Real Time Interactive Presentation Apparatus based on Depth Image Recognition

The research on human computer interaction. Human already thinking to overcome the way of interaction towards natural interaction. Kinect is one of the tools that able to provide user with Natural User Interface (NUI). It has capability to track hand gesture and interpret their action according to the depth data stream. The human hand is tracked in point of cloud form and synchronized simultaneously.The method is started by collecting the depth image to be analyzed by random decision forest algorithm. The algorithm will choose set of thresholds and features split, then provide the information of body skeleton. In this project, hand gesture is divided into several actions such as: waiving to right or left toward head position then it will interpret as next or previous slide. The waiving is measured in degree value towards head as center point. Moreover, pushing action will trigger to open new pop up window of specific slide that contain more detailed information. The result of implementations is quite fascinating, user can control the PowerPoint and event able to design the presentation form in different ways. Furthermore, we also present a new way of presentation by presenting WPF form that connected to database for dynamic presentation tool

Constructing Matrix Exponential Distributions by Moments and Behavior around Zero

This paper deals with moment matching of matrix exponential (ME) distributions used to approximate general probability density functions (pdf). A simple and elegant approach to this problem is applying Padé approximation to the moment generating function of the ME distribution. This approach may, however, fail if the resulting ME function is not a proper probability density function; that is, it assumes negative values. As there is no known, numerically stable method to check the nonnegativity of general ME functions, the applicability of Padé approximation is limited to low-order ME distributions or special cases. In this paper, we show that the Padé approximation can be extended to capture the behavior of the original pdf around zero and this can help to avoid representations with negative values and to have a better approximation of the shape of the original pdf. We show that there exist cases when this extension leads to ME function whose nonnegativity can be verified, while the classical approach results in improper pdf. We apply the ME distributions resulting from the proposed approach in stochastic models and show that they can yield more accurate results

Semantic data mapping technology to solve semantic data problem on heterogeneity aspect

The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mapping technology to be implemented with the real data in the specific application. The result of this research is the semantic data mapping file that contains all data structures in the application data source. This semantic data mapping file can be used to map, share and integrate with other semantic data mapping from other applications and can also be used to integrate with the ontology language

A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction

Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers

Android Malware Classification Using Optimized Ensemble Learning Based on Genetic Algorithms

The continuous increase in Android malware applications (apps) represents a significant danger to the privacy and security of users’ information. Therefore, effective and efficient Android malware app-classification techniques are needed. This paper presents a method for Android malware classification using optimized ensemble learning based on genetic algorithms. The suggested method is divided into two steps. First, a base learner is used to handle various machine learning algorithms, including support vector machine (SVM), logistic regression (LR), gradient boosting (GB), decision tree (DT), and AdaBoost (ADA) classifiers. Second, a meta learner RF-GA, utilizing genetic algorithm (GA) to optimize the parameters of a random forest (RF) algorithm, is employed to classify the prediction probabilities from the base learner. The genetic algorithm is used to optimize the parameter settings in the RF algorithm in order to obtain the highest Android malware classification accuracy. The effectiveness of the proposed method was examined on a dataset consisting of 5560 Android malware apps and 9476 goodware apps. The experimental results demonstrate that the suggested ensemble-learning strategy for classifying Android malware apps, which is based on an optimized random forest using genetic algorithms, outperformed the other methods and achieved the highest accuracy (94.15%), precision (94.15%), and area under the curve (AUC) (98.10%)

Android Malware Classification Using Optimized Ensemble Learning Based on Genetic Algorithms

The continuous increase in Android malware applications (apps) represents a significant danger to the privacy and security of users’ information. Therefore, effective and efficient Android malware app-classification techniques are needed. This paper presents a method for Android malware classification using optimized ensemble learning based on genetic algorithms. The suggested method is divided into two steps. First, a base learner is used to handle various machine learning algorithms, including support vector machine (SVM), logistic regression (LR), gradient boosting (GB), decision tree (DT), and AdaBoost (ADA) classifiers. Second, a meta learner RF-GA, utilizing genetic algorithm (GA) to optimize the parameters of a random forest (RF) algorithm, is employed to classify the prediction probabilities from the base learner. The genetic algorithm is used to optimize the parameter settings in the RF algorithm in order to obtain the highest Android malware classification accuracy. The effectiveness of the proposed method was examined on a dataset consisting of 5560 Android malware apps and 9476 goodware apps. The experimental results demonstrate that the suggested ensemble-learning strategy for classifying Android malware apps, which is based on an optimized random forest using genetic algorithms, outperformed the other methods and achieved the highest accuracy (94.15%), precision (94.15%), and area under the curve (AUC) (98.10%)

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi