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A Store-forward Method for Biosignal Acquisition in Smart Health Care System using Wearable IoT Device
Get PDFSmart Health Care System has been developed massively in the last decade. Internet of Things implementation has accelerated the development of smart health technology. Now people have realized the importance of doing independent health monitoring. Supported by more affordable e-health tools, motivation in using the smart health system is increasing. The availability of cloud as storage services triggers demands to always be connected to the Internet. However, there are times when interference occurs so the measurement results cannot be stored in the cloud database properly. The idea of using a store-forward mechanism to overcome the problem of disconnected smart healthcare system is proposed in this paper. From the test results, the data can be sent properly eventhough disconnected status occurs for 30 minutes. Smartwatch’s memory usage has increase 30 percent during temporary storage occupied
Application of Ant Colony Optimization for the Shortest Path Problem of Waste Collection Process
Get PDFThe search for the shortest path of the waste collection process is an interesting topic that can be applied to various cases, from a very practical basic problem to a complex automation system development. In a dense settlement, the waste collection system can be a challenging process, especially to determine the most optimized path. The obstacles can be circling streets, impassable roads, and dead-end roads. A wrong choice of method can result in wasteful consumption of energy. A possible method to solve the problem is the traveling salesman problem using ant colony search optimization, considering its relatively fast optimization process. Therefore, this paper proposes an application of ant colony and traveling salesperson problem in determining the shortest path of the waste collection process. The case study for the optimization algorithm application is the path UGM Sekip Lecturer Housing is considering. Firstly, the data was collected by measuring the distance between points. Then, the paths were modeled and then compared with the actual route used by waste transport vehicles. The last step is implementing the ant colony optimization and traveling salesman problem by determining the cost function and the parameters. The optimization process was conducted several times, considering the random generator within the algorithm. The simulation results show the probable shortest path with a value of about 752 meters so that the use of fossil fuels in waste transport vehicles can be more efficient. The results show that the algorithm can automatically recommend the minimized path length to collect waste
Development of Digital Ventilator with Internet of Things for Preparation of the Next Outbreak in Indonesia
Get PDFThe emergency of the healthcare device unit, such as a ventilator, has been experienced during the COVID-19 pandemic in 2020. Therefore, ventilator usage is not hard suggested anymore for COVID-19 patients compared to the outbreak beginning. Despite that, it is still essential to have the ventilator ready, if possible, in each house, for the upcoming respiratory syndrome outbreak. Therefore, in this paper, a digital ventilator development is presented. The digital ventilator is comprised of three main parts, such as respiration mechanism (I), controller Internet of Things (IoT) module (II), and website application (III). The developed digital ventilator has been tested by comparing the measurement of respiratory data between the developed digital ventilator and gas flow analyzer. Results show that the respiratory data, such as Pressure Peak (PPeak), Positive End Respiratory Pressure (PEEP), Inspiratory Expiratory Ratio (IE Ratio), Breath per Minute (BPM), and Tidal Volume can be monitored and controlled both directly and online via website application consistently (standard deviation around 10%) with PPeak absolute error of 1.35 mbar, the PEEP absolute error of 0.16 mbar. Furthermore, the average time response of the digital ventilator to the input command from the website application is 0.23 s. Therefore, it is safe to assume that the doctor can use the website application to control the digital ventilator remotely
Comparison Analysis of Multipath Routing Implementation in Software Defined Network
Get PDFMultipath routing is a path search method used as a data transmission process utilizing more than one available path on a network. The multipath routing concept is directed to substitute the single path routing concept for reducing network traffic congestion by distributing data transmission through several available paths. It can be implemented in the Software Defined Network (SDN) environment that separates the control plane and the data plane, which provides flexibility by deploying application-based solutions to resolve the problem. This paper is directed to implement the modified Deep First Search (DFS) multipath routing algorithm and compare the proposed method with Dijkstra and normal DFS multipath algorithm. The contribution was designed by combining the node, edge, path, and bucket weight using port statistics available in OpenFlow standard and manual calculation. The results of the system’s emulation showed that the overall algorithm could determine more than one path for the data transmission process. The average execution time on all algorithms produced 0.0903 ms for the modified DFS multipath algorithm, 0.0858 ms for DFS multipath algorithm, and 0.901 ms Dijkstra multipath algorithm, respectively. The QoS parameter testing results illustrated that the proposed method was better than another multipath routing algorithm in terms of throughput and jitter. However, based on packet loss percentage, the modified method was placed after normal DFS but still generated better results than Dijkstra. Overall, the implement multipath routing concept in SDN with all algorithms could be deployed to provide more than one data transmission path
Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction
Get PDFOne of the oldest known predictive analytics techniques is time series prediction. The target in time series prediction is use historical data about a specific quantity to predicts value of the same quantity in the future. Multivariate time series (MTS) data has been widely used in time series prediction research because it is considered better than univariate time series (UTS) data. However, in reality MTS data sets contain various types of information which makes it difficult to extract information to predict the situation. Therefore, UTS data still has a chance to be developed because it is actually simpler than MTS data. UTS prediction treats forecasts as a single variable problem, whereas MTS may employ a large number of time-concurred series to make predictions. Neural Network (NN) model could be built to predict the target variable given the other (predictor) variables. In this study, we used Particle Swarm Optimization (PSO) algorithm to optimize performance of NN on a UTS dataset. Our proposed model is validated using x-validation and and use RMSE to measure its performance. The experimental results show that NN performance after optimization using PSO produces good results compared to classical NN performance. This is evidenced by the value of RMSE = 0.410 which is the smallest RMSE value produced. The smaller the RMSE value, the better the model performance. It can be concluded that the proposed method can improve NN performance on UTS data
Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification
Get PDFMaize productivity growth is expected to increase by the year. However, there are obstacles to achieving it. One of the causes is diseases attack. Generally, maize plant diseases are easily detected through the leaves. This article discusses maize leaf disease classification using computer vision with a convolutional neural network (CNN). It aims to compare the deep convolutional neural network (CNN) AlexNet and Squeezenet. The network also used optimization, stochastic gradient descent with momentum (SGDM). The dataset for this experiment was taken from PlantVillage with 3852 images with 4 classes i.e healthy, blight, spot, and rust. The data is divided into 3 parts: training, validation, and testing. Training and validation are 80%, the rest for testing. The results of training with cross-validation produce the best accuracy of 100% for AlexNet and Squeezenet. Furthermore, the best weights and biases are stored in the model for testing data classification. The recognition results using AlexNet showed 97.69% accuracy. While the results of Squeezenet 44.49% accuracy. From this experiment environment, it can be concluded that AlexNet is better than Squeezenet for maize leaf diseases classification
ClusterMix K-Prototypes Algorithm to Capture Variable Characteristics of Patient Mortality With Heart Failure
Get PDFCardiovascular Disease (CVD) is one of the leading causes of many death worldwide, leading to heart failure incidence. The World Health Organization (WHO) says the number of people dying from cardiovascular disease from heart failure each year has an average of 17,9 million deaths each year, about 31 percent of the total deaths globally. Identify the mortality factors of heart failure patients that need to be formed, which reduces death due to heart failure. One of them is by using variable mortality due to heart failure by applying the k-prototypes algorithm. The clustering result is formed 2 clusters that are considered optimal based on the highest silhouette coefficient value of 0,5777. The results of the study were carried out as segmentation of patients with variable mortality of heart failure patients, which showed that cluster 1 is a cluster of patients who have a low risk of the chance of mortality due to heart failure and cluster 2 is a cluster of patients with a high risk of mortality due to heart failure. The segmentation is based on the average value of each variable of heart failure mortality factor in each cluster compared to normal conditions in serum creatine variables, ejection fraction, age, serum sodium, blood pressure, anemia, creatinine phosphokinase, platelets, smoking, gender, and diabetes
Optimization of System Authentication Services using Blockchain Technology
Get PDFWith the development of the era, one thing that must be considered for security is the Login System. In most cases, user login information is stored on the server. This gives access to sensitive informatio, many hackers easily break into data from users. Based on these problems, this research focuses on data security authentication in the form of usernames and passwords in the login system. Authentication using blockchain is used to reduce malicious access and increase security for the authentication process. One of the innovative technologies that can solve these problems is Blockchain Technology. Using blockchain technology, hackers will find it difficult to change and modify the same data on all computers at the same time because it takes a very long time to crack the encryption code on each block of data in the entire computer network. Data storage or transactions in the blockchain are stored in the form of hashes. This makes it difficult for hackers to break into it. Tests in this study using Wireshark tools and network miner. Based on the research conducted, the test was conducted as many as 5 times with two scenarios, namely authentication of the login system before using the blockchain and after using the blockchain. The results obtained. The, system built using blockchain can secure data. The test results obtained that data in the form of usernames and passwords were converted into hashes and with the immutable nature of the blockchain, data from users could not be changed or replaced by anyone
Study of Neuromarketing: Visual Influence with Decision Making on Impulse Buying
Get PDFMarketing trends have been increasing in the last few decades. Products need good branding and the right marketing strategy. Various marketing methods have been widely done, and one of them is with the study of neuroscience, especially neuromarketing. Neuromarketing is used to seek the influence of marketing stimuli on consumers and objective data through advances in neurology by utilizing human senses such as restraint, smell, taste, and touch. Measurements of neuromarketing responses to the brain can use electroencephalography signals (EEG). Measurement is done with the visual stimulus of consumers when making decisions. To analyze consumer interests, the majority still using qualitative methods, but it is still considered less effective due to many uncertain factors. In this study, neuromarketing responses were measured to the human brain using (EEG) signal analysis. Data collection was conducted on 11 respondents with a stimulus in the form of different product colors and was affected by changes in light intensity. For pre-processing used bandpass filters to get beta signals in the absence of noise. Then the data will be processed using Fast Fourier Transform (FFT) and energy extraction as characteristic extraction and classification of Support Vector Machines (SVM) in the signal pattern recognition process. The results of testing the best feature combination parameters showed an accuracy value of 72% with a combination of magnitude and phase features. By using the range of phase feature values obtained an accuracy of 67% for signal pattern recognition respondents