EMITTER International Journal of Engineering Technology
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Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization
No full textPreeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria. Â The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing
Real Performance Evaluation On MQTT and COAP Protocol in Ubiquitous Network Robot Platform (UNRPF) for Disaster Multi-robot Communication
No full textDisaster multi-robot has a significant role in a disaster area to do many tasks like detection of fire, search and rescue of victims, etc. It needs to build good communication between the operator and multi-robot and among multi-robot themselves to perform their tasks quickly and efficiently. This relates with the queue message protocol system. In this research, we implemented the queue message protocol on mesh topology and integrated it on the robot platform. Recently, development of IoT (Internet of Things) Technology causes development of communication protocol. MQTT and CoAP are among the communication protocols used for IoT needs. Both protocols performance were compared when  used and implemented into disaster multi-robot. We also integrated MQTT protocol and robot platform python based (UNR-PF). The result shows that MQTT protocol is easier to be implemented on to disaster multi-robot platform (UNR-PF) on mesh topology than CoAP, and that data transfer rate of MQTT protocol has data transfer rate higher than CoAP
Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure
No full textLeaf bone structure has a characteristic that can be used as a reference in digital image processing. One form of digital image processing is image edge detection. Edge detection is the process of extracting edge information from an image. In this research, Adaptive Ant Colony Optimization algorithm is proposed for edge image detection of leaf bone structure. The Adaptive Ant Colony Optimization method is a modification of Ant Colony Optimization, in which the initial an ant dissemination process is no longer random, but it is done by a pixel placement process that allows for an edge based on the value of the image gradient. As a comparison also performed edge detection using Robert and Sobel method. Based on the experiments performed, Adaptive Ant Colony Optimization algorithm is capable of producing more detailed image edge detection and has thicker borders than others. Keywords: edge detection, ant colony optimization, robert, sobe
Determination of Nearest Emergency Service Office using Haversine Formula Based on Android Platform
No full textEmergency Reporting Application is an android-based application that serves to help the community in reporting the emergency condition. This application allows users to choose and contact the emergency services office, without the need to notice their position and phone number. Selection of emergency services office is also automatically selected by the system by taking into account the distance between the complainant and the emergency services office. The selected emergency services office is the nearest emergency service office from the complainant so that the delay in coming assistance can be minimized. Therefore, this proposed application requires a GPS feature to recording, reporting and SMS positioning for message delivery of reports. The distance between the position of the complainant and the position of the emergency service office, in the form of latitude and longitude data, is requested using the Haversine formula taking into account the degree of curvature of the earth. Emergency service offices include police and hospital offices spread over 25 different districts. Furthermore, the reporter's position calculation results were compared with all selected emergency service offices and obtained 1 nearest emergency service office. Calculating the accuracy and delay value of the system will do system testing. Accuracy test results using the method of 100% Haversine and the average delay of the system is 4.5 seconds
Performance and Economic Analysis of Multi-Rotor Wind Turbine
No full textPower production of a wind turbine is dependent upon its rotor size and at present wind turbines with large rotor diameter (>175 m) are available in the market. However major problems associated with such large size conventional turbines are their cost & noise pollution. Due to these reason researchers have diverted their attention towards lower sized equivalent multi-rotor wind turbines. These turbines are found to be cheaper and good performers. Keeping it in view, in this paper an effort has been made to compare the energy yield and economics of two types of wind turbines i.e. single rotor & multi rotor wind turbine. Power, energy and cost models as proposed are used to determine the annual energy yield and economics of multi-rotor turbines. Simulation results as presented in this paper justify the suitability of multi-rotor wind turbine in place of single rotor configuration. Such turbines deliver more energy yield with low installation cost in contrast to single rotor turbines
Power Generation Forecasting of Dual-Axis Solar Tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks
No full textSolar power is a renewable energy interest many researchers around the world to be explored for human life beneficial especially for electric power generation. Photovoltaic (PV) is one of technology developed massively to exploit the solar power for this purpose. However, its performance is very sensitive to environmental condition such as solar irradiance, weather, and climatic behavior. Thus, the hybrid power generation systems are developed to solve this output uncertainty problem. To support this such hybrid system, this paper proposes an ensemble neural network based forecaster of the power output of PV systems which will lead an efficient power management. The object of this research is the PV systems equipped with two axes automated solar tracking with peak power 10Wp. The proposed ensemble forecaster model employs four multi-layer perceptron neural networks with two hidden layers as base forecasters while the input number of historical data is varied in order to exploit the forecaster diversity. The final prediction is calculated both by conventional averaging and simple weighting optimized by the least square fitting technique. According to the research results, the both proposed approaches provide low error rate. Moreover, in term of comparison, the ensemble model with averaging combining technique gives the highest accuracy comparing to the other ensemble and conventional neural network structures
Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance
No full textFinding victims at a disaster site is the primary goal of Search-and-Rescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using cross-validation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 61.70% precision, 77.60% accuracy, 74.36% recall and f-measure 67.44% to distinguish victim (positives) and non-victim (negatives)
Botnet Detection Using On-line Clustering with Pursuit Reinforcement Competitive Learning (PRCL)
No full textBotnet is a malicious software that often occurs at this time, and can perform malicious activities, such as DDoS, spamming, phishing, keylogging, clickfraud, steal personal information and important data. Botnets can replicate themselves without user consent. Several systems of botnet detection has been done by using classification methods. Classification methods have high precision, but it needs more effort to determine appropiate classification model. In this paper, we propose reinforced approach to detect botnet with On-line Clustering using Reinforcement Learning. Reinforcement Learning involving interaction with the environment and became new paradigm in machine learning. The reinforcement learning will be implemented with some rule detection, because botnet ISCX dataset is categorized as unbalanced dataset which have high range of each number of class. Therefore we implemented Reinforcement Learning to Detect Botnet using Pursuit Reinforcement Competitive Learning (PRCL) with additional rule detection which has reward and punisment rules to achieve the solution. Based on the experimental result, PRCL can detect botnet in real time with high accuracy (100% for Neris, 99.9% for Rbot, 78% for SMTP_Spam, 80.9% for Nsis, 80.7% for Virut, and 96.0% for Zeus) and fast processing time up to 176 ms. Meanwhile the step of CPU and memory usage which are 78 % and 4.3 GB for pre-processing, 34% and 3.18 GB for online clustering with PRCL, and  23% and 3.11 GB evaluation. The proposed method is one solution for network administrators to detect botnet which has unpredictable behavior in network traffic
Trusted Data Transmission Using Data Scrambling Security Method with Asymmetric Key Algorithm for Synchronization
No full textSecurity is a major concern of the internet world because the development of the Internet requires the security of data transmission. The security method helps us to store valuable information and send it over an insecure network so that it can not be read by anyone except the intended recipient. Security algorithm uses data randomization method. This method of data information randomization has a low computation time with a large number of bits when compared to other encryption algorithms. In general, the encryption algorithm is used to encrypt data information, but in this research the encryption algorithm is used for synchronization between the sender and the intended recipient. Number of bits on asymmetric key algorithm for synchronization are the 64-bits, 512-bits and 1024-bits. We will prove that security methods can secure data sent with low computational time with large number of bits. In the result will be shown the value of computing time with variable number of bits sent. When data are sent by 50 bytes, encryption time required 2 ms using 1024 bits for synchronization technique asymmetric key algorithm.Â
Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya
No full textDiskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms