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Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition [article]

Olasimbo Ayodeji Arigbabu
2020 arXiv   pre-print
This paper demonstrates our efforts to develop a collaborative decision fusion mechanism for integrating the predicted scores from multiple learning algorithms trained on smartphone sensor based human  ...  Human activity recognition serves an important part in building continuous behavioral monitoring systems, which are deployable for visual surveillance, patient rehabilitation, gaming, and even personally  ...  Our method combines the usefulness of automatically learned features with handcrafted ones. • Proposed the use of generalized entropy based on Tsallis entropy to obtain classifier fusion weights which  ... 
arXiv:2006.00367v1 fatcat:zug2sqrm4bgf5cdwkcfro6mr3u

Active learning approach to detecting standing dead trees from ALS point clouds combined with aerial infrared imagery

Przemyslaw Polewski, Wei Yao, Marco Heurich, Peter Krzystek, Uwe Stilla
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We explore how the use of semi-supervised classifiers with minimum entropy regularizers can benefit the learning process.  ...  We then perform active learning on this moderate-sized pool, using expected error reduction as the basic method.  ...  In pool-based active learning, the system is given a pool of unlabeled examples, a small initial training set, and a classifier family capable of producing a continuous measure of confidence alongside  ... 
doi:10.1109/cvprw.2015.7301378 dblp:conf/cvpr/PolewskiYHKS15 fatcat:2arkckgi6ze6vb6l5aucafg65e

P2P Lending Default Prediction Based on AI and Statistical Models

Po-Chang Ko, Ping-Chen Lin, Hoang-Thu Do, You-Fu Huang
2022 Entropy  
After that, three statistical models, namely, Logistic Regression, Bayesian Classifier, and Linear Discriminant Analysis (LDA), and five AI models, namely, Decision Tree, Random Forest, LightGBM, Artificial  ...  Neural Network (ANN), and Convolutional Neural Network (CNN), were utilized for data analysis.  ...  Decision Tree A Decision Tree is a supervised learning technique that uses a tree-based model.  ... 
doi:10.3390/e24060801 pmid:35741522 pmcid:PMC9222552 fatcat:qhxy6nqtt5fbhje44if63zsrki

A supervised active learning framework for recommender systems based on decision trees

Rasoul Karimi, Alexandros Nanopoulos, Lars Schmidt-Thieme
2014 User modeling and user-adapted interaction  
., labels) are known and are used for active learning purposes, the proposed framework is in fact a supervised active learning framework.  ...  A well-known solution for this problem is to ask new users to rate a few items to reveal their preferences and to use active learning to find optimally informative items.  ...  Algorithm 2 LAL based on decision trees ConstructDecisionTree Input: U t , D pool , q,r t Output: i * 1: for u ∈ U t do 2: compute RM SE 1 u based onr t 3: end for 4: for i ∈ D pool do 5: split U t into  ... 
doi:10.1007/s11257-014-9153-z fatcat:wor6ey2s3nf3pmluqdw55kctqq

A Survey on Brain Tumor Detection using Machine Learning

Mrunal Kurade
2020 International Journal for Research in Applied Science and Engineering Technology  
Tumor is noticed in brain MRI using Machine Learning algorithms.  ...  The conventional method for defect detection in magnetic resonance brain picture is human inspection. This method is impractical for ample quantity of data.  ...  The sympathetic level of Decision Trees algorithm is so informal related with other classification algorithms. The decision tree algorithm attempts to explain the problem, by using tree illustration.  ... 
doi:10.22214/ijraset.2020.6113 fatcat:4np7why24nennifxw3wlyq66y4

Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks

Glori Stephani Saragih, Zuherman Rustam, Dipo Aldila, Rahmat Hidayat, Reyhan E. Yunus, Jacub Pandelaki
2020 International Journal on Advanced Science, Engineering and Information Technology  
To carry out automatic diagnosis, machine learning and deep learning can be used, especially because of their ability to provide high accuracy prediction results.  ...  Based on the proposed method, the accuracy of testing is 100% when the percentage of the testing dataset is 10% and the number of trees more than 100 with criterion Gini or entropy.  ...  The more trees that are formed and used in the decision-making process, the more robust the results will be [15] . D.  ... 
doi:10.18517/ijaseit.10.5.13000 fatcat:hovxjc6xgre4xafe4xa7ai6nxa

Waste Management Using Machine Learning and Deep Learning Algorithms

Khan Nasik Sami, Zian Md Afique Amin, Raini Hassan
2020 International Journal on Perceptive and Cognitive Computing  
The model that we have used are classification models. For our research we did comparisons between four algorithms, those are CNN, SVM, Random Forest, and Decision Tree.  ...  Decision Tree have accomplished 55% and 65% respectively  ...  ACKNOWLEDGMENT The authors are grateful to Kulliyyah of Information and Communication Technology, International Islamic University Malaysia for their assistance and guidelines.  ... 
doi:10.31436/ijpcc.v6i2.165 fatcat:d4lzydb6pzd45e3tk6pviyuvjq

Using Bio-inspired intelligence for Web opinion Mining

George Stylios, Christos D. Katsis, Dimitris Christodoulakis
2014 International Journal of Computer Applications  
The obtained results are compared with a commonly used machine learning technique (decision trees-C4.5 algorithm).  ...  The proposed methodology could be easily integrated with a decision support system providing services in the fields of ecommerce or e-government in order to help merchants acquire customer satisfaction  ...  Sofia Stamou for her valuable suggestions and guidance.  ... 
doi:10.5120/15207-3610 fatcat:di3rpxkio5dsbiw5s5ukbni46a

Meta-Analysis of Artificial Intelligence Works in Ubiquitous Learning Environments and Technologies

Caitlin Sam, Nalindren Naicker, Mogiveny Rajkoomar
2020 International Journal of Advanced Computer Science and Applications  
2 = 99.83%) than intelligent decision support systems, intelligent systems and educational data mining.  ...  Using random-effects model, the estimated pooled estimate of artificial intelligence works in ubiquitous learning environments and technologies reported was 10% (95% CI: 3%, 22%; I 2 = 99.46%, P = 0.00  ...  ACKNOWLEDGMENT Kind acknowledgement goes to the Durban University of Technology for making the resources available for this research project.  ... 
doi:10.14569/ijacsa.2020.0110971 fatcat:i3d2m7hnsvfxnjivttudvbnxem

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Fatima Es-sabery, Khadija Es-sabery, Hamid Garmani, Abdellatif Hair, S. Krit
2021 E3S Web of Conferences  
This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS).  ...  Finally, we have used the General Fuzziness Reasoning (GFR) approach for classifying the new tweets.  ...  At every Mapper node level, we get a small fuzzy decision tree, so we used two reducer nodes to aggregate the obtained Mapper nodes results.  ... 
doi:10.1051/e3sconf/202129701052 fatcat:gs5ztgmb5bdh7okuj2itwaipne

Grading OSPE Questions with Decision Learning Trees: A First Step Towards an Intelligent Tutoring System for Anatomical Education

Jason Bernard, Bruce C. Wainman, O'llenecia Walker, Courtney Pitt, Alex B. Bak, Josh P. Mitchell, Anthony N. Saraco, Ilana Bayer, Ranil Sonnadara
2021 AAAI Fall Symposia  
To that end, decision tree learning was evaluated with, and without, spellchecking to produce a grading tool using the answer key developed by instructional assistants.  ...  Intelligent tutoring systems (ITSs) have been used for decades as a means for improving the quality of education for learners primarily by providing guidance to students based on a student model, e.g.,  ...  It begins with an overview of the decision tree learning (DTL) algorithm. This is followed by a description of how decision trees are used to evaluate OSPE questions.  ... 
dblp:conf/aaaifs/BernardWWPBMSBS21 fatcat:oe7zftptvzde7gb56kfe6p5vyq

Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT

Usman Ahmed, Jerry Chun-Wei Lin*, Gautam Srivastava
2022 ACM transactions on sensor networks  
The learned latent representation uses word position prediction and sentence-level attention to create a semantic framework.  ...  The development of an AIoMT tool requires labeling of data to achieve clinical-level performance.  ...  The dataset was subjected to machine learning methods such as Naive Bayes, Maximum Entropy, and Decision Tree.  ... 
doi:10.1145/3519304 fatcat:hpuecgatfnhlnas4itxp4dd4gy

A Comparative Analysis on Diagnosis of Diabetes Mellitus using Different Approaches – A Survey

Fareeha Anwar Qurat-Ul-Ain, Muhammad Yasir Ejaz
2020 Informatics in Medicine Unlocked  
A tree-based ensemble learning model was introduced for automatic diabetes prediction [18 Random Forest and Gradient Boosting used for classification.  ...  A decision support system was proposed in [35] that used the Ada-Boost algorithm with Decision Stump as a base classifier for classification.  ... 
doi:10.1016/j.imu.2020.100482 fatcat:3233o4r34ngbleyr4ufuyy3wdi

Wireless capsule endoscopy bleeding images classification using CNN based model

Furqan Rustam, Muhammad Abubakar Siddique, Hafeez Ur Rehman Siddiqui, Saleem Ullah, Arif Mehmood, Imran Ashraf, Gyu Sang Choi
2021 IEEE Access  
BIR uses the MobileNet model for initial-level computation for its lower computation power requirement and subsequently the output is fed to the CNN for further processing.  ...  The current study aims to devise a system that can perform the task of automatic analysis of WCE images to identify abnormalities and assist practitioners for robust diagnosis.  ...  will restrict each decision to maximum 300 level depths [40] .  ... 
doi:10.1109/access.2021.3061592 fatcat:xqmaub3ubzesdfhtxnpgohkaha

Design of Teaching Quality Analysis and Management System for PE Courses Based on Data-Mining Algorithm

Sen Li, Yanrui Luo, Xin Ning
2022 Computational Intelligence and Neuroscience  
In today's universities, the Web-based integrated academic management information system is widely used, promoting higher education management system innovation and improving the management level of education  ...  This article describes the design approach for a data mining-based analysis and management system for PE course teaching quality, as well as the application of information technology and data mining technology  ...  Based on the sample centroids' data, the criterion for selecting the attribute is the information entropy. e information entropy value is calculated based on the data, and then, the sizes of each information  ... 
doi:10.1155/2022/6830375 pmid:35685145 pmcid:PMC9173939 fatcat:fz4ejhcvqbcsxesxljlnwf4smu
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