9781450371803

In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： data mining machine learning algorithm big data analytics classification algorithm Naïve Bayes prediction student graduation

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A Chinese Text Classier Based on Strong Class Feature Selection and Bayesian algorithm

A Chinese Text Classier Based on Strong Class Feature Select...

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International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Chen, Yanqiu Sun, Peili Dalian Neusoft Inst Informat Dept Comp Sci Dalian Peoples R China Dalian Ocean Univ Econ Management Inst Dalian Peoples R China

ISBN: (纸本)9781728113074

For improve the efficiency and accuracy of Chinese text categorization, this paper presents a new Chinese text classier, in which word segmentation system is based on forward scan corpus, and word frequency statistics method is different in the training stage and the test stage, after that, a novel feature selection is proposed according to word frequency, mutual information and classificatory information, Finally a fast Bayes theory classier is designed. Experiments prove this classier is simple and effective.

关键词： Chinese text categorization feature selection feature weighting classification algorithm

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Improved Convolutional Neural Network for Lung Cancer Detection

Improved Convolutional Neural Network for Lung Cancer Detect...

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作者： Firdaous Essaf Yujian Li Seybou Sakho Pius Kwao Gadosey School of Computer Science and Technology Beijing University of Technology School of Artificial Intelligence Guilin University of Electronic Technology

The examination of the lungs is an important part of the annual physical examination. There are hundreds or thousands of cases in the physical examination, and each case contains many lung cross-sectional CT images. These all require professional doctors to screen for cases with pulmonary nodules one by one, which is not only a heavy workload but also a possibility of incorrect screening. Aiming at the above problems, a Convolutional Neural Network(CNN) is introduced to screen out the CT images for pulmonary nodules, and a classification algorithm based on CNN is proposed. The experimental results in the LIDC database show that compared with the widely used lenet-5 network, traditional methods, and other deep learning models, the use of customized convolutional neural networks improves the classification accuracy. The AUC value is 0.821 6, which is also the highest among several classifiers. Compared with other methods, this method can more accurately identify CT images of the lungs and can provide a more objective reference for clinical diagnosis as it can be used for CAD systems.

关键词： Pulmonary nodule Convolutional Neural Network classification algorithm deep learning

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An Heuristic Feature Selection algorithm to Evaluate Academic Performance of Students 10

An Heuristic Feature Selection Algorithm to Evaluate Academi...

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IEEE 10th Control and System Graduate Research Colloquium (ICSGRC)

作者： Ajibade, Samuel-Soma M. Ahmad, Nor Bahiah Shamsuddin, Siti Mariyam UTM Comp Sci Dept Johor Baharu Malaysia

ISBN: (纸本)9781728107554

The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student's performances. Feature Selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues.

关键词： educational data mining differential evolution student performance feature selection algorithm classification algorithm

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Improvement and Implementation of Feature Weighting algorithm TF-IDF in Text classification

Improvement and Implementation of Feature Weighting Algorith...

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Weisi Dai Department of Informatics Beijing University of Technology

ISBN: (纸本)9781510871076

Based on the introduction of the traditional feature weighting algorithm TF-IDF,based on the phenomenon that the eigenvalue extraction is not effective when the text to be classified is not uniform,an improved TF-IDF algorithm is proposed in this paper,which considers the uneven text distribution *** experimental results show that the results obtained by the classification algorithm using the improved algorithm are better than the original algorithm in terms of accuracy and recall and make up for the defects of the original TF-IDF algorithm.

关键词： classification algorithm TF-IDF

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An Investigation of classification algorithms for Intrusion Detection System - A Quantitative Approach

An Investigation of Classification Algorithms for Intrusion ...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Varghese, Josy Elsa Muniyal, Balachandra Manipal Univ Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India

ISBN: (纸本)9781509063673

Nowadays security issues are growing in a tremendous rate. So it is expedient to have a mechanism to keep track of its security issues in the network or host. The Intrusion Detection System (IDS) has a critical part for supervising the networks. The false alarm rate and accuracy are the two important factors to be considered in the design of competent IDS. The role of classification algorithms is indispensable in the decision making of IDS. The redundant and irrelevant features of dataset also affects the performance of classifiers which in turn degrading the evaluation of anomaly detection. The proposed work focuses on the detailed study of different classifiers on two feature selection techniques using NSL-KDD dataset, where Random Forest on Principal Component Analysis (PCA) gives the accuracy rate of 99.52% and false alarm rate is 0.48%.

关键词： Intrusion Detection System(IDS) classification algorithm Feature Selection Technique Principal Component Analysis(PCA) NSL-KDD dataset

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Research on Customer Deposit Order Based on Data Mining Technology 2019

Research on Customer Deposit Order Based on Data Mining Tech...

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Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

作者： Aiting Xu Yingying Zhou Hui Chen College of Statistics and Mathematics Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9781450376631

This paper takes the time deposit order data of banking institutions as the research object. Firstly, the data of banking institutions are analyzed and cleaned to convert the categorical data into numeric data. Then, to "split" preprocessing the data after processing, divided into training set and test set. In the training set, LR, Naive Bayes, K-NN, Decision Tree, Random Forests, Extra - trees, AdaBoost, GBDT, XGBoost, LightGBM and CatBoost algorithm (via the grid search method GridSearchCV) are established to study the customer's deposit ordering tendency respectively. Finally, the Stacking fusion algorithm in Ensemble Learning(The meta-learner adopts LR algorithm) is used to fuse each single model to build the best bank customer time deposit ordering classification model. The research results show that using python, based on the different dimensions of bank customer information, the Stacking fusion algorithm based on Ensemble algorithm has better prediction effect than the single model, and the Stacking fusion algorithm has better robustness. Therefore, this paper takes the Stacking fusion model as the final prediction model to effectively identify bank customers and help banking institutions understand customer subscription tendency.

关键词： Customer deposit ordering Stacking fusion algorithm classification algorithm Machine learning

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DeepDBP: Deep neural networks for identification of DNA-binding proteins

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Informatics in Medicine Unlocked 2020年 19卷

作者： Shadab, Shadman Alam Khan, Md Tawab Neezi, Nazia Afrin Adilina, Sheikh Shatabda, Swakkhar Department of Computer Science and Engineering United International University Plot-2 United City Madani Avenue Badda Dhaka 1212 Bangladesh

DNA-Binding proteins (DBP) are associated with many cellular level functions which includes but not limited to body's defense mechanism and oxygen transportation. They bind DNAs and interact with them. In the past DBPs were identified using experimental lab based methods. However, in the recent years researchers are using supervised learning to identify DBPs solely from protein sequences. In this paper, we apply deep learning methods to identify DBPs. We have proposed two different deep learning based methods for identifying DBPs: DeepDBP-ANN and DeepDBP-CNN. DeepDBP-ANN uses a generated set of features trained on traditional neural network and DeepDBP-CNN uses a pre-learned embedding and Convolutional Neural Network. Both of our proposed methods were able to produce state-of-the-art results when tested on standard benchmark datasets. DeepDBP-ANN had a train accuracy of 99.02% and test accuracy of 82.80%.And DeepDBP-CNN though had train accuracy of 94.32%, it excelled at identifying test instances with 84.31% accuracy. All methods are available codes and methods are available for use at: https://***/antorkhan/DNABinding. © 2020 The Authors

关键词： classification algorithm CNN Deep learning DNA-Binding proteins Feature Selection

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