DATA MINING CLASSIFICATION ALGORITHMS FOR DIABETES DATASET USING WEKA TOOL

Data mining explores a huge amount of data to extract the information to be meaningful. In the field of public health, data mining hold a crucial contribution in predicting disease in early stage. In order to detect diseases, the patients need to conduct various tests. In the context of disease predicion, Data mining techniques aims to reduce the test that patients need to accomplish. Also the techniques is used to increase the accuracy rate of detection. Nowadays, diabetes attacks many adults in the world. Moreover, in order to reduce the number of adult having diabetes, an effective and efficient diabetes detection mechanism should be found. This report will apply some data mining techniques on diabetes dataset that has been downloaded at UCI Machine Learning Repository.Three kind of classification algorithm such as Naïve Bayes Classifier, Multilayer Perceptrons (MLP’s) and Desicion Tree (J.48) have been performed on this dataset. Obtained outcomes indicated that Naïve Bayes Classifier achieved the highest accuracy with 76,30%. As the result, this algorithm is a good method to classify and diagnose diabetes diseases on studying dataset

Wallenius Naive Bayes

Traditional event models underlying naive Bayes classifiers assume probability distributions that are not appropriate for binary data generated by human behaviour. In this work, we develop a new event model, based on a somewhat forgotten distribution created by Kenneth Ted Wallenius in 1963. We show that it achieves superior performance using less data on a collection of Facebook datasets, where the task is to predict personality traits, based on likes.Faculty of Applied Economics, University of Antwerp, Belgium; Department of Information, Operations & Management Sciences, NYU Stern School of Busines

Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods

Modeling Complex Networks For (Electronic) Commerce

NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Artificial Intelligence in Materials Modeling and Design

In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented

Spell checker implementation to analyze the narrative essay of sixth-grade elementary school students in Indonesia

Learning Bahasa Indonesia correctly in terms of writing and reading is needed by students to understand well the language learned in school. Some problems arise when elementary school students still need guidance in writing Indonesian sentences in narratives, that are still lacking the standard or misspelling. Teachers usually read essays from students but it takes a lot for the teacher to learn. This research emerged with the aim of assisting teachers and students in correcting the spelling of the essay that was written so that it would be a perfect and perfect sentence. Applications built using internet technology so students can access the system anywhere. The results show that 87% of students experience spelling errors in writing narrative essay because they are inaccurate to what they write, so the application will provide automatic correction and already implemented in several schools at Malang

Wallenius Naive Bayes

Traditional event models underlying naive Bayes classifiers assume probability distributions that are not appropriate for binary data generated by human behaviour. In this work, we develop a new event model, based on a somewhat forgotten distribution created by Kenneth Ted Wallenius in 1963. We show that it achieves superior performance using less data on a collection of Facebook datasets, where the task is to predict personality traits, based on likes.Faculty of Applied Economics, University of Antwerp, Belgium; Department of Information, Operations & Management Sciences, NYU Stern School of Busines

Probabilistic inductive constraint logic

AbstractProbabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we consider the models produced by the inductive constraint logic system, represented by sets of integrity constraints, and we propose a probabilistic version of them. Each integrity constraint is annotated with a probability, and the resulting probabilistic logical constraint model assigns a probability of being positive to interpretations. To learn both the structure and the parameters of such probabilistic models we propose the system PASCAL for "probabilistic inductive constraint logic". Parameter learning can be performed using gradient descent or L-BFGS. PASCAL has been tested on 11 datasets and compared with a few statistical relational systems and a system that builds relational decision trees (TILDE): we demonstrate that this system achieves better or comparable results in terms of area under the precision–recall and receiver operating characteristic curves, in a comparable execution time