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Utilizing machine learning and data mining to Identify Diarrheal Diseases with the Decision Tree Method

Utilizing Machine Learning and Data Mining to Identify Diarr...

引用

international conference on Informatics and Computing (ICIC)

作者： Fuzy Yustika Manik T.H. F Harumy Dewi Sartika Ginting Suci Ramadani Anandhini Medianty Nababan Nur Wulan Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Sumatera Utara Indonesia Departement computerized accounting STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Informatic Engineering Universitas Harapan Medan Medan Sumatera Utara Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In Indonesia, diarrheal disease is still a global health problem. The degree of morbidity and mortality is quite high, ranking 3rd after TB and pneumonia. In developed countries, even though there have been improvements in people’s health and economy, the incidence of infectious diarrhea remains high and is still a health problem. The development of increasingly complex science encourages humans to learn data that can be processed and produce new information and knowledge. In computer science, machine learning (ML) facilitates computers to read and interpret previously existing data automatically. machine learning can be used to classify diseases, determine the type of disease, and predict a disease. The machine learning method used is a decision tree. The step is to collect a data set consisting of input variables in the form of symptoms and output variables in the form of types of diarrheal diseases. Then change the form of the data into a table which becomes a model tree by looking for the entropy and gain of each symptom. After the model is formed, change the tree into a rule and simplify the rule (pruning). The results of identifying diarrheal diseases using the decision tree method were excellent, with an accuracy of 96.4%. Then a simplification of the rules is also obtained from 20 rules from 17 symptoms to 5 rules with a depth of 4 levels. The most influential symptoms in forming rules are G10, G14, and G16.

关键词： Accuracy Pneumonia Input variables Mortality machine learning Entropy data models Decision trees Informatics Diseases

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Impact of Dimensionality Reduction on The Accuracy of data Classification

Impact of Dimensionality Reduction on The Accuracy of Data C...

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international conference on Engineering Technology and their Applications (IICETA)

作者： Amar A. Othman Taha M. Hasan Safwan O. Hasoon College of Sciences University of Diyala Baquba Iraq College of Computer Science and Mathematics University of Mosul Mosul Iraq

ISBN: (数字)9781728182315

ISBN: (纸本)9781728182322

One of the most essential research aspects that can be used in network security and information for data and information protection from unauthorized access is the system of intrusion detection. It was necessary to build systems that have the ability to Intrusion detection and ensure that the service is provided with the appropriate speed and time. In this paper introduced enhancing the robustness of machine learning systems via dimensionality reduction system. Firstly, the proposed system is chosen for the NSL-KDD dataset analyses. The data has been manipulated to make its formatting more relevant to the classification algorithms used and the second step was applied principal component analysis algorithm to reduce the dimensions. The data were classified according to the Naïve Bayes algorithm and Decision Tree (J48) algorithms and have achieved an accuracy of 99.29% with the attributes reduced to five attributes in decision Tree (J48) algorithm and test time models on test split: 0.3 seconds and build the model: 9.03 seconds compared to the same shorthand in Naïve Bayes algorithm whereas classification accuracy was 87.89 % and time range test model on test split: 0.73 seconds and build the model: 0.19 seconds, also well it was obtained some results for the reduced data in the classification stage, which exceeded the accuracy of the results of the original data without dimension reducing.

关键词： Classification algorithms data mining Dimensionality reduction Principal component analysis machine learning algorithms machine learning Training

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Packaging recognition Fusion Algorithm Based on Visual Imaging Technology

Packaging Recognition Fusion Algorithm Based on Visual Imagi...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Hanfei Zhao Xi’an Fanyi University Xi’an China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In the process of online terminal inspection of packaging bags, the traditional packaging inspection method is mainly carried out manually. However, due to factors such as work fatigue and emotional fluctuations, the efficiency of online terminal packaging inspection cannot be guaranteed. Manual testing also affects the surface cleanliness of packaging bags and consumes a lot of labor. At present, the packaging industry generally adopts intelligent packaging technology to solve the problems existing in the production process of traditional packaging products, among which machine vision technology is its core. However, nowadays packaging companies mostly use machine vision to check for labels, color defects, and other issues. This is beneficial at the end of the production line to achieve the integrity and aesthetics of the appearance information of packaging bags, thereby ensuring the production quality of products. However, currently, there are still problems such as skewness and folding in the inhalable plastic bags transported at the end of the assembly line, which leads to uneven packaging and affects the appearance of the product. In severe cases, it can also result in missing quantities. This article explored the research on packaging recognition fusion algorithms based on visual imaging technology, and verified through experiments that the accuracy of the recognition model based on CNN (Convolutional Neural Networks) algorithm was higher than 81% under different experimental times. Compared to CNN algorithm, the recognition accuracy of BP (Back Propagation) algorithm was much lower. The highest accuracy of the packaging recognition model based on BP algorithm was only about 79%.

关键词： Industries Visualization Image recognition Accuracy machine learning algorithms Imaging Manuals Packaging Inspection Convolutional neural networks

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A Novel Method for Leaf recognition Based on D-LLE and Polar Coordinate Feature Extraction

A Novel Method for Leaf Recognition Based on D-LLE and Polar...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Li Yang Jiao Ding Liheng Jiang Renrui Han Yingchun Bi Shangzhi Zheng School of Medical Information Wannan Medical College Wuhu City China Anhui Institute of Information Technology Wuhu City China College of Information Engineering Chaohu University Chaohu City China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

By extracting low-level features under the rectangular coordinate system, traditional leaf recognition methods typically have properties such as high dimensionality of extracted features, high-computational requirement and weak generalization performance. Based on the manifold learning algorithm D-LLE, we proposed a novel leaf recognition method under the polar coordinate system. The method first extracts from the leaf images high-dimensional features associated with polar coordinate as the preprocessing. Consequently, D-LLE is harnessed to reduce the features' dimensionality. In the low-dimensional space, we use the nearest neighbor classifier to make final determination. Experimental results exhibit higher effectiveness and efficiency of our method compared with classical traditional methods.

关键词： Image recognition Clustering algorithms Feature extraction Prediction algorithms Classification algorithms data mining Information systems

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Impact of Baggingon MLP Classifier for Credit Evaluation

Impact of Baggingon MLP Classifier for Credit Evaluation

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international conference on Computing for Sustainable Global Development

作者： Shashi Dahiya S. S. Handa N. P. Singh Manav Rachna International University Faridabad Manav Rachna International University Management Development Institute (MDI)

ISBN: (纸本)9781467394178

Credit risk is one of the critical and the major challenges faced by banks. Its evaluation is an important factor in decision making for grant of credit. The accuracy in credit risk evaluation techniques adds to the profitability of banks and avoids financial loss. data mining has played a very significant role in evaluating credit risk. Multi-Layer Perception Neural Network has been used individually and in combination with other machine learning or statistical techniques to build very efficient hybrid classifiers for credit risk evaluation. In this study, three different models are developed - Single MLP model, Feature Selection based MLP model and Bagging based hybrid MLP model. The results shows that the accuracy of single MLP classifier is significantly enhanced by using suitable feature selection method and is further enhanced by using the ensemble based bagging technique on MLP. The results also revealed that 70:30 percentage split of dataset contributed to the performance of all the classifiers.

关键词： Bagging Classifiers Credit Risk Ensemble Technique Feature Selection Hybrid Models

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Exploring the Potential Benefits of machine learning for Optimizing Real-Time data Analysis

Exploring the Potential Benefits of Machine Learning for Opt...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Ravindra Kumar Nitin N. Sakhare N. Thangarasu K.K. Senthilkumar Pawan S. Wawage A Rengarajan Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms.

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Face recognition Using Spatially Smooth and Maximum Minimum Value of Manifold Preserving

Face Recognition Using Spatially Smooth and Maximum Minimum ...

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2010 The 3rd international conference on Computational Intelligence and Industrial Application (PACIIA2010)

作者： Hao Wen School of Communication Engineering University of XiDian XiAn, Shaanxi Province, China Youkui Wen School of Economic Management University of XiDian XiAn, Shaanxi Province, China

Subspace learning based face recognition methods have attracted considerable interests in recently years. However, the accuracies of previous methods are not so high, because they don’t utilize the manifold of face image data sufficiently and neglect some particular characters of the images. Thus a new method to form graph of data is proposed in this paper, and the method is used to develop two face recognition algorithms. At the same time the pixels correlation in images is considered sufficient under the constrain of spatially smooth in the two developed algorithms. So the features of the projected subspace based on our algorithms have better classification ability. Therefore, the right recognition rates are enhanced by the two proposed algorithms. This is further confirmed by experiments.

关键词： manifold learning, face recognition, spatially smooth, subspace learning, pattern recognition.

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Combining different classifiers in Educational data mining

Combining different classifiers in Educational Data Mining

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2010 The 3rd international conference on Computational Intelligence and Industrial Application (PACIIA2010)

作者： He Chuan School of Computer Science Beijing University of Posts and Telecommunications Beijing,China Li Ruifan School of Computer Science Beijing University of Posts and Telecommunications Beijing,China Zhong Yixin School of Computer Science Beijing University of Posts and Telecommunications Beijing,China

Educational data mining is a crucial application of machine *** KDD Cup 2010 Challenge is a supervised learning problem on educational data from computer-aided *** task is to learn a model from students' historical behavior and then predict their future *** paper describes our solution to this *** use different classification algorithms,such as KNN,SVD and logistic regression for all the data to generate different results,and then combine these to obtainthe final *** is shown that our resultsarecomparable to the top-ranked ones in leader board of KDD Cup 2010.

关键词： data mining,logistic regression,k-nearest neighbor,singular value decomposition,classifiers combination

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Effective Visualization and Trend Forecasting of Crime data Through Big data Analytics and mining

Effective Visualization and Trend Forecasting of Crime Data ...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Pd Mahendhiran Apsara S K Harshitha Vasudevan Nithish S Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

A crime is an act that merits public condemnation and penalties, which is usually in the form of a fine or jail. It is a widespread social issue that affects a country's life style, economic progress, and credibility. People are at a worrisome point in terms of reducing criminal activity at a greater rate, as criminality rates have risen dramatically. If people can evaluate and predict the crime pattern and implement preventive steps ahead of time, crime rates can be lowered. In this research, the author has present Crime Cast, a system that analyses crime reports and anticipates trends and crime statistics based on a specified data set using deep learning techniques such as LSTM and DNN at a faster rate and with greater accuracy. Within a huge percentage of crime data, Big data Analytics is utilized to analyze and detect different crime patterns, their relationships, and trends. The information contains cases of kidnapping, murder, rape, stealing, and other crimes. With the help of the LSTM model and DNN several operations performed on the data. The expected outcomes will aid police and law enforcement agencies in clearer grasp crime trends, allowing them to track incidences and improve their decision-making processes. These results will allow the law enforcement and regulatory agencies to further immediately spot inappropriate behavior issues and provide information that will allow them to monitor activities and predict the likelihood of limiting the number of events, transporting property securely, and improving the dynamic cycle.

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data mining and Visualization to Understand Employee Attrition and Work Performance

Data Mining and Visualization to Understand Employee Attriti...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Mohammed Gollapalli Ata-Ur Rahman Abdurrahman Osama Ahmad Alfaify Muhammad Yassin Ahmad Alabdullah Department of Computer Information Systems College of Computer Science & Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Computer Science College of Computer Science & Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Throughout the course of careers, a large number of employees encounter a variety of normal life events that can have an impact on their performance and job environment satisfaction. Under these circumstances, some employees are compelled to leave their positions, not because of issues with their work environment, but due to personal circumstances. This is what we refer to as Employee Attrition, a pervasive phenomenon affecting numerous businesses and work environments in the present day. Employee attrition could occur in a variety of ways and be determined by several entities. Therefore, we look into various conditions, causes, indicators, and entities in order to study the attrition problem and determine its answers and enhancements. This study investigated the attrition of employees based on IBM data repository, together with the machine learning based experimental analysis and knowledge discoveries, to identify the most effective approaches to the attrition problem. The experimental data contained 1,471 records with 35 distinct characteristics. We formally employed Nave Bayes Classifier in this study using WEKA datamining software, and adequate knowledge has been extracted to better comprehend employee turnover and their work performance facets.

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