scholarly journals RUS Boost Tree Ensemble Classifiers for Occupancy Detection

2019 ◽  
Vol 8 (2S2) ◽  
pp. 272-277
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Ensemble Classifier ◽  
Sensor Data ◽  
Ensemble Classifiers ◽  
Humidity Ratio ◽  
Accuracy Rate ◽  
Weak Learner ◽  
Occupancy Detection ◽  
Save Energy

In this research paper, various ensemble classifiers are used to predict occupancy status using samples of light, temperature, humidity, CO2 , humidity ratio sensor data. Occupancy detection will save energy making room for smart buildings in smart cities. It paves ways to decide on heating, ventilation, cooling and lighting. To achieve 'white box' output and facilitate explanatory interpretation, decision tree was employed, Several weak learner decision trees were melded to form RUSBoosted Tree ensemble classifier. On investigation of the results, it is seen that RUSBoostedTree Ensemble gives the highest accuracy rate of 99%

scholarly journals A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Adeeb Noor ◽  
Muhammed Kürşad Uçar ◽  
Kemal Polat ◽  
Abdullah Assiri ◽  
Redhwan Nour
Keyword(s):  
Ensemble Classifier ◽  
Ensemble Classifiers ◽  
Subspace Method ◽  
Accuracy Rate ◽  
Novel Approach

In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

scholarly journals Ensemble Classification Approach for Sarcasm Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jyoti Godara ◽  
Isha Batra ◽  
Rajni Aron ◽  
Mohammad Shabaz
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Research Work ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Ensemble Model ◽  
Ensemble Classifiers ◽  
Text Data ◽  
Proposed Model ◽  
Pca Algorithm

Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K -means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.

scholarly journals PEDESTRIAN DEAD RECKONING USING SMARTPHONES SENSORS: AN EFFICIENT INDOOR POSITIONING SYSTEM IN COMPLEX BUILDINGS OF SMART CITIES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 905-912
Author(s):  
E. Saadatzadeh ◽  
A. Chehreghan ◽  
R. Ali Abbaspour
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Dead Reckoning ◽  
Principal Component ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Mean ◽  
Pedestrian Dead Reckoning

Abstract. This paper proposes an indoor positioning method using Pedestrian Dead Reckoning (PDR) based on the detection of the mode of the user’s smartphone. In the first step, to determine the mode of carrying the smartphone (Holding, Calling, Swinging) by suitably formed feature vectors based on sensor data, three classification algorithms (Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)) are evaluated. From the classification algorithm perspective, the decision tree algorithm had the best performance in terms of processing time and classification. Secondly, to determine the user position, the step detection is performed by defining the upper threshold and time threshold for Acceleration norm values. The orientation component is obtained by combining accelerometer, magnetometer, and gyroscope data using Complementary Filtering and Principal Component Analysis based on Global Acceleration (PCA-GA) methods. The mean standard deviation along the direct path for the three modes of carrying (Holding, Calling, and Swinging) were obtained 6.22, 6.82, and 14.68 degrees, respectively. Localization experiments were performed on 3 modes of carrying a smartphone in a rectangular geometry path. The mean final error of positioning from ordinary walking for the three modes of holding (Calling, Holding, Swinging) were obtained 2.11, 2.34, and 4.5 m, respectively.

scholarly journals Integrated sensor data processing for occupancy detection in residential buildings

2021 ◽  
Vol 237 ◽  
pp. 110810
Author(s):  
Chenli Wang ◽  
Jun Jiang ◽  
Thomas Roth ◽  
Cuong Nguyen ◽  
Yuhong Liu ◽  
...  
Keyword(s):  
Data Processing ◽  
Residential Buildings ◽  
Sensor Data ◽  
Integrated Sensor ◽  
Occupancy Detection

Practice Report “Smart Disaster Management” — Combining Smart City Data and Citizen Participation to Increase Disaster Resilience

i-com ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 177-193
Author(s):  
Daniel Wessel ◽  
Julien Holtz ◽  
Florian König
Keyword(s):  
Citizen Participation ◽  
Smart City ◽  
Development Process ◽  
Smart Cities ◽  
Government Support ◽  
Sensor Data ◽  
Specific Information ◽  
Mutual Support ◽  
The Everyday

Abstract Smart cities have a huge potential to increase the everyday efficiency of cities, but also to increase preparation and resilience in case of natural disasters. Especially for disasters which are somewhat predicable like floods, sensor data can be used to provide citizens with up-to-date, personalized and location-specific information (street or even house level resolution). This information allows citizens to better prepare to avert water damage to their property, reduce the needed government support, and — by connecting citizens locally — improve mutual support among neighbors. But how can a smart city application be designed that is both usable and able to function during disaster conditions? Which smart city information can be used? How can the likelihood of mutual, local support be increased? In this practice report, we present the human-centered development process of an app to use Smart City data to better prepare citizens for floods and improve their mutual support during disasters as a case study to answer these questions.

scholarly journals Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4132 ◽  
Author(s):  
Ku Ku Abd. Rahim ◽  
I. Elamvazuthi ◽  
Lila Izhar ◽  
Genci Capi
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Ensemble Methods ◽  
Daily Activities ◽  
Support Vector ◽  
Random Subspace ◽  
Ensemble Classifiers ◽  
Accuracy Rate

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

scholarly journals Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1928 ◽  
Author(s):  
Alfonso González-Briones ◽  
Fernando De La Prieta ◽  
Mohd Mohamad ◽  
Sigeru Omatu ◽  
Juan Corchado
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Energy Optimization ◽  
Sensor Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Perspectives ◽  
Wide Range ◽  
Multi Agent

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

scholarly journals C4.5 DECISION TREE IMPLEMENTATION IN SISTEM INFORMASI ZAKAT (SIZAKAT) TO AUTOMATICALLY DETERMINING THE AMOUNT OF ZAKAT RECEIVED BY MUSTAHIK

2014 ◽  
Vol 10 (1) ◽  
pp. 28 ◽  
Author(s):  
David Bayu Ananda ◽  
Ari Wibisono
Keyword(s):  
Information Systems ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Accuracy Rate ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Abstract In general, Zakat Information Systems is established to manage the zakat services, so that the data can be well documented. This study proposes the existence of a feature that will determine the amount of zakat received by Mustahik automatically using C4.5 Decision Tree algorithm. This feature is expected to make the process of determining the amount of zakat be done easy and optimal. The data used in this study are the data taken from Masjid An-Nur, Pancoran, South Jakarta. The experiment results show that the proposed feature produces an accuracy rate over 85%.

scholarly journals EVALUATING EFFECTIVENESS OF ENSEMBLE CLASSIFIERS WHEN DETECTING FUZZERS ATTACKS ON THE UNSW-NB15 DATASET

2020 ◽  
Vol 36 (2) ◽  
pp. 173-185
Author(s):  
Hoang Ngoc Thanh ◽  
Tran Van Lang
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Cyber Security ◽  
Experimental Results ◽  
Ensemble Classifiers ◽  
Research Results ◽  
Ensemble Techniques ◽  
F Measure ◽  
Classification Quality

The UNSW-NB15 dataset was created by the Australian Cyber Security Centre in 2015 by using the IXIA tool to extract normal behaviors and modern attacks, it includes normal data and 9 types of attacks with 49 features. Previous research results show that the detection of Fuzzers attacks in this dataset gives the lowest classification quality. This paper analyzes and evaluates the performance of using known ensemble techniques such as Bagging, AdaBoost, Stacking, Decorate, Random Forest and Voting to detect FUZZERS attacks on UNSW-NB15 dataset to create models. The experimental results show that the AdaBoost technique with the component classifiers using decision tree for the best classification quality with F-Measure is 96.76% compared to 94.16%, which is the best result obtained by using single classifiers and 96.36% by using the Random Forest technique.

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